Energy Management Technology Based on Swarm Intelligence in Wireless Sensor Networks

With the wide application of Wireless Sensor networks (WSN) in the Internet of Things, environmental monitoring, smart home and other fields, energy management issues have become one of the key factors limiting their performance and life cycle. Traditional energy management strategies mainly rely on fixed scheduling algorithms or rules, ignoring the complexity of dynamic changes in the network and the synergies between nodes. In recent years, Swarm Intelligence (SI) technology has shown great potential in energy management with its powerful adaptive capability and distributed collaboration. This paper proposes a novel energy management approach for wireless sensor networks based on swarm intelligence, designed to enhance energy efficiency, prolong network lifetime, and optimize network topology. Firstly, the application background of swarm intelligence in wireless sensor networks is analyzed. Particle Swarm Optimization, Ant Colony Optimization and Bee Colony Optimization (Bee Colony Optimization) are introduced. The basic principle and characteristics of swarm intelligence algorithms such as BCO. Additionally, leveraging the strengths of the ant colony algorithm, an energy scheduling model based on this algorithm is introduced. The balance between energy management and network performance is realized by multi-objective optimization method, which takes into account the energy consumption of nodes, network delay and data transmission quality. The experimental results demonstrate that the swarm intelligence-based energy management method achieves better energy efficiency and extends the network's lifetime compared to traditional algorithms.