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Hierarchical aggregate classification with limited supervision for data reduction in wireless sensor networks

Published: 01 November 2011 Publication History

Abstract

The main challenge of designing classification algorithms for sensor networks is the lack of labeled sensory data, due to the high cost of manual labeling in the harsh locales where a sensor network is normally deployed. Moreover, delivering all the sensory data to the sink would cost enormous energy. Therefore, although some classification techniques can deal with limited label information, they cannot be directly applied to sensor networks since they are designed for centralized databases. To address these challenges, we propose a hierarchical aggregate classification (HAC) protocol which can reduce the amount of data sent by each node while achieving accurate classification in the face of insufficient label information. In this protocol, each sensor node locally makes cluster analysis and forwards only its decision to the parent node. The decisions are aggregated along the tree, and eventually the global agreement is achieved at the sink node. In addition, to control the tradeoff between the communication energy and the classification accuracy, we design an extended version of HAC, called the constrained hierarchical aggregate classification (cHAC) protocol. cHAC can achieve more accurate classification results compared with HAC, at the cost of more energy consumption. The advantages of our schemes are demonstrated through the experiments on not only synthetic data but also a real testbed.

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cover image ACM Conferences
SenSys '11: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems
November 2011
452 pages
ISBN:9781450307185
DOI:10.1145/2070942
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 November 2011

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Author Tags

  1. classification
  2. data reduction
  3. sensor networks

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Overall Acceptance Rate 174 of 867 submissions, 20%

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  • (2019)Event Fusion and Decision Approach Based on Node Reliability in Dam Safety Monitoring2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService)10.1109/BigDataService.2019.00037(215-220)Online publication date: Apr-2019
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