Optimizing Border Surveillance with Energy-Efficient Wireless Sensor Networks

Border surveillance is crucial for national security, requiring the deployment of effective wireless sensor networks (WSNs) to monitor sensitive areas. This study introduces a new approach that combines a hybrid energy-efficient zone-based dynamic clustering method with routing, utilizing the Geometric Modified Ant colony Harris Hawk Optimization Algorithm (EECR-GeMACHHOA). The goal is to enhance energy efficiency and extend the network's lifespan. The method begins by identifying zone monitors through geometric translational symmetry, forming optimized zones that utilize a modified ant colony for selecting multiple cluster heads. This approach improves routing pathways by integrating geometric and nature-inspired optimization techniques, resulting in reduced energy consumption and increased packet transmission rates. The study reports a 6.66% reduction in network latency, a 15.03% decrease in energy usage, and a 9.86% increase in packets sent to the base station compared to alternative methods. The findings highlight a significant improvement in network lifetime and data transmission reliability over contemporary strategies. WSNs are particularly suited for border surveillance due to their ability to monitor vast areas and transmit data in real-time. However, challenges such as limited battery capacity and energy consumption can hinder their effectiveness. The proposed EECR-GeMACHHOA method addresses these issues by optimizing cluster head selection and routing, thereby balancing energy loads and minimizing communication overhead. This innovative approach not only enhances the performance of WSNs but also ensures their adaptability in dynamic environments, making it a valuable contribution to the field of border security and surveillance.