The rapid evolution of Beyond 5G (B5G) and 6G networks demands advanced research frameworks that enable the emulation and digital twinning of complex network topologies, the automation of experiments, and the investigation of cybersecurity challenges. This paper introduces CORE eXecutor (COREX), a novel automation framework designed to orchestrate the setup of emulated 5G/6G network topologies, execute predefined use cases and cyber ranges, and facilitate cybersecurity experiments. COREX interacts with key autonomous network domains —Access, Edge, Transport, and Core—while supporting multi-tenancy, user mobility, and end-to-end resource management. The experimental evaluation demonstrates the framework efficiency and scalability. Results show that execution time increases with the number of User Equipment (UE), ranging from 699 to 1191 seconds, due to the setup stage, where the orchestrator provides the network topology across multiple physical machines. Bandwidth tests indicate that the framework maintains expected performance at lower loads (128 Mbps) with up to 99.9% bandwidth efficiency at the Edge segment. The framework’s ability to automate experiment execution has been validated through a self-protection loop cybersecurity use case, demonstrating its capability to detect, plan, and mitigate cybersecurity threats in 5G / 6G networks. COREX presents a significant advancement in network emulation, providing researchers with a powerful tool to explore 5G and 6G cybersecurity, optimise network performance, and refine autonomous network principles.

The main contribution is the design, implementation and validation of a complete AI-driven electronic nose architecture to perform the classification of whiskey and acetones. This classification is of paramount important in the distillery production line of whiskey in order to predict the quality of the final product. In this work, we investigate the application of an e-nose (based on arrays of single-walled carbon nanotubes) to the distinction of two different substances, such as whiskey and acetone (as a subproduct of the distillation process), and discrimination of three different types of the same substance, such as three types of whiskies. We investigated different strategies to classify the odor data and provided a suitable approach based on random forest with accuracy of 99% and with inference times under 1.8 seconds. In the case of clearly different substances, as subproducts of the whiskey distillation process, the procedure presented achieves a high accuracy in the classification process, with an accuracy around 96%.

ABSTRACT Automated vehicles represent a rapidly expanding global market, drawing significant attention from both industry and academia. However, existing solutions often lack transparency, particularly in the disclosure of architectural designs, resulting in fragmented development approaches. To address these gaps, this paper introduces a novel, modular reference architecture tailored for Level 2 Automated Driving Systems (ADS). The proposed architecture ensures safety, scalability, and adaptability across diverse vehicle platforms. A comprehensive validation is conducted using OpenPilot, an open‐source Level 2 ADS implementation, demonstrating the architecture's practical feasibility in achieving reliable control tasks under real‐time constraints. This work bridges the gap between industrial and academic contributions, offering actionable insights and a robust foundation for future advancements in ADS development.

Abstract Radio frequency (RF) systems tend to become congested and overused due to the increasing number of users, devices and the multiple technologies involved in their deployment. This leads to the downgrading of quality of service (QoS) further caused by interference with different signals. Optical Wireless communications (OWC) are emerging as a feasible alternative as they offer unlicensed, interference-free spectrum by using the frequency range located in the visible and invisible light spectrum. Its applications can be found in various fields such as healthcare, education, finance and industry 4.0. Moreover, it enhances the security and privacy of communications. Nevertheless, the limited spectrum in OWC also requires optimised resource allocation to support the QoS of different applications or users whilst lacking established infrastructure to manage this. To address these challenges, this paper proposes a novel 5G-LiFi framework able to ensure QoS requirements by introducing network slicing in Light Fidelity (LiFi) networks integrated with 5G infrastructure. This paper has developed and deployed a 5G-LiFi architecture capable of providing network slicing capabilities over the LiFi segment of the hybrid network. It allows a full control over the network traffic and tailored, improved QoS capabilities. The proposed solution has been empirically validated and evaluated in a realistic testbed employing real-world LiFi and 5G network equipment, and yielded promising results in terms of bandwidth, delay, jitter and packet loss. This work concludes that the use of heterogeneous networks integrating OWC with RF is a suitable solution and it can lead to a better use and exploitation of the different spectrums, improving the QoS offered to end-users.

We introduce Quantitative Market Situation Embeddings (QMSEs), a pioneering artificial intelligence (AI)-driven methodology for encoding distinct temporal segments of stock markets into high-dimensional contextual embeddings exclusively leveraging quantitative stock data. Building upon prior research, we construe quantitative stock data analogously to Natural Language Processing (NLP) data, thereby adopting Doc2Vec methodologies to effectuate the embedding of stock data similar to document-level representations. We ascertain the efficacy of QMSEs in representing market dynamics by assessing their ability to discern various significant economic downturns post-2000, including but not limited to, the events of 9/11, the Subprime Crisis of 2008, and the Covid-induced market disruption. Moreover, we elucidate the practical utility of QMSEs through their application in employing distance metrics to gauge the rarity of market scenarios, serving as a regularizer in the training of quantitative stock AI models. Subsequently, we proceed to assess the algorithmic identification of analogous market conditions, aiming to elucidate their potential implications for future stock movements. Additionally, we demonstrate the efficacy of QMSEs in reducing data requirements for quantitative stock AI models by leveraging them as condensed representations of stock data.

Identification of animal species is a crucial aspect of biology and ecology, particularly in ornithology, where collaboration with other disciplines aims to develop effective methods for bird protection and environmental quality assessment. Leveraging artificial intelligence (AI) and Internet of Things (IoT) technologies, advancements in birdsong identification have been achieved. Machine learning and deep learning techniques, including Imagenet-based Convolutional Neural Networks (CNNs) like EfficientNet and MobileNet, have been employed for image feature comparison. Spectrograms of birdsongs have been analyzed, with Deep CNNs (DCNNs) proving effective in reducing model size for birdsong classification.

Data-driven machine learning based approaches have been playing increasingly important roles in 5 G and beyond (B5G) network management and optimisation. One of the major challenges is that the existence of class imbalance in networking datasets can greatly bias the classifier towards a majority classification. This discrepancy can pose a serious problem for AI-based models, which require large and diverse amounts of data to learn patterns and generate classifications. The generation of synthetic data is a process that has evolved over time from the application of statistical models to a more machine learning-centric approaches. In this research work we present the development, implementation, evaluation and comparison of four generative models for tabular data on a B5G network management dataset. These models have been previously optimised according to certain evaluation metrics of the generated synthetic data. The dataset presents a problem of imbalance between its classes, which is improved by using generative models to enrich it with synthetic data. The results show that machine learning based generative models obtain more accurate data than traditional statistical models, and are much faster in terms of conditional data sampling.

This study explores the execution of AI algorithms on open Unmanned Aerial Vehicles (UAVs) equipped with BeagleBone AI-64 (BBAI-64) boards, comparing their performance to high-performance computers equipped with GPUs. Key factors are evaluated, such as inference time, end-to-end processing time, CPU usage, or temperature on the board. Furthermore, this study presents the development of an open UAV platform based on an open-source flight controller (Durandal) executing an open-source autopilot (ArduPilot). This platform facilitates the integration of various sensors or cameras, regardless of brand or communication protocol. The study’s key findings show that the BBAI-64 offers advantages for smaller Artificial Intelligence (AI) models, and achieving comparable performance for larger models with high-performance computers. This work contributes to optimising AI execution on UAVs and supporting the development of versatile, sensor-agnostic open-source UAVs.

5G brings many benefits such as enlarged capacity and improved connectivity. However, it also poses challenges especially due to a significant increase in the amount of traffic on the network. This creates difficulties for operators to maintain the Quality of Service (QoS) for each of the services offered. Therefore, in order to improve the performance of such capabilities and, consequently, the experience of the users, it is necessary to identify which traffic requires more prioritisation. This would help allocate more resources to those services. This concept makes the identification and classification of traffic to gain more and more relevance and importance. In this paper, we propose a Long Short-Term Memory (LSTM) model to classify 5G Radio Access Network (RAN) behaviour into four different scenarios: streaming, video conferencing, Voice over IP (VoIP) and gaming. The results obtained show a $93 \%$ accuracy.

With the emergence of 6G, novel approaches are demanded to identify and address cyber-security, trust and privacy risks threatening the softwarised and virtualised networks and computing infrastructure, and next-generation services. One of the main innovations beyond State-of-the-Art envisioned is to deliver End-to-End Multi-domain Multi-tenant 6G Network Slicing capabilities over Zero-touch Security Network Management.This paper introduces a novel security enabler deployed in the data plane where network slicing is explored as a security mitigation mechanism. In this way, legitimate traffic can be isolated from harmful traffic and the attacker will have near zero vulnerability surface to compromise the implemented security measures. The proposed solution is centred on Network Self-Protection (NSP) based on the Open Virtual Switch (OVS) platform, to which significant extensions have been undertaken to support Network Slicing capabilities in multi-tenant multi-domain beyond 5G networks.Preliminary experiments show promising results in terms of overhead introduced in the data plane (in the order of microseconds) and high scalability when deploying up to 2048 network slices. The proposed software network slicing enabler is a suitable candidate for coping with network traffic with different levels of nested encapsulation associated with this kind of virtualised infrastructures.

This article addresses the challenge of integrating dynamic AI capabilities into ultralow-power (ULP) IoT devices, a critical necessity in the rapidly evolving landscape of 5G and potential 6G technologies. We introduce the Dynamic AI-IoT architecture, a novel framework designed to eliminate the need for cumbersome firmware updates. This architecture leverages Narrowband IoT (NB-IoT) to facilitate smooth cloud interactions and incorporates tailored firmware extensions for enabling dynamic interactions with Tiny Machine Learning (TinyML) models. A sophisticated memory management mechanism, grounded in memory alignment and dynamic AI operations resolution, is introduced to efficiently handle AI tasks. Empirical experiments demonstrate the feasibility of implementing a Dynamic AI-IoT system using ULP IoT devices on a 5G testbed. The results show model updates taking less than one second and an average inference time of approximately 46 ms.

Internet of Things (IoT) coupled with 5G and upcoming pre-6G networks will provide the scalability and performance required to deploy a wide range of new digital services in Smart Cities. This new digital services will undoubtedly contribute to an improvement in the quality of life of citizens. However, security is a major concern in IoT where low-powered constrained devices are a target for attackers who identify them as a vulnerable entry point to exploit the network weaknesses. This concern is exacerbated in Smart Cities where it is expected to deploy millions of heterogeneous yet unattended and vulnerable IoT devices throughout vast urban areas. A security breach in a Smart City allows attackers to target critical services such as the power grid network or the road traffic control or to expose sensitive health data to intruders. Thus, the security and privacy of citizens could be seriously compromised. Honeynets are an effective security mechanism to distract attackers from legitimate targets and collect valuable information on how they operate. Meanwhile, current honeynets lack functionality to protect the real and lure networks from large-scale volumetric Distributed Denial of Service (DDoS) attacks. This paper provides a novel solution to empower honeynet security tools with Network Slicing capabilities as an innovative way to isolate and minimize the network resources available from attackers. The proposed system supports the ambitious IoT scalability requirements associated to 5G networks and the forthcoming 6G networks. The solution has been empirically evaluated in a emulated testbed where promising results have been achieved when dealing with mMTC and eMBB traffic profiles. In mMTC scenarios where scalability is a challenge, the solution is able to deal with up to 1000 slices and 1 Million IoT devices sending traffic simultaneously. In eMBB use cases, the solution is able to cope with up to 19 Gbps of combined bandwidth. The gathered results demonstrate that the proposed solution is suitable as a security tool in 5G IoT multi-tenant infrastructures as those expected in Smart Cities deployments.

Abstract In recent years, Internet traffic patterns have been changing. Most of the traffic demand by end users is multimedia, in particular, video streaming accounts for over 53%. This demand has led to improved network infrastructures and computing architectures to meet the challenges of delivering these multimedia services while maintaining an adequate quality of experience. Focusing on the preparation and adequacy of multimedia content for broadcasting, Cloud and Edge Computing infrastructures have been and will be crucial to offer high and ultra-high definition multimedia content in live, real-time, or video-on-demand scenarios. For these reasons, this review paper presents a detailed study of research papers related to encoding and transcoding techniques in cloud computing environments. It begins by discussing the evolution of streaming and the importance of the encoding process, with a focus on the latest streaming methods and codecs. Then, it examines the role of cloud systems in multimedia environments and provides details on the cloud infrastructure for media scenarios. After doing a systematic literature review, we have been able to find 49 valid papers that meet the requirements specified in the research questions. Each paper has been analyzed and classified according to several criteria, besides to inspect their relevance. To conclude this review, we have identified and elaborated on several challenges and open research issues associated with the development of video codecs optimized for diverse factors within both cloud and edge architectures. Additionally, we have discussed emerging challenges in designing new cloud/edge architectures aimed at more efficient delivery of media traffic. This involves investigating ways to improve the overall performance, reliability, and resource utilization of architectures that support the transmission of multimedia content over both cloud and edge computing environments ensuring a good quality of experience for the final user.

Abstract Face detection in UAV imagery requires high accuracy and low execution time for real-time mission-critical operations in public safety, emergency management, disaster relief and other applications. This study presents UWS-YOLO, a new convolutional neural network (CNN)-based machine learning algorithm designed to address these demanding requirements. UWS-YOLO’s key strengths lie in its exceptional speed, remarkable accuracy and ability to handle complex UAV operations. This algorithm presents a balanced and portable solution for real-time face detection in UAV applications. Evaluation and comparison with the state-of-the-art algorithms using standard and UAV-specific datasets demonstrate UWS-YOLO’s superiority. It achieves 59.29% of accuracy compared with 27.43% in a state-of-the-art solution RetinaFace and 46.59% with YOLOv7. Additionally, UWS-YOLO operates at 11 milliseconds, which is 345% faster than RetinaFace and 373% than YOLOv7.

The heterogeneous requirements imposed by different vertical businesses have motivated a networking paradigm shift in the next generation of mobile networks (beyond 5G and 6G), leading to critical operation competitiveness of improved productivity, performance and efficiency. Furthermore, with the global digital revolution, such as Industry 4.0, and a connected world, network virtualisation together with high reliability and high performance communications have become crucial elements for mobile network operators. To minimise the negative effects that could affect critical services, network slicing is widely recognised as a key technology with the objective of meeting the Service-Level Agreements (SLAs) and Key Performance Indicators (KPIs) in future 6G networks. In this context, it is essential to introduce a programmable data plane able to enforce flexible Quality of Service (QoS) commitments, while providing high-performance packet processing and real-time monitoring capabilities. To this end, this paper is focused on designing, prototyping and evaluating a novel framework that leverages a set of hardware-based technologies including eXpress Data Path (XDP), extended Barkeley Packet Filter (eBPF) and Smart Network Interface Cards (SmartNICs) to offload network functionality with the objective of providing high-performance pre-6G front-, mid-and back-haul network communications and thus, decreasing the overhead incurs by the Linux Kernel. The proposed solution is implemented based on bypassing the Linux Kernel and accelerating the communication, while providing network slice control and real-time monitoring capabilities. The main aim of this framework is to ensure network communications in forthcoming 6G infrastructures by guaranteeing 6G KPIs and avoiding system overload. The empirical validation of this solution for Industry 4.0 services as an example use case demonstrates key performance improvements in terms of packet processing as high as about 25Gbps, 20M packet per second, 0% packet loss, 0.1ms of latency and less than 10% load on the CPUs.

Internet of Things (IoT) coupled with 5G networks enable unprecedented levels of scalability and performance in the computing industry. These enhanced performance features allow to offer and deploy a wide range of new use cases and services in scenarios such as Smart Cities, Smart Grid or Industry 5.0 just to mention a few. However, the inherent complexity of such networks is a serious concern in terms of security. Furthermore, the vulnerability and low-power constraints of IoT devices make such networks a targeted vector for cyber criminals. In this contribution, authors present an innovative topology-aware Cognitive Self-protection framework able to detect and mitigate attacks in an autonomous way with no human intervention in the wired segments of 5G-IoT multi-tenant networks. Preliminary tests carried out on a realistic emulated testbed show promising results in terms of time spent in stopping DDoS attacks (less than 47 seconds) and scalability for scenarios with different number of tenants and UEs (2 virtual tenants deployed in 4 Edge nodes and up to 64 IoT devices or sensors connected to the infrastructure).

With the evolution of softwarised industrial infrastructures, there is an increasing need for more sophisticated cyber security solutions that can protect industrial processes from a rapidly evolving landscape of cyber threats. In this context, we present an agent-based approach that provides process monitoring, predictive process behaviour, and process control to give the organisations appropriate situational awareness in relation to cyber security threats, enabling them to re-actively or pro-actively detect attacks and respond to advanced persistent threats and multi-vector attacks. Our architectural solution is based on four agents: Process Inventory Agent (PIA), Process Monitoring Agent (PMA), Process Forecasting Agent (PFA), and the Process Slicing Control Agent (PSCA), which work together to deliver a novel mitigation tool to secure softwarised industrial environments. The architecture has been designed, prototyped, and validated in order to demonstrate the effectiveness of our solution. Experimental results show that the proposed solution can successfully mitigate different attacks in the concerned context.

Internet of Things (IoT) is a major application area of the Fifth-Generation (5G) and beyond capable of providing massive machine-type communications (mMTC) at a large scale. It enables a wide range of applications such as smart cities, smart grids, smart factories and so on. In light of the huge number of devices involved, it is prohibitive to manage the massive large-scale cyber security scenarios manually. Therefore, closed automation loops are essential to automate such management. This paper proposes a new cognitive closed loop system to offer distributed dual-layer self-protection capabilities to battle against Distributed Denial of Service (DDoS) attacks. The proposed system features the novel usage of concurrent autonomous closed-loops for the different stakeholders’ business roles: Digital Service Providers (DSPs) and Infrastructure Service Providers (ISPs) respectively, suitable to provide a multi-layer self-protection defence mechanisms across multiple administrative domains. It has been designed, implemented and experimentally validated. Empirical results have shown that there is a high potential in the collaboration between the stakeholders to achieve the common goal of self-protection of infrastructures. It makes a major difference in the performance of the whole infrastructure for detecting, analysing and mitigating the threat when the proposed distributed dual-layer loops are applied instead of a standalone loop. The system has achieved a 78.12% of effectiveness compared with a 4.73% of the standalone counterpart, for a large scale attack when stopping 256 infected devices. Also, the proposed system has achieved a response time of 18 s whereas the standalone has required 57 s, achieving an optimization of performance of 316%.

The Fifth Generation (5G) of mobile networks and beyond have emerged with ambitions to facilitate the deployment and evolution of a wide spectrum of applications such as Industry 4.0 and 5.0 use cases. Despite this trend of increasing importance to upgrade the networked applications to the next generation, the use of 5G and beyond technologies can be a prohibitive barrier for some business sectors due to the high deployment costs that it can incur. To overcome this obstacle, more cost-effective approaches in networking are entailed. In this work, an innovative approach coupling 5G and Wi-Fi mesh networking is proposed and developed as a promising solution to extend 5G services to the indoor use case scenarios whilst being capable of keeping the capital expenditure of the network infrastructure significantly lower. In order to empirically validate and evaluate this new networking paradigm, a number of experiments have been performed over a testbed with a demanding video application as a representative use case. The experimental results prove the gained benefits from this new approach, especially, video users can be more than twice as far away without compromising the quality of the video consumption experience. Specifically, the results show that users can be 29% further away using a single router, and 100% further away if a second router is added.

The environmental psycho-acoustic annoyance is an important metric in the Smart City with Next Generation technologies. The use of such technologies can help to rapidly deploy large quantity of elements, which can dynamically be used for different applications. Also, the psycho-acoustic annoyance is usually based on the Zwicker’s model, but this model does not consider tonality of sounds to weight the subjective nuisance produced. In this work, we show a on-going work for the implementation of the nodes and EDGE/Fog to determine a modified version of the Zwicker’s model, to consider sound tonality (based on Aures’ method). This implementation has been designed to consider two options for offloading, one with sampling and computation on the EDGE and another with sampling in 5G-nodes and computation on the EDGE.

This study proposes a novel illumination-aware image fusion technique and a Convolutional Neural Network (CNN) called BlendNet to significantly enhance the robustness and real-time performance of small human objects detection from Unmanned Aerial Vehicles (UAVs) in harsh and adverse operation environments. The proposed solution is particular useful for mission-critical public safety applications such as search and rescue operations in rural areas. The operation environments of such missions are featured with poor illumination condition and complex background such as dense vegetation and undergrowth in diverse weather conditions, and the missions have to address the challenges of detecting humans from UAVs at high altitudes, with a moving platform and from various viewing angles. To overcome these challenges, the proposed solution register and fuse the images using Enhanced Correlation Coefficient (ECC) and arithmetic image addition with customised weights techniques. The result of this fusion is fuelled with our new BlendNet AI model achieving 95.01 % of accuracy with 42.2 Frames Per Second (FPS) on Titan X GPU with input size of 608 pixels. The effectiveness of the proposed fusion method has been evaluated and compared with other methods using the KAIST public dataset. The experimental results show competitive performance of BlendNet in terms of both visual quality as well as quantitative assessment of high detection accuracy at high speed.

Abstract Telescopic cranes are powerful lifting facilities employed in construction, transportation, manufacturing and other industries. Since the ground workforce cannot be aware of their surrounding environment during the current crane operations in busy and complex sites, accidents and even fatalities are not avoidable. Hence, deploying an automatic and accurate top-view human detection solution would make significant improvements to the health and safety of the workforce on such industrial operational sites. The proposed method (CraneNet) is a new machine learning empowered solution to increase the visibility of a crane operator in complex industrial operational environments while addressing the challenges of human detection from top-view on a resource-constrained small-form PC to meet the space constraint in the operator’s cabin. CraneNet consists of 4 modified ResBlock-D modules to fulfill the real-time requirements. To increase the accuracy of small humans at high altitudes which is crucial for this use-case, a PAN (Path Aggregation Network) was designed and added to the architecture. This enhances the structure of CraneNet by adding a bottom-up path to spread the low-level information. Furthermore, three output layers were employed in CraneNet to further improve the accuracy of small objects. Spatial Pyramid Pooling (SPP) was integrated at the end of the backbone stage which increases the receptive field of the backbone, thereby increasing the accuracy. The CraneNet has achieved 92.59% of accuracy at 19 FPS on a portable device. The proposed machine learning model has been trained with the Standford Drone Dataset and Visdrone 2019 to further show the efficacy of the smart crane approach. Consequently, the proposed system is able to detect people in complex industrial operational areas from a distance up to 50 meters between the camera and the person. This system is also applicable to the detection of any other objects from an overhead camera.

The Fifth-Generation (5G) mobile networks leverage virtualisation and softwarisation to reduce both capital and operating expenditures whilst benefiting from network programmability and flexibility for various use cases. Meanwhile, virtualisation and softwarisation introduce unprecedented complexity to network infrastructure topologies, which poses substantial challenges to network topology management. This paper proposes a novel architecture to enable network administrators to achieve real-time network discovery and spatial representation of the software data path, containers, virtual machines, and geographically distributed edges to help network troubleshooting and management tasks. Another important innovation is the flexibility to support a significant number of business roles by means of a novel method to perform data model alignment between different business roles. The architecture has been designed, prototyped and validated, and experiment results have demonstrated promising scalability of the proposed solution.

Environmental noise monitoring for smart cities need to be as much efficient as possible in order to mitigate its significant impact in the health of their inhabitants. 5G Internet of Things (IoT) systems offer a big opportunity to offload the computation from the sensor nodes, since it provides a series of new concepts for dynamic computing that the previous technologies did not offer. In this article, a complete 5G IoT system for psycho-acoustic monitoring has been designed and implemented using different options for offloading computation to different parts of the system. This offloading has been done by developing different functional splittings of the psycho-acoustic metrics algorithms to allocate such splits in different locations. Finally, a performance comparison among different functional splittings and their implementation are shown with a detailed discussion.

Abstract This paper proposes an acceleration technique to minimise the unnecessary operations on a state-of-the-art machine learning model and thus to improve the processing speed while maintaining the accuracy. After the study of the main bottlenecks that negatively affect the performance of convolutional neural networks, this paper designs and implements a discarding technique for YOLOv3-based algorithms to increase the speed and maintain accuracy. After applying the discarding technique, YOLOv3 can achieve a 22% of improvement in terms of speed. Moreover, the results of this new discarding technique were tested on Tiny-YOLOv3 with three output layers on an autonomous vehicle for pedestrian detection and it achieved an improvement of 48.7% in speed. The dynamic discarding technique just needs one training process to create the model and thus execute the approach, which preserves accuracy. The improved detector based on the discarding technique is able to readily alert the operator of the autonomous vehicle to take the emergency brake of the vehicle in order to avoid collision and consequently save lives.

Abstract The emerging fifth-generation (5G) mobile networks are empowered by softwarization and programmability, leading to the huge potentials of unprecedented flexibility and capability in cognitive network management such as self-reconfiguration and self-optimization. To help unlock such potentials, this paper proposes a novel framework that is able to monitor and calculate 5G network topological information in terms of advanced spatial metrics. These metrics, together with enabling and optimization algorithms, are purposely designed to address the complexity of 5G network topologies introduced by network virtualization and infrastructure sharing among operators (multi-tenancy). Consequently, this new framework, centred on a topology monitoring agent (TMA), enables on-demand 5G networks’ spatial knowledge and topological awareness required by 5G cognitive network management in making smart decisions in various autonomous network management tasks including but not limited to virtual network function placement strategies. The paper describes several technical use cases enabled by the proposed framework, including proactive cache allocation, computation offloading, node overloading alerting, and load balancing. Finally, a realistic 5G testbed is deployed with the central component TMA, together with the new spatial metrics and associated algorithms, implemented. Experimental results empirically validate the proposed approach and demonstrate the scalability and performance of the TMA component.

Location-aware recommendation is considered as one of human behavior cognitive analyses in the world of human-machine-environment system. The development of 5G technology and ubiquitous mobile devices has led to the emergence of a new online platform, location-based social networks (LBSNs), which allows users to share their locations. The essential feature of LBSNs is to provide users with location recommendations that help them explore new places and also to make LBSNs more prevalent to users. Most of the existing research is focusing on the introduction of new features and how these new features affect the check-in behaviors of the users. In addition, the dependencies between each feature and the probability of a user visiting the site is always a principle to follow. However, a user’s decision could be determined by considering several features at the same time. When a full model is applied by considering all the features, an overfitting problem could be occurred owing to the lack of sufficient data for each individual user. In this article, an intermediate solution was proposed to address all of these problems by fragmenting the model into several partial models, where each partial model is responsible for a few features. An additive strategy was also implemented to support the development of personalized partial models. Furthermore, a partition-based approach was introduced to explore the hidden patterns from the geographically clustered check-in data. The performance of the approaches has been evaluated by using the data sets from Foursquare and it demonstrates that the proposed approach outperforms the state-of-the-art approaches.

Abstract In educational context, a source of nuisance for students is carbon dioxide ( $$CO_2$$ C O 2 ) concentration due to closed rooms and lack of ventilation or circulatory air. Also, in the pandemic context, ventilation in indoor environments has been proven as a good tool to control the COVID-19 infections. In this work, it is presented a low cost IoT-based open-hardware and open-software monitoring system to control ventilation, by measuring carbon dioxide ( $$CO_2$$ C O 2 ), temperature and relative humidity. This system provides also support for automatic updating, auto-self calibration and adds some Cloud and Edge offloading of computational features for mapping functionalities. From the tests carried out, it is observed a good performance in terms of functionality, battery durability, compared to other measuring devices, more expensive than our proposal.

The new Fifth-Generation (5G) mobile networks entail next-generation network management solutions to manage both physical and virtual network infrastructures and services. The challenge is to effectively manage the increased complexity due to virtualization and softwarization, whilst attempting to reduce the operational costs for 5G operators. This paper focuses on the approach of the EU Horizon 2020 5G-PPP project SliceNet to meet this challenge. SliceNet is implementing an intelligence-based autonomic end-to-end slicing-friendly infrastructure for 5G networks. The paper describes SliceNet's virtualized Mobile/Multi-access Edge Computing (MEC) infrastructure segment as a solution to manage the combination of edge and cloud computing for the new services emerging on the vertical industries as part of the new 5G mobile networks. It presents the vision and recent development of the project on the MEC part of the architecture, and the artificial intelligence approach being investigated in the project. Moreover, the paper introduces three representative use cases to describe how the framework organizes between cloud and edge. These use cases show how the MEC and cloud computing can be combined for services in e-health, smart-grids, and smart-cities verticals.

In Next-Generation Technologies, the monitoring of environmental noise nuisance in the Smart City should be as efficient as possible. 5G IoT systems offer a great opportunity to offload the node calculation, as they provide a number of new concepts for dynamic computing that previous technologies did not offer. In this case, a complete 5G IoT system for psycho-acoustic monitoring has been implemented using different options to offload the calculation of the parameters to different parts of the system. This offloading has been implemented by directly computing the metrics in the node (as a Raspberry Pi), and in a ESP32 device (FiPy) and by sampling the audio and sending it to the EDGE in the psycho-acoustic metrics algorithm in order to evaluate the performance of the system and has been compared with the calculation at the node itself.

Lightnings are violent natural phenomena and can generate many expenditures, specially when they strike in urban areas. The identification of the concrete geographic area where they strike is of critical importance for emergency services in order to enhance their effectiveness by doing an intensive coverage of the affected area. To achieve this aim, this paper proposes a design, prototype and validation of a distributed network of Internet of Things (IoT) devices to enable detection and location of lightning strikes. The IoT devices are empowered with lightning detection capabilities and are synchronized with the other devices in the sensor network. All of them cooperate within a network that is able to locate different events thanks to a trilateration algorithm implemented in a big data environment. The designed low cost lightning detection system is based on the AS3935 sensor. This alone device has a limited range of effective detection, but when it is embedded in a IoT mesh network, the accuracy and performance is increased up to good levels, in the order of kilometres. A fully operational IoT network has been deployed and a functional validation and empirical measurements are provided.

There is a significant lack of software firewalls for 5G networks especially when the support for the Internet of Things (IoT) technologies such NB-IoT are considered. The main contribution of this research work is an advanced software firewall based on the Open Virtual Switch (OVS), which is able to provide firewall capabilities over these 5G IoT devices. The proposed software firewall is able to significantly scale up the number of rules to fulfill the 5G Key Performance Indicator of controlling 1 million IoT devices per square kilometer. Intensive experimental results are achieved in this work, validating the suitability of the proposed architecture for this remarkable level of scalability. In the most demanding conditions, where more than 1 million of firewall rules are installed and 1 million NB-IoT devices are sending traffic, yielding a total of 4 Gbps, the system shows only 8% of packet loss and 4 ms delay.

This paper presents an architecture to gather nontraditional metrics from 5G multi-tenant infrastructure using information about the network topology to take smart decisions on where to optimal placement VNFs that are used to provide services to network slices. The metrics considered are spatial metrics, where information about the shape and size of the network topology is taken into consideration. The architecture has been prototypical validated showing how optimal decisions are taken in an eMBB high-dense scenario, with topologies up to 65538 mobile users geographically concentrated on the same location. Our prototype is able to deal with the calculation of such spatial metrics over the 5G multi-tenant network with 65538 mobile users within 20 seconds, which make it viable at operational phase.

As the IoT adoption is growing in several fields, cybersecurity attacks involving low-cost end-user devices are increasing accordingly, undermining the expected deployment of IoT solutions in a broad range of scenarios. To address this challenge, emerging Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies can introduce new security enablers, thereby endowing IoT systems and networks with higher degree of scalability and flexibility required to cope with the security of massive IoT deployments. In this sense, honeynets can be enhanced with SDN and NFV support, to be applied into IoT scenarios thereby strengthening the overall security. IoT honeynets are virtualized services simulating real IoT networks deployments, so that attackers can be distracted from the real target. In this paper, we present a novel mechanism leveraging SDN and NFV aimed to autonomously deploy and enforce IoT honeynets. The system follows a security policy-based approach that facilitates management, enforcement and orchestration of the honeynets and it has been successfully implemented and tested in the scope of H2020 EU project ANASTACIA, showing its feasibility to mitigate cyber-attacks.

Summary This paper presents the design and prototype implementation of the SELFNET fifth‐generation (5G) mobile edge infrastructure. In line with the current and emerging 5G architectural principles, visions, and standards, the proposed infrastructure is established primarily based on a mobile edge computing paradigm. It leverages cloud computing, software‐defined networking, and network function virtualization as core enabling technologies. Several technical solutions and options have been analyzed. As a result, a novel portable 5G infrastructure testbed has been prototyped to enable the preliminary testing of the integrated key technologies and to provide a realistic execution platform for further investigating and evaluating software‐defined networking– and network function virtualization–based application scenarios in 5G networks.

Network slicing is a primary Fifth-Generation (5G) mobile networking technology to create virtualised and softwarised logical networks for various vertical businesses with diverging Quality of Service (QoS) requirements. Meanwhile, there is a clear gap in providing network slicing capabilities in 5G multi-tenant networks to enable guaranteed QoS in terms of well-defined network metrics for the multiple tenants sharing the same physical infrastructure. This article designs and implements novel software data path architecture that enables such network slicing with assured QoS in 5G multi-tenant networks. Highly flexible and customisable definition of network slicing is also allowed to be aligned with different existing definitions on demand and at run time. The proposed architecture has been prototyped based on the popular Open Virtual Switch (OVS), and empirically validated to demonstrate the deployment and management of network slices with the above capabilities. Intensive scalability results are provided where more than 8,192 network slices are achieved simultaneously with warranted QoS through performance isolation in terms of bandwidth and delay in a real softwarised 5G multi-tenant infrastructure at speeds of up to 10 Gbps.

The increasing popularity of video applications and ever-growing high-quality video transmissions (e.g., 4K resolutions), has encouraged other sectors to explore the growth of opportunities. In the case of health sector, mobile Health services are becoming increasingly relevant in real-time emergency video communication scenarios where a remote medical experts' support is paramount to a successful and early disease diagnosis. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for 5G video providers to deploy highly scalable and priorizable in-network video optimization schemes to meet the expectations of a large quantity of video treatments. This paper presents a novel 5G Video Optimizer Virtual Network Function (vOptimizerVNF) that leverages the latest technologies in 5G and video processing to address this important challenge. Advanced traffic filtering is coupled with Scalable H.265 video coding to enable run-time bandwidth-saving video optimization without compromising Quality of Service (QoS); kernel-space video processing is introduced to achieve further performance gains; and the use of a Virtual Network Function (VNF) facilitates dynamic deployment of virtualized video optimizers to achieve scalability and flexibility in this service. The proposed approach is implemented in a realistic 5G testbed and empirical results demonstrate the superior scalability and performance achieved.

Network Slicing is one of the fundamental capabilities of the new Fifth-generation (5G)networks. It is defined as several logical networks that are created to fulfil specific Quality of Service (QoS)and Quality of Experience (QoE)requirements and are available over the same physical infrastructure. This paper proposes a novel extension to P4-NetFPGA framework to achieve network slicing between different 5G users in the edge-to-core network segment. This solution provides hardware-isolation of the performance in terms of bandwidth, latency and packet loss of 5G network traffic. The work proposed has been validated in a real 5G infrastructure.

Video applications are expected to increasingly dominate the traffic of mobile networks in the 5G era, and thus a real-time adaptation of these high resource demanding network applications is crucial in optimising the overall 5G networks. In this manuscript, we leverage Virtual Network Function (VNF) techniques to implement a video adaptation service that automatically adapts the quality of video transmissions depending on the status of the network. Furthermore, Network Function Virtualisation (NFV) techniques are employed here to simplify, optimise and speed up the deployment process of the aforementioned video adapter service (vAdapter), and therefore, allowing its on-demand deployment in a flexible way. We design, implement and test the scheme in a realistic virtualised 5G testbed. Empirical results focus on the scalability evaluation and performance as well as demonstrates a significant bandwidth reduction without compromising the final user's video quality expectations.

Future networks including the Fifth Generation (5G) and beyond mobile networks shall manage, control and orchestrate the new services for users especially vertical sectors, thereby they shall maximize the potential of 5G infrastructures and their services. Network slicing has emerged as a major new networking paradigm for meeting the diverse requirements of various vertical businesses in virtualized and softwarised 5G networks. SliceNet is a project of the EU 5G Infrastructure Public Private Partnership (5G PPP) and focuses on network slicing as a cornerstone technology in 5G networks. This article describes how the SliceNet Control Plane shall evolve to meet the end-to-end needs of many different vertical businesses. SliceNet Control Plane shall span across multiple administrative domains, by integrating different technologies in each involved segments (RAN, MEC, CN, inter-connectivity). Moreover, SliceNet Control Plane is able to allow verticals to plug their own control logic on top of provisioned slices and specialize their services characteristics while optimizing the use of shared resources, providing dynamic configuration, dynamic management, resource isolation and scalability.

Object detection systems mounted on Unmanned Aerial Vehicles (UAVs) have gained momentum in recent years in light of the widespread use cases enabled by such systems in public safety and other areas. Machine learning has emerged as an enabler for improving the performance of object detection. However, there is little existing work that has studied the performance of the machine learning approach, which is computationally resource demanding, in a portable mobile platform for UAV based object detection in user mobility scenarios. This paper evaluates an integrated real-world testbed for this scenario, by employing commercial-off-the-shelf devices including a UAV system and a machine-learning-enabled mobile platform. It presents benchmarking results about the performance of popular machine learning and computer vision frameworks such as TensorFlow and OpenCV and the associated algorithms such as YOLO, embedded in a smartphone execution environment of limited resources. The results highlight opportunities and provide insights into technical gaps to be filled to realize real-time machine-learning-based object detection on a mobile platform with constrained resources.

Dynamic Adaptive Streaming over HTTP (DASH) is a popular over-the-top video content distribution technique that adapts the streaming session according to the user's network condition typically in terms of downlink bandwidth. This video quality adaptation can be achieved by scaling the frame quality, spatial resolution or frame rate. Despite the flexibility on the video quality scaling methods, each of these quality scaling dimensions has varying effects on the Quality of Experience (QoE) for end users. Furthermore, in video streaming, the changes in motion over time along with the scaling method employed have an influence on QoE, hence the need to carefully tailor scaling methods to suit streaming applications and content type. In this work, we investigate an intelligent DASH approach for the latest video coding standard H.265 and propose a heuristic QoE-aware cost-efficient adaptation scheme that does not switch unnecessarily to the highest quality level but rather stays temporarily at an intermediate quality level in certain streaming scenarios. Such an approach achieves a comparable and consistent level of quality under impaired network conditions as commonly found in Internet and mobile networks while reducing bandwidth requirements and quality switching overhead. The rationale is based on our empirical experiments, which show that an increase in bitrate does not necessarily mean noticeable improvement in QoE. Furthermore, our work demonstrates that the Signal-to-Noise Ratio (SNR) and the spatial resolution scalability types are the best fit for our proposed algorithm. Finally, we demonstrate an innovative interaction between quality scaling methods and the polarity of switching operations. The proposed QoE-aware scheme is implemented and empirical results show that it is able to reduce bandwidth requirements by up to 41% whilst achieving equivalent QoE compared with a representative DASH reference implementation.

The emerging Fifth Generation (5G) mobile networks have been attracting enormous attention from various stakeholders around the world. In particular, in the research community, prototyping 5G infrastructures and deploying 5G services have gained gears recently towards realising market-oriented 5G trials. However, accessing to and programming on real-world 5G infrastructure is almost prohibitive for most 5G researchers especially in academia. Therefore, it is critical to build realistic yet cost-efficient 5G infrastructure emulators for 5G research labs to enable credible 5G research activities. This paper proposes such a 5G infrastructure emulator that is able to emulate a realistic 5G network in a lab setting based on a small number of commercial-off-the-shelf servers by leveraging virtualization and other technologies. Moreover, this emulator allows a service provider to automatically deploy 5G services from `empty' machines through advanced automation. The emulation platform is described in details with the 5G infrastructure and service deployment procedure highlighted. Empirical results are presented to show the performance of the proposed emulator.

Currently, there is a lack of tools for real validation of 5G scenarios. The increasing traffic demand of 5G networks is pushing network operators to find new cost-efficient solutions. The selected solution is a multi-tenancy approach that, together with user mobility will impose some architectural changes. This approach increases service dynamism making it necessary to have tools that provide these new capabilities to be able to validate each development. This work presents a novel experimentation framework for the emulation of 5G scenarios providing them with real-time user mobility and multi-tenancy. The functionality of this novel framework has been validated through different experiments.

The evolution from the current Fourth-Generation (4G) networks to the emerging Fifth-Generation (5G) technologies implies significant changes in the architecture and poses demanding requirements on network infrastructures. One of the Key Performance Indicators (KPIs) in 5G is to ensure a secure network with zero downtime. In this paper, we focus on the provisioning of protection capabilities for 5G infrastructures. Our objective is to implement a new 5G firewall that allows the detection, differentiation and selective blocking of 5G network traffic in the edge-to-core network segment of a 5G infrastructure, using a hardware-accelerated framework based on Field Programmable Gate Arrays (FPGA), developed using the P4 language. The proposed 5G firewall has been prototyped with the new capabilities proposed empirically validated.

With the Fifth-Generation (5G) mobile networks set to arrive within the next years, this new generation will transform the industry with a profound impact on its customers as well as on the existing technologies and network architectures. Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) will play key roles for the network operators as they prepare the migration to 5G, allowing them to quickly scale their networks. This paper presents a research work undertaken on this new paradigm of virtualized and programmable networks, aiming to address Self-Organizing Networks (SON) scenarios in a NFV/SDN context, focusing on detection and prediction of potential network and service anomalies. Towards this end, the performance management system performs aggregation, correlation and analysis of data gathered from the virtualized and programmable network elements. In particular, customized catalog-driven tools are developed, and the results show that they are able to successfully address these requirements. Current performance management platforms in production are designed for non-virtualized (non-NFV) and non-programmable (non-SDN) networks, and the knowledge gathered from this research brings some new understanding on how management platforms must evolve in order to be prepared for the upcoming next-generation mobile networks.

With the paradigm shift from the current 4G to the forthcoming 5G networks, novel network topology management interfaces become essential mainly due to the associated complexity introduced by the new softwarisation and virtualisation architecture. Moreover, the expected increasing number of users will require a very efficient management of the network resources. This paper proposes a new topology management scheme to report real-time topological information about the mobile users in a softwarised and virtualised 5G network. The proposed API has been designed and prototyped using Software Defined Radio (SDR) and the proven open source software OpenAirInterface and validated using commercial off-the-shelf user equipment.

The next generation mobile networks 5G are currently being intensively developed and standardized globally, with commercial prototyping 5G connections already emerging. At the 5G system level, one of the Key Performance Indicators (KPIs) defining 5G is substantially reduced service creation time for 5G network operators and/or service providers to increase the system efficiency and thus reduce operational costs. In this work, we focus on realize this challenging KPI in terms of speedy creation of monitoring services for 5G operators from scratch (no operating system pre-installed). A new 5G infrastructure orchestrator UWSIO is proposed to achieve fully automated deployment of 5G monitoring services. The architecture of this orchestrator is presented, which is compliant with the ETSI MANO standard to deal with both physical and virtual resources towards establishing the services running over the infrastructure. The proposed orchestrator is implemented in a real-world testbed and the implementation details are provided. Experimental results demonstrate that the performance of the design and implementation of this orchestrator is able to meet the KPI requirement for 5G operators.

The next-generation 5G mobile networks are expected to bring a major shift on the management paradigm based on Network Function Virtualization (NFV) and Software-Defined Networking (SDN) compared with its precursor, 4G. Consequently, operators need to significantly change their network architectures, management mechanisms and business models to accommodate and address the 5G challenges. This paper contributes to advancing the operators' management capabilities in the monitoring and analytic domains, looking at the evolution of the existing performance management platform from a leading operator to a new SDN/NFV-enabled platform, in the context of the EU 5G project SELFNET. This work focuses on designing and prototyping the essential functionalities to perform real-time processing over network infrastructure data. This work devises a new Complex Event Processing (CEP) framework, a realtime framework for processing and aggregating big data using a dynamic rule-based approach. This CEP framework has been successfully implemented and deployed, with aggregation rules applied to a Self-Protection use case, which is able to provide in real-time information about detected botnets in the network. From a high-level perspective, this work brings some new understanding about the role of SDN/NFV network management tools for 5G network operators.

Internet of Things (IoT) is a key business driver for the upcoming fifth-generation (5G) mobile networks, which in turn will enable numerous innovative IoT applications such as smart city, mobile health, and other massive IoT use cases being defined in 5G standards. To truly unlock the hidden value of such mission-critical IoT applications in a large scale in the 5G era, advanced self-protection capabilities are entailed in 5G-based Narrowband IoT (NB-IoT) networks to efficiently fight off cyber-attacks such as widespread Distributed Denial of Service (DDoS) attacks. However, insufficient research has been conducted in this crucial area, in particular, few if any solutions are capable of dealing with the multiple encapsulated 5G traffic for IoT security management. This paper proposes and prototypes a new security framework to achieve the highly desirable self-organizing networking capabilities to secure virtualized, multitenant 5G-based IoT traffic through an autonomic control loop featured with efficient 5G-aware traffic filtering. Empirical results have validated the design and implementation and demonstrated the efficiency of the proposed system, which is capable of processing thousands of 5G-aware traffic filtering rules and thus enables timely protection against large-scale attacks.

The potential of telemedicine in respiratory health care has not been completely unveiled in part due to the inexistence of reliable objective measurements of symptoms such as cough. Currently available cough detectors are uncomfortable and expensive at a time when generic smartphones can perform this task. However, two major challenges preclude smartphone-based cough detectors from effective deployment namely, the need to deal with noisy environments and computational cost. This paper focuses on the latter, since complex machine learning algorithms are too slow for real-time use and kill the battery in a few hours unless specific actions are taken. In this paper, we present a robust and efficient implementation of a smartphone-based cough detector. The audio signal acquired from the device's microphone is processed by computing local Hu moments as a robust feature set in the presence of background noise. We previously demonstrated that pairing Hu moments and a standard k-NN classifier achieved accurate cough detection at the expense of computation time. To speed-up k-NN search, many tree structures have been proposed. Our cough detector uses an improved vantage point (vp)-tree with optimized construction methods and a distance function that results in faster searches. We achieve 18× speed-up over classic vp-trees, and 560× over standard implementations of k-NN in state-of-the-art machine learning libraries, with classification accuracies over 93%, enabling real-time performance on low-end smartphones.

With the advances of new‐generation wireless and mobile communication systems such as the fifth‐generation (5G) mobile networks and Internet of Things (IoT) networks, demanding applications such as Ultra‐High‐Definition video applications is becoming ever popular. These applications require real‐time monitoring and processing to meet the mission‐critical quality of service requirements and are expected to be supported by the emerging fog or edge computing paradigms. This paper presentsNFVMon, a novel monitoring architecture to enable flow monitoring capabilities of network traffic in a 5G multioperator mobile edge computing environment. The proposedNFVMonis integrated with the management plane of the Cloud Computing.NFVMonhas been prototyped and a reference implementation is presented. It provides novel capabilities to provide disaggregated metrics related to the different 5G mobile operators sharing infrastructures and also about the different 5G subscribers of each of such mobile operators. Extensive experiments for evaluating the performance of the system have been conducted on a mid‐sized infrastructure testbed.

The progress in realizing the Fifth Generation (5G) mobile networks has been accelerated recently towards deploying 5G prototypes with increasing scale. One of the Key Performance Indicators (KPIs) in 5G deployments is the service deployment time, which should be substantially reduced from the current 90 hours to the target 90 minutes on average as defined by the 5G Public‐Private Partnership (5G‐PPP). To achieve this challenging KPI, highly automated and coordinated operations are required for the 5G network management. This paper addresses this challenge by designing and prototyping a novel 5G service deployment orchestration architecture that is capable of automating and coordinating a series of complicated operations across physical infrastructure, virtual infrastructure, and service layers over a distributed mobile edge computing paradigm, in an integrated manner. Empirical results demonstrate the superior performance achieved, which meets the 5G‐PPP KPI even in the most challenging scenario where 5G services are installed from bare metal.

The paper elaborates on the technological and architectural innovations researched and developed by 5G-PPP Phase 1 projects and covering innovation areas such as 5G system design and evaluation, novel air interfaces, network management and security as well as virtualization and service deployment aspects.

Fourth-Generation (4G) mobile networks are based on Long-Term Evolution (LTE) technologies and are being deployed worldwide, while research on further evolution towards the Fifth Generation (5G) has been recently initiated. 5G will be featured with advanced network infrastructure sharing capabilities among different operators. Therefore, an open-source implementation of 4G/5G networks with this capability is crucial to enable early research in this area. The main contribution of this paper is the design and implementation of such a 4G/5G open-source testbed to investigate multioperator infrastructure sharing capabilities executed in virtual architectures. The proposed design and implementation enable the virtualization and sharing of some of the components of the LTE architecture. A testbed has been implemented and validated with intensive empirical experiments conducted to validate the suitability of virtualizing LTE components in virtual infrastructures (i.e., infrastructures with multitenancy sharing capabilities). The impact of the proposed technologies can lead to significant saving of both capital and operational costs for mobile telecommunication operators.

Monitoring of cloud computing infrastructures is an imperative necessity for cloud providers and administrators to analyze, optimize and discover what is happening in their own infrastructures. Current monitoring solutions do not fit well for this purpose mainly due to the incredible set of new requirements imposed by the particular requirements associated to cloud infrastructures. This paper describes in detail the main reasons why current monitoring solutions do not work well. Also, it provides an innovative monitoring architecture that enables the monitoring of the physical and virtual machines available within a cloud infrastructure in a non-invasive and transparent way making it suitable not only for private cloud computing but also for public cloud computing infrastructures. This architecture has been validated by means of a prototype integrating an existing enterprise-class monitoring solution, Nagios, with the control and data planes of OpenStack, a well-known stack for cloud infrastructures. As a result, our new monitoring architecture is able to extend the exiting Nagios functionalities to fit in the monitoring of cloud infrastructures. The proposed architecture has been designed, implemented and released as open source to the scientific community. The proposal has also been empirically validated in a production-level cloud computing infrastructure running a test bed with up to 128 VMs where overhead and responsiveness has been carefully analyzed.

This paper presents a novel monitoring architecture addressed to the cloud provider and the cloud consumers. This architecture offers a monitoring platform-as-a-Service to each cloud consumer that allows to customize the monitoring metrics. The cloud provider sees a complete overview of the infrastructure whereas the cloud consumer sees automatically her cloud resources and can define other resources or services to be monitored. This is accomplished by means of an adaptive distributed monitoring architecture automatically deployed in the cloud infrastructure. This architecture has been implemented and released under GPL license to the community as “MonPaaS”, open source software for integrating Nagios and OpenStack. An intensive empirical evaluation of performance and scalability have been done using a real deployment of a cloud computing infrastructure in which more than 3,700 VMs have been executed.

This study presents a scalable and elastic distributed system for monitoring cloud infrastructure based on a pure peer-to-peer architecture. Its distributed nature enables deploying long-living queries across the network to monitor a diverse set of entities and metrics, spanning across all layers of a cloud stack that can change rapidly. This allows for aggregating low-level metrics from operating systems, to higher-level application-specific metrics derived from services, databases or application log files. The observed metrics and information can be evaluated and used to reliably trigger policies to automate complex management tasks within a cloud environment. The architecture incorporates a query framework for obtaining high-level information and a policy framework to provide self-management capabilities to monitored cloud infrastructure. The system has been implemented as a proof of concept. Details and statistical results are provided to validate the scalability of the underlying architecture.

SUMMARY Cloud computing infrastructure services enable the flexible creation of virtual infrastructures on‐demand. However, the creation of infrastructures is only a part of the process for provisioning services. Other steps such as installation, deployment, configuration, monitoring and management of software components are needed to fully provide services to end‐users in the cloud. This paper describes a peer‐to‐peer architecture to automatically deploy services on cloud infrastructures. The architecture uses a component repository to manage the deployment of these software components, enabling elasticity by using the underlying cloud infrastructure provider. The life cycle of these components is described in this paper, as well as the language for defining them. We also describe the open‐source proof‐of‐concept implementation. Some technical information about this implementation together with some statistical results are also provided. Copyright © 2011 John Wiley & Sons, Ltd.

Authorisation is a crucial process in current information systems. Nowadays, many of the current authorisation systems do not provide methods to describe the semantics of the underlying information model which they are protecting. This fact can lead to mismatch problems between the semantics of the authorisation model and the semantics of the underlying data and resources being protected. In order to solve this problem, this paper describes an authorisation model based on Semantic Web technologies. This authorisation model uses the common information model (CIM) to represent the underlying information model. For this reason, a new conversion process of CIM into the Semantic Web languages has been proposed converting properly the semantics available in the CIM model. This representation provides a suitable information model based on a well-known logic formalism for implementing the authorisation model and a formal language for describing concisely the semantic of the information models being protected. The formal authorisation model supports role-based access control (RBAC), hierarchical RBAC, conditional RBAC and object hierarchies, among other features. Moreover, this paper describes an authorisation architecture for distributed systems taking into account aspects such as privacy among parties and trust management. Finally, some implementation aspects of this system have also been described.

As two major communication technologies, the internet and wireless, are maturing rapidly to dominate our civilised life, the authors urgently need to re-establish users’ confidence to harvest new potential applications of large-scale distributed systems. Service agents and distributed multi-agent systems (MASs) have shown the potential to help with this move as the lack of trust caused by heavily compromised security issues and concerns coupled with the out-of-date solutions are hindering the progress. The authors therefore seek new remedies to ensure that the continuity in developing new economies is maintained through building new solutions to address today's techno-economical problems. Following a scan of the literature the authors discuss the state-of-the-art progress followed by some observations and remarks for the researchers in the field. Here the authors recognise the need for new ‘distributed security’ solutions, as an overlay service, to rejuvenate and exploit the distributed artificial intelligence (AI) techniques for secure MAS as a natural solution to pave the way to enable a long awaited application paradigm of the near future.