Major parameters for handling the given tracking problems in computer experiments.
\r\n\tIn sum, the book presents a reflective analysis of the pedagogical hubs for a changing world, considering the most fundamental areas of the current contingencies in education.
",isbn:"978-1-83968-793-8",printIsbn:"978-1-83968-792-1",pdfIsbn:"978-1-83968-794-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"b01f9136149277b7e4cbc1e52bce78ec",bookSignature:"Dr. María Jose Hernandez-Serrano",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10229.jpg",keywords:"Teacher Digital Competences, Flipped Learning, Online Resources Design, Neuroscientific Literacy (Myths), Emotions and Learning, Multisensory Stimulation, Citizen Skills, Violence Prevention, Moral Development, Universal Design for Learning, Sensitizing on Diversity, Supportive Strategies",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 14th 2020",dateEndSecondStepPublish:"October 12th 2020",dateEndThirdStepPublish:"December 11th 2020",dateEndFourthStepPublish:"March 1st 2021",dateEndFifthStepPublish:"April 30th 2021",remainingDaysToSecondStep:"4 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Phil. Maria Jose Hernandez Serrano is a tenured lecturer in the Department of Theory and History of Education at the University of Salamanca, where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. in Education and Training in Virtual Environments by research with the University of Manchester, UK (2009).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"187893",title:"Dr.",name:"María Jose",middleName:null,surname:"Hernandez-Serrano",slug:"maria-jose-hernandez-serrano",fullName:"María Jose Hernandez-Serrano",profilePictureURL:"https://mts.intechopen.com/storage/users/187893/images/system/187893.jpg",biography:"DPhil Maria Jose Hernandez Serrano is a tenured Lecturer in the Department of Theory and History of Education at the University of Salamanca (Spain), where she currently teaches on Teacher Education. She graduated in Social Education (2000) and Psycho-Pedagogy (2003) at the University of Salamanca. Then, she obtained her European Ph.D. on Education and Training in Virtual Environments by research with the University of Manchester, UK (2009). She obtained a Visiting Scholar Postdoctoral Grant (of the British Academy, UK) at the Oxford Internet Institute of the University of Oxford (2011) and was granted with a postdoctoral research (in 2021) at London Birbeck University.\n \nShe is author of more than 20 research papers, and more than 35 book chapters (H Index 10). She is interested in the study of the educational process and the analysis of cognitive and affective processes in the context of neuroeducation and neurotechnologies, along with the study of social contingencies affecting the educational institutions and requiring new skills for educators.\n\nHer publications are mainly of the educational process mediated by technologies and digital competences. Currently, her new research interests are: the transdisciplinary application of the brain-based research to the educational context and virtual environments, and the neuropedagogical implications of the technologies on the development of the brain in younger students. Also, she is interested in the promotion of creative and critical uses of digital technologies, the emerging uses of social media and transmedia, and the informal learning through technologies.\n\nShe is a member of several research Networks and Scientific Committees in international journals on Educational Technologies and Educommunication, and collaborates as a reviewer in several prestigious journals (see public profile in Publons).\n\nUntil March 2010 she was in charge of the Adult University of Salamanca, by coordinating teaching activities of more than a thousand adult students. She currently is, since 2014, the Secretary of the Department of Theory and History of Education. Since 2015 she collaborates with the Council Educational Program by training teachers and families in the translation of advances from educational neuroscience.",institutionString:"University of Salamanca",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Salamanca",institutionURL:null,country:{name:"Spain"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"23",title:"Social Sciences",slug:"social-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"301331",firstName:"Mia",lastName:"Vulovic",middleName:null,title:"Mrs.",imageUrl:"https://mts.intechopen.com/storage/users/301331/images/8498_n.jpg",email:"mia.v@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6942",title:"Global Social Work",subtitle:"Cutting Edge Issues and Critical Reflections",isOpenForSubmission:!1,hash:"222c8a66edfc7a4a6537af7565bcb3de",slug:"global-social-work-cutting-edge-issues-and-critical-reflections",bookSignature:"Bala Raju Nikku",coverURL:"https://cdn.intechopen.com/books/images_new/6942.jpg",editedByType:"Edited by",editors:[{id:"263576",title:"Dr.",name:"Bala",surname:"Nikku",slug:"bala-nikku",fullName:"Bala Nikku"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"66215",title:"Use of Particle Multi-Swarm Optimization for Handling Tracking Problems",doi:"10.5772/intechopen.85107",slug:"use-of-particle-multi-swarm-optimization-for-handling-tracking-problems",body:'Generally, the task of tracking a moving target is an important subject as a real-world problem, for example, traffic management, mobile robot, safety guidance, image object recognition, industrial controls, etc. which are frequently taken up in various application fields [1, 2, 3, 4, 5]. In order to deal with dynamic optimization problems, many search methods are applied, and the approach of particle swarm optimization (PSO) in the area of swarm intelligence is one of them [6, 7, 8, 9, 10, 11].
The technique of PSO is very easy to implement and extend. Based on its basic search mechanism, main advantages have three built-in features: (i) information exchange, (ii) intrinsic memory, and (iii) directional search, compared to other existing heuristic and evolutionary techniques such as genetic algorithms (GA), evolutionary programming (EP), evolution strategy (ES), and so on [12, 13, 14, 15]. This is a reason why the technique of PSO is attracting attention and used in different fields such as science, engineering, technology, design, automation, communication, etc.
As is well-known, the search tasks handled by the technique of PSO are a mass of static optimization problems. The cause is simple for that the best information, that is, the best solution of swarm search and the best solution of each particle itself, is only recorded and renewed. Due to environmental change, the retained best information is not modified to normally search. Thus, its mechanism cannot adapt environment change or a moving target for dealing with tracking problems. Because of overcoming the disadvantage of the technique of PSO, and extending the range of its applications for dealing with dynamic optimization problems (including tracking problem), it is necessary to improve its search functions by adding some strategies into the mechanism of PSO [16, 17].
As prior work on handling the tracking problems by PSO, under a certain dynamic environment, we have proposed not only three single particle swarm optimizer with sensors, which are PSOS, PSOIWS, and CPSOS [18], but also four multiple particle multi-swarm optimizers with sensors which are MPSOS, MPSOIWS, MCPSOS, and HPSOS1 [19]. And for confirming the search effectiveness of these proposed methods, several computer experiments were carried out to handle the tracking problems of constant speed I type, variable speed II type, and variable speed III type that belong to a set of benchmark problems.
In general, the search ability and performance of multiple particle swarms are better than single particle swarm for handling same tracking problem. The comparative experiments on the finding were verified in literature [20]. According to the obtained experimental results of the four multiple particle swarm optimizers with sensors, MPSOIWS and HPSOS are better in search ability. MCPSOS is better in convergence. MPSOS is better in the robustness with respect to variation in sensor setting parameters. And many know-hows on the useful knowledge such as their experimental findings are obtained [19]. As the search characters of particle multi-swarm optimization (PMSO2), however, the search information (i.e., best solution) obtained from each particle swarm is not shared to explore. For dealing with this issue, we proposed a special strategy called information sharing and introduced it to effectively solve static optimization problems [21].
In order to acquire further the search ability and performance of PMSO in dealing with dynamic optimization problems, we innovate the strategy of information sharing into the previous four multiple particle multi-swarm optimizers with sensors and firstly propose the four new search methods, that is, multiple particle swarm optimizers with sensors and information sharing (MPSOSIS), multiple particle swarm optimizers with inertia weight with sensors and information sharing (MPSOIWSIS), multiple canonical particle swarm optimizers with sensors and information sharing (MCPSOSIS), and hybrid particle swarm optimizers with sensors and information sharing (HPSOSIS3).
This is a novel approach for the technology development and evolution of PMSO itself. The crucial idea is to add the special confidence term into the updating rule of the particle’s velocity by the best solution found out by particle multi-swarm search to enhance the intelligent level of whole particle multi-swarm and build a new framework of PMSO [22]. Based on the improvement of the confidence terms, it is expected to acquire the maximization of potential search ability and performance of the four basic search methods of PMSO under the context of any adjunctive computation resource.
Due to the revelation of the outstanding search ability and performance of the proposed MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS, we take more detailed data from the computer experiments. Based on these obtained data, furthermore, we clarify the characteristics and search ability of the proposed methods by analysis and comparison. This is the major goal of this research.
The rest of this chapter is organized as follows: Section 2 briefly introduces three basic search methods of PSO and these methods with sensors. Section 3 describes the proposed four search methods of PMSO in detail. Section 4 implements several computer experiments and analyzes the obtained results for investigating the search ability and performance of these new search methods. Finally, the concluding remarks and future research appear in Section 5.
In spite of the fact that there are a lot of search methods derived from the technique of PSO, they have evolved and developed from three basic search methods of PSO [23]. These search methods, that is, the particle swarm optimizer (the PSO) [24, 25], particle swarm optimizer with inertia weight (PSOIW) [26, 27], and canonical particle swarm optimizer (CPSO) [28, 29], are common ground for technology development of PSO and PMSO.
For the sake of convenience to the following specific description, let the search space be
The original particle swarm optimizer is firstly created by Kennedy and Eberhart in 1995. The method of a population-based stochastic optimization search is referred to as the PSO.
In beginning of the PSO search, the position and velocity of the
where
In the PSO,
For improving the convergence and search ability of the PSO, Shi and Eberhart modified the updating rule of the particle’s velocity shown in Eq. (2) by constant reduction of the inertia weight over time-step as follows:
where
where
Since the linear change of inertia weight from 0.9 to 0.4 in a search process, PSOIW has the characteristics of asymptotical/local search, and its convergence is so good in whole search process.
For the same purpose as the above described, Clerc and Kennedy modified the updating rule for the particle’s velocity in Eq. (2) by a constant inertia weight over time-step as follows:
where
It is clear that since the value of inertia weight,
We introduce the correspond to these foregoing search methods which are particle swarm optimizers with sensors to handle dynamic optimization problems. With adding sensors into the search methods of every particle swarm optimizer described in Sections 2.1–2.3, it is possible to sense environmental change and a moving target for improving the search ability and performance.
As an example, Figure 1 shows the positional relationship between the best solution and sensors.
Configuration of sensors.
In a search process, the best solution of entire particle swarm is always set as the origin of the sensor setting. Based on the sensing information (i.e., the measuring position and its fitness value) of each sensor, we can observe the change of the surrounding environment and the moving target. In particular, updating the best solution by Eq. (6) is an important search information:
where
On the other hand, regarding whether there are environmental change and a moving target or not, it is implemented by using the following judgment criterion:
where
If the judgment result of Eq. (7) is satisfied in a search process, the moving target has occurred. The particle swarm is initialized at the time and then continuous to begin particle swarm search. However, such initialization is not considered on the continuity of environmental change; it is implemented around the coordinate origin of the search range. As a new problem in the situation, if the distance before and after movement becomes smaller, the time loss to search is greater for finding out the new best solution.
By changing the coordinate origin of the initialization to the position of the best solution, the above difficulty can be dissolved. Therefore, the best solution of whole particle swarm is intermittently updated by sensing information.
And adding the judgment operation of Eqs. (6) and (7) into each method described in Sections 2.1–2.3, the constructions of the search methods, that is, particle swarm optimizer with sensors (PSOS), particle swarm optimizer with inertia weight with sensors (PSOIWS), and canonical particle swarm optimizer with sensors (CPSOS), can be conflated and completed to deal with the given tracking problems.
Formally, there are a lot of the methods about PMSO [32]. For understanding the formation and methodology of these proposed methods, let us assume that the multi-swarm consists of multiple single swarms. The corresponding three kinds of particle swarm optimizers described in Sections 2.1–2.3 can be generated by construction and parallel computation [33]. Therefore, these constructed particle multi-swarm optimizers, i.e. multiple particle swarm optimizers (MPSO), are multiple particle swarm optimizers with inertia weight (MPSOIW), multiple canonical particle swarm optimizers (MCPSO), and hybrid particle swarm optimizers (HPSO), respectively.
Based on the development of the search methods in Section 2.4, similarly, multiple particle swarm optimizers with sensors (MPSOS), multiple particle swarm optimizers with inertia weight with sensors (MPSOIWS), multiple canonical particle swarm optimizers with sensors (MCPSOS), and hybrid particle swarm optimizers with sensors (HPSOS) were acquired by programming [19].
However, all of their updating rules have two confidence terms in the Eqs. (2), (3), and (5) to be only used for calculating the particle’s velocity. Because of the use of the mechanism to search, they are called as the elementary basic methods with sensors of PMSO which have the same updating rule of the particle’s velocity [20, 34].
For improving the search ability and performance of the previous described elementary multiple particle swarm optimizers, furthermore, we add the special confidence term into the updating rule of the particle’s velocity by the best solution found out by the multi-swarm search, respectively. According to this extended procedure, the four basic search methods of PMSO, that is, MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS, can be constructed [18]. Consequently, these basic search methods of PMSO augmented with the strategy of multi-swarm information sharing are proposed [22].
It is clear that the added confidence term perfectly is in accordance with the fundamental construction principle of PSO. And the effectiveness of the methods has been verified by our experimental results [21].
On basis of the above description of PMSO, as the mechanism of the proposed MPSOSIS, the updating rule of each particle’s velocity is defined as follows:
where
Since
In same way as the mechanism of MPSOSIS, the updating rule of each particle’s velocity of the proposed MPSOIWSIS is defined as follows:
Since Eqs. (3) and (6) are alike in formulation, the description of the symbols in Eq. (9) is omitted. Similarly, the convergence of MPSOIWSIS is as same as that of PSOIW.
Similar to the mechanism of MPSOSIS, the updating rule of each particle’s velocity of the proposed MCPSOSIS is defined as follows:
Likewise, the description of the symbols in Eq. (10) is omitted. Since
Based on the three search methods described in Sections 3.1–3.3, there are the three updating rules of each particle’s velocity in the proposed HPSOSIS. The mechanism of HPSOSIS is determined by Eqs. (8)–(10).
Due to the mixed effect and performance in whole search process, global search and asymptotical/local search are implemented simultaneously for dealing with a given optimization problem. It is obvious that HPSOSIS has all search characteristics of the three basic methods, that is, PSO, PSOIW, and CPSO. Similarly, the convergence of HPSOSIS is as same as that of HPSOS.
Based on the development of these methods in Section 2.4, here, we propose the four basic methods with sensors of PMSO and describe the search methods with sensors, that is, MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS, respectively, by constructing the particle swarm optimizers with sensors described in Sections 3.1–3.3.
For indicating the image relation of the above described methods with sensors, Figure 2 simply shows the constitutional concept of the proposed four basic search methods with sensors of PMSO. It is clear that HPSOSIS is a mixed method which is composed of PSOSIS, PSOIWSIS, and CPSOSIS. Thus, HPSOSIS has different characteristics of the above methods as a special basic search method with sensors of PMSO [18].
The constitutional concept of the proposed four basic search methods with sensors of PMSO.
Regarding the convergence of the above proposed methods, it can be said that the MPSOSIS has the characteristics of global search, MPSOIWSIS has the characteristics of asymptotical/local search, and MCPSOSIS has the characteristics of local search. With different search features, HPSOSIS has the characteristics of the above three search methods. In a search process, it is expected to improve the potential search ability and performance of PMSO without additional calculation resource.
Due to the track of a moving target, the setting parameters of each proposed method described in Section 2.1 are used in every search case. The main parameters are shown in Table 1 for the following computer experiments.
Parameter | Value |
---|---|
Number of the used swarms, | 3 |
Number of particles in a swarm, | 10 |
Total number of particle search, | 800 |
Radius of moving target, | 2.0 |
Number of sensors, | 5, 8, 11, 14 |
Sensing distance, | 0.0, 0.1, |
Major parameters for handling the given tracking problems in computer experiments.
The computing environment and software tool are given as follows:
DELL: OPTIPLEX 3020, Intel(R) core (TM) i5-4590
CPU: 3.30GHz; RAM: 8.0GB
Mathematica: ver. 11.3
The tracking problems of constant speed I type, variable speed II type, and variable speed III type are used in the following computer experiments. A target object and its moving trajectories are shown in Figure 3. The search range of all cases is limited to
Trajectories of the moving target. (a) Target object, (b) moving trajectory of constant speed I type, (c) moving trajectory of variable speed II type, and (d) moving trajectory of variable speed III type.
The criterion of the moving target is expressed as follows:
where (
Specifically, for the moving trajectory of constant speed I type, (
where
The moving trajectories of variable speed II type and variable speed III type and their passing points, (
The difficulty index (
where
By concreting calculation, the
In this section, we implement the proposed methods, that is, MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS, respectively, for handling the three tracking problems shown in Figure 3 and investigating their search ability and performance in detail.
First, MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS were performed4 to handle the tracking problem of constant speed I type which has a low-level of difficulty, respectively. As an example, the obtained change patterns of the fitness value of the best solution and the moving trajectory are shown in Figure 4.
The moving trajectory of the best solution for handling the tracking problem of constant speed I type. Left part, time space; right part, search space. (a) MPSOSIS case, (b) MPSOIWSIS case, (c) MCPSOSIS case, and (d) HPSOSIS case.
We can see that the obtained variation of the best solution in whole search process from the left parts of Figure 4 and the search trajectories are beautifully drawn from the right parts of Figure 4(a)–(d), except for Figure 4(a). And comparing to the left parts of Figure 4(a)–(d), a big difference of the search state is clear with the origin of searching range as the center of initialization and the best solution as the center of initialization. The moving trajectories of the latter are relatively flat.
Moreover, when the target object moves, the fitness value of the best solution of the particle multi-swarm suddenly drops, then it rapidly rises with the subsequent search, and it is found that the peak of the target object is attained again. On the other hand, depending on the variation in the fitness value in the time space of Figure 4, the obtained results show that MPSOIWSIS, MCPSOSIS, and HPSOSIS have good search ability and tracking performance depending on the variation patterns of the fitness values on the search space.
Next, for handling the tracking problem of variable speed II type which has a middle level of difficulty, MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS were performed, respectively. The obtained experimental results are shown in Figure 5.
The moving trajectory of the best solution for handling the tracking problem of variable speed II type. Left part, time space; right part, search space. (a) MPSOSIS case, (b) MPSOIWSIS case, (c) MCPSOSIS case, and (d) HPSOSIS case.
We can see that the variation of the obtained best solution in whole search process from the left parts of Figure 5(a)–(d), and the moving trajectories of variable speed II type are drawn almost smoothly from the right parts of Figure 5 except for Figure 5(a). Then, compared to the variation in the fitness value in the time space of Figure 5, it is found that the falling range of the fitness value of the best solution is slightly bigger due to the increase in difficulty of the given search problem.
Subsequently, for handling the tracking problem of variable speed III type which has a high level of difficulty, MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS were performed, respectively. Figure 6 shows the obtained experimental results.
The moving trajectory of the best solution for handling the tracking problem of variable speed III type. Left part, time space; right part, search space. (a) MPSOSIS case, (b) MPSOIWSIS case, (c) MCPSOSIS case, and (d) HPSOSIS case.
Similarly, we can see that the variation of search patterns in the time space of Figure 6(a)–(d) for handling the given tracking problem. Except for the search result of Figure 6(a), the search trajectories of Figure 6(b)–(d) are roughly drawn. Then, compared with the variation in the fitness value in the time space of Figure 6, it is found that the falling variation of the fitness value of the best solution is bigger due to the increase in the difficulty of the given tracking problem.
The moving trajectories of MPSOIWSIS, MCPSOSIS, and HPSOSIS are roughly drawn. Corresponding to this situation, it is clear that the smoothness of the moving trajectory gradually deteriorated as the difficulty level of the tracking problem increased. In addition, we can see that MPSOIWSIS, MCPSOSIS, and HPSOSIS are more susceptible to target variation compared with MPSOSIS.
For objectively and quantitatively evaluating the tracking ability and performance of the proposed methods, we use an indicator such as cumulative fitness (
Consequently, by changing the number
Hereinafter, we change the number
First, computer experiments were carried out to handle the tracking problem of constant tracking I type. In this case, the obtained search results (average value of running ten times) are shown in Figure 7.
Effect of handling the tracking problem of constant speed I type with adjustment of the number m and sensing distance r of sensors. (a) MPSOSIS case, (b) MPSOIWSIS case, (c) MCPSOSIS case, and (d) HPSOSIS case.
Comparing the search results of MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS shown in Figure 7, it is found that the difference in tracking performance regarding the existence of sensors is very large with regard to the search ability. That is, when
On the other hand, when the sensing distance
Second, computer experiments were carried out to handle the tracking problems of variable speed II type and variable speed III type. The obtained search results are shown in Figures 8 and 9, respectively.
Effect of handling the tracking problem of variable speed II type with adjustment of the number m and sensing distance r of sensors. (a) MPSOSIS case, (b) MPSOIWSIS case, (c) MCPSOSIS case, and (d) HPSOSIS case.
Effect of handling the tracking problem of variable speed III type with adjustment of the number m and sensing distance r of sensors. (a) MPSOSIS case, (b) MPSOIWSIS case, (c) MCPSOSIS case, and (d) HPSOSIS case.
By comparing the search results shown in Figures 7–9, it is clear that each proposed search method has high tracking ability in each case. As the main search characteristics, we can see that as the sensing distance
In this section, we compare the search performance of the four proposed methods, that is, MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS, by handling the same tracking problem. Figure 10 shows the search results obtained by handling the tracking problem of constant speed I type.
Search ability of each proposed method for handling the tracking problem of constant speed I type. (a) m = 5 case, (b) m = 8 case, (c) m = 11 case, and (d) m = 14 case.
We can see clearly that the search performance of MPSOSIS is the lowest regardless of the number of sensors used. For the remaining three proposed methods, that is, MPSOIWSIS, MCPSOSIS, and HPSOSIS, it is obvious that the search performance of MCPSOSIS is good within a certain range of the sensing distance
Similarly, Figures 11 and 12 show the search results obtained by handling the tracking problems of variable speed II type and variable speed III type, respectively. Observing the obtained search results of both, it is almost the same as the finding obtained from the data analysis of the search result in Figure 10. In particular, it is found that the search performance of each proposed method is very lower when sensors are not used. In this case, the proposed methods (i.e., MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS) correspond to the existing methods (i.e., MPSOIS, MPSOIWIS, MCPSOIS, and HPSOIS). Thus, the important role of the used sensors is clearly shown.
Search ability of each proposed method for handling the tracking problem of variable speed type. (a) m = 5 case, (b) m = 8 case, (c) m = 11 case, and (d) m = 14 case.
Search ability of each proposed method for handling the tracking problem of variable speed III type. (a) m = 5 case, (b) m = 8 case, (c) m = 11 case, and (d) m = 14 case.
In order to investigate the effectiveness of the proposed methods under the situation of multiple particle swarm search, computer experiments on the existing search methods, that is, MPSOS, MPSOIWS, MCPSOS, and HPSOS, were implemented.
For intuitive comparison of both, the obtained results are shown in Figures 13 and 15, respectively. When there is no sensor, it is understood that the difference between them is the largest. It is also found that the attenuation of the cumulative fitness of the latter becomes relatively fast as the sensing distance
The best and average solutions for handling the tracking problem of constant speed I type. (a) MPSOSIS and MPSOS case, (b) MPSOIWSIS and MPSOIWS case, (c) MCPSOSIS and MCPSOS case, and (d) HPSOSIS and HPSOS case.
Except for the results in Figures 13(a), 14(a), and 15(a), we discovered that the search results of the proposed methods, that is, MPSOIWSIS, MCPSOSIS, and HPSOSIS, are better than the existing methods, that is, MPSOIWS, MCPSOS, and HPSOS, except for the MPSOSIS case. Therefore, the effectiveness of the information sharing strategy is confirmed even in the case of multiple particle swarm search. The obtained results in Figures 14 and 15 show that the attenuation of the existing methods becomes faster as
The best and average solutions for handling the tracking problem of variable speed II type. (a) MPSOSIS and MPSOS case, (b) MPSOIWSIS and MPSOIWS case, (c) MCPSOSIS and MCPSOS case, and (d) HPSOSIS and HPSOS case.
The best and average solutions for handling the tracking problem of variable speed III type. (a) MPSOSIS and MPSOS case, (b) MPSOIWSIS and MPSOIWS case, (c) MCPSOSIS and MCPSOS case, and (d) HPSOSIS and HPSOS case.
In this chapter, we proposed the four new search methods, that is, MPSOSIS, MPSOIWSIS, MCPSOSIS, and HPSOSIS, to deal with dynamic optimization problems. For investigating and comparing their tracking ability and performance, we modified the number of sensors and adjusted the sensing distance to implement computer experiments. As the given tracking problems, we used a set of benchmark problems of constant speed I type, variable speed II type, and variable speed III type.
Computer experiments were carried out to handle each given tracking problem. Based on various experimental results obtained, the prominent search ability and performance of each proposed search method is confirmed.
Specifically, regarding search performance of the proposed methods, it is found that the obtained search results of MPSOIWSIS, MCPSOSIS, and HPSOSIS are better than the existing methods, that is, MPSOIWS, MCPSOS, HPSOS, MPSOIWIS, MCPSOIS, and HPSOIS. Also, in addition to enhancing the processing capacity for dealing with the given tracking problems, the efficiency of the search itself is also improved. However, in order to obtain good tracking ability and performance, it is necessary to select an appropriate value for the sensing distance of the sensor.
As future research subjects, based on the sensing information obtained from the sensors, we will advance the development of PMSO [22], that is, introducing the strategy of sharing information during the search and raising the intellectual level in particle multi-swarm search. Alternatively, the proposal methods utilizing the excellent tracking ability of MPSOIWSIS and HPSOSIS are applied extensively to dynamic search problems such as identification of control systems and recurrent network learning.
Industry 4.0, that is, the fourth industrial revolution, represents industry and manufacturing digitalization bringing with it, among other things, the so-called smart factories. This transformation comes through the adoption of the Internet of Things (IoT) [1], which gives rise to the Industrial IoT (IIoT) and allows to interconnect humans, machines, and smart devices, as well as to share huge amounts of data among them.
\nIn order to cope with big data and predictive analytics [2], cloud computing is becoming another key enabler due to its computing, storage, and networking capabilities. It allows us to obtain meaningful information and valuable insights which will increase the efficiency, productivity, and performance of manufacturing processes and services. Several IIoT applications, such as system control, anomaly detection, or robot guidance, are time-critical, and therefore, they require millisecond response times. Thus, low-latency communications, as well as real-time analysis and monitoring, are indispensable for immediate decision-making.
\nAlthough the cloud offers high scalability, flexibility, and responsiveness, cloud-based analytics may introduce excessive latency, which would compromise the performance of time-critical applications. In order to accomplish a trade-off between latency and computation, IIoT deployments are moving cloud capabilities downwards to fog nodes to perform early analytics and minimize latency. Furthermore, most delay-critical applications not only require low-latency communications but also ensure high reliability. A promising technique that increases network reliability while reducing end-to-end latency is network coding (NC). Its properties are particularly beneficial for enhancing the robustness and reducing delays of wireless sensor network (WSN) communications [3]. Moreover, it improves the efficiency of distributed storage systems, regarding both data download speed and redundancy [4].
\nIn this chapter, we overview next-generation IIoT systems, which must provide low-latency communications as well as ensure their reliability in order to allow the performance of on-premise advanced cloud analytics for time-critical IIoT applications, that is, to bring the cloud to the fog (see Figure 1). This objective can be achieved by implementing a three-layer architecture based on IoT nodes, fog nodes and a multicloud environment, and also by exploiting the advantageous properties of NC techniques across the architecture.
\nBringing the cloud to the fog.
The rest of this chapter is organized as follows. First, we overview next-generation IIoT architectures, briefly describing and comparing the different layers as well as providing different use-cases in which these architectures are integrated. Next, we introduce some NC approaches and describe the benefits of NC regarding different scenarios. Then, we describe the communication process across the different levels of the architecture. We also summarize the benefits of merging IIoT architectures and NC techniques. Finally, we discuss existing issues and open challenges, and we report the final conclusions of the chapter.
\nNowadays, due to its scalability and big data management capabilities, cloud-based architectures are most widely used in Industry 4.0 applications. However, the integration of the IoT into industrial environments poses new challenges, which implies an architectural adaptation. As previously mentioned, IIoT applications are mostly delay-sensitive and require instant decision-making. This has led to the integration of fog nodes into the industrial systems in order to perform early analytics and closed-loop control. Moreover, systems of this nature must be robust. Thus, with the aim of providing a fault-tolerant architecture and guarantee system reliability, multicloud deployments are emerging as a promising solution. In addition to the latter, they enable to use the connections under the best conditions and therefore, delays can be reduced. Dependencies on a single cloud provider can also be avoided.
\nIt can be said that next-generation IIoT architectures, as shown in Figure 2, will consist of three layers, composed of IoT or smart devices, fog nodes and multiple clouds. The lowest layer, comprised of a variety of end-nodes, is responsible for sending taken measurements to actuators or fog devices. In the fog layer, time-critical analytics, as well as closed-loop control, can be performed. Finally, cloud servers are in charge of heavy data analytics and compute-intense workloads that manage a vast amount of data.
\nNext-generation IIoT architecture.
A description and comparison of the layers that comprise next-generation IIoT architectures are next provided.
\nWSNs can be considered the main communication technologies of IIoT due to the flexibility they offer to connect and manage a large number of sensors and actuators, independently of their location. A WSN consists of several IoT nodes, including sensors, actuators, and smart devices, which take several measurements. These devices are mainly battery, storage, and processing power constrained. This layer is responsible for gathering sensor data, such as machine temperature or vibration measurements, and for uploading them. It also receives instructions from the upper layers in order to perform a corresponding task or action.
\nThe fog can be considered as an intermediate layer between the cloud and IoT devices and so, it extends cloud computing capabilities to the edge of the network [5]. One of its main advantages is its closeness to the end-nodes, which makes possible to reduce communication latency and to enable real-time service support. Since fog computing allows early data processing, the amount of data sent to the cloud can be reduced. In addition, its mobility and location-awareness enable to deliver rich services to moving devices [6].
\nThe cloud can be described as several distributed remote servers which can be accessed via the Internet to store and manage big amounts of data [7]. Cloud computing enables the remote on-demand use of computing resources, that is, networks, servers, storage, applications, and services. It provides virtualized, elastic, and controllable services and powerful computational capabilities, enabling complex application systems at lower costs. The deployment of more than one cloud, in addition to the mentioned advantages, provides fault tolerance against service outages, and the system security level is improved since it is possible to store the information divided into different clouds. Furthermore, application requirements can be better adapted to available cloud resources and connectivity conditions.
\nWith the adoption of IoT, the number of things connected to the Internet is expected to grow up to 20 billion in 2020 [8]. Thus, a scalable architecture is required in order to adapt to such a huge number of devices. Moreover, the need for large amounts of data to be accessed more quickly is ever-increasing, where the inherent latency of the cloud can be detrimental. Latency issues become highly damaging, particularly for IIoT time-critical applications. Autonomous decisions are required in order to prevent failures or optimize production, and thus, milliseconds matter when trying, for instance, to prevent manufacturing line downtimes or to get the right decision in autonomous vehicles.
\nProcessing data directly in the end-devices would be the best solution in order to provide the lowest latency and jitter. However, the constrained nature of these nodes inhibits the performance of more advanced processing and analytics. Thus, fog computing can be the most suitable solution for applications that cannot afford the delay caused by the round trip to the cloud server. Nonetheless, fog computing requires local management of redundancy and data backup. Moreover, the integration of devices capable of performing remote data analytics implies the increase of the architecture complexity, as well as of the associated costs in hardware and software investments. Table 1 shows the most significant differences between WSNs, fog computing, and cloud computing.
\nFeature | \nWSNs | \nFog computing | \nCloud computing | \n
---|---|---|---|
Latency | \nVery low | \nLow | \nHigh | \n
Delay jitter | \nVery low | \nLow | \nHigh | \n
Server location | \n— | \nLocal | \nInternet | \n
Client–server distance | \n— | \nOne hop | \nMultiple hops | \n
Location awareness | \nYes | \nYes | \nNo | \n
Distribution | \nHighly distributed | \nDistributed | \nCentralized | \n
Mobility awareness | \nGuaranteed | \nSupported | \nLimited | \n
Real-time interactions | \nGuaranteed | \nSupported | \nLimited | \n
Comparison between WSN, fog, and cloud computing [9].
The three-layer architecture enables to exploit the efficiency and scalability of the fog while benefiting from the powerful storage and computing resources of the cloud. Next, we show some use-case examples.
\nWind energy-based smart grids require data analysis and real-time decision making. In a large wind farm, the health of the turbines is monitored by analyzing data collected by numerous sensors [10]. Each turbine can be monitored locally, that is, in the fog, and the collective performance can be improved by processing data on remote servers in the cloud. Thus, it enables to combine real-time response for early actions and advanced analyses for a deeper view of the whole wind farm. It can increase energy output, decrease operational costs, and increase turbine uptime.
\nSelf-driving vehicles, for instance, are equipped with an on-board system that, through real-time data analysis, allows controlling the car without human interaction. In such systems, highly reliable and low latency communication is crucial. Thus, critical decisions that require an instantaneous response are better managed with fog computing [11]. However, for monitoring the tracking performance of a truck fleet [12], as there is no need for real-time analytics, cloud computing is more suitable. Advanced cloud analytics based on information gathered from different parts of the truck can bring insights to improve the maintenance and lower repair costs.
\nSmart factories are able to perform predictive maintenance of their machines or improve product quality by real-time sensor analysis [13]. Fog computing is crucial for these delay-critical data processing. However, cloud computing can provide an overall system management as well as machine learning analytics that require greater computing power.
\nFog computing can provide a fast, real-time, and location-aware solution for many IoT use cases of smart cities, such as smart buildings [14]. Several sensors gather diverse measurements like temperature, energy usage, humidity, parking occupancy, air quality, elevators, smoke, and so on. The efficiency of the system can be improved by managing critical data at the fog layer in real time, as in traffic control, and by performing big data analytics in the cloud.
\nMost IIoT systems are deployed in harsh environments, where different devices within the architecture can be connected and disconnected from the network any time. Thus, besides providing low-latency communications, it is crucial to strengthen these communications in order to ensure a robust and highly reliable environment.
\nIIoT networks require system reliability, data availability and high communication quality. This may be difficult to achieve due to inherent constraints of these scenarios. WSNs, for example, may suffer from noise or multipath interferences, among others, which cause packet loss and inevitably degrades the quality of the communications. Moreover, the dynamic topology of these architectures in which devices connect intermittently, can destabilize communications and introduce variable delays. In order to overcome these issues, the integration of NC techniques across the shown architecture can be a suitable solution due to its properties.
\nNC breaks with the traditional store-and-forward transmission model [15] by allowing any intermediate node to recombine incoming packets into coded ones, which are decoded at destination. Its properties make it a promising solution to improve the performance of wireless and peer-to-peer networks. It exploits the broadcast nature of the wireless medium [3], which facilitates node cooperation to provide significant benefits in terms of communication robustness, stability, throughput, and latency.
\nMoreover, dependency on obtaining a particular packet is removed by applying NC since it is sufficient to get enough linearly independent packet combinations in order to recover the required data. Thus, in distributed systems, such as P2P [16] or multicloud environments [17], the use of NC can reduce additional data download or access delays in highly loaded conditions as well as improve the performance of data recovery and acquisition [4].
\nAdvanced NC techniques are based on the widely used NC approach random linear network coding (RLNC) [18], where the received \n
Several variants of this technique have been developed in order to adapt it to different scenarios and application requirements. Perpetual codes [20], for instance, can be considered as a supplement of RLNC. In manifold scenarios, particularly for large generation sizes, they can substantially increase the throughput due to their sparsity and the possibility of structured decoding.
\nFor heterogeneous networks with devices of different resources, fulcrum codes [21] allow to use binary GF operations in the network to achieve reduced overhead and computational cost, and reach compatibility with heterogeneous devices and data flows in the network, while providing the opportunity of employing higher coding finite fields end-to-end for greater performance. On the other hand, systematic coding [22] allows sending coded packets along with original ones, that is, uncoded packets, which can help to reduce overhead and improve the real-time decoding performance.
\nFor low-delay applications such as real-time control applications, on-the-fly or sliding window coding can be the most suitable solutions. Unlike block codes where all packets in a block need to be present to start generating useful coded packets, on-the-fly codes [23] are able to encode data while they become available and these packets are progressively decoded. Sliding window codes [24] are more flexible since they remove the limitation of fixed blocks by creating a variable-sized sliding window.
\nFinally, Tunable Sparse Network Coding (TSNC) [25], unlike RLNC that applies a fixed coding density for the entire process, tunes the density of coded packets during transmission to adjust to the trade-off between real-time performance and reliability. TSNC proposes to increase the coding density as the destination node receives more linearly independent packets since the probability of receiving innovative packets is lower, thereby reducing coding complexity.
\nThis section lists different benefits of NC, showing its suitability for IIoT systems and applications.
Distributed storage systems: IIoT systems require high reliability and availability. Thus, data must be distributed and stored in such a way as to ensure fault tolerance, for example in the event of a server failure. Packet loss, delay, and bandwidth fluctuation can hinder data distribution. The main benefit of NC over P2P environments is in relation to the coupon collector problem [16], being able to solve this issue due to the redundancy introduced in packet transmissions. Therefore, the performance of data streaming is enhanced since download times are minimized. With NC, the performance of the system depends much less on the underlying topology and schedule.
NC can help to increase the reliability of distributed storage systems like multicloud deployments [17]. In case of data loss, the amount of redundant data required for repair is minimized. In addition, each cloud is used at its maximum speed even in highly loaded conditions or dynamically changing environments. Thus, NC improves storage efficiency in terms of data retrieval time and storage space.
Dynamic topologies: NC techniques can be helpful for efficient content distribution [26] in changing environments. For example in Vehicular Ad-Hoc Networks (VANETs), in order to avoid possible accidents, vehicles exchange road state information among them. Even in dynamic road changing conditions, VANET applications, such as traffic live video broadcast, must guarantee a correct data reception. Since NC enhances network performance and reduces the number of required data transmissions, it can reduce transmission delays.
Due to the previous and together with its decentralized nature and robustness, NC can be extrapolated also to dynamic IIoT architectures, where end-nodes may connect periodically in order to save power or they can connect to different access points.
Constrained environments: the use of NC has been extended also to constrained environments. In satellite communications, for instance, bandwidth is usually limited and round-trip delays are high. The properties of this technique can be advantageous particularly in multibeam satellites [27]. On the one hand, NC improves throughput and bandwidth usage. On the other hand, it does not require any change at the physical layer and thus, it easies the implementation on already deployed satellite systems. NC techniques can also be used in applications aimed at energy-efficient data transmissions, such as Wireless Body Area Networks (WBANs), since they can provide reliable communications under low-energy constraints [28]. Therefore, IIoT applications can also profit from this technique as the majority of end-devices are resource constrained.
Poor quality channels: NC can improve the transmission performance in environments with unstable channel conditions which quality may not meet end-user quality of service (QoS) requirements, such as delay and reliability. An example of the previous are Underwater Sensor Networks (UWSNs) and Power Line Communication (PLC) systems. In UWSNs, the acoustic communications suffer high error rates and long propagation delays, which require efficient error recovery. NC can exploit the broadcast property of acoustic channels, improving data throughput [29]. PLC systems, on the other hand, are able to provide multicast and broadcast services by exploiting existing electrical wires. Due to the similarities between power line and wireless channels, NC protocols can be applied in order to achieve the implementation constraints [30] and provide reliable communications in harsh environments.
IIoT systems that relay on WSNs may deal with interferences or channel contention that cause QoS issues. Thus, NC-based techniques can help to improve channel resources as well as data rate while maintaining QoS.
In this section, we introduce NC into next-generation IIoT architectures reviewing related state-of-the-art works. We also outline some of the most relevant benefits and challenges.
\nIn IIoT systems, not only low-latency communications between end-nodes (things) and the cloud must be guaranteed but the whole system must be robust, including the connections and the provided service. Next, the communication process across the architecture is described.
\nImplementing NC techniques through the WSN, communication latency can be reduced [31] and its robustness [32, 33] improved. Here, sensors and actuators combine their measurements and transmit them across the network. By using NC, intermediate nodes recode received data and send them to one or more gateways which compose the fog layer. These devices are then in charge of uploading incoming data to the multicloud framework.
\nFog nodes can be any device with computing, storage, and network connectivity, such as controllers, routers, gateways, and so on. They can be deployed anywhere with a network connection, for instance, alongside a factory floor. They are interconnected among them, with the IoT devices and also with cloud servers, forming a distributed network. Therefore, NC-based techniques can be extrapolated from WSNs to the communication between the devices within the fog layer [34, 35].
\nThe fog layer is responsible for gathering data from end-devices and for distributing coded packets to the different clouds that comprise the multicloud deployment. The use of NC has also been demonstrated to be beneficial for data distribution [36]. Moreover, this technique is advantageous for distributed storage systems, since it can achieve an optimal trade-off between storage and repair traffic. Thus, it can also help to deal with fog storage nodes that may continuously leave the network without a replacement [37].
\nClouds within the multicloud deployment are responsible for storing incoming network-coded data from the lower layer. NC-based techniques can improve the process of lost data recovery, as well as enhance the efficiency of data redundancy [38]. As an example, Figure 3 illustrates the repair operation in case of a cloud failure using exact minimum-storage regenerating (EMSR) codes. A file is divided into for fragments, and both original and coded chunks are distributed as shown in the figure. Assuming Cloud 1 is down (A and B are lost), the surviving nodes XOR their own chunks to create new encoded ones in order to make possible the reconstruction of A and B.
\nRepair process with EMSR codes [17].
Clouds are then able to perform required operations for decision-making or further analytics. Data or action commands are transmitted to the corresponding devices based on the results of the performed analysis. The cloud response time can also be reduced, as in the upstream communication, due to the implementation of NC. In [39], for instance, if a file has been divided into \n
If the information is stored over distributed untrusted platforms, such as public clouds, the inherent use of NC schemes can provide, in addition to fault tolerance, a security level against eavesdroppers [40]. However, it is necessary to find a trade-off between fault-tolerance and security [41], since the more redundant data, the more vulnerable the system becomes.
\nWe overview the most relevant benefits the integration of IIoT architectures and NC provides. While this approach can bring significant advantages, it also poses some issues. We identify and describe future challenges that may arise with the implementation of next-generation IIoT architectures.
\nAs the IIoT architecture relies on a multicloud deployment, the reliability and availability of the entire system can be enhanced. Data are distributed across different clouds, and so, the possibilities of suffering a cyber-attack are reduced. Moreover, this information is stored differently from the original form. Thus, data privacy is improved. The multicloud environment, due to its fault tolerance, increases the robustness in case of service outages. Due to the implementation of multiple clouds, this architecture enables to distribute data to the most convenient cloud, which makes possible not only to choose the service provider that better fits the moment requirements but to use the connections under the best conditions. Thus, it helps to identify the right service architecture to optimize latency, location, and cost.
\nThe use of NC-based techniques can enhance the performance of the communications over congested WSNs, as well as of the data distribution and recovery processes over multicloud deployments. Since data redundancy is more efficient, reliability and availability of the provided service are improved. Besides, the integration of NC-based techniques into the architecture can lead to the reduction of end-to-end latency.
\nAll of the above advantages allow the computational power of the cloud to be available closer to the end nodes, improving the performance of delay-sensitive IIoT applications.
\nAs mentioned throughout the chapter, next-generation IIoT architectures aim to reduce the end-to-end communication latency and to increase the system reliability by merging both fog and multicloud-based schemes as well as NC techniques. However, the use of complex NC schemes can result in extra delays taking into account that IoT devices have limited computational resources. Thus, in order to exploit the benefits of this technique, it is crucial to choose the most suitable coding parameters as well as to design simpler coding schemes and adaptable scheduling and routing algorithms.
\nIIoT systems must also provide scalability and flexibility. A cloud environment is inherently a scalable architecture due to its capability to manage network topology variations while handling big amounts of data. However, in architectures such as the proposed, as devices may be intermittently connected to the network, not only the architecture itself needs to be scalable and adaptable to changing environments, but also the coding techniques.
\nThis chapter overviews next-generation IIoT systems which, in order to satisfy the demands of Industry 4.0 applications, must ensure low-latency and highly reliable communications. This will enable advanced analytics for time-critical IIoT applications. The previous objective can be achieved on one hand, by implementing a three-layer architecture based on IoT devices, fog nodes, and a multicloud deployment. On the other hand, the use of NC techniques across this architecture can improve the communication quality and increase the system reliability. In this chapter, we describe next-generation IIoT architectures and provide different application use-cases where they can be applied. We also review NC-based techniques and the benefits of this technique for different scenarios. Next, we describe the introduction of NC for the communications across the architecture. We also outline the advantages of the approach and finally, we present some challenges that may arise, such as the design of scalable and adaptive coding schemes and routing algorithms, and which may inspire future research lines.
\nThis work has been partially supported by the Basque Government through the Elkartek program, as well as the Spanish Ministry of Economy and Competitiveness through the CARMEN project (TEC2016-75067-C4-3-R) and the COMONSENS network (TEC2015-69648-REDC) and the H2020 research framework of the European Commission.
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I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. 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