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The 5G EVE Multi-site Experimental Architecture and Experimentation Workflow
... The Athens experimentation facility is comprised of the 5G-testbed owned by OTE, the largest Greek MNO, and created in OTE's research labs for 5G-EVE [10], and the stateof-the-art warehouse / Logistic-hub facilities of DIAKINISIS, the largest 3 rd Party Logistics (3PL) Greek operator. The 5Gtestbed will be upgraded to 3GPP Rel.16 compliant Stand Alone (SA) version and will be interconnected with the DIAKINISIS facilities with fiber connectivity. ...
... This UC will be validated over the Romanian 5G testbed, which is based on the Orange Romania testbed platform, using parts of the commercial 5G network as well as experimental open-source components, created by the 5G-EVE project [10]. The testbed will be progressively upgraded to a Rel.16 SA, while the upgraded 5G SA infrastructure will be enhanced during the deployment process with orchestration tools, such as ONAP or OSM, automatic services and VNFs onboarding and slicing orchestration. ...
As 5G networks are being deployed across the world, more and more vertical industries are discovering the benefits of 5G connectivity and the novel business and innovation models that it has to offer. The Transport & Logistics (T&L) industry is expected to be one of the key adopters of 5G technology, where the 5G enterprise market for T&L is estimated to reach €2.7 trillion by 2026 [1]. However, the adoption and penetration of 5G-based solutions in T&L may be hindered by the knowledge/expertise gap between the vertical industry, the telecommunication experts and the application developers. 5G based Network Applications (NetApps) represent a key enabler for the adoption of 5G solutions, as they can abstract the complexity of the underlying 5G infrastructure for T&L application developers, and significantly reduce the service creation and deployment times, as well as optimize the utilization of 5G resources. The European project VITAL-5G aims to advance the offered T&L services by showcasing the benefits of 5G-based NetApps via real-life trials over state-of-the-art vertical T&L facilities and advanced European 5G-testbeds. To support both internal and 3 rd-party experimentation, VITAL-5G will create an experimentation service portal and online repository to facilitate the creation, deployment, monitoring and (re)configuration of NetApps in the vertical environment.
... Perez et al. J Wireless Com Network (2021 First of all, (1) this monitoring platform has been designed and implemented within the scope of an European project related to the research on 5G networks: 5G EVE [6,7]. This project is deploying a validation 5G multi-site platform, involving four main facilities located in Spain, Italy, France and Greece, where verticals and other projects can execute extensive trials. ...
... More complex functions may estimate the average rate between two points in a defined window time. • Directly provide them to the different tools grouped in the Monitoring/Results collection/KPI tools entity, which is the entity consuming metrics from the Metrics aggregation or the Inter-site broker system, laying the ground for a set of value-added additional components that range from the KPI Framework for performance diagnosis already mentioned, which allows to fulfill requirement (7), to more complex modules such as data analytics platforms, SLA enforcement mechanisms or data visualization services, which can be fed from the monitoring data provided by the system. ...
The fifth generation (5G) of mobile networks is designed to accommodate different types of use cases, each of them with different and stringent requirements and key performance indicators (KPIs). To support the optimization of the network performance and validation of the KPIs, there exist the necessity of a flexible and efficient monitoring system and capable of realizing multi-site and multi-stakeholder scenarios. Nevertheless, for the evolution from 5G to 6G, the network is envisioned as a user-driven, distributed Cloud computing system where the resource pool is foreseen to integrate the participating users. In this paper, we present a distributed monitoring architecture for Beyond 5G multi-site platforms, where different stakeholders share the resource pool in a distributed environment. Taking advantage of the usage of publish-subscribe mechanisms adapted to the Edge, the developed lightweight monitoring solution can manage large amounts of real-time traffic generated by the applications located in the resource pool. We assess the performance of the implemented paradigm, revealing some interesting insights about the platform, such as the effect caused by the throughput of monitoring data in performance parameters such as the latency and packet loss, or the presence of a saturation effect due to software limitations that impacts in the performance of the system under specific conditions. In the end, the performance evaluation process has confirmed that the monitoring platform suits the requirements of the proposed scenarios, being capable of handling similar workloads in real 5G and Beyond 5G scenarios, then discussing how the architecture could be mapped to these real scenarios.
... Nonetheless, detection after an infection or an attack occurs might be already late, thus mechanisms to ensure the behavior of the elements to be deployed over the network are envisioned as necessary. In this line, the project 5GEVE [26] had as its primary goal the creation of an architecture to support the definition, execution, and validation of experiments, focusing on the testing and validation of the applications deployed on an end-to-end 5G infrastructure. We can also find the project VITAL-5G [8], with the aim of showcasing the benefits of 5G-based network applications via real-life trials by means of an experimentation service portal to create, deploy, monitor and (re)configure the network applications. ...
As 5th Generation (5G) and Beyond 5G (B5G) networks become increasingly prevalent, ensuring not only network security but also the security and reliability of the applications, the so-called network applications, becomes of paramount importance. This paper introduces a novel integrated model architecture, combining a network application validation framework with an AI-driven reactive system to enhance security in real-time. The proposed model leverages machine learning (ML) and artificial intelligence (AI) to dynamically monitor and respond to security threats, effectively mitigating potential risks before they impact the network infrastructure. This dual approach not only validates the functionality and performance of network applications before their real deployment but also enhances the network’s ability to adapt and respond to threats as they arise. The implementation of this model, in the shape of an architecture deployed in two distinct sites, demonstrates its practical viability and effectiveness. Integrating application validation with proactive threat detection and response, the proposed model addresses critical security challenges unique to 5G infrastructures. This paper details the model, architecture’s design, implementation, and evaluation of this solution, illustrating its potential to improve network security management in 5G environments significantly. Our findings highlight the architecture’s capability to ensure both the operational integrity of network applications and the security of the underlying infrastructure, presenting a significant advancement in network security.
... In the case of multi-site virtualized architectures, it is possible to deploy monitoring systems that allow the analysis of virtualized services in multiple geographical locations [47]. This makes it possible to create procedures for the use of 5G-enabled end-to-end platforms for the creation and performance analysis of vertical services [48]. ...
The evolution of telecommunication networks unlocks new possibilities for multimedia services, including enriched and personalized experiences. However, ensuring high Quality of Service and Quality of Experience requires intelligent solutions at the edge. This study investigates the real-time detection of race bib numbers using YOLOv8, a state-of-the-art object detection framework, within the context of 5G/6G edge computing. We train (BDBD and SVHN datasets) and analyze various YOLOv8 models (nano to extreme) across two diverse racing datasets (TGCRBNW and RBNR), encompassing varied environmental conditions (daytime and nighttime). Our assessment focuses on key performance metrics, including processing time, efficiency, and accuracy. For instance, on the TGCRBNW dataset, the extreme-sized model shows a noticeable reduction in prediction time when the more powerful GPU is used, with times decreasing from 1,161 to 54 seconds on a desktop computer. Similarly, on the RBNR dataset, the extreme-sized model exhibits a significant reduction in prediction time from 373 to 15 seconds when using the more powerful GPU. In terms of accuracy, we found varying performance across scenarios and datasets. For example, not good enough results are obtained in most scenarios on the TGCRBNW dataset (lower than 50% in all sets and models), while YOLOv8m obtain the high accuracy in several scenarios on the RBNR dataset (almost 80% of accuracy in the best set). Variability in prediction times was observed between different computer architectures, highlighting the importance of selecting appropriate hardware for specific tasks. These results emphasize the importance of aligning computational resources with the demands of real-world tasks to achieve timely and accurate predictions.
... In our approaches for QoE evaluation, the specific QoS metrics we consider are related only to RF signal strength, unlike those models involving packet loss, delays, throughput, and the like, as in [2,3,10,[20][21][22][23][24][25], and application-layer KPIs as in [7,22,26], all of which would require access to extensive user traces. Such approaches with simplified data collection are necessitated with the oncoming state of 'big data' for 5G communications [27] and the accompanying network management [28]. Although there have been some conclusions that such QoS measurements alone are insufficient to estimate QoE [14], it is possible to perform predictions necessary for handling network-related causes of QoE degradation, as we show in our work. ...
We propose an architecture for performing virtual drive tests for mobile network performance evaluation by facilitating radio signal strength data from user equipment. Our architecture comprises three main components: (i) pattern recognizer that learns a typical (nominal) behavior for application KPIs (key performance indicators); (ii) predictor that maps from network KPIs to application KPIs; (iii) anomaly detector that compares predicted application performance with said typical pattern. To simulate user-traces, we utilize a commercial state-of-the-art network optimization tool, which collects application and network KPIs at different geographical locations at various times of the day, to train an initial learning model. Although the collected data is related to an adaptive video streaming application, the proposed architecture is flexible, autonomous and can be used for other applications. We perform extensive numerical analysis to demonstrate key parameters impacting video quality prediction and anomaly detection. Playback time is shown to be the most important parameter affecting video quality, most likely due to video packet buffering during playback. We additionally observe that network KPIs, which characterize the cellular connection strength, improve QoE (quality of experience) estimation in anomalous cases diverging from the nominal. The efficacy of our approach is demonstrated with a mean-maximum F1-score of 77%.
... Either way, signal strength maps are expensive for both operators and crowdsourcing companies to obtain, and may not be available for all locations, times, frequencies, and other parameters of interest. The upcoming dense deployment of small cells at metropolitan scales will only increase the need for accurate and comprehensive signal maps to enable 5G network management [19,24]. ...
Signal maps are essential for the planning and operation of cellular networks. However, the measurements needed to create such maps are expensive, often biased, not always reflecting the metrics of interest, and posing privacy risks. In this paper, we develop a unified framework for predicting cellular signal maps from limited measurements. We propose and combine three mechanisms that deal with the fact that not all measurements are equally important for a particular prediction task. First, we design \emph{quality-of-service functions (Q)}, including signal strength (RSRP) but also other metrics of interest, such as coverage (improving recall by 76\%-92\%) and call drop probability (reducing error by as much as 32\%). By implicitly altering the training loss function, quality functions can also improve prediction for RSRP itself where it matters (e.g. MSE reduction up to 27\% in the low signal strength regime, where errors are critical). Second, we introduce \emph{weight functions} (W) to specify the relative importance of prediction at different parts of the feature space. We propose re-weighting based on importance sampling to obtain unbiased estimators when the sampling and target distributions mismatch(yielding 20\% improvement for targets on spatially uniform loss or on user population density). Third, we apply the {\em Data Shapley} framework for the first time in this context: to assign values () to individual measurement points, which capture the importance of their contribution to the prediction task. This can improve prediction (e.g. from 64\% to 94\% in recall for coverage loss) by removing points with negative values, and can also enable data minimization (i.e. we show that we can remove 70\% of data w/o loss in performance). We evaluate our methods and demonstrate significant improvement in prediction performance, using several real-world datasets.
... Mobile analytics companies (e.g., OpenSignal [42], Tutela [2]) crowdsource measurements directly from end-user devices, via standalone mobile apps or SDKs integrated into popular partnering apps, typically games, utility or streaming apps. The upcoming dense deployment of small cells for 5G at metropolitan scales will only increase the need for accurate and comprehensive signal maps [22,27]. Because cellular measurements are expensive to obtain, they may not be available for all locations, times and other parameters of interest, thus there is need for signal map prediction based on limited available such measurements. ...
We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices. We formulate the problem within the online federated learning framework: (i) federated learning (FL) enables users to collaboratively train a model, while keeping their training data on their devices; (ii) measurements are collected as users move around over time and are used for local training in an online fashion. We consider an honest-but-curious server, who observes the updates from target users participating in FL and infers their location using a deep leakage from gradients (DLG) type of attack, originally developed to reconstruct training data of DNN image classifiers. We make the key observation that a DLG attack, applied to our setting, infers the average location of a batch of local data, and can thus be used to reconstruct the target users' trajectory at a coarse granularity. We show that a moderate level of privacy protection is already offered by the averaging of gradients, which is inherent to Federated Averaging. Furthermore, we propose an algorithm that devices can apply locally to curate the batches used for local updates, so as to effectively protect their location privacy without hurting utility. Finally, we show that the effect of multiple users participating in FL depends on the similarity of their trajectories. To the best of our knowledge, this is the first study of DLG attacks in the setting of FL from crowdsourced spatio-temporal data.
... The complete 5G solution for both use cases is depicted in Figure 2. It comprises two vertical sites, namely the factory where all the machinery is installed and the headquarters where workers remotely control operations, and the PN interconnecting these sites. The 5Growth and the 5G-EVE [13] [14] platforms embody the 5G service platforms for the NPN (i.e., vertical premises) and the PN, respectively. 5G-EVE is a European platform for the validation and large-scale experimentation on 5G technology, providing full sets of 5G capabilities, including 5G new radio, backhaul, core and service technologies as well as slicing and orchestration. ...
5G is playing a paramount role in the digital transformation of the industrial sector, offering high-bandwidth, reliable, and low-latency wireless connectivity to meet the stringent and critical performance requirements of manufacturing processes. This work analyzes the applicability of 5G technologies as key enablers to support, enhance, and even enable novel advances in Industry 4.0. It proposes a complete 5G solution for two real-world Industry 4.0 use cases related to metrology and quality control. This solution uses 5Growth to ease and automate the management of vertical services over a soft-ware-defined network and network function virtualization based 5G mobile transport and computing infrastructure, and to aid the integration of the verticals' private 5G network with the public network. Finally, a validation campaign assesses the applicability of the proposed solution to support the performance requirements (especially latency and user data rate) of the selected use cases, and evaluates its efficiency regarding vertical service setup time across different domains in less than three minutes.
... In this setup, the 5Gr-VS interacts with an external PLMN domain managed through the 5G EVE platform [37] [38] to implement the multi-domain communication model based on peer CSMFs (i.e., communication service level). 5G EVE offers a Portal to request the experimentation of vertical services in configurable 5G environments, hosted in multiple 5G-enabled facilities across different European countries. ...
Private 5G networks has become a popular choice of various vertical industries to build dedicated and secure wireless networks in industry environments to deploy their services with enhanced service flexibility and device connectivity to foster industry digitalization. This article proposes multiple multi-domain solutions to deploy private 5G networks for vertical industries across their local premises and interconnecting them with the public networks. Such scenarios open up a new market segment for various stakeholders, and break the current operators’ business and service provisioning models. This, in turn, demands new interactions among the different stakeholders across their administrative domains. To this aim, three distinct levels of multi-domain solutions for deploying vertical’s 5G private networks are proposed in this work, which can support interactions at different layers among various stakeholders, allowing for different levels of service exposure and control. Building on a set of industry verticals (comprising Industry 4.0, Transportation and Energy), different deployment models are analyzed and the proposed multi-domain solutions are applied. These solutions are implemented and validated through two proof-of-concept prototypes integrating a 5G private network platform (5Growth platform) with public ones. These solutions are being implemented in three vertical pilots conducted with real industry verticals. The obtained results demonstrated the feasibility of the proposed multi-domain solutions applied at the three layers of the system enabling various levels of interactions among the different stakeholders. The achieved end-to-end service instantiation time across multiple domains is in the range of minutes, where the delay impact caused by the resultant multi-domain interactions is considerably low. The proposed multi-domain approaches offer generic solutions and standard interfaces to support the different private network deployment models.
... As a result, the implementation of the Monitoring architecture presented in Section 3 over the 5G EVE architecture [4] [18] results in the composition of a specific component chain, depicted in Figure 2. ...
The Fifth-Generation (5G) of mobile networks is designed to accommodate different types of use cases, each of them with different and stringent requirements and Key Performance Indicators (KPIs). To support the optimization of the network performance and validation of the KPIs, there exists the necessity of a flexible and efficient monitoring system, capable of realizing multi-site and multi-stakeholder scenarios. Nevertheless, for the evolution from 5G to 6G, the network is envisioned as a user-driven, distributed Cloud computing system where the resource pool is foreseen to integrate the participating users. In this scope, current monitoring solutions are limited, as they have to be able to maintain 5G performance in a distributed system with heterogeneous resources and still be efficient and sustainable. In this paper, we present a distributed monitoring architecture for Beyond 5G multi-site platforms, where different stakeholders share the resource pool in a distributed environment. Taking advantage of the usage of publish-subscribe mechanisms adapted to the Edge, the developed lightweight monitoring solution can manage large amounts of real-time traffic generated by the applications located in the resource pool. We assess the performance of the implemented paradigm, to confirm that it suits the requirements of the proposed scenarios, and discuss how the architecture could be mapped to other 5G or Beyond 5G scenarios.
The massive growth of live streaming, especially gaming-focused content, has led to an overall increase in global bandwidth consumption.
Certain services see their quality diminished at times of peak consumption, degrading the quality of the content.
This trend generates new research related to optimizing image quality according to network and service conditions.
In this work we present a gaming streaming use case optimization on a real multisite 5G environment. The paper outlines the virtualized workflow of the use case and provides a detailed description of the applications and resources deployed for the simulation. This simulation tests the optimization of the service based on the addition of Artificial Intelligence (AI) algorithms, assuring the delivery of content with good Quality of Experience (QoE) under different working conditions. The AI introduced is based on Deep Reinforcement Learning (DRL) algorithms that can adapt, in a flexible way, to the different conditions that the multimedia workflow could face. That is, adapt, through corrective actions, the streaming bitrate, in order to optimize the QoE of the content on a real-time multisite scenario. The results of this work demonstrate how we have been able to minimize content losses, as well as the fact of obtaining high audiovisual multimedia quality results with higher bitrates, compared to a service without an optimizer integrated in the system. In a multi-site environment, we have achieved an improvement of 20 percentage points in terms of blockiness efficiency and also 15 percentage points in block loss.
We consider the problem of predicting cellular network performance (signal maps) from measurements collected by several mobile devices. We formulate the problem within the online federated learning framework: (i) federated learning (FL) enables users to collaboratively train a model, while keeping their training data on their devices; (ii) measurements are collected as users move around over time and are used for local training in an online fashion. We consider an honest-but-curious server, who observes the updates from target users participating in FL and infers their location using a deep leakage from gradients (DLG) type of attack, originally developed to reconstruct training data of DNN image classifiers. We make the key observation that a DLG attack, applied to our setting, infers the average location of a batch of local data, and can thus be used to reconstruct the target users' trajectory at a coarse granularity. We build on this observation to protect location privacy, in our setting, by revisiting and designing mechanisms within the federated learning framework including: tuning the FL parameters for averaging, curating local batches so as to mislead the DLG attacker, and aggregating across multiple users with different trajectories. We evaluate the performance of our algorithms through both analysis and simulation based on real-world mobile datasets, and we show that they achieve a good privacy-utility tradeoff.
Signal maps are essential for the planning and operation of cellular networks. However, the measurements needed to create such maps are expensive, often biased, not always reflecting the performance metrics of interest, and posing privacy risks. In this paper, we develop a unified framework for predicting cellular performance maps from limited available measurements. Our framework builds on a state-of-the-art random-forest predictor, or any other base predictor. We propose and combine three mechanisms that deal with the fact that not all measurements are equally important for a particular prediction task. First, we design
quality-of-service functions ( Q )
, including signal strength (RSRP) but also other metrics of interest to operators, such as number of bars, coverage (improving recall by 76%-92%) and call drop probability (reducing error by as much as 32%). By implicitly altering the loss function employed in learning, quality functions can also improve prediction for RSRP itself where it matters (
e.g.
MSE reduction up to 27% in the low signal strength regime, where high accuracy is critical). Second, we introduce
weight functions
(
W
) to specify the relative importance of prediction at different locations and other parts of the feature space. We propose re-weighting based on importance sampling to obtain unbiased estimators when the sampling and target distributions are different. This yields improvements up to 20% for targets based on spatially uniform loss or losses based on user population density. Third, we apply the
Data Shapley
framework for the first time in this context: to assign values (
) to individual measurement points, which capture the importance of their contribution to the prediction task. This can improve prediction (
e.g.
from 64% to 94% in recall for coverage loss) by removing points with negative values and storing only the remaining data points (
i.e.
as low as 30%), which also has the side-benefit of helping privacy. We evaluate our methods and demonstrate significant improvement in prediction performance, using several real-world datasets.
Multi-Media applications are amongst the most demanding services, requiring high amounts of network capacity and computational resources for synchronous audio-visual streaming with high quality. Gaming streaming is one of the most demanding of these media applications. Recent technological advances in the 5G domain, precisely Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC), unlock media industries' potential by offering high-quality media services through dynamic and efficient resource allocation with low latency. This work presents a multi-site gaming streaming use case implemented over an end-to-end 5G-enabled platform provided by the EU H2020 5G-PPP 5G EVE project. Furthermore, we present the executed use case experiment scenarios to validate the use case performance following a set of defined Quality of Experience(QoE)-related Key Performance Indicators (KPIs). In particular, this paper discusses the design workflow and orchestration of the multi-site gaming streaming use case across two 5G EVE sites (i.e., Spain and Greece), providing a detailed description of the network function applications and resources utilized for the use case. Subsequently, leveraging the 5G EVE monitoring platform, this paper elaborates on the executed experiment scenarios that provide the defined KPI metrics data. Finally, this paper presents, discusses, and analyzes the obtained KPI metrics data results and provides recommendations on future works for these kinds of use cases.
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