The objective of this research is the creation of a coastal observing system that enables dense, in situ, 4D sensing through networked, sensor-equipped underwater drifters. The approach is to develop the technologies required to deploy a swarm of autonomous buoyancy controlled drifters, which are vehicles that can control their depth, but are otherwise carried entirely by the ocean currents. Such Lagrangian sampling promises to deliver a wealth of new data, ranging from applications in physical oceanography (mapping 3D currents), biology (observing the dispersion of larvae and nutrients), environmental science (tracking coastal pollutants and effluents from storm drains), and security (monitoring harbors and ports). This observing system fundamentally requires accurate positions of the drifters (to interpret the spatial correlations of data samples), swarm control algorithms (to achieve desired sampling topologies), and wireless communication (to coordinate between the individual drifters). This research will create distributed techniques to self-localize the drifter swarm, novel swarm control algorithms that enable topology manipulation while purely leveraging the stratified flow environment, and efficient wireless underwater communication for information sharing. This project has significant societal impact and educational elements. Underwater drifter swarms will enable novel insights into a wide array of scientific questions, including understanding plankton transport, accumulation and dispersion as well as monitoring harmful algal blooms. Undergraduates will play an active role in many aspects of this project, thereby offering them a uniquely interdisciplinary experience. Finally, outreach to high school students will occur through the UCSD COSMOS summer program.

Objectives and approaches. The objective of this research is to create a novel Cyber-Physical System, a self-reconfiguring ?second skin orthotic sleeve? consisting of programmable materials. The orthotic sleeve, worn over one or more limbs of brain-injured individuals, may restore brain function by promoting enriched exploration of self-produced limb movements. The approach consists of three steps (1) micro-fabricating sheets with embedded sensors and muscle-like collections of force-producing actuators, (2) conducting longitudinal recordings of kicking by typically developing and preterm brain-injured infants who wear a sensing, but not actuated micro-fabricated second skin, and (3) developing biologically-inspired programming techniques to help determine an algorithm with which the second skin embedded actuators may adaptively assist the ever-changing developmental pattern of infant kicking. The technology can be applied to many mobility-impaired populations,including children and adults with brain injury, the ageing population, and injured soldiers. The project will inform basic scientific and engineering research in areas such as formation of architectural structures by large-scale multi-agent robotic systems, and self-organization of swarming small-scale agents that act autonomously in cooperation with biological systems. The multi-institutional effort of this research endeavor will positively impact undergraduate and graduate science education via explorations of the intersection of biology and computation in cyber-physical systems. Innovation, teamwork, and the value of communication are encouraged. These efforts will promote education of an American work force that is technically expert, scientifically comprehensive, and socially aware to sustain national excellence in a future increasingly based on technologically complex systems.

The objectives of this research are to design a heterogeneous network of embedded systems so that faults can be quickly detected and isolated and to develop on-line and off-line fault diagnosis and prognosis methods. Our approach is to develop functional dependency models between the failure modes and the concomitant monitoring mechanisms, which form the basis for failure modes, effects and criticality analysis, design for testability, diagnostic inference, and the remaining useful life estimation of (hardware) components. Over the last few years, the electronic explosion in automotive vehicles and other application domains has significantly increased the complexity, heterogeneity, and interconnectedness of embedded systems. To address the cross-subsystem malfunction phenomena in such networked systems, it is essential to develop a common methodology that: (i) identifies the potential failure modes associated with software, hardware, and hardware-software interfaces; (ii) generates functional dependencies between the failure modes and tests; (iii) provides an on-line/off-line diagnosis system; (iv) computes the remaining useful life estimates of components based on the diagnosis; and (iv) validates the diagnostic and prognostic inference methods via fault injection prior to deployment in the field. The development of functional dependency models and diagnostic inference from these models to aid in online and remote diagnosis and prognosis of embedded systems is a potentially novel aspect of this effort. This project seeks to improve the competitiveness of the U.S. automotive industry by enhancing vehicle reliability, performance and safety, and by improving customer satisfaction. Other representative applications include aerospace systems, electrification of transportation, medical equipment, and communication and power networks, to name a few.

The objective of this research is to develop the scientific foundation for the quantitative analysis and design of control networks. Control networks are wireless substrates for industrial automation control, such as the WirelessHART and Honeywell's OneWireless, and have fundamental differences over their sensor network counterparts as they also include actuation and the physical dynamics. The approach of the project focuses on understanding cross-cutting interfaces between computing systems, control systems, sensor networks, and wireless communications using time-triggered architectures. The intellectual merit of this research is based on using time-triggered communication and computation as a unifying abstraction for understanding control networks. Time-triggered architectures enable the natural integration of communication, computation, and physical aspects of control networks as switched control systems. The time-triggered abstraction will serve for addressing the following interrelated themes: Optimal Schedules via Quantitative Automata, Quantitative Analysis and Design of Control Networks: Wireless Protocols for Optimal Control: Quantitative Trust Management for Control Networks. Various components of this research will be integrated into the PIs' RAVEN control network which is compatible with both WirelessHART and OneWireless specifications. This provides a direct path for this proposal to have immediate industrial impact. In order to increase the broader impact of this project, this project will launch the creation of a Masters' program in Embedded Systems, one of the first in the nation. The principle that guides the curriculum development of this novel program is a unified systems view of computing, communication, and control systems.