›› 2014, Vol. 29 ›› Issue (5): 887-900.doi: 10.1007/s11390-014-1476-z

Special Issue: Computer Networks and Distributed Computing

• Computer Networks and Distributed Computing • Previous Articles     Next Articles

Assessing Diagnosis Approaches for Wireless Sensor Networks: Concepts and Analysis

Rui Li1,2(李 瑞), Ke-Bin Liu3,4(刘克彬), Member, ACM, IEEE, Xiangyang Li2,5(李向阳), Senior Member, IEEE, Member, ACM, Yuan He3,4(何 源), Member, ACM, IEEE, Wei Xi2,*(惠 维), Member, CCF, ACM, IEEE, Zhi Wang2(王 志), Ji-Zhong Zhao2(赵季中), Member, CCF, ACM, IEEE, Meng Wan6(万 猛), Member, ACM, IEEE   

  1. 1. Institute of Software Engineering, Xidian University, Xi'an 710071, China;
    2. School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China;
    3. School of Software, Tsinghua University, Beijing 100084, China;
    4. Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;
    5. Department of Computer Science, Illinois Institute of Technology, Chicago, IL 60616, U. S. A. ;
    6. Center for Science and Technology Development, Ministry of Education, Beijing 100080, China
  • Received:2014-02-27 Revised:2014-06-27 Online:2014-09-05 Published:2014-09-05
  • About author:Rui Li received his B.S. degree in mathematics from the Department of Applied Mathematics of Xidian University in 2006, and the Ph.D. degree in computer science from the Department of Computer Science and Technology of Xi'an Jiaotong University in 2014. He is currently a lecturer in Xidian University. His main research interests include wireless ad hoc and sensor networks, and pervasive computing.
  • Supported by:

    This work is supported by the National Natural Science Foundation of China under Grant Nos. 61190110, 61325013, 61103187, 61170213, 61228202, 61170216, and 61422207, the National Basic Research 973 Program of China under Grant No. 2014CB347800, the Natural Science Foundation of USA under Grant Nos. CNS-0832120, CNS-1035894, ECCS-1247944, and ECCS-1343306, the Fundamental Research Funds for the Central Universities of China under Project No. 2012jdgz02 (Xi'an Jiaotong University), and the Research Fund for the Doctoral Program of Higher Education of China under Project No. 20130201120016.

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Diagnosis is of great importance to wireless sensor networks due to the nature of error prone sensor nodes and unreliable wireless links. The state-of-the-art diagnostic tools focus on certain types of faults, and their performances are highly correlated with the networks they work with. The network administrators feel difficult on measuring the effectiveness of their diagnostic approaches and choosing appropriate tools so as to meet the reliability demand. In this work, we introduce the D-vector to characterize the property of a diagnosis approach. The D-vector has five dimensions, namely the Degree of Coupling, the Granularity, the Overhead, the Tool Reliability and the Network Reliability, quantifying and evaluating the effectiveness of current diagnostic tools in certain networks. We employ a skyline query algorithm to find out the most effective diagnosis approaches, i.e., skyline points (SPs), from five dimensions of all potential D-vectors. The selected skyline D-vector points can further guide the design of various diagnosis approaches. In our trace-driven simulations, we design and select tailored diagnostic tools for GreenOrbs, achieving high performance with relatively low overhead.

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