Optimizing Worker Selection in Collaborative Mobile Crowdsourcing

Mobile crowdsourcing (MCS) is a promising way to monitor urban-scale data by leveraging the crowds’ power and has attracted much attention recently. How to recruit suitable workers for requesters to perform the published sensing tasks is always a crucial problem and also a research hotspot. Many attempts have been made in past literature to maximize social welfare or to motivate workers to participate in the mobile crowdsourcing (MCS). However, most existing works do not consider the individual sensing quality requirements of tasks, which may not be suitable for some special scenarios, such as monitoring tasks of locations with different importance levels. In this work, we investigate the optimal worker selection problem for collaborative MCS, in which we study the recruitment cost minimization problem to meet individual sensing quality requirements of tasks for the requester-centric MCS, as well as the profit maximization problem for the platform-centric MCS. Both of the studied problems are proved to be NP-hard, and thus we design corresponding approximation algorithms for them. Specifically, to solve the recruitment cost minimization problem for requester-centric MCS, we design two different polynomial time algorithms, both of which have performance guarantees. For the profit maximization problem for platform-centric MCS, we introduce a double-greedy-based algorithm and then use the iterative pruning technique to ensure the performance guarantee of our algorithm with a much weaker condition. Finally, we evaluate our algorithms through numerical simulation experiments and validate the effectiveness of our designs by comparing them with baselines under different parameter settings.