Search

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 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.

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...

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.