Deterministic boundary recognition and topology extraction for large sensor networks

TL;DR

This paper introduces a framework enabling large, immobile sensor networks to self-organize into structures reflecting their environment's topology without coordinate or distance measurements.

Contribution

It proposes algorithms for boundary recognition and topology extraction in sensor networks with minimal assumptions about node capabilities.

Findings

Successfully identifies environment boundaries

Extracts topological features of the environment

Enables large-scale network organization

Abstract

We present a new framework for the crucial challenge of self-organization of a large sensor network. The basic scenario can be described as follows: Given a large swarm of immobile sensor nodes that have been scattered in a polygonal region, such as a street network. Nodes have no knowledge of size or shape of the environment or the position of other nodes. Moreover, they have no way of measuring coordinates, geometric distances to other nodes, or their direction. Their only way of interacting with other nodes is to send or to receive messages from any node that is within communication range. The objective is to develop algorithms and protocols that allow self-organization of the swarm into large-scale structures that reflect the structure of the street network, setting the stage for global routing, tracking and guiding algorithms.