Machine learning based congestion control in wireless sensor networks

Abstract

The performance of wireless sensor networks strongly depends on the underlying transport protocol.The traffic characteristics in sensor networks are known to cause frequent congestion spots.This thesis studies artificial intelligence methods for congestion control in wireless sensor networks. It estimates likelihood of congestion and applies in turn, reinforcement learning to reduce fairness and packet drops in the long term.The second solution is an inference technique called Min-Sum. The minimization of congestion is transformed into smaller coordination problems involving fewer variables.The simulation results show that 15% improvement in energy efficiency is obtained over the recently proposed Fusion method. Although Min-Sum based methods allow accurate decision trade-offs, the message exchange is a limiting factor in the correctness of decisions.