An energy- aware algorithm for optimizing dynamic zone monitoring and multiple cluster head selection with routing in border surveillance WSNs

Border surveillance plays a vital role in national security, necessitating the deployment of efficient and strong Wireless Sensor Networks (WSNs) to reveal and protect sensitive areas. This research describes a unique approach for optimizing border surveillance that combines a hybrid energy efficient zone-based dynamic clustering method with routing utilizing Geometric Modified Ant colony Harris Hawk Optimization Algorithm (EECR-GeMACHHOA). Our proposed method aims to enhance energy efficiency and prolong the network lifespan. This technique starts with the identification of zone monitors using geometric translational symmetry capabilities, which enables the formation of zones. These zones are then optimized by making use of modified ant colony based on dynamic pheromone evaporation for multiple cluster head selection. The EECR-GeMACHHOA improves routing pathways by integrating geometric, nature-inspired Ant colony and Harris Hawk optimization methodologies in order to decrease energy utilization and maximize the packet transmission rate. The proposed approach reduces network latency by 6.66%, consumes 15.03% less energy, and sends 9.86% increase in packets sent to the base station relative to alternative methods. The outcomes demonstrate a very good increase in network lifetime, robust energy efficient system, and data packet transmission dependability when compared to contemporary strategies.