Smart City is a key component in Internet of Things (IoTs), so it has attracted much attention. The emergence of Mobile Crowd Sensing (MCS) systems enables many smart city applications. In an MCS system, sensing tasks are allocated to a number of mobile users. As a result, the sensing related context of each mobile user plays a significant role on service quality. However, some important sensing context is ignored in the literature. This motivates us to propose a Context-aware Multi-Armed Bandit (C-MAB) incentive mechanism to facilitate quality-based worker selection in an MCS system. We evaluate a worker’s service quality by its context (i.e., extrinsic ability and intrinsic ability) and cost. Based on our proposed C-MAB incentive mechanism and quality evaluation design, we develop a Modified Thompson Sampling Worker Selection (MTS-WS) algorithm to select workers in a reinforcement learning manner. MTS-WS is able to choose effective workers because it can maintain accurate worker quality information by updating evaluation parameters according to the status of task accomplishment. We theoretically prove that our C-MAB incentive mechanism is selection efficient, computationally efficient, individually rational, and truthful. Finally, we evaluate our MTS-WS algorithm on simulated and real-world datasets in comparison with some other classic algorithms. Our evaluation results demonstrate that MTS-WS achieves the highest cumulative utility of the requester and social welfare.
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Emu: engagement modeling for user studies
Mobile technologies that drive just-in-time ecological momentary assessments and interventions provide an unprecedented view into user behaviors and opportunities to manage chronic conditions. The success of these methods rely on engaging the user at the appropriate moment, so as to maximize questionnaire and task completion rates. However, mobile operating systems provide little support to precisely specify the contextual conditions in which to notify and engage the user, and study designers often lack the expertise to build context-aware software themselves. To address this problem, we have developed Emu, a framework that eases the development of context-aware study applications by providing a concise and powerful interface for specifying temporal- and contextual-constraints for task notifications. In this paper we present the design of the Emu API and demonstrate its use in capturing a range of scenarios common to smartphone-based study applications.
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- NSF-PAR ID:
- 10073126
- Date Published:
- Journal Name:
- Proc ACM Int Conf Ubiquitous Computing
- Page Range / eLocation ID:
- 959 to 964
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3123024.3124568
