Sendrayan Nandakumar

In the rapidly evolving landscape of wireless communication, the traditional model of data exchange predominantly relies on a centralized infrastructure, where all communications, even between nearby devices, are routed through a base station (BS) and potentially the core network. Device-to-device (D2D) communication emerges as a transformative paradigm that challenges this conventional model. The concept of D2D communication has gained considerable traction, especially with the advent of 4G LTE and its crucial role in 5G and beyond. D2D aims to unlock numerous benefits, including improved spectral efficiency, increased throughput, reduced latency, enhanced energy efficiency, and better network offloading. In this paper, we focus on secure D2D communication using D2D-ECDH key exchange protocol with blockchain smart contracts along with blockchain Verkle tree data structure. This approach helps to improve secure communication between IoT devices and avoids Man-in-the-Middle (MITM) attacks, replay attacks, and central points of failure. The Verkle tree approach provides a smaller proof size to verify the root commitment value compared to the traditional Merkle tree data structure. The simulation has been executed in both Python and Solidity coding. Python has executed in VS Code generator, and Solidity code has executed in Remix IDE, Ethereum network V0.65.0. The Remix IDE was developed in Ganache V2.7.1 for blockchain smart contracts. Compared to the traditional Merkle tree approach, the Verkle tree provides less proof size up to 33 times.

Fifth generation (5G) networks are desired to offer improved data rates employed for enhancing innovations of device-to-device (D2D) communication, small base stations densification, and multi-tier heterogeneous networks. In relay-assisted D2D communication, relays are employed to minimize data rate degradation when D2D users are distant from one another. However, resource sharing between relay-based and cellular D2D connections often results in mutual interferences, reducing the system sum rate. Moreover, traditional relay nodes consume their own energy to support D2D communication without gaining any benefit, affecting network sustainability. To address these challenges, this work proposes an efficient relay selection and resource allocation using the novel hybrid manta ray foraging with chef-based optimization (HMRFCO). The relay selection process considers parameters like spectral efficiency, energy efficiency, throughput, delay, and network capacity to attain effectual performance. Then, the data provided as the input to the adaptive residual gated recurrent unit (AResGRU) model for the automatic prediction of an optimal number of relays and allocation of resources. Here, the AResGRU technique’s parameters are optimized by the same HMRFCO for improving the prediction task. Finally, the designed AResGRU model offered the predicted outcome.

Enhancement in both energy efficiency and spectral efficiency in cellular networks is made possible by means of an advanced technique called device-to device (D2D) communication. The enhancement of these efficiencies is done by utilizing the cellular user (CU) resources once again to make communication with nearby cellular devices in an effective manner by spectral means. As the D2D communication technology is capable of providing a direct communication link with nearby devices effectively with enhanced spectral efficacy, this approach is considered as an ideal solution for the futuristic cellular communication network. A flexible and reliable relay-assisted communication by means of D2D technology is required that acts as an intermediate relay when the attenuation between the channels across the D2D devices becomes high. The throughput of the system is increased by utilizing D2D communication technology as it uses direct data transmission within cellular devices. When the cellular user is far apart from one another, the data loss in the D2D communication system is minimized with the utilization of the relay. When the channels are good, then the relay nodes (RNs) serve the cellular user. However, it is noted that the D2D systems are affected by issues such as higher consumption of energy and spectral sharing. Also, the sum rate gets degraded as a result of mutual interference between resource-sharing cellular devices in the relay-assisted D2D communication system. The transmission of the data in a traditional relay-assisted D2D communication system is carried out between the D2D receiver (DR) and D2D transmitter (DT) only with the utilization of its own energy by the RN. The issues in relay selection and resource allocation in the conventional joint resource allocation schemes are tackled by executing a scheme for performing the task of optimal relay selection and joint resource allocation. The enhancement of the overall sum rate in the D2D communication is the main motive behind the implemented scheme. This goal is attained along with the minimization of the link rates in the cellular and D2D networks. The ideal selection of the relay and the execution of the joint resource allocation are done with the utilization of a new optimization scheme called the hybrid flow direction with the chameleon swarm algorithm (HFDCSA), in which the flow direction algorithm (FDA) is fused along with chameleon swarm algorithm. This optimal selection of the relays is assisted by considering constraints like the network’s sum rate and energy efficiency in the network to achieve high performance. The data obtained from distinct sources are given to the adaptive multi-layer perceptron (AMLP) in which optimal resource allocation and the relay selection are performed with the help of the suggested HFDCSA. The parameters in the MLP are tuned by the same HFDCSA. Finally, the performance validation is conducted in the stage to verify the working of the suggested approach.

Wireless Body Sensor Network (WBSN) has gained increasing attention in health monitoring and service sector for real-time health monitoring and providing high-quality service. 6G wireless communication technologies overcome the limitations of the current 5G technology. An intelligent security system that prevents DDoS attack by applying solutions based on artificial intelligent is proposed. This method is used to identify the attackers in a heterogeneous environment by applying swarm intelligent-based Meta heuristic artificial intelligent algorithm called grey wolf optimisation (GWO). The improved GWO algorithm fine tunes user categorisation based on the service request from the WBSN devices. This approach includes KD tree (KDT) neighbour search algorithm to find the exact DDoS-attacked user electronic device by applying vertical and horizontal group division policy. The proposed method is evaluated with unsupervised dataset comprising 560 service requests, which include normal, anomaly and abnormal requests in proportions of 65%, 25% and 10%, respectively. The evaluation results show that the precision, recall, F1 Score and accuracy parameters were 92.3%, 95.2%, 93.7% and 91.5%, respectively for the predicting the abnormal electronic devices. Statistical analysis shows that the mean absolute error value is smaller than the other variants of GWO algorithms.

Cognitive radio (CR) is the best way to improve the efficiency of spectrum consumption for wireless multimedia communications. Spectrum sensing, which allows legitimate secondary users (SU) to find vacant bands in the spectrum, plays a vital role in CR networks. When cooperative sensing is used in CR networks, spectrum availability must be taken into account. In many ways, the shared cooperative spectrum sensing (CSS) data among SU. The presence of a malicious user (MU) in the system and sending false sensing data can degrade the performance of cooperative CR. The sharp rise in mobile data traffic causes congestion in the licensed band for the transmission of signals. Handling this security issue in real time, on top of spectrum sharing, is a challenge in such networks. In order to manage the spectrum and identify MU, blockchain-based CSS is developed in this article. To gauge the efficiency of the proposed topology, performance metrics like sensitivity, node selection, throughput measurement, and energy efficiency are used. This work suggests a unique, easier-to-use CSS method with MU suppression that outperforms the current one. According to simulation studies, the suggested topology can increase the likelihood of MU detection by roughly 15% when 40% of system users are malicious.

Cognitive radio (CR) is a novel concept developed to solve concerns such as spectrum underutilization and scarcity. Spectrum detection methods are employed in the blockchain-based CR to make optimum use of the spectrum reserves. In this paper, an attempt is made to evaluate the effectiveness of an energy sensor using collaborative spectrum detection. Wideband is defined as the frequency range between 470 MHz and 790 MHz, and additive white Gaussian noise (AWGN) is employed. The probability of detection () under different situations is examined using detection in the receiver operational curve (ROC). According to the findings, the increases with the number of samples. This form of sensing, which is thought to be the easiest and best, uses energy-detecting spectrum sensing. To address ambiguity, the M-ary QAM technique is provided, which increases aggregate effectiveness in terms of the percentage of false alarm () and probability of missed detection () by 5% at a comparable delay period. When CR encounters shadowing and impacts situations, the client cannot tell the difference between an underutilized zone and fading. In comparison to the existing model, this study increases the likelihood of detecting a 3 dBm SNR for a 64-QAM modulated signal by at least 15%.