Congestion Control in Wireless Sensor Networks Using Supervised Learning Method

Abstract

Wireless Sensor Networks is a Wireless Network consisting of spatially distributed automous devices using sensors to monitor physical or environmental conditions. Recent applications on wireless sensor networks demand networks with high and consistent data load. The performance becomes a crucial factor, congestion remains a serious problem and it is a highly undesirable situation. Since its appearance creates additional overhead to the already heavily loaded network. Then congestion control algorithm need to be applied in order to mitigate congestion. In this approach, routing in sensor networks maintains information on neighbor states and potentially many other factors in order to make informed decisions. Challenges arise both in (a) performing accurate and adaptive information discovery and (b) processing/analyzing the gathered data to extract useful features and correlations. And from that quality of a link can be estimated and by using rule learner algorithm good quality link is detected. Through the link alternate path is selected to reroute the packets. This algorithm is used to increase throughput of the network and decreases the data delay.