Impact of sensor-enhanced mobility prediction on the design of energy-efficient localization

  • Authors:

  • Chuang-Wen You;Polly Huang;Hao-hua Chu;Yi-Chao Chen;Ji-Rung Chiang;Seng-Yong Lau

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, CSIE Building, Taipei 106, Taiwan, ROC;Graduate Institute of Networking and Multimedia, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, CSIE Building, Taipei 106, Taiwan, ROC and Department of Electrical Engineering, Nationa ...;Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, CSIE Building, Taipei 106, Taiwan, ROC and Graduate Institute of Networking a ...;Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, CSIE Building, Taipei 106, Taiwan, ROC;Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, CSIE Building, Taipei 106, Taiwan, ROC;Department of Electrical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Boli Building, Taipei 106, Taiwan, ROC

  • Venue:
  • Ad Hoc Networks
  • Year:
  • 2008

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Abstract

Energy efficiency and positional accuracy are often contradictive goals. We propose to decrease power consumption without sacrificing significant accuracy by developing an energy-aware localization that adapts the sampling rate to target's mobility level. In this paper, an energy-aware adaptive localization system based on signal strength fingerprinting is designed, implemented, and evaluated. Promising to satisfy an application's requirements on positional accuracy, our system tries to adapt its sampling rate to reduce its energy consumption. The contribution of this paper is fourfold. (1) We have developed a model to predict the positional error of a real working positioning engine under different mobility levels of mobile targets, estimation error from the positioning engine, processing and networking delay in the location infrastructure, and sampling rate of location information. (2) In a real test environment, our energy-saving method solves the mobility estimation error problem by utilizing additional sensors on mobile targets. The result is that we can improve the prediction accuracy by 56.34% on average, comparing to algorithms without utilizing additional sensors. (3) We further enhance our sensor-enhanced mobility prediction algorithm by detecting the target's moving foot step and then estimate the target's velocity. This method can improve the mobility prediction accuracy by 49.81% on an average, comparing to previous sensor-enhanced mobility prediction algorithm. (4) We implemented our energy-saving methods inside a working localization infrastructure and conducted performance evaluation in a real office environment. Our performance results show as much as 68.92% reduction in power consumption.