Social Incentive Mechanism Based Multi-User Sensing Time Optimization in Co-Operative Spectrum Sensing with Mobile Crowd Sensing

Co-operative spectrum sensing emerging as a significant method to improve the utilization of the spectrum needs sufficient sensing users to participate. Existing related papers consider only the limited secondary users in current sensing system and assume that they will always perform the co-operative spectrum sensing out of obligation. However, this assumption is impractical in the realistic situation where the secondary users are rational and they will not join in the co-operative sensing process without a certain reward to compensate their sensing energy consumption, especially the ones who have no data transmitting in current time slot. To solve this problem, we take advantage of the mobile crowd sensing to supply adequate co-operative sensing candidates, in which the sensing users are not only the secondary users but also a crowd of widely distributed mobile users equipped with personal spectrum sensors (such as smartphones, vehicle sensors). Furthermore, a social incentive mechanism is also adapted to motivate the participations of mobile sensing users. In this paper, we model the interactions among the motivated sensing users as a co-operative game where they adjust their own sensing time strategies to maximize the co-operative sensing utility, which eventually guarantees the detection performance and prevents the global sensing cost being too high. We prove that the game based optimization problem is NP-hard and exists a unique optimal equilibrium. An improved differential evolution algorithm is proposed to solve the optimization problem. Simulation results prove the better performance in our proposed multi-user sensing time optimization model and the proposed improved differential evolution algorithm, respectively compared with the non-optimization model and the other two typical equilibrium solution algorithms.