QUIDS: Quality-informed Incentive-driven Multi-agent Dispatching System for Mobile Crowdsensing
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arXiv
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| Format: | Preprint |
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2025
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| author | Zhou, Nan Li, Zuxin Man, Fanhang Chen, Xuecheng Xu, Susu Dang, Fan Hong, Chaopeng Liu, Yunhao Zhang, Xiao-Ping Chen, Xinlei |
| author_facet | Zhou, Nan Li, Zuxin Man, Fanhang Chen, Xuecheng Xu, Susu Dang, Fan Hong, Chaopeng Liu, Yunhao Zhang, Xiao-Ping Chen, Xinlei |
| contents | This paper addresses the challenge of achieving optimal Quality of Information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) systems. The key obstacles are the interrelated issues of sensing coverage, sensing reliability, and the dynamic participation of vehicles. To tackle these, we propose QUIDS, a QUality-informed Incentive-driven multi-agent Dispatching System, which ensures high sensing coverage and reliability under budget constraints. QUIDS introduces a novel metric, Aggregated Sensing Quality (ASQ), to quantitatively capture QoI by integrating both coverage and reliability. We also develop a Mutually Assisted Belief-aware Vehicle Dispatching algorithm that estimates sensing reliability and allocates incentives under uncertainty, further improving ASQ. Evaluation using real-world data from a metropolitan NVMCS deployment shows QUIDS improves ASQ by 38% over non-dispatching scenarios and by 10% over state-of-the-art methods. It also reduces reconstruction map errors by 39-74% across algorithms. By jointly optimizing coverage and reliability via a quality-informed incentive mechanism, QUIDS enables low-cost, high-quality urban monitoring without dedicated infrastructure, applicable to smart-city scenarios like traffic and environmental sensing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_16325 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | QUIDS: Quality-informed Incentive-driven Multi-agent Dispatching System for Mobile Crowdsensing Zhou, Nan Li, Zuxin Man, Fanhang Chen, Xuecheng Xu, Susu Dang, Fan Hong, Chaopeng Liu, Yunhao Zhang, Xiao-Ping Chen, Xinlei Computer Vision and Pattern Recognition This paper addresses the challenge of achieving optimal Quality of Information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) systems. The key obstacles are the interrelated issues of sensing coverage, sensing reliability, and the dynamic participation of vehicles. To tackle these, we propose QUIDS, a QUality-informed Incentive-driven multi-agent Dispatching System, which ensures high sensing coverage and reliability under budget constraints. QUIDS introduces a novel metric, Aggregated Sensing Quality (ASQ), to quantitatively capture QoI by integrating both coverage and reliability. We also develop a Mutually Assisted Belief-aware Vehicle Dispatching algorithm that estimates sensing reliability and allocates incentives under uncertainty, further improving ASQ. Evaluation using real-world data from a metropolitan NVMCS deployment shows QUIDS improves ASQ by 38% over non-dispatching scenarios and by 10% over state-of-the-art methods. It also reduces reconstruction map errors by 39-74% across algorithms. By jointly optimizing coverage and reliability via a quality-informed incentive mechanism, QUIDS enables low-cost, high-quality urban monitoring without dedicated infrastructure, applicable to smart-city scenarios like traffic and environmental sensing. |
| title | QUIDS: Quality-informed Incentive-driven Multi-agent Dispatching System for Mobile Crowdsensing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.16325 |