New applications are motivating and informing the design of sensor/actuator networks, and, more broadly, research in cyber-physical systems (CPS). Our knowledge of many physical systems is uncertain, so that sensing and actuation must be mediated by inference of the structure and parameters of physical-system models. One CPS application domain of growing interest is ecological systems, motivated by the need to understand plant survival and growth as a function of genetics, environment, and climate change. For this effort to be successful, we must be able to infer coupled, data-driven predictive models of plant growth dynamics in response to climate drivers that allow incorporation of uncertainty. We are developing an architecture and implementation for precise fine-scale control of irrigation in an array of geographicallydistributed outdoor gardens on an elevational gradient of over 1500 m, allowing design of experiments that combine control of temperature and water availability. This paper describes a system architecture and implementation for this class of cybereco systems, including sensor/actuator node design, site-level networking, data assimilation, inference, and distributed control. Among its innovations are a modular, parallel-processing node hardware design allowing real-time processing and heterogeneous nodes, energy-aware hardware/software design, and a networking protocol that builds in trade-offs between energy conservation and latency. Throughout, we emphasize the changes in system architecture required as missions evolve from sensing-only to sensing, inference, and control. We also describe our developmental implementation of the architecture and its planned deployment. Future extensions will likely add negative control of precipitation using active rain-out shelters and additional plant-level control of air or soil temperature.
In the last decade, it has become apparent that the grand challenge problems of this century span disciplines. In spite of this, engineering curricula are still strongly stovepiped, even within each engineering discipline, and both inertia and downward budget pressures encourage curricular conservatism. At the same time, the need is urgent to expose students to the diversity and complexity of real-world problems where there is no "best" solution. How should we help students learn across disciplines and blend disciplinary knowledge to solve problems? This paper describes a laboratory project suitable for courses in areas of control and embedded systems that weaves critical aspects of control systems design with real-time embedded systems hardware and software, and along the way incorporates additional skills and tools. The project builds on previous efforts that have used the classic "ball-in-tube" experimental platform. We have developed an extremely low-cost experimental platform that student teams assemble from simple parts (e.g., shoeboxes and muffin fans), and that uses wireless communication between the real-time platform and a personal computer that provides a human interface and analytical tools. For real-time data acquisition and control, we adopted the CLIO platform that was designed for the experiential component of MUSE (Multi-University Systems Education, www.uvm.edu/~muse), an NSF-sponsored pedagogical effort to increase the ability of students to become conversant in skills related to systems thinking. In this spirit, the work discussed herein exposes students to experimentation, modeling and design across system layers. While tackling the project, students have also become more adept at (i) architecting distributed applications that integrate embedded and desktop computing systems, (ii) data acquisition, including measurement noise and signal conditioning, (iii) actuation, including motor control, and (iv) wireless communication. We present early assessment results evaluating how effectively the project helps students build critical systems-thinking skills, and the challenges of adopting resources for fast-tracking the development of new laboratory projects.
Abstract-Today's wirelessly networked embedded systems underlie a vast array of electronic devices, performing computation, communication, and input/output. A major design goal of these systems is energy efficiency. To achieve this goal, these systems are based on processors with numerous power and clock domains, variable clock rates, voltage scaling, and multiple hibernation states. These processors are designed into systems with sophisticated wireless transceivers and a diverse array of off-chip peripherals, and are linked through regulators to increasingly complex energy supplies. As a result, modern networked embedded systems are characterized by myriad power consumption states and significant power signal transients. Moreover, their power demands are multiscale in both magnitude and time, combining short bursts of high demand with long intervals of power-sipping sleep states. Thus the power supply signals have wideband spectra. In addition, due to noise, uniform relative precision across magnitude scales requires that measurement time increases with decreasing power. Tools are needed that support modeling, hardware/software optimization, and debugging for energy-centric embedded systems. This paper describes Prospector, an energy data acquisition system architecture for embedded systems that allows rapid, accurate, and precise assessment of system-level power usage. Prospector uses a distributed control architecture; each component contributes efficiently to control, precision and accuracy, analysis, and visualization. It is based on computerbased control of multimeters to maximize accuracy, precision, flexibility, and minimize target system overhead. Experimental results for a prototype Prospector system with a contemporary 16-bit ultra-low power microcontroller show that it can effectively measure power over the extreme time and magnitude scales found in today's embedded systems.
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