Data fusion in wireless sensor networks using swarm intelligence

The existing data fusion techniques in Wireless Sensor Networks compromise on either accuracy of data, time or energy consumption while transmitting data to the base station. The objective of the paper is to use the concepts of Swarm Intelligence (Particle swarm optimisation and Ant colony optimisation), to achieve the same data fusion without compromising on the above-mentioned parameters. The proposed work uses three algorithms from nature inspired computing namely, Particle Swarm Optimization, Ant Colony Optimization and Swarm Intelligence to improve the fusion technique. Particle Swarm Optimization is used in wireless sensor networks to find the probable time taken for each node to reach the base station. Ant Colony Optimization uses greedy algorithm to choose the best path to obtain each agent's goal. Swarm Intelligence is a concept used for filtering out data with different levels of desired data in them and fusing those with similar levels of desired data.