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With the pervasiveness of mobile devices, satisfying spatial-temporal coverage requirements in interested regions while considering the quality of the sensing data and the budget constraint is a major research challenge in mobile crowdsensing (MCS). In this paper, we define a novel coverage metric, quality coverage, which considers both the fraction of subareas covered by sensor readings and the quality of sensing data in each covered subarea, for diversified location-based MCS tasks. In our system, as participants with high reliability level can contribute a lot to sensing tasks, we design an incentive model that provides higher bonus to more reliable participants. After knowing the reward for the per amount of sensing data, each worker can submit an interested task set that contains several unit tasks. The platform assigns MCS tasks to these mobile workers by solving the task allocation optimization problems, which select the optimal mobile worker set under the budget of data requesters to maximize diverse and spatial coverage level in the whole sensing area. As the task allocation problems are proven to be NP-hard, we propose a heuristic and a polynomialtime greedy approximation algorithm to solve optimization problems in two different application scenarios. Simulations using both real and synthetic data sets show that our algorithms outperform existing approaches. more...

Rapidly evolving technologies and applications of mobile devices inevitably increase the power demands on the battery. However, the development of batteries can hardly keep pace with the fast-growing demands, leading to short battery life, which becomes the top complaints from customers. In this article, we investigate a novel energy supply technology, fuel cell (FC), and leverage its advantages of providing long-term energy storage to build a hybrid FC-battery power system. Therefore, mobile device operation time is dramatically extended, and users are no longer bothered by battery recharging.We examine real-world smartphone usage data and find that a naive hybrid power system cannot meet many users' highly diversified power demands. We thus propose an OS-level power management policy that reduces the device power consumption for each power peak to solve this mismatch. This technique trades the quality-of-service (QoS) for a larger FC ratio in the system and thus much longer device operation time. We further observe that the user's personality largely determines his/her satisfaction with the QoS degradation and the operation time extension. Thus, applying a hybrid system with fixed configuration (i.e., peak throttling level coupled with corresponding FC/battery ratio) fails to satisfy every user. We then explore customized hybrid system configuration based on each individual user's personality to deliver the optimal satisfaction for him/her. The experimental results show that our personality-aware hybrid FC-battery solution can achieve 4x longer operation time and 25% higher satisfaction score compared to the common setting for state-of-the-art mobile devices. [ABSTRACT FROM AUTHOR] more...