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.

As Artificial Intelligence (AI) permeates our lives more rapidly, robust regulations to ensure trustworthy AI applications are demanded by AI operators and consumers. The European Union’s AI Act addresses this by establishing a regulatory framework for high-risk AI systems, emphasizing the need for proper documentation to ensure compliance. This paper presents a novel approach to assess AI documentation using Large Language Model based methods: a GPT-4 prompting approach and fine-tuning DeBERTa and Mistral-7B. Due to the lack of relevant datasets, we constructed a novel benchmark dataset comprising text passages from AI research publications. These passages are matched by AI experts with regulatory requirements and are classified into different fulfilment classes. Using this dataset in our comparative study, our findings demonstrate that fine-tuning the DeBERTa model achieves 92% ± 1% accuracy in classifying compliance categories, outperforming the more complex GPT-4 and Mistral-7B significantly. Overall, this research advances AI governance by providing insights into automating documentation compliance checks. Finally, by making the models and datasets publicly available, we promote further research into enhancing transparency and accountability in AI systems.

This paper presents the design and implementation of an autonomous UAV-based search and rescue system developed within the Horizon Europe project P2CODE. The proposed system leverages a modular and scalable architecture integrating edge-based real-time video processing, AI-based human detection, asynchronous message communication, and persistent state logging, all orchestrated through a web-based operator interface. Central to the system is a swarm intelligence algorithm that partitions the search area among multiple UAVs, taking into account factors such as battery levels and initial positions to generate balanced and coherent flight paths. By combining a Divide Areas based on Robots' initial Positions (DARP) method with a Spanning Tree Coverage (STC) algorithm, the system ensures efficient and complete coverage of large outdoor regions. The operational workflow supports both fully autonomous exploration and reactive human-in-the-loop intervention in response to real-time detections. This work contributes a practical blueprint for large-scale, multi-agent coordination in dynamic and unstructured environments, advancing the state of the art in autonomous search and rescue missions.

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.

Emotion recognition promotes the evaluation and enhancement of Virtual Reality (VR) experiences by providing emotional feedback and enabling advanced personalization. However, facial expressions are rarely used to recognize users' emotions, as Head-Mounted Displays (HMDs) occlude the upper half of the face. To address this issue, we conducted a study with 37 participants who played our novel affective VR game EmojiHeroVR. The collected database, EmoHeVRDB (EmojiHeroVR Database), includes 3,556 labeled facial images of 1,778 reenacted emotions. For each labeled image, we also provide 29 additional frames recorded directly before and after the labeled image to facilitate dynamic Facial Expression Recognition (FER). Additionally, EmoHeVrdbincludes data on the activations of 63 facial expressions captured via the Meta Quest Pro VR headset for each frame. Leveraging our database, we conducted a baseline evaluation on the static FER classification task with six basic emotions and neutral using the EfficientNet-B0 architecture. The best model achieved an accuracy of 69.84% on the test set, indicating that FER under HMD occlusion is feasible but significantly more challenging than conventional FER.

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.

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 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.

Interactive technologies are increasingly being used as discursive objects for raising awareness about the environment in the cultural sector, but little is known about the user’s lived experience during an interaction. In this study, we present the development and evaluation of an interface designed to raise awareness about the environment within a speculative art installation. For this purpose, we drew on the concept of sentinel species, specifically the miner’s canary, to enable a multisensory experience with the state of the air. We then evaluated the interface with 14 participants while interacting in a prototypical arrangement in the laboratory. Overall, the findings indicate promising directions towards a sentinel-species-mimicking interface that communicates the state of the air through its physiological behavior and thus also engages with the user’s empathy on a cognitive and emotional level. Based on the findings, we highlight the implications of this study and point to further directions for human–atmosphere interactions.

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.

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.

This research addresses an interdisciplinary problem area concerning the long-term deployment of socially embedded technology in authentic environments. It concentrates on the case of ambient displays, where long-term research in the real world is still scant and evidentially requires methodological development. This study seeks to advance on this situation at both findings and methodological levels. To this end, we introduce our Ambient Surfaces solution that was deployed in the agile software development department of a company for circa 5 years. Classic grounded theory was chosen to methodologically guide the evaluation, while the theoretical contribution of this work is a substantive theory exemplified by its core category of Spontaneous utilization. The theory reveals insights on how ambient displays are utilized by practitioners in professional and large-scale agile environments. We found, among others, that staff members used the Ambient Surfaces largely not on purpose, that our solution evolved toward having a strong emphasis on progress tracking information, and that inter-team awareness as well as intra-team communication were encouraged.

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.

This paper introduces a preliminary taxonomy to bring the condition of air into the foreground of human perception. To create this taxonomy, we drew on the foundations of atmospheric research and studies in the field of human-computer interaction to provide an overview of different inputs and outputs that enable an interaction with the air. In addition, we present a potential use case that could benefit from a taxonomy to allow the development of atmospheric interfaces and empower the transfer of knowledge. We discuss our findings and conclude with challenges that can be addressed in future research.

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.

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.

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.

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.

During the last decades, concepts of Intelligent Transportation Systems (ITS) were continuously adapted and improved based on new insights into human travel behavior. Drivers for improvements are the quantity and quality of available mobility data, which increased significantly in recent years. Based on travel behavior, literature proposes a large number of different solutions for next step or future location prediction. However a holistic spatio-temporal prediction, which could further improve the quality of ITS, creates a more complex task. The prediction of medium-term mobility for one to seven days is challenging in particular for atypical travel behavior, since the weekdays' order delivers no reliable indication for the next day's travel behavior. With our contribution, we explore the benefits of various prediction approaches for medium-term mobility prediction and combine them dynamically to predict individual mobility behavior for a period of one week. The derived framework utilizes an exhaustive search approach to benefit from a machine learning based clustering method on location data. In conjunction with an Artificial Neural Network, the prediction framework is robust against prediction errors created by atypical behavior. With two data sets consisting of smartphone and vehicle data, we demonstrate the framework's real-world applicability. We show that clustering an individual's historical movement data can improve the prediction accuracy of different prediction methods that will be explained in detail and illustrate the interrelation of entropy and prediction accuracy.

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.

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 prediction of an individual's future locations is a significant part of scientific researches. While a variety of solutions have been investigated for the prediction of future locations, predicting departure and arrival times at predicted locations is a task with higher complexity and less attention. While the challenges of combining spatial and temporal information have been stated in various works, the proposed solutions lack accuracy and robustness. This paper proposes a simple yet effective way to predict not only an individual's future location, but also most probable departure and arrival times as well as the most probable route from origin to destination.

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.

This paper presents a new, practical infrared video based surveillance system, consisting of a resolution-enhanced, automatic target detection/recognition (ATD/R) system that is widely applicable in civilian and military applications. To deal with the issue of small numbers of pixel on target in the developed ATD/R system, as are encountered in long range imagery, a super-resolution method is employed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. To tackle the challenge of detecting extremely low-resolution targets, we train a sophisticated and powerful convolutional neural network (CNN) based faster-RCNN using long wave infrared imagery datasets that were prepared and marked in-house. The system was tested under different weather conditions, using two datasets featuring target types comprising pedestrians and 6 different types of ground vehicles. The developed ATD/R system can detect extremely low-resolution targets with superior performance by effectively addressing the low small number of pixels on target, encountered in long range applications. A comparison with traditional methods confirms this superiority both qualitatively and quantitatively.

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 upsurge of mobile devices paired with highly interactive social web applications generates enormous amounts of requests web services have to deal with. Consequently in our previous work, a novel request flow scheme with scalable components was proposed for storing interdependent, permanently updated resources in a database. The major challenge is to process dependencies in an optimal fashion while maintaining dependency constraints. In this work, three research objectives are evaluated by examining resource dependencies and their key graph measurements. An all-sources longest-path algorithm is presented for efficient processing and dependencies are analysed to find correlations between performance and graph measures. Two algorithms basing their parameters on six real-world web service structures, e.g., Facebook Graph API are developed to generate dependency graphs and a model is developed to estimate performance based on resource parameters. An evaluation of four graph series discusses performance effects of different graph structures. The results of an evaluation of 2,000 web services with over 850 thousand resources and 6 million requests indicate that resource dependency processing can be up to a factor of two faster compared to a traditional processing approach while an average model fit of 97 percent allows an accurate prediction.

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.

Context-aware systems for smart environments can be very complex and demanding for developers especially in distributed computing and communication environments. We propose a new development infrastructure, that targets this challenge by improving the general system's scalability and traceability. The infrastructure has been developed for and tested in two research labs for smart environments and human computer interaction. First measurements show that the platform has high scalability and low message latency that is perfectly suitable for interactive projects and virtual reality experiments.

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.

For time division duplex systems with increased bandwidth in 5G, the concept of self-contained transmission has been proposed. Aiming at reduced latency and more flexible resource allocation, this concept involves frequent uplink and downlink switching and relatively short transmission in both directions. In this work, we consider single OFDM symbol transmission within self-contained transmission time intervals. Focusing on OFDM with pulse shaping, we propose a pulse shape with smooth transients in the time domain. Performance evaluation shows that an OFDM system with the proposed pulse shape exhibits better robustness against both noise and self-interference in comparison to the OFDM system with cyclic prefix. From the implementation point of view, pulse shaping requires only small modification to the transceiver and negligible computational complexity increase.

Nowadays, web services have to accommodate a significant and ever-increasing number of requests due to high interactivity of current applications. Although the built-in elasticity offered by a cloud can mitigate this challenge, it is highly desirable that applications can be built in a scalable fashion. State-of-the-art Web Application Frameworks (WAFs) focus on the creation of application logic and do not offer integrated cloud scaling concepts. As the creation of such scaling systems is very complex, we proposed in our recent work the concept of Web Scaling Frameworks (WSFs) in order to offload scaling to another layer of abstraction. In this work, a detailed design for WSFs including necessary modules, interfaces and components is presented. A mathematical model used for performance rating is evaluated and enhanced on a computing cluster of 42 machines. Traffic traces from over 25 million real-world applications are analysed and evaluated on the cluster to compare the WSF performance with a traditional scaling approach. The results show that the application of WSFs can substantially reduce the number of total machines needed for three representative real-world applications-a social network, a trip planner and the FIFA World Cup 98 website-by 32, 63 and 92 percent, respectively.

Future 5G networks have stringent end-user requirements on data rate and error performance. In order to satisfy these requirements, innovative wireless networking technologies and models need be researched. One particular example is the two-way relaying channel, which can have as much as 100% higher theoretical data rate than current systems where transmissions are arranged in an orthogonal manner. However, benefits of this model cannot be achieved without the application of proper relaying protocols. This paper proposes a novel protocol that directly addresses the problems of existing protocols of two-way relaying models, e.g. analogy network coding and physical network coding, and has improved performance. By combining direct and differential demodulation-forward schemes based on wireless channel qualities and signal to noise ratio, a new hybrid protocol is created. Theoretical analysis and numerical experiments show that the proposed solution has lower error rate than the existing ones, and can thus be applied to support future 5G networks.

The social web and huge growth of mobile smart devices dramatically increases the performance requirements for web services. State-of-the-art Web Application Frameworks (WAFs) do not offer complete scaling concepts with automatic resource-provisioning, elastic caching or guaranteed maximum response times. These functionalities, however, are supported by cloud computing and needed to scale an application to its demands. Components like proxies, load-balancers, distributed caches, queuing and messaging systems have been around for a long time and in each field relevant research exists. Nevertheless, to create a scalable web service it is seldom enough to deploy only one component. In this work we propose to combine those complementary components to a predictable, composed system. The proposed solution introduces a novel class of web frameworks called Web Scaling Frameworks (WSFs) that take over the scaling. The proposed mathematical model allows a universally applicable prediction of performance in the single-machine- and multi-machine scope. A prototypical implementation is created to empirically validate the mathematical model and demonstrates both the feasibility and increase of performance of a WSF. The results show that the application of a WSF can triple the requests handling capability of a single machine and additionally reduce the number of total machines by 44%.

SHVC is the proposed scalable extension to the next-generation High Efficiency Video Coding (HEVC) standard, which delivers a 50% bandwidth reduction for the same video quality when compared to the current H.264/AVC standard. SHVC further offers a scalable format that can be readily adapted to meet network conditions or terminal capabilities. Both bandwidth saving and scalability are highly desirable characteristics of adaptive video streaming applications in bandwidth-constrained, wireless networks. We implement and evaluate temporal, spatial and quality scalability schemes for SHVC on a wireless testbed. However, there is little published work on video transmission using this important extension to the emerging HEVC standard. Quality scalability in SHVC is empirically shown to deliver peak signal to noise ratio (PSNR) improvements of up to 6.4dB over a previous streaming scheme for HEVC. Our SHVC streaming scheme also outperforms H.264/SVC with over 50% bandwidth saving for similar quality.

Wireless Mesh Networks (WMNs) integrate fast deployable backbone networks and distributed mobile ad hoc networks with self-healing, self-organization and self-configuration features. These capabilities make WMNs a promising technology for emergency management communications. An incident area network needs reliable routing paths during disaster recovery and emergency response operations, when pre-existing communication infrastructure and power resources have been destroyed. Power-aware routing plays an essential role in this context to provide prolonged emergency services. Nevertheless, most of the previous power-aware routing algorithms did not fully exploit the characteristics of WMNs. This paper proposes a power- and WMN node-type-aware routing algorithm (PNTARA), which selects optimized routes based on a joint consideration of the nodes' types and power levels along the path. Simulation results show that the proposed algorithm can significantly improve the network performance by reducing the power consumption and network overheads, whilst maintaining high data delivery ratio with low end-to-end delay.

There has been a significant amount of recent research in the computer networking community on the emerging wireless networking paradigm known as Mobile Networks; while the real-time image processing community have expended much effort on the development of scalable video encoding techniques and investigating the multipath streaming of real-time multimedia content. This work draws together the key elements of recent research in each of these areas and proposes a novel scheme for the multipath delivery of scalable video streams to users within multihomed mobile networks. We provide a hardware-based, multihomed mobile networks testbed and propose OPSSA, an optimised path selection and scheduling algorithm for use in multihomed mobile networks. Experimental results show our algorithm to outperform the representative algorithms when measured using the Peak Signal to Noise Ratio (PSNR) metric.

In this tutorial paper, we examine the H.264X family of standards for advanced, scalable or multiview video coding, the distributed video coding paradigm and the emerging H.265 standard. Detailed technical descriptions and insightful analysis are presented in each standard. The applications of selected video coding standards in emerging wireless networks are then introduced with an emphasis on scalable video streaming in multihomed mobile networks. Both research challenges and potential solutions are discussed along the description, and numerical results through simulations or experiments are provided to reveal the performances of selected coding standards and networking algorithms.