Computational Intelligence Routing for Lifetime Maximization in Heterogeneous Wireless Sensor Networks

In wireless sensor networks, sensor nodes are typically powerconstrained with limited lifetime, and thus it is necessary to know how long the network sustains its networking operations. Heterogeneous WSNs consists of different sensor devices with different capabilities. One of major issue in WSNs is finding the coverage distance and connectivity between sensors and sink. To increase the network lifetime, this paper proposed Swarm Intelligence, routing technique called Ant Colony Optimization (ACO). Ant colony optimization algorithm provides a natural and intrinsic way of exploration of search space of coverage area. Ants communicate with their nest-mates using chemical scents known as pheromones, Based on Pheromone trail between sensor devices the shortest path is found. By finding the coverage distance and sensing range, the network lifetime maximized and reduces the energy usage. Extensive Java Agent Framework (JADE) multi agent simulator result clearly provides more approximate, effective and efficient way for maximizing the lifetime of heterogeneous WSNs. Index Terms wireless sensor networks (WSNs), Ant colony optimization (ACO), connectivity, coverage, network lifetime.