CPS: Synergy: Self-Sustainable Data-Driven Systems In the Field

Abstract:

Data-driven intelligence is an essential foundation for physical systems in transportation safety and efficiency, area surveillance and security, as well as environmental sustainability. While sophisticated data analysis and synthesis can be well supported in large data centers, future intelligent systems require on-the-scene processing with faster responses and less dependence on the unreliable (often wireless) data communications in the field. Field processing must consume low power for easy deployment and self-sustainability (e.g., using solar energy harvesting and buffering). The combination of high-volume data processing with low-power computing and I/O creates critical challenges for future intelligent systems. This project develops a new computer system infrastructure for the efficient utilization of low-power resources toward in-network data processing, for secure management of data in the field, and for task deployment and migration. This project also develops new image and data processing approaches for resourceadaptive fidelity adjustment and function partitioning. The synergy between the self-sustainable hardware, system software support, and application data processing manifests through global coordination for quality-of-service, energy efficiency, and data privacy. Further synergy is demonstrated through software exploitation and adaptation of self-sustainable hardware characteristics (e.g., specialized low-power computing and energy leakage of supercapacitors).