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

TL;DR

This paper proposes a trust-based detection algorithm for identifying selfish packet-dropping nodes in wireless mesh networks, improving network reliability by accurately detecting malicious nodes using statistical inference.

Contribution

It introduces a novel detection algorithm utilizing statistical inference for reliable clustering of selfish nodes in WMNs, enhancing detection accuracy over existing methods.

Findings

High detection rate of selfish nodes

Low false positive rate in detection

Effective clustering based on local observations

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.