A digital twin (DT) framework for Internet-of-thing (IoT) networks is proposed where unmanned aerial vehicles (UAVs) acting as flying mobile edge computing (MEC) servers support the task offloading on the fly. The considered DT model is very well suitable for industrial automation with the strict constraints of mission-critical services' ultra-reliable low-latency communication (URLLC) links. To support low-latency IoT devices, we formulate the end-to-end (e2e) latency minimisation problem of digital twin-aided offloading UAV-URLLC. Specifically, the minimised latency is obtained by jointly optimising both communication and computation parameters, namely power, offloading factors, and the processing rate of IoT devices and MEC-UAV servers. Due to the highly non-convex optimisation problem, we first consider the K-means clustering algorithm to optimally deploy the on-demand UAVs. Then, an alternative optimisation approach combined with appropriate inner approximations is effectively exploited to tackle this challenge. We demonstrate the effectiveness of the proposed DT framework through representative numerical results.
- ORIGINAL RESEARCH
- alternating optimisation
- digital twin
- edge networks
- ultra‐reliable and low latency communications (URLLC)