A Hybrid Vehicle-Cloud Solution for Robust, Cost-Efficient Road Monitoring

Project Summary (NSF Awards 1527097,1527287)

Many of today's vehicles are equipped with GPS, networking capabilities, computational resources, and a variety of environment and vehicle performance sensors. The combination of these technologies and the growing presence of such vehicles offer a powerful platform for large-scale road sensing applications to assist drivers in route planning and safe driving. This project proposes an integrated approach to overcoming algorithmic and systems barriers to the cost and scalability challenges of collaborative road sensing. The project conducts four main research tasks: (1) obtain correlated measurements of vehicle sensor readings and network connectivity, (2) reduce the cost of vehicle-to-cloud (V2C) communication through vehicle-to-vehicle (V2V) message delegation, (3) develop new communication-efficient algorithmic approaches for collaborative road sensing, (4) evaluate the communication and algorithmic solutions in an emulation framework that pulls together vehicle sensor data, measurements of cellular and V2V connectivity, and traffic flow data from a I-90/94 corridor information system. Results of this project will include reliable connected vehicle communication architecture for both urban and rural settings and theoretically sound decentralized algorithms for sensing and estimation that will be of interest to the broader distributed computing systems community.

PIs

• Stacy Patterson (RPI)
• Mike Wittie Montana State University

Links

• MOVESET (MOdular VEhicle SEnsor Technology) Sensor Platform

Related Publications

• Yi, Y., Z. Zhang, and S. Patterson, "Scale-free Loopy Structure is Resistant to Noise in Consensus Dynamics in Complex Networks", IEEE Transactions on Cybernetics, 2020.
• Li, H., S. Patterson, Y. Yi, and Z. Zhang, "Maximizing the Number of Spanning Trees in a Connected Graph", IEEE Transactions on Information Theory, 2019 (Early Access).
• Obetz, M., S. Patterson, and A. Milanova, "Static Call Graph Construction in AWS Lambda Serverless Applications"'. HotCloud, 2019.
• Imai, I., C. Varela, and S. Patterson, "A Performance Study of Geo-Distributed IoT Data Aggregation for Fog Computing", Procedings of the 1st Workshop on Managed Fog-to-Cloud, 2018.
• Das, A., S. Patterson, and M. Wittie, "EdgeBench: Benchmarking Edge Computing Platforms", Proceedings of the 4th International Workshop on Serverless Computing, 2018.
• Mackin, E. and S. Patterson, "Submodular Optimization for Consensus Networks with Noise-Corrupted Leaders", IEEE Transctions on Automatic Control, 2019.
• Das, A., Y. Yi, S. Patterson, B. Bamieh, and Z. Zhang, "Convergence Rate of Consensus in a Network of Networks", Proceedings of the 57th IEEE Conference on Decision and Control, 2018.
• Shigeru Imai, Stacy Patterson, and Carlos A. Varela, Uncertainty-Aware Elastic Virtual Machine Scheduling for Stream Processing Systems, 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2018.
• Hollis, B., S. Patterson, and J. Trinkle, "Compressed Learning for Tactile Object Recognition, IEEE Robotics and Automation Letters, 2018.
• Hollis, B., S. Patterson, and J. Trinkle, Adaptive basis selection for compressed sensing in robotic tactile skins, IEEE Global Conference on Signal and Information Processing (GlobalSIP),2017.
• Patterson, S., N. McGlohon, and K. Dyagilev, "Optimal k-Leader Selection for Coherence and Convergence Rate in One-Dimensional Networks", IEEE Transactions on Control of Network Systems, 2017.
• Mackin, E., and S. Patterson, "Optimizing the Coherence of Composite Networks ", Proceedings of the American Control Conference, 2017.
• S. Patterson, "Optimizing Coherence in 1-D Noisy Consensus Networks with Noise-Free Leaders ", Proceedings of the American Control Conference, 2017.
• Imai, S., S. Patterson, and C. Varela, Maximum Sustainable throughput Prediction for Data Stream Processing over Public Clouds, Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), 2017.
• Hollis, B.. S. Patterson, and J. Trinkle, "Compressed Sensing for Scalable Robotic Tactile Skins", Tactile sensing for manipulation: new progress and challenges, workshop at IEEE-RAS International Conference on Humanoid Robots, 2016
• Chen, Q., B. Bellows, M. P. Wittie, S. Patterson, and Q. Yang, "MOVESET: MOdular VEhicle SEnsor Technology", IEEE Vehicular Networking Conference, 2016.
• Hollis, B., S. Patterson, and J. Trinkle, "Compressed Sensing for Tactile Skins", Proceedings of the International Conference on Robotics and Automation , 2016.
• Imai, S., S. Patterson, and C. Varela, "Cost-Efficient Elastic Stream Processing Using Application-Agnostic Performance Prediction", Proceedings of the16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing: Doctoral Symposium , 2016.
• McGlohon, N., and S. Patterson, "Distributed Semi-Stochastic Optimization with Quantization Refinement", Proceedings of the American Control Conference , 2016.
• Imai, S., S. Patterson, and C. Varela, "Elastic Virtual Machine Scheduling for Continuous Air Traffic Optimization", Proceedings of the16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, 2016.

News Articles

• Driving Conditions According to Cars and the Cloud, Inside Rensselaer, Feb. 2016.