The problem of coordinated data collection is studied for a mobile crowdsensing (MCS) system. A mobile crowdsensing platform (MCSP) sequentially publishes sensing tasks to the available mobile units (MUs) that signal their willingness to participate in a task by sending sensing offers back to the MCSP. From the received offers, the MCSP decides the task assignment. A stable task assignment must address two challenges: the MCSP's and MUs' conflicting goals, and the uncertainty about the MUs' required efforts and preferences. To overcome these challenges a novel decentralized approach combining matching theory and online learning, called collision-avoidance multi-armed bandit with strategic free sensing (CA-MAB-SFS), is proposed. The task assignment problem is modeled as a matching game considering the MCSP's and MUs' individual goals while the MUs learn their efforts online. Our innovative "free-sensing" mechanism significantly improves the MU's learning process while reducing collisions during task allocation. The stable regret of CA-MAB-SFS, i.e., the loss of learning, is analytically shown to be bounded by a sublinear function, ensuring the convergence to a stable optimal solution. Simulation results show that CA-MAB-SFS increases the MUs' and the MCSP's satisfaction compared to state-of-the-art methods while reducing the average task completion time by at least 16%.

翻译：针对移动群智感知（MCS）系统中的协调数据收集问题展开研究。移动群智感知平台（MCSP）顺序发布感知任务，可用移动单元（MU）通过向MCSP发送感知报价来表明参与意愿。MCSP根据收到的报价决定任务分配方案。稳定的任务分配需应对两个挑战：MCSP与MU之间的目标冲突，以及MU所需努力与偏好的不确定性。为克服这些挑战，本文提出一种融合匹配理论与在线学习的新型去中心化方法——带策略性自由感知的冲突避免多臂赌博机（CA-MAB-SFS）。该任务分配问题被建模为考虑MCSP与MU个体目标的匹配博弈，同时MU在线学习自身努力成本。所提出的“自由感知”机制在减少任务分配冲突的同时，显著提升了MU的学习过程。CA-MAB-SFS的稳定遗憾（即学习损失）被理论证明受亚线性函数约束，保证了算法收敛至稳定的最优解。仿真结果表明，与现有最优方法相比，CA-MAB-SFS在提升MCSP与MU满意度的同时，使平均任务完成时间降低至少16%。