Assessing Diagnosis Approaches for Wireless Sensor Networks:Concepts and Analysis  

作　　者：李瑞 刘克彬 李向阳 何源 惠维 王志 赵季中 万猛 

机构地区：[1]Institute of Software Engineering, Xidian University [2]School of Electronic and Information Engineering, Xi’an Jiaotong University [3]School of Software, Tsinghua University [4]Tsinghua National Laboratory for Information Science and Technology, Tsinghua University [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

出　　处：《Journal of Computer Science & Technology》2014年第5期887-900,共14页计算机科学技术学报（英文版）

基　　金：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);the Research Fund for the Doctoral Program of Higher Education of China under Project No.20130201120016

摘　　要：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 in measuring the effectiveness of their diagnosis 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 applications. 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.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 in measuring the effectiveness of their diagnosis 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 applications. 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.

关 键 词：diagnosis approach analysis and measurement wireless sensor network 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]