A Trust-Based Detection Algorithm of Selfish Packet Dropping Nodes in a Peer-to-Peer Wireless Mesh Network

📝 Abstract

Wireless mesh networks (WMNs) are evolving as a key technology for next-generation wireless networks showing raid progress and numerous applications. These networks have the potential to provide robust and high-throughput data delivery to wireless users. In a WMN, high speed routers equipped with advanced antennas, communicate with each other in a multi-hop fashion over wireless channels and form a broadband backhaul. However, the throughput of a WMN may be severely degraded due to presence of some selfish routers that avoid forwarding packets for other nodes even as they send their own traffic through the network. This paper presents an algorithm for detection of selfish nodes in a WMN that uses statistical theory of inference for reliable clustering of the nodes based on local observations. Simulation results show that the algorithm has a high detection rate and a low false positive rate.

💡 Analysis

Wireless mesh networks (WMNs) are evolving as a key technology for next-generation wireless networks showing raid progress and numerous applications. These networks have the potential to provide robust and high-throughput data delivery to wireless users. In a WMN, high speed routers equipped with advanced antennas, communicate with each other in a multi-hop fashion over wireless channels and form a broadband backhaul. However, the throughput of a WMN may be severely degraded due to presence of some selfish routers that avoid forwarding packets for other nodes even as they send their own traffic through the network. This paper presents an algorithm for detection of selfish nodes in a WMN that uses statistical theory of inference for reliable clustering of the nodes based on local observations. Simulation results show that the algorithm has a high detection rate and a low false positive rate.

📄 Content

A Trust-Based Detection Algorithm of Selfish Packet Dropping Nodes in a Peer-to-Peer Wireless Mesh Network Jaydip Sen

Innovation Lab, Tata Consultancy Services Ltd,
Bengal Intelligent Pak, Salt Lake Electronics Complex, Kolkata - 700091, India Jaydip.Sen@tcs.com Abstract. Wireless mesh networks (WMNs) are evolving as a key technology for next-generation wireless networks showing raid progress and numerous applications. These networks have the potential to provide robust and high- throughput data delivery to wireless users. In a WMN, high speed routers equipped with advanced antennas, communicate with each other in a multi-hop fashion over wireless channels and form a broadband backhaul. However, the throughput of a WMN may be severely degraded due to presence of some selfish routers that avoid forwarding packets for other nodes even as they send their own traffic through the network. This paper presents an algorithm for detection of selfish nodes in a WMN that uses statistical theory of inference for reliable clustering of the nodes based on local observations. Simulation results show that the algorithm has a high detection rate and a low false positive rate.
Keywords: Wireless mesh networks, AODV protocol, selfish nodes, clustering, node misbehavior. 1 Introduction Wireless mesh networking has emerged as a promising concept to meet the challenges in next-generation networks such as providing flexible, adaptive, and reconfigurable architecture while offering cost-effective solutions to the service providers. Unlike traditional Wi-Fi networks, with each access point (AP) connected to the wired network, in WMNs only a subset of the APs are required to be connected to the wired network. The APs that are connected to the wired network are called the Internet gateways (IGWs), while the others are called the mesh routers (MRs). The MRs are connected to the IGWs using multi-hop communication. In a community-based WMN, a group of MRs managed by different operators form an access network to provide last-mile connectivity to the Internet. As with any end-user supported infrastructure, cooperative behavior in these networks cannot be assumed a priori. Preserving scarce access bandwidth and power, as well as security concerns may induce some selfish users to avoid forwarding data for other nodes. The selfish MRs degrade the routing performance in WMN by decreasing the network throughput [1]. To enforce cooperation among nodes and detect selfish nodes in ad hoc wireless networks, various collaboration schemes have been proposed in the literature [2]. Majority of these proposals are based on trust and reputation frameworks which attempts to identify misbehaving nodes by suitable decision making algorithms. To address the issue of selfish nodes in a WMN, this paper presents a scheme that uses local observations in the nodes for detecting node misbehavior. The scheme is applicable for on-demand routing protocols like AODV, and uses statistical theory of inference and clustering techniques to make a robust and reliable classification of the nodes based on their packet forwarding activities. It also introduces some additional fields in the packet header for AODV protocol so that detection accuracy is increased. The rest of the paper is organized as follows. Section 2 presents some related work. Section 3 gives a brief background of the AODV protocol and a finite state machine model of the local observations of a node. The proposed scheme is described in Section 4. Section 5 presents simulations results, and Section 6 concludes the paper while identifying some potential future work.
2 Related Work The concept of neighborhood monitoring to check the activities of other nodes has been proposed by researchers in the context of wireless ad hoc networks. The idea of watchdog mechanism to monitor neighbors was first proposed by Marti et al. [3]. Buchegger et al. have proposed the CONFIDANT protocol that assigns a rating for every node in an ad hoc network based on watchdog and second-hand rating information gathered from other nodes [4]. Mahajan et al. have proposed a mechanism named CATCH [5], which consists of two modules: (i) anonymous challenge message (ACM) and (ii) anonymous neighbor verification (ANV). First, an ACM message from an unknown sender is sent to all its neighbors. In the ANV phase, a tester node sends cryptographic hash of a random token for rebroadcast and also records other hashes sent by others. The tester node releases the secret token to another node which successfully authenticates itself. Vigna et al. have proposed an approach to detect intrusions in AODV that works on stateful signature-based analysis of the observed traffic [6]. Pirzada et al. have described a model of building trust relationship between nodes in an ad hoc network [7]. Conti et al. have proposed a scheme in which a node exploits its local knowledge to estimate the

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