Telemedicine network latency management system in 5G telesurgery: a feasibility and effectiveness study.

BACKGROUND: Network latency is the most important factor affecting the performance of telemedicine. The aim of the study is to assess the feasibility and efficacy of a novel network latency management system in 5G telesurgery. METHODS: We conducted 20 telesurgery simulation trials (hitching rings to columns) and 15 remote adrenalectomy procedures in the 5G network environment. Telemedicine Network Latency Management System and the traditional "Ping command" method (gold standard) were used to monitor network latency during preoperative simulated telesurgery and formal telesurgery. We observed the working status of the Telemedicine Network Latency Management System and calculated the difference between the network latency data and packet loss rate detected by the two methods. In addition, due to the lower latency of the 5G network, we tested the alert function of the system using the 4G network with relatively high network latency. RESULTS: The Telemedicine Network Latency Management System showed no instability during telesurgery simulation trials and formal telesurgery. After 20 telesurgery simulation trials and 15 remote adrenalectomy procedures, the p-value for the difference between the network latency data monitored by the Telemedicine Network Latency Management System and the "Ping command" method was greater than 0.05 in each case. Meanwhile, the surgeons reported that the Telemedicine Network Latency Management System had a friendly interface and was easy to operate. Besides, when the network latency exceeded a set threshold, a rapid alarm sounded in the system. CONCLUSION: The Telemedicine Network Latency Management System was simple and easy to operate, and it was feasible and effective to use it to monitor network latency in telesurgery. The system had an intuitive and concise interface, and its alarm function increased the safety of telesurgery. The system's own multidimensional working ability and information storage capacity will be more suitable for telemedicine work.

5G-Based Remote Virtual Bronchoscopic Navigation-Guided Transbronchial Lung Biopsy for Diagnosis of Lung Cancer: Description of 2 Cases.

INTRODUCTION: Bronchoscopic navigation combined with endobronchial ultrasound-guided transbronchial lung biopsy (EBUS-TBLB) is an important approach for the diagnosis of peripheral pulmonary lesions (PPL). The fifth-generation (5G) network, characterized by low latency and high stability, has shown promising possibilities in telemedicine for remote areas. METHODS: We present two cases of PPL in primary hospitals without navigation equipment. The EBUS-TBLB was performed with the guidance of remote augmented reality virtual bronchoscopic navigation (VBN) based on a 5G network. In practice, the 5G network could enable the matching of actual/virtual bronchoscopic images and navigation paths in real time, as well as high-speed transmission at long distances (>20 km), without any visual delay (<500 ms). CONCLUSION: Both patients were successfully diagnosed with lung cancer after accurate positioning and obtaining biopsies of target lesions. This 5G-based remote VBN-guided EBUS-TBLB appears to be safe with reliable connections in both cases and shows potential for cost-effectiveness. It would be an optimal resource for undeveloped regions and/or regions lacking endoscopists with extensive experience in navigation-related procedures.

Optimal Resource Allocation for 5G Network Slice Requests Based on Combined PROMETHEE-II and SLE Strategy.

The network slicing of physical infrastructure is required for fifth-generation mobile networks to make significant changes in how service providers deliver and defend services in the face of evolving end-user performance requirements. To perform this, a fast and secure slicing technique is employed for node allocation and connection establishment, which necessitates the usage of a large number of domain applications across the network. PROMETHEE-II and SLE algorithms were used in this study's approach to network design for node allocation and link construction, respectively. The PROMETHEE-II approach takes into account a variety of node characteristics while constructing a node importance rank array (NIRA), including the node capacity, bandwidth of neighboring connections, degree of the node, and proximity centrality among others. The SLE method is proposed to record all possible link configurations for the network slice request (NSR) nodes to guarantee that the shortest path array (SPA) of the NSR has a high acceptance rate. Performance metrics such as the service revenue and acceptance ratio were considered to evaluate the effectiveness of the suggested approach. The effectiveness of network slicing has been further examined under different infrastructure models to determine whether a small-world network structure is beneficial to 5G network. For each scenario, simulations were carried out and the results were compared to previously published findings from other sources.

Intelligent Resource Allocation for V2V Communication with Spectrum-Energy Efficiency Maximization.

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum-energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.

Industrial Internet of Things over 5G: A Practical Implementation.

The next generation of mobile broadband communication, 5G, is seen as a driver for the industrial Internet of things (IIoT). The expected 5G-increased performance spanning across different indicators, flexibility to tailor the network to the needs of specific use cases, and the inherent security that offers guarantees both in terms of performance and data isolation have triggered the emergence of the concept of public network integrated non-public network (PNI-NPN) 5G networks. These networks might be a flexible alternative for the well-known (albeit mostly proprietary) Ethernet wired connections and protocols commonly used in the industry setting. With that in mind, this paper presents a practical implementation of IIoT over 5G composed of different infrastructure and application components. From the infrastructure perspective, the implementation includes a 5G Internet of things (IoT) end device that collects sensing data from shop floor assets and the surrounding environment and makes these data available over an industrial 5G Network. Application-wise, the implementation includes an intelligent assistant that consumes such data to generate valuable insights that allow for the sustainable operation of assets. These components have been tested and validated in a real shop floor environment at Bosch Termotecnologia (Bosch TT). Results show the potential of 5G as an enhancer of IIoT towards smarter, more sustainable, green, and environmentally friendly factories.

An E2E Network Slicing Framework for Slice Creation and Deployment Using Machine Learning.

Network slicing shows promise as a means to endow 5G networks with flexible and dynamic features. Network function virtualization (NFV) and software-defined networking (SDN) are the key methods for deploying network slicing, which will enable end-to-end (E2E) isolation services permitting each slice to be customized depending on service requirements. The goal of this investigation is to construct network slices through a machine learning algorithm and allocate resources for the newly created slices using dynamic programming in an efficient manner. A substrate network is constructed with a list of key performance indicators (KPIs) like CPU capacity, bandwidth, delay, link capacity, and security level. After that, network slices are produced by employing multi-layer perceptron (MLP) using the adaptive moment estimation (ADAM) optimization algorithm. For each requested service, the network slices are categorized as massive machine-type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low-latency communications (uRLLC). After network slicing, resources are provided to the services that have been requested. In order to maximize the total user access rate and resource efficiency, Dijkstra's algorithm is adopted for resource allocation that determines the shortest path between nodes in the substrate network. The simulation output shows that the present model allocates optimum slices to the requested services with high resource efficiency and reduced total bandwidth utilization.

Cooperative-Aware Radio Resource Allocation Scheme for 5G Network Slicing in Cloud Radio Access Networks.

The 5G network is designed to serve three main use cases: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (uRLLC). There are many new technological enablers, including the cloud radio access network (C-RAN) and network slicing, that can support 5G and meet its requirements. The C-RAN combines both network virtualization and based band unit (BBU) centralization. Using the network slicing concept, the C-RAN BBU pool can be virtually sliced into three different slices. 5G slices require a number of Quality of service (QoS) metrics, such as average response time and resource utilization. In order to enhance the C-RAN BBUs utilization while protecting the minimum QoS of the coexisting three slices, a priority-based resource allocation with queuing model is proposed. The uRLLC is given the highest priority, while eMBB has a higher priority than mMTC services. The proposed model allows the eMBB and mMTC to be queued and the interrupted mMTC to be restored in its queue to increase its chance to reattempt the service later. The proposed model's performance measures are defined and derived using a continuous-time Markov chain (CTMC) model and evaluated and compared using different methodologies. Based on the results, the proposed scheme can increase C-RAN resource utilization without degrading the QoS of the highest-priority uRLLC slice. Additionally, it can reduce the forced termination priority of the interrupted mMTC slice by allowing it to re-join its queue. Therefore, the comparison of the results shows that the proposed scheme outperforms the other states of the art in terms of improving the C-RAN utilization and enhancing the QoS of eMBB and mMTC slices without degrading the QoS of the highest priority use case.

Cellular Network Fault Diagnosis Method Based on a Graph Convolutional Neural Network.

The efficient and accurate diagnosis of faults in cellular networks is crucial for ensuring smooth and uninterrupted communication services. In this paper, we propose an improved 4G/5G network fault diagnosis with a few effective labeled samples. Our solution is a heterogeneous wireless network fault diagnosis algorithm based on Graph Convolutional Neural Network (GCN). First, the common failure types of 4G/5G networks are analyzed, and then the graph structure is constructed with the data in the network parameter, given data sets as nodes and similarities as edges. GCN is used to extract features from the graph data, complete the classification task for nodes, and finally predict the fault types of cells. A large number of experiments are carried out based on the real data set, which is achieved by driving tests. The results show that, compared with a variety of traditional algorithms, the proposed method can effectively improve the performance of network fault diagnosis with a small number of labeled samples.

Relevant Cybersecurity Aspects of IoT Microservices Architectures Deployed over Next-Generation Mobile Networks.

The design and implementation of secure IoT platforms and software solutions represent both a required functional feature and a performance acceptance factor nowadays. This paper describes relevant cybersecurity problems considered during the proposed microservices architecture development. Service composition mechanisms and their security are affected by the underlying hardware components and networks. The overall speedup of the platforms, which are implemented using the new 5G networks, and the capabilities of new performant IoT devices may be wasted by an inadequate combination of authentication services and security mechanisms, by the architectural misplacing of the encryption services, or by the inappropriate subsystems scaling. Considering the emerging microservices platforms, the Spring Boot alternative is used to implement data generation services, IoT sensor reading services, IoT actuators control services, and authentication services, and ultimately assemble them into a secure microservices architecture. Furthermore, considering the designed architecture, relevant security aspects related to the medical and energy domains are analyzed and discussed. Based on the proposed architectural concept, it is shown that well-designed and orchestrated architectures that consider the proper security aspects and their functional influence can lead to stable and secure implementations of the end user's software platforms.

A Secure Online Fingerprint Authentication System for Industrial IoT Devices over 5G Networks.

The development of 5G networks has rapidly increased the use of Industrial Internet of Things (IIoT) devices for control, monitoring, and processing purposes. Biometric-based user authentication can prevent unauthorized access to IIoT devices, thereby safeguarding data security during production. However, most biometric authentication systems in the IIoT have no template protection, thus risking raw biometric data stored as templates in central databases or IIoT devices. Moreover, traditional biometric authentication faces slow, limited database holding capacity and data transmission problems. To address these issues, in this paper we propose a secure online fingerprint authentication system for IIoT devices over 5G networks. The core of the proposed system is the design of a cancelable fingerprint template, which protects original minutia features and provides privacy and security guarantee for both entity users and the message content transmitted between IIoT devices and the cloud server via 5G networks.Compared with state-of-the-art methods, the proposed authentication system shows competitive performance on six public fingerprint databases, while saving computational costs and achieving fast online matching.

An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture.

Reaching a flat network is the main target of future evolved packet core for the 5G mobile networks. The current 4th generation core network is centralized architecture, including Serving Gateway and Packet-data-network Gateway; both act as mobility and IP anchors. However, this architecture suffers from non-optimal routing and intolerable latency due to many control messages. To overcome these challenges, we propose a partially distributed architecture for 5th generation networks, such that the control plane and data plane are fully decoupled. The proposed architecture is based on including a node Multi-session Gateway to merge the mobility and IP anchor gateway functionality. This work presented a control entity with the full implementation of the control plane to achieve an optimal flat network architecture. The impact of the proposed evolved packet Core structure in attachment, data delivery, and mobility procedures is validated through simulation. Several experiments were carried out by using NS-3 simulation to validate the results of the proposed architecture. The Numerical analysis is evaluated in terms of total transmission delay, inter and intra handover delay, queuing delay, and total attachment time. Simulation results show that the proposed architecture performance-enhanced end-to-end latency over the legacy architecture.

Design and Synchronization Procedures of a D&F Co-Operative 5G Network Based on SDR Hardware Interface: Performance Analysis.

Software defined radio (SDR) is a commonly used platform for its ease of operation and cost-effectiveness for the development and testing of real wireless communication systems. By supporting high transmission rates and enabling fast and cost-effective deployments, mainly in millimeter-wave (mmWave), the co-operative 5G network has been standardized by 3GPP Release 16. In this paper, a decode-and-forward (D&F) co-operative hardware network is proposed as one of the key technologies for future 5G/6G wireless networks. The proposed system consists of an emulated base station processing unit (gnodeB), a D&F protocol and the user equipment (UE). In particular, the design of the D&F relay node is based on an MIMO layer 2 relay technology. A testbed based on an SDR platform and MatlabTM software, in which the physical broadcast channel (PBCH) transmission, physical downlink control channel (PDCCH), physical downlink shared channel (PDSCH), and downlink shared channel (DL-SCH) for transport channel coding, according to the 3GPP standardized 5G downlink signal, has been designed. The key performance indicators (KPIs), namely EVM, BER, and throughput, were measured for 5G signals with 64-QAM and 256-QAM modulation schemes. The obtained results show that the D&F co-operative 5G network achieves substantially improved KPIs in the communication between the gnodeB and the UE in an outdoor-to-indoor scenario. Furthermore, it has been demonstrated that the D&F protocol presents a good performance and behavior being compared to one commercial equipment.

[Ultra-remote robot-assisted laparoscopic surgery for varicocele through 5G network: Report of two cases and review of the literature].

OBJECTIVE: At present, the longest network transmission distance (NTD) for 5G remote endoscopic surgery is reportedly only about 229 km, and the NTD longer than 5 000 km has not yet been reported in clinical application. In this study, we attempted the clinical application of 5G ultra-remote robot-assisted laparoscopic surgery in spermatic vein ligation. METHODS: This retrospective study included two cases of 5G ultra-remote robot-assisted laparoscopic spermatic vein ligation using the home-made Tumai Surgical Robot System. The operation table was located in Xinjiang Kezhou People's Hospital, with an NTD of about 5 800 km (a linear distance of about 3 800 km) from the surgeon's console in the Telemedical Center of the First Affiliated Hospital of Nanjing Medical University, the apparatuses connected through the public 5G network. We observed the network connection delay, network fluctuation, and data packet loss rate of the devices at both ends of the loop through the feedback value of the Ping command by real-time monitoring. RESULTS: The total operation time of the two cases was 45 and 40 minutes respectively, with a mean blood loss of < 5 ml. The patients resumed a liquid diet and out-of-bed activity on the first day, the abdominal drainage tubes removed on the second, and both discharged from the hospital on the third day. The intraoperative average two-way network delay was 130 ms, and the average continuous data packet loss rate was 1.4%. No adverse network events, such as network interruption, occurred during the operation. CONCLUSION: Through the public 5G network and home-made Tumai Surgical Robot System, ultra-remote robot-assisted laparoscopic surgery was performed safely and successfully.

5G ultra-remote robot-assisted laparoscopic surgery in China.

BACKGROUND: 5G communication technology has been applied to several fields in telemedicine, but its effectiveness, safety, and stability in remote laparoscopic telesurgery have not been established. Here, we conducted four ultra-remote laparoscopic surgeries on a swine model under the 5G network. The aim of the study was to investigate the effectiveness, safety, and stability of the 5G network in remote laparoscopic telesurgery. METHODS: Four ultra-remote laparoscopic surgeries (network communication distance of nearly 3000 km), including left nephrectomy, partial hepatectomy, cholecystectomy, and cystectomy, were performed on a swine model with a 5G wireless network connection using a domestically produced "MicroHand" surgical robot. The average network delay, operative time, blood loss, and intraoperative complications were recorded. RESULTS: Four laparoscopic telesurgeries were safely performed through a 5G network, with an average network delay of 264 ms (including a mean round-trip transporting delay of 114 ms and a 1.20% data packet loss ratio). The total operation time was 2 h. The total blood loss was 25 ml, and no complications occurred during the procedures. CONCLUSIONS: Ultra-remote laparoscopic surgery can be performed safely and smoothly with 5G wireless network connection using domestically produced equipment. More importantly, our model can provide insights for promoting the future development of telesurgery, especially in areas where Internet cables are difficult to lay or cannot be laid.

Factors Affecting Risk Perception of Electromagnetic Waves From 5G Network Base Stations.

The coverage of the fifth-generation network has increased steadily since the network was introduced in 2019. However, public protests around the globe against the construction of 5G network base stations have continued to occur for fear that electromagnetic (EM) waves emitted from the stations would cause adverse health effects. To identify factors that have contributed to such increased risk perception, we conducted a cross-sectional study using data obtained from a survey that assessed Korean adults' risk perception of EM wave-related objects. We found that female gender, high level of perceived exposure to EM waves, evaluation of public policies as ineffective, and high level of objective knowledge on EM waves were associated with increased risk perception. Furthermore, we found that higher ratings on a few risk characteristics such as "personal knowledge," "seriousness of the risk to future generations," "dreadfulness," and "severity of consequences" were also associated with increased risk perception as well. Bioelectromagnetics. © 2020 The Authors. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.

Sparse and Random Sampling Techniques for High-Resolution, Full-Field, BSS-Based Structural Dynamics Identification from Video.

Video-based techniques for identification of structural dynamics have the advantage that they are very inexpensive to deploy compared to conventional accelerometer or strain gauge techniques. When structural dynamics from video is accomplished using full-field, high-resolution analysis techniques utilizing algorithms on the pixel time series such as principal components analysis and solutions to blind source separation the added benefit of high-resolution, full-field modal identification is achieved. An important property of video of vibrating structures is that it is particularly sparse. Typically video of vibrating structures has a dimensionality consisting of many thousands or even millions of pixels and hundreds to thousands of frames. However the motion of the vibrating structure can be described using only a few mode shapes and their associated time series. As a result, emerging techniques for sparse and random sampling such as compressive sensing should be applicable to performing modal identification on video. This work presents how full-field, high-resolution, structural dynamics identification frameworks can be coupled with compressive sampling. The techniques described in this work are demonstrated to be able to recover mode shapes from experimental video of vibrating structures when 70% to 90% of the frames from a video captured in the conventional manner are removed.

Cognitive information measurements: A new perspective.

From a traditional point of view, the value of information does not change during transmission. The Shannon information theory considers information transmission as a statistical phenomenon for measuring the communication channel capacity. However, in modern communication systems, information is spontaneously embedded with a cognitive link during the transmission process, which requires a new measurement that can incorporate continuously changing information values. In this paper, we introduce the concept of cognitive information value and a method of measuring such information. We first describe the characteristics of cognitive information followed by an introduction of the concept of cognitive information in measuring information popularity. The new measurement is based on the mailbox principle in the information value chain. This is achieved by encapsulating the information as a mailbox for transmission where the cognition is continuously implemented during the transmission process. Finally, we set up a cognitive communication system based on a combination of the traditional communication system and cognitive computing. Experimental results attest to the impact of incorporating cognitive value in the performance of 5G networks.

A 5G-based telerobotic ultrasound system provides qualified abdominal ultrasound services for patients on a rural island: a prospective and comparative study of 401 patients.

PURPOSE: To explore the feasibility of a 5G-based telerobotic ultrasound (US) system for providing qualified abdominal US services on a rural island. METHODS: This prospective study involved two medical centers (the tele-radiologist site's hospital and the patient site's hospital) separated by 72 km. Patients underwent 5G-based telerobotic US by tele-radiologists and conventional US by on-site radiologists from September 2020 to March 2021. The clinical feasibility and diagnostic performance of the 5G-based telerobotic abdominal US examination were assessed based on safety, duration, image quality, diagnostic findings, and questionnaires. RESULTS: A total of 401 patients (217 women and 184 men; mean age, 54.96 ± 15.43 years) were enrolled. A total of 90.1% of patients indicated no discomfort with the telerobotic US examination. For the examination duration, telerobotic US took longer than conventional US (12.54 ± 3.20 min vs. 7.23 ± 2.10 min, p = 0.001). For image quality scores, the results of the two methods were similar (4.54 ± 0.63 vs. 4.57 ± 0.61, p = 0.112). No significant differences were found between the two methods in measurements for the aorta, portal vein, gallbladder, kidney (longitudinal diameter), prostate, and uterus; however, telerobotic US underestimated the transverse diameter of the kidney (p < 0.05). A total of 504 positive results, including 31 different diseases, were detected. Among them, 455 cases were identified by the two methods; 17 cases were identified by telerobotic US only; and 32 cases were identified by conventional US only. There was good consistency in the diagnosis of 29 types of disease between the two methods (κ = 0.773-1.000). Furthermore, more than 90% of patients accepted the telerobotic US examination and agreed to pay additional fees in future. CONCLUSION: The 5G-based telerobotic US system can expand access to abdominal US services for patients in rural areas, thereby reducing health care disparities.

Incremental RBF-based cross-tier interference mitigation for resource-constrained dense IoT networks in 5G communication system.

The deployment of resource-constrained and densely distributed Internet of Things (IoT) devices poses significant challenges for 5G communication systems due to the increased likelihood of inter-tier interference. This interference can degrade network performance and hinder the transmission of data in a reliable and efficient manner. Using an incremental Radial Basis Function (RBF) technique, this paper proposes a novel approach for cross-tier interference mitigation in 5G communication among resource-constrained dense IoT networks. Utilizing the incremental RBF method to model and optimize interference patterns in resource-constrained dense IoT networks is the primary innovation of our approach. In contrast to conventional interference mitigation techniques, which view interference as a static phenomenon, our method adapts to the dynamic nature of IoT networks by incrementally updating the RBF model. This enables precise modeling of the various interference scenarios and real-time modification of interference mitigation parameters. Utilizing the spatial distribution of IoT devices, this approach improves interference mitigation. The proposed method intelligently allocates resources and optimizes interference mitigation parameters based on the location and density of IoT devices. This adaptive resource allocation improves network capacity, reliability, and overall system performance by maximizing the utilization of available resources while minimizing interference. We demonstrate the effectiveness of the incremental RBF-based approach in mitigating cross-tier interference in resource-constrained dense IoT networks within the 5G ecosystem through extensive experiments and simulations. Our findings indicate substantial improvements in communication performance, including increased throughput, decreased packet loss, and decreased latency.

5G-Based Telerobotic Ultrasound System Improves Access to Breast Examination in Rural and Remote Areas: A Prospective and Two-Scenario Study.

OBJECTIVE: Ultrasound (US) plays an important role in the diagnosis and management of breast diseases; however, effective breast US screening is lacking in rural and remote areas. To alleviate this issue, we prospectively evaluated the clinical availability of 5G-based telerobotic US technology for breast examinations in rural and remote areas. METHODS: Between September 2020 and March 2021, 63 patients underwent conventional and telerobotic US examinations in a rural island (Scenario A), while 20 patients underwent telerobotic US examination in a mobile car located in a remote county (Scenario B) in May 2021. The safety, duration, US image quality, consistency, and acceptability of the 5G-based telerobotic US were assessed. RESULTS: In Scenario A, the average duration of the telerobotic US procedure was longer than that of conventional US (10.3 ± 3.3 min vs. 7.6 ± 3.0 min, p = 0.017), but their average imaging scores were similar (4.86 vs. 4.90, p = 0.159). Two cases of gynecomastia, one of lactation mastitis, and one of postoperative breast effusion were diagnosed and 32 nodules were detected using the two US methods. There was good interobserver agreement between the US features and BI-RADS categories of the identical nodules (ICC = 0.795-1.000). In Scenario B, breast nodules were detected in 65% of the patients using telerobotic US. Its average duration was 10.1 ± 2.3 min, and the average imaging score was 4.85. Overall, 90.4% of the patients were willing to choose telerobotic US in the future, and tele-sonologists were satisfied with 85.5% of the examinations. CONCLUSION: The 5G-based telerobotic US system is feasible for providing effective breast examinations in rural and remote areas.