NDLI: A New Hybrid Approach for Detection and Estimation in Wireless Sensor Networks

Content Provider	IEEE Xplore Digital Library
Author	Salhieh, A.M. Obeidallah, M.G.
Copyright Year	2009
Abstract	Wireless sensor network is a network that contains a set of autonomous inexpensive devices called sensors. These sensors communicate together to detect, track, and monitor physical environments. Since energy is the scarcest resource in wireless sensor networks, some necessary improvements should be made to current detection algorithms to reduce the amount of consumed energy.In this paper, two detection algorithms are studied named value fusion algorithm and decision fusion algorithm. Hybrid approach was developed as combinations between these two algorithms. In hybrid approach, event region was divided into a number of clusters. In each cluster, a cluster head is selected. Only one cluster is activated and participated in the detection process. Members of the active cluster perform the value fusion algorithm locally and the cluster head (CH) performs the decision fusion algorithm globally.To implement the hybrid approach, Hybrid Value-Decision (HVD) clustering protocol was developed as an extension to LEACH protocol.The results show that the hybrid algorithm is superior to value and decision algorithms in terms of energy consumption when more sensors are deployed in the event region (i.e. more scalable event region).
Starting Page	379
Ending Page	383
File Size	349225
Page Count	5
File Format	PDF
ISBN	9780769535074
DOI	10.1109/CSIE.2009.927
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computer science Wireless sensor networks Energy consumption Protocols Region 2 Event detection Wireless Sensor Network Computerized monitoring Detection and estimation Clustering algorithms Energy measurement Detection algorithms
Content Type	Text
Resource Type	Article

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