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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Targon, V. Cavallaro, A. |
| Copyright Year | 2013 |
| Description | Author affiliation: Alpen-Adria Univ., Klagenfurt, Austria (Cavallaro, A.) || Centre for Intell. Sensing, Queen Mary Univ. of London, London, UK (Targon, V.) |
| Abstract | We address the problem of energy reduction for a wireless sensor network performing an estimation task and propose an a posteriori measurement selection solution. The energy is saved by selecting in a distributed way a subset of the available measurements for transmission to a central processor. We show how to perform measurement selection for a network of radio-receivers tracking a target via the received signal strength indicator. The proposed measurement selection method is independent from the current estimate and based on the assumption that the best measurements have the largest received signal strength. The method operates through distributed max-consensus and schedules transmissions of the measurements without any centralized control. We compare the accuracy of our method with a centralized selection scheme and a Nearest Neighbor criterion. We show that the proposed measurement selection method consumes less energy for communication than centralized and distributed data fusion approaches, under the assumptions that radio nodes are always switched on to detect targets and that high-power long-range communication holds as a lower bound for the energy spent by the network for transferring data to the central processor. |
| Starting Page | 714 |
| Ending Page | 721 |
| File Size | 932637 |
| Page Count | 8 |
| File Format | |
| ISBN | 9786058631113 |
| e-ISBN | 9781479902842 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-07-09 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | ISIF ( Intl Society of Information Fusi |
| Subject Keyword | Energy measurement Time measurement Target tracking Sensors Data integration Position measurement Covariance matrices |
| Content Type | Text |
| Resource Type | Article |
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