Title: Lossy links diagnosis for wireless sensor networks by utilising the existing traffic information
Authors: Lixia Zhang; Weiping Wang; Jianliang Gao; Jianxin Wang
Addresses: School of Information Science and Engineering, Central South University, Changsha, 410083, China; School of Mathematics and Computer Science, Hunan Normal University, Changsha, 410081, China ' School of Information Science and Engineering, Central South University, Changsha, 410083, China ' School of Information Science and Engineering, Central South University, Changsha, 410083, China ' School of Information Science and Engineering, Central South University, Changsha, 410083, China
Abstract: Network diagnosis is very important for wireless sensor networks (WSNs) since many network-related faults, such as node and link failures, often occur in real applications. Diagnosis tools for WSNs usually consist of information collection and root-cause deduction, which deduce whether there are failures and which components are faulty. Compared to wired networks, the links in wireless sensor networks are prone to suffer from high packet loss rates, which cause the incomplete data at sinks. Therefore, to identify the poorly performing (lossy) links, lossy links diagnosis is crucial for WSNs. Existing diagnosis approaches usually need each sensor node to report a large amount of status information to the sink, thus introduce huge traffic overheads which is an enormous burden for a resource constrained and usually traffic sensitive sensor network. In this paper, we introduce a novel lossy link diagnosis approach to infer lossy links using only existing traffic information of sensor nodes. We propose an inference algorithm and a path information preprocessing algorithm in this paper. We evaluate the performance of our approach and the experimental results validate the scalability and effectiveness of our approach.
Keywords: wireless sensor networks; WSNs; network diagnosis; existing traffic information; lossy links; inference algorithm; path information preprocessing.
International Journal of Embedded Systems, 2014 Vol.6 No.2/3, pp.140 - 147
Received: 06 Sep 2013
Accepted: 11 Oct 2013
Published online: 31 Jul 2014 *