Power-Aware Classifier Selection in Wireless Sensor Networks

• Published in: Journal of Information Science and Engineering Vol. 32; no. 2; pp. 439 - 454
• Main Authors: 肖克江(Ke-Jiang Xiao), 王晓峰(Xiao-Feng Wang), 王睿(Rui Wang), 崔莉(Li Cui)
• Format: Journal Article
• Language: English
• Published: 社團法人中華民國計算語言學學會 01.03.2016
• Subjects: Algorithms; Classification; Classifiers; Cost function; Detection; Power consumption; Remote sensors; Wireless networks; wireless sensor networks; AdaBoost algorithm; 0-1 integer programming; classification accuracy; greedy algorithm; classifier selection; poweraware classification
• ISSN: 1016-2364
• DOI: 10.6688/JISE.2016.32.2.11

Many wireless sensor networks (WSNs) based sensing system are as intelligent as possible to accurately sense and recognize interested targets, while large amounts of sensing data generated by WSNs and the limited energy pose great challenges to target classification in WSNs. Thus, it is necessary to obtain tradeoff between power consumption and classification accuracy. Inspired by AdaBoost algorithm, we present a power- aware classification scheme named CSBoost to cluster right classifiers forming strong final classifier. CSBoost scheme can minimize system's power consumption subject to a lower bound on classification accuracy. Specifically speaking, in order to select appropri- ate classifiers, we first give the cost function and utility function according to upper error bound of the final classifier. Then we map the classifier selection problem into 0-1 integer programming problem and provide a heuristic greedy algorithm based method to solve the problem in a polynomial-time. Finally, we conduct experiment on real data to validate and evaluate our proposed scheme. The experimental results demonstrate that CSBoost scheme can get a better performance, comparing with traditional methods.