Certain investigations on optimization algorithms for energy efficient wireless communication networks

Abstract

With the arrival of wireless digital communication networks through its pervasive computing, Internet has rapidly evolved into a global technology called IoT through which devices are connected using different technologies such as 3G, 4G, 5G, LTE, etc., M2M technologies possess certain vital characteristics that enable them to facilitate reliable and seamless communication for IoT environment that comprises efficient power optimization, network architecture, routing protocols, security characteristics, and various QoS based services. To facilitate the above-mentioned services this research has three main investigations focused on clustering, optimization, and joint resource allocation in Wireless Sensor Network (WSN) and Machine to Machine (M2M) networks. An enhanced optimal solution to accommodate managerial aspects in wireless communication station systems has been proposed through the application of a bi-level programming approach together with an analysis of max-product fuzzy relation inequalities. In general, bi-level optimal models have the foundation based on first-level programming techniques where linear programming is adopted to minimize the intensity of electromagnetic radiation. To achieve optimal first-level programming problems may not be adequate as determining a monotonic rising function that supports effective management needs bi-level optimization. The next part is examining the energy efficiency issue to increase the lifetime and performance of WSNs to promote their applications in biomedical industries. Clustering is known to enhance productivity through Cluster Head (CH) categorization yet prevailing CH election operations begin with deciding on probable and feasible CH positions. This location-based model integrates requirements to offer speedy iv processing, accuracy in the selection, and avoiding redundant nodes being selected. A sampling-based Smart Spider Monkey Optimization (SSMO) has been proposed where the sample population nodes are varied and network nodes are chosen from them.