In this letter, we propose a novel digital twin scheme supported metaverse by jointly considering the integrated model of communications, computing, and storage through the employment of mobile edge computing (MEC) and ultra-reliable and low latency communications (URLLC). The MEC-based URLLC digital twin architecture is proposed to provide powerful computing infrastructure by exploring task offloading, and task caching techniques in nearby edge servers to reduce the latency. In addition, the proposed digital twin scheme can guarantee stringent requirements of reliability and low latency, which are highly applicable for the future networked systems of metaverse. For this first time in the literature, our paper addresses the optimal problem of the latency/reliablity in digital twins-enabled metaverse by optimizing various communication and computation variables, namely, offloading portions, edge caching policies, bandwidth allocation, transmit power, computation resources of user devices and edge servers. The proposed scheme can improve the quality-of-experience of the digital twin in terms of latency and reliability with respect to metaverse applications.

Many of the devices used in Internet-of-Things (IoT) applications are energy-limited, and thus supplying energy while maintaining seamless connectivity for IoT devices is of considerable importance. In this context, we propose a simultaneous wireless power transfer and information transmission scheme for IoT devices with support from the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communications. In particular, in the first phase, IoT devices harvest energy from the UAV through wireless power transfer; and then in the second phase, the UAV collects data from the IoT devices through information transmission. To characterise the agility of the UAV, we consider two scenarios: a hovering UAV and a mobile UAV. Aiming at maximising the total network sum-rate, we jointly optimise the trajectory and the power allocation of the UAV, the energy harvesting scheduling of IoT devices, and the phase-shift matrix of the RIS. We formulate a Markov decision process and propose two deep reinforcement learning algorithms to solve the optimisation problem of maximising the total network sum-rate. Numerical results illustrate the effectiveness of the UAV’s flying path optimisation and the network’s throughput of our proposed techniques compared with other benchmark schemes. Given the strict requirements of the RIS and UAV, the significant improvement in processing time and throughput performance demonstrates that our proposed scheme is well applicable for practical IoT applications.

The concept of digital twin (DT) is constantly revealing as a key enabling technology for the deployment of mobile communication services envisaged for the sixth-generation (6G) Internet-of-Things (IoT). This paper aims at providing a comprehensive review of the current state-of-the-art DT-enabled 6G oriented network services. The main characteristics of this new key enabling technology and its critical aspects are highlighted. An overview of the 6G network requirements for the deployment of its innovative envisioned services is firstly provided, emphasizing how the DT concept represents a complementary key enabling technology for them. This is followed by a brief introduction of the DT technology. Subsequently, a comprehensive classification and analysis of the research advancements on DT-enabled 6G services currently available in literature is provided. This paper is concluded by highlighting the most representative challenges and future directions necessary for the deployment of this promising and innovative technology.

Ultra-reliable low-latency communications (URLLCs) and the adoption of unmanned aerial vehicles (UAVs) for network coverage improvement have emerged as key enabling communication paradigms for the successful deployment of mobile communication services envisioned in both the fifth-generation (5G) and the sixth-generation (6G) networks. This paper provides a comprehensive survey about the current state-of-the-art (SoA) on UAV-enabled URLLC networks. The core idea is to highlight the main characteristics of this new network concept as well as its critical aspects. We first perform an overview of URLLC by illustrating the main features and related implementation challenges. Subsequently, an in-depth discussion on UAV-enabled networks is provided, with a special emphasis on how URLLCs and UAV communication can be classified as complementary paradigms. Finally, a comprehensive analysis and classification of the current research advancements on UAV-enabled URLLCs networks is carried out. This paper is concluded by pointing out some the open challenges and our visions related to future directions which should be undertaken in order to pave the way towards the practical implementation of this promising network architecture.

Introduction: Nowadays, mass screening campaigns for colorectal cancer diagnosis in the early and curable stage is essential yet limited due to many reasons, for example, invasiveness, fear of pain, and embarrassment for patients. Indeed, mass screening programs, allowing precancerous lesion detection, are the most cost-effective way to reduce mortality and potentially eradicate colorectal cancer threat. Accurate localization represents a key element in capsule endoscopy, that is, estimation of position and orientation of endoscopic capsule devices enables enhancement of technological and medical features, such as reliable closed-loop control of active-locomotion capsules, accurate lesions localization, retargeting of pathologies, and follow-up. Areas covered: This contribution provides an exhaustive and critical review of localization strategies, both internal and external, implemented so far for endoscopic capsules. Starting from basic theoretical principles, it describes the most promising methodologies, for example, magnetic and electromagnetic-based ones, but also other significant techniques, such as, ultrasound-based localization strategies. Expert commentary: Authors believe that the integration of external and internal localization methodologies with a multimodality approach can increase the overall accuracy and reliability of the endoscopic device pose estimation both for establishing an optimal control during the endoluminal procedure within a deformable environment and for autonomously identifying position of internal pathologies for retargeting and follow-up.

In this paper, we propose an aerial reconfigurable intelligent surface (RIS) system to support the stringent constraints of ultra-reliable low latency communication (URLLC). Specifically, unmanned aerial vehicles (UAVs) employed onboard RIS panels can act as repeaters to reflect the signal from macro base station (MBS) to all users in the networks. To overcome the dense networks’ interference, we propose to use zero-forcing beamforming and time division multiplexing access (TDMA) scheme where each UAV can serve a number of users in its own cluster. We formulate a optimisation framework in terms of UAVs’ deployment, power allocation at MBS, phase-shift of RIS, and blocklength of URLLC. Due to highly nonconvex and complex optimisation problem, we first consider to use a deep neural network (DNN) to solve the optimal UAVs’ deployment. Then, the optimal resource allocation is proposed to provide the maximal reliability of the considered system with respect to the users’ fairness. From the representative numerical results, our proposed scheme is shown to superior to other benchmarks which exhibits the positive impact of aerial RIS in supporting stringent demands of URLLC.