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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Keally, M. Gang Zhou Guoliang Xing Jianxin Wu |
| Copyright Year | 2011 |
| Description | Author affiliation: Nanyang Technological University, Singapore (Jianxin Wu) || Michigan State University, USA (Guoliang Xing) || College of William and Mary, USA (Keally, M.; Gang Zhou) |
| Abstract | Wireless sensor networks for human health monitoring, military surveillance, and disaster warning all have stringent accuracy requirements for detecting or classifying events while maximizing system lifetime. We define meeting such user accuracy requirements as confident sensing. To perform confident sensing and reduce energy, we must address sensing diversity: sensing capability differences among heterogeneous and homogeneous sensors in a specific deployment. We are among the first to explore the impact of sensing diversity on sensor collaboration, exploit diversity for sensing confidence, and apply diversity exploitation for confident sensing coverage. We show that our diversity-exploiting confident coverage problem is NP-hard for any specific deployment and present a practical solution, Wolfpack. Through a distributed and iterative sensor collaboration approach, Wolfpack maximizes a specific deployment's capability to meet user detection requirements and save energy by powering off unneeded nodes. Using real vehicle detection trace data, we demonstrate that Wolfpack provides confident event detection coverage for 30% more detection locations, using 20% less energy than a state of the art approach. |
| Starting Page | 1719 |
| Ending Page | 1727 |
| File Size | 608358 |
| Page Count | 9 |
| File Format | |
| ISBN | 9781424499199 |
| ISSN | 0743166X |
| e-ISBN | 9781424499212 |
| DOI | 10.1109/INFCOM.2011.5934969 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-04-10 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Sensors Collaboration Accuracy Vehicles Machine learning Event detection Vehicle detection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Science Electrical and Electronic Engineering |
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