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.
Alanazi et al. (Mon,) studied this question.