A Boundary Approximation Algorithm for Distributed Sensor Networks

TL;DR

This paper introduces a decentralized boundary approximation algorithm for sensor networks that reduces remote communication by using local sensor data and geometric structures, improving efficiency in large networks.

Contribution

The paper presents a novel boundary approximation algorithm leveraging Delaunay triangulations and Voronoi diagrams for decentralized computation in sensor networks.

Findings

Reduces remote communication compared to naive methods.

Effectively identifies boundaries based on sensor reading differences.

More beneficial as network size increases.

Abstract

We present an algorithm for boundary approximation in locally-linked sensor networks that communicate with a remote monitoring station. Delaunay triangulations and Voronoi diagrams are used to generate a sensor communication network and define boundary segments between sensors, respectively. The proposed algorithm reduces remote station communication by approximating boundaries via a decentralized computation executed within the sensor network. Moreover, the algorithm identifies boundaries based on differences between neighboring sensor readings, and not absolute sensor values. An analysis of the bandwidth consumption of the algorithm is presented and compared to two naive approaches. The proposed algorithm reduces the amount of remote communication (compared to the naive approaches) and becomes increasingly useful in networks with more nodes.