Localization of wireless sensor networks in the wild: pursuit of ranging quality

  • Authors:

  • Jizhong Zhao;Wei Xi;Yuan He;Yunhao Liu;Xiang-Yang Li;Lufeng Mo;Zheng Yang

  • Affiliations:

  • Xi'an Jiaotong University, Xi'an, China;Xi'an Jiaotong University, Xi'an, China;Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China;School of Software and Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China and Hong Kong University of Science and Technology, Hong Kong;Department of Computer Science, Illinois Institute of Technology, Chicago, IL;Xi'an Jiaotong University, Xi'an, China;Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing, China

  • Venue:
  • IEEE/ACM Transactions on Networking (TON)
  • Year:
  • 2013

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Abstract

Localization is a fundamental issue of wireless sensor networks that has been extensively studied in the literature. Our real-world experience from GreenOrbs, a sensor network system deployed in a forest, shows that localization in the wild remains very challenging due to various interfering factors. In this paper, we propose CDL, a Combined and Differentiated Localization approach for localization that exploits the strength of range-free approaches and range-based approaches using received signal strength indicator (RSSI). A critical observation is that ranging quality greatly impacts the overall localization accuracy. To achieve a better ranging quality, our method CDL incorporates virtual-hop localization, local filtration, and ranging-quality aware calibration. We have implemented and evaluated CDL by extensive real-world experiments in GreenOrbs and large-scale simulations. Our experimental and simulation results demonstrate that CDL outperforms current state-of-art localization approaches with a more accurate and consistent performance. For example, the average location error using CDL in GreenOrbs system is 2.9 m, while the previous best method SISR has an average error of 4.6 m.