Researchers from the University of Illinois at Urbana-Champaign and the University of Science and Technology of China have developed a new system aimed at improving the quality of information gathered through vehicular mobile crowdsensing. This technology could have significant implications for the energy sector, particularly in smart city applications.
The team, led by Nan Zhou and including Zuxin Li, Fanhang Man, Xuecheng Chen, Susu Xu, Fan Dang, Chaopeng Hong, Yunhao Liu, Xiao-Ping Zhang, and Xinlei Chen, introduced QUIDS, a Quality-informed Incentive-driven multi-agent Dispatching System. This system addresses the challenges of achieving optimal Quality of Information (QoI) in non-dedicated vehicular mobile crowdsensing (NVMCS) systems. The key issues in NVMCS are sensing coverage, sensing reliability, and the dynamic participation of vehicles. QUIDS tackles these problems by ensuring high sensing coverage and reliability under budget constraints.
One of the innovative aspects of QUIDS is the introduction of a new metric called Aggregated Sensing Quality (ASQ). This metric quantitatively captures QoI by integrating both coverage and reliability. The system also employs a Mutually Assisted Belief-aware Vehicle Dispatching algorithm. This algorithm estimates sensing reliability and allocates incentives under uncertainty, further improving ASQ.
The researchers evaluated QUIDS using real-world data from a metropolitan NVMCS deployment. The results showed that QUIDS improved ASQ by 38% over non-dispatching scenarios and by 10% over state-of-the-art methods. Additionally, it reduced reconstruction map errors by 39-74% across algorithms. These findings suggest that QUIDS can enable low-cost, high-quality urban monitoring without the need for dedicated infrastructure.
For the energy sector, this technology could be particularly useful in smart city applications such as traffic and environmental sensing. By leveraging existing vehicular networks, energy companies could gather more accurate and comprehensive data on urban energy usage patterns. This data could then be used to optimize energy distribution and reduce waste, ultimately leading to more efficient and sustainable energy systems. The research was published in the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.
This article is based on research available at arXiv.