Coverage and Energy Analysis of Mobile Sensor Nodes in Obstructed Noisy Indoor Environment: A Voronoi Approach

TL;DR

This paper introduces BISON, a Voronoi-based algorithm for deploying mobile sensor nodes in obstacle-rich, noisy indoor environments, optimizing coverage, energy use, and node distribution.

Contribution

It presents a novel bio-inspired Voronoi algorithm that enables sensor nodes to self-organize in complex indoor environments using local, noisy data.

Findings

BISON outperforms previous methods in convergence speed.

It reduces deployment costs significantly.

The approach maintains high coverage despite obstacles and noise.

Abstract

The rapid deployment of wireless sensor network (WSN) poses the challenge of finding optimal locations for the network nodes, especially so in (i) unknown and (ii) obstacle-rich environments. This paper addresses this challenge with BISON (Bio-Inspired Self-Organizing Network), a variant of the Voronoi algorithm. In line with the scenario challenges, BISON nodes are restricted to (i) locally sensed as well as (ii) noisy information on the basis of which they move, avoid obstacles and connect with neighboring nodes. Performance is measured as (i) the percentage of area covered, (ii) the total distance traveled by the nodes, (iii) the cumulative energy consumption and (iv) the uniformity of nodes distribution. Obstacle constellations and noise levels are studied systematically and a collision-free recovery strategy for failing nodes is proposed. Results obtained from extensive simulations show the algorithm outperforming previously reported approaches in both, convergence speed, as well as deployment cost.