In communication industry one of the most rapidly growing area is wireless technology and its applications. The efficient access to radio spectrum is a requirement to make this communication feasible for the users that are running multimedia applications and establishing real-time connections on an already overcrowded spectrum. In recent times cognitive radios (CR) are becoming the prime candidates for improved utilization of available spectrum. The unlicensed secondary users share the spectrum with primary licensed user in such manners that the interference at the primary user does not increase from a predefined threshold. In this paper, we propose an algorithm to address the power control problem for CR networks. The proposed solution models the wireless system with a non-cooperative game, in which each player maximize its utility in a competitive environment. The simulation results shows that the proposed algorithm improves the performance of the network in terms of high SINR and low power consumption.

Recently, there has been growing research on developing interference-aware routing (IAR) protocols for supporting multiple concurrent transmission in next-generation wireless communication systems. The existing IAR protocols do not consider node cooperation while establishing the routes because motivating the nodes to cooperate and modeling that cooperation is not a trivial task. In addition, the information about the cooperative behavior of a node is not directly visible to neighboring nodes. Therefore, in this paper, we develop a new routing method in which the nodes’ cooperation information is utilized to improve the performance of edge computing-enabled 5G networks. The proposed metric is a function of created and received interference in the network. The received interference term ensures that the Signal to Interference plus Noise Ratio (SINR) at the route remains above the threshold value, while the created interference term ensures that those nodes are selected to forward the packet that creates low interference for other nodes. The results show that the proposed solution improves ad hoc networks’ performance compared to conventional routing protocols in terms of high network throughput and low outage probability.

The Internet of Things (IoT) holds transformative potential in fields such as power grid optimization, defense networks, and healthcare. However, the constrained processing capacities and resource limitations of IoT networks make them especially susceptible to cyber threats. This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. We conducted a comparative analysis of three widely used DL models—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (biLSTM)—across four benchmark IoT intrusion detection datasets: BoTIoT, CiCIoT, ToNIoT, and WUSTL-IIoT-2021. Each model was assessed under balanced and imbalanced dataset configurations and evaluated using three loss functions (cross-entropy, focal loss, and dual focal loss). By analyzing model efficacy across these datasets, we highlight the importance of generalizability and adaptability to varied data characteristics that are essential for real-world applications. The results demonstrate that the CNN trained using the cross-entropy loss function consistently outperforms the other models, particularly on balanced datasets. On the other hand, LSTM and biLSTM show strong potential in temporal modeling, but their performance is highly dependent on the characteristics of the dataset. By analyzing the performance of multiple DL models under diverse datasets, this research provides actionable insights for developing secure, interpretable IoT systems that can meet the challenges of designing a secure IoT system.

In this paper we present an energy efficient route discovery method for reactive protocols in wireless ad hoc networks. In these types of protocols, the route discovery process typically uses expanding ring search discovery method. While expanding ring search method is efficient as compared to simple flooding, we present a method which further improves energy consumption and reduces route discovery overhead. The proposed method reduces the broadcast cost and as a result decreases the overall energy consumption. The expanding ring search method is modified and a new approach, which uses disks instead of rings, is presented. In this method, the node discovery process uses regions similar to disks, which have variable dimensions, and as a result, reduction in time and energy consumption is accomplished. The expanding disk search is suitable for various types of implementation scenarios ranging from time to energy critical applications. In addition, the analysis for the optimal number of disks in the expanding disk search algorithm is also presented.

Fifth-generation and beyond networks target multiple distributed network application such as Internet of Things (IoT), connected robotics, and massive Machine Type Communication (mMTC). In the absence of a central management unit, the device need to search and establish a route towards the destination before initializing data transmission. In this paper, we proposes a destination search and routing method for distributed 5G and beyond networks. In the proposed method, the source node makes multiple attempts to search for a route towards the destination by expanding disk-like patterns originating from the source node. The source node increases the search area in each attempt, accommodating more nodes in the search process. As a result, the probability of finding the destination increases, which reduces energy consumption and time delay of routing. We propose three variants of routing for high, medium, and low-density network scenarios and analyze their performance for various network configurations. The results demonstrate that the performance of the proposed solution is better than previously proposed techniques in terms of time latency and reduced energy consumption, making it applicable for 5G and beyond networks.