Recent Submissions

  • Autonomous vehicles in 5G and beyond: a survey

    Hakak, Saqib; Gadekallu, Thippa Reddy; Maddikunta, Praveen Kumar Reddy; Ramu, Swarna Priya; M, Parimala; De Alwis, Chamitha; Liyanage, Madhusanka; University of New Brunswick; Vellore Institute of Technology; University of Bedfordshire; et al. (Elsevier Inc., 2022-11-28)
    Fifth Generation (5G) mobile technology is the latest generation of mobile networks that is being deployed to facilitate emerging applications and services. 5G offers enhanced mobile broadband, massive machine type communication, and ultra reliable low latency communication. Hence, the capabilities of 5G can be harnessed to satisfy the requirements of autonomous vehicles (AV). AVs are developed to offer comfort, safety and efficient driving. However, the capabilities of 5G are yet to be harness by AVs and related technologies. In response, this survey provides a comprehensive review on AVs in the 5G and beyond era. The paper provides a discussion on the current advancements in AVs, automation levels, enabling technologies and the requirement of 5G networks. Furthermore, the paper focuses on emerging technologies enabling the integration of 5G with AVs, the impact of 5G and B5G for AVs and the envisaged security concerns in AVs. The paper also provides a comprehensive understanding of recent developments in terms of standardisation activities and projects on 5G AV technologies. The article also provides lessons learnt, existing challenges, and future research directions to facilitate the development of AV technologies in the 5G and beyond era.
  • Detection of credit card frauds with machine learning solutions: an experimental approach

    Mabani, Courage; Christou, Nikolaos; Katkov, Sergey; University of Bedfordshire (Springer, 2022-12-31)
    In many cases frauds in payment transactions could be detected by analysing the customer’s behaviour. Only in the United States fraudulent transactions led to financial losses of 300 billion a year. Machine learning (ML) and Data Mining techniques were shown to be efficient for detection of fraudulent transactions. This paper proposes an experimental way for designing a ML solution to the problem, which allows practitioners to minimise financial losses by analysing the customer’s behaviour and common patterns of using credit cards. The solution designed within a Random Forest (RF) strategy is examined on a public data set available for the research community. The results obtained on the benchmark data show that the proposed approach provides a high accuracy of detecting fraudulent transaction based on the customer’s behaviour patterns that were learnt from data. This allow us to conclude that the use of the RF models for detecting credit card fraud transactions allows practitioners to design an efficient solution in terms of sensitivity and specificity. Our experimental results show that practitioners using the RF models can find new insights into the problem and minimise the losses.
  • A study of search user interface design based on Hofstede’s six cultural dimensions

    Chessum, Karen; Liu, Haiming; Frommholz, Ingo; University of Bedfordshire; University of Southampton; University of Wolverhampton (Scitepress Digital Library, 2022-11-09)
    An information seeker’s cultural background could influence their preference for search user interface (UI) design. To study cultural influences Geert Hofstede’s cultural dimensions have been applied to website design for a number of years. In this paper, we examine if Hofstede’s six cultural dimension can be applied to inform the design of search engine user interfaces. The culturally designed search user interfaces have been evaluated in a study with 148 participants of different cultural backgrounds. The results have been analysed to determine if Hofstede’s cultural dimensions are appropriate for understanding users’ preferences on search user interface design. Whilst the key findings from the study suggest Hofstede cross-cultural dimensions can be used to model users’ preferences on search interface design, further work is still needed for particular cultural dimensions to reinforce the conclusions.
  • Fuzzy logic-based cluster-head election-led energy efficiency in history-assisted cognitive radio networks

    Safdar, Ghazanfar Ali; Syed, Tazeen Shabana; Ur-Rehman, Masood; University of Bedfordshire; University of Hertfordshire; University of Glasgow (IEEE, 2022-10-11)
    The performance and the network lifetime of cooperative spectrum sensing (CSS) infrastructure-based cognitive radio (CR) networks are hugely affected by the energy consumption of the power-constrained CR nodes during spectrum sensing, followed by data transmission and reception. To overcome this issue and improve the network lifetime, clustering mechanisms with several nodes inside a single cluster can be employed. It is usually the cluster head (CH) in every cluster that is responsible for aggregating the data collected from individual CR nodes before it is being forwarded to the base station (BS). In this article, an energy-efficient fuzzy logic-based clustering (EEFC) algorithm is proposed, which uses a novel set of fuzzy input parameters to elect the most suitable node as CH. Unlike most of the other probabilistic as well as fuzzy logic-based clustering algorithms, EEFC increments the fuzzy input parameters from three to four to obtain improved solutions employing the Mamdani method for fuzzification and the Centroid method for defuzzification. It ensures that the best candidate is selected for the CH role by obtaining the crisp value from the fuzzy logic rule-based system. While compared to other well-known clustering algorithms such as low-energy adaptive clustering hierarchy (LEACH), CH election using fuzzy logic (CHEF), energy-aware unequal clustering using fuzzy logic (EAUCF), and fuzzy logic-based energy-efficient clustering hierarchy (FLECH), our proposed EEFC algorithm demonstrates significantly enhanced network lifetime where the time taken for first node dead (FND) in the network is improved. Moreover, EEFC is implemented in the existing history-assisted energy efficient infrastructure CR network to analyze and demonstrate the overall augmented energy efficiency of the system.
  • High-reliability graphene-wrapped nanoprobes for scanning probe microscopy

    Cao, Liang; Liu, Ri; Zhang, Wenxiao; Wang, Ying; Wang, Guoliang; Song, Zhengxun; Weng, Zhankun; Wang, Zuobin; Changchun University of Science and Technology; University of Bedfordshire (IOP Publishing, 2022-01-29)
    The nanoprobe is a powerful tool in scanning probe microscopy (SPM) that is used to explore various fields of nanoscience. However, the tips can wear out very fast due to the low stability of conventional probes, especially after the measurement of high currents or lateral friction, which results in image distortion and test imprecision. Herein, a novel functional nanoprobe is presented using graphene sheets in a high-quality graphene solution wrapped round a plasma-treated conventional Pt-Ir coated nanoprobe, which shows highly stability and resistance to degradation, leading to a significantly increased lifetime. Furthermore, we show that the graphene-wrapped nanoprobes have the advantages of enhanced electrical conductivity and reduced tip–sample friction, compared with Pt-Ir coated nanoprobes. The simplicity and low cost of this method make it valuable to various functional graphene-wrapped nanoprobes and applications.
  • A survey on the use of blockchain for future 6G: technical aspects, use cases, challenges and research directions

    Kalla, Anshuman; De Alwis, Chamitha; Porambage, Pawani; Gür, Gürkan; Liyanage, Madhusanka; Uka Tarsadia University; University of Bedfordshire; University of Sri Jayewardeneprua; VTT Technical Research Centre of Finland; Zurich University of Applied Sciences; et al. (2022-10-17)
    While 5G is at the early deployment state around the globe, the research and industrial communities have already started concentrating their efforts on formulating the overall 6G vision comprising requirements, key enabling technologies, performance indicators, and applications. Following the trend, it is evident that 6G will emerge as highly softwarized and open networks allowing the participation of multiple stakeholders. This undoubtedly will make 6G more flexible, agile, autonomous, intelligent, and cost-efficient networks. However, the programmability and openness will make 6G networks more prone to issues like security, privacy, traceability, interoperability, auditability, resource manageability, spectrum efficiency, and 3D mobility. To address these issues, a deep integration of blockchain technology with 6G networks is foreseen. Thus, we aim to put together blockchain and 6G under a magnifying lens to gain a comprehensive understanding of the role of blockchain in the 6G ecosystem. We begin by providing an overview of the envisioned 6G networks and blockchain technology. Next, we present a high-level view of the role of blockchain for 6G trends and requirements. Following that, we conduct an in-depth study on how the blockchain can provide a secure, transparent, and decentralized underpinning to various technical aspects and use cases of 6G. Thereafter, we discuss the deployment challenges to be faced while integrating blockchain in 6G and the possible solutions. Finally, future research directions are expounded to set the floor for further advancements in the blockchainized 6G.
  • Slicing enabled 5G experimentation platform for the robotics vertical industry

    Mhatre, Suvidha Sudhakar; Ramantas, Kostantinos; Qiu, Renxi; Verikoukis, Christos; Iquadrat Informatica S.L.; University of Bedfordshire; Centre Tecnològic Telecomunicacions Catalunya (IEEE, 2021-12-31)
    This paper will discuss a network slicing 5G architecture and framework to enable efficient robotics operation, while improving Quality of Service (QoS). Furthermore, it will focus on a user-centric paradigm of integrating vertical knowledge into the existing 5G solutions. The software architecture and test- bed includes open air Interface (OAI) and open source MANO (OSM) based 5G testing platform to provide wireless connectivity to robots. It targets on minimising developers need for the comprehension of 5G when developing vertical applications of autonomous robotic systems. Different types of slicing framework considerations, edge computing aspects and orchestration options are evaluated as part of the architecture. The work is carried out by analysing various 5G architecture especially the potentials of relevant work already launched under 5GPPP Phases, 3GPP and ETSI, as well as by studying key robotic workflows of verticals sectors.
  • Edge intelligence case study on medical Internet of Things security

    Feng, Xiaohua (Elsevier, 2023-02-01)
    MIoT (Medical Internet of Things) systems produced many of sensing data in the world. Consequently, there is a demand of scientific research in this field. Edge intelligence fit in this trends, as one of the developing cutting-edge technology. A systematic approach had been applied on the health informatics edge intelligence devices’ investigation. The observing and recording action that occurs in the process of this research to date had been satisfed. This work had been reported here. The analyzing of the case study data was carried out. Eventually, some results have been summarized based on the investigation. Furthermore, a solution is proposed for the kind of medical edge intelligence device data cyber security problem-solving. Edge intelligence was defined as “the devices available at the edge layer have some limited amount of computing resource which can be utilized and incorporated with machine learning or AI (Artificial intelligence) algorithms to perform RT (real time) data analytics”. Studying more in the category of edge intelligence would influence MIoT system. This survey on edge intelligence had been carried out on investigated recent MIoT, Robot, Raspberry Pi and AV (autonomous vehicle) and so on as edge terminal - edge intelligence devices and the challenges they had encounter. In particular, AI application in edge intelligence device handle medical data security threat. AI face more challenges in edge intelligence computing. In this survey, through some case studies, some advantages and disadvantages had been studied. MIoT edge intelligence device challenges on big data security issues had been discussed.
  • A novel integrated framework for securing online instructor-student communication

    Salem, Maher; Samara, Khalid; AlDaheri, Mohammed Saeed (Inderscience, 2019-01-08)
    Academic advising is a time-consuming effort in educational institutions. Advising and consulting students are critical, which can cause latency and additional overhead for both the instructor and student. This paper proposes an effective and secured online framework to enrich the advising experience between the instructor and student and enhance time management. It provides two main services: reserved time slots for each student and common discussion between students and instructors. Reserving a time slot means that the student can communicate with the advisor anywhere. It can start an embedded web-based virtual machine to interact with the instructor in real time. The second service is to share a general post with all students and instructors to come up with a generic solution. Security plays a dominant role in the framework. It uses a strong authentication mechanism and encrypts the entire traffic to keep the data confidential.
  • EPPR: using blockchain for sharing educational records

    Alkouz, Akram; HaiYasien, Ahmed; Alarabeyyat, Abdulsalam; Samara, Khalid; Al-Saleh, Mohammed; Higher Colleges of Technology; Al-Balqa’ Applied University (IEEE Xplore, 2020-04-23)
    There have been recent, marked increases in the challenges of privacy, data interoperability and quality of Educational Professional Personal Record (EPPR). This calls into question the current model, in which different parties generate, exchange and monitor massive amounts of personal data related to EPPR. Ethereum blockchain has demonstrated that trusted, auditable transactions is visible using a decentralized network of nodes accompanied by a general ledger. Thus, the rapid development of educational and professional data generators such as online universities and distance learning, learners need to engage in detail into their EPPR as well as the educational and professional data generators. In this paper, we propose a novel decentralized approach to manage EPPR using Ethereum blockchain technology. The decentralized approach provides the owner of the EPPR a comprehensive immutable log and ease of access to their educational records across the educational record editors and consumers. Utilizing Ethereum blockchain features, can provide solutions with the main concerns of exchanging data between parties such as privacy, accountability and data interoperability. The aim of this approach is to also facilitate educational stakeholders (universities and employing agencies) to participate in the network as blockchain miners rewarded by pseudonymized data in compliance with General Data Protection Rules (GDPR) in United Arab Emirates (UAE).
  • Outgoing data filtration for detecting spyware on personal computers

    Afzulpurkar, Aishwarya; Alshemaili, Mouza; Samara, Khalid (Springer, Cham, 2019-02-06)
    One of the most critical issues emerging from the Internet is the diverse number of spyware and bots. When a spyware is installed in your PC then it will be difficult to detect, mainly because it deploys covert channels to communicate with outbound data transmissions. These attacks are usually sent from PCs infected with a bot that communicates with malicious controllers over an encrypted channel. However, the available pattern-based intrusion detection system (IDS) and antivirus systems (AVs) are unable to detect the infected PC. This paper presents a Monitoring and Filtering method (SMF) for outgoing packets based on machine learning and behavioral-based methods that can help in the protection of PCs. In addition, this paper presents recent research contributions and emerging tools in the field of spyware detection and identifies existing gaps in the literature. The paper then presents a High-level Architecture to inspect the outgoing packet from the hardware and the software installed in PCs as a solution.
  • Optimisation of perfect preventive maintenance and component replacement schedules (PPMCRS) using SPEA2

    Ikechukwu, Anthony O.; Nggada, Shawulu H.; Quenum, Jose G.; Samara, Khalid; Namibia University of Science and Technology; Higher Colleges of Technology (IEEE Xplore, 2020-04-23)
    As the proliferation in processes and technological options increases, the complexity of designing safety-critical systems may pose challenges to meeting the requirements to ensure that these systems are dependable. The number of the failures occurring can be minimized through preventive maintenance and replacement (PMR). Although different components have different PM times (aT), each preventive maintenance (PM) interval T, reduces the effective age of a component of the system. The degree of age reduction is hinged upon the effectiveness of PM and the coefficient of maintenance interval (CoMI). The Perfect Preventive Maintenance (PPM) policy renews the component being maintained is adopted in this paper. This paper investigates the effects of component replacement on component and system level reliability, and optimising PPM schedules taking into account component replacement as an improvement parameter on system's unavailability.
  • Predicting carpark prices indices in Hong Kong using AutoML

    Li, Rita Yi Man; Song, Lingxi; Li, Bo; Crabbe, M. James C.; Yue, Xiao-Guang; Hong Kong Shue Yan University; Rajamangala University of Technology Tawan-Ok; Jinke Property Group Co., Ltd.; Oxford University; University of Bedfordshire; et al. (Tech Science Press, 2022-07-06)
    The aims of this study were threefold: 1) study the research gap in carpark and price index via big data and natural language processing, 2) examine the research gap of carpark indices, and 3) construct carpark price indices via repeat sales methods and predict carpark indices via the AutoML. By researching the keyword “carpark” in Google Scholar, the largest electronic academic database that coversWeb of Science and Scopus indexed articles, this study obtained 999 articles and book chapters from 1910 to 2019. It confirmed that most carpark research threw light on multi-storey carparks, management and ventilation systems, and reinforced concrete carparks. The most common research method was case studies. Regarding price index research, many previous studies focused on consumer, stock, press and futures, with many keywords being related to finance and economics. These indicated that there is no research predicting carpark price indices based on an AutoML approach. This study constructed repeat sales indices for 18 districts in Hong Kong by using 34,562 carpark transaction records from December 2009 to June 2019.Wanchai’s carpark price was about four times that of Yuen Long’s carpark price, indicating the considerable carpark price differences in Hong Kong. This research evidenced the features that affected the carpark price indices models most: gold price ranked the first in all 19 models; oil price or Link stock price ranked second depending on the district, and carpark affordability ranked third.
  • Behavior-neutral smart charging of plugin electric vehicles: reinforcement learning approach

    Dyo, Vladimir; University of Bedfordshire (IEEE, 2022-06-16)
    High-powered electric vehicle (EV) charging can significantly increase charging costs due to peak-demand charges. This paper proposes a novel charging algorithm which exploits typically long plugin sessions for domestic chargers and reduces the overall charging power by boost charging the EV for a short duration, followed by low-power charging for the rest of the plugin session. The optimal parameters for boost and low-power charging phases are obtained using reinforcement learning by training on EV’s past charging sessions. Compared to some prior work, the proposed algorithm does not attempt to predict the plugin session duration, which can be difficult to accurately predict in practice due to the nature of human behavior, as shown in the analysis. Instead, the charging parameters are controlled directly and are adapted transparently to the user’s charging behavior over time. The performance evaluation on a UK dataset of 3.1 million charging sessions from 22,731 domestic charge stations, demonstrates that the proposed algorithm results in 31% of aggregate peak reduction. The experiments also demonstrate the impact of history size on learning behavior and conclude with a case study by applying the algorithm to a specific charge point.
  • Artificial intelligence robot safety: a conceptual framework and research agenda based on new institutional economics and social media

    Li, Rita Yi Man; Crabbe, M. James C. (Springer, 2022-05-15)
    According to "Huang's law", Artificial intelligence (AI)-related hardware increases in power 4 to 10 times per year. AI can benefit various stages of real estate development, from planning and construction to occupation and demolition. However, Hong Kong's legal system is currently behind when it comes to technological abilities, while the field of AI safety in built environments is still in its infancy. Negligent design and production processes, irresponsible data management, questionable deployment, algorithm training, sensor design and/or manufacture, unforeseen consequences from multiple data inputs, and erroneous AI operation based on sensor or remote data can all lead to accidents. Yet, determining how legal rules should apply to liability for losses caused by AI systems takes time. Traditional product liability laws can apply for some systems, meaning that the manufacturer will bear responsibility for a malfunctioning part. That said, more complex cases will undoubtedly have to come before the courts to determine whether something unsafe should be the manufacturer's fault or the individual's fault, as well as who should receive the subsequent financial and/or non-financial compensation, etc. Since AI adoption has an inevitable relationship with safety concerns, this project intends to shed light on responsible AI development and usage, with a specific focus on AI safety laws, policies, and people's perceptions. We will conduct a systematic literature review via the PRISMA approach to study the academic perspectives of AI safety policies and laws and data-mining publicly available content on social media platforms such as Twitter, YouTube, and Reddit to study societal concerns about AI safety in built environments. We will then research court cases and laws related to AI safety in 61 jurisdictions, in addition to policies that have been implemented globally. Two case studies on AI suppliers that sell AI hardware and software to users of built environment will also be included. Another two case studies will be conducted on built environment companies (a contractor and Hong Kong International Airport) that use AI safety tools. The results obtained from social media, court cases, legislation, and policies will be discussed with local and international experts via a workshop, then released to the public to provide the international community and Hong Kong with unique policy and legal orientations.
  • Crowdsourced linked data question answering with AQUACOLD

    Collis, Nick; Frommholz, Ingo; University of Bedfordshire; University of Wolverhampton (IEEE, 2021-12-29)
    There is a need for Question Answering (QA) to return accurate answers to complex natural language questions over Linked Data, improving the accessibility of Linked Data (LD) search by abstracting the complexity of SPARQL whilst retaining its expressiveness. This work presents AQUACOLD, a LD QA system which harnesses the power of crowdsourcing to meet this need.
  • Bi-layered disulfiram-loaded fiber membranes with antibacterial properties for wound dressing

    Xie, Chenchen; Yan, Jin; Cao, Siyuan; Liu, Ri; Sun, Baishun; Xie, Ying; Qu, Kaige; Zhang, Wenxiao; Weng, Zhankun; Wang, Zuobin; et al. (Springer, 2021-10-29)
    In this study, the bi-layered disulfiram-loaded fiber membranes with the antibacterial activity and different surface wettabilities are prepared using electrospinning technology. In the application of wound dressing, the hydrophilic surface of fiber membranes is beneficial for cell adhesion and drug release to heal the wound. Meanwhile, the outside hydrophobic surface is able to block water penetration to reduce the probability of wound infection. The obtained bi-layered drug-loaded fiber membranes are composed of polyvinylidene fluoride (PVDF) bottom surface and disulfiram (DSF)/polylactic acid (PLA) top surface. To modify the top surface wettability, the oxygen plasma modification of bi-layered membranes was carried out. The morphology, wettability, and chemical compositions of bi-layered drug-loaded fiber membranes were analyzed using the scanning electronic microscope (SEM), drop shape analysis instrument, X-ray diffractometer (XRD), and X-ray photoelectron spectrometer (XPS). The bi-layered disulfiram-loaded membranes showed the potent antibacterial activity in vitro against both Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive). It was found that the bi-layered membranes had good biocompatibility with L929 cells. Thus, the obtained bi-layered disulfiram-loaded fiber membranes are suitable for wound dressing application.
  • Real-time user clickstream behavior analysis based on Apache Storm streaming

    Pal, Gautam; Li, Gangmin; Atkinson, Katie (Springer, 2021-12-22)
    This paper presents an approach to analyzing consumers’ e-commerce site usage and browsing motifs through pattern mining and surfing behavior. User-generated clickstream is first stored in a client site browser. We build an ingestion pipeline to capture the high-velocity data stream from a client-side browser through Apache Storm, Kafka, and Cassandra. Given the consumer’s usage pattern, we uncover the user’s browsing intent through n-grams and Collocation methods. An innovative clustering technique is constructed through the Expectation-Maximization algorithm with Gaussian Mixture Model. We discuss a framework for predicting a user’s clicks based on the past click sequences through higher order Markov Chains. We developed our model on top of a big data Lambda Architecture which combines high throughput Hadoop batch setup with low latency real-time framework over a large distributed cluster. Based on this approach, we developed an experimental setup for an optimized Storm topology and enhanced Cassandra database latency to achieve real-time responses. The theoretical claims are corroborated with several evaluations in Microsoft Azure HDInsight Apache Storm deployment and in the Datastax distribution of Cassandra. The paper demonstrates that the proposed techniques help user experience optimization, building recently viewed products list, market-driven analyses, and allocation of website resources.
  • Predicting cumulative effect of lifestyle risk factors for complex disease

    Effiok, Emmanuel; Liu, Enjie; Hitchcock, Jonathan James; University of Bedfordshire (IET/Wiley, 2022-03-17)
    In medical domain, risk factors are often used to model disease predictions. In order to make the most use of the predictive models, linking the model with real patient data generates personalized disease progression and predictions. However, the risk factors are fragmented all over medical literature, certain risks can be accumulated for a disease and the aggregated probability may increase or decrease the occurrence of a disease. In this paper, we propose a risk predictive framework which forms a base for a complete risk prediction model that can be used for various health applications.
  • A systematic approach of impact of GDPR in PII and privacy

    Feng, Xiaohua; Feng, Yunzhong; Asante, Audrey; University of Bedfordshire; Hebei Normal University (International Journal of Engineering Science Invention, 2021-01-31)
    Since EU (European Union) published GDPR (General Data Protection Regulation) in 2016, every countries related have started to pay more attention on PII (Personally Identifiable Information) and personal privacy. GDPR and Data Protection Act 2018 lawsbrought people’s attention on how to cope with data privacy, especially in the current pandemic.Conventional personal privacy breach crimes had been boostedwith the rapid development of ICT technology. The Internet had brought rise in cybercrimes even though it had changed the stages of activities, communications, socialization and way of access to information. Internet had now been applied as a tool by many cyber criminals hunt PII and personal privacy in order to performing their malicious activities. One of the reason behind Internet being frequently exploited by most cyber criminals had been that Internet was a low-cost, relative easyapproach for interaction [Schneier,2019]. Although there were different strategies had been developed and approved to control these cybercrimespotentially,people in the society realized handling of these crimes were seriouslysignificant. Attacks carried online by offenders or perpetrates were considered to have importantimpact, which could be severe when compared to attacks carried out offline and in the physical domain [Lipton 2011]. A strategy was proposed with feasible method to improve on privacy protection, in terms of enhance people’s awareness on PII privacy in our society.

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