A Gibbs Sampler Approach for Optimal Distributed Monitoring of Multi-channel Wireless Networks

Wireless monitoring employing distributed sniffers has been shown to complement wire side monitoring using SNMP and base station logs since it reveals detailed PHY (e.g., signal strength, spectrum density) and MAC behaviors (e.g, collision, retransmissions), as well as timing information (e.g., back-off time), which are often essential for network diagnosis. Due to hardware limitations, wireless sniffers typically can only collect information on one channel at a time. Thus, it is important to determine the optimal channel allocation of sniffer nodes to maximize the information collected. In this paper, we propose a Gibbs sampler approach for optimal distributed monitoring of multi-channel wireless networks with provable convergence. Simulation studies show that in general the proposed method has low computation complexity while achieving optimal or near optimal solutions.