Digital Twin-assisted Offloading for Low-Latency and Energy-Efficient Multi-Layer Network

The sixth-generation (6G) is expected to offer ubiq-uitous connectivity, high data rate, low latency, energy efficiency, and edge intelligence for Internet of things (IoT) applications. To achieve this, the digital twin is considered as a potential technology in multi-layer wireless networks with IoT devices on the ground, unmanned aerial vehicles (UAVs) as mobile edge computing (MEC) servers, and cloud server. Multi-layer processing is used to handle time-sensitive and computationally intensive tasks by IoT devices. This paper proposes a digital twin-assisted multi-layer network for low-latency and energy-efficient communication and computation. We mathematically formulate an optimization problem to minimize IoT devices' latency and energy consumption by optimizing their association with the UAV-MECs, computation resources, communication resources, and offloading portions of tasks. We propose a multi-stage solution based on a learning algorithm and interior point method to solve the problem. The simulation results demonstrate the usefulness of the proposed multi-layer network.