TY  - CONF
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044223067&doi=10.1109%2fTENCON.2017.8228159&partnerID=40&md5=167cd6a5236b0a0bde0c5f15c2594dbf
A1  - Alam, M.K.
A1  - Aziz, A.A.
A1  - Awang, A.
A1  - Latif, S.A.
VL  - 2017-D
EP  - 1852
Y1  - 2017///
SN  - 21593442
PB  - Institute of Electrical and Electronics Engineers Inc.
N1  - cited By 0; Conference of 2017 IEEE Region 10 Conference, TENCON 2017 ; Conference Date: 5 November 2017 Through 8 November 2017; Conference Code:133992
N2  - 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. © 2017 IEEE.
KW  - Accidents; Data transfer; Electroencephalography; Energy utilization; Low power electronics; Monitoring; Sensor networks; Topology; Wireless sensor networks
KW  -  Communication load; Continuous monitoring systems; Driver safety; Electro-encephalogram (EEG); Network topology; Performance analysis; Sensor network topology; WESN
KW  -  Data communication systems
SP  - 1847
TI  - Performance analysis of different network topologies for driver safety monitoring application in WESN
ID  - scholars8052
AV  - none
ER  -