An Ant Odor Analysis Approach to the Ant Colony Optimization Algorithm for Data-Aggregation in Wireless Sensor Networks

In this paper we present an ant odor analysis approach to the ant colony optimization (ACO) algorithm for data aggregation in wireless sensor networks. We provide a modification to the existing ACO data aggregation scheme by accounting for multiple sinks and sources in an energy-constrained wireless sensor network. By forming families of ants (FoA), an intrinsic way is provided to explore the search space for routing and for determining optimal data aggregation. An ant odor identification model (AOIM) serves to determine and increase the correlation between data at the various nodes by trying to include hitherto different data under the same family (FoA). Simulation results show that the energy-efficiency achieved is high with less associated costs even for a larger number of sources and sinks