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CLC number: TP393

On-line Access: 2017-05-24

Received: 2016-05-10

Revision Accepted: 2016-11-10

Crosschecked: 2017-04-13

Cited: 0

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Citations:  Bibtex RefMan EndNote GB/T7714


Wen-yan Cui


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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.5 P.689-707


An efficient lossy link localization approach for wireless sensor networks

Author(s):  Wen-yan Cui, Xiang-ru Meng, Bin-feng Yang, Huan-huan Yang, Zhi-yuan Zhao

Affiliation(s):  College of Information and Navigation, Air Force Engineering University, Xian 710077, China; more

Corresponding email(s):   cwy_edu@163.com

Key Words:  Lossy link localization, Redundancy eliminating algorithm, Set-covering, Wireless sensor networks (WSNs), Network diagnosis

Wen-yan Cui, Xiang-ru Meng, Bin-feng Yang, Huan-huan Yang, Zhi-yuan Zhao. An efficient lossy link localization approach for wireless sensor networks[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(5): 689-707.

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author="Wen-yan Cui, Xiang-ru Meng, Bin-feng Yang, Huan-huan Yang, Zhi-yuan Zhao",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

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%A Bin-feng Yang
%A Huan-huan Yang
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601247

T1 - An efficient lossy link localization approach for wireless sensor networks
A1 - Wen-yan Cui
A1 - Xiang-ru Meng
A1 - Bin-feng Yang
A1 - Huan-huan Yang
A1 - Zhi-yuan Zhao
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 5
SP - 689
EP - 707
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Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601247

Network fault management is crucial for a wireless sensor network (WSN) to maintain a normal running state because faults (e.g., link failures) often occur. The existing lossy link localization (LLL) approach usually infers the most probable failed link set first, and then gives the fault hypothesis set. However, the inferred failed link set contains many possible failures that do not actually occur. That quantity of redundant information in the inferred set can pose a high computational burden on fault hypothesis inference, and consequently decreases the evaluation accuracy and increases the failure localization time. To address the issue, we propose the conditional information entropy based redundancy elimination (CIERE), a redundant lossy link elimination approach, which can eliminate most redundant information while reserving the important information. Specifically, we develop a probabilistically correlated failure model that can accurately reflect the correlation between link failures and model the nondeterministic fault propagation. Through several rounds of mathematical derivations, the LLL problem is transformed to a set-covering problem. A heuristic algorithm is proposed to deduce the failure hypothesis set. We compare the performance of the proposed approach with those of existing LLL methods in simulation and on a real WSN, and validate the efficiency and effectiveness of the proposed approach.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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