Localizability of Wireless Sensor Networks: Beyond Wheel Extension

TL;DR

This paper introduces a distributed algorithm that efficiently identifies a broad class of localizable wireless sensor networks using only connectivity data, extending beyond existing wheel extension methods.

Contribution

It presents a novel distributed technique capable of recognizing a larger class of localizable networks without requiring distance measurements.

Findings

The algorithm successfully identifies localizable networks using only connectivity.

It extends the class of networks detectable beyond wheel extensions.

The method operates efficiently in distributed environments.

Abstract

A network is called localizable if the positions of all the nodes of the network can be computed uniquely. If a network is localizable and embedded in plane with generic configuration, the positions of the nodes may be computed uniquely in finite time. Therefore, identifying localizable networks is an important function. If the complete information about the network is available at a single place, localizability can be tested in polynomial time. In a distributed environment, networks with trilateration orderings (popular in real applications) and wheel extensions (a specific class of localizable networks) embedded in plane can be identified by existing techniques. We propose a distributed technique which efficiently identifies a larger class of localizable networks. This class covers both trilateration and wheel extensions. In reality, exact distance is almost impossible or costly. The proposed algorithm based only on connectivity information. It requires no distance information.