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| Content Provider | ACM Digital Library |
|---|---|
| Author | Jiang, Hao Hallstrom, Jason O. |
| Copyright Year | 2013 |
| Abstract | Due to the limited computational and energy resources available on existing wireless sensor platforms, achieving high-precision classification of high-level events in-network is a challenge. In this article, we present $\textit{in-network}$ implementations of a Bayesian classifier and a condensed $\textit{kd-tree}$ classifier for identifying events of interest on resource-lean embedded sensors. The first approach uses preprocessed sensor readings to derive a multidimensional Bayesian classifier used to classify sensor data in real time. The second introduces an innovative condensed kd-tree to represent preprocessed sensor data and uses a fast nearest-neighbor search to determine the likelihood of class membership for incoming samples. Both classifiers consume limited resources and provide high-precision classification. To evaluate each approach, two case studies are considered, in the contexts of human movement and vehicle navigation, respectively. The classification accuracy is above 85% for both classifiers across the two case studies. |
| Starting Page | 1 |
| Ending Page | 22 |
| Page Count | 22 |
| File Format | |
| ISSN | 15564665 |
| e-ISSN | 15564703 |
| DOI | 10.1145/2491465.2491470 |
| Volume Number | 8 |
| Issue Number | 2 |
| Journal | ACM Transactions on Autonomous and Adaptive Systems (TAAS) |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-07-01 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Bayesian classification Wireless sensor networks Classification Event detection Kd-tree |
| Content Type | Text |
| Resource Type | Article |
| Subject | Control and Systems Engineering Software |
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