Results and submissions : REF 2021

Data Driven Intelligent and Autonomous Systems

Submitting institution: University of the West of Scotland
Unit of assessment: 11 - Computer Science and Informatics
Summary impact type: Technological
Is this case study continued from a case study submitted in 2014?: No

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1. Summary of the impact

Ground-breaking technological advancements in data analytics by the UWS research team have led to wide-ranging socio-economic impacts globally. From smart farming adoption in Nepal, Bhutan and Thailand, marketing and sales (Golden Casket Ltd.), social housing indoor air quality improvements (BRS Technology Ltd.) to the I3Q smart trolley deployment in 19 Chinese airports (Wuxi Chigoo Interactive Technology Co. Ltd China) and award-winning search and rescue drone developments (Police Scotland and Swedish Police), the team have demonstrated the global reach and significance of their pioneering research.

2. Underpinning research

Data analytics research undertaken by Artificial Intelligence, Visual Communication and Networks (AVCN) research centre at UWS, involves three cohesive areas of data anonymization, personalised service assistant, and video analytics. Valuable insight gained from data analytics helped to develop the state-of-the-art and create impact in various sectors, including social housing, customer engagement, smart farming, journey planner, and search and rescue.

Data anonymization realising data-driven services

Ensuring privacy of data is of utmost importance for data-driven services. Supported by gLink project [3.E] we have thus developed a partition-based approach to handle the uncertainty in IoT data streams [3.1], instead of widely-used structured approaches. From extensive review of the state-of-the-art in privacy preserving methodologies, it was evident that existing anonymization approaches could not support processing of IoT data streams, without significantly relaxing the privacy restrictions. In our approach, partitions were initialised based on tuple description of the IoT data streams - each partition conforming to data usability, and privacy requirements. This approach helped to achieve the desired level of data quality in the published data whilst ensuring privacy of the IoT data streams; thus, has been utilised in smart farming (SunSpace project **[3.C]**) and servicing sector (SmartLink project **[3.D]**) . To ensure anonymised data can deliver intelligence for data-driven services, expiration-band mechanism (X-BAND) [3.2] was proposed to handle missingness in the live IoT data streams. X-BAND novel weighted distance function reduced missingness of published data, thus ensuring reduced information-loss and guaranteed privacy perseveration.

Personalised Service Assistant

To address the challenges of indoor location-based data analytics, Simultaneous Localization and Mapping (SLAM) was developed to utilise the measurement of ambient magnetic fields present in all indoor environments [3.3]. Service Recommendation Systems (SRS) are mainly driven by data analytics to understand the context and improve the quality of service. SRS for individual users were extensively studied, however group-based recommendation never got reasonable attention by the research community. SLAM, on the other hand, used a pioneering, exponentially weighted particle filter to estimate the pose distribution of the object and a Kriging interpolation method was used to update the map of magnetic fields. SLAM was embedded in a ground-breaking Smart Trolley project – utilising RFID, IRID (Infrared ID), geomagnetic data for locating and navigation of passengers at airports. The insight gained from analysed trajectory data through SLAM was utilised to route passengers to the boarding gates whilst ensuring their shopping preferences [3.4].

Video Analytics

To address the challenge of a lack of high-quality video data in hard environments, AVCN made advancements through the AALART project [3.A]. A new resolution-enhanced automatic target detection and recognition for extremely small numbers of pixels was developed [3.5], specifically useful for long-range and low-quality video-based surveillance. Video-based analytics mainly rely on high resolution and continuous video stream for detection and recognition of anomalies or regions of interest. The novel machine learning driven surveillance system is capable of detecting and recognising at extremely low resolutions: vehicles at 9×9 pixels, and humans and animals at 11×11 pixels.

Building on AVCN’s research achievements, novel machine learning algorithms were developed based on extending the Tiny-YOLO (You Only Look Once) and combining Path Aggregation Network and additional image processing technologies [3.5] and [3.6]. The video analytics system was demonstrated through drone-based video surveillance for Police Scotland’s Search & Rescue (SAR) operations in collaboration with Thales UK and CENSIS [3.B].

3. References to the research

3.1 Otgonbayar, A., Pervez, Z., Dahal, K. and Eager, S., (2018), ‘K-VARP: K-anonymity for varied data streams via partitioning’, Information Sciences, 467, pp.238-255. https://doi.org/10.1016/j.ins.2018.07.057

3.2 Otgonbayar, A., Pervez, Z. and Dahal, K., (2019), ‘X-BAND: Expiration Band for Anonymizing Varied Data Streams’, IEEE Internet of Things Journal, 7(2), pp.1438-1450. https://doi.org/10.1109/JIOT.2019.2955435

3.3 Wang, X., Zhang, C., Liu, F., Dong, Y. and Xu, X., (2017), ‘Exponentially weighted particle filter for simultaneous localization and mapping based on magnetic field measurements’, IEEE Transactions on Instrumentation and Measurement, 66(7), pp.1658-1667. https://doi.org/10.1109/TIM.2017.2664538

3.4 Naserian, E., Wang, X., Dahal, K., Wangy, Z. and Wang, Z. (2018) ‘Personalized Location Prediction for Group Travellers from Spatial-Temporal Trajectories’, Future Generation Computing, 83, pp.278-292, https://doi.org/10.1016/j.future.2018.01.024

3.5 Martinez‐Alpiste, I., Casaseca‐de‐la‐Higuera, P., Alcaraz‐Calero, J.M., Grecos, C. and Wang, Q., (2020), ‘Smartphone‐based object recognition with embedded machine learning intelligence for unmanned aerial vehicles’, Journal of Field Robotics, 37(3), pp.404-420. https://doi.org/10.1002/rob.21921

3.6 Martinez-Alpiste, I., Casaseca-de-la-Higuera, P., Alcaraz-Calero, J., Grecos, C. and Wang, Q., (2019), ‘Benchmarking Machine-Learning-Based Object Detection on a UAV and Mobile Platform’. In 2019 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-6). IEEE. https://doi.org/10.1109/WCNC.2019.8885504

Grants

3.A Casaseca, P., Luo, C., *Thales-Challenge: Low-pixel Automatic Target Detection and Recognition (ATD/ATR),*Scottish Funding Council, October 2015 to October 2016, GBP50,000

3.B Casaseca, P., Alcaraz-Calero, J., Wang, Q., Smart Unmanned Aerial System for Real-Time Object Detection, CENSIS, November 2018 to October 2019, GBP50,000

3.C Dahal, K., Pervez, Z., SUNSpACe – SUstainable developmeNt Smart Agriculture Capacity, EU Erasmus+, January 2019 to January 2022, EUR163,862 (~GBP142,600).

3.D Dahal, K., Pervez, Z., SmartLink (South-east-west Mobility for Advanced Research, Learning, Innovation, Network and Knowledge), EU, Erasmus Mundus, July 2014 to December 2018, EUR3,049,600.

3.E Dahal, K., Pervez, Z., gLINK (Sustainable Green Economies through Learning, Innovation, Networking and Knowledge Exchange), EU, Erasmus Mundus, July 2014 to December 2018, EUR3,036,625

3.F Gilardi, M., Dahal, K., Johnston, J., Gisbert, H., Developing intelligent applications for confectionary business, InnovateUK: KTP with Golden Casket, April 2019 to March 2021, GBP115,717

3.G Pervez, Z., Konanahalli. A., Ramzan. N., To embed IoT and machine learning capabilities for developing air quality monitoring framework for large scale deployment, Innovate UK: KTP with BRS Technology, August 2019 to July 2021, GBP123,502

4. Details of the impact

Process of research leading to the impact: Research projects coordinated and participated by AVCN, supported 36 staff and student mobilities across 10 Asian research institutes, including lead author of [3.1] and [3.2]. Research advancements made through these projects has enabled 11 students to secure higher degrees (PhD, MPhil, and Masters). Research from these projects has resulted in two ongoing KTP projects in the areas of data analytics for indoor air quality, and ML-driven business intelligence with BRS Technology Limited, and Golden Casket. In addition to this, four PDRAs trained from SmartLink and gLink projects, secured KTP Associate, and Senior Research Scientist posts with BRS Technology Limited, KeyFM, Bradford University, and Mechatherm International Limited.

Economic impact: AVCN has successfully up-skilled conventional farming practices in Nepal, Bhutan, and Thailand, resulting from smart farming adoption and practices beyond Europe through EU SunSpaCe project. The project has already trained over 30 conventional farmers from Chiang Mai and Khon Kaen provinces in Northern Thailand through the train the trainer program [5.1a, 5.1b]. This training has up-skilled farmers to use IoT devices and AI-based data analytics techniques for smart farming to improve the agro-health, use of farming-resources, sustainable organic farming, and access global markets. The consortium acknowledges that the AVCN played “the pivotal task of developing data analytic models and associated learning materials for the training programmes”.

Wuxi Chigoo Interactive Technology Co. Ltd China, in collaboration with AVCN deployed the I3Q smart trolley in 19 Chinese airports including Guangzhou Baiyun (China's third-busiest, and the world's 11th-busiest airport) serving dozens of millions of passengers annually [5.2] – resulting in a significant increase in revenue from advertising. The underpinning research [3.3] and [3.4] helped identify the group users, making recommendations based on spatio-temporal and contextual data. This industry-focused research has been featured in local and international news with a viewership of 2,000,000 ( e.g. The National, Evening Times, The Herald, The Herald online, and Paisley Daily Express).

Police Scotland are using our new aerial drone system to help in searches for missing and vulnerable people. The system is now scheduled for trials with Swedish Drone Rescue in liaison with Police Sweden. This CENSIS (GBP100,000) funded search and rescue operations (SAR) has enabled AVCN’s drone-based AI video analytics platform to be ported to the Police Scotland platforms, which are being deployed across their SAR teams - Glasgow, Aberdeen, and Inverness with a significant proportion of rural areas being remote [5.3, 5.4]. The system ultimately saves live by speeding up the highly resource intensive, dangerous and time-consuming process of tracing and tracking missing people in inaccessible geographies. The project has received a range of high-profile media coverage, particularly after it was reported by BBC News [5.5a] and UK Authority [5.5b]. The project won the Knowledge Exchange/Transfer Initiative of the Year at the Times Higher Education (THE) Awards in 2020 [5.9a]. The project also won the CeeD-Scotland Industry Awards 2020 - Innovation Award [5.9b]. Furthermore, enhancements made to AI-enabled video analytics is applied to detect ground personnel with complex backgrounds in the SmartCrane project for the lifting industry in Oil & Gas, construction and other related sectors [5.6, 5.7]. Another AI-enabled video analysis application for Low-Pixel Automatic Target Detection and Recognition was covered by BBC [5.8] and won a ‘Special Commendation’ in the Multiparty Collaboration category at the 2018 Scottish Knowledge Exchange Awards [5.9.c]. AVCN’s AI-enabled video analytics demonstrates numerous applications in smart city, smart building and utility infrastructure management, intruder detection, and animal tracking, amongst others.

AVCN’s data stream analytics research in the two ongoing KTP’s with BRS Technology Ltd [3.G] and Golden Casket [3.F] embeds ML/AI expertise within the companies, creating commercial and financial growth opportunities [5.10a, 5.10b]. The research has applications in the areas of indoor air quality, predictive maintenance, BI-informed sales decision-making and sales operations. In addition, these partnerships are set to deliver GBP5,290,000 accrued profit impact to the UK economy.

In the social housing sector, the technology can be used to enhance living conditions. The BRS Technology KTP focuses on indoor air quality monitoring for preventive mould development interventions. The AI-based analytics and privacy preserving achievements [3.1], and [3.2] are embedded into BRS Technology to develop a predictive analytics framework for condensation and mould detection. This has a significant impact on housing associations, supporting the UK’s ambitious target of at least an 80% reduction in net carbon emissions by 2050 and creating a healthy space for families, thus having research impact beyond the technological spheres. The BRS Technology testimonial is clear: “ *This [project] KTP has significantly helped BRS Technology to demonstrate a clear growth trajectory to its core team thus **improving staff retention by 80%**” [5.10a].

On the other hand, the KTP project with confectionary company Golden Casket is utilising AVCN’s BI and data analytics research to transform the way the company operates by providing mobile computing and dynamic customer service with BI informed decision-making capacity to improve customer engagement, marketing and sales resulting in a profit of more than GBP3,000,000 over five years post-KTP. The project has addressed multiple challenges that currently inhibit growth: improving customer engagement (current and new), improving decision-making based on robust data analytics and culture change transforming the business into a sector leader in the use of digital technology [5.10b].

Efficient and Robust Signal/Image Processing and Machine Learning Methods for Real ...

Submitting institution: University of the West of Scotland
Unit of assessment: 11 - Computer Science and Informatics
Summary impact type: Technological
Is this case study continued from a case study submitted in 2014?: No

Download case study PDF

1. Summary of the impact

Novel signal/image analysis and machine learning methods have been developed and efficiently implemented into different applications and devices even with low computational capabilities. Highly innovative UWS research is achieving significant impact in several areas including electronics and computing industries, where top multinational companies such as Cirrus Logic and SME’s like Codeplay Software Ltd; MODO Systems Ltd; and Lumen Research Ltd implemented our methods to realise socio-economic benefits. The applicability of our developments has gained recognition from the Government, implementations in the Health and Social Care sector and have informed the standardisation and open access community.

2. Underpinning research

The Affective and Human Computing for Smart Environment (AHCSE) research centre aims at the creation of state-of-the-art knowledge in signal processing (1D/2D) and machine learning, with a specific focus on making all our developments applicable in real life challenging scenarios. Combining all the disciplines, we have created accurate, robust and efficient algorithms and applications:

Emotion recognition by physiological signals (EEG, and ECG) and implemented in Emotional Gym for social care sector; we developed efficient and robust algorithms for automated recognition of human affect (Emotion). To address it, we created a DREAMER, a pioneering open access database consisting of recordings of EEG and ECG signals captured while audio-visual stimuli was presented to the participants in order to elicit specific emotions [3.1]. Portable, wearable, and wireless devices were used for both the EEG and ECG capture allowing the evaluation of affect recognition algorithms on signals that can be conveniently captured in everyday scenarios, providing the means to integrate affect computing methods into a wide variety of tasks. After assessing the quality of the participants’ ratings, a baseline for this database was then established by evaluating EEG and ECG-based features through participant-wise supervised classification experiments. To demonstrate the feasibility of integrating affective computing methods to everyday applications [3.2] through the use of portable/wearable equipment like Emotional Gym project. The initial research was carried out through an innovation grant funded by Loretto Care and followed by the research grant from Construction Scotland Innovation Centre with Wheatley group, a leading and award-winning provider of care and support services [3.I].

Continuous real-time monitoring of respiratory patients in connected health scenarios by detecting and analysing cough episodes from audio signals acquired in challenging noisy scenarios; we proposed a more robust approach to cough detection with high sensitivity (up to 88.51%) and specificity (up to 99.77%) in a variety of noisy environments [3.3] by considering two dimensional spectrograms and feature extraction techniques. However, the implementation of such methods together with easily customisable machine learning algorithms resulted in fast battery drainage from the mobile device (less than 2 hours), so, we proposed efficient machine learning implementations for the smartphone that offers accuracy of more than 93% when continuously monitoring cough for 48 hours while keeping the smartphone at full functionality [3.4]. The original research was carried out during a ground-breaking Smartcough project funded by the Scottish Funding Council (SFC) [3.A]. We exploited the use of this technology for Early Detection of Lung Cancer funded by Cancer Research UK [3.B].

Other major research strands include: video decoder optimisation, which is based on the parallel decoder architecture for latest video coding standard High Efficiency Video Codec (HEVC) (in 2014) that was proposed for mobile platforms. In addition, the subjective and objective quality evaluation of HEVC standard was conducted under the global standardisation body MPEG, a working group within the International Organization for Standardization (ISO) and the International Electrotechnical Commission (ICE). MPEG is responsible for the development of international standards for compression, decompression, processing, and coded representation of moving pictures, audio and their combination [3.5, 3.C, 3.G]. Real-time content based image retrieval (CBIR) framework has been developed based on the highly efficient deep learning within mobile platform [3.6]; Furthermore, alongside the CBIR framework, a state-of-the-art person identification and recognition framework was developed and implemented within Mobile platform [3.D, 3.F, 3.I]. The research team has also developed the first real-time mobile eye tracking framework based on deep learning. It implements the stages of data acquisition, large data storage, data cleansing and mining, feature extraction, developing new machine learning models, testing these models, adjusting the parameters of the models and deploying the newly invented AI models in a distributed environment [3.E].

3. References to the research

3.1 Katsigiannis, S. and Ramzan, N., (2018) DREAMER: A Database for Emotion Recognition Through EEG and ECG Signals from Wireless Low-cost Off-the-Shelf Devices. IEEE Journal of Biomedical and Health Informatics, 22 (2): 98-107. https://doi.org/10.1109/JBHI.2017.2688239.

3.2 Katsigiannis, S., Willis, R. and Ramzan, N., (2019) A QoE and Simulator Sickness Evaluation of a Smart-Exercise-Bike Virtual Reality System via User Feedback and Physiological Signals. IEEE Transactions on Consumer Electronics, 65(1): 119-127. https://doi.org/10.1109/TCE.2018.2879065.

3.3 Monge-Alvarez, J., Hoyos-Barcelo, C., Lesso, P. and Casaseca-de-la-Higuera, P., (2019) Robust Detection of Audio-Cough Events Using Local Hu Moments. IEEE Journal of Biomedical and Health Informatics, 23(1): 184-196. https://doi.org/10.1109/JBHI.2018.2800741.

3.4 Hoyos Barceló, C., Monge-Álvarez, J., Shakir, M. Z., Alcaraz Calero, J. M., & Casaseca, J. P. (2017). Efficient k-NN implementation for real-time detection of cough events in smartphones. IEEE Journal of Biomedical and Health Informatics, 22(5): 1672-1771. https://doi.org/10.1109/JBHI.2017.2768162.

3.5 Tan, T., Weerakkody, R., Mrak, M., Ramzan, N., Baroncini, V., Ohm, J. and Sullivan, G., 2016. Video Quality Evaluation Methodology and Verification Testing of HEVC Compression Performance. IEEE Transactions on Circuits and Systems for Video Technology, 26(1): 76-90. https://doi.org/10.1109/TCSVT.2015.2477916.

3.6 Alzu'bi, A., Amira, A., Ramzan, N., (2017) “Content-based image retrieval with compact deep convolutional features”, Neurocomputing, 249: 95-105. https://doi.org/10.1016/j.neucom.2017.03.072.

Grants

3.A *Casaseca, P. , SMARTCOUGH: Continuous intelligent cough detection and identification using smartphones, Scottish Funding Council (Digital Health & Care Institute (DHI)), June 2015 to November 2016, GBP69,940.84.

3.B Casaseca, P., Usoro, I., Dahal, K., Audio Technology to detect lung cancer earlier, Cancer Research UK, June 2016 to November 2018 , GBP19,244.00

3.C Ramzan, N., Video processing and computer vision for mobile environment, Innovate UK: KTP with CodePlay Software Ltd, September 2015 to September 2017, GBP85,821

3.D Ramzan, N., Shakir, MZ., Pervez, Z., To develop the next generation of digital content management platform, Innovate UK: KTP with MODO Systems Ltd, July 2018 to June 2020, GBP109,918 .

3.E Ramzan, N., Keir, P., To develop a software tool for predicting a viewer’s likely response to a range of visual communications, Innovate UK: KTP with Lumen Research, April 2018 to September 2020, GBP144,733

3.F Ramzan, N., Pervez ,Z ., To develop an automated safeguarding platform to prevent misuse of collaborative platforms used within the education, Innovate UK: KTP with Seric Systems, August 2019 to January 2022, GBP149,569

3.G -* Ramzan, N., Gisbert, H., To develop the next generation of automated surveillance system to enhance security & public safety, Innovate UK: KTP with Visual Management Systems Limited, February 2020 to January 2023, GBP180,278.

3.H Ramzan, N., Pervez, Z., Keir, P., Johnstone, J, To develop an advanced remote health monitoring and behavioural analysis solution utilising machine vision and artificial intelligence, Innovate UK: KTP with Kibble Education and Care Centre, April 2020 to December 2022, GBP124,366

3.I Ramzan, N., Shakir, MZ., Pervez, Z., Advanced Training in Health Innovation Knowledge Alliance (ATHIKA), Erasmus+, January 2019 to December 2021, EUR945,060

4. Details of the impact

Significant socio-economic impacts have been achieved by the UWS research team.

Societal Impact: Pioneering new care home solution for the elderly has been developed directly from our research [5.9]. Our Emotional Gym solution deployed at two different care home sites funded by Construction Scotland Innovation Centre (CSIC) and Loretto Care has enabled care home residents to improve their mobility, exercise and stay physically active for longer periods. Our research has created new emotional technology driven health and care management systems and its originality and reach have been recognised in 2018 with the following awards:

• Laing Buisson Awards: Innovation & Leaders Category – Innovation in Care Award

• Holyrood Connect Digital Health and Care Awards - Digital Innovation Award

Our expertise in mobile app development and signal processing has resulted in the development of a novel publicly available mobile app - Stay Safe Scotland in partnership with the Council of Ethnic Minority Voluntary Sector Organisations (CEMVO) Scotland, with funding from the Scottish Government to help tackle the disproportionate number of ethnic minority people affected by Covid-19 [5.10]. The app seeks to overcome major barriers faced by ethnic minority communities in Scotland by providing social distancing guidelines in a variety of different languages and predicting footfall data at nearly 100 supermarkets throughout Scotland to help users schedule visits at quieter times, and to avoid queuing and overcrowding.

Public Impact: Loretto Care and Wheatley Group have implemented our life changing research on emotion recognition based on physiological signals, resulting in the development of an innovative stationary exercise bike integrated with in-house developed super-interactive games to monitor health and wellbeing of general public with limited mobility (e.g. elderly people). Our results have shown that more than 30% residents in care homes are highly likely to participate in physical activities and sports thereby improving the health and wellbeing of the public.

Over 93% accuracy has been achieved by using our pioneering SmartCough App for detecting cough and enabled the health service operators and general public to monitor the respiratory conditions remotely, enabling accurate prediction of changes in the severity in respiratory diseases [5.10]. Our robust and energy efficient smartphone-based cough detector mobile app exploits some advanced machines learning algorithms.

Knowledge Impact: Our emotion recognition research has produced first ever publicly available open source dataset (DREAMER) that can be used to create new industrial research and development programmes based on physiological signals [5.1]. After its publication (https://zenodo.org/record/546113\), the dataset has attracted a significant attention from the international professional audiences and researchers (e.g. Facebook/Occulus). Up until 21/08/2020, 3757 requests for access to the dataset had been made via the “zenodo.org” platform, 5000 full text views of the publication via the “IEEE Xplore” platform.

Furthermore, the work on video quality evaluation has been recognised worldwide and accelerated the verification of the HEVC video coding standard. The outcome of this research was two contribution to MPEG standard [5.3] and has been selected for the Best of IET and IBC papers [5.2] and awarded with the Best Paper Award of IEEE Transaction of Circuit and System for Video Technology [5.2] and downloaded by more than 21,000.

Economic Impact: Codeplay Software Ltd has achieved x10 predicted impact in financial terms and a doubling of business size (headcount), gain international level recognition in the field of AI positioning them along Google, Intel, Arm, NVIDIA and resulting in both significant business growth (e.g. company has established long term collaborative partnerships with the ARM, Intel, Imagination and Renesas **[5.6]**) and accrued significant profit (e.g. company awarded with seven-figure contract during the period of the KTP project). This global success directly resulted from the InnovateUK funded project with the UWS research team.

Our cloud computing project with MODO Ltd funded by Innovate UK has integrated an advanced machine algorithm (e.g. CBIR and person identification with MODO’s core product architecture) and thereby enhanced an overall customer proposition. Our results have enabled the company to secure a six-figure investment and has significantly raised the company’s valuation and profile for future growth [5.4]. This project received top “outstanding” rating by Innovate UK [5.5].

Lumen Ltd. have leveraged AI/ML within its innovative product that helps to generate 50% more revenue and almost doubling the headcount [5.7] as a result of the research with UWS. This huge success stems from the InnovateUK-funded project that has enabled our team to develop the first mobile eye tracking framework on the market. This real-time mobile eye tracking framework is based on deep learning and has generated significant industry interest, boosting Lumen’s visibility and market share.

Significant InnovateUK successes have since substantially increased industry interest in pioneering UWS research, resulting in three further KTP projects ( VMS, SERIC with Education Scotland), and Kibble) as well as research interest demonstrated in two Erasmus+ projects (ATHIKA and Remote Health Monitoring)

The above industry-focused successes of the team and its leadership have been crowned with the Knowledge Exchange Champion award 2020 [5.8] awarded to Ramzan for contributing to over GBP15,000,000 to UK economy via 8 KTPs projects with leading industrial and commercial partners in the UK.

Leading international 5G innovations and shaping global standards and policy

Submitting institution: University of the West of Scotland
Unit of assessment: 11 - Computer Science and Informatics
Summary impact type: Technological
Is this case study continued from a case study submitted in 2014?: No

Download case study PDF

1. Summary of the impact

University of the West of Scotland (UWS) plays a pivotal role in Europe’s global leadership in 5G – a vital technology used across a wide range of crucial sectors, including energy, healthcare, security etc. UWS’ ground-breaking research has led the development of Europe’s 5G Golden Nugget innovation, and visions encapsulated within official White Papers. UWS’ 5G network management and control solutions have been deployed in the 5G trial testbeds of several leading 5G industries. Furthermore, UWS’ pioneering research has directly led to the development of the ITU (International Telecommunication Union) and ETSI (European Telecommunications Standards Institute) global ICT standards – essential to interoperability and innovation of international ICT systems. Further impact on national policies was achieved in the Scotland’s AI Strategy.

2. Underpinning research

Research originality, rigor and quality: University of the West of Scotland’s (UWS) Beyond5GHub team with the AVCN (Artificial-Intelligence, Visual Communications and Networks) Centre has been intensively researching 5G since 2014, leveraging UWS and industry partners’ 5G testbeds, and producing internationally leading 5G innovations in enabling technologies and applications. The outstanding quality of the research was recognised by an international expert evaluation panel (“Exceptional results” in the official EU project evaluation report), and worldwide peer reviewers for leading journal publications (including 10+ IEEE transaction/journal papers).

Research outcomes and significance: Highlighted UWS outcomes include solutions for a novel autonomous network management framework to achieve ‘self-organising’ 5G networks. These solutions enable the automatic detection, and resolution, of a range of common network problems and fulfil essential network management tasks (traffic optimisation, service creation and infrastructure deployment and protection, etc.) that are currently manually addressed by network operators, resulting in substantial operating expenditure reduction. 5G real-time traffic 'self-optimisation' capability and multi-tenant-aware network monitoring are enabled by novel 5G-UHD [3.1] and 5G-QoE techniques to reduce up to 50% of bandwidth requirements for video traffic – which itself represents 80% of worldwide traffic – without compromising users’ Quality of Experience (QoE), even when the network is experiencing cyberattacks or congestion. Moreover, UWS led the achievement of one of the six European 5G-PPP KPIs on the reduction of service creation time, decreasing from 90 hours to 90 minutes, by producing a novel on-demand one-click 5G infrastructure deployment solution [3.2] to automatically deploy and configure a new 5G edge network from bare-metal commodity computers in less than 40 minutes, significantly reducing both capital expenditure and operating expenditure. The pioneering self-organising framework was further applied to ‘self-protection’ against cyberattacks [3.3].

UWS has also focused on developing the cornerstone 5G technology ‘Network Slicing’, and successfully achieved massive (approximately 1,000,000 per 4 software switches) data-plane network slices [3.4] in super-fast networks (up to 100 Gbps) with guaranteed Quality of Service for multiple verticals over the same infrastructure, with significant reduction in capital expenditure and new business models enabled. The solutions were demonstrated in various use cases; for example, saving lives by enabling 5G ambulance eHealth [3.5] for early stroke assessment during transit, and Smart Grid self-healing to protect critical energy infrastructure. To enable intuitive management of complex virtualised 5G networks, a novel augmented reality-based holographic network monitoring and management solution [3.6] was created, enabling visualisation of millions of 5G network nodes and multiple virtual network operators concurrently. Furthermore, UWS collaborated with other international 5G projects, including ANASTACIA on 5G-IoT security management [3.3], and MATILDA on 5G Smart City lighting.

International research leadership: The above outcomes were achieved through several UWS-led international 5G projects, including two large EU Horizon 2020 Research & Innovation projects, SELFNET (Phase-1) and SliceNet (Phase-2). UWS technically led the projects, coordinating the R&D with global 5G players and beneficiaries including Orange, DellEMC, Altice (“Portugal Telecom”), Ericsson, IBM, EFACEC, etc.

Generate further income towards 6G: UWS has secured two new EU Horizon2020 5G Phase-3 projects, 6G-BRAINS (H2020-ICT-2020-2/101017226, EUR5,700,000) and 5G-INDUCE (H2020-ICT-2020-2/101016941, EUR6,000,000), from 2020 to 2022 to investigate advanced 5G towards 6G.

3. References to the research

3.1 Salva-Garcia, P., Alcaraz-Calero, J., Wang, Q., Arevalillo-Herraez, M. and Bernal Bernabe, J., (2020) Scalable Virtual Network Video-Optimizer for Adaptive Real-Time Video Transmission in 5G Networks. IEEE Transactions on Network and Service Management, 17(2): 1068-1081. https://doi.org/10.1109/TNSM.2020.2978975.

3.2 Chirivella-Perez, E., Calero, J., Wang, Q. and Gutiérrez-Aguado, J., (2018) Orchestration Architecture for Automatic Deployment of 5G Services from Bare Metal in Mobile Edge Computing Infrastructure. Wireless Communications and Mobile Computing: 5786936. https://doi.org/10.1155/2018/5786936.

3.3 Zarca, A., Bernabe, J., Skarmeta, A. and Alcaraz Calero, J., (2020) Virtual IoT HoneyNets to Mitigate Cyberattacks in SDN/NFV-Enabled IoT Networks. IEEE Journal on Selected Areas in Communications, 38(6): 1262-1277. https://doi.org/10.1109/JSAC.2020.2986621.

3.4 Matencio-Escolar, A., Wang, Q. and Alcaraz Calero, J., (2020) SliceNetVSwitch: Definition, Design and Implementation of 5G Multi-Tenant Network Slicing in Software Data Paths. IEEE Transactions on Network and Service Management, 17(4): 2212-2225. https://doi.org/10.1109/TNSM.2020.3029653.

3.5 Wang, Q., Alcaraz-Calero, J., Ricart-Sanchez, R., Weiss, M., Gavras, A., Nikaein, N., Vasilakos, X., Giacomo, B., et.al, (2019) Enable Advanced QoS-Aware Network Slicing in 5G Networks for Slice-Based Media Use Cases. IEEE Transactions on Broadcasting, 65(2): 444-453. https://doi.org/10.1109/TBC.2019.2901402.

3.6 Sanchez-Navarro, I., Serrano Mamolar, A., Wang, Q. and Alcaraz Calero, J., (2021) 5GTopoNet: Real-time topology discovery and management on 5G multi-tenant networks. Future Generation Computer Systems, 114: 435-447. https://doi.org/10.1016/j.future.2020.08.025.

Grants

3.A Wang, Q., Calero, J., Keshav, D.,“ SELFNET: Framework for Self-Organised Network Management in Virtualized and Software Defined Networks” European Commission: Horizon 2020 5G-PPP scheme, Jul 2015 to June 2018, EUR6,866,496.

3.B Wang, Q. , Calero, J., “ SliceNet: End-to-End Cognitive Network Slicing and Slice Management Framework in Virtualised Multi-Domain, Multi-Tenant 5G Networks” European Commission: Horizon 2020 5G-PPP scheme, June 2017 to June 2020, EUR7,979,030

4. Details of the impact

Develop Europe’s 5G vision, innovation and trials

5G Infrastructure Public Private Partnership (5G-PPP) is the leading joint programme between the European Commission (EC) and European ICT industry, to deliver 5G architectures, technologies and standards – UWS research and influence has played an important role in shaping its decisions.

5G-PPP Technology Board (TB) has adopted the following 5G-PPP programme-level “Golden Nuggets” (flagship European 5G innovation achievements) – influenced by UWS research though UWS Board members Alcaraz Calero and Wang. These include: 5G PPP Phase 1 Golden Nuggets [5.1]: “Autonomic network management” [3.1, 3.3], “Automated physical and virtual infrastructure deployment” [3.2], and “Multi-level, multi-tenant-aware network monitoring” [3.6]; and 5G PPP Phase 2 Golden Nuggets [5.1]: “AI-empowered cognitive network management & orchestration” [3.5], and “End-to-end multi-domain multi-tenants network slicing” [ 3.4, 3.5].

10 high-profile and widely-cited official 5G-PPP White Papers commissioned by the EC, and co-authored by UWS, have led the EC to adopt the vision on 5G Autonomous network management architecture [5.2] and 5G Software-Defined Networking (SDN) and Network Function Virtualisation (NFV) integration [5.2], strategies on 5G innovations for new business opportunities, especially service creation and security [5.3], and network slicing trials for eHealth [5.3].

UWS was the only Scottish university in 5G-PPP, and one of the very few UK universities that technically led both Phase-1 (SELFNET) and Phase-2 (SliceNet) projects; thereby being positioned to influence Europe’s 5G vision, strategies, innovation and trials, and bringing to bear world-leading UK academic expertise.

Create global impact through international standardisation

Working with the International Telecommunication Union (ITU) and European Telecommunications Standards Institute (ETSI), UWS has led the standardisation in relation to six official standardisation Work Items, focusing on cognitive/intelligent management and control of Network Slicing – a key enabler in 5G for network operators and vertical businesses. Two of these Work Items have been approved as ITU Draft Recommendations [5.4, 5.5] (“The impact of interoperability and innovation enabled by the above work items extend beyond the current networks into foreseeable future”, as acknowledged by ITU FG-AN Vice Chair)], one approved as ETSI POC (Proof-of-Concept) etc. [5.6] in collaboration with Orange, Altice, DellEMC, IBM etc. ITU is the United Nations' world-leading standardisation organisation for mobile telecommunications. ETSI, on the other hand, is responsible for establishing European and globally applicable ICT standards. Such exceptional achievements were particularly appreciated in the project final review [5.9].

Moreover, UWS influenced de-facto industry standards via open source projects: Open vSwitch (de-facto software-networking platform) benefited from UWS’ enabling 5G-compatible processing capabilities [5.6]; OpenAirInterface (leading global 5G infrastructure platform) benefited from UWS’ 5G topology management facility [5.6].

Impact on Europe’s commercial 5G networks and vertical business trials

Orange Group's first commercial 5G network in Romania was launched on 5 November 2019, benefiting from UWS-led SliceNet and UWS development of mobile edge computing (MEC) (as acknowledged by Orange Development & Innovation Manager [5.7] and CEO **[5.9]**). UWS’ 5G network slice solutions have been deployed in the 5G trial testbeds of leading European 5G industries including DellEMC (Ireland), Orange Romania, Altice (Portugal) etc.

Importantly, UWS developed Network Slicing trials for service quality assurance in eHealth, with DellEMC and Irish Ambulance Services, to reduce fatalities of onboard stroke patients by 25% [5.3] (as acknowledged by DellEMC Principal Research Scientist **[5.8]**), and in Smart Grid Self-Healing with the largest Portuguese energy company EFACEC, to reduce maintenance costs by up to 20% (as acknowledged by EFACEC Director of Technology and Innovation **[5.8]**). UWS technologies were also applied to smart city lighting system in Bucharest with Orange Romania, to reduce energy costs by 77% (as acknowledged by Orange Development & Innovation Manager **[5.7]**). The UWS-led 5G autonomous network management solution was deployed in Altice, leading to the ETSI POC [5.6] (as acknowledged by Altice AI/Cognitive Solution Architect **[5.7]**).

Policy and wider impact

UWS’ beneficial impacts, in terms of knowledge transfer to industries, have been further recognised through a highly prestigious national accolade: UK Times Higher Education (THE) Awards 2020 - Knowledge Exchange/Transfer Initiative of the Year Award [5.10]. “The judges applauded how the team had built on successive projects to demonstrate life-saving applications, commending the ‘growing stature of the academic team and their work to create AI-based business solutions’.” [5.10].

In recognition of its demonstratable world-leading expertise in 5G-AI [5.10], UWS was invited as a Member of Scotland’s AI Strategy - Developing AI & AI Enabled Services and Products Working Group [5.10], to have helped the Scottish Government in defining its nationwide AI strategy for businesses and research through extensive consultation in 2020. The Scotland’s AI Strategy has been recently launched [5.10].

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