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CHEST 2022 Research Project Abstracts

P7_22: Assuring Trust in CHEST Devices with Near-Optimal Data Collection and Distributed Sensors
Topic Areas:
Principal investigator: Dr. Negin Alemazkoor, University of Virginia
Co-Principal investigator(s): Dr. Arsalan Heydarian, Virginia
Co-Principal investigator(s): Dr. James H. Lambert, Virginia
PI Email

Distributed integrated-circuit (IC) sensors with CHEST technologies promise to enable transformational improvements in the operations of complex systems. Spatially distributed sensor nodes cooperatively monitor a physical system and transmit a potentially vast amount of sensed data to central base stations for analysis. The wide range of applications includes indoor and outdoor environments, military and homeland security, infrastructure, agriculture, meteorology, health, space, and many other critical applications. Undoubtedly, the constant monitoring provided by the ICs can substantially improve the management of the systems of interest. However, blind data collection can lead to diminished trust in the IC devices when data communication and storage capacities are oversubscribed. In 2010, Lt. Gen. David A. Deptula, former Air Force deputy chief of staff for intelligence, predicted that “We’re going to find ourselves in the not too distant future swimming in sensors and drowning in data.” The prediction has been confirmed by several reports, recognizing the flood of data coming from the intelligence, surveillance, and reconnaissance systems used by intelligence analysts and commanders as a major vulnerability. To address loss of trust in ICs, this project will develop computational algorithms that preserve appropriate trust in IC devices while substantially reducing the volume of data, while maintaining its value, by minimizing the redundancies of the data. Particularly, these algorithms that involve both hardware design and network operations will achieve: a) trust in IC devices that are of current interest in the CHEST, b) optimal sensor placement by exploiting mobile sensors and data fusion, c) efficient prediction-based event-triggered data collection, and d) optimal spatial data collection from moving IC sensors. Together, these algorithms will achieve (near)-optimal data collection from the network of sensors with trust in the IC devices and accurate system state estimation within acceptable error bounds.