Energy- and Cost-Efficient Healthcare Task Offloading via Network Edge Digital Twin

This paper proposes a digital twin (DT)-enabled edge computing framework for healthcare task offloading in smart medical environments. The DT maintains a continuously synchronized virtual representation of the physical edge system, capturing queue states, resource utilization, and energy dynamics. Based on this virtual state, a deterministic optimization model is formulated to support real-time offloading and scheduling decisions under latency, energy, and fairness constraints. Unlike prediction-only approaches, the proposed DT operates in a closed-loop manner, where state estimation and synchronization directly influence scheduling feasibility and system performance. The offloading problem is formulated as a mixed-integer linear programming (MILP) model to jointly optimize task allocation, delay minimization, and energy efficiency. The simulated workload includes 10,000 heterogeneous healthcare applications. At each time slot, one to five tasks are generated and uniformly selected from ECG, video-processing, or medical-imaging workloads, with average task sizes of 90 KB, 280 KB, and 150 KB, respectively. This setup aims to emulate diverse real-world healthcare edge workloads with varying communication and computation demands. The proposed approach significantly reduces average latency by up to 57%, eliminates task drops in all evaluated scenarios, and improves load balancing compared with hospital-only and round-robin baselines. Although total energy consumption increases moderately, energy efficiency per completed task improves due to more effective scheduling by stability, reducing deadline violations and enhancing resource utilization. We further analyze the impact of DT freshness and updated frequency and show that outdated or misaligned DT updates can degrade performance by increasing delay and leading to suboptimal decisions, while overly frequent updates introduce additional coordination overhead. These results highlight the importance of jointly designing DT synchronization mechanisms and optimization-based scheduling strategies for reliable and cost-efficient healthcare edge systems.