Fairness task assignment strategy with distance constraint in Mobile CrowdSensing

Mobile CrowdSensing(MCS) has become a promising paradigm and attracted significant attention from academia, in which mobile users can complete various sensing tasks. In most existing works, the platform asssigns tasks based on the fairness of users, i.e., user's processing ability, to minimize assignment cost, which ignores the fairness of tasks. Although some works have considered tasks' fairness, they still suffer from either of two limitations: (i) Fairness of the user and task cannot be guaranteed simultaneously; (ii) In real-world scenarios, the distance a user can travel in a certain period is limited, which affects the assignment performance. Motivated by this, we investigate the fairness task assignment problem under distance constraint. We argue that it is necessary to not only make full use of all users' ability to process tasks (e.g., not exceeding the maximum capacity of each user while also not letting any user idle too long), but also consider distance constraint and satisfy the assignment frequency of all corresponding tasks (e.g., how many times each task should be assigned within the whole system time) to ensure a long-term, double-fair and stable participatory sensing system. We first model two fairness constraints simultaneously by converting them to user processing queue and task virtual queue. Then we propose a Fairness Task Assignment Strategy with Distance constraint(FTAS-D), which first utilizes Lyapunov optimization technology to find a feasible assignment solution, and then we introduce simulated annealing algorithm to iteratively find the best solution. Finally, extensive simulations have been conducted over three real-life mobility traces: Changchun/taxi, Epfl/mobility, and Feeder. The simulation results prove that the proposed strategy can achieve a trade-off between the objective of minimizing the cost and fairness of tasks and users compared with other baseline approaches.