A greedy-proof incentive-compatible mechanism for group recruitment in mobile crowd sensing

As crowd sensing gains more popularity, the issue of recruiting the best workers for a fair cost becomes more relevant. In this paper, we tackle the problem of workers’ greediness, where workers overprice their data to improve their profit, by presenting an incentive mechanism for group recruitment crowd sensing systems. The proposed incentive mechanism, which consists of a selection and a payment mechanism, considers not only the Quality of Information (QoI) of the worker and his contribution to the overall QoI of the group, but also the cost of his contribution compared with other workers. This technique avoids the selection of greedy members since their costs will be high compared to the QoI they offer, hence forcing them to align with the other members by advertising their true cost. On the other hand, the proposed payment mechanism rewards workers based on the improvement they offer to the QoI and the cost of the group. We prove that the proposed incentive mechanism is strategy-proof and demonstrate its effectiveness by adapting it to an existing group recruitment framework Azzam et al. (2016), and comparing it with the original model through experiments with real datasets.