Open AccessIdentifying key node in multi-region opportunistic sensor network based on improved TOPSIS
Author(s) -
Linlan Liu,
Wei Wang,
Guirong Jiang,
Zhang Jiang
Publication year - 2021
Publication title -
computer science and information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.244
H-Index - 24
eISSN - 2406-1018
pISSN - 1820-0214
DOI - 10.2298/csis200620019l
Subject(s) - betweenness centrality , computer science , topsis , key (lock) , centrality , weighting , node (physics) , graph , topology (electrical circuits) , data mining , theoretical computer science , mathematics , operations research , statistics , medicine , computer security , structural engineering , combinatorics , engineering , radiology
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.