This project investigates algorithmic innovations targeting the cyber-physical system (CPS) opportunities emerging from cloud, transportation, and power networks. The envisioned advances pertain to models and algorithms for learn-and-adapt approaches to managing CPS; online data-driven workload balancing for sustainable data center networks; real-time traffic management for transportation networks in smart cities; and learning-aided online decentralized energy management for smart power networks.
The proposed research has great potential for social, environmental, and economic benefits, as it directly impacts the wireless Internet, scalable cloud networks, intelligent transportation, and microgrids supporting smart homes and cars. Cloud users' experience can be enhanced with markedly low request delay. Smart cars can end up consuming less fuel, and experience less waiting time at road intersections due to efficient signal control and vehicle routing. Finally, power grid operators can benefit from relaxed battery capacity requirements and fast electric vehicle charging at substations. Broader transformative impact will be effected by reaching out to middle-school students and the community, involving undergraduates in research, increasing participation of under-represented minorities, and promoting a scientifically literate public.
