Secure and Energy-Efficient Resource Optimization Framework for Real-Time Task Execution in Industrial IoT

This paper presents a secure and energy-efficient resource optimization framework for Industrial IoT (IIoT) task scheduling. Although prior studies have explored various aspects of cost, performance, energy efficiency, compatibility, and security, few have incorporated all these factors into a unified framework. To consider computational cost while satisfying real-time deadline constraints, we adopt public edge servers and ensure secure execution against potential threats from the host OS and privileged administrators. Unlike conventional application-level TEEs (Trusted Execution Environments), our framework extends security to the entire virtual machine level, enabling seamless compatibility between local execution and offloaded tasks. Efficient task scheduling requires the simultaneous optimization of processor voltage scaling, memory placement, and offloading decisions based on workload intensity and resource conditions. Through a detailed analysis, we identify strong interdependencies among system resources and propose a task model that accounts for these interactions. By incorporating fine-grained execution time and energy models, which precisely capture processor-memory interactions, parallelism between local and remote executions, and TEE-added offloading overheads, our framework guarantees real-time deadline compliance while minimizing energy consumption. Additionally, it enables secure and compatible IIoT deployment, effectively balancing cost, performance, security, and energy efficiency.