NDLI: Tracking Coverage throughout Epochs with Bounded Uncertainty

Content Provider	IEEE Xplore Digital Library
Author	Fan Zhou Trajcevski, G. Avci, B.
Copyright Year	2011
Abstract	This work addresses the problem of managing the sensor-coverage and organizing the epochs in a manner that balances the trades-offs between the accuracy and energy consumptions during target tracking in Wireless Sensor Networks (WSN). While the typical target tracking approaches are based on movement prediction, we only assume a knowledge of some maximal speed of the target during certain time-intervals. This, in turn, restricts its whereabouts to a disk-bound area throughout such intervals. In such settings, we seek to determine a sensor cover, a subset of all the nodes that need to be awake, which ensures that the target can be detected during the given epoch. Towards this, we propose sensor-cover selection methodologies, Greedy Uncertain Moving Object coverage sensor set selection (GUMO) and PAttern Based coverage sensor set selection (PAB). GUMO is a heuristic maximizing the coverage gain at each step, while PAB is an approach based on optimal deployment pattern of sensor nodes achieving coverage of the disk area bounding the target's whereabouts. We conduct extensive simulations to evaluate the performance of the algorithms, and the results reveal that GUMO and PAB not only provide substantial energy saving due to reduction in the communications involved in selecting tracking participant-nodes and principal(s), while assuring a bounded error on the target's location.
Starting Page	67
Ending Page	74
File Size	582901
Page Count	8
File Format	PDF
ISBN	9781457710520
DOI	10.1109/NCA.2011.17
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Target tracking Sensors Wireless sensor networks Uncertainty Scheduling Silicon Approximation algorithms
Content Type	Text
Resource Type	Article

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