Connectivity-Aware Pheromone Mobility Model for Autonomous UAV Networks

TL;DR

This paper introduces a connectivity-aware pheromone mobility model for autonomous UAV networks that balances area coverage and network connectivity using stigmergy-based coordination.

Contribution

It proposes a novel decentralized mobility model that enhances coverage and connectivity in UAV networks through digital pheromone maps and local connectivity data.

Findings

Improved network connectivity among UAVs during search and rescue operations.

Enhanced area coverage efficiency with decentralized coordination.

Maintained high connectivity while optimizing coverage.

Abstract

UAV networks consisting of reduced size, weight, and power (low SWaP) fixed-wing UAVs are used for civilian and military applications such as search and rescue, surveillance, and tracking. To carry out these operations efficiently, there is a need to develop scalable, decentralized autonomous UAV network architectures with high network connectivity. However, the area coverage and the network connectivity requirements exhibit a fundamental trade-off. In this paper, a connectivity-aware pheromone mobility (CAP) model is designed for search and rescue operations, which is capable of maintaining connectivity among UAVs in the network. We use stigmergy-based digital pheromone maps along with distance-based local connectivity information to autonomously coordinate the UAV movements, in order to improve its map coverage efficiency while maintaining high network connectivity.