OBJECTIVE: A proof-of-concept study aimed at designing and implementing Visual & Interactive Engagement With Electronic Records (VIEWER), a versatile toolkit for visual analytics of clinical data, and systematically evaluating its effectiveness across various clinical applications while gathering feedback for iterative improvements. MATERIALS AND METHODS: VIEWER is an open-source and extensible toolkit that employs natural language processing and interactive visualization techniques to facilitate the rapid design, development, and deployment of clinical information retrieval, analysis, and visualization at the point of care. Through an iterative and collaborative participatory design approach, VIEWER was designed and implemented in one of the United Kingdom's largest National Health Services mental health Trusts, where its clinical utility and effectiveness were assessed using both quantitative and qualitative methods. RESULTS: VIEWER provides interactive, problem-focused, and comprehensive views of longitudinal patient data (n = 409 870) from a combination of structured clinical data and unstructured clinical notes. Despite a relatively short adoption period and users' initial unfamiliarity, VIEWER significantly improved performance and task completion speed compared to the standard clinical information system. More than 1000 users and partners in the hospital tested and used VIEWER, reporting high satisfaction and expressed strong interest in incorporating VIEWER into their daily practice. DISCUSSION: VIEWER provides a cost-effective enhancement to the functionalities of standard clinical information systems, with evaluation offering valuable feedback for future improvements. CONCLUSION: VIEWER was developed to improve data accessibility and representation across various aspects of healthcare delivery, including population health management and patient monitoring. The deployment of VIEWER highlights the benefits of collaborative refinement in optimizing health informatics solutions for enhanced patient care.

Digital Twins (DT) have become crucial to achieve sustainable and effective smart urban solutions. However, current DT modelling techniques cannot support the dynamicity of these smart city environments. This is caused by the lack of right-time data capturing in traditional approaches, resulting in inaccurate modelling and high resource and energy consumption challenges. To fill this gap, we explore spatiotemporal graphs and propose the Reinforcement Learning-based Adaptive Twining (RL-AT) mechanism with Deep Q Networks (DQN). By doing so, our study contributes to advancing Green Cities and showcases tangible benefits in accuracy, synchronisation, resource optimization, and energy efficiency. As a result, we note the spatiotemporal graphs are able to offer a consistent accuracy and 55% higher querying performance when implemented using graph databases. In addition, our model demonstrates right-time data capturing with 20% lower overhead and 25% lower energy consumption.

The surge in popularity of live video streaming has spurred the development of various bitrate adaptation techniques, all aimed at enhancing user Quality of Experience (QoE). Compared to streaming Video-on-Demand, achieving low-latency live video streaming under fluctuating network conditions poses additional challenges. It requires finding the balance between rebuffering avoidance and latency, as a small client buffer is required to achieve low latency. Video players can also employ playback speed control to help optimize this balance. Specifically, when client buffer occupancy is high and hence latency is high, the player may increase playback speed to reduce the latency; and conversely, when client buffer occupancy is low and hence the risk of rebuffering is high, the player may reduce playback speed to increase buffer occupancy. Based on this rationale, a variety of playback speed control methods have been proposed. This paper evaluates, using a real-world testbed, the effectiveness of various playback speed control mechanisms when applied to a set of bitrate adaptation algorithms, with the evaluation also encompassing variations in target latency and network conditions. Our findings show a lack of coordination between adaptive bitrate (ABR) algorithms and playback speed control mechanisms. This leads us to conclude that there is a need for new playback speed control methods designed in conjunction with ABR algorithms.

Abstract HTTP Adaptive Streaming (HAS), the most prominent technology for streaming video over the Internet, suffers from high end-to-end latency when compared to conventional broadcast methods. This latency is caused by the content being delivered as segments rather than as a continuous stream, requiring the client to buffer significant amounts of data to provide resilience to variations in network throughput and enable continuous playout of content without stalling. The client uses an Adaptive Bitrate (ABR) algorithm to select the quality at which to request each segment to trade-off video quality with the avoidance of stalling to improve the Quality of Experience (QoE). The speed at which the ABR algorithm responds to changes in network conditions influences the amount of data that needs to be buffered, and hence to achieve low latency the ABR needs to respond quickly. Llama (Lyko et al. 28) is a new low latency ABR algorithm that we have previously proposed and assessed against four on-demand ABR algorithms. In this article, we report an evaluation of Llama that demonstrates its suitability for low latency streaming and compares its performance against three state-of-the-art low latency ABR algorithms across multiple QoE metrics and in various network scenarios. Additionally, we report an extensive subjective test to assess the impact of variations in video quality on QoE, where the variations are derived from ABR behaviour observed in the evaluation, using short segments and scenarios. We publish our subjective testing results in full and make our throughput traces available to the research community.

Ambient computing is gaining popularity as a major technological advancement for the future. The modern era has witnessed a surge in the advancement in healthcare systems, with viable radio frequency solutions proposed for remote and unobtrusive human activity recognition (HAR). Specifically, this study investigates the use of Wi-Fi channel state information (CSI) as a novel method of ambient sensing that can be employed as a contactless means of recognizing human activity in indoor environments. These methods avoid additional costly hardware required for vision-based systems, which are privacy-intrusive, by (re)using Wi-Fi CSI for various safety and security applications. During an experiment utilizing universal software-defined radio (USRP) to collect CSI samples, it was observed that a subject engaged in six distinct activities, which included no activity, standing, sitting, and leaning forward, across different areas of the room. Additionally, more CSI samples were collected when the subject walked in two different directions. This study presents a Wi-Fi CSI-based HAR system that assesses and contrasts deep learning approaches, namely convolutional neural network (CNN), long short-term memory (LSTM), and hybrid (LSTM+CNN), employed for accurate activity recognition. The experimental results indicate that LSTM surpasses current models and achieves an average accuracy of 95.3% in multi-activity classification when compared to CNN and hybrid techniques. In the future, research needs to study the significance of resilience in diverse and dynamic environments to identify the activity of multiple users.

The Internet of Things (IoT), in combination with advancements in Big Data, communications and networked systems, offers a positive impact across a range of sectors including health, energy, manufacturing and transport. By virtue of current business models adopted by manufacturers and ICT operators, IoT devices are deployed over various networked infrastructures with minimal security, opening up a range of new attack vectors. Conventional rule-based intrusion detection mechanisms used by network management solutions rely on pre-defined attack signatures and hence are unable to identify new attacks. In parallel, anomaly detection solutions tend to suffer from high false positive rates due to the limited statistical validation of ground truth data, which is used for profiling normal network behaviour. In this work we go beyond current solutions and leverage the coupling of anomaly detection and Cyber Threat Intelligence (CTI) with parallel processing for the profiling and detection of emerging cyber attacks. We demonstrate the design, implementation, and evaluation ofCitrus: a novel intrusion detection framework which is adept at tackling emerging threats through the collection and labelling of live attack data by utilising diverse Internet vantage points in order to detect and classify malicious behaviour using graph-based metrics as well as a range of machine learning (ML) algorithms. Citrus considers the importance of ground truth data validation and its flexible software architecture enables both the real-time and offline profiling, detection and classification of emerging cyber-attacks under optimal computational costs. Thus, establishing it as a viable and practical solution for next generation network defence and resilience strategies.

The Intent-Based Northbound Interface (NBI) offers users the ability to express what they want to achieve instead of how to achieve it, enabling improvements to network management and reducing operational costs. However, development of an Intent-Based NBI remains in its infancy. Existing solutions do not allow users to express high-level operational targets that appropriately capture business objectives, nor link these to lower-level management policies and operations. We propose an extensible Intent-based NBI framework and a higher-level declarative intent expression to enable service-oriented intents with different targets. We focus on the creation of intents and their mapping from high-level expressions to low-level policies, and consider this from the perspective of an intent developer in the context of a Cloud CDN use case.

In the recent years, HTTP Adaptive Bit Rate (ABR) streaming including Dynamic Adaptive Streaming over HTTP (DASH) has become the most popular technology for video streaming over the Internet. The client device requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces high latency compared to traditional broadcast methods, mostly in the client buffer which needs to hold enough data to absorb any changes in network conditions. Clients employ an ABR algorithm which monitors network conditions and adjusts the quality at which segments are requested to maximise the user's Quality of Experience. The size of the client buffer depends on the ABR algorithm's capability to respond to changes in network conditions in a timely manner, hence, low latency live streaming requires an ABR algorithm that can perform well with a small client buffer. In this paper, we present Llama - a new ABR algorithm specifically designed to operate in such scenarios. Our new ABR algorithm employs the novel idea of using two independent throughput measurements made over different timescales. We have evaluated Llama by comparing it against four popular ABR algorithms in terms of multiple QoE metrics, across multiple client settings, and in various network scenarios based on CDN logs of a commercial live TV service. Llama outperforms other ABR algorithms, improving the P.1203 Mean Opinion Score (MOS) as well as reducing rebuffering by 33% when using DASH, and 68% with CMAF in the lowest latency scenario.

Despite the Internet being an apex of human achievement for many years, malicious activity and cyber attacks are becoming more prevalent than ever before. Large scale data collection using threat sources such as honeypots have recently been employed to gather information relating to these attacks. While this data naturally details attack properties, there exists challenges in extracting the relevant information from vast data sets to provide valuable insight and a standard description of the attack. Traditionally, threats are identified through the use of signatures that are crafted manually through the composition of IOCs (Indicators of Compromise) extracted from telemetry captured during an attack process, which is often administered by an experienced engineer. These signatures have been proven effective in their use by IDSs (Intrusion Detection Systems) to detect emerging threats. However, little research has been made in automating the extraction of emerging IOCs and the generation of corresponding signatures which incorporate host artefacts. In this paper we present Citrus: a novel approach to the generation of signatures by incorporating host based telemetry extracted from honeypot endpoints. Leveraging this visibility at an endpoint grants a detailed understanding of bleeding edge attack tactics, techniques, and procedures gathered from host logs.

HTTP Adaptive Streaming (HAS) technologies such as MPEG DASH are now used extensively to deliver television services to large numbers of viewers. In HAS, the client requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces significant end to end latency compared to traditional broadcast, due to the the client requiring a large enough buffer for the ABR algorithm to react to changes in network conditions in a timely manner. The recently standardised Common Media Application Format (CMAF) has helped address the issue of latency by defining segments as composed of independently transferable chunks. In this paper, we describe a simulation model we have developed to evaluate the performance of four popular ABR algorithms using DASH and CMAF in various low latency live streaming scenarios. Realistic network conditions are used for the evaluation, which are based on throughput data taken from the CDN logs of a commercial live TV service. We quantify the performance of the ABR algorithms using a selection of QoE metrics, and show that CMAF can significantly improve ABR performance in low delay scenarios.

When designing Software Defined Networks (SDNs), there is a risk that the additional abstractions available can result in reduced scalability and performance. One such abstraction, intents, are a way in which network administrators can express policies rather than having to define specific forwarding rules. This provides a benefit to administrators in allowing automatic network reconfiguration and fault tolerance. In this paper, we highlight the performance overheads associated with the intents framework from a popular SDN controller, ONOS. We propose a novel prototype that leverages source-based routing and programmable data planes using P4 in order to reduce the overheads of intent-based forwarding.

Despite the relative maturity of the Internet, the computer networks of today are still susceptible to attack. The necessary distributed nature of networks for wide area connectivity has traditionally led to high cost and complexity in designing and implementing secure networks. With the introduction of software-defined networks (SDNs) and network functions virtualization, there are opportunities for efficient network threat detection and protection. SDN's global view provides a means of monitoring and defense across the entire network. However, current SDN-based security systems are limited by a centralized framework that introduces significant control plane overhead, leading to the saturation of vital control links. In this paper, we introduce TENNISON, a novel distributed SDN security framework that combines the efficiency of SDN control and monitoring with the resilience and scalability of a distributed system. TENNISON offers effective and proportionate monitoring and remediation, compatibility with widely available networking hardware, support for legacy networks, and a modular and extensible distributed design. We demonstrate the effectiveness and capabilities of the TENNISON framework through the use of four attack scenarios. These highlight multiple levels of monitoring, rapid detection, and remediation, and provide a unique insight into the impact of multiple controllers on network attack detection at scale.

The emerging network paradigm of Software Defined Networking (SDN) has been increasingly adopted to improve the Quality of Experiences (QoE) across multiple HTTP adaptive streaming (HAS) instances. However, there is currently a gap between research and reality in this field. QoE models, which offer user-level context to network management processes, are often tested in a simulation environment. Such environments do not consider the effects that network protocols, client programs, and other real world factors may have on the outcomes. Ultimately, this can lead to models not functioning as expected in real networks. On the other hand, setting up an experiment that reflects reality is a time consuming process requiring expert knowledge. This paper shares designs and guidelines of an SDN experimentation framework (SDQ), which offers rapid evaluation of QoE models using real network infrastructures.

The growing demand for online distribution of high quality and high throughput content is dominating today's Internet infrastructure. This includes both production and user-generated media. Among the myriad of media distribution mechanisms, HTTP adaptive streaming (HAS) is becoming a popular choice for multi-screen and multi-bitrate media services over heterogeneous networks. HAS applications often compete for network resources without any coordination between each other. This leads to quality of experience (QoE) fluctuations on delivered content, and unfairness between end users, while new network protocols, technologies, and architectures, such as software defined networking (SDN), are being developed for the future Internet. The programmability, flexibility, and openness of these emerging developments can greatly assist the distribution of video over the Internet. This is driven by the increasing consumer demands and QoE requirements. This paper introduces a novel user-level fairness model UFair and its hierarchical variant UFairHA, which orchestrate HAS media streams using emerging network architectures and incorporate three fairness metrics (video quality, switching impact, and cost efficiency) to achieve user-level fairness in video distribution. UFairHAhas also been implemented in a purpose-built SDN testbed using open technologies, including OpenFlow. Experimental results demonstrate the performance and feasibility of our design for video distribution over future networks.

Network operators recognise that Content Delivery Networks are essential for meeting user Internet application and content demands. The infrastructure must be tightly integrated to provide request routing, content caching, load balancing, scalability and reliability, whilst minimising deployment time and complexity. A major step towards achieving these goals is to embrace recent Software Defined Network and Network Function Virtualisation objectives and design principles. This paper outlines the OpenCache API; an interface used to define the behaviour and operation of an SDN-based content delivery platform in real-time. We demonstrate the applicability and effectiveness of such an API by implementing load-balancing and fail-over functionalities as part of an experimental deployment.

High quality online video streaming, both live and on-demand, has become an essential part of consumers' every-day lives. The popularity of video streaming has placed a heavy burden on the network infrastructure that now has to transfer an enormous amount of data very quickly to the end-user. To further exacerbate the situation, the Video-on-Demand (VoD) distribution paradigm uses a unicast independent flow for each user request. This results in multiple duplicate flows carrying the same video assets many times end-to-end. We present OpenCache: a highly configurable, efficient and transparent in-network caching service that aims to improve the VoD distribution efficiency by caching video assets as close to the end-user as possible. OpenCache leverages Software Defined Networking to benefit last mile environments by improving network utilisation and increasing the Quality of Experience for the end-user. Our evaluation on a pan-European OpenFlow testbed uses adaptive video streaming and demonstrates that with the use of OpenCache, the external link utilisation is reduced by 100%. Furthermore the streaming application receives better quality video and observes higher throughput, lower latency and shorter start up and buffering times.

A recent proposed update to TCP congestion control, TCP-newCWV, has targeted congestion control for rate-limited applications. These methods need to be explored in the context of rate-adaptive applications, such as DASH. The new method enables a client to exploit the persistence of a DASH connection and enables the DASH server to rapidly resume transmission of a series of video segments using a single TCP connection. Another technique, called 'Pacing' smoothes DASH bursts when there is no TCP ACK clock, and is shown to significantly reduce burst loss. These two methods in combination can increase the application performance. This paper investigates the effect of implementing these techniques on a DASH flow in different congestion scenarios and whether the method can promote better capacity sharing while minimizing the latency experienced by other flows sharing a common network bottleneck. The results confirm that newCWV with Pacing provides a benefit as a platform for DASH transport.