research-article

DeResolver: a decentralized negotiation and conflict resolution framework for smart city services

Authors:

John Stankovic,

Shan LinAuthors Info & Claims

ICCPS '21: Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems

Pages 98 - 109

https://doi.org/10.1145/3450267.3450538

Published: 19 May 2021 Publication History

Abstract

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 data set 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.

References

[1]

N. S Altman. 1992. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician (1992).

[2]

T. Baarslag, M. JC Hendrikx, K. V Hindriks, and C. M Jonker. 2016. Learning about the opponent in automated bilateral negotiation: a comprehensive survey of opponent modeling techniques. Autonomous Agents and Multi-Agent Systems (2016).

[3]

Z B. Celik, G. Tan, and P. D McDaniel. 2019. IoTGuard: Dynamic Enforcement of Security and Safety Policy in Commodity IoT. In NDSS.

[4]

F. V Cespedes, A. M Ciechanover, and M. Eiran. 2018. BreezoMeter: Making Air Pollution Data Actionable. (2018).

[5]

R. M. Coehoorn and N. R. Jennings. 2004. Learning on Opponent's Preferences to Make Effective Multi-Issue Negotiation Trade-Offs. In ICEC '04. ACM.

[6]

U. Endriss. 2006. Monotonic Concession Protocols for Multilateral Negotiation. In AAMAS '06. ACM.

[7]

J. Friedman, T. Hastie, and R. Tibshirani. 2001. The elements of statistical learning. Vol. 1. Springer series in statistics New York.

[8]

K. Hindriks and D. Tykhonov. 2008. Opponent Modelling in Automated Multi-issue Negotiation Using Bayesian Learning. In AAMAS.

[9]

T. K. Ho. 1995. Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition. IEEE.

[10]

C. Hou. 2004. Predicting agents tactics in automated negotiation. In IEEE/WIC/ACM IAT '04.

[11]

Intel. 2019. Intelligent traffic management system. https://solutionsdirectory.intel.com/solutions-directory/Intelligent_Traffic_Management_System

[12]

T. Ito, H. Hattori, and M. Klein. 2007. Multi-issue Negotiation Protocol for Agents: Exploring Nonlinear Utility Spaces. In IJCAI.

[13]

S. Ji, Y. Zheng, Z. Wang, and T. Li. 2019. A Deep Reinforcement Learning-Enabled Dynamic Redeployment System for Mobile Ambulances. ACM IMWUT (2019).

[14]

D. G Kleinbaum, K Dietz, M Gail, M. Klein, and M. Klein. 2002. Logistic regression.

[15]

G. Lai, C. Li, and K. Sycara. 2006. Efficient multi-attribute negotiation with incomplete information. Group Decision and Negotiation (2006).

[16]

L. Li, Y. Lv, and F. Wang. 2016. Traffic signal timing via deep reinforcement learning. IEEE/CAA Journal of Automatica Sinica (2016).

[17]

M. Li, H. Li, and Z. Zhou. 2009. Semi-supervised document retrieval. Information Processing & Management 45, 3 (2009), 341--355.

Digital Library

[18]

C. Mike Liang, B. F. Karlsson, N. D. Lane, F. Zhao, J. Zhang, Z. Pan, Z. Li, and Y. Yu. 2015. SIFT: Building an Internet of Safe Things. In IPSN '15. ACM.

[19]

R. Lin, S. Kraus, J. Wilkenfeld, and J. Barry. 2008. Negotiating with bounded rational agents in environments with incomplete information using an automated agent. Artificial Intelligence 172, 6-7 (2008), 823--851.

Digital Library

[20]

R. Liu, Z. Wang, L. Garcia, and M. Srivastava. 2019. RemedioT: Remedial Actions for Internet-of-Things Conflicts. In BuildSys '19. ACM.

[21]

Y. Lou and S. Wang. 2016. Approximate representation of the Pareto frontier in multiparty negotiations: Decentralized methods and privacy preservation. European Journal of Operational Research 254, 3 (2016), 968--976.

[22]

M. Ma, S. M. Preum, and J. A Stankovic. 2017. Cityguard: A watchdog for safety-aware conflict detection in smart cities. In IoTDI. ACM.

Digital Library

[23]

M. Ma, S M. Preum, W Tarneberg, M. Ahmed, M. Ruiters, and J. Stankovic. 2016. Detection of runtime conflicts among services in smart cities. In SMARTCOMP.

[24]

M. Ma, J. A Stankovic, and L. Feng. 2018. Cityresolver: a decision support system for conflict resolution in smart cities. In ICCPS '18.

[25]

S. Munir and J. A Stankovic. 2014. Depsys: Dependency aware integration of cyber-physical systems for smart homes. In ICCPS '14.

Digital Library

[26]

J. F Nash Jr. 1950. The bargaining problem. Econometrica: Journal of the Econometric Society (1950), 155--162.

[27]

NYC. 2020. NYC Open Data. https://opendata.cityofnewyork.us/

[28]

City of Newark. 2020. City of Newark Open Data. http://data.ci.newark.nj.us/

[29]

Graz University of Technology. 2019. New traffic light system automatically recognizes pedestrians' intent to cross the road. Retrieved Nov 24, 2019 from https://phys.org/news/2019-05-traffic-automatically-pedestrians-intent-road.html

[30]

R. K. Pasumarthi, S. Bruch, X. Wang, C. Li, M. Bendersky, Ma. Najork, J. Pfeifer, N. Golbandi, R. Anil, and S. Wolf. 2019. TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank. In KDD '19. ACM.

[31]

A. Piscitello, F. Paduano, A. A Nacci, M. D Noferi, D. and Santambrogio, and D. Sciuto. 2015. Danger-system: Exploring new ways to manage occupants safety in smart building. In 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT).

[32]

R. S Sutton and A. G Barto. [n.d.]. Reinforcement learning: An introduction.

[33]

K. Sycara and D. Zeng. 1997. Benefits of learning in negotiation. In AAAI '97.

[34]

M. Szummer and E. Yilmaz. 2011. Semi-supervised learning to rank with preference regularization. In CIKM '11. ACM.

[35]

R. M. B. S. Thais, B. R. Linnyer, and A. F. L. Antonio. 2010. How to conciliate conflicting users' interests for different collective, ubiquitous and context-aware applications?. In IEEE Local Computer Network Conference. 288--291.

Digital Library

[36]

S. Wang, T. He, D. Zhang, Y. Shu, Y. Liu, Y. Gu, C. Liu, H. Lee, and S. H. Son. 2018. BRAVO: Improving the Rebalancing Operation in Bike Sharing with Rebalancing Range Prediction. ACM IMWUT (2018).

Digital Library

[37]

F. V. Webster. 1958. Traffic signal settings. Technical Report.

[38]

H. Wei, N. Xu, H. Zhang, G. Zheng, X. Zang, C. Chen, W. Zhang, Y. Zhu, K. Xu, and Z. Li. 2019. Colight: Learning network-level cooperation for traffic signal control. In CIKM '19.

[39]

H. Wei, G. Zheng, H. Yao, and Z. Li. 2018. IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control. In KDD.

[40]

M. Wiering. 2000. Multi-agent reinforcement learning for traffic light control. In ICML '2000. 1151--1158.

[41]

H. Yang, S. Tsai, K. Liu, S. Lin, and J. Gao. 2019. Patrol Scheduling Against Adversaries with Varying Attack Durations. In AAMAS '19.

[42]

Y. Yuan, D. Zhang, F. Miao, J. Chen, T. He, and S. Lin. 2019. p^ 2Charging: Proactive Partial Charging for Electric Taxi Systems. In ICDCS.

[43]

D. Zhang, Y. Li, F. Zhang, M. Lu, Y. Liu, and T. He. 2013. CoRide: Carpool Service with a Win-Win Fare Model for Large-Scale Taxicab Networks. In SenSys '13.

Cited By

Roy BGhosh SChoudhury NRoy PPaul S(2024)The Role of AI in Conflict Resolution and NegotiationNavigating Organizational Behavior in the Digital Age With AI10.4018/979-8-3693-8442-8.ch016(461-484)Online publication date: 25-Oct-2024
https://doi.org/10.4018/979-8-3693-8442-8.ch016
Hong ZCao DWang HWang GHe TZhang DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)AutoBuild: Automatic Community Building Labeling for Last-mile DeliveryProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614658(4623-4630)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614658
Joshi HJoshi S(2022)A Decision Support Framework to Conceptualize the Impact of 5G on Smart City Ecosystem2022 International Conference on Decision Aid Sciences and Applications (DASA)10.1109/DASA54658.2022.9765185(1229-1233)Online publication date: 23-Mar-2022
https://doi.org/10.1109/DASA54658.2022.9765185

Index Terms

DeResolver: a decentralized negotiation and conflict resolution framework for smart city services
1. Computer systems organization
  1. Embedded and cyber-physical systems
2. Computing methodologies
  1. Modeling and simulation

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Published In

cover image ACM Conferences

ICCPS '21: Proceedings of the ACM/IEEE 12th International Conference on Cyber-Physical Systems

May 2021

242 pages

ISBN:9781450383530

DOI:10.1145/3450267

General Chairs:
Martina Maggio
Lund University
,
James Weimer
University of Pennsylvania
,
Program Chairs:
Mohammad Al Faruque
University of California at Irvine
,
Meeko Oishi
University of New Mexico

Copyright © 2021 ACM.

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Published: 19 May 2021

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ICCPS '21: ACM/IEEE 12th International Conference on Cyber-Physical Systems

May 19 - 21, 2021

Tennessee, Nashville

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Cited By

Roy BGhosh SChoudhury NRoy PPaul S(2024)The Role of AI in Conflict Resolution and NegotiationNavigating Organizational Behavior in the Digital Age With AI10.4018/979-8-3693-8442-8.ch016(461-484)Online publication date: 25-Oct-2024
https://doi.org/10.4018/979-8-3693-8442-8.ch016
Hong ZCao DWang HWang GHe TZhang DFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)AutoBuild: Automatic Community Building Labeling for Last-mile DeliveryProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3614658(4623-4630)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3614658
Joshi HJoshi S(2022)A Decision Support Framework to Conceptualize the Impact of 5G on Smart City Ecosystem2022 International Conference on Decision Aid Sciences and Applications (DASA)10.1109/DASA54658.2022.9765185(1229-1233)Online publication date: 23-Mar-2022
https://doi.org/10.1109/DASA54658.2022.9765185

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