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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Yick, J. Mukherjee, B. Ghosal, D. |
| Copyright Year | 2005 |
| Description | Author affiliation: Dept. of Comput. Sci., California Univ., Davis, CA, USA (Yick, J.; Mukherjee, B.; Ghosal, D.) |
| Abstract | Target tracking in wireless sensor networks requires efficient coordination among sensor nodes. Existing methods have focused on tree-based collaboration, selective activation, and group clustering. This paper presents a prediction-based adaptive algorithm for tracking mobile targets. We use adaptive Kalman filtering to predict the future location and velocity of the target. This location prediction is used to determine the active tracking region which corresponds to the set of sensors that needs to be "lighted". The velocity prediction is used to adaptively determine the size of the active tracking region, and to modulate the sampling rate as well. In this paper, we quantify the benefits of our approach in terms of energy consumed and accuracy of tracking for different mobility patterns. Our simulation results show that advance resource reservation coupled with adaptively changing the size of the active tracking region and the sampling rate reduces the overall energy consumed for tracking without affecting the accuracy in tracking. |
| Starting Page | 753 |
| Ending Page | 760 |
| File Size | 377397 |
| Page Count | 8 |
| File Format | |
| ISBN | 0780392760 |
| DOI | 10.1109/ICBN.2005.1589681 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-10-07 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Target tracking Wireless sensor networks Sampling methods Clustering algorithms Collaboration Adaptive algorithm Kalman filters Adaptive filters Filtering |
| Content Type | Text |
| Resource Type | Article |
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