Credible and energy-aware participant selection with limited task budget for mobile crowd sensing

Crowd sensing campaigns encourage ordinary people to collect and share sensing data by using their carried smart devices. However, new challenges that must be faced have arisen. One of them is how to allocate tasks to the most appropriate participants when considering their different incentive requirements and credibility, in order to best satisfy the quality-of-information (QoI) requirements of multiple concurrent tasks, with different, and limited budget constraints. Another challenge is how to maximize participants' rewards to encourage them to contribute sensing data continuously. To this end, in this paper, we first propose a crowd sensing system, that aims to address the above two challenges, where the system considers the benefits of both platform and participants. Then, a participant reputation definition and update method is proposed, that takes participant's willingness and contributed data quality into consideration. Last, we introduce two metrics called "QoI satisfaction" and "Difficulty of Task (DoT)". The former quantifies how much collected sensing data can satisfy the multi-dimensional task's QoI requirements in terms of data quality, granularity and quantity, and the later aids participants to choose proper tasks to maximize their rewards. Finally, we compare our proposed scheme with existing methods via extensive simulations based on a real dataset. Extensive simulation results well justify the effectiveness and robustness of our approach.