Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/13940
 Title: SNAP: Fault tolerant event location estimation in sensor networks using binary data Authors: Panayiotou, Christos G.Michaelides, Michalis P. Major Field of Science: Engineering and Technology Field Category: Electrical Engineering - Electronic Engineering - Information Engineering Keywords: Maximum likelihood estimation;Fault tolerance;Fault tolerant systems;Wireless sensor networks;Estimation;Probability density function;Noise Issue Date: 25-Aug-2009 Source: IEEE Transactions on Computers, 2009, vol. 58, no. 9, pp. 1185 - 1197 Volume: 58 Issue: 9 Journal: IEEE Transactions on Computers Abstract: This paper investigates the use of wireless sensor networks for estimating the location of an event that emits a signal that propagates over a large region. In this context, we assume that the sensors make binary observations and report the event (positive observations) if the measured signal at their location is above a threshold; otherwise, they remain silent (negative observations). Based on the sensor binary beliefs, a likelihood matrix is constructed whose maximum value points to the event location. The main contribution of this work is Subtract on Negative Add on Positive (SNAP), an estimation algorithm that provides an efficient way of constructing the likelihood matrix by simply adding \pm 1 contributions from the sensor nodes depending on their alarm state (positive or negative). This simple estimation procedure provides very accurate results and turns out to be fault tolerant even when a large percentage of the sensor nodes report erroneous observations. © 2009 IEEE. ISSN: 0018-9340 DOI: 10.1109/TC.2009.60 Rights: © IEEE Type: Conference Papers Affiliation : University of Cyprus Appears in Collections: Δημοσιεύσεις σε συνέδρια /Conference papers or poster or presentation

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