The goal of the study presented in this work is to analyze all recent advances in the context of the computing continuum and meta-operating systems (meta-OSs). The term continuum includes a variety of diverse hardware and computing elements, as well as network protocols, ranging from lightweight Internet of Things (IoT) components to more complex edge or cloud servers. To this end, the rapid penetration of IoT technology in modern-era networks, along with associated applications, poses new challenges towards efficient application deployment over heterogeneous network infrastructures. These challenges involve, among others, the interconnection of a vast number of IoT devices and protocols, proper resource management, and threat protection and privacy preservation. Hence, unified access mechanisms, data management policies, and security protocols are required across the continuum to support the vision of seamless connectivity and diverse device integration. This task becomes even more important as discussions on sixth generation (6G) networks are already taking place, which they are envisaged to coexist with IoT applications. Therefore, in this work the most significant technological approaches to satisfy the aforementioned challenges and requirements are presented and analyzed. To this end, a proposed architectural approach is also presented and discussed, which takes into consideration all key players and components in the continuum. In the same context, indicative use cases and scenarios that are leveraged from a meta-OSs in the computing continuum are presented as well. Finally, open issues and related challenges are also discussed.

As the discussions on sixth-generation (6G) wireless networks progress at a rapid pace, various approaches have emerged over the last few years regarding new architectural concepts that can support the 6G vision. Therefore, the goal of this work is to highlight the most important technological efforts in relation to the definition of a 6G architectural concept. To this end, the primary challenges are first described, which can be viewed as the driving forces for the 6G architectural standardization. Afterwards, novel technological approaches are discussed to support the 6G concept, such as the introduction of artificial intelligence and machine learning for resource optimization and threat mitigation, cell-free deployments, and novel physical layer techniques to leverage high data rates. In the same context, open-access protocols for flexible resource integration, security, and privacy protection in the 6G era, as well as the digital twin concept, are discussed as well. Finally, recent research efforts are analyzed, with an emphasis on the combination of the aforementioned aspects towards a unified 6G architectural approach. To this end, limitations and open issues are highlighted as well.

The goal of the study presented in this paper is to evaluate the performance of a proposed adaptive beamforming approach in cell-free massive multiple input multiple output (CF-mMIMO) orientations. To this end, mobile stations (MSs) can be served by multiple access points (APs) simultaneously. In the same context, the performance of a dynamic physical resource block (PRB) allocation approach is evaluated as well, where the set of assigned PRBs per active MS is constantly updated according to their signal strength and the amount of interference that cause to the rest of the co-channel MSs. Performance evaluation takes place in a two-tier wireless orientation, employing a system-level simulator designed for parallel Monte Carlo simulations. According to the presented results, a significant gain in energy efficiency (EE) can be achieved for medium data rate services when comparing the cell-free (CF) resource allocation approach to single AP links (non-CF). This is made feasible via cooperative beamforming, where on one hand, the radiation figures of the APs that serve a particular MS are jointly updated to ensure quality of service (QoS), and on the other hand, the effects of these updates on the other MSs are evaluated as well. Although EE for high data rate services decreases compared to the non-CF scenario, the proposed dynamic PRB allocation strategy significantly lowers the number of active radiating elements required to meet minimum QoS standards, thereby reducing both hardware and computational demands.

The goal of the study presented in this work is to evaluate the performance of a proposed adaptive beamforming approach when combined with non-orthogonal multiple access (NOMA) in cell-free massive multiple input multiple output (CF m-MIMO) orientations. In this context, cooperative beamforming is employed taking into consideration the geographically adjacent access points (APs) of a virtual cell, aiming to minimize co-channel interference (CCI) among mobile stations (MSs) participating in NOMA transmission. Performance is evaluated statistically via extensive Monte Carlo (MC) simulations in a two-tier wireless orientation. As the results indicate, for high data rate services, various key performance indicators (KPIs) can be improved compared to orthogonal multiple access, such as the minimum number of users in the topology as well as the available PRBs for downlink transmission. Although in NOMA transmission more directional beamforming configurations are required to compensate for the increased CCI levels, the increase in the number of hardware elements is reduced compared to the corresponding gain in the considered KPIs.

The full deployment of sixth-generation (6G) networks is inextricably connected with a holistic network redesign able to deal with various emerging challenges, such as integration of heterogeneous technologies and devices, as well as support of latency and bandwidth demanding applications. In such a complex environment, resource optimization, and security and privacy enhancement can be quite demanding, due to the vast and diverse data generation endpoints and associated hardware elements. Therefore, efficient data collection mechanisms are needed that can be deployed at any network infrastructure. In this context, the network data analytics function (NWDAF) has already been defined in the fifth-generation (5G) architecture from Release 15 of 3GPP, that can perform data collection from various network functions (NFs). When combined with advanced machine learning (ML) techniques, a full-scale network optimization can be supported, according to traffic demands and service requirements. In addition, the collected data from NWDAF can be used for anomaly detection and thus, security and privacy enhancement. Therefore, the main goal of this paper is to present the current state-of-the-art on the role of the NWDAF towards data collection, resource optimization and security enhancement in next generation broadband networks. Furthermore, various key enabling technologies for data collection and threat mitigation in the 6G framework are identified and categorized, along with advanced ML approaches. Finally, a high level architectural approach is presented and discussed, based on the NWDAF, for efficient data collection and ML model training in large scale heterogeneous environments.

The goal of this paper is the performance evaluation of a deep learning approach when deployed in fifth-generation (5G) millimeter wave (mmWave) multicellular networks. To this end, the optimum beamforming configuration is defined by two neural networks (NNs) that are properly trained, according to mean square error (MSE) minimization. The first network has as input the requested spectral efficiency (SE) per active sector, while the second network has the corresponding energy efficiency (EE). Hence, channel and power variations can now be taken into consideration during adaptive beamforming. The performance of the proposed approach is evaluated with the help of a developed system-level simulator via extensive Monte Carlo simulations. According to the presented results, machine learning (ML)-adaptive beamforming can significantly improve EE compared to the standard non-ML framework. Although this improvement comes at the cost of increased blocking probability (BP) and radiating elements (REs) for high data rate services, the corresponding increase ratios are significantly reduced compared to the EE improvement ratio. In particular, considering 21.6 Mbps per active user and ML adaptive beamforming, the EE can reach up to 5.3 Mbps/W, which is significantly improved compared to the non-ML case (0.9 Mbps/W). In this context, BP does not exceed 2.6%, which is slightly worse compared to 1.7% in the standard non-ML case. Moreover, approximately 20% additional REs are required with respect to the non-ML framework.

In this paper the performance of an adaptive beamforming framework is evaluated, when deployed in fifth-generation massive multiple-input multiple-output millimeter wave cellular networks. To this end, active beams are formed dynamically according to traffic demands, in order to maximize spectral and energy efficiency (SE, EE) with reduced hardware and algorithmic complexity. In the same context, a machine learning (ML) approach is considered as well, where the configuration of the active beams per cell is directly related to the requested throughput in the cell’s angular space. According to the presented results, the ML-assisted beamforming framework can improve EE with reduced algorithmic complexity compared to the non-ML case, depending on the tolerable amount of blocking probability.

Motion Shield is an automatic crash notification system that uses a mobile phone to generate automatic alerts related to the safety of a user when the user is boarding a means of transportation. The objective of Motion Shield is to improve road safety by considering a moving vehicle's risk, estimating the probability of an emergency, and assessing the likelihood of an accident. The system, using multiple sources of external information, the mobile phone sensors' readings, geolocated information, weather data, and historical evidence of traffic accidents, processes a plethora of parameters in order to predict the onset of an accident and act preventively. All the collected data are forwarded into a decision support system which dynamically calculates the mobility risk and driving behavior aspects in order to proactively send personalized notifications and alerts to the user and a public safety answering point (PSAP) (112).

Driven by the emerging trend for transparent, open and programmable communications, Open Radio Access Network (O-RAN) constitutes the dominant architectural approach for deploying the future wireless networks. Towards standardizing and specifying the building blocks and principles of O-RAN, a coordinated global effort has been observed, mainly comprised of the O-RAN Alliance, the operators and several research activities. This paper presents the architectural aspects and the current status of O-RAN deployments, integrating both existing and ongoing activities from the O-RAN enablers. Furthermore, since the Artificial Intelligence and Machine Learning (AI/ML) act as key pillars for realizing O-RANs, a comprehensive view on the AI/ML functionality is provided as well. Additionally, a Network Telemetry (NT) architecture is also proposed to ensure end-to-end data collection and real-time analytics. To concretely illustrate the O-RAN supporting mechanisms for hosting AI/ML, we implemented two realistic ML algorithms: (i) a Supervised Learning (SL) based algorithm for cell traffic prediction using the training data of an open dataset and (ii) a Deep Reinforcement Learning (DRL) based algorithm for energy-efficiency maximization using a 5G-compliant simulator to obtain RAN measurements. We schematically demonstrate the AI/ML workflow for both ML-assisted algorithms through the usage of xApps running on the Radio Intelligent Controller (RIC), as well as we outline the role of the O-RAN components involved in the AI/ML loop. Combining the high-level architectural descriptions with a detailed presentation of ML-empowered resource allocation schemes, the paper discusses and summarizes the O-RAN disaggregation principles and the role of AI/ML embedded in future O-RAN deployments.

The goal of this paper is to evaluate the performance of an adaptive beamforming approach in fifth-generation millimeter-wave multicellular networks, where massive multiple-input multiple-output configurations are employed in all active base stations of the considered orientations. In this context, beamforming is performed with the help of a predefined set of configurations that can deal with various traffic scenarios by properly generating highly directional beams on demand. In parallel, a machine learning (ML) beamforming approach based on the k-nearest neighbors (k-NN) approximation has been considered as well, which is trained in order to generate the appropriate beamforming configurations according to the spatial distribution of throughput demand. Performance is evaluated statistically, via a developed system level simulator that executes Monte Carlo simulations in parallel. Results indicate that the achievable spectral efficiency (SE) and energy efficiency (EE) values are aligned with other state of the art approaches, with reduced hardware and algorithmic complexity, since per user beamforming calculations are omitted. In particular, considering a two-tier cellular orientation, then in the non-ML approach EE and SE can reach up to 5 Mbits/J and 36 bps/Hz, respectively. Both metrics attain the aforementioned values when the ML-assisted beamforming framework is considered. However, beamforming complexity is further reduced, since the ML approach provides a direct mapping among the considered throughput demand and appropriate beamforming configuration.

Monitoring of photovoltaic (PV) plants is crucial for optimal operation and timely fault diagnosis as well as system maintenance. In this work, we propose a telemetry system, based on IoT open technologies, to support the work of specialized personnel in charge of monitoring the operation of various PV plants. PV installations use various types of inverters and technologies which vary between providers as well as a variety of communication methods. The proposed system collects all data concerning the production and operation of several PV plants in one platform, with a user-friendly interface and mobile application. Data from different telemetry systems is integrated and displayed in a unified manner, allowing monitoring at a glance, reduced entry delays when using different proprietary platforms and providing the basis for remote fault detection systems using advanced machine learning techniques. The architecture of the system is described and results are shown from its implementation in five PV plants with different generation capacities, topologies, and communication systems.

In this paper the performance of realistic massive multiple input multiple output configurations operating at the millimeter wave frequency band is evaluated. Performance is evaluated statistically by executing a sufficient number of Monte Carlo simulations with the help of a developed semi-static simulator in a fifth-generation multicellular orientation. According to the results, although the increase in the number of radiating elements potentially leads to improved performance metrics (e.g., increased throughput, reduced blocking probability and transmission power), the deployment of an increased number of highly directional beams in predefined angular locations can lead to performance degradation. In particular, for 128 radiating elements and 8 directional beams per base station (BS), total throughput can reach 2320 Mbps with an equivalent transmission power per BS less than 2 W. However, system performance in terms of accepted users and blocking probability can deteriorate significantly when considering an equivalent grid of beams with 16 active sectors.

The era of 5G broadband wireless networks is inextricably connected with the provision of high data rates to mobile users, as well as bandwidth demanding and low latency applications. While large scale deployments of 5G Public Networks are ongoing, enterprises are interested to deploy their own 5G Non Public Networks (NPNs), customized to better serve their specific use cases. To this end, the goal of this paper is to present an architectural approach for cost-efficient 5G Standalone NPN deployment, leveraging cell densification, disaggregated RAN with open interfaces, edge computing and AI/ML-based network optimization. For this purpose, open solutions, such as O-RAN and MANO frameworks for cloud native micro-service deployments are adopted. Furthermore, research, development and deployment challenges are also discussed.

In this paper the performance of a proposed non-orthogonal multiple access (NOMA) scheme in a fifth-generation (5G) multicellular orientation is evaluated. To this end, a hybrid system-link level simulator has been developed, making the performance evaluation of 5G orientations feasible for various radio resource management strategies. In particular, simulations were performed for a two-tier cellular network (i.e., 57 active sectors) and multiple antennas at both transmission ends (multiple input multiple output - MIMO configuration). According to the presented results, considering a $2 \times 2$ MIMO configuration, the total network throughput can be significantly increased when NOMA is employed, without any mean bit error rate (BER) deterioration compared to orthogonal multiple access (OMA) transmission. Importantly, this improvement is achieved with minimum transceiver complexity since the results were derived without employing successive interference cancellation at the receiver side.

Power allocation is strongly related to the coverage and capacity of wireless networks, playing a critical role in the development of 5G networks. This paper proposes a Demand-Driven Power Allocation (DDPA) algorithm aiming to fulfill the requested throughput of individual users and accommodate their needs. DDPA is based on model-free Deep Reinforcement Learning (DRL) approaches and has the ability to proactively adjust the power levels of network transmitters. The performance of the developed algorithm is evaluated for a variety of simulation parameters and variable user demands. According to the presented results, the DDPA scheme exhibits a near-optimal performance for up to 50 users in the network area (i.e. satisfaction percentage exceeds 95%), with each one requesting 1 Mbps. Moreover, performance comparison between DDPA and two typical baseline methods reveals that the former results into enhanced total allocated throughput solutions (i.e. a performance increase by a factor of approximately 9% against baseline methods).

Hardware complexity reduction is a key concept towards the design and implementation of next generation broadband wireless networks. To this end, the goal of the study presented in this paper is to evaluate the performance of an adaptive hybrid analog-digital beamforming approach in fifth-generation (5G) massive multiple input multiple output (MIMO) millimeter wave (mmWave) wireless cellular orientations. In this context, generated beams are formed dynamically according to traffic demands, via an on-off analog activation of radiating elements per vertical antenna array, in order to serve active users requesting high data rate services without requiring any expensive and mechanical complex steering antenna system. Each vertical array, which constitutes a radiating element of a circular array configuration, has a dedicated radio frequency chain (digital part). The performance of our proposed approach is evaluated statistically, by executing a sufficient number of independent Monte Carlo simulations per MIMO configuration, via a developed system-level simulator incorporating the latest 5G-3GPP channel model. According to the presented results, the adaptive beamforming approach can improve various key performance indicators (KPIs) of the wireless orientation, such as total downlink transmission power and blocking probability. In particular, when studying/analyzing a MIMO configuration with 15 vertical antenna arrays and10 radiating elements per array, then, depending on the tolerable amount of transmission overhead, the proposed adaptive algorithm can significantly reduce the number of active radiating antenna elements compared to the static grid of beams case. In the same context, when keeping the number of radiating elements constant, then the total downlink transmission power as well as the blocking probability can be significantly reduced. It is important to note that all the KPIs have been extracted when deploying the developed array configuration in complex cellular orientations (two tiers of cells around the central cell).

In this paper the performance of realistic massive multiple input multiple output configurations operating at the millimeter wave frequency band is evaluated. Performance is evaluated statistically by executing a sufficient number of Monte Carlo simulations with the help of a developed semi–static simulator in a fifth-generation multicellular orientation. According to the results, although the increase in the number of radiating elements potentially leads to improved performance metrics (e.g., increased throughput, reduced blocking probability and transmission power), the deployment of an increased number of highly directional beams in predefined angular locations can lead to performance degradation. In particular, for 128 radiating elements and 8 directional beams per base station (BS), total throughput can reach 2320 Mbps with an equivalent transmission power per BS less than 2 W. However, system performance in terms of accepted users and blocking probability can deteriorate significantly when considering an equivalent grid of beams with 16 active sectors. Keywords—5G, massive MIMO, mmWave transmission, System level simulations

The provision of high data rate services to mobile users combined with improved quality of experience (i.e., zero latency multimedia content) drives technological evolution towards the design and implementation of fifth generation (5G) broadband wireless networks. To this end, a dynamic network design approach is adopted whereby network topology is configured according to service demands. In parallel, many private companies are interested in developing their own 5G networks, also referred to as non-public networks (NPNs), since this deployment is expected to leverage holistic production monitoring and support critical applications. In this context, this paper introduces a 5G NPN architectural approach, supporting among others various key enabling technologies, such as cell densification, disaggregated RAN with open interfaces, edge computing, and AI/ML-based network optimization. In the same framework, potential applications of our proposed approach in real world scenarios (e.g., support of mission critical services and computer vision analytics for emergencies) are described. Finally, scalability issues are also highlighted since a deployment framework of our architectural design in an additional real-world scenario related to Industry 4.0 (smart manufacturing) is also analyzed.

Drift Detection Method (DDM) emerges as a critical problem faced in the Industry 4.0 era, especially for the identification of concept drift in continuously-gathered data streams. In this study, a small-scale comparison with regard to the impact of different classifiers in DDM is presented. Six well-established classifiers are introduced in order to compare their performance and evaluate their impact on DDM, namely Naïve Bayes (NB), Hoeffding Tree (HT), k-Nearest Neighbors (KNN), Passive Aggressive (PAC), Stochastic Gradient Descent (SGD) and Very Fast Decision Rules (VFDRC) classifiers. Streaming Ensemble Algorithm (SEA dataset) was selected to validate our simulations due to its suitability regarding concept drift detection and handling, incorporating abrupt concept changes. Confusion matrices were used to effectively juxtapose the performance of the considered algorithms in the SEA dataset. The findings suggest that KNN exhibits the best overall accuracy (87%) and seems to be the most stable algorithm compared to NB (86%), HT (86%) and VFDRC (84%) classifiers. Overall, these algorithms show increased performance, while the presence of noise in the data has a significant impact on the model accuracy.

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.