Routing in Wireless Sensor Networks using swarm intelligence

Wireless Sensor Networks consisting of nodes with limited power are deployed to collect and distribute useful information from the field to the other sensor nodes. Energy consumption is a key issue in the sensor's communications since many use battery power, which is limited. The sensors also have limited memory and functionality to support communications. Ant Colony Optimization, a swarm intelligence based optimization technique, is widely used in network routing. This paper describes a new routing approach for Wireless Sensor Networks consisting of stable nodes based an Ant Colony Optimization algorithm that explore the network and learn good routes, using a novel variation of reinforcement learning. Simulation results show that proposed algorithm provides promising solutions allowing node designers to efficiently operate routing tasks.