EdgeAgentX-DT: Integrating Digital Twins and Generative AI for Resilient Edge Intelligence in Tactical Networks
This work addresses the problem of resilient edge AI in contested tactical networks for military applications, representing an incremental improvement over existing frameworks.
The paper tackles the challenge of enhancing edge intelligence in military networks by integrating digital twins and generative AI for scenario training, resulting in faster learning convergence, higher throughput, reduced latency, and improved resilience against jamming and node failures compared to baseline methods.
We introduce EdgeAgentX-DT, an advanced extension of the EdgeAgentX framework that integrates digital twin simulations and generative AI-driven scenario training to significantly enhance edge intelligence in military networks. EdgeAgentX-DT utilizes network digital twins, virtual replicas synchronized with real-world edge devices, to provide a secure, realistic environment for training and validation. Leveraging generative AI methods, such as diffusion models and transformers, the system creates diverse and adversarial scenarios for robust simulation-based agent training. Our multi-layer architecture includes: (1) on-device edge intelligence; (2) digital twin synchronization; and (3) generative scenario training. Experimental simulations demonstrate notable improvements over EdgeAgentX, including faster learning convergence, higher network throughput, reduced latency, and improved resilience against jamming and node failures. A case study involving a complex tactical scenario with simultaneous jamming attacks, agent failures, and increased network loads illustrates how EdgeAgentX-DT sustains operational performance, whereas baseline methods fail. These results highlight the potential of digital-twin-enabled generative training to strengthen edge AI deployments in contested environments.