SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted The Impact of Digital Transformation on Enhancing the Medical Supply Chain(Saudi Digital Library, 2025) Abdulaziz, Albaraa; Theo, FotisThe current thesis aims to investigate how digital transformation (DT) technologies, artificial intelligence (AI), the Internet of Things (IoT), and blockchain can be used to improve the medical supply chain in Saudi Arabia through the Vision 2030 metric. A systematic review based on PRISMA was used to identify and screen the studies (n=6); narrative and thematic synthesis were performed as part of the study, and three general themes were created: operational excellence, economic viability, and implementation readiness. According to the findings, DT enhances visibility, predictability, responsiveness, and resilience, but the strength of evidence was not consistent, and some of the studies have indicated a high implementation cost and organisational barriers. It is important to note that the synthesis has revealed the convergence as well as tensions among studies that technical interoperability and workforce readiness are important in the effective adoption of DT. Some of these recommendations are gradual execution of technology, capacity building initiatives and sponsorship of national policy to enable equal adoption in all the facilities. Its implications go beyond Saudi Arabia, and the fact that DT serves as an agent of predictive and agile systems and the global discussions about how digital health transformation strategies are to be implemented.6 0Item Restricted THE IMPACT OF INDUSTRY 4.0 TECHNOLOGIES ON SUPPLY CHAIN RESILIENCE IN SAUDI ARABIA(Saudi Digital Library, 2025) Alanazi, Jawaher; Greasley, AndrewThis dissertation examines the impact of Industry 4.0 technologies on supply chain resilience in Saudi Arabia, where initiatives such as Internet of Things and the Global Supply Chain Resilience Initiative (GSCRI) are central to industrial transformation. A systematic literature review (SLR) guided by the PRISMA framework was conducted, covering academic and grey literature published between 2020 and 2025. The search initially yielded 315 records; after applying inclusion and exclusion criteria, 26 studies were selected for detailed analysis. The findings indicate thatInternet of Things (AI), blockchain, the Internet of Things (IoT), and digital twins are the leading technologies supporting resilience capabilities, particularly in risk mitigation, operational continuity, agility, and sustainability. However, barriers such as high implementation costs, limited absorptive capacity, institutional inertia, and fragmented infrastructure constrain their full potential. This research contributes by integrating insights from policy initiatives, industry practices, and recent empirical studies to show how Industry 4.0 adoption strengthens resilience in supply chains within an emerging economy context. It underscores the strategic role of digital transformation in enabling continuity, flexibility, and sustainability. The study concludes with practical recommendations for policymakers and practitioners in Saudi Arabia, emphasizing the need to strengthen digital infrastructure, enhance workforce skills, and foster cross-sector collaboration. It also highlights avenues for future research, including empirical studies on digital maturity and comparative analyses across sectors and regions.26 0Item Restricted Resource Scheduling Strategies to Optimise QoS in Integrated IoT and Fog Computing Environments(Saudi Digital Library, 2025) Alshammari, Naif Hamdan; Pervaiz, Haris; Ahmed, Hasan; Ni, QiangIn recent years, there has been a tremendous increase in the internet and its applications, which has attracted the attention of scholars and industries to investigate how to improve the quality of services (QoS). Improving QoS is considered a major challenge for users of IoT devices. To address this challenge, several technologies have emerged to extend cloud computing, including fog computing, which provides computational resources at the network’s edge. Nevertheless, fog computing faces limitations due to the constrained resources, limited computational processes and storage in fog devices compared to cloud infrastructure. This thesis investigates resource scheduling strategies to optimise QoS in fog computing, focusing on task scheduling and resource allocation approaches. The thesis begins with a qualitative comparative analysis of existing resource management approaches to optimise QOS. It classifies resource management approaches into several categories: application placement, task scheduling, resource allocation, task offloading, load balancing, and resource provisioning. These categories are either task-oriented, such as application placement, task scheduling, and task offloading, or resource-oriented, including resource allocation, load balancing, and resource provisioning. It also introduces a novel intelligent resource scheduling model using gated graph convolution neural networks (GGCNs) to trade off between delay and network usage with a limited number of fog nodes. The GGCN model outperforms various other existing approaches like PSO, FCFS, and JSF by 86.09%, 98.53%, and 98.02% respectively, in terms of total network usage. Additionally, in terms of loop delay, it achieves improvements of 68.64% over PSO, 92.07% over FCFS, and 76.26% over SJF. Furthermore, it presents a novel multi-objective scheduling framework utilising an enhanced multi-layer perceptron (eMLP). This new mechanism optimises several parameters, including delay, power consumption, and cost, while simultaneously optimising bandwidth. Experimental results show that eMLP reduces delay, network usage and cost by 75%, 65%, and %70 respectively, compared to other benchmark schemes such as GNN, SMA, FCFS, and SJF. Finally, the thesis discusses the current gaps and future directions for enhancing and further investigating QoS through fog computing.15 0Item Restricted Design, Development and Deployment of Customisable Mobile and IoT Systems to Enhance Mosquito Surveillance(University College London, 2025-04-10) Aldosery, Aisha; Kostkova, PattyMosquito-borne diseases pose significant public health challenges in tropical and subtropical regions, requiring precise and efficient surveillance methods. Traditional field data collection methods often lack the accuracy, timeliness and efficiency required to control outbreaks. This research advances mosquito field surveillance by designing, developing, and deploying two digital solutions customised for distinct environmental settings in Northeast Brazil and Madeira Island: a Mobile Surveillance System and an Internet of Things (IoT) Environmental Monitoring System. Developed through iterative processes and stakeholder engagement, these adaptable and scalable systems enhance the accuracy and granularity of data collection. The Mobile Surveillance System was adapted to regional requirements: in Brazil, it comprises a mobile app for field agents and a web platform for supervisors; in Madeira, it combines both functionalities into a single, unified mobile app. The implementation of both applications shares a common architecture that not only proves the systems’ generalisability but also boosts operational efficiency and data accuracy by digitising field data, supporting field agents, and aiding supervisors in managing activities. The IoT-based Environmental Monitoring System, featuring a five-layer architecture, including Arduino microcontrollers, weather and water sensors, and a water pump, autonomously and continuously captures high-resolution data, offering deeper insights into environmental influences on mosquito populations and supporting precise, location-specific predictive modelling. Detailed statistical analyses using Bayesian hierarchical models and correlation studies were conducted to pinpoint critical environmental predictors of mosquito breeding. Furthermore, a neural network classification-based predictive model was developed, enhancing weekly mosquito presence predictions by analysing temporal and sequential environmental patterns. This research distinguishes itself through its real-world deployments, addressing infrastructure, logistics, and technology challenges, while underscoring the importance of a process-oriented approach in tool development and recommending longitudinal deployments to assess the long-term impact of these technologies on mosquito population control and disease management in diverse environments.18 0Item Restricted Software-based Fault-Tolerant Internet of Things (IoT) Multi-Sensor Device using the BEAM Virtual Machine(Newcastle University, 2024-08-22) Alghamdi, Abdulrahman; Bystrov, AlexThe use of Internet of Things (IoT) devices in many industries, such as healthcare, agriculture, and transportation, has led to the reliability of such devices to become an essential requirement. It was argued that future business models would be dependent on IoT infrastructure. This project aimed to implement fault-tolerant IoT software using the Erlang virtual machine (BEAM) on the Raspberry Pi. The faults addressed are software faults, stuck-at-fault, and data loss faults. The objective was to build a multi-sensor IoT device that links the sensors to the cloud. It was decided to use the Elixir programming language as it had better support for external dependency and embedded systems. As for hardware, two sensors connected to the Raspberry Pi were used. A supervision tree was implemented using the Elixir language in Raspberry Pi, and experiments were then conducted to test the implementation. The implementation achieved a mean time to recovery (MTTR) of 2.16 milliseconds and 296 milliseconds in publish time. Moreover, it was found that increases in BEAM processes tend to be efficient in CPU usage due to a logarithmic relationship. The results proved BEAM as a substantial solution for IoT to meet digital business needs. The author is confident to recommend the BEAM as the tool for future reliable IoT devices.23 0Item Restricted Adaptive Resilience of Intelligent Distributed Applications in the Edge-Cloud Environment(Cardiff University, 2024-04) Almurshed, Osama; Rana, OmerThis thesis navigates the complexities of Internet of Things (IoT) application placement in hybrid fog-cloud environments to improve Quality of Service (QoS) in IoT applications. It investigates the optimal distribution of a Service Function Chain (SFC), the building blocks of an IoT application, across the fog-cloud infrastructure, taking into account the intricate nature of IoT and fog-cloud environments. The primary objectives are to define a platform architecture capable of operating IoT applications efficiently and to model the placement problem comprehensively. These objectives involve detailing the infrastructure's current state, execution requirements, and deployment goals to enable adaptive system management. The research proposes optimal placement methods for IoT applications, aiming to reduce execution time, enhance dependability, and minimise operation costs. It introduces an approach to effectively manage trade-offs through the measurement and analysis of QoS metrics and requires the implementation of specialised scheduling and placement strategies. These strategies employ concurrency to accelerate the planning process and reduce latency, underscoring the need for an algorithm that best corresponds to the specific requirements of the IoT application domain. The study's methodology begins with a comprehensive literature review in the area of IoT application deployment in hybrid fog-cloud environments. The insights gained inform the development of novel solutions that address the identified limitations, ensuring the proposal of robust and efficient solutions.32 0Item Restricted An information security model for an Internet of Things-enabled smart grid in the Saudi Energy Sector(University of Southampton, 2024-07-22) Akkad, Abeer; Rezazadeh, Reza; Wills, Gary; Hoang, SonThe evolution of an Internet of Things-enabled Smart grid affords better automation, communication, monitoring, and control of electricity consumption. It is now essential to supply and transmit the data required, to achieve better sensing, more accurate control, wider information communication and sharing, and more rational decision-making. However, the rapid growth in connected entities, accompanied by an increased demand for electricity, has resulted in several challenges to be addressed. One of these is protecting energy information exchange proactively before an incident occurs. It is argued that Smart Grid systems were designed without any regard for security, which is considered a serious omission, especially for data security, energy information exchange, and the privacy of both consumers and utility companies. This research is motivated by the gap identified in the requirements and controls for maintaining cybersecurity in the bi-directional data flow within the IoT-enabled Smart Grid. Through literature and industry standards, the initial stages of the research explore and identify the challenges and security requirements. Threat modelling analysis identified nine internet-based threats, proposing an initial information security model. This initial model is validated using expert reviews, resulting in a reference model that includes seven security requirements and 45 relevant security controls. To demonstrate the usefulness of this reference model as a foundation for further research, a segment of the reference model is elaborated using Event-B formal modelling. This approach assists in incorporating additional details during refinements and confirming the consistency of those details. The formal modelling process begins by formulating the functional requirements in a consistent model and then augmenting it with security controls. The effectiveness of these security controls is validated and verified using formal modelling tools. The contribution of this research, therefore, is the unique approach to developing a framework for an IoT-enabled Smart Grid (SG) by utilising threat analysis and expert reviews in combination with formal methods. As the field of security continues to evolve, this generic framework and formal template can be reused as a foundation for further analysis of other components or access points, and to implement new security controls. The resulting model enables field experts, security practitioners, and engineers to verify any changes made, ensuring they do not compromise the security of information flow within the IoT-enabled Smart Grid during the initial design stages of the system life cycle.48 0Item Restricted IAI-CGM: A Novel Theoretical Framework for Internet of Things -Enabled Continuous Glucose Monitoring Adoption for Self-Empowerment Perspectives Among Saudi Patients with Type 1 Diabetes(University of Sussex, 2024-07-11) Almansour, Hamad; Beloff, Natalia; White, MartinBackground: The alarming surge in the occurrence of diabetes in Saudi Arabia has been primarily linked to the adoption of a "westernised" lifestyle, especially in dietary practices. Despite the existence of treatment facilities, projections indicate that diabetes will affect approximately 25% of Saudi Arabia's adult population by 2030. Addressing this worrying situation regarding type 1 diabetes mellitus (T1DM) requires a paradigm shift in health control dynamics. The emphasis is moving from relying solely on doctors and physicians to placing greater responsibility in the hands of patients. This shift implies that patients should possess enhanced knowledge and the means for self-empowerment over their diet and nutrition to address their health-related issues. This is where smart technology assumes significance, empowering patients to adopt self-care management roles with Internet of Things (IoT)- enabled devices. However, it is imperative that use of IoT-enabled continuous glucose monitoring (IoT-CGM) be implemented at diabetes primary care centres in order for this practice to be normalized among all patients in Saudi Arabia. It is challenging to accurately assess the current rate of smart technology adoption by patients and IoT integration in the Saudi healthcare sector. Patients’ IoT-CGM adoption may be caused by numerous factors, such as practical, technological, and user behaviour factors. The study seeks to gauge the extent to which Saudi Arabian patients with diabetes are ready to embrace IoT-CGM for self- empowerment. Aims and Objectives: The research aims to assess the readiness and willingness of primary diabetes care patients in Saudi Arabia to wear CGM devices, thereby allowing self-empowerment. This research examines the literature that represents the challenges and concerns influencing the adoption of IoT-CGM, taking into account the experiences of T1DM patients in the environment of Saudi Arabia. The theoretical framework of the adoption of IoT-CGM is based on the technology acceptance model (TAM). Consequently, a theoretical framework is proposed as intention to adopt internet of things-enabled continuous glucose monitoring (IAI-CGM) to assess the willingness of Saudi Arabian T1DM patients for self- empowerment. Methods: The quantitative primary data were collected from 873 T1DM patients in Saudi Arabia, aged at least 18 years old. Primary data were analysed using the research IAI-CGM framework. Next, the validity and reliability of instrument were measured after checking data normality in SPSS and then the hypotheses were analysed using structural equation modelling (SEM) in AMOS. In the following step, qualitative data were collected through 15 comprehensive semi-structured interviews to capture the viewpoints of T1DM patients. A thematic analysis was performed to explore themes grounded on the theoretical IAI-CGM framework to identify the significance of practical, technological, and user behaviour factors that influence the adoption intention of T1DM patients. Results: The results consolidate the critical factors into the proposed IAI-CGM framework, identifying the main elements crucial for the framework in the context of T1DM patients in Saudi Arabia. The comprehensive theoretical IAI-CGM framework, based on the TAM, was applied and extended to comprehend the factors affecting the intention to adopt IoT-CGM in the context of Saudi Arabia. The results indicate the significance of practical, technological, and user behaviour factors, both quantitatively and qualitatively. Conclusion: This study investigated the critical factors found in the theoretical IAI-CGM framework, such as practical, technological, and user behaviour factors, in the environment of Saudi Arabia. The research findings give valuable information regarding the willingness of Saudi Arabian T1DM patients to adopt IoT-CGM, which necessitates its integration into the Saudi healthcare system.14 0Item Restricted Security Countermeasures for Topology and Flooding Attacks in Low Power and Lossy Networks(University of Bristol, 2023-10-06) Algahtani, Fahad Mohammed F; Oikonomou, GeorgeInternet of Things have become an integral part in many industries such as health- care, home automation, automobile, and agriculture. Many applications of IoT use networks of unattended micro battery-operated devices with limited compu- tational power and unreliable communication systems. Such networks are called Low-Power and Lossy Network (LLN) which is based on a stack of protocols de- signed to prolong the life of an application by conserving battery power and mem- ory usage. Most commonly used routing protocol is the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). RPL suffers from vulnerabilities related to routing paths formation, network maintenance, and response to some of its control messages. Specifically, compro- mised nodes can advertise falsified routing information to form sub-optimised paths or trigger network reformations. Furthermore, they can flood a network with join- ing requests to trigger a massive number of replies. No standardised RPL solutions provide the security against such attacks. Moreover, existing literature works are mostly based on using monitoring architectures, public key infrastructure (PKI), or a blacklisting approach. Any monitoring devices must be physically secured and utilising only secure communications which is not easily scaleable. Using PKI in LLNs is still a challenge as certificates management is unsuitable for LLN devices. Blacklisting nodes using their advertise addresses is clearly vulnerable to identity spoofing. Moreover, attacks described in few sentences could miss details which transforms any discussion on impact analysis to be subject to interpretation. Therefore, the aim of this dissertation is to first implement attacks using a developed framework to launch multiple attacks simultaneously on different nodes during specified times. Second, to analyse the strategies of an adversary when launching the aforementioned attacks. Then, the impact of the instigated attacks in each strategy is analysed to establish a baseline for countermeasures evaluation. Finally, security countermeasures for the aforementioned attacks are proposed as well as their performances are evaluated. In countering the attack responsible for forming sub-optimised routing paths, preloading a minimum relative location in each node has filtered out any future attempts to accept false routing metrics. As for the attack causing unnecessary net- work reformations, nodes will only accept cryptographically authenticated routing information to trigger future network rebuilds. Lastly, any faster interarriving join- ing requests will be evaluated against thresholds with hysteresis to adjust RPL’s response to potential floods.30 0Item Restricted Profiling IoT Botnet Activity(University of Glasgow, 2024-02-09) Almazarqi, Hatem Aied; Marnerides, AngelosUndoubtedly, Internet of Things (IoT) devices have evolved into a necessity within our modern lifestyles. Nonetheless, IoT devices have proved to pose significant security risks due to their vulnerabilities and susceptibility to malware. Evidently, vulnerable IoT devices are enlisted by attackers to participate into Internet-wide botnets in order to instrument large-scale cyber-attacks and disrupt critical Internet services. Tracking these botnets is challenging due to their varying structural characteristics, and also due to the fact that malicious actors continuously adopt new evasion and propagation strategies. This thesis develops BotPro framework, a novel data-driven approach for profiling IoT botnet behaviour. BotPro provides a comprehensive approach for capturing and highlighting the behavioural properties of IoT botnets with respect to their structural and propagation properties across the global Internet. We implement the proposed framework using real-world data obtained from the measurement infrastructure that was designed in this thesis. Our measurement infrastructure gathers data from various sources, including globally distributed honeypots, regional Internet registries, global IP blacklists and routing topology. This diverse dataset forms a strong foundation for profiling IoT botnet activity, ensuring that our analysis accurately reflects behavioural patterns of botnets in real-world scenarios. BotPto encompasses diverse methods to profile IoT botnets, including information theory, statistical analysis, natural language processing, machine learning and graph theory. The framework's results provide insights related to the structural properties as well as the evolving scanning and propagation strategies of IoT botnets. It also provides evidence on concentrated botnet activities and determines the effectiveness of widely used IP blacklists on capturing their evolving behaviour. In addition, the insights reveal the strategy adopted by IoT botnets in expanding their network and increasing their level of resilience. The results provide a compilation of the most important autonomous system (AS) attributes that frequently embrace IoT botnet activity as well as provide a novel macroscopic view on the influence of AS-level relationships with respect to IoT botnet propagation. Furthermore, It provides insights into the structural properties of botnet loaders with respect to the distribution of malware binaries of various strains. The insights generated by BotPro are essential to equip next generation automated cyber threat intelligence, intrusion detection systems and anomaly detection mechanisms with enriched information regarding evolving scanning, establishment and propagation strategies of new botnet variants. Industry will be equipped with even more improved ways to defend against emerging threats in the domains of cyber warfare, cyber tourism and cyber crime. The BotPro framework provides a comprehensive platform for stakeholders, including cybersecurity researchers, security analysts and network administrators to gain deep and meaningful insights into the sophisticated activities and behaviour exhibited by IoT botnets.51 0