Formation-based Selection of Drone Swarm Services

TL;DR

This paper introduces a formation-guided framework for selecting drone swarm services in delivery missions, optimizing energy use by considering formations and environmental factors like wind.

Contribution

It presents a novel formation-based approach for SDaaS selection, including fixed and adaptive algorithms that improve energy efficiency under real-world conditions.

Findings

Formation impacts energy consumption significantly.

Proposed algorithms outperform baseline methods.

Framework effectively accounts for environmental constraints.

Abstract

Swarm of drones are increasingly being asked to carry out missions that can't be completed by one drone. Particularly, in delivery, issues arise due to the swarm's limited flight endurance. Hence, we propose a novel formation-guided framework for selecting Swarm-based Drone-as-a-Service (SDaaS) for delivery. A detailed study is carried out to highlight the effect of swarm formations on energy consumption. Two SDaaS selection approaches, i.e. Fixed and Adaptive, are designed considering the different formation decisions a swarm can take. The proposed framework considers extrinsic constraints including wind speed and direction. We propose SDaaS selection algorithms for each approach. Experimental results prove the efficiency of the proposed algorithms.