ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2017, Vol. 54 ›› Issue (9): 2021-2031.doi: 10.7544/issn1000-1239.2017.20160645

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Multiple Attribute Decision Making-Based Prediction Approach of Critical Node for Opportunistic Sensor Networks

Liu Linlan1, Zhang Jiang1, Shu Jian2, Guo Kai1, Meng Lingchong1   

  1. 1(School of Information Engineering, Nanchang Hangkong University, Nanchang 330063);2(School of Software, Nanchang Hangkong University, Nanchang 330063)
  • Online:2017-09-01

Abstract: If critical nodes have been predicted, the network can be optimized according to the information of the critical nodes. Furthermore, maintenance time and cost of network can be dramatically reduced by checking the critical nodes at the first time when the network is crashed. Unfortunately, the existing methods of predicting critical nodes in static wireless sensor networks are not suitable for opportunistic sensor networks (OSNs). According to the characteristics of dynamic changes of network topology and high latency, for multi-region OSNs (MOSNs) with hierarchical structure, this paper analyzes the message transferring process. The stage contribution is defined to reflect the contribution of Ferry nodes in the process of message transmission, and the region contribution is defined to reflect the contribution of Ferry nodes to regions. In terms of the comprehensive contribution of Ferry nodes, the prediction method of critical nodes is proposed, which is based on multiple attribute decision making—technique for order preference by similarity to ideal solution (TOPSIS). The experimental results show that the prediction method with improved TOPSIS algorithms achieves better accuracy. Furthermore, test bed is established so as to validate the proposed method. The test bed experimental results show that the prediction method with improved TOPSIS algorithms achieves better accuracy as well.

Key words: opportunistic sensor networks (OSNs), critical node, region contribution, stage contribution, multiple attribute decision making

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