An Adaptive Congestion Alleviating Protocol for Healthcare Applications in Wireless Body Sensor Networks: Learning Automata Approach

Abstract

Wireless Body Sensor Networks (WBSNs) involve a convergence of biosensors, wireless communication and networks technologies. WBSN enables real-time healthcare services to users. Wireless sensors can be used to monitor patients’ physical conditions and transfer real time vital signs to the emergency center or individual doctors. Wireless networks are subject to more packet loss and congestion. To alleviate congestion, the source transmission rate and node arrival rate should be controlled. In this paper, we propose Learning based Congestion Control Protocol (LCCP) for wireless body sensor networks. LCCP joins active queue management and rate adjustment mechanism to alleviate congestion. The proposed system is able to discriminate different physiological signals and assign them different priorities. Thus, it would be possible to provide better quality of service for transmitting highly important vital signs. The simulation results confirm that the proposed protocol improves system throughput and reduces delay and packet dropping. We also evaluate the performance of the AQM mechanism with no rate adjustment mechanism to show the advantage of using both AQM and rate adjustment mechanism together.