Journal of Sensors

Volume 2016, Article ID 8246030, 8 pages

http://dx.doi.org/10.1155/2016/8246030

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

^{1}Internet of Things Technology Institute, Nanchang Hangkong University, Nanchang 330063, China^{2}School of Software, Nanchang Hangkong University, Nanchang 330063, China^{3}School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China

Received 14 January 2016; Accepted 29 March 2016

Academic Editor: Fei Yu

Copyright © 2016 Qifan Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### 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.

#### 1. Introduction

In Opportunistic Sensor Network (OSN), the critical nodes are very important to keep normal operation of networks. In practical applications, 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. In network maintenance, 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. Therefore, predicting critical nodes of OSN has great significance.

#### 2. Related Work

OSN is a kind of Wireless Sensor Networks. It perceives the surrounding environment by sensor nodes and transports messages by the meeting opportunities of Ferry nodes. Hence, it has the characteristics of Mobile Opportunity Network [1] and Wireless Sensor Network [2]. The current study of OSN critical nodes is very little. Nevertheless, in some related fields, researchers have made some progress such as node importance evaluation [3–7] and network cut-vertex judgement [8–14].

Corley and Sha [15] proposed that the critical nodes in a weighted network are those whose removal from the network results in the greatest increase in shortest distance between two specified nodes. This method could be applied to estimate the end-to-end nodes. However, it is powerless to estimate the critical nodes in the whole network. Chen et al. [16] studied a method to estimate the relative importance of nodes by comparing the number of spanning trees. Although this method could estimate the critical nodes of the whole network, it has the problem of high computing complexity. So it is not suitable in practical applications. In resistance network, Xiao et al. [17] did the research on the energy consumption model to evaluate the importance degree of nodes. This method estimates the critical node by comparing the increase of the average energy consumption of the network after the nodes are removed. Goyal and Caffery [18] discussed the split of ad hoc networks. They utilized network survivability concepts to detect the critical links in an ad hoc wireless network. This method is based on the precondition that the nodes can locate themselves, which has great application limitations.

With the research above, it is always one-sided to evaluate the network through a single evaluation index. Considering the influence of node degree, node closeness, node betweenness, equivalent topology, and neighbor lists, Hu et al. [19] conducted experiments with three real typical networks to show that their method is more accurate than using a single evaluation index. Liu et al. [20] proposed a method to estimate critical nodes by combining the residual lifetime of nodes and the network energy consumption. Due to the shortcomings of the methods of node deletion and node contraction, Zhou et al. [21] exploited the evaluation matrix of node efficiency and node importance to determine the critical nodes in complex network. This method reflects the significance of divergences between two nodes and can evaluate the importance exactly. In a similar way, Fan and Liu [22] discussed the local and global importance of nodes and presented an evaluation method based on transfer efficiency matrix which takes not only the interactions between adjacent nodes but also the nonadjacent nodes’ contributions into account, thus obtaining a more accurate node importance evaluation result.

As a dynamic network, current static prediction methods of critical nodes are not appropriate for OSN. Depending upon the researches above, in this paper, the stage contribution and the region contribution are proposed to evaluate the node importance. Then an algorithm is designed which is based on the multiple attribute decision making (MADM) to predict the critical nodes of OSN.

#### 3. Scenario Model and Definitions

##### 3.1. Scenario Model

The monitoring areas of application scenarios like environmental monitoring are very large. Therefore, the maintainers tend to monitor the key regions instead of the whole network. In OSN, the messages of the network are collected through the communication opportunities supported by mobile nodes. As shown in Figure 1(a), our research is proposed for the OSN with multiple regions and the nodes in the regions are fixed. There are Ferry nodes between regions supporting communication opportunities to Sink nodes and the trajectory of Ferry nodes could be a specific way or a random way.