Wireless mesh networks (WMNs) are evolving as a key technology fornext-generation wireless networks showing raid progress and numerousapplications. These networks have the potential to provide robust andhigh-throughput data delivery to wireless users. In a WMN, high speed routersequipped with advanced antennas, communicate with each other in a multi-hopfashion over wireless channels and form a broadband backhaul. However, thethroughput of a WMN may be severely degraded due to presence of some selfishrouters that avoid forwarding packets for other nodes even as they send theirown traffic through the network. This paper presents an algorithm for detectionof selfish nodes in a WMN that uses statistical theory of inference forreliable clustering of the nodes based on local observations. Simulationresults show that the algorithm has a high detection rate and a low falsepositive rate.
展开▼
