Autonomous Conflict Resolution for Smart City Services - ICCPS 2021 Best Paper Winner
DeResolver: A Decentralized Negotiation and Conflict Resolution Framework for Smart City Services
- Yukun Yuan, Stony Brook ECE student
- Meiyi Ma, University of Virginia
- Songyang Han, University of Connecticut
- Desheng Zhang, Rutgers University
- Fei Miao, University of Connecticut
- John Stankovic, University of Virginia
- Shan Lin, Stony Brook ECE Faculty
The ACM/IEEE 11th International Conference on Cyber-Physical Systems (ICCPS), May
Winner, Best Paper Award
Novel Technical Contribution
To the best of our knowledge, we are the first to propose a decentralized negotiation framework, called DeResolver, for conflict resolution among city services. As service conflicts demonstrate high spatiotemporal locality in the physical world, the decentralized resolution allows smart services to mitigate cross-domain conflicts in an efficient and robust manner.
As the number of smart services increases, city managers are facing more and more potential conflicts across the deployed services. Since services may have self-interested objectives and different stake holders, resolving these conflicts become a challenging task. This work provides a general and efficient resolution framework for services to autonomously reach social optimal decisions based on city requirements.
As various smart services are increasingly deployed in modern cities, many unexpected conflicts arise due to various physical world couplings. Existing solutions for conflict resolution often rely on centralized control to enforce predetermined and fixed priorities of different services, which is challenging due to the inconsistent and private objectives of the services. Also, the centralized solutions miss opportunities to more effectively resolve conflicts according to their spatiotemporal locality of the conflicts. To address this issue, we design a decentralized negotiation and conflict resolution framework named DeResolver, which allows services to resolve conflicts by communicating and negotiating with each other to reach a Pareto-optimal agreement autonomously and efficiently. Our design features a two-level semi-supervised learning-based algorithm to predict acceptable proposals and their rankings of each opponent through the negotiation. Our design is evaluated with a smart city case study of three services: intelligent traffic light control, pedestrian service, and environmental control. In this case study, a data-driven evaluation is conducted using a large dataset consisting of the GPS locations of 246 surveillance cameras and an automatic traffic monitoring system with more than 3 million records per day to extract real-world vehicle routes. The evaluation results show that our solution achieves much more balanced results, i.e., only increasing the average waiting time of vehicles, the measurement metric of intelligent traffic light control service, by 6.8% while reducing the weighted sum of air pollutant emission, measured for environment control service, by 12.1%, and the pedestrian waiting time, the measurement metric of pedestrian service, by 33.1%, compared to priority-based solution.