A Reinforcement Learning-Based Incentive Mechanism for Task Allocation Under Spatiotemporal Crowdsensing
Bibliographic Data
| ID | 22108243 |
|---|---|
| Authors | Kaige Jiang (0009-0006-1141-5684, Yantai University), Yingjie Wang (0000-0003-0920-9305, Yantai University), Haipeng Wang (0000-0002-5297-6510, Institute of Information Fusion, Naval Aviation University, Yantai, China), Zhaowei Liu (0000-0003-3262-8962, Yantai University), Qilong Han (0000-0002-5185-8387, Harbin Engineering University), Ao Zhou (0000-0001-5743-9418, Beijing University of Posts and Telecommunications), Chaocan Xiang (0000-0002-1473-6006, Chongqing University), Zhipeng Cai (0000-0001-6017-975X, Georgia State University) |
| Year | 2024 |
| Volume | 11 |
| Issue | 2 |
| Pages | 2179-2189 |
| Publication date | 2024-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3263821 |
| OpenAlex | W4364302108 |
| Language | EN |
| Citations received | 5 |
| References cited | 39 |
With the development of the Industrial Internet of Things (IoT), the work of large-scale data collection makes spatiotemporal crowdsensing (SC) play an important role. Mobile devices equipped with sensors could act as workers to collect and process data for uploading. In the task allocation process, a fully static allocation fails to meet the needs of realistic conditions, while a completely dynamic allocation fails to achieve the desired results. Therefore, we assume a task-scheduled execution scenario that combines the above two conditions. In the pre-allocation process, an original time location constraints (ORTA) allocation algorithm is first proposed. Then it is optimized (OPTA) to fully utilize the remaining time of the workers and increase the matched number. In addition, the design of the incentive mechanism is an effective means to improve the task completion rate of the platform. To efficiently utilize the limited platform budget in the long run, a Q-learning-based algorithm is proposed to identify target inspire tasks and subsequently increase their reward to attract workers’ participation. Finally, comparison experiments are conducted on real datasets to verify the effectiveness of our algorithm. Furthermore, the experiments on a Raspberry Pi local terminal are conducted under a satellite-based environment
Computer security · Crowdsensing · Distributed computing · Incentive · Operating system · Real-time computing · Reinforcement learning · Task Analysis · Time allocation · Upload · Computer Science · Engineering · IoT and Edge/Fog Computing · Mobile Crowdsensing and Crowdsourcing · Privacy-Preserving Technologies in Data · Artificial Intelligence
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DRL-Upps
Solving the Task Allocation Problem Under Market Fluctuations via Group Role Assignment
| Unique citing works | 5 |
|---|---|
| Citations per year | 2,5 |
| Citation span | 2024 - 2026 (3) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 5 |