International Conference on Computer and Knowledge Engineering

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Energy-Aware Dynamic Digital Twin Placement in Mobile Edge Computing

Authors :

Mahdi Hematyar¹ Zeinab Movahedi²

1- School of Computer Engineering, Iran University of Science and Technology 2- School of Computer Engineering, Iran University of Science and Technology

Keywords :

IoT،Digital twin،Placement،Mobile Edge Computing،5G

Abstract :

5G and beyond networks promise to revolutionize device connectivity and interaction with seamless connectivity, fast response times, and intelligent features. With the development of IoT and its need for high-speed, low-latency connectivity, these networks will play a critical role in enabling the next generation of smart and connected systems. Digital twin technology will further enhance the capabilities of these systems by providing accurate and dynamic simulations of physical assets, enabling organizations to make better decisions and optimize their operations. Despite the prior research conducted in the field of optimal placement of digital twins, a few of these studies have overlooked the impact of user transmission delay, while none have addressed user energy consumption. The focus is on optimizing the positioning of digital twins at the edge servers while taking into account the energy consumption of users and ensuring reduced latency between physical devices and their corresponding digital twins. Additionally, the paper explores the potential for social-aware arrangements of digital twins to streamline the process of data exchange and service discovery across the Social Internet of Things (SIoT) network. This work utilized the CPLEX optimization tool to obtain the optimal placement of IoT digital twins at the edge servers, taking into account both user transmission delay and energy consumption. The evaluation results indicate that the proposed model enables optimal placement of digital twins in edge servers, resulting in 8% reduction in latency and 10% decrease in energy consumption, compared to alternative work.