This paper presents and evaluates two location sensing algorithms that we have developed and demonstrated. We present comparative accuracy results, complexity of training the system, and total power consumption required to perform scanning. Our method reduces training complexity by a factor of eight, and yields noticeable better accuracy. The paper also introduces a location information privacy model and reports on user study results. Our results indicate that users expect two unique behaviors from the privacy system, an introvert model where privacy is preferred, and an extrovert model where availability of information is preferred.

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Sketching and streaming algorithms are in the forefront of current research directions for cut problems in graphs. In the streaming model, we show that (1 − ε)-approximation for MaxCut must use n 1−O(ε) space; moreover, beating 4/5-approximation requires polynomial space. For the sketching model, we show that r-uniform hypergraphs admit a (1 + ε)-cut-sparsifier (i.e., a weighted subhypergraph that approximately preserves all the cuts) with O(ε −2 n(r + log n)) edges. We also make first steps towards sketching general CSPs (Constraint Satisfaction Problems).

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