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

Qifan Chen,Zhiyong Yang,Linlan Liu,Kai Guo

doi:10.1155/2016/8246030

Abstract

Predicting critical nodes of Opportunistic Sensor Network (OSN) can help us not only to improve network performance but also to decrease the cost in network maintenance. However, existing ways of predicting critical nodes in static network are not suitable for OSN. In this paper, the conceptions of critical nodes, region contribution, and cut-vertex in multiregion OSN are defined. We propose an approach to predict critical node for OSN, which is based on multiple attribute decision making (MADM). It takes RC to present the dependence of regions on Ferry nodes. TOPSIS algorithm is employed to find out Ferry node with maximum comprehensive contribution, which is a critical node. The experimental results show that, in different scenarios, this approach can predict the critical nodes of OSN better.

Highlights

In Opportunistic Sensor Network (OSN), the critical nodes are very important to keep normal operation of networks
The black thick lines show that most of the communication opportunities of region ra are provided by Ferry node fa and the dotted lines show that the communication opportunities supported by fe and fg between ra and Sink node are very few, which indicates that node fa is the critical node of the network in Scenario A
Considering the dynamic of OSN, this paper proposed a multiple attribute decision making (MADM) based method to predict critical nodes

Summary

Introduction

In Opportunistic Sensor Network (OSN), the critical nodes are very important to keep normal operation of networks. If the critical nodes can be predicted, the network could be optimized according to the attributes of critical nodes, which helps improving the robustness of the network. Maintainers can focus on monitoring the status of critical nodes so that the failures of the network could be resolved immediately, which can dramatically reduce the time and the cost of network maintenance. Predicting critical nodes of OSN has great significance

Related Work

Scenario Model and Definitions

MADM Based Prediction Method of Critical Node of OSN

Experiments and Analysis

Result

Conclusions