Optimized Energy-Efficient Routing and Adaptive Coverage in Wireless Sensor Networks for Multi-Access Edge Environments

Abstract

The growing demand for edge computing necessitates optimizing wireless sensor networks (WSNs) for efficient real-time data processing. Achieving energy efficiency and dynamic coverage in WSNs, particularly in multi-access edge environments, is a key challenge. This paper introduces a framework with three core algorithms to enhance network coverage and routing in such environments. The first algorithm uses Delaunay Triangulation to detect coverage gaps, identifying areas with insufficient sensor deployment. To address these gaps, the second algorithm applies a dynamic node placement strategy, where additional sensors are strategically positioned, optimized using clustering and refined Delaunay Triangulation to integrate new nodes. The third algorithm proposes an adaptive tree-based routing protocol using a Minimum Spanning Tree (MST) to conserve energy by rerouting data through energy-efficient paths. Together, these algorithms form a resilient WSN system that adapts to environmental changes, improving data reliability and system performance. Simulation results demonstrate significant improvements in network lifetime and efficiency within multi-access edge environments.