  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. It uses statistical theory of inference for reliable
clustering of the nodes and is based on local observations by the nodes.
Simulation results show that the algorithm has a high detection rate while
having a low rate of false positives.
