Truthful and Dual-direction Combinatorial Multi-Armed Bandit Scheme to Maximize Profit for Mobile Crowd Sensing

Nowadays, Mobile Crowd Sensing (MCS) has become a popular paradigm for large-scale data collection using ubiquitous mobile sensing devices. However, most existing works do not consider that requester's payments are unknown prior, and assume that workers are honest, which may not be true in practice. To address these problems, we propose a novel Truthful and Dual-direction Combinatorial Multi-Armed Bandit (TD-CMAB) scheme, which maximizes the total profit of the dual-direction platform for both the worker side and the requester side. Specifically, for the worker side, to overcome the problem that the platform is not clear whether sensed data are true, we propose a worker recruitment strategy that identifies and recruits honest workers at low cost through the Upper Confidence Bound (UCB) algorithm based on truth data discovery. For the requester side, where requesters' payments are unknown prior, we model requester selection as a CMAB problem and solve it by the proposed adaptive UCB algorithm. Furthermore, we theoretically prove the worst regret bound of the TD-CMAB. Finally, we evaluate the effectiveness of the TD-CMAB scheme through extensive experiments using the Beijing taxi dataset.