Minimum-cost mobile crowdsourcing with QoS guarantee using matrix completion technique

Abstract Mobile crowdsourcing is a promising solution for data collection, whereby a crowd of participants are recruited and paid for participation in data collection. To minimize the total crowdsourcing cost while guaranteeing the quality of service (QoS) of the tasks, this paper proposes a novel Matrix Completion Technique based Data Collection (MCTDC) scheme. Specifically, we explore the multi-dimensional correlation of data to reduce the data amount required while guaranteeing the QoS, by means of Matrix Completion Technique. Furthermore, to select the minimum set of appropriate participants, we redefine the contribution degree as the ratio of the valid data from a given participant and the total amount data it collects. The participants with high contribution degree are recruited to sense and report data. By doing so, the system can satisfy the demand of application quickly with less participants and less data amount, namely, with minimum cost and QoS guarantee. Extensive simulation results are provided, which demonstrates the proposed MFTDC scheme can significantly reduce the data redundancy and the number of participants.