  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.
