Programming a Sensor Network as an Amorphous Medium

In many sensor network applications, the network is deployedto approximate a physical space. The network itself is not ofinterest: rather, we are interested in measuring the propertiesof the space it fills, and of establishing control over thebehavior of that space.The spatial nature of sensor network applications meansthat many can be expressed naturally and succinctly in termsof the global behavior of an amorphous medium---a continuouscomputational material filling the space of interest. Althoughwe cannot construct such a material, we can approximateit using a sensor network.Using this amorphous medium abstraction separates sensornetwork problems into two largely independent domains.Above the abstraction barrier we are concerned with longrangecoordination and concise description of applications,while below the barrier we are concerned with fast, efficient,and robust communication between neighboring devices.We apply the amorphous medium abstraction with Proto,a high-level language for programming sensor/actuator networks.Existing applications, such as target tracking andthreat avoidance, can be expressed in only a few lines of Protocode. The applications are then compiled for execution on akernel that approximates an amorphous medium. Programswritten using our Proto implementation have been verified insimulation on over ten thousand nodes, as well as on a networkof Berkeley Motes

Infrastructure for Engineered Emergence on Sensor/Actuator Networks

The ability to control emergent phenomena depends on decomposingthem into aspects susceptible to independent engineering. Forspatial self-managing systems, the amorphous-medium abstraction lets youseparate the systemÂs specification from its implementation

Programming Manifolds

Many programming domains involve the manipulation of values distributed through a manifold - examples include sensor networks, smart materials, and biofilms. This paper describes a programming semantics for manifolds based on the amorphous medium abstraction, which places a computational device at every point in the manifold. This abstraction enables the creation of programs that automatically scale to networks of different size and device density. This semantics is currently implemented in our language Proto and compiles for execution on Mica2 Motes and several other platforms

Constraint and Restoring Force

Long-lived sensor network applications must be able to self-repair and adapt to changing demands. We introduce a new approach for doing so: Constraint and Restoring Force. CRF is a physics-inspired framework for computing scalar fields across a sensor network with occasional changes. We illustrate CRFs usefulness by applying it to gradients, a common building block for sensor network systems. The resulting algorithm, CRF-Gradient, determines locally when to self-repair and when to stop and save energy. CRF-Gradient is self-stabilizing, converges in O(diameter) time, and has been verified experimentally in simulation and on a network of Mica2 motes. Finally we show how CRF can be applied to other algorithms as well, such as the calculation of probability fields

Continuous Space-Time Semantics Allow Adaptive Program Execution

A spatial computer is a collection of devices filling spacewhose ability to interact is strongly dependent on theirproximity. Previously, we have showed that programmingsuch a computer as a continuous space can allow self-scalingacross computers with different device distributionsand can increase robustness against device failure. Wehave extended these ideas to time, allowing self-scalingacross computers with different communication and executionrates. We have used a network of 24 Mica2 Motes todemonstrate that a program exploiting these ideas showsminimal difference in behavior as the time between programsteps ranges from 100 ms to 300 ms and on a configurationwith mixed rates

Fast Self-Healing Gradients

We present CRF-Gradient, a self-healing gradient algorithm that provably reconfigures in O(diameter) time. Self-healing gradients are a frequently used building block for distributed self-healing systems, but previous algorithms either have a healing rate limited by the shortest link in the network or must rebuild invalid regions from scratch. We have verified CRF-Gradient in simulation and on a network of Mica2 motes. Our approach can also be generalized and applied to create other self-healing calculations, such as cumulative probability fields

CELLO: A fast algorithm for Covariance Estimation

We present CELLO (Covariance Estimation and Learning through Likelihood Optimization), an algorithm for predicting the covariances of measurements based on any available informative features. This algorithm is intended to improve the accuracy and reliability of on-line state estimation by providing a principled way to extend the conventional fixed-covariance Gaussian measurement model. We show that in experiments, CELLO learns to predict measurement covariances that agree with empirical covariances obtained by manually annotating sensor regimes. We also show that using the learned covariances during filtering provides substantial quantitative improvement to the overall state estimate. © 2013 IEEE.United States. National Aeronautics and Space AdministrationSiemens Corporate ResearchUnited States. Office of Naval Research. Multidisciplinary University Research InitiativeMicro Autonomous Consortium Systems and Technolog

Active fuzzing for testing and securing cyber-physical systems

National Research Foundation (NRF) Singapore under its National Satellite of Excellence Programm

Iterative Structure-Based Peptide-Like Inhibitor Design against the Botulinum Neurotoxin Serotype A

The botulinum neurotoxin serotype A light chain (BoNT/A LC) protease is the catalytic component responsible for the neuroparalysis that is characteristic of the disease state botulism. Three related peptide-like molecules (PLMs) were designed using previous information from co-crystal structures, synthesized, and assayed for in vitro inhibition against BoNT/A LC. Our results indicate these PLMS are competitive inhibitors of the BoNT/A LC protease and their Ki values are in the nM-range. A co-crystal structure for one of these inhibitors was determined and reveals that the PLM, in accord with the goals of our design strategy, simultaneously involves both ionic interactions via its P1 residue and hydrophobic contacts by means of an aromatic group in the P2′ position. The PLM adopts a helical conformation similar to previously determined co-crystal structures of PLMs, although there are also major differences to these other structures such as contacts with specific BoNT/A LC residues. Our structure further demonstrates the remarkable plasticity of the substrate binding cleft of the BoNT/A LC protease and provides a paradigm for iterative structure-based design and development of BoNT/A LC inhibitors