Analysis and Design of Advanced Optimization Techniques for Solving Coverage Problem and Congestion Control in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSN) are one of the most demanding areas of study within information technology. A WSN comprises interlinked sensors that function with restricted battery resources. Due to these limitations, it becomes crucial to emphasize precise deployment and routing strategies to enhance the Quality of Service (QoS) in the WSN framework. Within the realm of WSN modelling, one of the significant challenges is optimization amidst multiple conflicting objectives. The primary motive of the proposed work is to design a model for multi-objective optimization (MOO) to improve QoS in WSN. This research suggests a new algorithm, Enhance Non-dominated Sorting Genetic Algorithm(ENSGA), which is reference-based with a new dynamic weight-based cluster scheduling algorithm. ENSGA uses multi-parent order crossover (MPOX) to enhance fresh child to produce the best Pareto Fronts (PF). The optimization study considers the number of sensor nodes, network coverage, and energy consumption as the three objectives metrics. In real-world applications, these metrics frequently present conflicting demands. Thus, a reasonable balance of the trade-offs among them is imperative for optimizing the comprehensive performance of WSNs. ENSGA shows promising results compared to NSGA-II and NSGA-III. newlineAnother part of this research focuses on improving QoS and energy efficiency in application specific WSNs, i.e., in healthcare. The Research on Wireless Sensor Networks (WSNs) in healthcare monitoring has expanded notably due to the unique challenges it can tackle. Contemporary healthcare technologies predominantly emphasize wearable and implantable Wireless Body Area Networks (WBANs), a specific subset of WSNs. Medical sensors, implanted within or attached to the body, form the basis of WBANs and communicate wirelessly. This innovation enables the acquisition of physiological data from the human body. The WBAN infrastructure consists of various node coordinators that collect data from instruments (e.g., heartbeat sensors) and forward i