EdgeGuard-AI: Zero-Trust and Load-Aware Federated Scheduling for Secure and Low-Latency IoT Edge Networks
Year: 2026
Type: article
Abstract: Edge computing is now widely used to support real-time and safety-critical IoT services. However, current edge schedulers usually optimize only performance, while security verification and trust assessment are handled as separate modules. This separation creates a practical risk: tasks may be assigned to lightly loaded but compromised edge nodes, or secure nodes may become overloaded, violating latency requirements. We propose EdgeGuard-AI, a unified trust-driven and load-aware scheduling framework inspired by zero-trust security principles for next-generation IoT edge networks. The framework jointly learns dynamic node trust and short-term workload patterns from distributed edge data and integrates both signals into scheduling decisions. Experimental results on a realistic IoT edge security dataset show a task success rate of 97.3 percent, average scheduling latency of 58.1 ms during stress periods, unsafe offloading below 2 percent, and trust discrimination AUC of 0.971.
Source: Sensors
DOI: 10.3390/s26061989
Authors: Abdulaziz G. Alanazi, Haifa A. Alanazi
Institution: Northern Border University
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funder: Northern Border University
OpenAlex ID: W7140080860