A Nature Inspired Optimal Path Finding Algorithm to Mitigate Congestion in WSNs

Abstract

In resource constrained wireless sensor networks, congestion control is an extremely important issue that need to be addressed. The individual capacities of the channels are exceeded by the bulk traffic and creates adverse effects on the performance of the network. Therefore, to resolve the congestion problems in wireless sensor network the challenge lies in developing more sophisticated routing techniques which are able to fairly deliver the data between source and destination with minimum consumption of energy and reduced congestion. In the recent times, various swarm intelligence based routing approaches are proposed that aided in congestion detection and control mechanisms. Most of them are found to be with lower convergence rate. Therefore, a nature inspired hierarchical routing technique which aims to reduce congestion and energy consumption with network longevity and faster convergence rate is proposed. In this technique, a static partition of the target area based on node density is done to optimize energy efficiency. Firefly behavior based routing is modeled to select the optimal path for data transmission. This approach is concerned with exploiting global behavioral patterns emerging from local interactions. The proposed technique aims to minimize congestion by applying network load balance.