Abstract
Spectrum sensing is an important issue in cognitive radio networks. The unlicensed users can access the licensed wireless spectrum only when the licensed wireless spectrum is sensed to be idle. Since mobile terminals such as smartphones and tablets are popular among people, spectrum sensing can be assigned to these mobile intelligent terminals, which is called crowdsourcing method. Based on the crowdsourcing method, this paper studies the distributed scheme to assign spectrum sensing task to mobile terminals such as smartphones and tablets. Considering the fact that mobile terminals’ positions may influence the sensing results, a precise sensing effect function is designed for the crowdsourcing-based sensing task assignment. We aim to maximize the sensing effect function and cast this optimization problem to address crowdsensing task assignment in cognitive radio networks. This problem is difficult to be solved because the complexity of this problem increases exponentially with the growth in mobile terminals. To assign crowdsensing task, we propose four distributed algorithms with different transition probabilities and use a Markov chain to analyze the approximation gap of our proposed schemes. Simulation results evaluate the average performance of our proposed algorithms and validate the algorithm’s convergence.
Highlights
According to Cisco’s report, the wireless traffic has increased sharply in the past few years and the global mobile data traffic grew by 63% in 2016 [1]
Considering the impact of mobile terminals’ positions, we propose a precise sensing effect function of the crowdsourcing-based sensing task assignment
Compared to the spectrum sensing in recent studies, the paper solves sensing task assignment in distributed ways with the two major differences: (i) an objective function, considering different sensing outcomes in various subregions, is introduced to represent sensing effect; (ii) aiming to achieve higher sensing effect, four distributed algorithms, with different transition probabilities, are designed to tackle the problem of sensing task assignment
Summary
According to Cisco’s report, the wireless traffic has increased sharply in the past few years and the global mobile data traffic grew by 63% in 2016 [1]. In this paper, based on the crowdsourcing method, we propose distributed algorithms to assign mobile terminals the spectrum sensing task. Considering the impact of mobile terminals’ positions, we propose a precise sensing effect function of the crowdsourcing-based sensing task assignment. We aim to maximize the sensing effect function and cast this optimization problem to address crowdsensing task assignment in cognitive radio networks. We propose four distributed algorithms with different transition probabilities and use a Markov chain to analyze the approximation gap of our proposed schemes. (ii) It is difficult to assign crowdsensing task for the reason that the complexity of this problem increases exponentially with the growth in mobile terminals.
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