SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted .Improving the Sustainability of IoT and CMMS Integration in Engineering Management for Saudi Arabian Consultancy Firms(University of the West of England, 2025) Alsubaie, Saud; Jumbo, RaphaelThis study investigated the adoption of IoT and CMMS in engineering management by Saudi Arabian consultancy companies, focusing on the sustainability in line with Saudi Arabia Vision 2030. This research investigated the benefits of these transformational potentials of IoT and CMMS in asset reliability, operational efficiency, and environmental responsibilities. Key benefits such as the capability to detect failures in real time, predicting failures, and implementing optimised resource management to reduce downtime, extend asset lifecycle, and support sustainability impact were identified. The study, however, identified several challenges such as infrastructure limitations, security of data, interoperability among others and high implementation costs. In attempt to address these challenges, the study adopted the use of quantitative methodology to employ data from 190 industry participants to provide current practice, barriers and perceived use of IoT and CMMS in asset management. Results from the findings were used to develop a sustainable integration framework that addressed these barriers and optimizes the use of IoT and CMMS for enhancing asset management and operational sustainability. The findings provided knowledge on engineering management and sustainability by providing practical guidance for navigating technological and organisational challenges, particularly with IoT and CMMS in asset management and operational sustainability. This research aligns with Saudi Vision 2030’s strategic goals, and would help consultancy firms expand their innovative, sustainable, and efficient infrastructure development.14 0Item Restricted Scalable Network Fingerprinting for IoT Devices(University of Southampton, 2024) Alyahya ,Tadani Nasser; Aniello, Leonardo; Sassone, VladimiroRecognising IoT devices through network fingerprinting contributes to enhancing the security of IoT networks and supporting forensic activities. Network fingerprinting for IoT devices involves analysing the traffic from these devices to accurately identify them without relying on explicit identifiers within the transmitted packets, which can be spoofed. Machine learning techniques have been extensively utilised in the literature to optimise IoT fingerprinting accuracy. Given the rapid proliferation of new IoT devices, a current challenge in this field is around how to make IoT fingerprinting scalable, which involves efficiently updating the used machine learning model to enable the recognition of new IoT devices. Some approaches have been proposed to achieve scalability, but they all suffer from limitations like large memory requirements to store training data and accuracy decrease for older devices. In this research, we propose a novel, scalable network fingerprinting method for IoT devices that leverages online stream learning and fixed-size session payloads. This approach enables the model to be updated periodically without needing to retain data, ensuring scalability and maintaining high recognition accuracy. Moreover, our method includes a mechanism for detecting unknown IoT devices. Our contributions are multifaceted, beginning with a comprehensive survey of passive IoT device fingerprinting that leverages machine learning and network characteristics, systematically reviewing the literature and detailing the network traffic features used for device identification. We identify key open research problems and future directions in this domain, highlighting significant challenges and gaps. A notable advancement is the introduction of ScaNeF-IoT, a scalable IoT fingerprinting approach utilising online stream learning and fixed-size traffic payload sessions, demonstrating high accuracy and adaptability. The scalability of the approach lies in its ability to continuously update the machine learning model with minimal resource overhead, allowing for the seamless recognition of new IoT devices without retraining from scratch. We further investigate the feature extraction method, which indicates the instances of interest from network traffic, such as packets, flows, or sessions, for further analysis and feature extraction, finding that fixed-size payload sessions outperform others with an accuracy of over 99.5% and an average false positive rate of 2.25%. Additionally, our scalable system is able to detect unknown IoT devices using online stream learning and z-score analysis, showcasing efficiency and adaptability. Our scalable IoT device fingerprinting approach achieves 100% accuracy in detecting unknown devices and 94% average accuracy in identifying known devices in streaming data.11 0Item Restricted Integrating Digital Technologies with Customer Relationship Management (CRM) to Enhance Customer Satisfaction and Loyalty in Luxury Hotels(Manchester metropolitan university, 2024) Assiri, Tarek; Cosser, GillianThis study investigates the integration of digital technologies—namely Artificial Intelligence (AI), Internet of Things (IoT), and Big Data analytics—into Customer Relationship Management (CRM) systems in luxury hotels. The research evaluates the impact of these technologies on customer satisfaction and loyalty through a quantitative approach, utilizing data from surveys conducted with hotel front-office employees. Findings reveal a varied adoption of digital tools, with IoT significantly enhancing operational efficiency, Big Data analytics improving customer retention strategies, and AI demonstrating underutilization due to staff training challenges. The study underscores the importance of aligning technology adoption with employee proficiency and guest expectations to optimize CRM effectiveness. Strategic recommendations include enhanced staff training programs, expansion of IoT applications, and leveraging Big Data for predictive analytics to strengthen customer relationships in the luxury hospitality sector. Limitations, such as the focus on luxury hotels and the exclusion of guest perspectives, highlight areas for future research15 0Item Restricted Integrating Industry 4.0 in Project Management: A Systematic Literature Review(De MontFort University, 2024-09-20) almehaize, Ghannam nasser; Oyinlola, AdewaleThis thesis investigates Industry 4.0 technologies with the aim of integrating them into project management methodologies to improve efficiency, decision-making, and overall project success. The study investigates the existing studies on the influence of these technologies on project management processes and evaluates the present status of their integration across a variety of sectors. This is accomplished via a comprehensive examination of the available literature and studies. Industry 4.0 technologies have the potential to revolutionise project management by enabling the sharing and analysis of real-time data, according to the results. In addition, they present challenges regarding organisational culture, communication, and skill limitations. This thesis shows that project managers need technical understanding, leadership, and flexibility. This thesis ultimately emphasises the potential of Industry 4.0 technologies to enhance project performance, while also emphasising the need for organisations to modify their project management frameworks in order to prosper in a digital environment that is swiftly evolving. In order to enable organisations to fully realise the promise of these technologies for successful and sustainable development, the study's conclusion calls for further research to develop frameworks that facilitate the effective integration of these technologies.14 0Item Restricted Assessing and Enhancing Protection Measures for Internet of Things (IoT) in Cybersecurity(University of Portsmouth, 2024-09) Alshehri, Abdulrahman; Bader-El-den, MohammedThe Internet of Things (IoT) revolution sweeps across Saudi Arabia, connecting devices, transforming industries, enhancing lives. But with great connectivity comes great vulnerability - cybersecurity threats loom large in this digital frontier. This study delves into the heart of IoT security in the Kingdom, surveying the landscape, probing the defenses, seeking solutions. Through the lens of cybersecurity professionals, we explore current practices, uncover challenges, envision improvements. Our findings paint a picture of a nation at a crossroads: frequent audits needed, authentication protocols lacking, employee training insufficient, encryption underutilized. Yet hope springs eternal in the form of correlations discovered - more vigilant monitoring begets stronger authentication desires. From this research emerges a roadmap for the future: recommendations for policymakers to craft robust regulations, guidelines for organizations to fortify their digital fortresses, advice for end-users to navigate the IoT maze safely. In the rapidly evolving technological tapestry of Saudi Arabia, this study weaves a thread of security consciousness, contributing to a safer, more reliable IoT ecosystem. As the Kingdom marches towards its Vision 2030, may it do so with cybersecurity as its steadfast companion.20 0