One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs.

Software‐defined networking (SDN) is a programmable architecture for networking domain in which the security is provided by devising the network policies with the help of the network administrator. This is very cumbersome for the administrator to handle different attacks at various planes in SDN architecture. Blockchain can be used to prevent various attacks in SDN by providing a decentralization authentication environment. In this article, a secure Blockchain‐based privacy‐preserving protocol is proposed to thwart various security vulnerabilities in the SDN architecture. The proposed approach uses Modified‐Delegated Proof of Stake as the consensus protocol to ensure safety and reliability in the network. Besides, a security protocol is designed using cryptographic primitives and analyzed using a detailed security analysis. Initially, the consensus protocols are implemented using solidity smart contracts and deployed to the public Blockchain using Ethereum. Consequently, the proposed approach is simulated on OMNet++ using INET framework. The experimental results show that the proposed SDN‐Chain is secure, efficient, less incentive to centralize, and practically implementable in resource‐limited wireless and mobile environments.

The recent developments in biosignal acquisition devices for continuous supervision of cardiovascular signs of high-risk patients require a high-precision and low-power Analog Front End (AFE) circuit. The proposed design adopts Continuous-Time (CT) Sigma-Delta Modulator (ΣΔM) architecture to achieve high resolution and SIgnal-to-Noise And Distortion ratio (SINAD) requirements. The proposed modulator is a second-order CT-ΣΔM with Cascade of Integrators Feed-Forward (CIFF) architecture that consists of a CT loop filter, a single-bit quantizer, and a Digital-to-Analog Converter (DAC). The use of single-bit quantization in the design reduces circuit complexity and power consumption. To use the designed ΣΔM for measuring ECG signals, a bandwidth (B_w) of 150 Hz is considered with a sampling frequency (f_s) of 153.6kHz to achieve an oversampling ratio of 512. The design is simulated in a standard Cadence Virtuoso EDA tool at 180nm CMOS technology, operating at 1.8V supply voltage at the block level. The simulation results for the designed modulator show that SINAD is 104.5dB, the Effective Number Of Bits (ENOB) is 17.06bits, with power consumption of 24µW, and achieves Schreier’s Figure-Of-Merit (FOM) equal to 172.45dB.

D2D (Device-to-device) communication has a major role in communication technology with resource and power allocation being a major attribute of the network. The existing method for D2D communication has several problems like slow convergence, low accuracy, etc. To overcome these, a D2D communication using distributed deep learning with a coot bird optimization algorithm has been proposed. In this work, D2D communication is combined with the Coot Bird Optimization algorithm to enhance the performance of distributed deep learning. Reducing the interference of eNB with the use of deep learning can achieve near-optimal throughput. Distributed deep learning trains the devices as a group and it works independently to reduce the training time of the devices. This model confirms the independent resource allocation with optimized power value and the least Bit Error Rate for D2D communication while sustaining the quality of services. The model is finally trained and tested successfully and is found to work for power allocation with an accuracy of 99.34%, giving the best fitness of 80%, the worst fitness value of 46%, mean value of 6.76 and 0.55 STD value showing better performance compared to the existing works.