دانلود رایگان ترجمه مقاله مدل نظارت همکاری بر اساس شافر- دمپستر جهت آشکارسازی وسایل نقلیه بدرفتار (نشریه الزویر 2014)

مدل نظارت همکاری بر اساس شافر- دمپستر جهت آشکارسازی وسایل نقلیه بدرفتار

A cooperative watchdog model based on Dempster–Shafer for detecting misbehaving vehicles

Abstract

In this paper, we address the problem of detecting misbehaving vehicles in Vehicular Ad Hoc Network (VANET) using Quality of Service Optimized Link State Routing (QoS-OLSR) protocol. According to this protocol, vehicles might misbehave either during the clusters’ formation by claiming bogus information or after clusters are formed. A vehicle is considered as selfish or misbehaving once it over-speeds the maximum speed limit or under-speeds the minimum speed limit where such a behavior will lead to a disconnected network. As a solution, we propose a two-phase model that is able to motivate nodes to behave cooperatively during clusters’ formation and detect misbehaving nodes after clusters are formed. Incentives are given in the form of reputation and linked to network’s services to motivate vehicles to behave cooperatively during the first phase. Misbehaving vehicles can still benefit from network’s services by behaving normally during the clusters’ formation and misbehave after clusters are formed. To detect misbehaving vehicles, cooperative watchdog model based on Dempster–Shafer is modeled where evidences are aggregated and cooperative decision is made. Simulation results show that the proposed detection model is able to increase the probability of detection, decrease the false negatives, and reduce the percentage of selfish nodes in the vehicular network, while maintaining the Quality of Service and stability.

1 Introduction

Vehicular Ad Hoc Network (VANET) [16,20,19,9] is a new kind of ad hoc networks that is characterized by its highly mobile topology. Like Mobile Ad hoc Network (MANET), VANET encounters the problem of selfish nodes that may hinder the implementation of any protocol dedicated to it. However, dealing with these nodes in VANET is more challenging due to the increased ambiguity in the detection caused by the high mobility of vehicles. The Quality of Service Optimized Link State Routing (QoS-OLSR) protocol [10] is a proactive routing protocol modeled to cope with mobile ad hoc networks. It is based on electing a set of optimal cluster-heads and dividing the network into clusters. These heads are then responsible for selecting a set of designated nodes charged of transmitting the network topology information and forwarding the traffic flows. Such nodes are called MultiPoint Relay (MPR) nodes. This protocol is an enhanced version of QOLSR [1] that prolongs the network lifetime by considering the energy of nodes while calculating the QoS function since the nodes, in MANET, have limited energy resources. However, the energy parameter has a minimal importance in VANET due the long battery lifetime of vehicles. In order to extend such a protocol to VANET, velocity and residual distance parameters must be added to the QoS function instead of the residual energy to improve the network stability.

According to this protocol, vehicles might misbehave either during the clusters’ formation by claiming bogus information or after clusters are formed. A vehicle is considered as selfish or misbehaving once it over-speeds the maximum road limit or underspeeds the minimum road limit. Such a behavior is considered as a passive malicious since vehicles do not aim to attack or impede the network functioning, but rather they tend to optimize their own gain neglecting the welfare of others [11]. They entail hence negative implications on the whole network such as the (1) increase in the percentage of MPRs, (2) decrease in the network stability, (3) increase in the clusters disconnections, and (3) increase in the average path length.

To address the above problems, we propose a two-phase model that (1) motivates vehicles to behave normally during clusters’ formation and (2) detects misbehaving vehicles after clusters’ formation. In phase one, incentives are given in the form of reputation where networks’ services are offered based on vehicle’s accumulative reputation. Misbehaving vehicles can still benefit from networks’ services by behaving normally during the clusters’ formation and misbehave after clusters are formed. Thus, the main challenge that we are addressing in this paper and as phase two of our model is the detection of misbehaving vehicles after clusters formation. This is done by the means of cooperative watchdog model based on Dempster–Shafer theory [4] where evidences are correlated cooperatively in order to improve the probability of detection and reduce the false alarms. Thus, we overcome the problem of ambiguity in the detection resulting from packets collision, high mobility of vehicles, and untrustworthy watchdogs. The cluster-members, including the cluster heads, are designated as watchdogs to monitor the behavior of their MPRs where the evidences of any suspicious MPR are shared among all. To overcome the problem of initial trust estimates that the Dempster–Shafer suffers from, we use the reputation calculated in phase one for this purpose. In summary, our contribution is a cooperative detection model based on Dempster–Shafer that is able to increase the probability of detection and reduce the false alarms.

The remainder of the paper is organized as follows. Section 2 reviews the related work. Section 3 formulates the problem. Section 4 motivates the work. Section 5 explains the proposed approach in details. Section 6 explains the model used for simulation and presents empirical results. Finally, Section 7 concludes the paper.