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

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