Network Localization by Shadow Edges

TL;DR

This paper introduces a novel framework for sensor network localization that leverages shadow edges, representing the absence of communication, to reduce positional uncertainty and require only bi-connected, rigid communication graphs.

Contribution

It proposes a new localization method using shadow edges, enabling networks to be localized with bi-connected, rigid graphs instead of globally rigid ones.

Findings

Shadow edges effectively reduce localization uncertainty.

Bi-connected, rigid graphs suffice for accurate localization.

The approach relaxes traditional connectivity requirements.

Abstract

Localization is a fundamental task for sensor networks. Traditional network construction approaches allow to obtain localized networks requiring the nodes to be at least tri-connected (in 2D), i.e., the communication graph needs to be globally rigid. In this paper we exploit, besides the information on the neighbors sensed by each robot/sensor, also the information about the lack of communication among nodes. The result is a framework where the nodes are required to be bi-connected and the communication graph has to be rigid. This is possible considering a novel typology of link, namely Shadow Edges, that account for the lack of communication among nodes and allow to reduce the uncertainty associated to the position of the nodes.