\n\t\t\t\t\t\tApplication areas and products that use polymer/ clay nanocomposites.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"2073",leadTitle:null,fullTitle:"Sustainable Forest Management - Case Studies",title:"Sustainable Forest Management",subtitle:"Case Studies",reviewType:"peer-reviewed",abstract:"The concept of forest sustainability dates from centuries ago, although the understanding of sustainable forest management (SFM) as an instrument that harmonizes ecological and socio-economic concerns is relatively new. 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Each step of such activities produces various types of agricultural waste that include crop residue, on-farm livestock and fisheries waste, forest waste, agro-industrial waste, etc. Currently, handling and managing agricultural waste is a challenging task worldwide, especially in the context of environmental pollution control and sustainable agriculture. Thus, efficient management in terms of reuse, recycling, and reduction of agricultural waste is principally needed not only for the green economy but also for farmers' profitability. This would also contribute to minimizing environmental pollution, greenhouse gas emissions, and climate change to meet the 2030 UN-SDGs. Therefore, this book aims to address agricultural waste production and management in the multidimensional aspects of crop residue, biodegradables, biomass, composting and vermiculture, agricultural waste economics, air pollution, environmental safety, waste management, and handling, on-farm waste reuse, and agricultural waste value addition. Authors are encouraged to submit original research, reviews, modeling and simulation, case studies, and recent progress and scenarios in the above-mentioned subject areas.
\r\n\t
On 29th December 1959, the physicist Richard Feynman delivered a lecture titled “There is plenty of room at the bottom” atthe American Physical Society. Such a lecture is a landmark of nanotechnology, asFeymann proposed the use of nanotechnology to store information as well as a series of new techniques to support this technology [1]. From then on, the technological and scientific mastership ofnanometric scale is becoming stronger due to the new research tools and theoretical and experimental developments. In this scenario, the worldwide nanotechnology market, in the next five years, is expected to be ofthe order of 1 trillion dollars [2].
\n\t\t\tRegarding polymer/ clay nanocomposite technology, the first mention in the literature was in 1949 and is credited to Bower that carried out the DNA absorption by the montmorillonite clay[3]. Moreover, other studies performed in the 1960s demonstrated that clay surface could act as a polymerization initiator [4,5] as well as monomers could be intercalated between clay mineral platelets [6]. It is also important to mention that, in 1963, Greeland prepared polyvinylalcohol/ montmorillonite nanocomposites in aqueous medium [7].
\n\t\t\tHowever, until the early1970s, the minerals were only used in polymers as fillers commercially aiming to reduce costs, since these fillers are typically heavier and cheaper than the added polymers. During the 1970s, there was a vertiginous and successive increase in thepetroleum price during and after the 1973 and 1979 crises [8]. These facts, coupled with polypropylene introduction in commercial scale, besides the development of compounds with mica, glass spheres and fibers, talc, calcium carbonate, led to an expansion of the ceramic raw materials as fillers and initiated the research as these fillers interacted with polymers.
\n\t\t\tNevertheless, only in the late 1980swas the great landmark in the polymer clay nanocomposite published by Toyota regarding the preparation and characterization of polyamide 6/ organophilic clay nanocomposite to be used as timing belts in cars [9-11]. This new material, that only had 4.2 wt.%, had an increase of 40% in the rupture tension, 68% in the Young modulus and 126% in the flexural modulus as well as an increase in the heat distortion temperature from 65oC to 152oC in comparison with pure polymer [12]. From then on, several companies introducedthermoplastic nanocomposites, such as polyamide and polypropylene,inautomotive applications [13]. Another highlightedapplication is as gas barrier, by using polyamides or polyesters [14].
\n\t\tPolymer/ clay nanocomposites are a new class of composites with polymer matrix in which the dispersed phase is the silicate constituted by particles that have at least one of its dimensions in the nanometer range (10-9 m).
\n\t\t\tThe mineral particles most used in these nanocomposites are the smectitic clays, as, for example, montmorillonite, saponite and hectorire [15,16]. These clays belong to the philossilicate 2:1 family and they are formed by layers combined in a sucha waythat the octadedrical layers that have aluminum are between two tetrahedrical layers of silicon (Figure 1). The layers are continuous in the
The clay thickness is around 1 nm and the side dimensions can vary from 30 nm to various micrometers, depending on the clay. The layer stacking by Van der Waals and weak electrostatic forces originates the interlayer spaces or the galleries. In the layers, aluminum ions can be replaced by iron and magnesium ions, as well as magnesium ions can be replaced by lithium ions and the negative charge is neutralized by the alkaline and terrous- alkalinecations that are between the clay layers. Moreover, between these layers, water molecules and polar molecules can enter this space causing an expansion in the
Schematic representation of montmorillonite.
Polymers are constituted by largemolecules, called macromolecules, in which the atoms are linked between each other through covalent bonds. The great majority of the polymers are composed oflong and flexible chains whose rough sketch is generally made of carbon atoms (Figure 2). Such carbon atoms present two valence electrons notshared in the bonds between carbon atoms that can be part of the bonds between other atoms or radicals.
\n\t\t\t\tThese chains are composed ofsmall repetitive units called
When all the
Representation of an organic polymer chain.
Regarding the polymer molecular structure, polymers are linear when the
Schematic representation of: (a) linear, (b) ramified, (c) with crossed bonds and (d) network [
Polymers can be amorphous or semi-crystalline according to their structure. It is reasonable that the polymers that have a great number of radicals linked to the main chain are not able to have their molecules stacked as close as possible and, for this reason, the polymer chains are arranged in a disorganized manner, originating amorphous polymers. The polymers with linear chains and small groups are grouped in a more oriented form, forming crystals.
\n\t\t\t\tAs a consequence of the polymer structure, there are two types of polymers: thermoplastic andthermofixes. Thermoplastic polymers can be conformed mechanically several times with reheating by the shear of the intermolecular bonds. Generally, linear and ramified polymers are thermoplastic and network polymers are thermofixes.
\n\t\t\t\tThermofix polymers do not soften with temperature since there are crossed bonds in the 3D structure. Therefore, they cannot be recycled [21]
\n\t\t\tDepending on the interphase forces between polymer and clay, different morphologies are thermodynamically accepted (Figure4):
\n\t\t\t\tintercalated nanocomposite: the insertion of the polymer matrix in the silicate structure is crystalographicallyregular, alternating clay and polymer;
\n\t\t\t\tflocculated nanocomposites: it would be the same structure of the intercalated nanocomposite, except forthe formation of floccus due to the interaction between the hydroxile groups of the silicate;
\n\t\t\t\texfoliated nanocomposites: individual clay layers are randomically separated in a continuous polymer matrix ata distance that depends on the clay charge [22,23]
\n\t\t\t\tPolymer/ clay nanocomposites morphologies.
The formation and consequent morphology of the nanocomposites are related to entropic (ex.: molecular interactions) and enthalpic (changes in the configurations of the components) factors. Hence, efforts have been made to describe these systems. As an example, Vaia and Giannelis developed a model to predict the structure above according to the free energy variation of the polymer/ clay mixture in function of the clay layer separation.
\n\t\t\t\tThe free energy variation, ∆H, associated to the clay layer separation and polymer incorporation is divided into two terms: the term related to the intern energy variation, ∆U, associated to the configuration changes of various components.
\n\t\t\t\tWhere h and h0 are the initial and final separation of the clay layers.Then, when ∆H<0, the intercalation process is favorable.
\n\t\t\t\tSuch model presents as a limitation the separation of the configuration term, theintermolecular interactions and the separation of the entropy terms of various components.
\n\t\t\t\tOther mathematical models were also developed for studies of simulation of the thermodynamics of the polymer/ clay nanocomposites. These models consider the nanocomposite thermodynamics and architecture, the interaction between clay and polymer to the free energy and the polymer and clay conformation.
\n\t\t\t\tSpecifically for polyamide 6 and 66/ clay nanocomposites, the study of the molecular dynamics was employed, which uses the bond energy between the various components that composes the nanocomposite.
\n\t\t\t\tThe kinetics of polymer/ clay nanocomposite formation is also a very important issue to predict the resulting nanocomposite. Studies of the molecular dynamics were also employed to understand the system kinetics. Other mathematical models were also used to describe the system kinetics, but kinetics is less understood than thermodynamics.
\n\t\t\t\tThere is still the needof developing models that are explored in individual time and length scales, besides the integration of concepts that permeate from smaller to larger scales, that is, in the quantum, molecular, mesoscopic and macroscopic dominium [24].
\n\t\t\tThree methods are widely used in the polymer/ clay nanocomposite preparation. The first one is the
The second method is solution dispersion. Silicate is exfoliated in single layers by using a solvent in which the polymer or pre-polymer is soluble. Such silicate layers can be easily dispersed in a solvent through the entropy increase due to the disorganization of the layers that exceed the organizational entropy of the lamellae. Polymer is then sorved in the delaminated layers and when the solvent is evaporated, or the mixture is precipitated, layers are reunited, filled with the polymer.
\n\t\t\t\tMoreover, there is also the fusion intercalation, amethod developed by Vaia et al. in 1993 [25]. In this method, silicate is mixed with a thermoplastic polymer matrix in its melted state. Under these conditions, the polymer is dragged to the interlamellae space, forming a nanocomposite. The driving force in this process is the enthalpic contribution of the interactions between polymer and clay.
\n\t\t\t\tBesides these three techniques, the use of supercritical carbon dioxide fluids and sol-gel technology can also be mentioned [26].
\n\t\t\tAs explained before, the great majority of polymers are composed of a carbon chain and organic groups linked to it, thus presentinga hydrophobic character. On the other hand, clays are generally hydrophilic, making them, at a first view, not chemically compatible. Aiming to perform clay dispersion and polymer chains insertion, it is necessary to modify these materials.
\n\t\t\tThere are two possibilities to form nanocomposites: clay organomodification that will decrease clay hydrophilicity and the use of a compatibilizing agent in the polymer structure, by grafting, to increase polarity. The concepts that govern each of these modifications will be explored in this chapter.
\n\t\t\tThis method consists in the interlamellae and surface cation exchange (generally sodium and calcium ions) by organic molecules that hold positive chains and that will neutralize the negative charges from the silicate layers, aiming to introduce hydrophobicity and then, producing an organophilic clay. With this exchange, the clay basal space is increased and the compatibility between the hydrophilic clay and hydrophobic polymer. Therefore, the organic cations decrease surface energy and improve the wettability by the polymer matrix.
\n\t\t\t\tThe organomodification, also called as organophilization, can be reached through ion exchange reactions. Clay is swelled with water by using alkali cations. As these cations are not structural, they can be easily exchanged by other atoms or charged molecules, whichare called exchangeable cations.
\n\t\t\t\tThe greaterdistance between the silicate galleries due to the size of the alquilammonium ions favor polymer and pre-polymer diffusion between the galleries. Moreover, the added cations can have functional groups in their structure that can react with the polymer or even begin the monomer polymerization. The longerthe ion chain is and the higher the charge density is, the greaterthe clay layer separation will be [4,11].
\n\t\t\tGenerally, a compatibilizing agent can be a polymer which offers a chemically compatible nature with the polymer and the clay. By a treatment, such as the graftization of a chemical element that has reactive groups, or copolymerization with another polymer which also has reactive groups, compatibility between the materials will form the nanocomposite. Amounts of the modified polymer are mixed with the polymer without modification and the clay to prepare the nanocomposites.
\n\t\t\t\tParameters such as molecular mass, type and content of functional groups, compatibilizing agent/ clay proportion, processing method, among others, should be considered to have compatibility between polymer and clay. Maleic anidride is the organic substance most used to compatibilize polymer, especially with the polyethylene and polypropylene, since the polar character of maleic anidride results in favorable interactions, creating a special affinity with the silicate surfaces [27,28].
\n\t\t\tIn this item, examples of studies about the most important polymers that are currently employed in the polymer/ clay nanocomposite preparation will be presented. Fora better understanding, polymers are divided into general-purpose polymers, engineering plastics, conductive polymers and biodegradable polymers.
\n\t\t\tGeneral-purpose polymers, also called commodities, represent the majority of the total worldwide plastic production. These polymers are characterized by being used in low-cost applications due to theirprocessing ease and low level of mechanical requirement. The formation of nanocomposites is a way to addvalue to these commodities.
\n\t\t\t\tPE is one of the polymers that most present scientific papers related to nanocomposite formation. Maleic anidride grafted PE/ Cloisite 20A nanocomposites were prepared by two techniques: fusion intercalation and solution dispersion. Only the nanocomposites produced by the first method produced exfoliated morphology. The LOI values, related to the material flammability, were lower in all composites and were highly reduced in the exfoliated nanocomposites due to the high clay dispersion [29].
\n\t\t\t\t\tAnother work presented the choice of a catalyzer being supported on the clay layers that are able to promote
Rosseau et al. prepared maleic anidride grafted PP/ Cloisite 30B nanocomposites by water assisted extrusion and by simple extrusion. The use of water improved clay delamination dispersion and, consequently, the rheological, thermal and mechanical properties [29].
\n\t\t\t\t\tThe use of carbon dioxide in the extrusion of PP/ Cloisite 20A nanocomposites enabled a higher separation between the clay layers. The use of clay at lower contents in the foam formation also suppressed the cell coalescence, demonstrating that the nanocomposite is also favorable to produce foams [31].
\n\t\t\t\tThe use of different clays (calcium, sodium and organomodified montmorillonite, aluminum magnesium silicate clay and magnesium lithium silicate clay) was studied in the preparation of rigid foam PVC nanocomposites. Although the specific flexure modulus and the density have been improved by the nanocomposite formation, the tensile strength and modulus have their values decreased in comparison with pure PVC [32].
\n\t\t\t\tEngineering plasticsare materials that can be used in engineering applications, as gear and structural parts, allowing the substitution of classic materials, especially metals, due to their superior mechanical and chemical properties in relation to the general-purpose polymers [33]. These polymers are also employed in nanocomposites aiming to explore their properties to the most.
\n\t\t\t\tAmong all engineering plastics, this is the polymer that presents the highest number of researches involving the preparation of nanocomposites. PA/ organomodified hectorite nanocomposites were prepared by fusion intercalation. Advanced barriers properties were obtained by increasing clay content [34]. The flexure fatigue of these nanocomposites were studied in two environments: air and water. It was observed that the clay improved this property in both environments [35].
\n\t\t\t\tPSf/ montmorillonite clay nanocomposite membranes were prepared by using solution dispersion and also the method most employed in membrane technology, wet-phase inversion. A hybrid morphology (exfoliated/ intercalated) was obtained, and itsdispersion was efficient to increase a barrier to volatilization of the products generated by heat and, consequently, initial decomposition temperature. By the strong interactions between
\n\t\t\t\t\tpolymers and silicate layers, the tensile strength was increased and elongation at break was improved by the rearrangement of the clay layers in the deformation direction. Furthermore, hydrophobicity was also increased,so that membranes couldbe used in water filtration operations, for example [36].
\n\t\t\t\tBy in situ polycondensation, PC/ organophilic clay exfoliated nanocomposites were prepared. Although exfoliated nanocomposites were produced, transparency was not achieved [37].
\n\t\t\t\tConductive polymers, also called synthetic metals, have electrical, magnetic and optical properties that can be compared to thoseof the semiconductors. They are also called conjugated polymers, since they have conjugated C=C bonds in their chains which allow the creation of an electron flux in specific conditions.
\n\t\t\t\tThe conductive polymer conductivity is dependent on the polymer chains ordering that can be achieved by the nanocomposite formation.
\n\t\t\t\tPANI is the most studied polymer in the polymer/ clay nanocomposite technology. Exfoliated nanocomposites wereprepared with montmorillonite which contained transition by
PEO nanocomposites werepreparedwiththreetypes of organophilicclays (Cloisite 30B, Somasif JAD400 e Somasif JAD230) by fusion intercalation. The regularity and spherulites size of the PEO matrix were altered by only using Cloisite 30B. An improvement in the storage modulus of the other nanocomposites was not observed since the spherulites were similar in the other nanocomposites [39].
\n\t\t\t\tBiodegradable polymers are those that, under microbial activity, have their chains sheared. To have the polymer biodegradabilization, specific conditions, such as pH, humidity, oxygenation and the presence of some metals were respected. The biodegradable polymers can be made from natural resources, such as corn; cellulose can be produced by bacteria from molecules such as butyric, and valeric acid which produce polyhydrobutirate and polyhydroxivalerate or even can derive from petroleum; or fromthe biomass/ petroleum mixture, as the polylactides [40].
\n\t\t\t\tThe PHB disadvantages are stiffness, fragility and low thermal stability and because of this, improvements should be performed. One of the ways to improve these properties is by preparing nanocomposites.
\n\t\t\t\t\tPHB nanocomposites were prepared with the sodium montmorillonite and Cloisite 30B by fusion intercalation. A better compatibility between clay and polymer was established by using Cloisite 30 B and an exfoliated/ intercalated morphology was formed. Moreover, an increase in the crystallization temperature and a decrease in the spherulite size were also observed. The described morphology was responsible for increasing the Young modulus [41]. Besides that, thermal stability was increased in PHB/ organomodified montmorillonite in comparison with pure PHB [42].
\n\t\t\t\tApproximately 80% of the polymer/ clay nanocomposites is destined to the automotive, aeronautical and packaging industry.
\n\t\t\tThe car part industry pioneered in the use of polymer/ clay nanocomposites, since these nanocomposites present stiffness and thermal and mechanical resistances able to replacemetals, and its use in car reduces powerconsumption. Moreover, its application is possible due to the possibility of being painted together with other car parts, as well as of undergoing the same treatments as metallic materials in vehicle fabrication.
\n\t\t\tGeneral Motors was the first industry to use nanocomposites in car, reducing its mass byalmost one kilogram. In the past, car parts weremade of polypropylene and glass fillers, which hadthe disharmony with the other car partsas a disadvantage. By using lower filler content, as in the case of the nanocomposites, materials with a higher quality are obtained, as is the case of the nanoSealTM, which can be used in friezes, footboards, station wagon floors and dashboards. Basell, Blackhawk, Automotive Plastics, General Motors, Gitto Global produced polyolefines nanocomposites with, for example polyethylene and polypropylene, to be used in footboards of the Safari and Astro vehicles produced by General Motors.
\n\t\t\tCar parts, such as handles, rear view mirror, timing belt, components of the gas tank, engine cover, bumper, etc. also used nanocomposites, specially with nylon (polyamide), produced by the companies Bayer, Honeywell Polymer, RTP Company, Toyota Motors, UBE and Unitika.
\n\t\t\tIn the packaging industry, the superior oxygen and dioxide carbon barrier properties of the nylon nanocomposites have been used to produce PET multilayer bottles and films for food and beverage packaging.
\n\t\t\tIn Europe and USA, nanocomposites are used in soft drink and alcoholic beverage bottles and meat and cheese packaging since these materials present an increase in packaging flexibility and tear resistance as well as a humidity control.
\n\t\t\tNanocor produced Imperm, a nylon MDXD6/ clay nanocomposite used as a barrier in beer and carbonated drink bottles, in meat and cheese packaging and in internal coating of juice and milk byproduct packaging. The addition of 5% of Imperm in PET bottles increase the shelf time bysix months and reduce the dioxide carbon lossto less than 10%.
\n\t\t\tAnother commercial products can be cited, as for example the M9TM, produced by the Mitsubish Gas Chemical Company, for application in juice and beer bottles and multilayer films; Durethan KU2-2601, a polyamide 6 nanocomposite produced by Bayer for coating juice bottles as barrier films and AEGISTM NC which is polyamide 6/ polyamide nanocomposites, used as barrier in bottles and films, produced by Honeywell Polymer.
\n\t\t\tIn the energy industry, the polymer nanocomposites positively affect the creation of forms of sustainable energy by offering new methods of energy extraction from benign and low-cost resources. One example is the fuel cell membranes; other applications include solar panels, nuclear reactors and capacitors.
\n\t\t\tIn the biomedical industry, the flexibility of the nanocomposites is favorable, which allows their use in a wide range of biomedical applications as they fill several necessary premises for application in medical materials such as biocompatibility, biodegradability and mechanical properties. For this reason and forthe fact of being finely modulated by adding different clay contents, they can be applied in tissue engineering – the hydrogel form, in bone replacement and repair, in dental applications and in medicine control release.
\n\t\t\tMoreover, there is the starch/ PVA nanocomposite, produced by Novamont AS (Novara, Italy) that can replace the low density PE films to be used as water-soluble washing bags due to its good mechanical properties.
\n\t\t\tOther commercial applications include cables due to the slow burning and low released heat rate; the replacementof PE tubes withpolyamide 12 nanocomposites (commercial name SETTM), produced by Foster Corporation and in furniture and domestic appliances withthe nanocomposite with brand name ForteTM produced by Noble Polymer.
\n\t\t\t\n\t\t\t\tTable 1 presents a summary of the application areas and products in which polymer/ clay nanocomposites are used.
\n\t\t\tThe consumption of polymer/ clay nanocomposites was equal to 90 million dollars with a consumption of 11,300 ton in 2005. In 2011, a consumption of 71,200 ton was expected,corresponding to 393 million dollars.
\n\t\t\tThe scenario that correspond to the areas in which polymer/ clay nanocomposite was used in 2005 is presented in Figure 5. By the end of 2011, the barrier applications were expected to exceed the percentage related to car parts.
\n\t\t\tIn a near future, the PP nanocomposites produced by Bayer are expected to replace car parts that use pure PP and the PC nanocomposites produced by Exaltec are expected to be used in car glasses due to an improved abrasion resistance without loss of optical transparency.
\n\t\t\tAutomotive | \n\t\t\t\t\t\tPackaging | \n\t\t\t\t\t\tEnergy | \n\t\t\t\t\t\tBiomedical | \n\t\t\t\t\t\tConstruction | \n\t\t\t\t\t\tHome furnishings | \n\t\t\t\t\t
---|---|---|---|---|---|
-footboards, -friezes, - station wagon floors, - dashboards, -timing belts, -handle, -gas tank components, -engine covers, - bumpers. \n\t\t\t\t\t\t\t \n\t\t\t\t\t\t | \n\t\t\t\t\t\t- beer and soft drink bottles, -meat and cheese packaging, -internal films of juice boxes, | \n\t\t\t\t\t\t-fuel cells, -lithium batteries, - solar panels - nuclear reactors, -capacitors. | \n\t\t\t\t\t\t-artificial tissues; -dental and bone prosthesis, -medicines. \n\t\t\t\t\t\t | \n\t\t\t\t\t\t-tubes, - cords. | \n\t\t\t\t\t\t-furniture, -home appliances. | \n\t\t\t\t\t
\n\t\t\t\t\t\tApplication areas and products that use polymer/ clay nanocomposites.
The research about the application of these nanocomposites in car parts is still being developed since a reduction in the final car mass corresponds to benefits to the environment. The large use of nanocomposites would reduce 1.5 billion liters of gasoline a year and the CO2 emission in more than 5 billion kilograms.
\n\t\t\tAnother thriving field is the barrier applications, the use of which can increase food shelf life besides maintaining film transparency. As an example, by using Imperm nanocomposite in a Pet bottle, beer shelf life is increased to 28.5 weeks.
\n\t\t\tGreat attention has been also paid to the biodegradable polymers which present a variety of applications. Moreover, another potential application is in nanopigment as an alternative to cadmium and palladium pigments which presenthigh toxicity.
\n\t\t\tThe distant future of the applications of polymer/ clay nanocomposites is dependent on the results obtained from researches, commercial sectors, existing markets and the improvement level of the nanocomposite properties. Furthermore, the relevance of their application in large scale, the capital to be invested, production costs and the profits should be taken into account.
\n\t\t\tApplications of polymer/ clay nanocomposites in 2005.
Due to the aforementioned reasons, a considerable increase in investigations and the commercialization of nanocomposites in the packaging area, selective catalyzers, conductive polymers and filtration of toxic materials are expected. A light growth in the applications related to an increase of catalysis efficient and of material conductivity, new types of energy, storage information and improved membranes are also expected.
\n\t\t\tAlthough nanocomposites present a series of advanced properties, their production is still considered low in comparison with other materials due to the production costs. Once they become cheaper, polymer/ clay nanocomposites can be largely used in a series of applications [11, 43-45].
\n\t\tThe first decade of the twenty-first century belongs to a new wireless world indeed! The rapid growth of cellphones, Wireless Local Area Networks (WLANs), and recently the wireless Internet, in short, wireless communication is driving the whole world toward greater integrity with wireless communications. By 2020, two-thirds i.e. 66% of total IP traffic shall be occupied by Wi-Fi and mobile devices whereas wired devices will account for 34% of IP traffic in access network [1]. Licensed bands claim to be heavily congested but different research work shows that the channels in the form of time and frequency are still available. In future, wireless networks may face the problem to find suitable frequency spectrum to fulfill the demands of future services. To solve the problem of inefficient use of spectrum utilization, a new concept is evolved known as cognitive radio (CR) [2]. In 1999, Joseph Mitola III introduced the concept of CR. This new concept of CR which is called as intelligent wireless communications is capable of sensing its environment and dynamically accessing the technology. It adjusts according to the input variations of statistical data for: (a) very dependable communication wherever and whenever needed; and (b) efficiently utilizing the radio spectrum [3, 4]. This can be done by sensing the radio environment: (i) by finding spectrum bands which are unused by the PU (i.e., licensed user), and (ii) by allocating unused bands of radio spectrum to SU requesting service [5]. The underused frequency bands of PUs are called as in-band spectrum holes [6]. The spectrum holes can be used to allocate the channels to CR user. However, to ensure efficient communication for such unlicensed communication, the Quality of Service (QoS) parameters need to be considered. Quality of Service can be defined as a set of specific requirements provided by a network of users, which are necessary in order to achieve the required functionality of a service.
Cognitive radio (CR) concept is based on vacant spectrum in licensed band which sometimes referred to as combination of channels. In telecommunication, a channel refers either to a physical transmission medium such as wire or to a logical connection over a multiplexed medium such as a radio channel. Global System for Mobile Communication-900 (GSM-900) has been allocated an operational frequency from 890 to 960 MHz. GSM uses the frequency band 890–915 MHz for uplink (reverse) transmission, and for downlink (forward) transmission, it uses the frequency band 935–960 MHz. The available 25 MHz spectrum with 100 kHz guard band at two edges of the spectrum is divided into 124 Frequency Division Multiplexing (FDM) channels, each occupying 200 kHz as mentioned in Figure 1.
Frequency channels in GSM-900.
A large amount of information is transmitted between the MS and the BS, particularly, user information (voice or data) and control or signaling data. Depending on the type of information transmitted, different logical channels are used. These logical channels are mapped onto the physical channels (time slots). In the GSM system, a traffic channel will be made by a combination of a 200 kHz frequency channel and one of the eight time slots. For example, digital speech is carried by the logical channel called the traffic channel which during transmission can be allocated to a certain physical channel. There are two basic types of logical channels in GSM: traffic channels (TCHs) and control channels (CCHs). TCHs are used to carry either encoded speech or user data both in the uplink (UL) and downlink (DL) directions. The CCHs are used to communicate service between network equipment nodes.
Code Division Multiple Access (CDMA or Interim Standard-95) uses the frequency band 824–849 MHz for uplink (reverse) transmission, and for downlink (forward) transmission, it uses the frequency band 869–894 MHz. With CDMA, all users share the same 1.25 MHz wide carrier, but unique digital codes are used to differentiate subscribers. The codes are shared by both the mobile station and the base station and are called “pseudo-random code sequences”. Base stations in the system distinguish themselves from each other by transmitting different portions of the code at a given time. In other words, the base stations transmit time-offset versions of the same pseudo-random code.
The 3rd Generation Partnership Project (3GPP) and 3rd Generation Partnership Project 2 (3GPP2) have indicated that orthogonal frequency division multiple access (OFDMA) is the choice for the physical-layer transmission technology in 4G standards. In OFDM, usable bandwidth is divided into a large number of smaller bandwidths that are mathematically orthogonal using fast Fourier transforms (FFTs). Reconstruction of the band is performed by the inverse fast Fourier transform (IFFT).
CR utilizes both licensed and unlicensed bands for communication. Among these bands, GSM bands have less attenuation; their wavelength is more resilient to phenomenon like diffraction, absorption, scattering, etc. GSM channels use FDM-TDM technique with low bandwidth of 200 kHz and hence better scalable. In practice, technologies like CDMA, OFDM, etc. uses large bandwidth and total allotted spectrum and hence, only chance to obtain large bandwidth is the un-allotted part of the licensed band. Cognitive radio users (human and machine) are low end users (users in lower or lowest economic bracket or free public utility users with minimum vocabulary or information) and expected mainly to use voice, short message and short data services. These reasons make GSM is a good choice for cognitive radio implementation.
The QoS for mobile services which has been defined by ITU-T includes different parameters of QoS like availability, accessibility, maintainability and user perception of service. These parameters have been defined in context of cognitive radio in Section 2. Availability refers to detection of unused spectrum by way of signal strength measurements. In conventional method, the signal strength of a received radio signal is measured. The measurement setup used for detection of spectrum holes in CR along with the cognitive radio issues for availability has been discussed in detail in Section 4. The proposed work calculates blocking probabilities both on immediate minute occupancy basis and its preceding 60 min basis at the instant of service request by SU. The new concept of channelized blocking probability has been defined along with the general definitions of blocking probabilities in Section 5. An algorithm has been developed to accept SU service requests with different classified Quality of Service (QoS) from a set of PU channels. Allocation of a PU vacant channel on SU call request is done based on prediction that the channel will remain vacant for more than the assessed holding time of SU. The channel allocation model works based on inputs from (a) the channel call arrival rate prediction model and (b) SU holding time assessment model and has been discussed in Section 6. The model accepts collected data as input in time serial manner for running through residual lifetime based prediction model program. The comparison of proposed work has also been done and its results and conclusion has been discussed in Section 6.
Quality of Service (QoS) is the capability of a network to offer better service to selected network traffic over specific underlying technologies [7, 8]. The various parameters for QoS are:
Availability: The operator maintains a dynamic list of available channels. When the user wants to communicate, operator is liable to assign one or more communication channel to the user as per his demand and within tolerable specified time limit. In case of telecom service, this delay is maximum 6 s but usually, the delay noticed is less than a second. This function is referred to as availability.
Accessibility: When the operator assigns channels to the user, the user equipment (UE) should be capable to use the allocated spectrum to the extent possible. For example, when a 200 kHz channel is allocated for some time τ, the user handset should be able to communicate at highest modulation supported by operator and RF condition. This phenomenon is called accessibility. Proper handshaking shall take place between UE and access network (AN) before establishing communication at acceptable speed by both ends.
Maintainability: In mobile communication, as the user is mobile, there is a continuous change in environment and RF condition. The operator has to take into consideration various parameters like speed of communication, handover, etc. for proper maintenance of established communication. This is known as maintainability.
User perception of service: It is the ability to deliver the service meeting the user’s quality of expectations. It is measured by the customer satisfaction using access equipment behavior audit, drive test for mobile as pseudo customer and actual satisfaction through interrogation by customer survey specialists.
Over the last few years, a lot of research has undergone on spectrum sensing (SS) techniques for the detection of spectrum holes [9]. Energy detection (ED) approach, also known as radiometry or periodogram, is a popular technique for spectrum sensing due to low computational and implementation complexities [10]. The conventional SS method includes waveform-based sensing (WBS), matched filter-based sensing (MFBS) and cyclostationary-based sensing (CBS). WBS is a coherent method that correlates the received signal with the previous patterns available in database [11]. This technique is susceptible to synchronization errors which can cause false detection of primary users [10]. MFBS is the best detecting method where the received signal is interrelated with the transmitted signal [12]. The periodic characteristics of the received signals i.e., pilot sequences, carrier tones, etc. is explored by CBS technique [13]. It requires less time to achieve high processing gain due to coherent detection. In MFBS technique, it is assumed that it has the previous information of the primary’s signal. It indicates that method is not suitable in some bands as some of the communication technologies are not operating with the previous information. On the other hand, CBS is unfeasible for signals that don’t show cyclostationarity properties. CBS has high computational complexity [14]. Energy-based sensing (EBS) is the easiest SS method [15, 16]. This technique does not require any previous knowledge of primary user’s signal but its performance is less when noise’s variance is unknown or at the higher side [17]. Energy-based sensing based on sub-Nyquist sampling shall be beneficial as per as sensing duration is concerned [18]. The performance of the EBS is characterized where the PUs reflects a constant characteristic during the sensing period as well as during the sensing period where PUs can alter their ON/OFF status, thus, affecting the spectrum sensing decision [19]. A brief comparison various SS techniques is enlisted in Table 1 as follows [10, 20]:
In mobile communication, primary user occupied channels are known to network. So, a new call is eligible to occupy any of the vacant channels. In contrast, in cognitive radio network, a dynamic spectrum management is used which shall include information about the traffic pattern of the channels occupied by primary users at an instant. Basically, a CR should characterize whether the traffic pattern is static or dynamic and based on that it should use different methods for idle time prediction before selecting a channel.
Much of the spectrum below 50 GHz is available for low-powered unlicensed use. Based on environmental variations, the utilization of the licensed band is approximately 15–85% [21]. The actual utilization of mobile communication spectrum in licensed band has not yet been taken into consideration. The variation of channel utilization for various types of cities has also not been studied. These studies may be very useful to perfectly recognize the frequency channels with no active or low occupancy so that the CR technology can be successfully deployed. Few such studies has been mentioned below:
A spectrum measurement campaign for a frequency band of 75 MHz to 3GHz was conducted through a survey in an outdoor urban environment for a continuous period of 48 h at Barcelona, Spain [22]. The six consecutive frequency bands of 500 MHz were formulated and it was found that only 22.57% of the whole frequency range was utilized.
To find the spectrum occupancy for the frequency range from 80 MHz to 5.85 GHz, another survey was conducted at Institute for Infocomm Research’s building in Singapore for 24-h over 12 weekday periods [23]. It was observed that the average utilization of frequency band was only 4.54%.
At the Loring Commerce Centre, a similar survey was conducted during a normal work week for 72 h for the frequency band of 100 MHz to 3 GHz [24]. In the survey, it was found that only 17% of the average spectrum is utilized during the measurement period. The ISM bands and mobile licensed bands are partly utilized and the remaining part of the spectrum band resembles noise.
In India, the RFs are being used for different types of services like mobile communication, broadcasting, radio navigation, satellite communication, defense communication, etc. The wireless equipment are developed and manufactured based on the spectrum utilization in the country as decided by the National Frequency Allocation Plan (NFAP). The various frequency spectrums allotted to mobile communication services is shown in Table 2 [25, 26].
Comparison of different spectrum sensing methods.
Licensed spectrum of various wireless technologies.
* As per available information.
CR technology has been developed to dynamically access and release channels in licensed bands. There is a scope of getting the unutilized channels in licensed spectrum with or without having a stable infrastructure for CR. Thus it is expected that at zero cost public authorities providing public utility services may be authorized to operate over unutilized spectrum even though licensed. However, such public utility service providers are very limited. Field test should be essentially conducted for the evaluation of quasi-permanently unused channels for use of in-band common control signaling purposes.
To dynamically measure the occupancy rate of the PUs and to calculate the quantum of vacant channels available for CR use, a measurement setup called drive test equipment is used that collects data on a moving vehicle. A motor vehicle containing mobile radio network air interface measurement equipment is used in the drive test. The equipment measures different types of virtual and physical parameters of mobile cellular service in a given geographical region. Data relating to the network itself is collected by drive test equipment, radio frequency scanner information, services running on the network such as voice or data services and GPS information to provide location logging. The hardware and software used in the setup includes data cable and global positioning system (GPS), digital radio frequency (RF) scanner, laptop with charger and USB hub license dongle for TEMS, engineering handsets with 4 (2G/3G) SIMs of different operators mounted simultaneously and cable terminal, cell site database and link budget, clutter diagram from Google website, MapInfo software. In the setup, data collection software is installed in the laptop where mobile set is used along with GPS. Data related to signal strength, downlink and uplink frequency etc. is collected by the mobile whereas GPS collects the data of latitude and longitude of each point. All the information is stored with its geographical locations along with their respective time and date.
Data was collected for spectrum utilization measurements in GSM 900 MHz band in an outdoor environment of other cities viz. Bhopal, Ranchi, Patna, Dibrugarh, Shillong & Port Blair with population in the range of 1.5 million to 6.6 million as per 2011 census. The study reveals that there is 74.19% spectrum occupancy in lower band in Bhopal, while in Ranchi it is only 52.42% as it switches to upper band where it has spectrum occupancy of 83%. In the lower band of Patna, the measurements indicate that there is 75.8% spectrum occupancy. Shillong, the capital of Meghalaya state of India is located at 25.57°N and 91.88°E on a plateau in the eastern part of the state. The population of the city is 1.43 million where spectrum occupancy is 54%. Port Blair located at 11° 40’ N and 92° 46′ E is the capital city of Andaman and Nicobar Islands in India. The next survey was conducted at Port Blair which is the municipal council in the southern part of Andaman, a part of India’s Union Territory. Being the lowest populated area, it has spectrum occupancy of 43%. It is evident that most of the bands in lower band of various cities are quasi-permanently vacant. These vacant channels can be used for control signaling in CR communication.
The spectrum occupancy of eight cities of India is represented in Figure 2. It is shown in the diagram that there is 30% occupancy for the most sub-urban area with less population. For population between 1 million to 4 million, the increase is almost linear. In the range of population between 4 million to 7 million, it is observed that the occupancy reaches a saturation level. Also, with projected expansion of highly populated city core areas to 8 million, occupancy level is projected to reach up to 86%, leaving a clear space of 14% of channels for CR use [25].
Population spectrum graph.
As population increases, there will be requirement of more number of channels and this need can be managed through effective optimization methods. This can be mathematically calculated as negative requirement of channels and graphically expressed as saturation. Due to continuous growth in population, operators can request access for TCHs from higher frequency band and consequently, the occupancy at lower frequency band is reduced. Thus, the channel occupancy with growth in population can empirically be given as:
where x = size of population in millions, y = channel occupancy percentage in lower frequency band.
Thus, it is found that 20% or more of the licensed bandwidth is almost practically unused even in a saturated market environment. In other words, more than 1/8th part of all bands were not in use which closely matches the need of one signaling channel for 7 traffic channels. This is more than the channel demand of 12% of the whole band to get access to the whole of the bandwidth at a time by cognitive radio and is adequate to take additional MAC level overhead required for CR. There is no urgent requirement of these channels by the licensed operators, whereby it can be carefully allotted as common control channel for CR purpose. Further, to doubly enhance protection of common control channel, a disaster recovery common control channel may be designated. It will hold the replica of allotments and processing status of CR Primary Common Control Channel. The above findings for common control channel are highly dynamic and sensed information may be able to provide user mobility in a most competitive environment at an economically affordable cost. The approximation of channel occupancy is possible depending upon the population and hence CR technology planners can propose a long term plan for efficient use of it for public benefits.
As per as the need of QoS is concerned, CR networks should have the ability to choose the best frequency band for use [27]. Spectrum decision is based on the channel characteristics and operations of PUs. Spectrum decision follows two steps: (i) every spectrum band is distinguished based on the statistical information of PUs and the local observations of CR users [28]. The available spectrum holes represent different characteristics that differ over time. (ii) After the available spectrum bands are characterized by considering spectrum characteristics and the QoS requirements, the most appropriate spectrum band should be selected. To minimize capacity variation, spectrum decision method is used which incorporates minimum variance-based spectrum decision (MVSD) scheme. To maximize the total network capacity, a maximum capacity-based spectrum decision (MCSD) scheme is used [29]. Accordingly a database is maintained where the data is purified based on signal to noise ratio (SNR), vacant holding time, etc. which is then used for channel allocation to SU.
After the holes are detected and best selected in licensed band, the next function of the CR user includes accessing the channel which is known as spectrum sharing. The wireless channel needs the synchronization of transmission attempts between CR users. The spectrum sharing aims to address four aspects:
As per the architecture, the classification can be distributed or centralized. In centralized spectrum sharing, a central entity controls the procedures of spectrum allocation and access. In distributed spectrum sharing, local or probably global policies that are independently executed by each node decide the spectrum allocation and access [30].
Based on allocation behavior, spectrum access can be cooperative or non-cooperative. In cooperative (or collaborative) allocation, the interference measurements of each node is exploited in such a way that it considers the effect of the communication of one node on other nodes. In non-cooperative allocation, only a single node is considered. To promote cooperation among conflicting decision makers, efficient spectrum sharing schemes such as game theory have been used for more efficient, flexible, and fair spectrum usage [31, 32, 33, 34].
Based on the access technology, it is of two types: overlay and underlay. In overlay spectrum sharing, nodes access the network using the spectrum band that has not been used by PUs so as to minimize interference to the primary network. In underlay spectrum sharing, the spread spectrum techniques are exploited such that the transmission of a CR node is regarded as noise by PUs [35].
Spectrum sharing methods are concentrated on two types of solution: where spectrum sharing can be within a CR network, which is called intranetwork spectrum sharing; and among multiple coexisting CR networks, which is called internetwork spectrum sharing.
The conventional telecom uses hourly prediction for estimating mobile communication traffic. In hourly prediction, peak time is not determined neither in the beginning of the hour nor at the end of hour. The clock hour is not authentic as it does not tell exactly about the peak time at which the traffic was maximum. Hourly traffic data is insufficient to decide whether a call can be initiated at a particular instant of time. Thus, it becomes necessary to study minutewise traffic pattern. Minutewise occupancy data is computed on hourly basis for various cells of varying channel numbers, e.g., each cell with 7/14/28/60 channels for 1/2/4/8 radio frequencies (RFs) of GSM system to assess the traffic behavior of PU. There are channels with same number of RFs which are lightly loaded at some places as well as highly loaded at some other locations.
Conventionally, a telecom operator analyzes the total traffic on hourly basis and identifies the busy hour where total traffic is maximum. Hourly data arranged on weekly basis does not give a clear picture of the peak hour as it contains many peaks. The traffic variations within a clock hour are not predictable in weekly analysis. Thus, data arranged on daily basis is taken to estimate the behavior of hourly traffic. For example, the minutewise collected occupancy data was taken on hourly basis for 50 cells of different channel numbers. Figure 3 depicts daily occupancy pattern for the locations with 60 channels (8 RFs) and differently loaded at various traffic places [36, 37]. The results are similar for other RF counts also. The figure indicates double heaps in the channel occupancy. The heap pattern shows near parabolic nature from 07:00 to 15:00 and 17:00 to 22:00 h.
Daily channel occupancy pattern for three cells with 60 channels.
Usually, peak traffic is bell-shaped around peak few minutes. Hence, the busy hour may or may not include peak traffic minute which is of serious concern for prediction of channel availability. The clock hour is not authentic as it does not exactly tell about the peak time at which the traffic was maximum. The PU busy hour is redefined in context of CR as 1 h during which peak channel occupancy occurs and calculates the growth and decay of traffic 30 min each around peak traffic minutes.
The determination of QoS provided by a particular network configuration is required for an efficient design of communication networks. The Grade of Service (GoS) is a benchmark used to define the desired performance of a particular cellular communication system by specifying a desired probability of a mobile subscriber obtaining channel access given a specific number of channels available in the system. The concept of trunking allows a large number of mobile subscribers to share the relatively small number of available channels in a cell by providing access to each mobile subscriber, on demand, from a pool of available channels.
Cellular communication systems are examples of trunked radio systems in which each mobile subscriber is allocated a channel on a per-call request basis. Upon termination of the call, the previously occupied channel is immediately returned to the pool of available channels. When a mobile subscriber requests service and in case all of the radio channels are already busy, the incoming subscriber call is blocked, or denied access to the system. In some communication systems, a queue may be used to hold the requesting mobile subscribers until a channel becomes available.
The GoS is a measure of the ability of a mobile subscriber to access a cellular system during the busiest hour. The busy hour is based upon the subscriber’s demand for the service from the system at the busiest hour during a week, month or year. It is necessary to estimate the maximum required capacity in terms of available channels and to allocate the proper number of channels in order to meet the GoS. GoS is typically specified as the probability that a call is blocked, or the probability of a call experiencing a delay greater than the predefined queuing time. A call which cannot be completed at the time of call request made by a mobile subscriber is referred to as a blocked call or lost call. This may happen due to channel congestion or non-availability of a free channel. In other words, GoS is a measure of channel congestion which is specified as the probability of a call being blocked, or the probability of a call being delayed beyond a specified time.
When the offered traffic exceeds the maximum capacity of the system in terms of the allocated number of channels, the carried traffic becomes limited due to the limited number of channels. The maximum traffic is the total number of channels in Erlangs. Let us consider a cellular system that is designed for a GoS of 2% blocking. This implies that the channel allocations for cell sites are designed in such a way so that 2 out of 100 calls requested by mobile subscribers will be blocked due to channel congestion during the busiest hour.
Practically, there are two types of trunked cellular systems. The first type of trunked cellular system offers no queuing for call requests, which is known as “Erlang B” system. This means that for every mobile subscriber making a service request; it is assumed that there is no set up time to a requesting mobile subscriber. He mobile subscriber is given immediate access to a channel if it is available. If no channels are available, the requesting mobile subscriber is blocked without access to the system and is free to try again later. This type of trunking is called “blocked calls cleared or blocked calls lost”. It assumes that calls arrive as determined by a Poisson distribution.
In performance evaluation of cellular systems or telephone networks, Erlang B formula is a formula for estimating the call blocking probability for a cell (or a sector, if sectoring is used) which has N “trunked” channels and the amount of (“offered”) traffic is A Erlang [38]:
where, i = 1 to N denotes the steady-state number of busy servers. It is directly used to determine the probability B that call requests will be blocked by the system because all channels are currently used.
The second type of trunked cellular system is called “blocked calls delayed” and its measure of GoS is defined as the probability that a call is blocked after waiting a specific length of time in a queue. In this system, a queue is provided to hold the calls requested which are blocked. If a channel is not available immediately, the call request may be delayed until a channel becomes available. Customers who find all N servers busy join a queue and wait as long as necessary to receive service. The probability of a call not having immediate access to a channel is determined by the “Erlang C” formula [38]. If no channels are immediately available, the call is delayed. The Erlang C formula is expressed in terms of blocking probability as:
where, N = number of trunks or service channels, A = offered load.
The basic unit of channel busy/idle status is recorded for each frequency and each time slot. All the channel activities (busy/idle) during each time slot and frequency correction are monitored through different counters. In addition, user friendly graphical user interface (GUI) is available from where the data can be collected and stored in backup support. Conventionally, the data related occupancy of channel is collected on hourly basis from the counters like m15, m16, m25, m17, m18, m23, m147, and m148. Telecom occupancy related data is stored in several counters of base station controller (BSC). To get secondwise accurate data, an interrupt driven learning mechanism is required which is practically not used in telecom network because the requirement is purely academic. In earlier telecommunication, very few processors were used in the radio access logic boards. The speed of working of processors was much less as compared to present day. Further, minutewise transfer of counter data to a central computer adds to transport overhead and hence avoided. Presently, the data speed is available in processors along with high speed links. Hence, capturing of minutewise data is now feasible. Thus, the counters are read every minute for free/occupied status. A scale below minutes was not explored due to the reason that the measurement traffic is a great appreciable part of the total signaling traffic. Thus, disastrous situation cannot be introduced in a live system.
The data for individual subscriber was taken offline from Billing Center at extreme leisure hour for few subscribers. It was expected that a similar set of users shall be the SUs also. For example, the minutewise occupancy of 32 channels during a busy hour has been taken into consideration and is shown in Figure 4 which helps to determine the availability of spectrum holes. The red color cell indicates that the channel is busy or in dedicated mode and cannot be used for channel allocation to CR. The green color cell indicates that the channel is free and can be used for CR use after ensuring its QoS parameters. The parameters like call arrival rate and user holding time of PUs is predicted for the purpose of utilization of channel by SUs.
Minutewise occupancy chart for 32 channels in a day during busy hour.
Blocking probability can be estimated by channel occupancy during last clock hour, e.g., 9 am–10 am at 10 am, 10 am–11 am at 11 am, etc., as in classical teletraffic theory and this estimation has been further improved through prediction models. In present chapter, clock has been considered only for hourly prediction purpose. For channel allocation, considering the instant of channel request as origin, an observation hour is defined in 2 more ways viz., (a) each hour has been composed of 60 immediately preceding minutes or channelized minutes, (b) current minute, or instantaneous minute.
For a lost call system, the GoS for CR shall be measured by using modified Poisson’s model, as proposed in this chapter is given by the equation:
where, k = 0 to (c−1) with c = total number of trunked channels, N = Np + Ns, Np = count of PUs in the system, Ns = σNp + offset = count of SUs in the system, where, 0 < σ ≤1. A portion of the PU, σ (known as SU factor) can be considered for the calculation of the blocking probability of a secondary call combined with PUs traffic in the system. Also, 0 < offset <1 such that Ns is an integer of higher value. These values of GoS help to determine whether the channel allocation to SU shall be successful or fail.
Consider a network with ‘n’ licensed channels (j = 1 to n) where the wireless nodes are static. A CRN is located within the licensed coverage area of licensed operator. The CRNs are equipped with spectrum sensor devices. The sensors monitor and report channel states to the central node via dedicated channels. Also, the outcome of the sensor state can be represented by binary signal {0,1}, where ‘0’ represents the vacant state and ‘1’ represents the occupied state of observed channels at an instant of time, t. All the channels are sensed assuming that the sensing time is very less than the duration of idle and busy time. The history database is periodically updated with the new sensing information. The collected database of different channels can be used to compute the different blocking probabilities as described below to estimate GoS.
The probability computed by autoregressive moving average (ARMA) model that is a mathematical model of the persistence, or autocorrelation, in a time series is called as PBP. In ARMA model, a time series is observed for total number of calls (y1,y2,….yT). To predict the total number of calls in dth day, forecast is done by minimizing the mean squared error (MSE), i.e., Min.y’ T + d E = ((yT + k – y′T + d)2). In that case, the best forecast is the mean of yT + d, conditional on the information up to T, (y1,y2,….yT):
The BS monitoring system records the minutewise channel occupancy of licensed users for continuously 7 days of a week. The predicted value of offered load during the 8th day is calculated by using data of total calls of a particular hour for 7 days (i.e., T = 1 to 7) using ARMA model and has been depicted in Table 3 [39]. The predicted value of total calls of 8th day of a particular hour is taken for computation of blocking probability using the formula:
where i = 0 to c = total channels in the system.
Prediction of offered load in a particular hour using ARMA model.
The blocking probability provided by the system at an instant of time, (t + 1), is called as IBP. The IBP is on every minute basis as shown in Table 4 [39]. In this case, the offered load,
where, i = 0 to c = total channels in the system.
Calculation of offered load at an instant of time t = (t + 1). (a snapshot taken from software).
The blocking probability provided by the system at an instant of time (t + 1) considering the traffic of the preceding 60 min is called as CBP and is depicted in Table 5 [39]. The offered load in this case is defined as,
Calculation of offered load based on immediate preceding 60 min data.
where, i = 0 to c = 60×n = total channels in the system.
The values of CBP and IBP helps to decide the probability of success whenever a SU initiates a request. The data has been chosen at peak busy hours for 50 channels and minutewise occupancy for 300 min calls is practically taken for estimation purpose for various trunk servers ranging from 7 to 50 channels. The CBP as shown in Table 5 can be computed by the program developed by the author.
Figure 5 is plotted for comparison of CBP and PBP for consecutive 4 h. It is evident that the standard deviation of PBP is fixed with respect to IBP but the standard deviation of CBP matches with that of IBP during the busy hour which shows that the CBP is better than PBP [39]. The CBP is much more prominent during the peak hours where random variation of instantaneous values is more.
IBP, CBP and PBP vs. time in minutes in the system with trunk servers (c) = 22.
The error is estimated by the computation of standard deviation between IBP and PBP, and IBP and CBP. The standard deviation of the sample is the degree to which individual data within the sample differ from the sample mean. Since PBP is fixed for a clock hour, the error between IBP and PBP is given by:
where x = value of IBP, x
As CBP varies minutewise, the error between IBP and CBP is given by:
where, xi
It is evident from Table 6 [39] that as the number of trunk server increases, error between IBP and CBP {calculated using equation (Eq.(10))} is less than that of error between IBP and PBP {calculated using equation (Eq.(9))}. Thus, the estimation of CBP is a better method than the estimation of PBP. The eligible list of channels available for use by SU can be formed where the channels have blocking probability ≤0.02.
Difference between standard deviation of IBP & CBP vs. IBP & PBP.
Whenever a SU initiates a call, at an instant, the blocking probability P from Eq. (4) is measured at that instant for all channels in the cell. The value of blocking probability must be less than some pre-determined value. An observation for different channels was made with c = 29, 44, 60 servers to assess the blocking probability and is shown in Figure 6 [37].
Call blocking probability with trunk servers (c) = 29 using Poisson’s model.
It is observed from Figure 6 that when the primary channel occupancy <50% then the CR-BS is capable of providing mobile channel to the SU with blocking probability less than 0.02 which is equivalent to wireline. The channels which have blocking probability less than 50% are eligible used for allocation to SUs.
After capturing the best available spectrum by CR, the user may change its operating frequency band(s) that may require modifications to the operation parameters, based on the PU activity. This process is referred to as spectrum mobility. The purpose of the spectrum mobility management in CRN is to ensure smooth and fast transition that may lead to minimum performance degradation during a spectrum handoff.
CR users and CR infrastructure are essentially the identical as licensed authorized user system. But CR systems shall follow the guideline that: (a) only free channels of PU are to be used and when PU is active, the channels shall be returned to PU immediately, (b) it will not create any noise to PU system. Thus, architecture of SU should have some extra logic than PU system, otherwise they are similar. Therefore, there is a necessity to understand complete system architecture of PU along with PU traffic behavior for making conclusions about CR traffic handling effectively. This can be done by using prediction models.
The parameters like call arrival rate and user holding time of PUs can be predicted for the purpose of utilization of channel by SUs. The probability that the channel would be accessible for a given time period is evaluated according to the prediction or estimation results. The evaluated probability is then compared with some threshold, according to which, SUs can decide whether to use this channel or not. For the purpose of prediction, the study has been arranged in two broad divisions viz. (a) daily traffic analysis for long term prediction; (b) minutewise traffic analysis for immediate prediction of availability of vacant channels.
Primary channel is allotted by the network operator according to demand. The channel occupancy is recorded during each hour. The traffic pattern of each channel is seasonal in terms of daily traffic. The present study uses long term prediction model to compute call arrival rate of the PU. It takes the weekly values of call arrival rate during a particular hour as an input and predicts its weekly values for the same hour. These predicted values can be used to assess the hourly traffic of PU, based on which SU channel allocation is done.
In SARIMA, weekly data of each cell was gathered and organized on hourly basis and one particular hour was selected and analyzed. SARIMA model was used to forecast call arrival rate of weekly data for a specific hour depending on monthly monitored data of the same hour. The prediction of traffic pattern for a week follows the following relationship:
where, i = 1 to 7 for 1 week is assumed as a seasonal unit, j = 1 to n, n = count of days for observation, Si = seasonal coefficients; and,
where, A0 & A1 = intercept coefficients obtained from SARIMA modeling.
A study of channel occupancy pattern shows that the occupancy varies every hour in a day and again daily occupancy pattern has variations over the days of a week. SARIMA uses moving average and auto regression methods which assures sample variations from predicted channel occupancy rate
where, α, β and γ are the smoothing parameters and usually their values are chosen heuristically; at is the smoothed level at time t, bt is the change in the trend at time t, st is the seasonal smooth at time t, p is the number of periods per season.
Here, term j is omitted from λ(j,t), and is written as λt for simplicity. The Holt-Winters algorithm requires starting (or initializing) values given by equations as below:
The HW forecasts are then calculated using the latest estimates given by the equations (Eq. (13)), (Eq. (14)) and (Eq.(15) that have been applied to the series. Thus, the predicted value for
where su is the smoothed estimate of the appropriate seasonal component at u, bu is the smoothed estimate of the change in the trend value at time u and au is the smoothed estimate of the level at time u.
The minutewise occupancy data is used as compared to hourly occupancy data that has been used in traditional traffic prediction models.
The conventional telecom uses hourly prediction for estimating mobile communication traffic. In hourly prediction, peak time is not determined neither in the beginning of the hour nor at the end of hour. The clock hour is not authentic as it does not tell exactly about the peak time at which the traffic was maximum. Hourly traffic data is insufficient to decide whether a call can be initiated at a particular instant of time. Thus, it becomes necessary to study minutewise traffic pattern. In the present work, minutewise occupancy data was computed on hourly basis for various cells of varying channel numbers.
The nature of traffic distribution for few cells at busy hours around the peak for half an hour on both the sides with time resolution of 1 min is studied. For prediction of channel occupancy by PU, it has been established that: “The rate of change of occupancy at a particular point of time near peak time is proportional to its separation from peak time” [43]. Mathematically, it can be expressed as:
where, y = occupancy of primary channels at time t, tp = expected time where peak occupancy occurs, m
which is the equation of a parabola with: h = −b/2a, n = ah2 + bh + c; (h, n) are the equation of the vertex. h = tp.
The peak of parabola may be different from peak occupancy minute. Also peak occupancy projected at peak of parabola shall be different from actual peak obtained.
The authors Hao Chen and L. Trajkovic had captured 92 days (2208 h) of traffic data to study calling behavior of users [44]. They concluded that (a) time scale of minutes is too small for recording the calling activity as an average holding time of a call is usually 3–5 min, (b) time scale larger than an hour (day) is too coarse to capture. The other authors Xiukui Li and Seyed A. (Reza) Zekavat have used the concept of accessing the channel for SU which is vacant with maximum duration [45].
Hence, most of the computations in telecommunication industry are based on hourly number of calls. Accordingly, existing literatures have indicated the need for counting free lifetime of a channel as a probabilistic parameter based on hourly occupied time and hence unaware about residual lifetime, particularly in case of ON/OFF traffic channel conditions.
Methods like dynamic spectrum access (DSA) are proposed to access the channel but the actual channel allocation is not taken into consideration. In case of CR, spectrum or channel mobility is the main challenge as the SU can only access a call without interfering the PU. The program in the present work shall determine the best channel eligible for allocation to SU using the concept of mean residual lifetime (MRL). The procedure for computation of MRL has been described below.
The PU channel state shall be considered as {Ho, H1} where, Ho = free and H1 = occupied. The primary status as sensed or predicted by the SU is shown in Figure 7 [JAF1]. False alarm and missed detection occurs during assessment which leads either to an inefficient system or interference with PU.
Channel occupancy in binary state.
Considering a small unit of time ‘τ’ which is the minimum time period such that BS can upload scanned RF data to Mobile Switching Centre (MSC) and fusion center without affecting routine CR operation. ‘T’ is the time period during which the traffic is recorded based on pulled data from different CR-BS counters and used for statistical records e.g. number of seizures of a channel per hour, total holding time of the channel per hour etc. A SU can request for a channel anytime within τ. The request is conveyed to the fusion center where the decision for allocation of a suitable channel is taken based on MRL and particular requesting SU’s channel holding time profile.
T will be taken as an hour and (1/λ) in minutes. It is also considered that ‘τ’ is in minutes. It is also further considered that:
τ is the atomic unit of time and further decomposition of it is not practically feasible,
MSC is updated by BS every τ units of time,
MSC updates warehouse every T units of time
MSC updates SU traffic data in warehouse every T units of time
Channel occupancy request (PU&SU) is instantly passed on by the BS to MSC in real time t
These aspects will be taken up for application in different models.
Let λ is the number of calls arrived on a particular traffic data acquisition interval T. If th is the call holding time of a SU requesting a free PU channel at any time t, then the probability that none of the PU occupies a channel till t = t + th is given by:
where,
Let F be the lifetime distribution with discrete random sample and no call arrival intervals Γ1, Γ2, ……, Γn in the span of observation T. We arrange them in order such that:
The empirical mean residual lifetime (MRL) is defined as:
and, mn(t) = 0 for te ≥ Γnn and k = 0,1,2,….,(n-1).
where, mn(te)|j = mean residual lifetime of jth channel which has ‘n’ number of vacant intervals at observation instant ‘t’ which can be offered a SU call.
Here, te is the time which has elapsed since it became free; Γj = mean residual lifetime of jth channel at an instant t of SU call offer, and j = 1,2,…., r with r = total number of vacant channels at an instant ‘t’ of offer.
The program developed by the author computes probability of success using the method proposed by Li and Zekavat, i.e., without MRL and with MRL. Figure 8 depicts the probability of success with and without MRL for various trunk servers vs. time demanded by CR. The Figure 8 clearly depicts that the proposed model using MRL method is superior than the method used by previous researchers [46].
Comparison of probability of success with and without MRL vs. time demanded by CR for trunk servers = 43 and 50.
A traffic model is required to represent traffic characteristics and to estimate the performance evaluation of the volume of traffic load place on network capacity and subscriber mobility. The present work assumes that the arrival rate is Poisson’s distributed. The inter-arrival rate is also assumed to be exponentially distributed. The traffic model is determined assuming a certain number of channels in a cell system. The two crucial factors for mobile communication in the traffic pattern are called arrival rate of the channel and user call holding time. Hence, the traffic model is developed based on the prediction of call arrival rate and the study of holding time distribution of individual customers.
A telecommunication network consists of expensive hardware (trunks, switches, etc.) which carries telecommunications traffic (phone calls, data packets, etc.). The physical network is fixed, but the traffic is random for which it is designed, i.e., the call arrival rate and the user holding time is unpredictable. Thus, to accommodate this random demand of traffic, the network designers must predict the call arrival rate for allocation of resources. The usual assumption in classical teletraffic theory is that the call arrivals follow a Poisson’s process. The Poisson’s assumption is consistent with data for voice traffic when the calls are generated by a large number of independently acting subscribers.
The French Mathematician Simeon Denis Poisson developed Poisson’s formula. It states that for non-overlapping events, arriving at an average rate λ, the probability of ‘s’ arrivals in time t equals:
Poisson’s distribution is taken for traffic measurement as it is based on memoryless system and it generally gives a better estimate of the traffic related parameters.
Telecom occupancy related data is stored in several counters of Base Station Controller (BSC). To get secondwise accurate data, an interrupt driven learning mechanism is required which is practically not used in telecom network because the requirement is purely academic. In earlier telecommunication, very few processors were used in the radio access logic boards. The speed of working of processors was much less as compared to present day. Further, minutewise transfer of counter data to a central computer adds to transport overhead and hence avoided. Presently, the data speed is available in processors along with high speed links. Hence, capturing of minutewise data is now feasible.
The channel occupancy duration depends on individual person depending upon profession, status, time of day, etc. and varies widely at different hours of a day. This is most predominant in case of speech communication when the channel holding time depends upon caller and various customers called parties. Thus, the holding time data shows different variation at different levels of the time series and hence, a transformation of the data series can be useful. The Box- Cox method is used to transform this data into normality. The Box-Cox method obtains a normal distribution of the transformed data (after transformation) and a constant variance.
Let us denote original observations hi,u,p as hp for the ith user at uth hour and write the series as h1,h2,---,ht and transform the observations as w1,w2,---,wt. According to Box-Cox principle [47]:
where, ζ is a parameter used to compute the confidence level (CL). Using the values of ζ = {−2,−1,−0.5, 0, 0.5, 1, 2} and to gain confidence level (CL) limit up to 95%, the optimum value of ζ shall be used to assess hp as:
where, hp = optimal holding (service) time of the user at time t.
The Box-Cox method can be used to decide the optimum holding time of the user.
On placement of service request by an SU, all vacant channels from eligible list has to be evaluated for allocation based on (i) predicted call arrival rate during the hour created through long term table; (ii) IBP at the instant for primary decision on allocation; (iii) mean residual lifetime of the channel at the time of service request; (iv) expected service holding time of a particular user requesting service; (v) CBP at the instant based on short term table. Finally, the best channel with highest probability of survival is selected for offer to the incoming SU traffic [46].
The interworking of different blocks for channel allocation is shown in Figure 9 and is described below:
Channel traffic updation: The Gateways (GWs) monitor PU activities using dedicated RF scanners. G1,G2,…,Gk are responsible for monitoring PU activities as well as for SUs. Any change in channel occupancy is passed on by GW to CR-BS and MSC in real time t. BS maintains several counters for traffic recording purposes. The counts of the counters are polled by MSC every ‘τ’ interval and then the counters are reset. When T = kτ where, k = 2, 3,… MSC prepares a table for the call arrival rate for T interval for each channel and deposit to the warehouse where the data is stored in format λ(j, u) where, j = channel number and u = current T period number. This module also provides idle time information d1, d2,…, etc. in τ units since a channel is free.
SU traffic updation: SU traffic information is recorded in the billing register after the completion of each call. The traffic details in respect of each SU are stored in warehouse. It is used for predicting holding time of SU at the time of service request.
Channel traffic prediction: Primary channel is allotted by network operator according to demand. The channel occupancy is recorded during each T. The predicted value for channel occupancy
As soon as a new predicted value of call arrival rate is available during a particular hour, HW updates its estimated three components (level, trend, seasonal) for that particular hour. The value of smoothing constant for each component falls between zero and one. Larger smoothing constants mean more weight is placed on the value suggested by the new predicted value and less on the previous estimate. This means that the method will adapt more quickly to genuine changes in the call arrival pattern.
The list of channels {C1, C2,…} which satisfies the above condition are predicted during the last hour and are then transferred to the channel allocation model for assessment of holding time during the current hour of allocation.
SU holding time assessment: The holding time data shows different variation at different levels of the time series and hence their transformation is done by Box-Cox method using (Eq.(25)) and the optimum holding time needed by SU is obtained by (Eq.(26)).
Channel allocation model: The set of eligible channels obtained by (Eq.(22)) are with residual lifetime:
Channel selection block diagram.
where, hp = service time needed by SU at time t.
The eligible channel set {c1, c2,…, cr} is arranged and the probability of the success in the offered jth channel shall be:
A sequential flowchart of data flow from all the blocks and computation of MRL has been given in Figure 10. Based on the measurement of the call arrival rate, holding time of PUs and various parameters of QoS, the channel allocation is done.
Process for channel allocation.
Minutewise traffic data acquisitioned online is collected in a table in OMC-R. SU can raise service request at any time. To serve channel allocation engine inputs,
At the beginning of each hour, for all channels predicted call arrival rate
estimated holding time in minutes for the service request from SU holding time assessment model;
all channel occupancy status is ‘free’ or ‘busy’ mode for last 60 min starting from current minute from channel traffic updation module is taken, and
their MRL is computed.
Finally, channels with highest probability of survival given by Eq. (28) are selected for offer to the incoming SU traffic.
A trial run for 25 times each for available channels 7, 15, 22, 29, 36, 43 and 50 were carried for holding times from 1 to 14 min in the program developed by the author. A snapshot of the program is depicted in Figure 11, where different SUs demand varying holding times. The ‘red’ color indicates that the channel is busy while the ‘green’ color indicates that the channel is free. Whenever an SU requests a channel at time t for a certain holding time, the channel is allocated to that particular SU if the channel is free for continuous holding time demanded by SU, and is shown in ‘yellow’ color in Figure 11. If the channel is busy even for the last holding time demanded by SU, the program indicates that the channel allocation is unsuccessful and is shown with ‘red’ color in continuation with yellow color.
A snapshot of the program for channel allocation at an instant of time t.
To study success rate for channel allocation various cases were considered with varying available number of channels and different holding times demanded by SU, where the total number of channels in the cell is denoted by tch.
Case (i): Consider tch = 15 and the time demanded by the SU = 2 min. The result is obtained when the program is initialized at 100th min and stopped at 102nd min out of 300 min total available data. At 102nd min, when a call request is made by SU, the total available vacant channels are 5 out of 15 channels. The values of blocking probabilities are obtained as IBP = 0.03649 and CBP = 0.0308. The MRL is computed for all the five vacant channels. The MRL of the five channels are 4.7, 0.82, 0.5, −27 and −27.25. As one of the channels has maximum MRL and has value > holding time demanded (2 min), it is selected for channel allocation to SU. The result is depicted in snapshot given in Figure 12 and is shown with continuity of yellow color for 2 min, which signifies that the channel allocation was successful. Table 7 shows analysis of success rate for 15 channels with various holding times demanded by CR.
Validation result for total channels = 15; SU holding time demanded = 2 min.
Computation of success rate for total channels = 15 for CR holding time = 1 to 14 at various runs of the program.
Case (ii): Consider tch = 43 and the time demanded by the SU = 9 min. The result is obtained when the program is initialized at 80th min and stopped at 82nd min out of 300 min total available data. At 82nd min, when a call request is made by SU, the total vacant channels are 14 out of 43 channels. The values of blocking probabilities are obtained as IBP = 1.102×10−3 and CBP = 6.442×10−4. The MRL is computed for all the 14 vacant channels. The MRL of the 14 channels are: 4, 0.75, 0.444444, −0.6875, −1.5, −2.6, −4, −5.66667, −5.8, −11.6667, −12.5, −13.5714, −13.8, −14.2222, −20. As the MRL of one of the channels has value > holding time demanded (9 min); thus, the channel allocation was successful. The result is depicted in snapshot given in Figure 13 and is shown with yellow color which signifies that the channel allocation was successful. Table 8 shows analysis of success rate for 43 channels with various holding times demanded by CR.
Validation result for total channels = 43; SU holding time demanded = 9 min.
Computation of success rate for total channels = 43 for CR holding time = 1 to 14 at various runs of the program.
Thus, the channel allocation is successful as the count of channels increases in the system with the precondition that the MRL > time demanded by the SU.
The data was chosen at busy hours for various channels ranging from 15 to 50 and minutewise occupancy for 300 min calls is taken for simulation purpose. A program has been developed for real time offering and verification if the call request succeeds or fails for the duration demanded by SU. The program calculates the predicted λ upto last hour at background and has been included in simulation program as offline. Similarly, holding time needed for SU has also been imprinted interactively. The program accepts any number of PU channels upto 50 selectively at the time of trial. The position of occupancy can be seen on screen starting from any instant after 60 min upto 300 min for any duration. Also, one or more SU calls can be offered to the system and minute by minute observation of SU call progress can be monitored on screen.
The program was run repetitively and at random, under various channel availability conditions and differently demanded holding time. The result has been plotted in Figure 14, where the probability of success (or QoS) has been calculated as:
where, number of trials were 25 times for each run condition at random input time.
Probability of success rate vs. time demanded by CR for various trunk servers.
As depicted in Figure 14, in case, if the threshold probability in licensed band is taken to be 0.8, the success rate for CR users is still achieved if the channels are ≥36 and holding time ≤2.5 min demanded by the SU [46]. When the threshold probability for SU is 0.5, the success rate is achieved if the channels are ≥22 with minimum holding time ≤ 2.5 min demanded by the SU. Thus, when the PU channel occupancy is 50%, the CR-BS shall provide mobile channel to SU with blocking probability ≤0.02, where industry standard for blocking is 2% (0.02).
Thus, the CR-BS shall be capable of providing vacant channels to SU. Grade of Service (GoS) of SU is at par with GoS standard specified for PU when traffic intensity is below 50%. Success rate of channel allocation is increased as number of channels increases in the system.
In mobile communication network, despite heavy usage of communication channels, vacant channels are available which can be used for cognitive radio network. From the present work it is found that practically 20% or more of the total licensed bandwidth is permanently unused or vacant even in a crowded region This is well above the need of 1 out of 8th part of the band to get access to the whole of the bandwidth at a time by CR and adequate to take additional MAC level overhead required for CR. Remaining 80% of the licensed bands are dynamically vacant which can be used for traffic purpose. Based on results, an empirical relationship has been established for channel occupancy for a city, where such survey has not been conducted but population is known. The blocking probability of the channels are available for allocation and duration of remaining unoccupied can be mapped to the instant status of the channels. The present work has established that when the PU channel occupancy is 50%, the CR-BS shall provide voice channel to SU with blocking probability ≤0.02, where industry standard for blocking is 0.02. The GoS improves linearly with total number of channels in the system at a given per channel availability. It is also evident that Erlang theory is effective for Poisson’s distribution theorem with ≥20 channels for GoS to achieve.
A program that has been developed to accept SU service requests with different QoS from a set of PU channels can be used for dynamic allocation to SU. A SU call request can be placed in such a dynamic environment and status of the SU call progress can be noticed till the end of requested holding time. The mean residual lifetime (MRL) of the free channels was computed based on requesting SU call holding time for PU channel allocation to a requesting SU. The channel with highest MRL is allocated. If the threshold probability in licensed band is taken to be 0.8, the success rate for CR users is achieved if the channels are ≥36 and holding time ≤ 2.5 min demanded by the SU. When the threshold probability for SU is 0.5, the success rate is achieved if the channels are ≥22 with minimum holding time ≤ 2.5 min demanded by the SU. Thus, success rate of channel allocation increases with the increase in number of channels in the system.
The proposed model shall be deployed to provide services for IoT through wireless communication. The implementation of the proposed work shall be a good solution where high volume of devices with low mobility is required for new wireless technologies.
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\n\nThe Open Access Publishing Fee of your IntechOpen Compacts, Monograph or Edited Book depends on the volume of the publication and includes: project management, editorial and peer review services, technical editing, language copyediting, cover design and book layout, book promotion and ISBN assignment.
\n\nWe will send you your price quote and after it has been accepted (by both the author and the publisher), both parties will sign a Statement of Work binding them to adhere to the agreed upon terms.
\n\nAt this step you will also be asked to accept the Copyright Agreement.
\n\n5. LANGUAGE COPYEDITING, TECHNICAL EDITING AND TYPESET PROOF
\n\nYour manuscript will be sent to Straive, a leader in content solution services, for language copyediting. You will then receive a typeset proof formatted in XML and available online in HTML and PDF to proofread and check for completeness. The first typeset proof of your manuscript is usually available 10 days after its original submission.
\n\nAfter we receive your proof corrections and a final typeset of the manuscript is approved, your manuscript is sent to our in house DTP department for technical formatting and online publication preparation.
\n\nAdditionally, you will be asked to provide a profile picture (face or chest-up portrait photograph) and a short summary of the book which is required for the book cover design.
\n\n6. INVOICE PAYMENT
\n\nThe invoice is generally paid by the author, the author’s institution or funder. The payment can be made by credit card from your Author Panel (one will be assigned to you at the beginning of the project), or via bank transfer as indicated on the invoice. We currently accept the following payment options:
\n\nIntechOpen will help you complete your payment safely and securely, keeping your personal, professional and financial information safe.
\n\n7. ONLINE PUBLICATION, PRINT AND DELIVERY OF THE BOOK
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\n\nIf you feel that IntechOpen Compacts, Monographs or Edited Books are the right publishing format for your work, please fill out the publishing proposal form. For any specific queries related to the publishing process, or IntechOpen Compacts, Monographs & Edited Books in general, please contact us at book.department@intechopen.com
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The routine management of oral cancers is firstly surgical resection with or without postoperative adjuncts and other therapies such as the use of postoperative chemoradiation and radiation. Successful treatment of oral cancer patients is a complex issue that requires a multidisciplinary approach, including oral and maxillofacial surgeons, oral and maxillofacial radiologists, ENT specialists, medical and radiological oncologists, prosthodontists, dentists, speech therapists, supportive care experts, and also pathologists or, if possible, oral and maxillofacial pathologists.",book:{id:"8631",slug:"prevention-detection-and-management-of-oral-cancer",title:"Prevention, Detection and Management of Oral Cancer",fullTitle:"Prevention, Detection and Management of Oral Cancer"},signatures:"Nihat Akbulut and Ahmet Altan",authors:[{id:"262769",title:"Dr.",name:"Nihat",middleName:null,surname:"Akbulut",slug:"nihat-akbulut",fullName:"Nihat Akbulut"},{id:"268500",title:"Dr.",name:"Ahmet",middleName:null,surname:"Altan",slug:"ahmet-altan",fullName:"Ahmet Altan"}]},{id:"63395",title:"The Impact of Sequencing Human Genome on Drug Design to Treat Oral Cancer",slug:"the-impact-of-sequencing-human-genome-on-drug-design-to-treat-oral-cancer",totalDownloads:892,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Of all the known cancers, oral cancer is the most preventable and it is the second most deadly cancer after the breast cancer. 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The sources related to thyroid surgery show that the success of the neck masses with the surgical intervention was limited until the second half of the nineteenth century. Among the names leading the development of thyroid surgery in contemporary times are Emil Theodor Kocher, Theodor Billroth, William James Mayo, and William Stewart Halsted. 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His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:58,paginationItems:[{id:"81961",title:"Antioxidants as an Adjuncts to Periodontal Therapy",doi:"10.5772/intechopen.105016",signatures:"Sura Dakhil Jassim and Ali Abbas Abdulkareem",slug:"antioxidants-as-an-adjuncts-to-periodontal-therapy",totalDownloads:3,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Dental Trauma",coverURL:"https://cdn.intechopen.com/books/images_new/11567.jpg",subseries:{id:"2",title:"Prosthodontics and Implant Dentistry"}}},{id:"82357",title:"Caries Management Aided by Fluorescence-Based Devices",doi:"10.5772/intechopen.105567",signatures:"Atena Galuscan, Daniela Jumanca and Aurora Doris Fratila",slug:"caries-management-aided-by-fluorescence-based-devices",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Dental Caries - The Selection of Restoration Methods and Restorative Materials",coverURL:"https://cdn.intechopen.com/books/images_new/11565.jpg",subseries:{id:"1",title:"Oral Health"}}},{id:"81894",title:"Diet and Nutrition and Their Relationship with Early Childhood Dental Caries",doi:"10.5772/intechopen.105123",signatures:"Luanna Gonçalves Ferreira, Giuliana de Campos Chaves Lamarque and Francisco Wanderley Garcia Paula-Silva",slug:"diet-and-nutrition-and-their-relationship-with-early-childhood-dental-caries",totalDownloads:11,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Dental Caries - The Selection of Restoration Methods and Restorative Materials",coverURL:"https://cdn.intechopen.com/books/images_new/11565.jpg",subseries:{id:"1",title:"Oral Health"}}},{id:"81595",title:"Prosthetic Concepts in Dental Implantology",doi:"10.5772/intechopen.104725",signatures:"Ivica Pelivan",slug:"prosthetic-concepts-in-dental-implantology",totalDownloads:27,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Current Concepts in Dental Implantology - From Science to Clinical Research",coverURL:"https://cdn.intechopen.com/books/images_new/10808.jpg",subseries:{id:"2",title:"Prosthodontics and Implant Dentistry"}}}]},overviewPagePublishedBooks:{paginationCount:8,paginationItems:[{type:"book",id:"6668",title:"Dental Caries",subtitle:"Diagnosis, Prevention and Management",coverURL:"https://cdn.intechopen.com/books/images_new/6668.jpg",slug:"dental-caries-diagnosis-prevention-and-management",publishedDate:"September 19th 2018",editedByType:"Edited by",bookSignature:"Zühre Akarslan",hash:"b0f7667770a391f772726c3013c1b9ba",volumeInSeries:1,fullTitle:"Dental Caries - Diagnosis, Prevention and Management",editors:[{id:"171887",title:"Prof.",name:"Zühre",middleName:null,surname:"Akarslan",slug:"zuhre-akarslan",fullName:"Zühre Akarslan",profilePictureURL:"https://mts.intechopen.com/storage/users/171887/images/system/171887.jpg",biography:"Zühre Akarslan was born in 1977 in Cyprus. She graduated from Gazi University Faculty of Dentistry, Ankara, Turkey in 2000. \r\nLater she received her Ph.D. degree from the Oral Diagnosis and Radiology Department; which was recently renamed as Oral and Dentomaxillofacial Radiology, from the same university. \r\nShe is working as a full-time Associate Professor and is a lecturer and an academic researcher. \r\nHer expertise areas are dental caries, cancer, dental fear and anxiety, gag reflex in dentistry, oral medicine, and dentomaxillofacial radiology.",institutionString:"Gazi University",institution:{name:"Gazi University",institutionURL:null,country:{name:"Turkey"}}}]},{type:"book",id:"7139",title:"Current Approaches in Orthodontics",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7139.jpg",slug:"current-approaches-in-orthodontics",publishedDate:"April 10th 2019",editedByType:"Edited by",bookSignature:"Belma Işık Aslan and Fatma Deniz Uzuner",hash:"2c77384eeb748cf05a898d65b9dcb48a",volumeInSeries:2,fullTitle:"Current Approaches in Orthodontics",editors:[{id:"42847",title:"Dr.",name:"Belma",middleName:null,surname:"Işik Aslan",slug:"belma-isik-aslan",fullName:"Belma Işik Aslan",profilePictureURL:"https://mts.intechopen.com/storage/users/42847/images/system/42847.jpg",biography:"Dr. Belma IşIk Aslan was born in 1976 in Ankara-TURKEY. After graduating from TED Ankara College in 1994, she attended to Gazi University, Faculty of Dentistry in Ankara. She completed her PhD in orthodontic education at Gazi University between 1999-2005. Dr. Işık Aslan stayed at the Providence Hospital Craniofacial Institude and Reconstructive Surgery in Michigan, USA for three months as an observer. She worked as a specialist doctor at Gazi University, Dentistry Faculty, Department of Orthodontics between 2005-2014. She was appointed as associate professor in January, 2014 and as professor in 2021. Dr. Işık Aslan still works as an instructor at the same faculty. She has published a total of 35 articles, 10 book chapters, 39 conference proceedings both internationally and nationally. Also she was the academic editor of the international book 'Current Advances in Orthodontics'. She is a member of the Turkish Orthodontic Society and Turkish Cleft Lip and Palate Society. She is married and has 2 children. Her knowledge of English is at an advanced level.",institutionString:"Gazi University Dentistry Faculty Department of Orthodontics",institution:null}]},{type:"book",id:"7572",title:"Trauma in Dentistry",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7572.jpg",slug:"trauma-in-dentistry",publishedDate:"July 3rd 2019",editedByType:"Edited by",bookSignature:"Serdar Gözler",hash:"7cb94732cfb315f8d1e70ebf500eb8a9",volumeInSeries:3,fullTitle:"Trauma in Dentistry",editors:[{id:"204606",title:"Dr.",name:"Serdar",middleName:null,surname:"Gözler",slug:"serdar-gozler",fullName:"Serdar Gözler",profilePictureURL:"https://mts.intechopen.com/storage/users/204606/images/system/204606.jpeg",biography:"Dr. Serdar Gözler has completed his undergraduate studies at the Marmara University Faculty of Dentistry in 1978, followed by an assistantship in the Prosthesis Department of Dicle University Faculty of Dentistry. Starting his PhD work on non-resilient overdentures with Assoc. Prof. Hüsnü Yavuzyılmaz, he continued his studies with Prof. Dr. Gürbüz Öztürk of Istanbul University Faculty of Dentistry Department of Prosthodontics, this time on Gnatology. He attended training programs on occlusion, neurology, neurophysiology, EMG, radiology and biostatistics. In 1982, he presented his PhD thesis \\Gerber and Lauritzen Occlusion Analysis Techniques: Diagnosis Values,\\ at Istanbul University School of Dentistry, Department of Prosthodontics. As he was also working with Prof. Senih Çalıkkocaoğlu on The Physiology of Chewing at the same time, Gözler has written a chapter in Çalıkkocaoğlu\\'s book \\Complete Prostheses\\ entitled \\The Place of Neuromuscular Mechanism in Prosthetic Dentistry.\\ The book was published five times since by the Istanbul University Publications. Having presented in various conferences about occlusion analysis until 1998, Dr. Gözler has also decided to use the T-Scan II occlusion analysis method. Having been personally trained by Dr. Robert Kerstein on this method, Dr. Gözler has been lecturing on the T-Scan Occlusion Analysis Method in conferences both in Turkey and abroad. Dr. Gözler has various articles and presentations on Digital Occlusion Analysis methods. 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Dr. Al Ostwani is an assistant professor and faculty member at IUST University since 2014. \nDuring his academic experience, he has received several awards including the scientific research award from the Union of Arab Universities, the Syrian gold medal and the international gold medal for invention and creativity. Dr. Al Ostwani is a Member of the International Association of Dental Traumatology and the Syrian Society for Research and Preventive Dentistry since 2017. He is also a Member of the Reviewer Board of International Journal of Dental Medicine (IJDM), and the Indian Journal of Conservative and Endodontics since 2016.",institutionString:"International University for Science and Technology.",institution:{name:"Islamic University of Science and Technology",institutionURL:null,country:{name:"India"}}}]}]},openForSubmissionBooks:{paginationCount:3,paginationItems:[{id:"11570",title:"Influenza - New Approaches",coverURL:"https://cdn.intechopen.com/books/images_new/11570.jpg",hash:"157b379b9d7a4bf5e2cc7a742f155a44",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"May 10th 2022",isOpenForSubmission:!0,editors:[{id:"139889",title:"Dr.",name:"Seyyed Shamsadin",surname:"Athari",slug:"seyyed-shamsadin-athari",fullName:"Seyyed Shamsadin Athari"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"11569",title:"Bacterial Sexually Transmitted Infections - New Findings, Diagnosis, Treatment, and Prevention",coverURL:"https://cdn.intechopen.com/books/images_new/11569.jpg",hash:"069d6142ecb0d46d14920102d48c0e9d",secondStepPassed:!0,currentStepOfPublishingProcess:3,submissionDeadline:"May 31st 2022",isOpenForSubmission:!0,editors:[{id:"189561",title:"Dr.",name:"Mihaela Laura",surname:"Vica",slug:"mihaela-laura-vica",fullName:"Mihaela Laura Vica"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null},{id:"11568",title:"Staphylococcal Infections - Recent Advances and Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11568.jpg",hash:"92c881664d1921c7f2d0fee34b78cd08",secondStepPassed:!1,currentStepOfPublishingProcess:2,submissionDeadline:"July 8th 2022",isOpenForSubmission:!0,editors:[{id:"59719",title:"Dr.",name:"Jaime",surname:"Bustos-Martínez",slug:"jaime-bustos-martinez",fullName:"Jaime Bustos-Martínez"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},onlineFirstChapters:{paginationCount:10,paginationItems:[{id:"82380",title:"Evolution of Parasitism and Pathogenic Adaptations in Certain Medically Important Fungi",doi:"10.5772/intechopen.105206",signatures:"Gokul Shankar Sabesan, Ranjit Singh AJA, Ranjith Mehenderkar and Basanta Kumar Mohanty",slug:"evolution-of-parasitism-and-pathogenic-adaptations-in-certain-medically-important-fungi",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Fungal Infectious Diseases - Annual Volume 2022",coverURL:"https://cdn.intechopen.com/books/images_new/11400.jpg",subseries:{id:"4",title:"Fungal Infectious Diseases"}}},{id:"82367",title:"Spatial Variation and Factors Associated with Unsuppressed HIV Viral Load among Women in an HIV Hyperendemic Area of KwaZulu-Natal, South Africa",doi:"10.5772/intechopen.105547",signatures:"Adenike O. 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He also obtained an MSc in Molecular and Genetic Medicine, and a Ph.D. in Clinical Immunology and Human Genetics from the University of Sheffield, UK. He also completed a short-term fellowship in Pediatric Clinical Immunology and Bone Marrow Transplantation at Newcastle General Hospital, England. Dr. Rezaei is a Full Professor of Immunology and Vice Dean of International Affairs and Research, at the School of Medicine, Tehran University of Medical Sciences, and the co-founder and head of the Research Center for Immunodeficiencies. He is also the founding president of the Universal Scientific Education and Research Network (USERN). Dr. Rezaei has directed more than 100 research projects and has designed and participated in several international collaborative projects. He is an editor, editorial assistant, or editorial board member of more than forty international journals. He has edited more than 50 international books, presented more than 500 lectures/posters in congresses/meetings, and published more than 1,100 scientific papers in international journals.",institutionString:"Tehran University of Medical Sciences",institution:{name:"Tehran University of Medical Sciences",country:{name:"Iran"}}},{id:"180733",title:"Dr.",name:"Jean",middleName:null,surname:"Engohang-Ndong",slug:"jean-engohang-ndong",fullName:"Jean Engohang-Ndong",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180733/images/system/180733.png",biography:"Dr. Jean Engohang-Ndong was born and raised in Gabon. After obtaining his Associate Degree of Science at the University of Science and Technology of Masuku, Gabon, he continued his education in France where he obtained his BS, MS, and Ph.D. in Medical Microbiology. He worked as a post-doctoral fellow at the Public Health Research Institute (PHRI), Newark, NJ for four years before accepting a three-year faculty position at Brigham Young University-Hawaii. Dr. Engohang-Ndong is a tenured faculty member with the academic rank of Full Professor at Kent State University, Ohio, where he teaches a wide range of biological science courses and pursues his research in medical and environmental microbiology. Recently, he expanded his research interest to epidemiology and biostatistics of chronic diseases in Gabon.",institutionString:"Kent State University",institution:{name:"Kent State University",country:{name:"United States of America"}}},{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",slug:"emmanuel-drouet",fullName:"Emmanuel Drouet",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",biography:"Emmanuel Drouet, PharmD, is a Professor of Virology at the Faculty of Pharmacy, the University Grenoble-Alpes, France. As a head scientist at the Institute of Structural Biology in Grenoble, Dr. Drouet’s research investigates persisting viruses in humans (RNA and DNA viruses) and the balance with our host immune system. He focuses on these viruses’ effects on humans (both their impact on pathology and their symbiotic relationships in humans). He has an excellent track record in the herpesvirus field, and his group is engaged in clinical research in the field of Epstein-Barr virus diseases. He is the editor of the online Encyclopedia of Environment and he coordinates the Universal Health Coverage education program for the BioHealth Computing Schools of the European Institute of Science.",institutionString:null,institution:{name:"Grenoble Alpes University",country:{name:"France"}}},{id:"131400",title:"Prof.",name:"Alfonso J.",middleName:null,surname:"Rodriguez-Morales",slug:"alfonso-j.-rodriguez-morales",fullName:"Alfonso J. Rodriguez-Morales",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/131400/images/system/131400.png",biography:"Dr. Rodriguez-Morales is an expert in tropical and emerging diseases, particularly zoonotic and vector-borne diseases (especially arboviral diseases). He is the president of the Travel Medicine Committee of the Pan-American Infectious Diseases Association (API), as well as the president of the Colombian Association of Infectious Diseases (ACIN). He is a member of the Committee on Tropical Medicine, Zoonoses, and Travel Medicine of ACIN. He is a vice-president of the Latin American Society for Travel Medicine (SLAMVI) and a Member of the Council of the International Society for Infectious Diseases (ISID). Since 2014, he has been recognized as a Senior Researcher, at the Ministry of Science of Colombia. He is a professor at the Faculty of Medicine of the Fundacion Universitaria Autonoma de las Americas, in Pereira, Risaralda, Colombia. He is an External Professor, Master in Research on Tropical Medicine and International Health, Universitat de Barcelona, Spain. He is also a professor at the Master in Clinical Epidemiology and Biostatistics, Universidad Científica del Sur, Lima, Peru. In 2021 he has been awarded the “Raul Isturiz Award” Medal of the API. Also, in 2021, he was awarded with the “Jose Felix Patiño” Asclepius Staff Medal of the Colombian Medical College, due to his scientific contributions to COVID-19 during the pandemic. He is currently the Editor in Chief of the journal Travel Medicine and Infectious Diseases. His Scopus H index is 47 (Google Scholar H index, 68).",institutionString:"Institución Universitaria Visión de las Américas, Colombia",institution:null},{id:"332819",title:"Dr.",name:"Chukwudi Michael",middleName:"Michael",surname:"Egbuche",slug:"chukwudi-michael-egbuche",fullName:"Chukwudi Michael Egbuche",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/332819/images/14624_n.jpg",biography:"I an Dr. Chukwudi Michael Egbuche. I am a Senior Lecturer in the Department of Parasitology and Entomology, Nnamdi Azikiwe University, Awka.",institutionString:null,institution:{name:"Nnamdi Azikiwe University",country:{name:"Nigeria"}}},{id:"284232",title:"Mr.",name:"Nikunj",middleName:"U",surname:"Tandel",slug:"nikunj-tandel",fullName:"Nikunj Tandel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/284232/images/8275_n.jpg",biography:'Mr. Nikunj Tandel has completed his Master\'s degree in Biotechnology from VIT University, India in the year of 2012. He is having 8 years of research experience especially in the field of malaria epidemiology, immunology, and nanoparticle-based drug delivery system against the infectious diseases, autoimmune disorders and cancer. He has worked for the NIH funded-International Center of Excellence in Malaria Research project "Center for the study of complex malaria in India (CSCMi)" in collaboration with New York University. The preliminary objectives of the study are to understand and develop the evidence-based tools and interventions for the control and prevention of malaria in different sites of the INDIA. Alongside, with the help of next-generation genomics study, the team has studied the antimalarial drug resistance in India. Further, he has extended his research in the development of Humanized mice for the study of liver-stage malaria and identification of molecular marker(s) for the Artemisinin resistance. At present, his research focuses on understanding the role of B cells in the activation of CD8+ T cells in malaria. Received the CSIR-SRF (Senior Research Fellow) award-2018, FIMSA (Federation of Immunological Societies of Asia-Oceania) Travel Bursary award to attend the IUIS-IIS-FIMSA Immunology course-2019',institutionString:"Nirma University",institution:{name:"Nirma University",country:{name:"India"}}},{id:"334383",title:"Ph.D.",name:"Simone",middleName:"Ulrich",surname:"Ulrich Picoli",slug:"simone-ulrich-picoli",fullName:"Simone Ulrich Picoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334383/images/15919_n.jpg",biography:"Graduated in Pharmacy from Universidade Luterana do Brasil (1999), Master in Agricultural and Environmental Microbiology from Federal University of Rio Grande do Sul (2002), Specialization in Clinical Microbiology from Universidade de São Paulo, USP (2007) and PhD in Sciences in Gastroenterology and Hepatology (2012). She is currently an Adjunct Professor at Feevale University in Medicine and Biomedicine courses and a permanent professor of the Academic Master\\'s Degree in Virology. She has experience in the field of Microbiology, with an emphasis on Bacteriology, working mainly on the following topics: bacteriophages, bacterial resistance, clinical microbiology and food microbiology.",institutionString:null,institution:{name:"Universidade Feevale",country:{name:"Brazil"}}},{id:"229220",title:"Dr.",name:"Amjad",middleName:"Islam",surname:"Aqib",slug:"amjad-aqib",fullName:"Amjad Aqib",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229220/images/system/229220.png",biography:"Dr. Amjad Islam Aqib obtained a DVM and MSc (Hons) from University of Agriculture Faisalabad (UAF), Pakistan, and a PhD from the University of Veterinary and Animal Sciences Lahore, Pakistan. Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:null},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}},{id:"338856",title:"Mrs.",name:"Nur Alvira",middleName:null,surname:"Pascawati",slug:"nur-alvira-pascawati",fullName:"Nur Alvira Pascawati",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universitas Respati Yogyakarta",country:{name:"Indonesia"}}},{id:"441116",title:"Dr.",name:"Jovanka M.",middleName:null,surname:"Voyich",slug:"jovanka-m.-voyich",fullName:"Jovanka M. Voyich",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Montana State University",country:{name:"United States of America"}}},{id:"330412",title:"Dr.",name:"Muhammad",middleName:null,surname:"Farhab",slug:"muhammad-farhab",fullName:"Muhammad Farhab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"349495",title:"Dr.",name:"Muhammad",middleName:null,surname:"Ijaz",slug:"muhammad-ijaz",fullName:"Muhammad Ijaz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Veterinary and Animal Sciences",country:{name:"Pakistan"}}}]}},subseries:{item:{id:"95",type:"subseries",title:"Urban Planning and Environmental Management",keywords:"Circular economy, Contingency planning and response to disasters, Ecosystem services, Integrated urban water management, Nature-based solutions, Sustainable urban development, Urban green spaces",scope:"