Latency and Energy Modeling of Blockchain-Digital Twin-Relied Edge Computing Task Scheduling

Integration of blockchain and digital twin technologies has proven to offer distributed and efficient solutions for addressing edge computing task scheduling. However, limited research has comprehensively addressed the modeling, optimization, and analysis of latency and energy consumption within such integrated frameworks. To fill this gap, this paper presents a generic architecture that combines blockchain and digital twin layers to facilitate edge computing task scheduling. A system model is developed across all layers, from which a multi-objective optimization problem is formulated to minimize both overall latency and energy consumption. Furthermore, the feasibility of this approach is demonstrated through three representative task scheduling algorithms—Mixed Integer Linear Programming (MILP), First Come First Serve (FCFS), and Shortest Job First (SJF)—achieving latency reductions of up to 55.43 ms and energy consumption as low as 0.296 J.