Incentive mechanisms have been commonly proposed to encourage people to participate in mobile crowdsensing (MCS). 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 (LDSTs). In this paper, we focus on location-dependent MCS systems and propose a demand-driven 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 (AHP) to calculate the relative demands of all sensing tasks and then determine their rewards accordingly. Moreover, we consider two task selection problem with participatory users and opportunistic users, respectively, and prove that both of them are NP-hard. We propose an optimal dynamic programming based solution for participatory scenario and an optimal backtracking based solution for opportunistic scenario to help each user select tasks while maximizing its profit. Extensive experiments show that the demand-driven dynamic incentive mechanism outperforms existing incentive mechanisms.

Location information of workers are usually required for optimal task allocation in mobile crowdsensing, which however raises severe concerns of location privacy leakage. Although many approaches have been proposed to protect the locations of users, the location protection for task allocation in mobile crowdsensing has not been well explored. In addition, to the best of our knowledge, none of existing privacy-preserving task allocation mechanisms can provide personalized location protection considering different protection demands of workers. In this paper, we propose a personalized privacy-preserving task allocation framework for mobile crowdsensing that can allocate tasks effectively while providing personalized location privacy protection. The basic idea is that each worker uploads the obfuscated distances and personal privacy level to the server instead of its true locations or distances to tasks. In particular, we propose a Probabilistic Winner Selection Mechanism (PWSM) to minimize the total travel distance with the obfuscated information from workers, by allocating each task to the worker who has the largest probability of being closest to it. Moreover, we propose a Vickrey Payment Determination Mechanism (VPDM) to determine the appropriate payment to each winner by considering its movement cost and privacy level, which satisfies the truthfulness, profitability, and probabilistic individual rationality. Extensive experiments on the real-world datasets demonstrate the effectiveness of the proposed mechanisms.