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Performance analysis of different network topologies for driver safety monitoring application in WESN

Recent reports have indicated that driver fatigue is one of the main causes in vehicle accidents leading to traffic fatalities and severe injuries. Therefore, developing fatigue driving monitoring system is of outmost importance to avoid fatigue-related accidents. Electroencephalogram (EEG)-based continuous monitoring system is one of the effective ways to predict both psychological and physiological fatigue during driving. This system requires driver to wear a fabric cap consisting of many EEG sensors which are battery-powered, small-size and lightweight. The EEG data of driver are continuously monitored and sent to a base station using a special network called a wireless EEG sensor network (WESN). Unfortunately, the network suffers from high communication load due to abundant transmission activities of high-density sensors placed on the small circumference of the human scalp. This article aims to identify the suitable sensor network topology that results in low power data transmission for WESN. The results show that the cluster-based topology performs better compared to far-end and near-end with respect to energy consumption in transmit, received and idle mode, throughput and delay.