Identifying Key Node in Multi-region Opportunistic Sensor Network based on Improved TOPSIS

Linlan Liu1, Wei Wang1, Guirong Jiang2 and Jiang Zhang1

  1. School of Information Engineering, Nanchang Hangkong University
    330063 Nanchang, China
    765693987@qq.com, 919269210@qq.com, zhangjiangky@163.com
  2. School of Software, Nanchang Hangkong University
    330063 Nanchang, China
    1132153564@qq.com

Abstract

The topology of multi-region opportunistic sensor networks is evolving, and it is difficult to identify the key nodes in the networks by traditional key node identification methods. In this paper, a novel method based on the improved TOPSIS method is proposed to identify the key node from the ferry node. The dynamic topology information is represented by the graph model which is modeled by the temporal reachable graph. Based on the temporal reachable graph, three attributes are constructed to identify the key node, which are average degree, betweenness centrality and message forwarding rate. The game theory with a combination weighting method is employed to combine the subjective weight and objective weight, so as to obtain the combined weight of each attribute. The TOPSIS method is improved by the combined weight. The key node is identified by the improved TOPSIS. The experiments in three simulation situations show that, compared with the TOPSIS method and MADM_TOPSIS method, the proposed method has better accuracy for the key node identification in the network.

Key words

multi-region opportunistic sensor network, key node, combination weight, TOPSIS

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200620019L

Publication information

Volume 18, Issue 3 (June 2021)
Year of Publication: 2021
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Liu, L., Wang, W., Jiang, G., Zhang, J.: Identifying Key Node in Multi-region Opportunistic Sensor Network based on Improved TOPSIS. Computer Science and Information Systems, Vol. 18, No. 3, 1041–1056. (2021), https://doi.org/10.2298/CSIS200620019L