Optimal Crowdsourced Channel Monitoring in Cognitive Radio Networks

Crowdsourcing is an emerging paradigm for spectrum access rule enforcement in dynamic spectrum sharing, which leverages a large number of mobile users to help monitoring and detecting spectrum violations and misuse. Its main advantages compared with traditional dedicated monitoring architecture includes enhanced coverage, effectiveness and lower costs. However, how to optimally assign mobile users to monitor the channel usage has not been studied in the crowdsourced setting. The main challenges are: the large number of channels to monitor while mobile users may not be available all the time, the need to consider monitoring costs and incentives, as well as the uncertainty of each channel's traffic patterns. In this paper, we tackle such challenges by formulating a stochastic optimization problem that optimizes the spectrum monitoring task for crowdsourced mobile users. We consider the availability pattern of the mobile users and we assume they are given payments as incentives for participating in monitoring. Simulations show that our method outperforms the risk-averse scenario and has a small gap with the solution under perfect information.