CfP: Intelligent Systems for Smart Cities Workshop (InSys 2021)

CALL FOR PAPERS

Intelligent Systems for Smart Cities Workshop (InSys 2021)

part of CPS-IoT Week 2021

Important Dates

• January 15: Call for papers out
• February 15: Submission Deadline
• March 15: Author Notification
• March 25: Camera-ready Submission Deadline

Intelligent Systems for Smart cities are emerging as a priority for Cyber-Physical Systems (CPS) research and development across the world. Artificial Intelligence and Machine Learning algorithms have played a large part in automating and advancing city operations and aiding the development of CPS in cities. Increasingly, data-driven modeling and intelligent decision-making under uncertainty are forming the basis for advancing transportation, safety, connectivity, and health services. For example, advanced traffic solutions, improved public transportation systems, smart emergency response, energy modeling, and autonomous driving are some of the applications that have benefited from approaches to principled decision-making.

There are many challenges pertaining to decision-making for CPS in smart cities. With the advent of IoT, sensor data is being generated at a pace and volume that is difficult to process and make inferences from. Further, the needs of the cities dictate that much of the processing happens on the edge, making it imperative that fast and tractable approaches to decision-making are designed. At the same time, there is a growing need for automated applications to be fair, secure, and resilient. Participants in the workshop will exchange ideas on these and allied topics, including data science and open-source data sets for smart cities, decision making for smart cities, design of intelligent systems in smart cities, and challenges in deployment, equity and faireness in smart cities, and security and privacy in AI for cities. Authors can choose to include their papers in the ACM CPS Week proceedings or opt out.

We invite full papers (8 pages), short papers (4 pages), and concept papers (2 pages) on the following topics (all page limits include references). Authors can choose to include their papers in the ACM CPS Week proceedings or opt out. We welcome prior work published in conferences or journals.

• Data Science and Open-Source Data Sets for Smart Cities
• Frameworks and ML models for solving the challenge of integrating heterogeneous and cross-domain data in smart cities.
• Approaches for engaging communities, citizen science, and crowdsourcing, and subsequently using such uncertain and noisy information to make inferences.
• Predictive analytics for smart cities; using data to inform policies.
• Open source datasets, descriptions, and benchmark results for the research community.
• Decision Making for smart cities, design of intelligent systems in smart cities, and challenges in deployment
• Approaches to model complex decision-making tasks in smart cities and how they tackle uncertainty.
• Principled heuristics to design scalable decision-making in city-scale CPS.
• Challenges faced and lessons learned in deploying intelligent systems in smart cities.
• Ethical considerations in smart city applications.
• Validation and verification of intelligent systems deployed in smart cities.
• Equity and fairness in Smart Cities
• Evaluating and creating metrics for evaluating equity and fairness.
• Empirical results on fairness of existing algorithmic approaches deployed in cities.
• Modeling fairness and equity in predictive methodologies and decision making pipelines.
• Security and Privacy in AI for Cities
• Trustworthy Analytics and Privacy control.
• Anomaly Detection in Smart Connected Communities.
• Security vs Privacy Tradeoffs in Smart Connected Communities.
• Interactions between privacy, security, resilience, reliability, and safety from both theoretical and operational perspectives.

Workshop Organizers

• Ayan Mukhopadhyay, Vanderbilt University (Workshop Chair)
• Aron Laszka, University of Houston (Workshop Chair)
• Abhishek Dubey, Vanderbilt University
• Ram Rajagopal, Stanford University
• Danny Huang, New York University