Storms and floods cause 70% of the world’s natural disasters. These natural disasters affect on average up to 200 million people in a year, with economic losses between US$50 billion to US$100 billion annually. Monitoring these flash flood prone areas proactively in populated areas and providing just-in-time notification to the city officials can help in effectively prioritizing, controlling, and mitigating such disastrous events.

Sensor energy efficiency is the top critical concern that hinders long-term monitoring in energy-constrained Internet-of-things (IoT) applications. Conventional compressive sensing techniques fail to achieve satisfactory performance in IoT and especially wearable applications due to the lack of prior knowledge about signal models and the overlook of individual variability. The research goal of this CAREER plan is to develop a data-driven and hardware-friendly IoT framework to fundamentally address the unmet energy efficiency need of IoT and especially wearable applications.

The goal of this project is to leverage and improve upon the capabilities of honey bees as agricultural pollinators by incorporating them into Bio-Cyber Physical systems. Rapid advances are needed to aid a dwindling agricultural workforce, increase crop yield to sustain the growing population, and provide targeted crop care to limit the need for broad pesticide treatments. These challenges may well be addressed by autonomous mobile robots and sensor networks; unfortunately, agricultural landscapes represent vast, complex, and dynamic environments that complicate long term operation.

Recent years have seen an explosion in the use of ad-hoc, IP-based and Wifi networks for control of spatially-distributed physical systems, with applications including automotive fleets; swarms of UAVs; remote surgery; and optimization of sensor networks. These new forms of communication have dramatically decreased the cost, energy, and maintenance associated with remote regulation, but have added fundamental challenges in the form of delay, packet drops, and intermittent feedback.

In this poster, we briefly present the overview of this CPS project and our research progress in its first year. The overall goals of this project are to develop onboard-adaptable and personalizable human driver models, create revolutionary driver-specific and personalized vehicle active motion control systems, design dynamic onboard real-time computation task scheduling methods, and integrate real-time V2V communications with driver-vehicle-pair-specific inter-vehicle motion control methods.

Rapid and customized part realization in all industrial sectors imposes stringent demands on part attributes, e.g., mechanical properties, microstructure, surface finish, geometry, etc. However, part attributes can very rarely be directly measured and/or controlled in the production process. Instead, measurements are taken of accessible and measurable primary process responses that are known to influence the part's attributes. These primary process responses are then controlled through the manipulation of a set of controllable process parameters.

The goal of this research is to gain a fundamental understanding of the integrated actuation, embedded sensing, reactive control, and distributed control needs of a cyber-physical, synthetic, distributed sensing, soft and modular tissue (sTISSUE). Realizing this cyber-physical, physiological testbed will enable surgically relevant tasks, procedures, and devices to be much more refined ahead of animal testing, which can be dramatically reduced with such high-fidelity simulators.

The objective of this project is to formulate and validate a methodology for creating secure algorithms in cyber-physical systems.

The overall research objective of the project is to establish and demonstrate a generic motion- sensing co-design procedure that significantly reduces the complexity of mission design for swarm- ing CPS, and greatly facilitates the development of effective and efficient control and sensing strategies.