Saudi Cultural Missions Theses & Dissertations
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Item 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.15 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.21 0Item Restricted Efficient Deep Learning for Plant Disease Classification in Resource Constrained Environment(The University of Georgia, 2024) Alqahtani, Ola; Ramaswamy, LakshmishDeep Neural Networks (DNNs) have been widely used in today’s applications. In many applications such as video analytics, face recognition, computer vision, and classification problems like plant disease classification, etc. DNN models are constrained by efficiency constraints (e.g., latency). Many deep learning applications require low inference latency, which must fall within the parameters set by a service level objective. The prediction of the inference time of DNN models raises another problem which are the limited resources of Internet of Things devices. These devices need an effective way to run DNN models on them. One of the most widely discussed technological developments since the Internet of Things is edge machine learning (Edge ML), and with good reason. Edge Machine Learning is a fast-growing well-known technological improvement since the existence of the Internet of Things (IoT). Edge ML allows smart devices to use machine learning and deep learning techniques to analyze data using servers locally or at the device level, which reduces the need for cloud networks. This is caused by a variety of issues, including poor internet access, expensive cloud resources, low-resource edge devices, and a high failure rate of Internet of Things (IoT) devices, either because of battery or connection issues. Finding a way to effectively run the DNN models locally on IoT devices is crucial.30 0Item Restricted Detecting Makeup Activities using Internet-of-Things(University of Maryland Baltimore County, 2019-07-30) Alqurmti, Fatimah; Roy, NirmalyaThe make-up market is one of the most furnished fashion markets in product retailing and training demands. Each of the makeup activities involves very delicate movements of hands and requires good amount of training and practice for perfection. The available choices in the make-up training industry depends on practical workshops by professionalinstructors, and still evaluating the perfection of makeup activities lacks certainty. In this work, we introduced a novel application for human activity recognition using sensors’ data and a supervised machine learning approaches for rendering make-up activities. We considered five make-up activities in our work, such as, applying cream, lipsticks, blusher, eyeshadow, and mascara and collected data from ten participants. We built supervised make-up activity recognition using different predictive machine learning algorithms i.e. Naïve Bayes, Simple Logistic, k-nearest neighbors’, and the random forest algorithms. We investigated the models' performance for detecting five make-up activities with or without instructions. Our results show that shallow machine learning algorithms achieve up to 92% accuracy in detecting make-up activities.20 0Item Restricted COMPREHENSIVE SYSTEM ARCHITECTURE FOR HOUSEHOLD REPLENISHMENT SYSTEM: SIMULATION OPTIMIZATION FOR INVENTORY REPLENISHMENT POLICY CONSIDERING QUALITY DEGRADATION & STOCHASTIC DEMAND(Binghamton University, 2024) Almassar, Khaled; Khasawneh, MohammadFood wastage because of the lack or incompletion of a Household Replenishment System is an essential topic to be addressed. Appropriately using the Internet of Things (IoT) and Artificial Intelligence (AI) technologies with particular components is needed to design a smart Household Replenishment System to reduce food waste. This dissertation develops a unified framework and conceptual system architecture for the implementation of a Household Replenishment System, presents an object recognition framework to identify labeled and unlabeled items inside a smart refrigerator, and showcases a low-cost installation model for a smart refrigerator. It develops and validate a simulation optimization model of perishable items inside a smart refrigerator for an optimal replenishment policy. To accomplish those goals, this dissertation initially provides comprehensive analyses and a summary of the literature using IoT and AI tools for perishable items storage compartments, as they are always full of items that need to be monitored. This comprehensive research followed the PRISMA search strategy, which was conducted to point out the approaches, contributions, components used, and limitations of the reviewed papers in developing a unified framework for a household replenishment system. More specifically, 70 papers were examined in chronological order starting from 2000 when LG Electronics invented the first smart refrigerator, and research on technology involvement in food storage compartments increased. The analysis found 43 approaches using IoT technology, 27 using AI, and in the past couple of years, the use of AIoT has been trending. The future directions for researchers were acquired from the limitations of the reviewed papers, and they could enhance the household replenishment system by adding the features to smart food storage compartments v The comprehensive research helps fulfill an objective of this dissertation, system architecture framework The system architecture acts as a road map for developers to implement a Household Replenishment System. It sheds more light on one of the most important techniques of AI, object recognition. A framework of object recognition is developed. The developed object recognition provides insight into the type of information about the stored items that could be obtained by the Household Replenishment System. A practical example of a cost model is presented. The developed cost model minimizes the total installation cost of the smart refrigerator based on household preferences using linear programming, adoption of capital budgeting, and multidimensional knapsack problems. The object recognition framework presented is conceptual. Therefore, several assumptions were used to develop a simulation optimization model of the Household Replenishment System. The simulation optimization model uses discrete-event simulations and a periodic policy considering the review period, minimum stock level, and maximum stock level (T, s, S). The simulation optimization model finds the optimal replenishment policy to minimize the total Household Replenishment Systems’ inventory cost. The simulation optimization model considers holding, shortage, wastage, and order costs as components in the objective function, which accounts for stochastic demand, variation in life span, and quality degradation rates of stored items. The simulation optimization model was tested on single and multiple items, with different scenarios for the multipleitem cases. Experimental runs of the simulation optimization model were completed, validated, and analyzed. The design of the experiment and sensitivity analyses were applied. The simulation optimization model successfully generated a set of top five optimal replenishment policies for the household to choose from. Further investigation into smart home appliances would lead to extensive approaches like smart shops, vi industries, and eventually smart cities. Future work for this dissertation could be achieved by enlarging the scope of research to involve patents, dissertations, and theses that used Artificial Intelligence of Things (AIoT) technologies to improve the Household Replenishment System.21 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.38 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.12 0Item Restricted Understanding and Improving the Usability, Security, and Privacy of Smart Locks from the Perspective of the End User(University of North Carolina at Charlotte, 2024) Hazazi, Hussein; Shehab, MohamedOver the past two decades, the Internet of Things (IoT) has seen a significant expansion in both the sophistication and variety of its applications. These applications span several domains, including enhancing and automating services in healthcare, advancing smart manufacturing processes, and elevating home living standards through smart home technologies. These technologies empower individuals with greater control over their home appliances. Smart locks are smart home devices that were introduced as replacements for traditional locks. Smart locks, designed to go beyond the basic functionality of traditional locks by offering additional features, have seen a surge in market growth and competitiveness. According to the Statista Research Department, it is projected that the global market for smart locks will surpass four billion dollars by 2027. A number of studies have examined end users' concerns, needs, and expectations regarding smart homes in general. However, little research has been conducted to examine these aspects of the smart lock in particular. To address this gap, we conducted a series of user studies that aim to elucidate how smart locks are integrated and interact within smart home environments, focusing on user interactions both with the locks themselves and when they are part of broader automation scenarios. This dissertation contributes to a deeper understanding of smart lock technology from a user-centric viewpoint. It offers insights into user motivations, concerns, and preferences regarding smart lock usage and automation. It also highlights the importance of balancing convenience and security, the pivotal role of trust, and the complexities of integrating smart locks into broader smart home systems.34 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.29 0Item Restricted Factors Influencing Medical Internet of Things Adoption by Riyadh Hospital Staff in Saudi Arabia: A Quasi-Experimental and Model Testing Study(University of La Trobe, 2024-02-07) Alomari, Abdulaziz S.; Soh, BenMedical IoT (mIoT) has tremendous capabilities in healthcare, ranging from monitoring patients’ basic health statistics remotely to more complex healthcare solutions, such as timely prediction of fatal life events and prevention of lethal communicable diseases. Saudi Arabia faces significant challenges in smoothly transitioning to mIoT. Previous experiences with eHealth solutions have encountered obstacles, indicating the potential hurdles ahead. For instance, the underutilisation of EHR functionalities has been reported across the board due to a lack of interest associated with low computer literacy. Other factors resistant to eHealth technology adoption are insufficient user knowledge, time constraints, and a lack of appreciation for the importance and functionality of technologies. To that end, this thesis aims to investigate the mIoT knowledge, perceptions and determinants that influence mIoT adoption, in conjunction with the role of evidence-based awareness videos and personal demographics in hospitals in Riyadh, Saudi Arabia. A study framework is proposed utilising UTAUT as the base model. The proposed framework incorporates these six study factors: Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Computer and English language Self-Efficacy (CESE), Perceived Threat to Autonomy (PTA) and Confidentiality and Privacy Concerns (CPC). A quasi-experimental and model testing design is incorporated into our study. This study finds that the determinants (PE, EE, CESE, SI, PTA, CPC) proposed in the study framework hold significant predictive power in explaining the adoption behaviour of hospital care staff towards mIoT. Notably, the findings related to PTA yield valuable insights characterised by novelty, complexity, variability, and relevance in understanding mIoT adoption. The thesis concludes that the determinants should be incorporated during the initial stage of mIoT adoption in the healthcare sector. Future large-scale studies, including the involvement of sufficient numbers of doctors, are required to increase confidence and expand the relevance of the framework developed in this thesis.33 0