Publications

@conference{Trab2013237,

title = {Energy minimization algorithm based on Bayesian approach for fault tolerant detection in Wireless Sensor Network},

author = {S. Trab and B. Boussaid and A. Zouinkhi and M. N. Abdelkrim},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84899072736\&doi=10.1109%2fSTA.2013.6783137\&partnerID=40\&md5=79d84a1dc1459675fc18af59b9cd0f2a},

doi = {10.1109/STA.2013.6783137},

year  = {2013},

date = {2013-01-01},

journal = {14th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering, STA 2013},

pages = {237-242},

publisher = {IEEE Computer Society},

abstract = {Wireless Sensor Networks (WSNs) have been a significant system considerably exploited in numerous fields including medical, environmental, military and household applications. Yet, its high energy consumption and its potential fault vulnerability still their major drawback. Both of these issues require the implementation of fault distributed detection methods, capable of distinguishing faulty nodes from normal ones while minimizing energy. Thus, several approaches are proposed to detect these distributed errors, mentioning the Bayesian approach insuring the best choice of a proper sensor neighbourhood size n for best fault detection as well the energy conservation. In this paper, we define an energy minimization algorithm based on the Bayesian approach which ensures preventive sensor nodes pre-deployment against faults distribution. The Bayesian approach represents a study oscillating between Theory of Signal Detection (TSD), fault tolerance and energy minimization by determining the optimal parameters required to achieve this fault tolerance detection. This method includes the error probability due to the sensor itself which is included in the fault detection scheme thus the network will be more accurate. The experimental simulations will join a perceptive study allowing to the best choice of algorithm inputs then we will prouve that the proposed algorithm is able to provide a better detection performance while optimizing energy in WSN. © 2013 IEEE.},

note = {cited By 2},

keywords = {Algorithms; Automation; Bayesian networks; Control; Energy utilization; Fault detection; Fault tolerance; Military applications; Optimization; Process control; Sensors; Wireless sensor networks, Bayesian approaches; Distributed fault detections; Energy minimization; Energy minimization algorithms; Experimental simulations; Fault-tolerant detections; High energy consumption; Wireless sensor network (WSNs), Sensor nodes},

pubstate = {published},

tppubtype = {conference}

}