On Connectivity-Aware Distributed Mobility Models for Area Coverage in Drone Networks

TL;DR

This paper compares three connectivity-aware mobility models for drone networks, analyzing their effectiveness in area coverage, connectivity, and communication, and highlights the superior performance of certain models in maintaining network quality.

Contribution

It provides a comparative analysis of three known connectivity-aware mobility models and evaluates their performance in drone network coverage and connectivity.

Findings

DPR model offers decent coverage performance.

Connectivity-based and Connected Coverage models improve connectivity and communication.

Non-connectivity-aware models serve as control benchmarks.

Abstract

Drone networks are becoming increasingly popular in recent years and they are being used in many applications such as area coverage, delivery systems, military operations, etc. Area coverage is a broad family of applications where a group of connected drones collaboratively visit the whole or parts of an area to fulfill a specific objective and is widely being researched. Accordingly, different mobility models have been designed to define the rules of movements of the participating drones. However, most of them do not consider the network connectivity which is crucial, plus many models lack the priorities and optimization strategies that are important for drone networks. Therefore within this study, three known connectivity-aware mobility models have been analyzed comparatively. Two non-connectivity-aware mobility models have further been implemented to catch the placebo effect if any. Per the detailed experiments on the mobility models, coverage rates, connectivity levels, and message traffic have been evaluated. The study shows that the Distributed Pheromone Repel (DPR) model provides a decent coverage performance, while the Connectivity-based model and the Connected Coverage model provide better connectivity and communication quality.