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Fast, Accurate Event Classification on Resource-Lean Embedded Sensors

Fast, Accurate Event Classification on Resource-Lean Embedded Sensors Fast, Accurate Event Classification on Resource-Lean Embedded Sensors HAO JIANG and JASON O. HALLSTROM, Clemson University 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 in-network implementations of a Bayesian classifier and a condensed 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. Categories and Subject Descriptors: C.2.4 [Computer-Communication Networks]: Distributed Systems General Terms: Design, Algorithms, Performance Additional Key Words and Phrases: Wireless sensor networks, classification, Bayesian classification, kd-tree, event detection ACM Reference Format: Jiang, H. and Hallstrom, J. O. 2013. Fast, accurate event classification http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Autonomous and Adaptive Systems (TAAS) Association for Computing Machinery

Fast, Accurate Event Classification on Resource-Lean Embedded Sensors

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References (42)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1556-4665
DOI
10.1145/2491465.2491470
Publisher site
See Article on Publisher Site

Abstract

Fast, Accurate Event Classification on Resource-Lean Embedded Sensors HAO JIANG and JASON O. HALLSTROM, Clemson University 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 in-network implementations of a Bayesian classifier and a condensed 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. Categories and Subject Descriptors: C.2.4 [Computer-Communication Networks]: Distributed Systems General Terms: Design, Algorithms, Performance Additional Key Words and Phrases: Wireless sensor networks, classification, Bayesian classification, kd-tree, event detection ACM Reference Format: Jiang, H. and Hallstrom, J. O. 2013. Fast, accurate event classification

Journal

ACM Transactions on Autonomous and Adaptive Systems (TAAS) – Association for Computing Machinery

Published: Jul 1, 2013

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