Improved ACO for Energy-Efficient Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are increasingly becoming an integral part of many applications, including environmental monitoring, healthcare, and smart cities. However, a significant challenge faced by WSNs is energy consumption, as the sensor nodes are typically powered by batteries, which deplete over time, limiting the network's longevity. Traditional routing protocols often fail to optimize energy usage effectively, resulting in an uneven depletion of energy and premature node failures. In this paper, we introduce an improved Ant Colony Optimization (ACO) algorithm designed to optimize energy consumption and extend the operational life of WSNs. The enhanced ACO leverages multi-objective optimization and dynamic parameter adaptation, allowing for more energy-efficient routing by accounting for both the residual energy of nodes and the distance between them. This approach helps mitigate common issues such as the energy hole problem, imbalance in energy consumption, and rigid parameter settings seen in conventional algorithms. Our experiments demonstrate that the enhanced ACO significantly improves energy efficiency, achieving 19.6% more residual energy and reducing energy consumption by 38% compared to traditional ACO methods. Additionally, the network's lifetime is extended, maintaining over 90% connectivity for a large portion of the operational time. These results highlight the effectiveness and scalability of the proposed algorithm, particularly for large-scale deployments. Looking forward, the integration of machine learning techniques and support for mobile sensor networks could further enhance the algorithm's flexibility and performance in dynamic environments.