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Currently, the smart cities, smart vehicles, and smart gadgets will improve the way of living standard. Cloud connectivity of IoT sensed devices will capture real-time data in the cloud which helps to improve the system performance and quick response to queries. Electric Vehicle battery health diagnosis plays an important role in the proper functioning of the battery management system, guarantees safety, and warranty claim. Society 5.0 develops with the advancement in the road, infrastructure, better connectivity, transportation, and options available to purchase. Battery health cannot be measured directly. There are internal and external factors that affect battery health such as State of Charge, model parameters, charging/discharging method, temperature, Depth of Discharge, C-rate, battery chemistry, form factor, thermal management, and load change effect. Battery degrades due to both calendar ageing and cyclic ageing. Artificial Intelligence plays a significant role in Battery management system due to the nonlinear behavior of lithium-ion battery. Prediction of battery health accurately and in due time will reduce the risk of recklessness. Timely maintenance will reduce the risk of fatal accidents. This paper presents different batteries analysis under different discharge voltage and capacity conditions. Different machine learning algorithms such as Neural Network, Modified Support Vector Machine (M-SVM) and Linear Regression are used to predict state of health. The proposed M-SVM performs well with less error for all four-battery discharge data.

In this paper, the authors have emphasized on the perspectives of the Terahertz channel modeling in Internet of multimedia nano things (IoMNT) networks. A modulation technique targeting body-centric network is discussed. An analogy of a real Terahertz antenna is developed within a terahertz multi-layer modelling channel for a human skin tissue. As a result, the investigation of how signals at THz frequency band interact and transmit within the skin biomaterial. The human skin model used to collect data was selected to have four layers: epidermis, dermis, blood, and hypodermis, with the depth of the layers varying between normal human body values. It is revealed from the literature that the frequency and content have a substantial impact on path failure. The estimated path loss could thus differ considerably, but for a human skin model with depths of 0.21 mm, 1.23 mm, 1.38 mm, and 3.76 mm, the frequencies of 0.5-1.5 THz at the end distance resulted in a path loss estimated about 250-350 dB.

With proper tuning in the phase shift of reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), which consist of huge number of passive reflecting elements, are the most promising and transforming technology to reconfigure the wireless propagation ecosystems to improve the “spectral-and energy-efficiency” for the future generation wireless communication system. This article presents a systematic review of RIS/IRS technology and conceptualizes the prospective requirement, operations, and application. Further, we have presented its capability for execution in a forthcoming smart industrial ecosystem to assist the development of Industry 5.0 with a series of emerging applications. Finally, we discuss the potential, open research challenges, and prospects of RIS technology in the emerging intelligent manufacturing industry to encourage future research in this area.

Robots are rapidly growing technologies in simple words. Robots are artificial living things in this world that are created by humans for reducing human involvement in the top domains such as agriculture, medicine, and industries. Based on the requirements of work, robots are classified into six major categories. Robotic vehicles come under the division of autonomous mobile robots and automated guided vehicles. These vehicles are more popular in factories for supporting humans. Integration of artificial intelligence (AI) in a robotic vehicle gives the brain to the vehicle that can sense the surrounding environment and work accordingly without human operation. This article describes bibliometric research on the integration of AI in robotic vehicles. During the research, 1,196 documents are extracted from the Scopus database between 2015 till now. The types of publications included in this bibliometric analysis are articles, conference papers, reviews, book chapters, books, and short surveys that help understand the global research domain. The pictorial representation was made with the help of open-source platforms such as VOSviewer, GPS visualizer, Gephi, ScienceScape, and word cloud. This analysis helps understand the research gap in this domain.

In this article, the dynamic spectrum accessing techniques-the overlay, underlay, and hybrid spectrum-sharing approaches-are exploited for the military communication. Furthermore, the importance of the energy efficiency of cognitive radio (CR) networks (CRNs) for military applications is discussed. We also emphasize security threats over the physical layer and their mitigation in CRNs. Finally, we explore why the cross-layer design of protocols is preferred for greater energy efficiency and security.

We have perceived the potentials of spectrum accessing techniques in the cognitive radio networks where the hybrid spectrum access (HSA) is an advanced technique which exploits the interweave and underlay spectrum access strategies simultaneously to enhance the spectral efficiency and throughput. This chapter presents a novel hybrid‐cum‐improved spectrum access technique (HISA) for significant improvement in the throughput, data‐loss rate, and interference at the PU. The spectrum accessing strategies and frame structures are the key elements of the HISA techniques; therefore, the work related to these elements is presented. The proposed frame structures and their combination with HSA are illustrated. The chapter also consists of the mathematical analysis of the throughput and data loss. The throughput and data loss optimization by constraining various metrics is an exigent task which is a potential issue for the future research.” The numerically simulated results of the proposed approach are illustrated and compared with the conventional approach.

The next‐generation communication systems demand huge spectral and energy efficiency, low latency, high scalability, improved connectivity and reliability, as well as advanced security due to massive growth in the wireless connected devices such as internet‐of‐things, wireless sensor networks, wireless body area networks, etc. The prominent and effective approaches to fulfill these demands are the cognitive radio (CR), non‐orthogonal multiple access (NOMA), multi‐antennas or multiple‐input‐multiple‐output, cooperative communication, network function virtualization, millimeter‐wave communication, ultra‐classification, etc. This chapter proposes the potential CR‐NOMA frameworks with the implementation challenges and future research directions. It presents the spectrum accessing strategies in CR. The chapter comprises the fundamentals of NOMA. The chapter also presents the prominent frameworks of NOMA in CR for uplink and downlink scenarios. It also comprises the challenging issues in CR‐NOMA frameworks and their recommended solutions.

After perceiving the idle channels using spectrum sensing, the cognitive user (CU) accesses these channels using suitable medium access control (MAC) protocol where the decision on optimal spectrum sensing, transmission time, and proper coordination with other CUs are the key characteristics of the MAC protocol. In the conventional MAC protocol, the CUs exploit the common control channel for the spectrum sensing information exchange in order to negotiate the channel access. After the spectrum sensing, each CU contends for the access of idle channel using various spectrum access protocols. The system model and proposed hybrid self‐scheduling multichannel‐MAC (HSMC‐MAC) protocol are illustrated. The chapter consists of the performance analysis of proposed protocol for the perfect and imperfect spectrum sensing scenarios. It presents the simulation results for the perfect and imperfect spectrum sensing scenarios. An improved frame structure for the distributed cognitive radio networks is proposed in order to enhance the throughput by exploiting the spectrum efficiently.

Due to the random spectrum sensing of channels in cognitive radio networks, the probability of channel to be sensed active is significantly high, resulting in high data loss due to which the effective throughput of the network decreases significantly. In this chapter, the challenging issues regarding the selection of channel for spectrum sensing after spectrum prediction are exploited to enhance the throughput of cognitive user (CU). First, the chapter illustrates the system model for the proposed approach. The expressions of throughput are then presented. Next, the chapter discusses the simulation results and finally discusses the future scope. A new approach for additional enhancement in the throughput of CU by exploiting the underlay communication in the spectrum sensing and prediction phases is presented and the results are compared with pre‐obtained results of Approach‐1. The results are evidence for the significant improvement in the throughput by using this approach.

The PU channels are highly utilized in the high‐traffic cognitive radio networks (HTCRNs) which results the decrease in the idle probability of channels. In this chapter, the concepts of spectrum prediction and hybrid spectrum access techniques are explored to improve the throughput by alleviating the spectrum sensing errors. Moreover, in the data transmission phase, the cognitive user (CU) transmits data only on the idle sensed channels whereas it needs to wait on the active sensed channels which cause the problem of wait/switch/stop states. To conquer the issue of wait states, a novel approach is proposed in which the hybrid spectrum access technique named as Approach‐1 is exploited. Moreover, the concept of spectrum monitoring during the data transmission period is exploited to lighten the data loss of CU and interference‐at‐PU, and this is named as Approach‐2. The performance analysis of the proposed system models is illustrated. Further, the simulation results and analysis are presented.

An automobile is a computer on wheels after the integration of electronics. This handshake of electronics and mechanical systems makes a vehicle smart, and comfortable; driver assistance for achieving this involves data exchange and surroundings sensing. Devices such as sensors, telematics, protocols, etc., are responsible for data exchange and data sensing. This process contains some loopholes that are the preliminary sources for the attacker to attack the vulnerable devices to control the vehicle. This article provides a review of possible attacks and defenses on autonomous and connected vehicles. The attacker’s area of autonomous and connected vehicles is classified into three categories that are safety system attacks, connectivity attacks, and diagnostics attacks, and provided all possible defenses for those attacks. In addition, we provided an analysis of the domain to understand the scenarios in this domain, recommendations, and future scope in this area for further work.

This paper has collectively exploited cognitive radio (CR), non-orthogonal multiple access (NOMA) and multiple-input-multiple-output (MIMO) communication systems in order to propose a spectral-and interference-efficient framework which is named as MIMO-based CR–NOMA communication system. The proposed framework is analysed for the uplink and downlink scenarios. Further, the closed-form expressions for throughput at each user due to the number of transmitting-and receiving-antennas are derived numerically for the downlink-and uplink-scenarios. In addition to this, the total/sum throughput for different frameworks such as CR–NOMA, CR–MIMO and MIMO-based CR–NOMA systems is derived for both the downlink and uplink scenarios. Moreover, in order to satisfy the interference constraints at the primary user due to cell-edge/far-user [cognitive user (CU)-4] transmission in the uplink scenario, a new metric known as interference efficiency is derived. Furthermore, the proposed frameworks are simulated for downlink and uplink scenarios and the relationship between throughput of cell-centred/near-user (CU-1) and CU-4 are presented. Moreover, the total throughput for different frameworks and interference efficiency is also illustrated with the simulation results. The simulation results reveal that the proposed MIMO-based CR–NOMA system outperforms the existing MIMO–NOMA, CR–NOMA and CR–OMA systems in terms of the CUs' individual throughput, total throughput and interference efficiency of the system.

Free space optical communication is a potential technology that exploits the visible light frequency band for communication. In this paper, a free-space optical link (FSOL) optimization in visible light communication (VLC) is used to improve the channel performance for signal strength and analyze the effect of signal failures in a communication system. The data transmission and reception phenomenon are established using a light-emitting diode (LED) as a carrier at transmitter and photodiode as a detector at the receiver, respectively. The OptiSystem tool uses to optimize the component parameters of this proposed communication system. The bit error rate (BER) analyzer displays the signals as an eye diagram with the challenges of components uncertainty for better signal performance. The optimized components solve the problem encountered transmitting improve and enhance the signals strength. The acceptable eye diagram displayed from the BER analyzer has been resolved as a result of component optimizations by adding and removing noise to ascertain the behavior of the channel. A better signal retrieve by the display of the acceptable eye diagram from the OptiSystem software used. The FSOL components optimization improve the loss of data, weak signal, loss of power, signal failure, and signal errors which guarantee multi-mobile users with the shared signal. OptiSystem software package used to fully characterize the FSOL system.

The underlay spectrum access is a very prominent technique in cognitive radio (CR) communication systems in which the primary users (PUs) share the spectrum with the cognitive users (CUs), simultan...

Communication industry demands the frameworks, which allow significant improvement in energy efficiency with spectral efficiency, however, these two do not always coincide and may even conflict sometimes in cognitive radio networks. In addition to this, the distributed network structures are preferred over centralised structures due to ease of deployment as well as less information exchange between the cognitive users which reduce the network-overhead. Therefore, in this study, the authors have proposed a framework to enhance the spectral as well as energy efficiency by exploiting the novel frame structure in the distributed cognitive radio networks. A self-scheduled multichannel-medium access control (SMC-MAC) protocol has been proposed in which the contention and sharing intervals are exploited for the efficient spectrum and energy utilisation. Moreover, the closed-form expressions of the spectral efficiency, power consumption and energy efficiency for the proposed SMC-MAC protocols are derived. Furthermore, the relation between energy and spectral efficiency for the proposed framework is also derived. The numerically simulated results are presented to validate the proposed work and Monte-Carlo simulations are performed for the random events in order to support the numerically simulated results.

The main aim of the physical layer is to boost the data rates over wireless channel with better quality of services (QOS), thereby, increasing the efficiency and reliability of new generation wireless communication systems especially 5G/6G system. Performance of various space time block codes like quasi orthogonal space time block codes and rotational quasi orthogonal space time block codes is investigated using various decoding algorithms like Sphere Decoder, K 1, K 2, K 3, K 4 Sphere decoders. The Sphere Decoder has been modified in terms of K detection algorithms based on spectral efficiency. Different modulations are used to validate the analytic analysis. Simulations are performed in MATLAB software and the results obtained shows the significant incremented spectral efficiency.

COVID-19 pandemic created a global health crisis affecting every nation. The essential smart medical devices/accessories, quarantine facilities, surveillance systems, and related digital technologies are in huge demand. Healthcare, manufacturing industries, and educational institutions need technologies that allow working from a safe location. Digital technologies and Industry 4.0 tools have the potential to fulfil these customized requirements during and post COVID-19 crisis. The purpose of this research is to provide understanding to healthcare professionals, government policymakers, researchers, industry professionals, academics, and students/learners of the paradigm of different Digital technologies, Industry 4.0 tools, and their applications during the COVID-19 pandemic. Digital technologies, Industry 4.0 tools and their current and potential applications have been reviewed. The use of different Digital technologies and Industry 4.0 tools is identified. Digital technologies and Industry 4.0 tools (3D Printing, Artificial Intelligence, Cloud Computing, Autonomous Robot, Biosensor, Telemedicine service, Internet of Things (IoT), Virtual reality, and holography) offer opportunities for effective delivery of healthcare service(s), online education, and Work from Home (WFH) environment. The article emphasises the usefulness, most recent development, and implementation of Digital technologies, Industry 4.0 techniques, and tools in fighting the COVID-19 pandemic worldwide.

This paper proposes the analysis of the power allocation (PA) technique in visible light - non orthogonal multiple access (VL-NOMA) communication systems using uniform probability distribution function (UPDF) for improved system performance. A light-emitting diode (LED) transmits data to two mobile users (near- and far-users) in the indoor environment. The issues of better reception in a room and in distances to detect the signal prompted this study. NOMA decodes the signals for achievable rate based on the inequality agreement of the two mobile users while UPDF concentrates on the percentage at which the two mobile users are placed within the room. We have developed a new approach named UPDF-NOMA to solve the problem for PA in the VL-NOMA scenario. The UPDF-NOMA approach will decide the detection area for transmitted signal in percentage within the system. We observe that UPDF-NOMA performs 60% detection of the signal at 2m to 2.1m distance in the system. The simulation analysis presents the detected signals using the MATLAB software tool.

Energy and Spectrum are the two basic requirements in the realm of Internet-of-Things (IoT). The network of IoT is becoming larger day by day and the design of spectrum and energy efficient solution is a quite challenging task because of the rapid increase of connecting devices in IoT network. To make the system more energy and spectral efficient, energy harvesting and cognitive radio (CR) are the proficient solutions, respectively. This paper introduces a spectral and energy efficient design for CR based sensor networks. We present a network architecture, in which nodes or other sensing devices can use the spectrum opportunistically and energy harvesting can be done from different ambient sources. We then propose an 1) energy alancing scheme for heterogeneous network in which nodes will have different energy levels and 2) Cluster head (CH) selection scheme which will only be performed on the few nodes of network having the highest current energy to accomplish the ultimate goal of energy balancing in network, this analysis is performed with in the cluster. Furthermore, for the spectral efficiency, we propose a channel management scheme based on cognitive radio to allot the best available channel having highest reliability in respect of the bit error rate (BER) using. Comprehensive results exhibit the effectiveness in the performance of the proposed spectral and energy efficient schemes and show better performance over other schemes.

The growing demand of the radio spectrum is an important part in the multi-agent intelligence management system of the vehicles. Cognitive radio is used for reducing the restricted access to the wavelength of the spectrum and utilizing the radio spectrum is dynamic allocation method. With the advent in the cognitive radio arrangement, the CR in vehicular ad hoc networks allow the operator to sense and hop from one to another system network in the desired frequency of the spectrum based on the environment of the cognitive radio. In existing method implemented a cluster formation mechanism used for data transmission one to another vehicle nodes. In this mechanism used CR-VANET network is divided into subgroups or clusters and achieve accuracy rate among vehicles. In this work, has implemented a cluster formation mechanism with Bacterial foraging optimization algorithm method in Cognitive radio in VANETs. In planned technique a self-motivated system network is established on the basis of clusters using BFOA network goals to achieve better throughput in data transmission one node to another node with RSU (Road Side Units). In the experimental result improves accuracy of the data transmission over the network. In proposed research, vehicles and road side units are deployed in the network. When there is loss of the data packets during the transmission in the network, then optimized clustering phase has implemented. In addition, the selections of the cluster heads are maintained the path and optimization (BFOA) phase implement to recover the path losses and improve the network performance such as overhead, energy consumption, E2E delay and Network Throughput and compared with existing method (Cluster-Formation). Simulation tool used in this proposed work is MATLAB 2016a.

The spectrum sensing is a key process of the cognitive radio technology in which the cognitive users identify the unutilized/underutilized primary users (PUs)/licensed users spectrum for its efficient utilization. The sensing performance of cognitive radio (CR) is generally measured in terms of false-alarm probability ( $$ P_{f} $$ ) and detection probability ( $$ P_{d} $$ ). IEEE 802.22 wireless regional area network is one of the typical cognitive radio standards to access unused licensed frequencies of TV band and according to this standard, the false-alarm probability of CR should be ≤ 0.1 and the detection probability must be ≥ 0.9. Further, the detection and false-alarm probabilities are greatly affected by the selected threshold value in the spectrum sensing approach and selection of threshold is a crucial step to yield the status (presence/absence) of PU. In most of the available literatures, the threshold is decided by fixing one parameter ( $$ P_{f} $$ or $$ P_{d} $$ ) and optimizing the other parameter ( $$ P_{d} $$ or $$ P_{f} $$ ). Moreover, at low SNR, while achieving one of the targeted sensing parameter, there is significant degradation in the other sensing parameter. Therefore, in this paper, we are motivated to decide the optimal threshold at low SNR (signal-to-noise ratio) in such a way where we can jointly achieve both sensing matrices ( $$ P_{f} $$ ≤ 0.1 and $$ P_{d} \ge 0.9 $$ ) and provided better sensing performance in comparison to that of the traditional constant false-alarm rate and constant detection rate (CDR) threshold selection approaches. Further, we have illustrated that at low SNR, the proposed optimal threshold selection approach has provided better throughput as compare to that of the threshold selected by traditional CDR approach. The proposed approach has improved throughput approximately 24.63% when compared with CDR at chosen SNR.

This paper introduces the concept of imperfect spectrum monitoring for realistic wireless communication environments with high-traffic cognitive radio networks (HTCRN). The effect of imperfect spectrum monitoring in the HTCRN is analyzed in terms of the probability of data-loss (in the form of packets) and interference at primary user (PU). Further, two algorithms are proposed to compute the data-loss and interference-at-PU considering different scenarios of the traffic-intensity and spectrum monitoring error. Moreover, the closed-form expressions for the ratio of achieved-throughput to the data-loss, power-wastage, interference-efficiency as well as energy-efficiency are derived, and numerically simulated results are presented to support the proposed work. Furthermore, the randomness in emergence of PU is considered and the Monte-Carlo simulations are exploited to validate the numerically simulated results.

This paper focuses on potential issues related to the random selection of a sensing channel that occurs after the prediction phase in a Cognitive Radio Network (CRN). A novel approach (Approach-1) for improved selection is proposed, which relies on the probabilities of channels by which they are predicted idle. Further, closed-form expressions are derived for the throughput of Cognitive Users (CUs) in the conventional and proposed approaches. In addition to this, a fundamental approach for computing the prediction probabilities is also proposed. Moreover, a new challenging issue named “sense and stuck” was observed in the conventional approach. The proposed approach is validated by comparing the results achieved with the results of the conventional approach. However, to achieve the prediction probabilities, the pre-channel-state-information is a prerequisite, but it may be unavailable for particular scenarios; therefore, a modified selection method is introduced to avoid the sense and stuck problem. An algorithm to evaluate the throughput using the random, improved, and modified selection methods is presented with its space and time complexities. Furthermore, for additional improvement in the throughput of the CU, a new frame structure is introduced, in which the spectrum prediction and sensing periods are exploited for simultaneous transmission of data via the underlay spectrum access technique (Approach-2). The simulated results of Approach-2 are compared with our pre-obtained results of Approach-1, which confirm significant improvement in the throughput. Keywords: Cognitive radio, Interweave, Prediction, Spectrum accessing, Throughput, Underlay

The spectrum-monitoring is a prominent technique to detect the reappearance of the primary user (PU) during the cognitive users' (CUs) data transmission. However, the imperfect spectrum monitoring (SM) causes a delay in the identification of PU that introduces interference at PU as well as data-loss. The cooperation among CUs for SM is a proficient solution to diminish these impairments of imperfection. Thus, in this study, a scenario of the heterogeneous cognitive radio network (CRN) is presented in which the CUs perform cooperation among them for SM and have analysed the impact of cooperation on several performance metrics such as the data-loss, interference efficiency and energy efficiency. An algorithm is presented for the evaluation of data-loss for different circumstances of the traffic intensity of PU and probability of SM error in the proposed heterogeneous CRN. In addition to this, the closed-form expressions for the cooperative and non-cooperative SM of these metrics are derived. Furthermore, the simulation results are illustrated for different state-of-affairs of the probability of SM error and traffic intensity.

In this chapter, we have explored several power allocation (PA) techniques for visible light—nonorthogonal multiple access (VL-NOMA) communication systems. A systematic and state-of-the-art study of PA techniques have been presented to enhance the performance of communication systems in terms of the received signal strength that results improved throughput, bit error rate (BER), etc. To enhance the received signals strength directly at the end users, the PA techniques have been employed to solve the potential challenges of future-generation communication systems. Further, to solve the problem of overall accomplishable data-rate and user-fairness, a potential framework has been presented with different power allocation techniques for VL-NOMA communication systems. We also discuss and provide solutions to the problems encountered. We then compare all the techniques in terms of the user fairness, total achievable data rate, power level in the channel, algorithm, decoding order, optimally power, and channel to the users.

The spectrum sensing (SS) is employed to identify the status of licensed channel in the cognitive radio/user (CR/CU). Arbitrarily choosing the licensed channel for sensing in high traffic intensity network results into reduced throughput of cognitive user due to wait state problem in interweave mode. However, the wait state problem is overcome with hybrid spectrum access, resulting into improvement in throughput of CU. In addition, the sensing decisions without cooperation increases the sensing error and affect the sensing performance of CU resulting into reduction in throughput of CU. In this paper, we have proposed new approach that is Hybrid Spectrum Access with Spectrum Prediction and Cooperation (HSA-SPC) of cognitive users in which we have collectively employed spectrum prediction, cooperation and hybrid spectrum access to improve the sensing performance and throughput of CU. Further, for the proposed approach we have considered a new frame structure in multiuser environment and derived the closed form expression for the throughput. In addition to this, we have also shown the computation of energy efficiency for our proposed approach. Further, with the help of simulation results, we have shown the improvement in CU throughput with our proposed approach over reported literature. Comparison of various approaches with the proposed method HSA-SPC is also illustrated.

The mixed population of semi-/full-autonomous and conventional vehicles on the roads originates a heterogeneous network with moving nodes. Therefore, it is worth to explore the potential frameworks and challenges for the implementation of next generation Internet-of-Vehicles (IoVs) networks. In this paper, we have presented the potential frameworks of IoVs network for heterogeneous environment. Further, various simulation tools for mimicking and analyzing the IoVs network are presented and compared on the bases of their performance. Moreover, the key challenges for the implementation of IoVs networks except the challenges faced by conventional wireless ad-hoc networks are illustrated.

Recent and rapid progress in the field of computing, micro/nano/elecromechanical systems, communication, and the Internet have encouraged researchers from industry as well as academia to achieve the feasibility of Industry 4.0, which is the acronym for the fourth industrial revolution. The Internet-of-Things (IoT) is an integral and very important technique that deals with the sensing, analyzing, actuating, as well as secure and reliable communication of wireless-connected devices. This race toward Industry 4.0 also influences the medical industry and has given birth to a subdomain of the IoT that is the Internet of Medical Things (IoMT), where devices/nodes are used to diagnose, monitor, and treat patients, such as electrocardiograms, pulse oximeters, dosimeters, etc. The specific part of the IoMT is wireless body area networks (WBANs), which deal with devices/nodes implanted on or inside the human body and are responsible for reliable, secure, and interference-free communication. All these nodes and devices communicate with each other and remote controlling units, for which they need spectrum/frequency channels; however, the allocated spectrum in the IEEE 802.15.6 will be inadequate in the future and will result in spectrum scarcity for WBANs. A potential solution for spectrum scarcity is cognitive radio that relies on the concept of dynamic spectrum access where the unlicensed/cognitive users are permitted to communicate on the licensed/primary users’ channel. Therefore, in this chapter, we present the potential frameworks that exploit the cognitive-inspired IoMT so that the issue of spectrum scarcity in WBANs is resolved. The potential frameworks with their pros and cons as well as major research challenges with probable solutions are discussed. As “security and interference with human body organs” is of primary concern due to direct human body involvement, further security concerns and interference management techniques are illustrated.

With the advancement in the wireless communication, there has been an immense growth in the number of vehicles on the road. The unpredictable nature of vehicular ad hoc network (VANET) due to random increase and decrease of the nodes/users (vehicle) on the roads is a challenging issue. Moreover, the increase in the number of nodes creates the problem of spectrum scarcity due to shortage of licensed spectrum for vehicular services. In order to solve the issue of spectrum scarcity, the cognitive radio network (CRN) has been developed which exploits the unlicensed spectrum for communication without affecting the licensed communication that is using interference avoidance. The CRNs are more vulnerable to the security and the privacy of the networks because the transmission parameters required for communication avoid the interference with the licensed and unlicensed users. Moreover, the safety message among the vehicles ensures the safety of the vehicles in the cognitive radio vehicular ad hoc network (CR–VANET) and also manages the sharing of the licensed/primary and the unlicensed/secondary users in the network. The routing and network topologies are a challenging issue due to mobility of vehicles. Therefore, in this review paper, we present the applications and various routing topologies for CR–VANET.

In this paper, we have exploited the key technologies of the next generation communication systems which are cognitive radio (CR), non-orthogonal multiple access (NOMA) and multiple-input-multiple-output (MIMO) and have proposed a framework of simultaneous use of these technologies for the spectral efficient design, named as MIMO based CR-NOMA communication systems. The proposed communication system is defined for the specific case that is MIMO with 2 × 2 antennas and NOMA serving two users. Moreover, the data rate at each antenna of users in derived numerically and further, the sum-data rate for the cognitive users (CUs) is computed. The relationship between sum-data rates of two NOMA users with two antennas each, is described with simulation results.

The imperfect spectrum monitoring (SM) is a major obstacle to detect the emergence of primary user (PU) quickly during the cognitive users’ (CUs’) data transmission which results data-loss and introduces the interference at PU. The cooperation in CUs for SM is an effective solution to improve its performance. Therefore, in this paper, a scenario, where CUs can cooperate with each other for SM is presented and have analyzed the effect of cooperation on various performance metrics namely, the data-loss, interference efficiency, and energy efficiency. An algorithm is illustrated for the computation of data-loss under various conditions of the traffic intensity of PU and probability of SM error. Moreover, the closed-form expressions of these metrics are derived for the cooperative and non-cooperative SM. Further, the simulation results are presented for various scenarios of traffic intensity, probability of SM error and channel gain between the CUs’ transmitter to PU receiver. Furthermore, the Monte-Carlo simulation results are exploited to consider the random nature of the PUs’ traffic intensity as well as to support the numerically simulated results.

The spectrum mobility during data transmission is an integral part of the cognitive radio network (CRN) which is conventionally two types for instance reactive and proactive. In the reactive approach, the cognitive user (CU) switches its communication after the emergence of the primary user (PU), where the detection of emergence of PU relies either on spectrum sensing and/or monitoring. Due to certain limitations of the reactive approach such as: (1) loss at least one packet on the emergence of PU and (2) resource (bandwidth) wastage if the periodic sensing is used for mobility, the researchers have introduced the concept of proactive spectrum mobility. In this approach, the emergence of PU is predicted on the bases of pre-available spectrum information, and switching is performed before true emergence of the PU, in order to avoid even the single packet loss. However, the imperfect spectrum prediction is a major milestone for the proactive spectrum mobility. Recently, due to introduction of the spectrum monitoring simultaneous to the data transmission, the reactive approach has come into lime-light again, however, it suffers from the single packet loss and imperfect spectrum monitoring issues. Therefore in this paper, we have exploited the spectrum monitoring and prediction techniques, simultaneously for the spectrum mobility, in order to enhance the performance of cognitive radio network (CRN). In the proposed strategy, the decision results of the spectrum prediction and monitoring techniques are fused using AND and OR fusion rules, for the detection of emergence of PU during the data transmission. Further, the closed-form expressions of the resource wastage, achieved throughput, interference power at PU and data-loss for the proposed approaches as well as for the prediction and monitoring approaches are derived. Moreover, the simulation results for the proposed approaches are presented and validation is performed by comparing the results with prediction and monitoring approach. In a special case, when the prediction error is zero, the graphs of all metric values overlies the spectrum monitoring approach, which further validates the proposed approach.

In this letter, we have exploited a novel hybrid-cum-improved spectrum access technique for significant improvement in the throughput and data-loss rate. This proposed technique comprises of hybrid spectrum access and improved frame structure strategies of the cognitive radio communication system. Also, the closed-form expressions for throughput and data-loss of these approaches are derived. Moreover, the proposed approaches are validated numerically as well as with reported literature.

In this paper, the hybrid spectrum access and prediction techniques are exploited simultaneously in the high-traffic cognitive radio communication system, in order to enhance the throughput and overcome the problem of waiting states. The hybrid spectrum access is responsible for throughput enhancement by escaping the waiting states whereas the spectrum prediction alleviates the sensing errors in the high-traffic communication environment. The closed-form expression for the throughput of cognitive user (CU) communication is derived and validated the proposed approach with the reported literature. Moreover, a new framework is proposed to conquer the sharing issues of conventional and proposed approaches. In addition to this, the performance metrics of proposed framework such as the data-loss, energy-loss of the CU and interference at the PU have been analyzed.

In the area of Internet-of-Things, devices are supposed to collect or exchange the data by using limited number of resources in which spectrum utilization is one of the most essential factor. This paper presents the overview, applications, characteristics of Internet-of-Things & Cognitive radio as well as the combination of both for better spectrum utilization. In today's scenario, IoT is becoming the hot topic in communication world because everyday devices are being combined with internet connectivity. Numbers of devices are connecting in this network so a lot of spectrum is needed. Since the spectrum is finite resource therefore we introduced the idea of Cognitive radio networks (CRNs) in Internet-of-Things for providing the better and efficient way of utilizing spectrum in communication world.

In this paper, we have analyzed the effects of cooperative spectrum sensing and threshold selection over the performance of cognitive radio network (CRN) using energy detector in the fading environment. Firstly, we have compared the receiver operating characteristics (ROC) curve of both the cooperative and non-cooperative scenario for different fading environments. Further, we have illustrated the probability density function (PDF) of binary hypothesis for proposed CRN system, and analyzed the necessity of dynamic selection of threshold over that of the fixed threshold selection. In addition to this, we have compared the spectrum sensing performance and throughput of proposed CRN for fixed and dynamic threshold selection approaches on the spectrum sensing error at low and high signal-to-noise (SNR) ratio. Moreover, we have improved the sensing performance of proposed CRN by employing cooperative spectrum sensing with dynamic threshold selection scheme. Further, the percentage improvement in detection probability, throughput and total error probability of the proposed system model has been presented.