Online Stable Task Assignment in Opportunistic Mobile Crowdsensing With Uncertain Trajectories
| Autor: | Eyuphan Bulut, Fatih Yucel |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science media_common.quotation_subject Carry (arithmetic) Stability (learning theory) Machine learning computer.software_genre Computer Science Applications Task (project management) Crowdsensing Hardware and Architecture If and only if Signal Processing Quality (business) Artificial intelligence business computer Information Systems media_common |
| Zdroj: | IEEE Internet of Things Journal. 9:9086-9101 |
| ISSN: | 2372-2541 |
| DOI: | 10.1109/jiot.2021.3118134 |
| Popis: | In opportunistic mobile crowdsensing, participants (workers) accept to carry out the requested sensing tasks only if they are already close to or within the regions of interest. Thus, the existence of an assignment opportunity between a workertask pair strictly depends on whether or not the worker will visit the task region. However, when worker trajectories are uncertain and hence not known in advance, existing solutions fail to produce an effective task assignment. Besides, a satisfactory task assignment should respect the preferences and capacity constraints of workers and task requesters, which are generally neglected in literature. In this study, we address all of these issues together and propose novel task assignment algorithms for different settings, which we prove to be optimal in terms of preference-awareness (or stability). Extensive simulations performed on both synthetic and real data sets validate our theoretical results, and demonstrate that the proposed algorithms significantly outperform the existing solutions in terms of preference-awareness and average quality of sensing attained in the final task assignment in almost all scenarios. |
| Databáze: | OpenAIRE |
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