Pay On-Demand: Dynamic Incentive and Task Selection for Location-Dependent Mobile Crowdsensing Systems

Abstract

With the rich sensing capacity and ubiquitous usage of smartphones, crowdsensing leveraging the power of the crowd of mobile users has become an effective technique to collect data for various sensing applications. Many incentive mechanisms have been proposed to encourage people to participate in crowdsensing. However, most of them set unchangeable rewards for sensing tasks, while the inherent inequality and on-demand feature of sensing tasks have been long ignored, especially for location-dependent sensing tasks. In this paper, we focus on location-dependent crowdsensing systems and propose a demand-based dynamic incentive mechanism that dynamically changes the rewards of sensing tasks at each sensing round in an on-demand way to balance their popularity. A demand indicator is introduced to characterize the demand of each sensing task by considering its deadline, completing progress, and number of potential participants. At each sensing round, we use the Analytic Hierarchy Process to calculate the relative demands of all sensing tasks and then determine their rewards accordingly. Moreover, we prove that the distributed task selection problem with time budget is NP-hard. We propose an optimal dynamic programming based solution and a greedy solution to help each user select tasks while maximizing its profit. Extensive experiments show that the demand-based dynamic incentive mechanism outperforms existing incentive mechanisms.