Adaptive Clustering based Dynamic Routing of Wireless Sensor Networks via Generalized Ant Colony Optimization

Wireless sensor networks (WSNs) use battery-powered sensor nodes for sensing, thus the energy efficiency is critical to extend the lifespan. The performance depends on the trade-off among energy consumption, latency and reliability. Data aggregation is a fundamental approach to eliminate redundancy and minimize transmission cost so as to save energy. Dynamic clustering based routing is proposed to achieve good performance via adaptive algorithms. The generalized Ant Colony Optimization (ACO) is applied to increase the reliable lifespan of sensor nodes with energy constraints. Each sensor node is modeled as an artificial ant and dynamic routing is modeled as ant foraging. The ant pheromone is released when an energy efficient channel from the source to sink is secured. Route discovery, data aggregation and information loss are modeled as the processes of pheromone diffusion, accumulation and evaporation. Each sensor node estimates the residual energy and dynamically calculates probabilities to select an optimal channel to extend the lifespan of WSNs.