One of the major challenges in deployed wireless sensor networks (WSN) is to curb down congestion in network’s traffic without compromising the energy consumption of the sensor nodes. Congestion disrupts the continuous flow of data, increases loss of information, delays data delivery to the destination and significantly and unnecessarily increases energy consumption in already energy-strapped nodes. Obviously, in healthcare WSN applications, particularly those that cater to medical emergencies or monitor patients in critical conditions, it is desirable to prevent congestion from occurring in the first place.
In this work, the authors address the problem of congestion in the nodes of healthcare WSN using a learning automata (LA)-based approach. The primary objective is to adaptively equate the processing rate (data packet arrival rate) in the nodes to the transmitting rate (packet service rate), so that the occurrence of congestion in the nodes can be avoided. The authors maintain that the proposed algorithm, named as Learning Automata-Based Congestion Avoidance Algorithm in Sensor Networks (LACAS), can counter the congestion problem in healthcare WSN effectively. An important feature of LACAS is that it intelligently “learns” from the past and improves its performance significantly as time progresses. The proposed LA-based model is evaluated using simulations representing healthcare WSN. The results obtained through the experiments with respect to performance criteria have important implications in the healthcare domain. For example, the number of collisions, the energy consumption at the nodes, the network throughput, the number of unicast packets delivered, the number of packets delivered to each node, the signals received and forwarded to the Medium Access Control (MAC) layer, and the change in energy consumption with variation in transmission range, have shown that the proposed algorithm is capable of successfully avoiding congestion in typical healthcare WSNs requiring a reliable congestion control mechanism.