Truthful Incentive Mechanisms for Geographical Position Conflicting Mobile Crowdsensing Systems

Sensor-embedded smartphones have become ubiquitous nowadays, further leveraging the popularity of mobile crowdsensing. A mobile crowdsensing platform gathers sensory data from smartphone users and makes payments to them in return. Due to the spatial correlation of sensory data in various applications, users close to each other in geographical positions usually provide similar sensory data, and it is quite an economic waste for a mobile sensing platform to buy duplicated sensory data with multiple payments to geographically close users. Unfortunately, the existing works do not take this matter into consideration. To prevent waste, our paper considers geographical position conflicting mobile crowdsensing systems in which any two users within a limited geographical distance cannot obtain payments simultaneously while participating in crowdsensing tasks. Two algorithms are proposed to select appropriate mobile crowdsensing participants and calculate the payments to them. Solid theoretical proofs are presented to demonstrate the beneficial properties of our proposed algorithms. The extensive experiment results based on real-world datasets indicate that our proposed algorithms are efficient while providing beneficial properties.