Research

Sponsored Research Projects:

RINGS: Enabling Joint Sensing, Communication, and Multi-tenant Edge AI for Cooperative Perception Systems

NSF RINGS Award: #2148353

Nationally, 40 percent of all traffic crashes involve intersections, with a quarter of all traffic deaths and about half of all injuries occurring in such locations. Recent advancements in vehicle communication and perception systems have led to significant improvement of situational awareness in modern connected and semi-autonomous vehicles. However, these light-based perception systems are limited by view blockage in busy traffic intersections and thus cannot reliably detect all objects. To improve sensing reliability, many autonomous systems rely heavily on multiple active sensors including 3D LiDAR, Radar and Ultrasound sensors. However, the improved perception reliability comes at the cost of bigger and more complex systems with multiple vendor-specific components, which cause much higher power consumption and increased maintenance, security and reliability issues. Moreover, there are no coordination or multiple access control mechanisms for nearby active sensors, thus potentially making nearby perception systems interfere with each other in busy traffic intersections. The goal of this project is to design a next generation system with multi-modal sensing and low-latency communication capabilities that share the same essential hardware including the spectrum band, baseband processing, and computing units. This design will result in a low-cost and compact perception device that provides cooperative sensing with nearby devices with improved resilience. To improve the device utilization and harness the power of multiple artificial intelligence (AI) services, this project proposes an edge computing framework to enable multi-tenant AI capabilities on the proposed perception system.

Fighting Hunger, Feeding People: A Blockchain-based Intelligence System for Improving Food Rescue Efficiency

SJSU Multidisciplinary RSCA Stimulus Award

Modeling, Identification, and Estimation of Distributed Parameter Systems Using Mobile Sensor Networks

NSF CMMI Award #1663073, #1917300

This project will formulate a general-purpose mathematical framework using mobile sensor networks (MSNs) that will allow an efficient and accurate prediction of information about the behavior of complex real-world systems such as weather forecasting, wildfire control, disaster recovery, explosive materials detection, etc. Such systems are known to be distributed parameter systems (DPS) and modeling of such systems and accurately predicting their behavior in real-time is highly complex and computationally challenging. The current state-of-the-art techniques use static sensor networks to help obtain solutions from complex mathematical models which are often inaccurate and cannot provide timely information. The mobility and adaptability of MSNs make them great candidates for overcoming these challenges. This project will develop a unified framework for modeling, identifying, estimating, and predicting the behavior of such distributed parameter systems. The project will also develop strongly integrated research, educational, and outreach program by providing graduate students with interdisciplinary and challenging research experiences, by providing undergraduate students with the opportunity of early involvement in research activities through algorithm development and test-bed experiments, and by motivating K-12 students by giving them hands-on experiences through university's Engineering Ambassadors(EA) program.

CPS: Synergy: Collaborative Research: Towards Effective and Efficient Sensing-Motion Co-Design of Swarming Cyber-Physical Systems

NSF CPS Award #1446461

The project focuses on swarming cyber-physical systems (swarming CPS) consisting of a collection of mobile networked agents, each of which has sensing, computing, communication, and locomotion capabilities, and that have a wide range of civilian and military applications. Different from conventional static CPS, swarming CPS relies on mobile computing entities, e.g., robots, which collaboratively interact with phenomena of interest at different physical locations. This unique feature calls for novel sensing-motion co-design solutions to accomplish a variety of increasingly complex missions. Towards this, the overall research objective of this project is to establish and demonstrate a generic motion-sensing co-design procedure that will significantly reduce the complexity of the mission design for swarming CPS, and greatly facilitate the development of effective, efficient and adaptive control and sensing strategies under various environment uncertainties. This project aims to offer a comprehensive scientific understanding of the dynamic nature of swarming CPS, contribute to generic engineering principles for designing collaborative control and sensing algorithms, and advance the enabling technologies of practically applying CPS in the challenging environment. The research solutions of this project aim to bring a significant advance in environmental sustainability, homeland security, and human well-being. The project provides unique interdisciplinary training opportunities for graduate and undergraduate students through both research work and related courses that the PIs will develop and offer.