5GhNet: an intelligent QoE aware RAT selection framework for 5G-enabled healthcare network.
Ontology highlight
ABSTRACT:
SUBMITTER: Priya B
PROVIDER: S-EPMC8617375 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Along with the adoption of 5G, the development of neutral host solutions provides a unique opportunity for mobile networks operators to accommodate the needs of emerging use-cases and in the consolidation of new business models. By exploiting the concept of network slicing, as one key enabler in the transition to 5G, infrastructure and service providers can logically split a shared physical network into multiple isolated and customized networks to flexibly address the specific demands of those tenant slices. Motivated by this reality, the H2020 5GCity project proposed a novel 5G-enabled neutral host framework for three European cities: Barcelona (ESP), Bristol (UK), and Lucca (IT). This article revises the main achievements and contributions of the 5GCity project, focusing on the deployment and validation of the proposed framework. The developed neutral host framework encompasses two main parts: the infrastructure and the software platform. A detailed description of the framework implementation, in terms of functional capabilities and practical implications of city-wide deployments, is provided in this article. This work also presents the performance evaluation of the proposed solution during the implementation of real vertical use cases. Obtained results validate the feasibility of the neutral host model and the proposed framework to be deployed in city-wide 5G infrastructures.
| S-EPMC8659788 | biostudies-literature
Project description:Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.
| S-EPMC6679261 | biostudies-literature
Project description:The intelligent reflecting surface (IRS) is a ground-breaking technology that can boost the efficiency of wireless data transmission systems. Specifically, the wireless signal transmitting environment is reconfigured by adjusting a large number of small reflecting units simultaneously. Therefore, intelligent reflecting surface (IRS) has been suggested as a possible solution for improving several aspects of future wireless communication. However, individual nodes are empowered in IRS, but decisions and learning of data are still made by the centralized node in the IRS mechanism. Whereas, in previous works, the problem of energy-efficient and delayed awareness learning IRS-assisted communications has been largely overlooked. The federated learning aware Intelligent Reconfigurable Surface Task Scheduling schemes (FL-IRSTS) algorithm is proposed in this paper to achieve high-speed communication with energy and delay efficient offloading and scheduling. The training of models is divided into different nodes. Therefore, the trained model will decide the IRSTS configuration that best meets the goals in terms of communication rate. Multiple local models trained with the local healthcare fog-cloud network for each workload using federated learning (FL) to generate a global model. Then, each trained model shared its initial configuration with the global model for the next training round. Each application's healthcare data is handled and processed locally during the training process. Simulation results show that the proposed algorithm's achievable rate output can effectively approach centralized machine learning (ML) while meeting the study's energy and delay objectives.
| S-EPMC8627228 | biostudies-literature
Project description:The ongoing transition towards 5G technology expedites the emergence of a variety of mobile applications that pertain to different vertical industries. Delivering on the key commitment of 5G, these diverse service streams, along with their distinct requirements, should be facilitated under the same unified network infrastructure. Consequently, in order to unleash the benefits brought by 5G technology, a holistic approach towards the requirement analysis and the design, development, and evaluation of multiple concurrent vertical services should be followed. In this paper, we focus on the Transport vertical industry, and we study four novel vehicular service categories, each one consisting of one or more related specific scenarios, within the framework of the "5G Health, Aquaculture and Transport (5G-HEART)" 5G PPP ICT-19 (Phase 3) project. In contrast to the majority of the literature, we provide a holistic overview of the overall life-cycle management required for the realization of the examined vehicular use cases. This comprises the definition and analysis of the network Key Performance Indicators (KPIs) resulting from high-level user requirements and their interpretation in terms of the underlying network infrastructure tasked with meeting their conflicting or converging needs. Our approach is complemented by the experimental investigation of the real unified 5G pilot's characteristics that enable the delivery of the considered vehicular services and the initial trialling results that verify the effectiveness and feasibility of the presented theoretical analysis.
| S-EPMC8778894 | biostudies-literature
Project description:Accurate artificial intelligence (AI) for disease diagnosis could lower healthcare workloads. However, when time or financial resources for gathering input data are limited, as in emergency and critical-care medicine, developing accurate AI models, which typically require inputs for many clinical variables, may be impractical. Here we report a model-agnostic cost-aware AI (CoAI) framework for the development of predictive models that optimize the trade-off between prediction performance and feature cost. By using three datasets, each including thousands of patients, we show that relative to clinical risk scores, CoAI substantially reduces the cost and improves the accuracy of predicting acute traumatic coagulopathy in a pre-hospital setting, mortality in intensive-care patients and mortality in outpatient settings. We also show that CoAI outperforms state-of-the-art cost-aware prediction strategies in terms of predictive performance, model cost, training time and robustness to feature-cost perturbations. CoAI uses axiomatic feature-attribution methods for the estimation of feature importance and decouples feature selection from model training, thus allowing for a faster and more flexible adaptation of AI models to new feature costs and prediction budgets.
| S-EPMC9537352 | biostudies-literature
Project description:BACKGROUND:Some natural systems are big in size, complex, and often characterized by convoluted mechanisms of interaction, such as epistasis, pleiotropy, and trophism, which cannot be immediately ascribed to individual natural events or biological entities but that are often derived from group effects. However, the determination of important groups of entities, such as genes or proteins, in complex systems is considered a computationally hard task. RESULTS:We present Pyntacle, a high-performance framework designed to exploit parallel computing and graph theory to efficiently identify critical groups in big networks and in scenarios that cannot be tackled with traditional network analysis approaches. CONCLUSIONS:We showcase potential applications of Pyntacle with transcriptomics and structural biology data, thereby highlighting the outstanding improvement in terms of computational resources over existing tools.
| S-EPMC7576925 | biostudies-literature
Project description:Microbial community classification enables identification of putative type and source of the microbial community, thus facilitating a better understanding of how the taxonomic and functional structure were developed and maintained. However, previous classification models required a trade-off between speed and accuracy, and faced difficulties to be customized for a variety of contexts, especially less studied contexts. Here, we introduced EXPERT based on transfer learning that enabled the classification model to be adaptable in multiple contexts, with both high efficiency and accuracy. More importantly, we demonstrated that transfer learning can facilitate microbial community classification in diverse contexts, such as classification of microbial communities for multiple diseases with limited number of samples, as well as prediction of the changes in gut microbiome across successive stages of colorectal cancer. Broadly, EXPERT enables accurate and context-aware customized microbial community classification, and potentiates novel microbial knowledge discovery.
| S-EPMC9677468 | biostudies-literature
Project description:The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading around the globe since December 2019. There is an urgent need to develop sensitive and online methods for on-site diagnosing and monitoring of suspected COVID-19 patients. With the huge development of Internet of Things (IoT), the impact of Internet of Medical Things (IoMT) provides an impressive solution to this problem. In this paper, we proposed a 5G-enabled fluorescence sensor for quantitative detection of spike protein and nucleocapsid protein of SARS-CoV-2 by using mesoporous silica encapsulated up-conversion nanoparticles (UCNPs@mSiO2) labeled lateral flow immunoassay (LFIA). The sensor can detect spike protein (SP) with a detection of limit (LOD) 1.6 ng/mL and nucleocapsid protein (NP) with an LOD of 2.2 ng/mL. The feasibility of the sensor in clinical use was further demonstrated by utilizing virus culture as real clinical samples. Moreover, the proposed fluorescence sensor is IoMT enabled, which is accessible to edge hardware devices (personal computers, 5G smartphones, IPTV, etc.) through Bluetooth. Medical data can be transmitted to the fog layer of the network and 5G cloud server with ultra-low latency and high reliably for edge computing and big data analysis. Furthermore, a COVID-19 monitoring module working with the proposed the system is developed on a smartphone application (App), which endows patients and their families to record their medical data and daily conditions remotely, releasing the burdens of going to central hospitals. We believe that the proposed system will be highly practical in the future treatment and prevention of COVID-19 and other mass infectious diseases.
| S-EPMC7954646 | biostudies-literature
Project description:The wave of modernization around us has put the automotive industry on the brink of a paradigm shift. Leveraging the ever-evolving technologies, vehicles are steadily transitioning towards automated driving to constitute an integral part of the intelligent transportation system (ITS). The term autonomous vehicle has become ubiquitous in our lives, owing to the extensive research and development that frequently make headlines. Nonetheless, the flourishing of AVs hinges on many factors due to the extremely stringent demands for safety, security, and reliability. Cutting-edge technologies play critical roles in tackling complicated issues. Assimilating trailblazing technologies such as the Internet of Things (IoT), edge intelligence (EI), 5G, and Blockchain into the AV architecture will unlock the potential of an efficient and sustainable transportation system. This paper provides a comprehensive review of the state-of-the-art in the literature on the impact and implementation of the aforementioned technologies into AV architectures, along with the challenges faced by each of them. We also provide insights into the technological offshoots concerning their seamless integration to fulfill the requirements of AVs. Finally, the paper sheds light on future research directions and opportunities that will spur further developments. Exploring the integration of key enabling technologies in a single work will serve as a valuable reference for the community interested in the relevant issues surrounding AV research.
| S-EPMC9963447 | biostudies-literature
Project description:Wireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors' knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being.
| S-EPMC8413293 | biostudies-literature