Adaptive Chaotic Ant Colony Optimization for Energy Optimization in Smart Sensor Networks

• Published in: Journal of sensors Vol. 2021; no. 1
• Main Authors: Jia, Wenxian, Liu, Menghan, Zhou, Jie
• Format: Journal Article
• Language: English
• Published: New York Hindawi 05.07.2021; John Wiley & Sons, Inc
• Subjects: Adaptive algorithms; Algorithms; Ant colony optimization; Artificial intelligence; Clustering; Communication; Data collection; Design; Energy consumption; Energy efficiency; Genetic algorithms; Optimization; Particle swarm optimization; Power consumption; Sensors; Smart sensors; Transition probabilities; Unmanned aerial vehicles
• ISSN: 1687-725X; 1687-7268; 1687-7268
• DOI: 10.1155/2021/5051863

Smart sensor network has the characteristics of low cost, low power consumption, real time, strong adaptability, etc., and it has a wide range of application prospects in the agricultural field. However, the smart sensor node is limited by its own energy; it also faces many bottlenecks in agricultural applications. Therefore, balancing the energy consumption of nodes and extending the life of the network are important considerations in the design of efficient routing for smart sensor networks. Aiming at the problem of energy constraints, this paper proposes an intelligent sensor network clustering algorithm based on adaptive chaotic ant colony optimization (ACACO). ACACO introduces logical chaotic mapping to interfere with the pheromone on the initial path and uses the adaptive strategy to improve the transition probability formula. After selecting the best next hop node, the advancing ants are released to update the local pheromone, and the current pheromone content is adjusted by the chaos factor. When the ants determine the path, they release subsequent ants to update the global pheromone. The simulation results show that ACACO has obvious advantages over genetic algorithm (GA) and particle swarm optimization (PSO).