Download Algorithmic Aspects of Wireless Sensor Networks: Fourth by Roger Wattenhofer (auth.), Sándor P. Fekete (eds.) PDF

By Roger Wattenhofer (auth.), Sándor P. Fekete (eds.)

This publication constitutes the reviewed lawsuits of the Fourth foreign Workshop on Algorithmic elements of instant Sensor Networks, ALGOSENSORS 2008, held in Reykjavik, Iceland, Wroclaw, Poland, July 12, 2008.

The workshops aimed toward bringing jointly learn contributions regarding different algorithmic and complexity-theoretic features of instant sensor networks. the subjects comprise yet will not be constrained to optimization difficulties, noise and likelihood, robots and excursions.

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Extra resources for Algorithmic Aspects of Wireless Sensor Networks: Fourth International Workshop, ALGOSENSORS 2008, Reykjavik, Iceland, July 2008. Revised Selected Papers

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In [24], the authors look at the problem of counting the number of people in a crowd using image sensors. They subtract background from the image and then count number of visual hulls to count number of people. Their focus is on geometric hull computation and our techniques can be used on top of their algorithms to provide the bounds and counts. In [25] the authors use topological integration theory to provide expected target counts as compared to the deterministic bounds provided in this paper.

Neither of these assumptions are critical to our algorithms and analysis, as we later discuss, but provide a convenient framework to understand the fundamental limits of target counting. Because the communication requirements of our collaborative counting are so minimal (each sensor only needs to communicate its reading), we abstract away all networking issues in our discussion. In particular, we assume that all the processing occurs at a base station, or a tracker node, that knows the precise geometry of the sensors’ locations and ranges.

Suri We begin our discussion by considering the problem in a one-dimensional setting. We imagine targets as points arranged on a line, and a collection of sensors, each with a unit-interval sensing range. It turns out that the exact counting of targets is non-trivial even in this simple setting, and leads to some interesting results. The 1-dimensional setting is also a useful framework in many practical situations, such as counting targets along a road or counting objects in a crowd using far away cameras.

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