Self-Sustainable Data-Driven Systems In the Field

Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. This project develops new computer system infrastructure and algorithms for self-sustainable data-driven systems in the field. Research outcomes of the project include (a) a low-maintenance, environmentally-friendly hardware platform with solar energy harvesting and super capacitor-based energy storage, (b) virtualization software infrastructure for low-power nodes to enable inter-operability among distributed field nodes and from/to the data center, and (c) new image and data processing approaches for resource-adaptive fidelity adjustment and function partitioning. The synergy between the self-sustainable hardware, system software support, wireless communications management, and application data processing manifests through global coordination for quality-of-service, energy efficiency, and data privacy.

This picture illustrates our deployment on a seven-story building rooftop that includes a camera, a block of solar panels, and a system box. The system box contains a Nexus 7 tablet computer (without its internal rechargeable battery) sustained by eight Maxwell 3000 Farad supercapacitors (wrapped in black tapes), and a custom-built controller board (at the top of the box). The energy buffering capacity of the supercapacitors is about 1.4 times that in the original Nexus 7 battery.

In broader impacts, this project enables data-driven intelligence in the field for important physical system domains. Integration of the technologies involved is accomplished through real-world system deployment and experimentation, including an intelligent campus traffic and parking management system and collaborative work with industry collaborators. The results of this project will further enhance the technological competitiveness for US industries in key areas such as intelligent transportation. The education component includes cross-disciplinary curriculum enhancements and the development of a new instructional platform for realistic experiments with cyber-physical systems. Within the scope of this project, the PIs perform mentoring and outreach activities to recruit/retain women and minorities in science and engineering.

Project Personnel:

Kai Shen at Computer Science; Wendi Heinzelman, Gaurav Sharma, and Tolga Soyata at Electrical and Computer Engineering.


Related Sites:


This project is supported by the National Science Foundation under Grant No. 1239423. Link to the award abstract.


Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.