Scalable Information Systems. 4th International ICST Conference, INFOSCALE 2009, Hong Kong, June 10-11, 2009, Revised Selected Papers

Research Article

A Self-adaptive Fault-Tolerant Mechanism in Wireless Sensor Networks

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  • @INPROCEEDINGS{10.1007/978-3-642-10485-5_17,
        author={Wei Xiao and Ming Xu and Yingwen Chen},
        title={A Self-adaptive Fault-Tolerant Mechanism in Wireless Sensor Networks},
        proceedings={Scalable Information Systems. 4th International ICST Conference, INFOSCALE 2009, Hong Kong, June 10-11, 2009, Revised Selected Papers},
        proceedings_a={INFOSCALE},
        year={2012},
        month={5},
        keywords={Fault-tolerant self-adaptive sensor networks},
        doi={10.1007/978-3-642-10485-5_17}
    }
    
  • Wei Xiao
    Ming Xu
    Yingwen Chen
    Year: 2012
    A Self-adaptive Fault-Tolerant Mechanism in Wireless Sensor Networks
    INFOSCALE
    Springer
    DOI: 10.1007/978-3-642-10485-5_17
Wei Xiao,*, Ming Xu1, Yingwen Chen1
  • 1: National University of Defense Technology
*Contact email: xiaowei@hunnu.edu.cn

Abstract

This work was motivated by the idea of getting admirable faulttolerant and efficient performance in application when smart sensors are engaged in ‘in-network’ computing in wireless sensor networks. In such applications, the objective of the sensor network is to repeatedly compute and then deliver to a server some results based on the values measured at the sensors. It is crucial for the sensors to form an optimal network topology and tune to transmission attempt rates in a way that optimize network throughput. However, we cannot ignore the influence from the fault node occurring in the network when the optimal network topology is constructed, which might decrease reliability of data transmitting using formed topology in some applications. So, in this paper, we proposed a self-adaptive way which identifies the node’s confidence rate in accordance with its fault possibility, which in turn figures out the weighted volume between any two adjacent nodes. Moreover, we discussed and tried to solve the problems on FTMAWSS (fault-tolerant maximum average-weighted spanning subgraph) in weighted connected graph initiating from clustering WSNs. Because of the use of the tolerant fault, the correctness and efficiency about sensing values in data processing could be guaranteed in our mechanism. Simulation results confirmed the validity of the proposed algorithm with a high degree of accuracy and demonstrated that our proposed way could be scaled to large networks.