User Recruitment for Optimizing Requester’s Profit in Self-Organized Mobile Crowdsensing

Mobile crowdsensing is a novel sensing scheme, where some mobile users utilize the equipped mobile phones to jointly participate in a sensing activity. In this paper, we focus on a self-organized mobile crowdsensing, in which the requesters publish a sensing task to sense the specific data for an area (for example, taking a photo), and the users move around the area, when they enter the specific area, they could be recruited, and then they could take the sensing data, and deliver the data to the activity requester. If the requester recruits a user, a cost should be paid, however, if the sensing data is successfully delivered to the requester, the requester could get a high-value achievement. In order to maximize the requester's profit, in this paper, we propose a user recruitment strategy for self-organized mobile crowdsensing (UROC), which first estimate the expected profit of recruiting a user, compared the profit with the recruiting cost, a decision is made in terms of whether to recruit the user. We have done the simulations based on the random-waypoint mobility pattern and a real-world trace: roma/taxi. Simulation results show that, UROC achieves an approximate sensing task delivery ratio, while the highest requester profit, when compared with the other strategies.