BackgroundAtrial fibrillation (AF) is a chronic cardiovascular condition with a lifetime risk of 1 in 3 and a prevalence of 3% among adults. AF’s prevalence is predicted to more than double during the next 20 years due to better detection, increasing comorbidities, and an aging population. Due to increased AF prevalence, telerehabilitation has been developed to enhance patient engagement, health care accessibility, and compliance through digital technologies. A telerehabilitation program called “Future Patient—telerehabilitation of patients with AF (FP-AF)” has been developed to enhance rehabilitation for AF. The FP-AF program comprises two modules: (1) an education and monitoring module using telerehabilitation technologies (4 months) and (2) a follow-up module, where patients can measure steps and access a data and knowledge-sharing portal, HeartPortal, using their digital devices. Those patients in the FP-AF program measure their heart rhythm, pulse, blood pressure, weight, steps, and sleep. Patients also complete web-based questionnaires regarding their well-being and coping with AF. All recorded data are transmitted to the HeartPortal, accessible to patients, relatives, and health care professionals. ObjectiveThis paper aims to describe the research design, outcome measures, and data collection techniques in a clinical trial of the FP-AF program for patients with AF. MethodsThis is a multicenter, mixed methods, randomized controlled trial. Patients are recruited from AF clinics serving the North Jutland region of Denmark. The telerehabilitation group will participate in the FP-AF program, while the control group will follow the conventional care regime based on physical visits to the AF clinic. The primary outcome measure is AF-specific health-related quality of life, to be assessed using the Atrial Fibrillation Effect on Quality-of-Life Questionnaire. Secondary outcomes are knowledge of AF; measurement of vital parameters; level of anxiety and depression; degree of motivation; burden of AF; use of the HeartPortal; qualitative exploration of patients’, relatives’, and health care professionals’ experiences of participating in the FP-AF program; cost-effectiveness evaluation of the program; and analysis of multiparametric monitoring data. Outcomes are assessed through data from digital technologies, interviews, and questionnaires. ResultsPatient enrollment began in January 2023 and will be completed by December 2024, with a total of 208 patients enrolled. Qualitative interviews conducted in spring 2024 will be analyzed and published in peer-reviewed journals in 2025. Data from questionnaires and digital technologies will be analyzed upon study completion and presented at international conferences and published in peer-reviewed journals by the fall of 2025. ConclusionsResults from the FP-AF study will determine whether the FP-AF program can increase quality of life for patients with AF and increase their knowledge of symptoms and living with AF in everyday life compared to conventional AF care. The cost-effectiveness evaluation will determine whether telerehabilitation can be a viable alternative for rehabilitation of patients with AF. Trial RegistrationClinicalTrials.gov NCT06101485; https://clinicaltrials.gov/study/NCT06101485 International Registered Report Identifier (IRRID)DERR1-10.2196/64259

The increasing demand for enhanced communication systems, driven by applications such as real-time video streaming, online gaming, critical operations, and Internet-of-Things (IoT) services, has necessitated the optimization of cellular networks to meet evolving requirements while addressing power consumption challenges. In this context, various initiatives undertaken by industry, academia, and researchers to reduce the power consumption of cellular network systems are comprehensively reviewed. Particular attention is given to emerging technologies, including Software-Defined Networking (SDN), Network Function Virtualization (NFV), and Cloud-Radio Access Network (C-RAN), which are identified as key enablers for reshaping cellular infrastructure. Their collective potential to enhance energy efficiency while addressing convergence challenges is analyzed, and solutions for sustainable network evolution are proposed. A conceptual architecture based on SDN, NFV, and C-RAN is presented as an illustrative example of integrating these technologies to achieve significant power savings. The proposed framework outlines an approach to developing energy-efficient cellular networks, capable of reducing power consumption by approximately 40 to 50% through the optimal placement of virtual network functions.

Background:Atrial fibrillation (AF) is a prevalent chronic condition with increasing incidence worldwide. AF increases the risks of stroke, heart failure, and myocardial infarction and imposes a substantial burden on the health care system. Cardiac rehabilitation programs, while effective, often have low patient adherence. Recent evidence suggests that cardiac telerehabilitation, where patients are given home monitoring devices, could enhance adherence and outcomes. The program "Future Patient-Telerehabilitation of Patients with AF" (FP-AF) was created to assess the effects and potential benefits of cardiac telerehabilitation on patients with AF. Objective:The objective of this study is to explore the experiences of patients participating in the FP-AF program. Methods:This qualitative sub-study is part of the multicenter, randomized controlled FP-AF trial, which included 208 patients. Semi-structured interviews were conducted on 14 patients, randomly selected from participants in the intervention arm of the FP-AF program. The patient interviews, guided by self-determination theory, focused on patients' experiences with the FP-AF program, including the use of telerehabilitation technologies and a web-based portal called the "HeartPortal." Interview responses were analyzed using NVivo software (version 14.0; QSR International), with thematic coding based on interview guides and methodological guidance elaborated by Brinkmann & Kvale. The study adhered to ethical guidelines, with informed consent obtained from all participants. Results:Based on the interviews, the following themes were identified: the home monitoring devices are viewed positively by the patients; the HeartPortal is a useful digital toolbox; patients develop new coping strategies for living with AF; the measured values are useful for the patients; the community of practice is beneficial; and the FP-AF program creates a sense of security. Conclusions:Participation in the FP-AF program enhanced patients' sense of security, empowerment, and knowledge about AF. This improvement was due largely to a combination of patients' use of the HeartPortal and the educational sessions at health care centers. Telerehabilitation for patients with AF may be a useful way of researching this group of patients with a focus on rehabilitation and may be an effective means of offering rehabilitation to this group in the future.

Wide Area Network (WAN) management faces significant complexity in characterizing heterogeneous traffic from diverse services, which complicates unified feature extraction for operational tasks like anomaly detection. Key challenges persist in difficulty of creating generic feature sets across diverse traffic patterns and insufficient historical data for machine learning (ML) generalization. Meta-learning algorithms can be applied to learn dynamic feature sets to improve model generalizability on a limited number of data records. However, prior research focuses on algorithm design, with a lack of study on its application to in-band network monitoring. This work proposes a workflow to adaptively collect new feature sets and promptly learn from them. An adaptive in-band feature selection and extraction method is proposed for programmable switch. Meta learning algorithm is introduced for prompt decision in controller based on few-shot records. Evaluation results have shown that it outperforms prior methods in accuracy and inference time on public datasets.

This paper compares the power consumption of Long-Term Evolution (LTE), 5G Non-Standalone (5G-NSA), 5G-Standalone (5G-SA), and private 5G-SA networks at two locations. Our findings reveal that 5G-NSA consumes 92% to 111% more energy than LTE, while 5G-SA consumes 79% more. Although 5G-SA is 6.5% more efficient than 5G-NSA, both are less efficient than LTE. Notably, private 5G-SA is 63% more energy-efficient than public 5G-SA. These results highlight the need for optimized network design and technology selection based on specific use case requirements to ensure energy-efficient mobile communication.

This paper presents a comparative analysis of the power consumption of Long-Term Evolution (LTE), 5G Non-Standalone (5G-NSA), 5G-Standalone (5G-SA), and Private 5GSA networks on the user equipment (UE) side across two locations. In our results, 5G-NSA consumed between ${9 2 \%}$ and 111% more energy than LTE, while 5G-SA consumed 79% more energy than LTE. 5G-SA was ${6. 5 \%}$ more efficient than 5G-NSA, though both technologies remained less efficient than LTE. The most significant finding is that Private 5G-SA was 63% more energy-efficient than 5G-SA, highlighting the potential for energy savings through custom network designs. These findings offer insights for optimizing power consumption in next-generation mobile networks and highlight the need to establish guidelines for technology selection based on specific use case requirements.

The widespread use of IoT devices has unveiled overlooked security risks. With the advent of ultra-reliable lowlatency communications (URLLC) in 5G, fast threat defense is critical to minimize damage from attacks. IoT gateways, equipped with wireless/wired interfaces, serve as vital frontline defense against emerging threats on IoT edge. However, current gateways struggle with dynamic IoT traffic and have limited defense capabilities against attacks with changing patterns. In-network computing offers fast machine learning-based attack detection and mitigation within network devices, but leveraging its capability in IoT gateways requires new continuous learning capability and runtime model updates. In this work, we present P4Pir, a novel in-network traffic analysis framework for IoT gateways. P4Pir incorporates programmable data plane into IoT gateway, pioneering the utilization of in-network machine learning (ML) inference for fast mitigation. It facilitates continuous and seamless updates of in-network inference models within gateways. P4Pir is prototyped in P4 language on Raspberry Pi and Dell Edge Gateway. With ML inference offloaded to gateway’s data plane, P4Pir’s in-network approach achieves swift attack mitigation and lightweight deployment compared to prior ML-based solutions. Evaluation results using three public datasets show that P4Pir accurately detects and fastly mitigates emerging attacks (>30% accuracy improvement and sub-millisecond mitigation time). The proposed model updates method allows seamless runtime updates without disrupting network traffic.

This paper presents a Software-Defined Networking (SDN)-based reconfigurable edge architecture designed to optimize the allocation of services across multiple edge servers in a railway environment. The proposed system, named the ‘Intelligent Traffic Router,” dynamically assigns primary, secondary, and tertiary edge servers based on real-time load and capacity, ensuring optimal resource utilization and maintaining Quality of Service (QoS) for critical services such as autonomous and teleoperated driving. A novel mathematical model and optimization algorithm are introduced to formalize the service allocation process, considering factors such as service priority, train mobility, and server capacity. The SDN controller continuously monitors server load and capacity, reallocating resources as needed to prevent overload and maintain service continuity. Performance evaluation using Mininet-WiFi and the Open Network Operating System (ONOS) SDN controller demonstrates the system’s effectiveness in dynamically managing server resources, handling edge server disconnections, and prioritizing high-priority services under heavy load conditions. Notably, the system successfully reroutes traffic to available edge servers when CPU usage exceeds 85%, ensuring uninterrupted service. The results highlight the system’s ability to enhance network stability, reliability, and efficiency, making it a viable solution for future railway transport systems.

In this comprehensive study, Cellular-IoT (C-IoT) i.e. Narrowband-IoT (NB-IoT) and Long-Term Evolution Machine Type Communication (LTE-M) are thoroughly analyzed for their performance in terms of coverage, latency, mobility support, and repetition parameters. The findings revealed that both technologies excel in providing good signal strength for outdoor IoT applications, with NB-IoT showing a notable advantage in certain scenarios, particularly in corner locations and the basement of a building. In terms of mobility support, LTE-M demonstrated robustness even at higher driving speeds, while NB-IoT encountered packet loss above 50 km/h. Latency-wise, LTE-M exhibited lower latency in specific outdoor and indoor scenarios. Furthermore, NB-IoT displayed superior uplink and downlink repetition parameters, positioning it as a favourable choice for deep indoor IoT applications, while LTE-M is well-suited for near real-time monitoring and outdoor tracking applications.

In the near future, there will be a greater emphasis on sharing network resources between roads and railways to improve transportation efficiency and reduce infrastructure costs. This could enable the development of global Cooperative Intelligent Transport Systems (C-ITSs). In this paper, a software-defined networking (SDN)-based common emergency service is developed and validated for a railway and road telecommunication shared infrastructure. Along with this, the developed application is capable of reducing the chances of distributed denial-of-service (DDoS) situations. A level-crossing scenario is considered to demonstrate the developed solution where railway tracks are perpendicular to the roads. Two cases are considered to validate and analyze the developed SDN application for common emergency scenarios. In case 1, no cross-communication is available between the road and railway domains. In this case, emergency message distribution is carried out by the assigned emergency servers with the help of the SDN controller. In case 2, nodes (cars and trains) are defined with two wireless interfaces, and one interface is reserved for emergency data communication. To add the DDoS resiliency to the developed system the messaging behavior of each node is observed and if an abnormality is detected, packets are dropped to avoid malicious activity.

The expanding use of Internet-of-Things (IoT) has driven machine learning (ML)-based traffic analysis. 5G networks' standards, requiring low-latency communications for time-critical services, pose new challenges to traffic analysis. They necessitate fast analysis and response, preventing service disruption or security impact on network infrastructure. Distributed intelligence on IoT edge has been studied to analyze traffic, but introduces delays and raises privacy concerns. Federated learning can address privacy concerns, but does not meet latency requirements. In this article, we propose FLIP4: an efficient federated learning-based framework for in-network traffic analysis. Our solution introduces a lightweight federated tree-based model, offloaded and running within network devices. FLIP4 consumes less resources than previous solutions and reduces communication overheads, making it well-suited for IoT edge traffic analysis. It ensures prompt mitigation and minimal impact on services in the presence of false alerts using two approaches (metering and dropping), thereby balancing learning accuracy and privacy requirements. CCS Concepts: center dot Networks- In-network processing; Network security; center dot Computing methodologies- Machine learning algorithms;

Cellular coverage measurements for 5th generation mobile networks (5G), Long-Term Evolution (LTE), Narrowband-IoT (NB-IoT), and LTE for Machine Type Communication (LTEM) have been carried out above 450 feet in the airspace OD 1 near Hans Christian Andersen (HCA) Airport, Denmark, and the results for Reference Signal Received Power (RSRP) and Signal to Interference & Noise Ratio (SINR) is presented. The main objective of this empirical work is to investigate the reachability of cellular technology for drone communication and navigation. The results conclude that 5G has good signal strength and acceptable communication signal quality compared to LTE, LTE-M, and NB-IoT.

The numerous applications of Unmanned Aerial Vehicles (UAVs) have the prospects of stirring the global economic growth. Besides having a relatively small carbon footprint, the operating cost of UAVs is significantly less than that of manned aircrafts. Conventionally, civilian UAVs are flown such that the UAV controller is in Visual Line of Sight (VLOS) with the UAV. This mode of operation limits possible UAV applications to the visual acuity of the UAV operator. To explore all possible applications of UAVs, it is often required that they operate in large quantities and Beyond Visual Line of Sight (BVLOS). In this article, the radio channel parameters between a UAV and a live 5G NR (New Radio) cellular network, operating on 3.5 GHz frequency was measured and analyzed. This was done by field measurements with TSMA6B - an industrial-grade mobile frequency scanner, attached to a rotary-wing UAV. Using the network configurations obtained by the MNO (Mobile Network Operator), measurement campaigns were carried out at altitudes (22 - 35 meters) within the elevation angle of the transmitting antenna. Collected results were used to fit a path loss regression line for each altitude using the measured power of the reference signal and the instantaneous distance of the UAV from the base station. Findings from the analysis show that the path loss model is height dependent with altitude-specific cell edge requirements. In conclusion, the findings indicate that a 3.5GHz radio channel, deployed in a rural scenario, approximates a free space channel with an increase in altitude. This is because the channel conditions are similar to a line-of-sight scenario with increased altitude.

The ease of use and flexibility provided by drones or Unmanned Aerial vehicles (UAV) is attracting different industries and researchers across domains (e.g., delivery, agriculture, security, etc.). Although maintaining a reliable and secure command and control communication channel is still an open challenge and primary limitation for using drones. Satellite and 5G are considered viable solutions for drone communication. In this survey paper, we have explored specifications and proposed enhancements in cellular technology specified by 3GPP to command and control UAVs. It also describes the required network Quality of Service (QoS) parameters for drone communication. Such as end-to-end latency to send and receive a command and control message (C2), reliability, and message size. Along with these, it also emphasizes defining the reliability in terms of communication and navigation of UAVs, based on cellular technology 5G additional investigation and standardization should be executed.

Data processing architectures are currently evolving to enable the deployment of critical real-time applications, particularly in the railway environment. Indeed, these new computing architectures could contribute to the definition of innovative services: platooning, remote driving, autonomous trains, etc. This is why many studies today aim at setting up optimal Edge Computing architectures that would allow critical services to be deployed as close as possible to the end user. It is therefore necessary to design a powerful simulation/emulation environment that could be used to validate the proposed solutions. In this paper we demonstrate how the platform we have implemented, Emu5GNet, could be used to enable the rapid design and evaluation of new Edge Computing solutions. We present the potential applications of Emu5GNet and a reusable use case comparing Edge and Cloud deployment as well as different strategies for Edge resources management.

The rise of IoT-connected devices has led to an increase in collected data for service and traffic analysis, but also to emerging threats and attacks. In-network machine learning-based attack detection has proven effective in fast response, but scaling to distributed IoT edge devices risks increasing communication overheads and raising data privacy concerns. To address these concerns, we present FLIP4, a distributed in-network attack detection framework based on federated tree models. FLIP4 maintains data privacy by enabling distributed machine learning training while keeping data local on IoT edge, and provides in-network inference within the programmable data plane on edge gateway for timely attack labeling and mitigation. Evaluation results show that FLIP4 can accurately detect attacks while maintaining source data privacy and enabling lightweight deployment on IoT edge.

MQTT is currently one of the most widely used protocols at the application layer for the Internet of Things (IoT). To manage an increasing number of IoT devices and optimize the transmission of information, the distribution of the MQTT broker (MQTT cluster) and the integration of the SDN technology in the MQTT architecture are nowadays strongly promoted ideas. Unfortunately, existing proposals have several limitations: 1) they are incompatible with currently deployed solutions, 2) they induce significant delays for information exchange between MQTT brokers within the same cluster, and 3) they do not take into account the distribution of data to the end users (subscribers). That is why, in this paper, we propose a new SDN-based Real-Time Distributed MQTT Broker: SoD-MQTT. The architecture and protocols described aim to minimize communication delays between brokers and to support very low latency applications (e-health, transportation). By leveraging the advantages of SDN, subscription, and publishing can also be efficiently managed. The conducted evaluations demonstrate the relevance of the solution in terms of latency and network utilization optimization compared to existing SDN-based MQTT brokers.

In the past generation of wireless technologies, support for a wide range of applications and use cases was very limited. However, the introduction of 5G and cellular Internet of Things (IoT) with features such as Ultra Reliable Low Latency (URLLC), Massive Machine-to-Machine (M2M) communication, and enhanced Mobile Broadband (eMBB) opens up new possibilities. One such critical application of 5G which is heavily investigated is smart healthcare. Connected smart hospitals, remote at-home patient monitoring, remote surgery over the network, etc. are some of the use cases which are part of smart healthcare. One such use case is smart ambulances which are connected to 5G for real-time data transmission. Ambulances play a critical role in the pre-hospital diagnosis and treatment of patients. Smart ambulances amongst other things should be able to transmit new data from the ambulance and access the historic data recorded in the Electronic Health Registry (EHR) with the intention of reducing the time before the patient is diagnosed or treated. In this paper, the authors have focused on the wireless communication links between ambulances and the smart hospital. The experiments were designed with the goal of evaluating the state of the art of 5G, Long-Term Evolution (LTE), and Narrow Band Internet of Things (NB-IoT) coverage within a specific region of the country. We identified a specific test region for this experiment and used an ambulance from that area with the network measurement equipment. The authors believe that this work could serve as a good starting point in understanding the current capabilities of the communication networks to support future smart use cases.

Since the beginning of 2020, many societal systems have been used to extend the health care system, which were not planned for, and as such, there is concern for its collapse. Clearly, the collapse of the health care system, primarily hospitals, has been a key concern, and many initiatives, including lockdown and curfew, were taken to avoid such a collapse. The internet was the key platform used to enable people to work from home, provide remote teaching, conduct meetings on the web, etc. However, when it comes to data communication and processing, the risk of collapse is not the only risk, and maybe not even the biggest one. Many systems were not properly adapted for used in such a hurry, which did not allow time (and concern) for a proper risk and privacy assessment. This paper presents internet performance statistics and analyzes how this knowledge can be used in future designs of internet-based telemedical solutions. Statistics regarding traffic increases and security attacks on the internet during 2020 and 2021 were analyzed. The internet did not collapse during the COVID pandemic—as many people had predicted. However, the massive use of the internet, in new innovative ways, created a number of new opportunities for cybersecurity breach. Especially, the use videoconferences enabled made-in-middle attacks, phishing, and other classical breaches in new ways due to insufficient authentication and content encryption. Even though a large amount of experience has been gathered with respect to scaling eHealth systems, a minimum amount of improvement with respect to privacy and security has been identified.

IoT gateways are vital to the scalability and security of IoT networks. As more devices connect to the network, traditional hard-coded gateways fail to flexibly process diverse IoT traffic from highly dynamic devices. This calls for a more advanced analysis solution. In this work, we present P4Pir, an in-network traffic analysis solution for IoT gateways. It utilizes programmable data planes for in-band traffic learning with self-driven machine learning model updates. Preliminary results show that P4Pir can accurately detect emerging attacks based on retraining and updating the machine learning model.