This paper studies the optimal sniffer-channel assignment for reliable monitoring in multi-channel wireless networks. This problem concerns how to deploy certain sniffers in a network (and tune their channels) so that they can overhear and verify communication among the other nodes, referred to as normal nodes. Prior works have studied the optimal sniffer-channel assignment, but they assume perfect sniffers. However, in practice, sniffers may probabilistically make errors in monitoring, e.g., due to poor reception and compromise by an adversary. Hence, to maintain acceptable monitoring quality, a node needs to be overheard by multiple sniffers. We show that the optimal sniffer-channel assignment with sniffer redundancy differs fundamentally from the previous works due to the absence of a desirable property called submodularity. As a result, in our problem, the prior approximation algorithms no longer maintain their performance guarantees. We propose a variety of approximation algorithms based on two approaches - greedy strategy and relaxation-and-rounding approach. We present an empirical performance analysis of the proposed algorithms through simulations in practical networks. Our results suggest that our two algorithms show a performance trade-off between coverage and running time and are therefore suitable for different kinds of deployment.
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