Abstract: Programming wireless embedded networks is challenging due to severe limitations on processing speed, memory, and bandwidth. Staged programming can help bridge the gap between high level code refinement techniques and efficient device level programs by allowing a first stage program to specialize device level code. Here we introduce a two stage programming system for wireless sensor networks. The first stage program is written in our extended dialect of Scala, called Scalaness, where components written in our type safe dialect of nesC, called nesT, are composed and specialized. Scalaness programs can dynamically construct TinyOS-compliant nesT device images that can be deployed to motes. A key result, called cross-stage type safety, shows that successful static type checking of a Scalaness program means no type errors will arise either during programmatic composition and specialization of WSN code, or later on the WSN itself. Scalaness has been implemented through direct modification of the Scala compiler. Implementation of a staged public-key cryptography calculation shows the sensor memory footprint can be significantly reduced by staging.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).