Security, privacy and safety evaluation of dynamic and static fleets of drones

Chaumette, Serge; Akram, Raja Naeem; Markantonakis, Konstantinos; Mayes, Keith; Habachi, Oussama; Sauveron, Damien · 2017 · OpenAlex-citations

DOI: 10.1109/dasc.2017.8101984

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Summary

This paper addresses the growing integration of Unmanned Aerial Vehicles (UAVs) into civilian, commercial, and military sectors, focusing on the security, privacy, and safety challenges of operating drone fleets. The authors argue that while individual drones are useful, fleets—particularly those utilizing Swarm Intelligence (SoD)—offer superior robustness, cost-effectiveness, and operational flexibility. However, current management models often rely on centralized ground control, which is vulnerable to communication latency, congestion, and failure in challenging terrains. The research is motivated by the need for autonomous, self-aware fleets that can perform mission-critical operations (flight control, obstacle avoidance, cybersecurity) independently, especially in dynamic environments where drones may join or leave the fleet. The study employs a conceptual analysis and literature review rather than empirical experimentation. It categorizes drone fleets into static (fixed membership), dynamic (changing membership), and hybrid structures. The authors propose a conceptual architecture for SoDs that leverages swarm intelligence to distribute computational loads and decision-making processes among drones, thereby reducing reliance on ground control stations. The paper evaluates existing related work in swarm robotics, secure communication protocols, and energy management, identifying gaps in holistic approaches that simultaneously address security, safety, and resource constraints. It further outlines specific use cases, including rescue operations in remote areas, infrastructure monitoring, military surveillance, and data collection from wireless sensor networks, to illustrate the practical applicability of autonomous fleets. Key findings highlight the trade-offs between autonomy and resource constraints. The authors identify that while swarm intelligence enables resilience and adaptability, it imposes significant computational and energy burdens on resource-constrained drones. Security challenges include potential physical capture of drones, signal jamming, and unauthorized data access, necessitating lightweight cryptographic protocols that preserve privacy without excessive energy consumption. The paper concludes that a holistic approach is required, where security, safety, and performance metrics are integrated into the swarm’s design. Specifically, load-sharing mechanisms and efficient algorithms are essential to mitigate the power consumption associated with encryption and real-time decision-making. The significance of this work lies in its comprehensive framework for evaluating and designing secure, autonomous drone fleets. By shifting the focus from centralized control to decentralized swarm intelligence, the paper provides a roadmap for developing resilient UAV systems capable of operating in unpredictable environments. It emphasizes that future drone deployments must balance the benefits of autonomy with the strict requirements of energy efficiency and cybersecurity, offering a foundation for further research into standardized protocols and architectures for dynamic drone fleets.

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