Channel and active component abstractions for WSN programming - a language model with operating system support

To support the programming of Wireless Sensor Networks, a number of unconventional programming models have evolved, in particular the event-based model. These models are non-intuitive to programmers due to the introduction of unnecessary, non-intrinsic complexity. Component-based languages like Insense can eliminate much of this unnecessary complexity via the use of active components and synchronous channels. However, simply layering an Insense implementation over an existing event-based system, like TinyOS, while proving efficacy, is insufficiently space and time efficient for production use. The design and implementation of a new language-specific OS, InceOS, enables both space and time efficient programming of sensor networks using component-based languages like Insense

Graph Sparsification by Edge-Connectivity and Random Spanning Trees

We present new approaches to constructing graph sparsifiers --- weighted subgraphs for which every cut has the same value as the original graph, up to a factor of $(1 \pm \epsilon)$. Our first approach independently samples each edge $uv$ with probability inversely proportional to the edge-connectivity between $u$ and $v$. The fact that this approach produces a sparsifier resolves a question posed by Bencz\'ur and Karger (2002). Concurrent work of Hariharan and Panigrahi also resolves this question. Our second approach constructs a sparsifier by forming the union of several uniformly random spanning trees. Both of our approaches produce sparsifiers with $O(n \log^2(n)/\epsilon^2)$ edges. Our proofs are based on extensions of Karger's contraction algorithm, which may be of independent interest