Unmanned aerial vehicle‐aided edge networks with ultra‐reliable low‐latency communications: A digital twin approach

Abstract

IET Signal ProcessingEarly View ORIGINAL RESEARCHOpen Access Unmanned aerial vehicle-aided edge networks with ultra-reliable low-latency communications: A digital twin approach Yijiu Li, Yijiu Li School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UKSearch for more papers by this authorDang Van Huynh, Dang Van Huynh orcid.org/0000-0002-2314-4934 School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UKSearch for more papers by this authorTan Do-Duy, Tan Do-Duy Department of Computer and Communications Engineering, HCMC University of Technology and Education, Hochiminh, VietnamSearch for more papers by this authorEmi Garcia-Palacios, Emi Garcia-Palacios School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UKSearch for more papers by this authorTrung Q. Duong, Corresponding Author Trung Q. Duong [email protected] orcid.org/0000-0002-4703-4836 School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK Correspondence Trung Q. Duong, School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Queen's Road, Belfast BT7 1NN, UK. Email: [email protected] for more papers by this author Yijiu Li, Yijiu Li School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UKSearch for more papers by this authorDang Van Huynh, Dang Van Huynh orcid.org/0000-0002-2314-4934 School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UKSearch for more papers by this authorTan Do-Duy, Tan Do-Duy Department of Computer and Communications Engineering, HCMC University of Technology and Education, Hochiminh, VietnamSearch for more papers by this authorEmi Garcia-Palacios, Emi Garcia-Palacios School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UKSearch for more papers by this authorTrung Q. Duong, Corresponding Author Trung Q. Duong [email protected] orcid.org/0000-0002-4703-4836 School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK Correspondence Trung Q. Duong, School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Queen's Road, Belfast BT7 1NN, UK. Email: [email protected] for more papers by this author First published: 25 April 2022 https://doi.org/10.1049/sil2.12128 This paper has been accepted in part for presentation in the first International Conference on 6G Networking (6GNet 2022) to be held on July 06-08, 2022 in Paris, France [ 1]. AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract 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. 1 INTRODUCTION 1.1 Literature review Digital Twin (DT) is an emerging technology that is able to create virtual twins of physical objects in order to facilitate the processing of control and to manage cyber-physical systems. DT can be exploited in networking and communications for many aspects, such as system modelling, physical data processing, cloud computing, and edge computing [2]. Therefore, studies of DT are attracting much attention from active researchers [3-6]. More specifically, in [3], a DT-assisted task offloading in mobile edge computing (MEC) was investigated to address the problem of minimising power and time overhead. Another work in reducing offloading latency for DT edge network was introduced in [4]. In [5], DT was proposed for intelligent authorisation in the beyond 5G smart grid applications. DT was exploited in [6, 7] to empower edge networks for the industrial Internet of things environment. These representative studies demonstrate the huge potential of DT in various domains, especially in networked systems. In recent years, unmanned aerial vehicles (UAVs) have been under the spotlight due to their flexible configuration and mobile characteristics [8, 9]. Numerous research studies have been conducted to enhance the control performance of UAVs. In [10], authors carried out the ground test of UAV, evaluating the performance of its entire flight control system through rotor speed, roll attitude, etc. The flight test [11] and collision avoidance [12] have also been studied to give a stronger control over this smart vehicle. UAVs achieve even better performance in many research areas by combining with other advanced technologies. To integrate with the intelligent reflecting surface (IRS) [13], a well-performed UAV-assisted IRS symbiotic radio system has been formed. The system performs better as data information is transferred via UAV by optimising the UAV trajectory and the IRS phase shifts. Applying a deep reinforcement learning algorithm [14], the decision making of UAVs can be autonomous rather than pre-planned. UAV's low consumption of energy also attracts public attention. Through optimisation algorithms, resource allocation can be optimised, and the total energy consumption in a multi-UAV network framework can be minimised [15, 16]. Based on their outstanding advantages, UAVs are now being developed and used in military and civil applications [17]. These applications are in many fields, such as environment monitoring, traffic control, public safety, damaged buildings detection, and industrial automation [18-20]. Particularly, with the help of UAV, Yang X et al. [21] develop a method for high-precision geolocation of distant targets, which is more effective than the conventional one-shot localisation way. Search and rescue operations with UAVs participation increase the speed of rescue and thus improve the survival rate of people [22, 23]. In Ref. [24], UAVs are used as flying base stations to ensure the connectivity of communication networks in unexpected disasters. These aerial vehicles also play a role in smart cities [19]. With the rapid development of the 5G network, ultra-reliable and low-latency communication (URLLC) emerges as a promising paradigm to ensure a certain quality of service (QoS). With strict requirements of extremely low latency (from 1 ms to few milliseconds) and ultra-high reliability (over 99.999%) [25], URLLC plays an indispensable role in remote healthcare, autonomous driving, immersive virtual reality, cloud robotics, deterministic communication, and many other areas [26]. This novel communication service uses short packet transport, which allows optimising the transmission of control information [27]. Evolved from cloud computing, MEC has been widely considered as a key application in 5G communication. This promising technology extends the capabilities of cloud computing at the network edge [28] and performs excellently in smart manufacturing, industrial Internet of things (IoT) as well as many other areas [29-31]. In recent years, many efforts have been put into MEC. In Ref. [32], the author demonstrates a well-established MEC architecture and integrates an application deployment use case, establishing a proof of concept, which is very similar to the actual deployment of the MEC system in a 5G environment. Combining with the optimising method, MEC is an appropriate solution to improve the quality of service. In Ref. [33], the author proposes a Reinforcement Learning (RL)-based optimisation framework to minimise the cost of delay and energy consumptions for user equipment in a time-variant dynamic MEC system. The considered MEC system outperforms other baseline solutions according to the demonstration, whereas Wu J et al. [34] adopt an offloading strategy in MEC that considers delay and energy consumptions of cost optimisation. Two schemes named optimised OMA and hybrid NOMA are proposed in [35] for solving the problem of joint power and time allocation for MEC offloading. Through these extensive studies of MEC, this evolving technology has been used in various use cases, for example, an audience metre [36]. In this particular use case, MEC modules are used to improve the algorithm's performance, detecting the estimated number of participants in an event over the entire time period. MEC and URLLC techniques are closely related to each other. Under the sufficiently powerful computing of MEC, applications can be processed in real time. By reducing the transmission processing time and reception processing time, M