An Efficient Algorithm for Detection of Selfish Packet Dropping Nodes in Wireless Mesh Networks

TL;DR

This paper introduces an efficient statistical inference-based algorithm to detect selfish nodes in wireless mesh networks, significantly improving network reliability by identifying malicious routers that drop packets.

Contribution

The paper proposes a novel detection algorithm utilizing statistical inference to accurately identify selfish nodes in WMNs, enhancing network security and performance.

Findings

High detection rate of selfish nodes

Low false positive rate

Effective in simulated WMN scenarios

Abstract

In a wireless mesh network (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. WMNs provide reliable connectivity and fault-tolerance, as each node is connected to several other nodes. If a node fails due to hardware problems, its neighbors can find another route. Extra capacity can be achieved by introducing additional nodes in the network. 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. Simulation results show that the algorithm has a high detection rate and a low rate of false positives.