Adaptive learning solution for congestion avoidance in wireless sensor networks

One of the major challenges in wireless sensor network (WSN) research is to curb down congestion in the network's traffic, without compromising with the energy of the sensor nodes. In this work, we address the problem of congestion in the nodes of a WSN using Learning Automata (LA)-based adaptive learning approach. Our primary objective, using this approach, is to adaptively make the processing rate (data packet arrival rate) in the nodes equal to the transmitting rate (packet service rate), so that the occurrence of congestion in the nodes is seamlessly avoided. We maintain that the proposed algorithm, named as Learning Automata-Based Congestion Avoidance Algorithm in Sensor Networks (LACAS), can counter the congestion problem in WSNs effectively. The results obtained through the experiments with respect to important performance criteria showed that the proposed algorithm is capable of successfully avoiding congestion in typical WSNs requiring a reliable congestion control mechanism.