The essential circadian rhythm genes in mammals.
\\n\\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\nThank you all for being part of the journey. 5,000 times thank you!
\\n\\nNow with 5,000 titles available Open Access, which one will you read next?
\\n\\nRead, share and download for free: https://www.intechopen.com/books
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\nDr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\nSeeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\nOver these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\nWe are excited about the present, and we look forward to sharing many more successes in the future.
\n\nThank you all for being part of the journey. 5,000 times thank you!
\n\nNow with 5,000 titles available Open Access, which one will you read next?
\n\nRead, share and download for free: https://www.intechopen.com/books
\n\n\n\n
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"3797",leadTitle:null,fullTitle:"Industrial Robotics: Programming, Simulation and Applications",title:"Industrial Robotics",subtitle:"Programming, Simulation and Applications",reviewType:"peer-reviewed",abstract:"This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology.\r\nThis book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here.\r\nThe authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein.",isbn:null,printIsbn:"3-86611-286-6",pdfIsbn:"978-953-51-5808-0",doi:"10.5772/40",price:159,priceEur:175,priceUsd:205,slug:"industrial_robotics_programming_simulation_and_applications",numberOfPages:702,isOpenForSubmission:!1,isInWos:1,hash:"1e417b44016323de5c12e77210148af6",bookSignature:"Low Kin Huat",publishedDate:"December 1st 2006",coverURL:"https://cdn.intechopen.com/books/images_new/3797.jpg",numberOfDownloads:149315,numberOfWosCitations:140,numberOfCrossrefCitations:92,numberOfDimensionsCitations:185,hasAltmetrics:0,numberOfTotalCitations:417,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 9th 2013",dateEndSecondStepPublish:"September 30th 2013",dateEndThirdStepPublish:"January 4th 2014",dateEndFourthStepPublish:"April 4th 2014",dateEndFifthStepPublish:"May 4th 2014",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,editors:[{id:"134111",title:"Dr.",name:"Kin Huat",middleName:null,surname:"Low",slug:"kin-huat-low",fullName:"Kin Huat Low",profilePictureURL:"https://mts.intechopen.com/storage/users/134111/images/system/134111.jpg",biography:"Dr. Low Kin Huat is currently a professor in the School of Mechanical and Aerospace Engineering, Nanyang Technological University in Singapore. 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Everyday life is organized according to three different clocks: the solar clock which gives us light and temperature during the day, the social clock which determines the working day, and the biological clock which we notice during shift work or when we adjust to a reduced amount of daylight. In real life, the circadian clock is synchronized within 24 hours of the solar clock [1, 2].
It is known that almost all cardiovascular events occur in a circadian manner with a higher frequency in the morning after waking [3]. In the peripheral clocks of the cardiovascular tissues or cells, there is daily expression of the clock-controlled genes (CCG) synchronized and regulated by central clock [4, 5]. Disturbances of circadian rhythm can lead to cardiovascular disease.
Today we are facing a global epidemic of cardiovascular disease. In 2015 cardiovascular disease was the cause of 17.7 million deaths worldwide or 31% of total mortality. Of these 7.4 million deaths were caused by ischemic heart disease and 6.7 million by cerebrovascular diseases, according to World Health Organization (WHO) [6].
Circadian rhythm is controlled by a molecular clock located in almost every cell. A hierarchical system organizes molecular clocks—the master clock is located in the suprachiasmatic nucleus (SCN) in the hypothalamus [2, 7], while the peripheral clocks are located in each organ or cell. The central clock regulates physiological functions via the autonomic nervous system, humoral mediators, and other still unknown factors [7, 8]. In the maintenance and generation of circadian or biological rhythm in humans, a whole series of anatomical (suprachiasmatic nucleus), neurological (retinohypothalamic paths) and neuroendocrine (melatonin) systems are involved, indicating that the biology of the circadian rhythm of humans is similar to that of animals [9].
The master clock in the SCN consists of about 100,000 neurons in humans. It is the only molecular clock that receives light as an input signal from the retina. Internal clocks are synchronized with light depending on the time of day. SCN receives a direct light signal from the retina via the optic nerve from the photoreceptor called the intrinsically photoreceptive retinal ganglion cell (ipRGC), which expresses the circadian photopigment, melanopsin [10]. The signal is further transmitted to peripheral clocks via the endocrine system [11, 12]. The central clock synchronizes each of the peripheral clocks in the body, and the primary circadian hormone is melatonin [13]. The pineal gland secretes melatonin during the night. Melatonin plays an essential role in maintaining the circadian rhythm depending on the period of light or darkness. The main difference between the master and peripheral clocks is in their degree of intercellular interaction. Peripheral clocks are under the influence of the master clock from the SCN via hormones, chemical signals and other metabolites (such as food), as well as by changes in the body, such as body temperature [11, 12].
On the other hand, due to the high degree of neuron connection the master clock in the SCN is not under the influence of internal signals but only under the influence of light [14]. Peripheral tissues integrate central clock signals with environmental factors (including sleepiness, physical activity, and feeding) and their autonomic rhythms which regulate the metabolism in a circadian manner [10]. The rhythm of the peripheral clocks in humans is measured by direct measurement of physiological changes, or by determining the expression of the clock genes. Central and peripheral clocks together control the daily circadian rhythm of the metabolism [15].
Feeding time is one of the key triggers or external factors that sets the phases of the peripheral clocks [15]. Complex feedback loops connect the circadian clock with metabolic pathways and integrate these systems independently of light [10]. It is believed that the central clock regulates the metabolism by hormones (primarily cortisol and melatonin) and synaptic signaling (via the autonomic nervous system) [10]. Feeding is a circadian event that serves not only as the output of the central clock, but also as an input signal for peripheral clocks because peripheral tissues communicate with the brain through ghrelin, leptin, glucose, and insulin. Circadian feeding contributes to the interworking of the clock and metabolism, which is crucial for metabolic homeostasis [16]. The central clock rhythm is primarily related to light, whereas peripheral tissue rhythms derive from the input of signals from the central clock, external factors (light, physical activity, feeding, and sleepiness) and the availability of numerous metabolites [15]. All these signals affect the molecular clock, creating a complex correlation between the circadian clock and physiological processes [10]. SCN coordinates all cellular circadian clocks in the organs and tissues through its rhythmic outcomes, to adapt physiology to Earth’s rotation [17].
The two clock systems become desynchronized when their drivers or stimuli do not coincide because different stimuli affect the phases of the central and peripheral clocks. This mismatch disrupts the metabolism because the two clock systems coordinate interlinked metabolic pathways. Circadian rhythm mismatch increases the risk of developing metabolic diseases [15].
The central clock is primarily triggered by light, and its rhythm is often measured by determining the concentrations of melatonin, cortisol or body temperature [15]. The expression of the clock genes is disrupted in pathological conditions. Such a change may result in different tissue response to external signals and accelerate tissue damage. The loss of synchronization can lead to various diseases, including an increased incidence of cardiovascular disease [18].
The central clock genes are expressed in a circadian manner in the SCN, and light is one of the main initiators (so-called zeitgeber) and can reset the phase of the rhythm. The first circadian rhythm gene discovered was the Per gene in the fruit fly in 1971 [19, 20], while the first circadian rhythm gene discovered in the vertebrae was the CLOCK gene [21]. There are about 10 circadian rhythm genes known to regulate cyclic expression of mRNA and protein, via transcription and translation feedback loops [22]. In the SCN there are four essential proteins: ARNTL (Aryl Hydrocarbon Receptor Nuclear Translocator-Like) and CLOCK (Circadian Locomotor Output Cycles Caps) are activators, while PER (Period) and CRY (Cryptochrome) are transcription inhibitors. The feedback of the circadian rhythm gene maintains circadian oscillations in one cell at the transcriptional and posttranscriptional levels, and the transition from light to dark triggers these oscillations. The whole process of activation and repression of gene expression within the loop lasts for about 24 hours. These transcriptional factors trigger numerous physiological changes by acting on the expression of the same genes, and other clock-controlled genes [23, 24].
ARNTL and CLOCK heterodimers bind to regulatory elements of the promoters and enhancers (E-box) of the PER and CRY genes and stimulate their expression and the expression of other clock-controlled genes. Overnight the amount of PER and CRY proteins gradually increases, and heterodimers are created in the cytoplasm. The phosphorylated PER-CRY heterodimers are translocated into the nucleus where they inhibit the ARNTL-CLOCK protein complex. Therefore, during the day, transcription of PER and CRY genes is reduced, while the levels of PER and CRY protein decrease due to their degradation by ubiquitin. The PER-CRY heterodimers directly bind to the ARNTL-CLOCK complex, and as PER2 contains histone deacetylase, the chromatin structure changes, resulting in transcription termination. Also, the PER-CRY heterodimer is in interaction with RNA-binding proteins and helicase that are important in stopping transcription independently of the interaction with the ARNTL-CLOCK complex. Additionally, PER-CRY heterodimers regulate the transcription of various nucleic hormone receptors [25, 26, 27, 28].
During the day a new cycle begins by the termination of the ARNTL-CLOCK heterodimer inhibition. Casein kinase 1 (CK1) controls the amount of phosphorylation or degradation of PER-CRY heterodimers and thereby determines the amount of PER-CRY heterodimer entering the nucleus and inhibiting the ARNTL-CLOCK complex. CK1 phosphorylates the proteins and thus regulates their activity [29].
The additional negative loop is REV-ERBα that binds to the REV-ERB/ROR response element (RRE) of the ARNTL and CLOCK genes, and prevents their transcription. Also, RORα (Retinoic Acid Receptor-related Orphan Receptor) binds to the same regulatory elements of the ARNTL gene as well as REV-ERBα. With REV-ERBα degradation overnight, RORα promotes transcription of the ARNTL gene [30]. The second regulatory loop consists of ARNTL-CLOCK heterodimers which promote the transcription of the nucleic receptors REV-ERBα and RORα [31] (Figure 1).
The molecular mechanism of circadian rhythm in humans. ARNTL and CLOCK activate transcription of CRY and PER, nuclear receptors (REV-ERBα and RORα) and other clock-controlled genes. CRY and PER heterodimerize and phosphorylate by casein kinases and translate into the nucleus where they prevent binding of the ARNTL-CLOCK to the regulatory regions of target genes. In the second feedback loop, REV-ERBα prevents the transcription of ARNTL because it binds to the RRE element, while overnight the same regulatory elements bind RORα and activate transcription of ARNTL. Also, ARNT-CLOCK heterodimers activate transcription of the REV-ERBα and RORα proteins. ARNTL—aryl hydrocarbon receptor nuclear translocator-like, CLOCK—circadian locomotor output cycles kaput, CRY—cryptochrome, PER—period, P—phosphate, RORα—retinoic-related orphan receptor alpha, RRE element—REV-ERB/ROR response element, Ub—ubiquitin.
Circadian clock genes have an essential role in many physiological processes. Thus, animal models demonstrate that the ARNTL gene plays an essential role in lipid metabolism because it induces the expression of genes involved in lipogenesis in adipose tissue in a circadian manner [32]. Pancreatic beta cells have a circadian clock dependent on ARNTL and CLOCK protein oscillations, which regulate insulin secretion depending on the stage of alertness. Abnormalities of the pancreas clock may trigger the onset of diabetes [33]. It was found that CLOCK polymorphisms are associated with body weight, the risk for metabolic syndrome and insomnia in humans [9, 32], and polymorphisms of the PER2 and PER3 genes are associated with sleep disorders [34, 35]. Some variants of CRY1 and CRY2 genes are associated with metabolic syndrome, particularly hypertension and increased triglyceride levels in the blood [36]. Many variants of the circadian rhythm genes are associated with the risk factors for the development of cardiovascular diseases such as blood pressure, glucose concentration [23, 37]. An overview of the essential circadian rhythm genes with their roles is shown in Table 1 [38, 39].
Gene | Function |
---|---|
ARNTL (Aryl hydrocarbon Receptor Nuclear Translocator-Like) | Rhythmically expressed. Physically associates with CLOCK. Promotes transcription of PER and CRY. It is involved in the risk for hypertension, adipogenesis, and glucose metabolism. |
CK1ε (Casein kinase 1 ε) | Physically associates with and phosphorylates PER. Affects PER stability and nuclear localization. |
CLOCK (Circadian Locomotor Output Cycles Kaput) | Constitutively expressed. Physically associates with ARNTL. Promotes transcription of PER and CRY. It is involved in the platelet rhythmic activity, response of cardiomyocytes to fatty acids, lipid, and glucose metabolism. |
CRY1 (Cryptochrome 1) CRY2 (Cryptochrome 2) | Physically associates with and stabilizes PER. Negative regulator of Per and Cry transcription. |
PER1 (Period 1) PER2 (Period 2) PER3 (Period 3) | Physically associates with CRY. Positive regulator of ARNTL. They are involved in the aortic endothelial function. |
REV-ERBα (nuclear receptor subfamily 1 group D member 1) | Associates with regulatory elements and negative regulator of the ARNTL and CLOCK transcription. It is involved in triglyceride and lipid metabolism, and circadian activity of PAI-1. |
RORα (Retinoic-related orphan receptor alpha) | Associates with regulatory elements and positive regulator of the ARNTL. It is involved in lipid metabolism. |
TIM (Timeless) | Circadian function not known. Physically associates with CRY. Negative regulator of PER and CRY transcription in vitro. |
The essential circadian rhythm genes in mammals.
Numerous studies on animal models, as well as human populations, have confirmed the association of the circadian clock gene with metabolic syndrome and cardiovascular diseases [15, 40, 41].
The WHO data for 2017 show that cardiovascular diseases were the cause of 19.9 million deaths worldwide, and about 80% of deaths from cardiovascular diseases were due to myocardial infarction and stroke [42]. It is estimated that by 2030, 23.6 million people will die annually due to cardiovascular diseases [43].
Cardiovascular diseases are the primary cause of death in developed countries of the world, and in less developed parts of the world, this mortality is rising and overtaking mortality rates for infectious diseases [44].
There are variable and constant risk factors for cardiovascular disease. The variable risk factors are those that can be affected by therapy and lifestyle change, such as smoking, hyperlipoproteinemia, hypertension, and to some extent diabetes and homocysteinemia. The constant risk factors cannot be affected, namely age, genetic predisposition, gender, and menopause. The general risk factors which can be altered most are smoking, hypertension and hyperlipidemia, and obesity and diabetes whose prevalence has risen in the last few decades. However, some recent risk factors (fibrinogen, lipoprotein (a), homocysteine) should not be ignored. All of these contribute to total cardiovascular risk [45].
Cardiometabolic risk factors are determined by a cluster of metabolic and cardiovascular changes. Diabetes and obesity are also associated with reduced quality of life and increased economic burden on the person and society [46, 47]. Cardiovascular diseases and type 2 diabetes share common pathophysiological mechanisms of insulin resistance and risk factors for cardiovascular diseases, such as metabolic syndrome. Excessive weight plays a significant role because fatty tissue becomes an active endocrine organ that secretes low-level inflammation mediators, and these stimulate the development of metabolic syndrome and vascular diseases [32, 48, 49].
Myocardial infarction is the leading cause of mortality in developed countries and developing countries. It can be caused and triggered by different pathophysiological processes. Myocardial infarction is an inflammatory disease due to the death of myocardial cells because of complete coronary circulation interruption, which is in most cases a consequence of thrombotic occlusion of the coronary artery at the site of the activated atherosclerotic plaque. An electrocardiogram (ECG) of the ST elevation is only an indirect indicator of the fact that ischemia affects all three layers of cardiac muscle (endocardium, myocardium, and epicardium) [50].
ST-elevation myocardial infarction (STEMI) is the most severe form of acute coronary syndrome. Myocardial infarction is accompanied by increased cellular oxidative stress in the pericardial cavity [51]. The primary cause of infarction is platelet aggregation in the coronary artery. The platelet activity is the highest in the morning. Also, in the morning hours, mental and physical activity increase due to cortisol and catecholamine elevation, which increases cardiac output [45]. The biochemical markers of myocardial damage are cardiac troponin T or troponin I in serum. These biochemical markers are more reliable than those previously used, such as measurement of creatine kinase (CK). The initial increase in troponin in peripheral blood in patients with infarction occurs over 3–4 hours with a permanent increase for up to 2 weeks after infarction. In order to confirm or exclude myocardial damage, troponin T serum levels are repeated in the first 6–12 hours after severe chest pain [52].
The classification of myocardial infarction in five types was introduced in 2007 and established the clinical criteria for its exact differentiation [50]. Type 1 is related to a coronary plaque rupture, fissuring, or dissection with resulting intraluminal thrombosis. Type 2 myocardial infarction is secondary to myocardial ischemia resulting from increased oxygen demand or decreased supply. Type 3 myocardial infarction is linked to unexpected cardiac death when cardiac biomarkers are unavailable. Types 4 and 5 myocardial infarction are procedure related [53].
In the study of Saaby et al., [54] it was shown that the most significant number of patients with myocardial infarction come under Type 1 (72%). In patients with Type 1 myocardial infarction, changes in the ECG are seen either as the elevation or depression of the ST segment; the troponin T level also increases in the blood and serves as a diagnostic marker. The troponin T level in the blood is higher in patients with Type 1 myocardial infarction than Type 2 [55].
For manifestation of infarction, apart from lifestyle, a genetic background of myocardial infarction is also essential. A positive family history of myocardial infarction is a major cardiovascular risk factor [56]. Coronary artery disease and myocardial infarction have a genetic background in 50–60% of cases. Many genes are found in the genetic background of myocardial infarction. Whole genome association studies have revealed many variants of genes associated with increased risk for myocardial infarction [56]. So far the most frequently explored genes with increased risk for myocardial infarction are involved in the metabolic pathways of lipid metabolism and development of type 2 diabetes. The relationship between the circadian rhythm genes and the onset of myocardial infarction will be discussed below.
Many cardiovascular events and diseases have a circadian pattern of appearance. The normal circadian blood pressure shows the two highest values during the day, around 9 am and 7 pm, while there is a slight decrease around 3 pm. It is considered that circadian variations in the tone of coronary vessels and endothelial function play an essential role in the onset of myocardial infarction. As myocardial infarction is significant medical stress, it causes increased cortisol levels in plasma [3]. In the acute phase of myocardial infarction, the phase of the circadian clock in the ischemic part of the heart differs from the non-ischemic part of the heart. The arrhythmia may occur because of the difference in the phase of the rhythm, or different expressions of the circadian clock genes. The loss of synchronization of the circadian rhythm between organs or tissues occurs more often than we would expect [4]. Circadian regulation of physiological processes is regulated locally. Peripheral tissue clocks control tissue-specific expression [27].
Homeostatic changes, gene expression changes, and external triggers can cause a stressful environment and cause damage to the atherosclerotic plaque in the coronary arteries in the morning, when prothrombin is increased [57]. Many intrinsic vasoactive and cardioactive substances, such as angiotensin II, melatonin, plasminogen activator inhibitor 1 (PAI-1), glucocorticoids, epinephrine, norepinephrine, and nitrogen oxide, show a specific circadian pattern. The fibrinolytic system, which regulates PAI-1, shows a circadian pattern of occurrence in both healthy patients and those with ischemic heart disease. The concentration and activity of PAI-1 depend on the circadian rhythm and are the highest in the morning [28]. CLIF (Cycle-Like Factor) expression in endothelial cells creates heterodimers with CLOCK protein, and binds to the E-box of the PAI-1 gene promoter and promotes its expression, while PER2 and CRY1 inhibit expression of PAI-1 by blocking heterodimer CLOCK-CLIF. CLIF controls the circadian rhythm of PAI-1 in endothelial cells, which might explain the higher incidence of myocardial infarction in the morning [26, 58]. As a result of this, the fibrinolytic system in patients with MI might be a potential goal for chronotherapy, to treat acute cardiovascular events. The circadian clock regulates the endothelial response to vascular injury. The main factor that can be affected by potential chronotherapy is PAI-1 because it is a crucial fibrinolysis inhibitor [59]. Chronotherapy includes the accurate timing of drug taking and can improve the therapeutic efficacy of the drug, while limiting its toxicity [41]. That is why many studies support chronotherapy for cardiovascular disease by limiting pathogenesis and improving treatment after the occurrence of acute cardiovascular events [59].
It is known that melatonin levels decrease during the night in coronary heart disease and infarction. Melatonin is an antioxidant that can inhibit the action of reactive oxygen radicals during heart ischemia. It also plays a vital role in regulating blood pressure, depending on the circadian rhythm. Animal studies have shown that animals with the pineal gland removed develop hypertension. Clinical examinations have shown that in patients with hypertension melatonin drugs taken daily before bedtime reduced blood pressure [3].
The appearance of myocardial infarction has two peaks during the day. The highest incidence of myocardial infarction is during the morning, and the second peak occurs late at night [60]. The beta blockers prevent increased sympathetic activity, catecholamine concentration, heart rate, blood pressure and lack of oxygen in the heart, and these are the physiological reasons for the existence of two peaks of myocardial infarction [57, 61].
Ischemia occurs in the morning due to increased oxygen demand, whereas in the evening it is due to decreased coronary blood flow. The appearance of myocardial infarction depends on ethnic origin, and the British differ from Asians in the frequency of the infarction [3]. In the Mediterranean, the highest incidence of myocardial infarction is between midday and midnight, while in the UK the highest incidence is between midnight and midday [62]. Numerous factors might affect the later occurrence of infarction in the Mediterranean, such as the number of sunlight hours, inequality in the prevalence of risk factors for cardiovascular disease, and the habit of afternoon rest or ‘siesta’ [63]. It has also been noted that the incidence of myocardial infarction is higher in the winter [3]. The specific circadian pattern of infarction symptoms has been observed, and the correlation of the circadian rhythm gene with the infarction investigated. The role of the molecular circadian clock in myocardial activity was initially investigated on animal models. It has been observed that the clock gene mutations of the circadian rhythm affect the heart rate, myocardial contractility, energy metabolism, which altogether leads to ischemia [64, 65]. In contrast, variants of the Per2 gene in mice reduce the severity of the injury after myocardial infarction because it does not only reduce inflammatory response, but also reduces apoptosis, induces cardiovascular hypertrophy, and thus preserves cardiac function [65].
Different variations of circadian rhythm genes are associated with many risk factors for cardiovascular disease. Thus, CLOCK gene variations are associated with metabolic syndrome in humans, type 2 diabetes, and some with stroke [64, 66, 67, 68], while CRY2 and PER2 gene variations are associated with myocardial infarction [69]. Expression of CRY1 and PER2 genes in fatty tissue is associated with metabolic syndrome in humans [64, 70]. Metabolic syndrome is a significant risk factor for cardiovascular disease and contributes to the common pathophysiological processes leading to the development of diabetes and cardiovascular diseases [48]. Atherosclerotic changes in blood vessels in patients with diabetes are more severe than those with normal glucose concentration [71]. It has been shown that the risk of cardiovascular disease in diabetic patients is two to three times higher than in healthy subjects [72]. Patients with diabetes usually have a higher heart rate in sleep and lower heart rate variability over the day than people without diabetes, which causes unnecessary oxygen consumption in the myocardium, with reduced nutritional blood supply. Biological and epidemiological studies suggest a direct link between lifestyle and metabolic disorders [12], although the genetic and biochemical linkage of human circadian rhythm with metabolic disorders has not been fully explored. Accordingly, the importance of the circadian rhythm in maintaining ‘energy’ homeostasis and metabolism is evident.
A peripheral clock is also found in cardiomyocytes, and the internal molecular mechanism of cardiomyocytes, such as the circadian clock, might contribute to cardiovascular disease [73]. Similar to SCN, cardiomyocytes have a circadian expression of clock genes in response to serum shock or norepinephrine. Several genes are associated with intracellular metabolism or physiological activity that has a circadian expression in cardiomyocytes [74]. After development, cardiomyocytes do not replicate, although they possess a meager and permanent rate of renewal. Cardiomyocytes renew cellular structure with their new proteins and membrane lipids every few weeks [75]. The ischemic precondition is an adaptation of cardiomyocytes to hypoxia, and once the heart has suffered an ischemic insult, cardiomyocytes become more resistant to MI because of PER2 and hypoxia inducible factor (HIF)-1a [74]. Circadian genes regulate a group of genes encoding for cardiac metabolic enzymes, and it is considered that a significant role of circadian genes in the heart is to synchronize cardiomyocyte metabolic activity with the availability of nutrients in the blood (i.e., feeding time) [29]. It is known that PER2 plays an essential role in carbohydrate metabolism during myocardial ischemia [76].
The cardiomyocyte circadian clock affects the daily variations in the heart. Studies show that the cardiomyocyte circadian clock affects myocardial contractions, the metabolism and gene expression. This clock is vital since impairment of the cardiomyocyte circadian clock might significantly alter cardiac function, cardiovascular disease pathogenesis, and treatment strategies for cardiovascular diseases (e.g., chronopharmacology) [77]. Desynchronization between different cell types (e.g., cardiomyocytes, vascular smooth muscle cells, endothelial cells) could occur within the organs (e.g. the heart) during certain physiological or pathological conditions [78]. The cardiomyocyte circadian clock allows the heart to predict circadian rhythm by extracellular stimuli, allowing rapid and temporally response [77]. The cardiomyocyte circadian clock has a crucial role in mediating the daily rhythm in myocardial metabolism and affects the cardiovascular function [79]. The cardiomyocyte circadian clock changes during illness, and this molecular mechanism might affect the etiology of cardiovascular disease [78].
Circadian rhythm adjusts the physiological functions of an individual on a daily basis. Daily variations of physiological parameters in the cardiovascular system maintain cardiovascular function according to the needs of different activities during the day. This information suggests that we need to know not only how, but also when to treat heart disease, and also to treat pathological changes not only symptomatically but to treat non-symptomatic but potentially harmful changes in the circadian rhythm.
Understanding the pathophysiological processes involved in the onset of myocardial infarction requires additional studies to assess the crucial elements of the circadian rhythm. In today’s personalized medicine, knowledge of the circadian rhythm (i.e., the genetic background) of an individual can be significant for treatment and should be included as an essential part of the diagnostic process.
Authors declare no conflict of interest.
Generally, wireless networks consist of low capacity links with nodes that rely on batteries. An efficient communication scheme for such networks should minimize both congestion in the links and control information in the nodes. Security is a critical parameter in wireless applications and any efficient communication scheme has to integrate security vulnerabilities of the system in its implementation. Unfortunately, existing schemes have network security implemented at the upper layer such as the application layer; meanwhile parameters such as congestion, which affect data throughput, are the physical layer. Hence, any attempt to increase the security level in a communication system greatly compromises data throughput. In [1], the authors developed a metric to estimate a timeframe for cyberattacks using the RSA public key cryptography. In the analysis, the authors estimated the attacker’s human time in carrying out a successful attack based on the key length. Such an implementation at the upper layer will curb any security attack at the prescribed time but will greatly compromise data throughput at the physical layer due to the huge modular exponentiation involved in its implementation. In [2], the authors developed a secure and efficient method for mutual authentication and key agreement protocol with smart cards. The implementation, which is based on the constant updating of the password, will involve a considerable amount of control information, which is detrimental to the optimum functioning of the nodes. Research work using different information-theoretic models to develop physical layer security based on the characteristics of the wireless links has been carried out [3, 4]. However, the existing methods for implementing physical layer security under the different information-theoretic security models is expensive and requires assumptions about the communication channels that may not be accurate in practice [5]. Hence any deployment of the physical-layer security protocol to supplement a well-established upper layer security scheme will be a pragmatic approach for robust data transmission and confidentiality [6]. It is in this light that, a new cross-layer approach is presented in this research. Major research efforts have targeted cross-layer implementation of security schemes in wireless networks [7, 8, 9]. In this research, the proposed cross-layer security scheme uses signal processing techniques as well as efficient coding and well-established cryptographic algorithm to implement a security scheme, which greatly enhances security-throughput trade-off, and curb many security threats common to wireless networks.
The rest of the chapter is organized in eight subsequent sections. In Section 2, we present the background knowledge required for the design and implementation of the new cross layer security scheme. This will involve a review of the different techniques used in the development of the new security scheme. The first subsection presents the implementation of the multi-level convolutional cryptosystem. This implementation involves the combination of subband coding and a new non-linear convolutional coding. Next, a review of the residue number system (RNS) with brief description of the Chinese remainder theorem (CRT) is presented. We conclude the section with an overview of RSA public-key cryptography. Section 3 presents the protocol for the implementation of the new cross layer security scheme. The FPGA-based implementation applied to CDMA using the new layered security scheme will be presented in Section 4. In Section 5, cryptanalysis of the cross-layer security scheme will be carried out in order to quantify the security. Quantification of data throughput is performed in section 6 while different security threats which could be circumvented by the cross-layer security scheme are presented in Section 7. We end the chapter in Section 8 with conclusions of our work.
This section presents the background knowledge required for the design and implementation of the new cross layer security scheme. It involves a review of the different techniques used in the development of the new security scheme.
The multi-level convolutional cryptosystem constitutes the second stage of implementation at the physical layer. It receives integers from the RNS implemented at the first stage. The multi-level cryptosystem is implemented using subband coding and non-linear convolutional cryptosystem.
The integers from the RNS block are split into different levels of decomposition based on subband coding. Subband coding is implemented using integer wavelet lifting scheme [10, 11]. It is shown in [12] that, a judicious choice of filter banks could result into an integer transform despite the fact that, wavelet transform is an approximation process. A four-tap Daubechies polyphase matrix, which results into integer transforms, is given as follows [12]:
where h and g are filter coefficients with suffix e and o denoting even and odd coefficients. The factorization of the polyphase matrix is as follows [12, 13, 14].
(Eq. (2)) forms the basis of integer to integer wavelet transform which in effect is progressive transmission. The factored coefficients
where an and dn are the approximation and detail sequences of wavelet coefficients of the nth sample. The subsequent transmissions are fed into the non-linear convolutional coding block as depicted in Figure 1 for the first level kth detail and approximation sequences [12, 15]. The processing blocks (PEs) shown in the figure depicts the computations of (Eq. (3)).
Synopsis for the computation of the kth coefficients for the 1st and 2nd levels of decomposition.
The final decomposition level which comprises one data point will give one approximation coefficient and one detail coefficient.
At the destination, the inverse wavelet transform is performed to obtain the successive approximation sequences. (Eq. (3)) is used to perform the inverse transform by reversing the operations for the forward transform and flipping signs [12, 15]. The process starts with the approximation and detail coefficients a0 and d0 respectively obtained at the final decomposition level of the forward transform. The first stage of the inverse transform is shown in Figure 2 [12, 15].
The first stage of the inverse transform.
The (↑2) symbol represents upsampling by 2, which means that zeros are inserted between samples while H2 and H3 are the filter coefficients used in (Eq. (3)).
A major advantage of symmetric cryptography is the ability of composing primitives to produce stronger ciphers although on their own the primitives will be weak. Hence, the vulnerable convolutional code block will be cascaded into different stages using the product ciphers obtained from the S-box and P-box to form a non-linear convolutional cryptosystem.
Key generation: The specifications of the private keys used in the implementation of the cascaded convolutional cryptosystem are as follows [12, 15, 16]:
States of each transducer or convolutional code block in the cascade given by the contents of the sub-matrices in the generator matrix;
The transition functions. These are mappings used to compare the input bits and present state and switches to the appropriate next state;
n-bit S-boxes. They are used to shuffle the output bits.
n-bit P-boxes. They are used for the different permutations per level of decomposition.
For illustrative purposes, an (8, 8, 2) convolutional encoder will be considered to demonstrate the keys generation process.
For an 8 × 8 matrix, there are at least 216 ways or keys in which the connections of a register to the modulo-2 adder could be made. A possible key which gives the contents of the 8 × 8 matrix are shown in (Eq. (4))
The generator matrix is used to specify the following set of 8 vectors.
X(7) := A1(7) ⊕ A3(7); X(6) := A1(7) ⊕ A1(6) ⊕ A3(6).
X(5) := A1(5) ⊕ A3(5) ⊕ A3(6); X(4) := A1(4) ⊕ A3(4) ⊕ A3(5).
X(3) := A1(3) ⊕ A3(3) ⊕ A3(4); X(2) := A1(2) ⊕ A3(2) ⊕ A3(3).
X(1) := A1(1) ⊕ A3(1); X(0) := A1(0) ⊕ A3(0).
It should be recalled that, for an (n,k,L) convolutional encoder, each vector has Lk dimensions and contains the connections of the encoder to the modulo-2 adder.
The structure of the (8, 8, 2) convolutional encoder is shown in Figure 3 with A2, A3 representing the registers
Structure of an (8, 8, 2) convolutional encoder.
For an (8,8,2) convolutional code, there are 28 = 256 mappings or keys. There are two sets of transition functions denoted as f1 for the two possible states.
For example, a transition function that compares input data to state 1 and remains in state 1 is given as follows:
In (Eq. (5)), if the input data is any of the sequences {[00000000], [00000001], [00000010], [00000011], [00000100], [00000101], [00000110], [00000111]}, the present state of the transducer which is state 1 is retained.
A transition function that compares input data to state 1 and switches to state 2 is given as follows:
In (Eq. (6)), if the input data is any of the sequences {[00001000], [00001001], [00001010], [00001011], [00001100], [00001101], [00001110], [00001111]}, the present state of the transducer which is state 1 is changed to state 2.
At the destination, the transition functions are similar to those for the encoder at the source but change roles. The transition functions are very critical in the implementation of convolutional cryptosystem since it accounts for its dynamic nature, hence an increase in security level.
For an (8,8,2) convolutional code, using 2-bit shuffling boxes, there are 16 S-boxes or keys. For higher n-bit shuffling boxes, the number of keys increases, for example 8-bit shuffling boxes will give 28 keys. The four 2-bit S-boxes used to illustrate the scheme are shown in Table 1. Given an 8-bit data sequence as [A7, A6, A5, A4, A3, A2, A1, A0], the look-up S-box, Sub1,1 is used to shuffle the first pair of bits, [A7, A6], Sub1,2 is used to shuffle the second pair [A5, A4], Sub1,3 is used to shuffle the third pair [A3, A2], and Sub1,4 is used to shuffle the last pair [A1, A0].
2-bit shuffle look-up-table.
The interconnections between inputs and outputs are implemented using a permutation set look-up table. For an (8,8,2) code, the eight (08) inputs and outputs could be permuted or interconnected in at least 77 = 823,543 ways. A permissible permutation is shown in Table 2 [12, 15].
Input–output interconnect look-up-table for encoder.
After the specification of the keys, the vulnerable convolutional code block will be cascaded into different stages using the product ciphers obtained from the S-box and P-box. Using two (02) stages, a non-linear (8, 8, 2) 2-cascaded is as shown in Figure 4.
Initial structure of the cascade before encoding.
In Figure 4, Sub1,1, Sub1,2, Sub1,3 and Sub1,4 are S-boxes used for pairwise bit shuffling and the input vector to the first transducer, {X7, X6, X5, X4, X3, X2, X1, X0} is the output set from the subband encoding block while the output vector {Y7, Y6, Y5, Y4, Y3, Y2, Y1, Y0} is the ciphertext from the second transducer stage.
It is worth noting that the security level could be greatly increased by increasing the number of stages to be cascaded.
The residue number system uses the Chinese remainder theorem (CRT) to compute unknown values from the remainders left or residues when unknown values are divided by known numbers.
The modular Chinese remainder theorem states that [17, 18]:
Assume m1, m2, …, mN are positive integers, relatively prime pairs: (mi, mk) = 1 if i # k. Let {b1, b2, …, bN} be arbitrary integers, then the system of simultaneous linear congruence
has exactly one solution modulo the product m1, m2, …, mN. The solution to the simultaneous linear congruence is formally given as [15, 17, 18].
where
(Eq. (8)) establishes the uniqueness of the solution. In this research, the integers
Key creation
Choose secrete primes p and q and compute m = p.q
Choose encryption exponent, e
Compute d satisfying e.d ≡ 1 mod ((p – 1). (q – 1)).
Public key: (m, e) and Private key: d
Security services achieved using RSA cryptography are authentication and non-repudiation based on digital signatures and confidentiality based on encryption. Implementation of these services are summarized in Table 3
Authentication and non-repudiation | Confidentiality |
---|---|
Choose plaintext X. Compute Xs ≡ Xd (mod m) Send (X, Xs) to Alice. Xs is the RSA digital signature of message, X | Choose plaintext X. Use Bob’s public key (m, e) to compute C ≡ Xe (mod m). Send ciphertext, C to Bob |
Summary of security services of RSA public-key cryptography.
The new scheme is implemented at the application and physical layers. The detail operations of the application and physical layers at the source and destination are as follows:
Source:
Application layer:
Traditional RSA encryption
Physical layer:
Step 1: Residue number system (RNS) converts the message points into residues based on the moduli set;
Step 2: RNS-based RSA ciphertext is converted into different levels of decomposition using subband coding;
Step 3: Symmetric encryption using Convolutional cryptosystem;
At the destination, the entire process is reversed starting with convolutional decoding at the physical layer and ending with RSA decryption at the application layer.
Consider an arbitrary array of integers for plaintext as follows {398, 453, 876, 200, 356, 165, 265, 897}.
Source
Application layer: Traditional RSA encryption
Primes, p = 13; q = 37 ⇒ n = p. q = 481
Encryption key, e = 5
Decryption key: e. d ≡ 1 mod (432) ⇒ d = 173
Array due to RSA encryption is given as {151, 293, 252, 135, 304, 315, 265, 182}
Physical layer:
Step 1: Moduli set of {107, 109, 113} is used to convert the RSA ciphertext into 8-bit data point arrays. The residue set, r1 for m1 = 107 is as follows:
r1 = {44, 79, 38, 28, 90, 101, 51, 75}
Step 2: Subband coding is performed to split residues obtained using moduli set into three levels of decomposition since m = 8 = 23 data points are used. Subband coding is basically down sampling by 2 using (Eq. (3)). The corresponding arrays for the first level of decomposition are as follows:
r11 = {−9, −48, −79, −27}; − r12 = {−9, −42, −75, −21}; − r13 = {−9, −30, −67, −9}
Note that r11 refers to first level of decomposition array for modulus, m1 = 107 and the first element is obtained using (Eq. (3)) with integer lifting filter coefficients set, h = {2, 0, 0} as follows: r1(1) – 2 × r1(0) = 79–2 × 44 = −9.
The same procedure is performed for the second and third levels of decomposition.
Step 3: Table 4 summarizes the manual computation of the encryption and decryption process of the convolutional cryptosystem for the data r11(0) = −9 from the subband encoding stage based on the entries of the product cipher and combinational logic of the non-linear (8,8,2) 2-cascaded convolutional transducer in Figure 4.
Destination
Manual computation for the first sample of first level of decomposition for modulus 107.
The first two stages of the wavelet inverse transform.
The (↑2) symbol represents upsampling by 2, which means that zeros are inserted between samples. The QMF bank are the coefficients derived from the 4-tap Daubechies filter bank [12, 13, 19]. Hence, using upsampling and the QMF bank coefficients the residue sets r1, r2 and r3 are retrieved.
Figure 5 will be used to perform a numerical illustration of subband decoding for the first level of decomposition of the array of modulus m1 = 107 to obtain approximation coefficients a1.
The moduli sets obtained from subband encoding for the three levels of decomposition for m1 = 107 are as follows:
- Level 3: r31 = {2, 44}; − Level 2: r21 = {−50, −20}; − Level 1: r11 = {−9, −48, −79, −27}.
For subband decoding, the entire process is reversed with level 3 of encoding becoming level 1 for decoding.
r31 from subband encoding namely a0 = 2 and d0 = 44 is used. From Figure 5, upsampling performed on the approximation, a0 and detail, d0 data points gives the sets y_1 = {2, 0} and z_1 = {44, 0} respectively. Using 4-tap Daubechies integer lifting filter coefficients set, h = {2, 0, 0} we have.
w_1(0) = z_1(0) – h(2)*y_1(0) – h(3)*y_1(1) = 44–0 – 0 = 44
w_1(1) = z_1(1) – h(2)*y_1(0) – h(3)*y_1(1) = 0–0 – 0 = 0
a_1(0) = y_1(0) – h(1)*w_1(0) = 2–2*44 = −86
a_1(1) = y_1(1) – h(1)*w_1(1) = 0–2*0 = 0
Hence the first level approximation data points, a1 are obtained as follows.
a1(0) = w_1(0) = 44 and a1(1) = w_1(1) + z_1(0) = 0–86 = −86
⇒ a1 = {44, −86}.
The process is repeated to obtain the second and third levels approximation data points. The third level approximation data points, a3 should be equal to residue set, r1 obtained from the RNS-based RSA ciphertext using the modulus, m1 = 107.
RNS-based Chinese Remainder Theorem (CRT): It is applied to the residue sets r1, r2 and r3.
(Eq. (8))will be used to retrieve the RSA ciphertext from the residue sets. Using (Eq. (8)) and moduli set, m = {107, 109, 113} to compute the first data point of the ciphertext set we have.
The same process is repeated to obtain all the other data points of the RSA ciphertext set. The RSA ciphertext set is fed to the RSA decryption block at the application layer.
Application layer: RSA decryption
Decryption of the first data point is given as M = 151d mod n = 151173 mod 481 = 398 which represents the original data which was sent at the source.
The same process is repeated to obtain all the other data points of the plaintext set.
In this section, FPGA implementation of new scheme applied to CDMA, the new VHDL code package to implement A mod n operations, synthesis report and behavioral simulation results will be presented.
Code division multiple access (CDMA) enables several users to transmit messages simultaneously over the same channel bandwidth in such a way that each transmitter/ receiver user pair has its own distinct signature code for transmitting over the common channel bandwidth. This distinct signature is ensured by using spread spectrum techniques whereby the message from each user is transmitted using orthogonal waveforms. In orthogonal signaling, the residues are mapped to orthogonal waveforms which constitute the CDMA signal [20]. The orthogonal waveforms used in this research are Walsh functions.
Considering an (8, 8, 2) multi-level cryptosystem for illustrative purposes, the mapping process will involve M = 28 = 256 orthogonal waveforms. Using the dynamic range of (−128, 126), a set of M = 28 = 256 orthogonal waveforms is required to completely represent all the integers or symbols. Based on this, the corresponding Hadamard matrix obtained from the procedure elaborated in [21] is as follows:
The H256 matrix is a large matrix comprising of 256 rows and 256 columns. The Hadamard matrix results into a multi–dimensional array. Multi–dimensional arrays are arrays with more than one index. Multi–dimensional arrays are not allowed for hardware synthesis. One way around this is to declare two one–dimensional array types. This approach is easier to use and more representative of actual hardware. The VHDL code used to declare the two one–dimensional array types is shown in Figure 6 [22].
VHDL code for synthesizable 256 × 256 Hadamard matrix.
The other operations in the hardware Walsh function generator implementation are trivial since they involve modulo–2 addition with built–in operators in VHDL code to handle such operations.
In this research, a new algorithm is presented which implements modular exponentiation without the use of the Montgomery algorithm. A package is developed in the VHDL code to extract residues similarly to the X mod N operation for any randomly generated data. Meanwhile, the large operand lengths which resulted from the modular exponentiation are reduced using binary exponentiation and the RNS.
The principle used to develop the package is as follows:
To perform the x = X mod N calculation where X has a large operand length of b bits say b = 1024 bits and N is modulus of small operand length of b1 bits say b1 = 8 bits, the following steps are used:
1. X is converted to binary equivalent;
2. The b1 least significant bits of the b bits of X are chosen;
3. The integer equivalent, x1 of the chosen b1 bits is determined;
4. The residue, x = X mod N is obtained from the following equation;
5. If the residue, x is greater than the modulus, N the process is iterated until the residue is less than the modulus.
(Eq. (9)) forms the basis for the x = X mod N calculation.
Based on this new algorithm which implements modular exponentiation without the use of the Montgomery algorithm, the entire physical layer security scheme could fit into a single FPGA chip.
In order to verify the performance of the proposed architecture, a VHDL programme was written and implemented on a Xilinx Virtex-4 FPGA chip (device: xc4vlx 200, package: ff 1513, speed grade − 11) [22]. Sixteen (16) randomly generated integers were fed into the FPGA. For this value, the number of bonded IOBs is 760 out of 960 resulting to 79% resource used. The behavioral simulation results for array {39,870, 45,378, 87,654, 20,087, 35,689, 16,592, 564, 276,509, 89,732, 56,287, 4527, 89,065, 4321, 7654, 5489, 512} using moduli set {111, 115, 119} are displayed in [15].
In [15], the complete synthesis report showing device utilization summary is presented. Due to the additional implementation of orthogonal signaling compared to the implementation in [15], the following parameters are different compared to results displayed in [15]:
The device utilization summary is as follows:
Number of slices: 5411 out of 89,088 6%
Number of slice flip flops: 60 out of 178,176 0%
Number of four input LUTs: 7452 out of 178,176 4%
Total REAL time to Router completion: 24 min 7 s.
Total REAL time to place and route (PAR) completion: 24 min 41 s.
Pin delays less than 1.00 ns: 21928 out of 30,651 71.5%.
The cryptanalysis will be performed separately at the application and physical layers and later combined in the cross-layer scheme to demonstrate the high security level of the new scheme compared to separate implementations.
The RSA public key cryptography is implemented at the application layer. The most successful method to break the RSA cryptosystem is the Number Field Sieve (NFS) method used for partial key exposure attacks. The NFS is based on a method known as “Fermat Factorization”: one tries to find integers x, y, such that x2 ≡ y2 mod n but x ≠ ± y mod n [12]. We assume that the two primes p and q should be close and approximately equal to the square root of n, where n = p.q. If one of the integers could be written as x = (p + q)/2 then number of steps, S1 required to determine the other integer, y could be computed as follows [23].
It is partial key exposure attack since the number of steps, S1 required for the attack depends on one of the primes.
Table 5 gives a summary of the number of steps required to break the traditional RSA cryptography implemented at the application layer using Fermat Factorization.
Operand key length | Total number of steps |
---|---|
16-bit | 1 |
32-bit | 1 |
64-bit | 1.8 × 108 |
128-bit | 8.0 × 1017 |
256-bit | 1.26 × 1025 |
512-bit | 2.53 × 1063 |
1024-bit | 3.3 × 10140 |
Number of steps required to break the traditional RSA.
Security at the physical layer is ensured by the multi-level convolutional cryptosystem which encrypts already encrypted data emanating from the RNS-based RSA. The cryptanalysis of the multi-level convolutional cryptosystem will be based on the ciphertext-only attack whereby, it is assumed that the attacker knows ciphertext of several messages encrypted with the same key and/or several keys. The keys used in the encryption are those mentioned in Section 2.1.2 for the non-linear (8, 8, 2) 2-cascaded convolutional cryptosystem.
It is shown in [12] that, for an (n, k, L) convolutional code, each generator matrix reveals at most p – k – 1 values of a private parameter, using Gaussian elimination for p blocks of input data. Hence, if q is the number of states, then to completely break the (k, k, L) N-cascaded cryptosystem, the minimum number of plaintext-ciphertext pairs (u, v) required is [12].
For an (8, 8, 2) 2-cascaded cryptosystem, k = 8 and the least number of plaintext-ciphertext blocks required is p = 10 due to the number of rows and columns in the generator matrix. Assuming q = 2 states, S2 could be as
Table 6 gives a summary of the number of steps required to break the (8, 8, 2) 2-cascaded cryptosystem using ciphertext-only attack.
Operand key length | Total number of steps |
---|---|
8-bit | 7.8 × 1034 |
16-bit | 2.1 × 1057 |
32-bit | 1.4 × 1057 |
64-bit | 1.5 × 1062 |
128-bit | 6.1 × 1071 |
256-bit | 9.87 × 1087 |
512-bit | 2.68 × 10112 |
1024-bit | 1.42 × 10134 |
Number of steps required to break the (8, 8, 2) 2-cascaded cryptosystem.
At the upper layer, huge key lengths such as 1024 bits and 2048 bits are used to implement the RSA. Such implementations will greatly compromise throughput at the physical layer due to modular exponentiation. Hence, the main objective of the new cross-layer security scheme is to increase security level at the physical layer despite the small valued data points transmitted derived from the RNS-based RSA in order to enhance throughput. Cryptanalysis is performed on the small residue RSA encrypted values. The analysis will be based on partial key exposure and ciphertext-only attacks at the physical layer for eavesdropper who could wiretap the transmitted data. The number of steps, S required to break the new cross-layer security scheme should be a product of S1 and S2 given as [12].
Table 7 gives a summary of the number of steps required to break the new cross-layer security scheme by using partial key exposure attack and ciphertext-only attacks for different cascaded stages.
Operand key length | Total number of steps | ||
---|---|---|---|
N = 2 | N = 3 | N = 4 | |
16-bit | 2.1 × 1057 | 9.6 × 1085 | 4.4 × 10114 |
32-bit | 1.4 × 1057 | 5.2 × 1085 | 1.9 × 10114 |
64-bit | 1.5 × 1062 | 1.8 × 1093 | 2.3 × 10124 |
128-bit | 6.1 × 1071 | 4.8 × 10107 | 3.7 × 10143 |
256-bit | 9.87 × 1087 | 9.8 × 10131 | 9.7 × 10175 |
512-bit | 2.68 × 10112 | 4.4 × 10168 | 7.2 × 10224 |
Number of steps required to break the cross-layer security scheme.
Comparing Tables 5–7, it can be seen that high security levels comparable to the traditional 1024-bit RSA implemented at the upper layer could be attained using short operand key lengths of 128 bits and 256 bits for cross-layer security implemented at the physical layer. It is worth noting that, the security level could be much higher compared to the values displayed in Table 7 if the S-boxes were implemented using 4-bit and 8-bit shuffling instead of the aforementioned 2-bit shuffling.
The data throughput, T could be given as [24].
where Pe is the bit error probability, N is the number of bits in the block length and R is a fixed transmission rate for the frames. For Pe << 1, the throughput could approximate to
From (Eq. (15)) it could be seen that, for a fixed transmission rate, R the throughput, T could be increased by either minimizing N or Pe. In this section, it will be shown how convolutional coding could be used to achieve both conditions through orthogonal signaling and forward error correction respectively.
It is shown in [16, 25] that, for coded orthogonal signaling, the bit error probability to transmit k-bit symbols is as follows:
where ad denotes the number of paths of distance d from the all-zero path which merge with the all-zero path for the first time and dfree = 3 in this case, is the minimum distance of the code. dfree is also equal to the diversity, L.
For illustrative purposes, the transfer function, T(D) of smaller convolutional codes such as (2, 2, 2) and (4, 4, 2) will be used.
The transfer function T(D) for the (2, 2, 2) code is given as follows [16]:
The transfer function for the (4, 4, 2) code is given as follows [16]:
For both the (2, 2, 2) code and the (4, 4, 2) code, dfree = L = 3. Using the values of L and {ad}, the probability of a binary digit error, Pb as a function of the SNR per bit,
Performance of coded orthogonal signaling for k = 2 and k = 4.
The curves illustrate that, the error probability increases with an increase in k for the same value of SNR. Hence, better performance for wireless transmission should involve lower order codes and many independent parallel channels rather than higher order codes with fewer independent parallel channels. Hence, high data throughput could be attained by using small number of bits in the block length, N.
The Viterbi algorithm [25] is the most extensively decoding algorithm for Convolutional codes and has been widely deployed for forward error correction in wireless communication systems. In this sub-section Viterbi algorithm will be applied to the non-linear convolutional code. The constraint length, L for a (n,k,m) convolutional code is given as L = k(m-1). The constraint length is very essential in convolutional encoding since a Trellis diagram which gives the best encoding representation populates after L bits. Hence to encode blocks of n bits, each block has to be terminated by L zeros (0 s) before encoding.
For illustrative purposes, a non-linear (4,2,3) convolutional code will be used to demonstrate encoding and Viterbi decoding. A possible non-linear (4,2,3) convolutional code showing mod-2 connections and the product cipher is shown in Figure 8.
2-stage non-linear (4,2,3) convolutional code.
Encoding process
The constraint length, L = k(m-1) = 2(3–1) = 4.
Hence 4 zeros will be appended to message M before encoding. The modified message becomes M’ = 10110000. Transition tables in appendix are used to encode the modified message.
Using transition tables in appendix, the transmitted sequence from the 1st stage is given as Tin = 10 01 01 11
S-box output is given as S = 00 11 11 01
P-box output is given as P = 00 11 11 10
Transmitted sequence into the 2nd stage is given as P = 00 11 11 10
Using transition tables in appendix, the final transmitted sequence which is the output bits from the 2nd stage is given as Tout = 0000 1111 0101 1001
Viterbi decoding process
In performing the Viterbi algorithm, a bit in the sequence Tout will be altered. Let the received sequence be TR = 1000 1111 0101 1001 instead of Tout = 0000 1111 0101 1001. The Viterbi algorithm applied to the 2nd stage is summarized in Table 8.
Viterbi algorithm applied to 2nd stage of (4,2,3) code.
The bits above the arrows will constitute the retrieved sequence from the 2nd stage. Hence, the retrieved sequence is given as, R1 = 00 11 11 10. This sequence is fed to the P-box.
P-box output is given as P1 = 00 11 11 01. Sequence, P1 is fed to the S-box
S-box output is given as S1 = 10 01 01 11
Sequence, S1 is fed into the 1st stage to retrieve the final correct message. The Viterbi algorithm applied to the 1st stage is summarized in Table 9.
Viterbi algorithm applied to 1st stage of (4,2,3) code.
For a good trellis, the final state is the all-zero state as seen in the winning path in Table 9. The final received sequence is identical to the original transmitted message of M’ = Rfinal = 10110000 despite the first bit error. Hence, using the non-linear convolutional code, the error bit was identified and corrected. The forward error correction capability will therefore enhance throughput, since the bit error rate, Pe is reduced.
Most attacks in wireless networks are classified into two categories: passive and active. Passive attacks such as eavesdropping and traffic analysis do not interfere with normal network operations as opposed to active attacks. Some of the attacks could be circumvented by the cross-layer security scheme presented in this research due to the following characteristics inherent in its implementation:
RSA cryptographic algorithm at the upper layer: Table 3 summarized the security services, which could be achieved by implementing RSA cryptography such as authentication, non-repudiation and confidentiality. These services are essential network security requirements which are vital in curbing attacks such as eavesdropping, masquerade attack and information disclosure since there will be a possibility of not attaining the final all-zero state if message is modified.
Convolutional cryptosystem at the physical layer: The different keys generated are essential in ensuring confidentiality while the forward error correction capability is essential in curbing message modification attack.
Other attacks such as denial of service and replay attack could be circumvented if the cross-layer security scheme is associated with Transmission Control Protocol (TCP).
In this chapter, we have described a new cross-layer security scheme which has the advantage of enhancing both security and throughput as opposed to existing schemes which either enhances security or throughput but not both. The new scheme is implemented using the residue number system (RNS), non-linear convolutional coding and subband coding at the physical layer and RSA cryptography at the upper layers. By using RSA cryptography, the scheme could be used in encryption, authentication and non-repudiation with efficient key management as opposed to existing schemes, which had poor key management for large wireless networks since their implementation, was based on symmetric encryption techniques. Results show that, the new algorithm exhibits high security level for key sizes of 64, 128 and 256 bits when using three or more convolutional-cascaded stages. The security level is far above the traditional 1024-bit RSA which is already vulnerable. The vulnerability of 1024-bit RSA has led to the proposal of implementing higher levels such as 2048-bit and 4096-bit. These high level RSA schemes when implemented will greatly compromise throughput due to modular exponentiation. Hence the usefulness of a scheme such as the one presented in this chapter. In addition, Viterbi algorithm was performed for the new non-linear convolutional code in order to highlight the error correction capability. It was shown that, by using error correction codes on many small block lengths compared to one huge block length, throughput increases. Hence non-linear convolutional code is very critical in the implementation of the new scheme, since it contributes in enhancing both security and throughput. The entire scheme could be implemented at different access points in a wireless network since it fits in a single FPGA. Finally, the new cross-layer security scheme is essential in circumventing some attacks in wireless and computer networks.
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\\n\\nIntegrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
\n\nOpenness - We communicate honestly and transparently. We are open to constructive criticism and committed to learning from it.
\n\nDisruptiveness - We are eager for discovery, for new ideas and for progression. We approach our work with creativity and determination, with a clear vision that drives us forward. We look beyond today and strive for a better tomorrow.
\n\nIntechOpen is a dynamic, vibrant company, where exceptional people are achieving great things. We offer a creative, dedicated, committed, and passionate environment but never lose sight of the fact that science and discovery is exciting and rewarding. We constantly strive to ensure that members of our community can work, travel, meet world-renowned researchers and grow their own career and develop their own experiences.
\n\nIf this sounds like a place that you would like to work, whether you are at the beginning of your career or are an experienced professional, we invite you to drop us a line and tell us why you could be the right person for IntechOpen.
\n\nPlease send your CV and a cover letter to jobs@intechopen.com.
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