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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Fanaei, M. Valenti, M.C. Schmid, N.A. |
| Copyright Year | 2014 |
| Description | Author affiliation: Lane Dept. of Comput. Sci. & Electr. Eng., West Virginia Univ., Morgantown, WV, USA (Fanaei, M.; Valenti, M.C.; Schmid, N.A.) |
| Abstract | Most studies that consider the problem of estimating the location of a point source in wireless sensor networks assume that the source location is estimated by a set of spatially distributed sensors, whose locations are fixed. Motivated by the fact that the observation quality and performance of the localization algorithm depend on the location of the sensors, which could be randomly distributed, this paper investigates the performance of a recently proposed energy-based source-localization algorithm under the assumption that the sensors are positioned according to a uniform clustering process. Practical considerations such as the existence and size of the exclusion zones around each sensor and the source will be studied. By introducing a novel performance measure called the estimation outage, it will be shown how parameters related to the network geometry such as the distance between the source and the closest sensor to it as well as the number of sensors within a region surrounding the source affect the localization performance. |
| Starting Page | 186 |
| Ending Page | 192 |
| File Size | 1131845 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781479946402 |
| DOI | 10.1109/ICCW.2014.6881194 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Distributed source localization distributed estimation wireless sensor networks Estimation Vectors binomial point process with repulsion Geometry energy detector Wireless sensor networks Quantization (signal) spatial randomness uniform clustering process fusion center Sensors Signal to noise ratio |
| Content Type | Text |
| Resource Type | Article |
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