Designing Secure Fog-to-Cloud Architectures for Resilience and Low-Latency in Military Navigation Networks
Year: 2025
Type: article
Abstract: Military navigation and command systems face escalating challenges in GPS-degraded or denied environments, necessitating architectures that are both resilient and secure.This study proposes and evaluates a fog-to-cloud framework engineered for real-time, low-latency communication across edge devices-including UAVs, ground vehicles, and maritime units-linked to cloud-based central command.The architecture incorporates adaptive routing algorithms, lightweight encryption, and AI-driven anomaly detection to maintain operational continuity in hostile or electromagnetically contested scenarios.Extensive simulations assessed performance under variable node loads, link disruptions, and cyber-attack conditions.Results revealed that adaptive fog node configurations reduced command latency by up to 48% compared to baseline systems, while maintaining throughput consistency even under high-traffic loads.Packet delivery rates exceeded 95% in predictive routing conditions, and command success ratios remained stable above 92% despite node failure events.Fault tolerance mechanisms, including trust-based link reconfiguration and traffic rerouting, demonstrated resilience and rapid recovery.The architecture also supports horizontal scalability across multi-domain platforms, making it suitable for Joint All-Domain Command and Control (JADC2) strategies and aligned with Department of Defense zero-trust mandates.By integrating distributed intelligence at the fog layer and ensuring encrypted pathways to the cloud, this system offers a robust solution for mission-critical military operations.Future deployments can extend the model to incorporate post-quantum cryptography and realworld testbeds in tactical field environments.
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411144604