Data Transmission Between a Drone Swarm and a Ground Base: Modern Methods and Technologies—A Narrative Review
Year: 2025
Type: review
Abstract: BACKGROUND The objective of this study was to analyse contemporary approaches, identify key challenges, and provide recommendations for optimising data exchange with regard to speed, reliability, energy efficiency, and security. MATERIALS AND METHODS This work presents a structured narrative review analysing modern methods and technologies for data transmission between a drone swarm and a ground base, enabling an assessment of the effectiveness of various approaches in ensuring stable, rapid, and secure communication under conditions of high mobility and dynamic network topology. RESULTS The research indicated that 5G provides superior bandwidth (up to 10 Gbit/s) and negligible latency (under 1 ms), although its implementation is constrained by range (up to 1 km) and the requirement for advanced infrastructure. MANETs and DTNs offer adaptability in dynamic settings but encounter latency (up to several minutes) and connection stability challenges. Wi-Fi mesh networks provide effective coverage and stability. Nonetheless, they necessitate energy consumption optimisation for autonomous operation. Principal problems encompass interference, restricted drone power, and the necessity for improved security in conveyed data. CONCLUSION Combined approaches are proposed, incorporating dynamic routing, adaptive frequency management, and blockchain integration to improve authentication and data protection. Additionally, the use of machine learning algorithms for real-time threat detection and more efficient network resource allocation is explored. The findings can be further applied to enhance wireless communication systems in unmanned networks.
Source: Premier journal of science.
DOI: 10.70389/pjs.100116
Authors: Roman Zaivyi, Oleh Melnychok, Vitalii Skochelias
Institution: Lviv Polytechnic National University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415063702