Q-Learning-Based Optimized Routing in Biomedical Wireless Sensor Networks

Abstract

ABSTRACTThe information regarding any abnormal behaviour in the physiological parameters of a patient should reach the remote healthcare personal immediately in real time, so as to take corrective action promptly and save the life. Biomedical wireless sensor network (BWSN) plays a vital role in this connection because it can be attached to the patient without causing any inconvenience to him/her and can communicate to any remote healthcare office without any delay. BWSN consists of individual nodes to collect the patient's information and communicate it to the remote health centre if the value of sensed signal is beyond normal range. The nodes deployed within the patients form a BWSN, and the network has to send the information from the source to the remote sink in an efficient way. The network should choose an optimized path for this communication, so that the node’s lifetime is increased. This paper presents a Q-learning-algorithm-based routing concept to route the sensed information if required from the individual node to the remote healthcare station. Simulations are made with a set of mobile biomedical wireless sensor nodes within an area of 100 m × 100 m flat space operating for 600 seconds of simulation time. Results show that the Q-learning-based approach requires less time to route the packet from the source node to the destination remote station.