PSO تحسين استهلاك الطاقة باختيار العقد الرئيسية في شبكات الاستشعار اللاسلكية عبر نسخة مبسطة من خوارزمية

Abstract

Energy consumption and network lifetime optimization represent critical challenges in Wireless Sensor Networks (WSNs), primarily due to the constrained resources and non replaceable batteries of sensor nodes. This work aims to propose an efficient and high performance mechanism for Cluster Head (CH) and active node selection to reduce energy consumption within the network by adapting nature-inspired metaheuristic optimization algorithms. In this thesis, we propose a tailored and developed variant of the Binary Simplified Particle Swarm Optimization (BSPSO) algorithm to address the Cluster Head selection problem. The core idea relies on minimizing the computational complexity of the standard PSO by eliminating redundant parameters (such as the inertia weight and random coefficients) and integrating the Sigmoid function to seamlessly transition into a binary search space representing node states. Simulation and performance evaluation results demonstrate that the proposed simplified approach significantly optimizes Cluster Head selection, decreases computational overhead, and effectively extends the wireless sensor network's operational lifetime compared to conventional methods.