Saudi Cultural Missions Theses & Dissertations

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Enhancing Robustness and Energy Efficiency of IoT-Coupled Machine Learning Applications
(University of Georgia, 2025) AlShehri, Yousef; Ramaswamy, Lakshmish
Machine learning (ML) applications continue to revolutionize many domains. In recent years, there has been considerable research interest in building novel ML applications for a variety of Internet of Things (IoT) domains, such as precision agriculture, smart cities, and smart manufacturing. However, IoT characteristics pose several fundamental challenges to designing and implementing effective ML applications at the edge, specifically, inference-time data incompleteness and quality induced by sensor failures and the energy management of such applications. This dissertation comprises four components aimed at addressing the aforementioned challenges to enhance IoT ML-based systems’ robustness, energy efficiency, and reliability against sensor failures at inference time. First, to tackle the sudden and concurrent missing data of sensors at the inference time of the ML application, it introduces an ensemble of a few sub-models, each trained with distinct subsets of sensors chosen based on correlation to make ML robust against missing data. This ensemble-based approach maintains the accuracy of the IoTMLapplication during the occurrence of missing data at inference time via performing predictions using sub-model(s) that are built using the correlated sensors to the faulty sensors, excluding the faulty sensors. The second component involves an extensive study to identify energy bottlenecks for operating our ensemble-based approach, SECOE, on a resource-constrained edge device and find the number of ensemble models that further maintain the ML application’s performance while optimizing energy consumption. The third component introduces an energy-aware ensemble of models with enhanced energy management capabilities for handling data incompleteness during inference time to fit resource-constrained IoT devices. Finally, our research offers an innovative autoencoder model for correcting inaccurate/faulty sensor readings caused by the malfunctioning of multiple sensors simultaneously at inference time. This model treats different faulty readings (e.g., drift and bias) as one type via a correlation-based masking mechanism. The experimental results have demonstrated that our methods have effectively alleviated the impact of IoT sensor failures on the performance of theMLapplication during the inference time while optimizing its energy consumption, thereby decisively outperforming existing methods and significantly improving the overall reliability of ML coupled IoT applications.
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OPTIMIZING INTRUSION DETECTION IN IOT NETWORK ENVIRONMENTS THROUGH DIVERSE DETECTION TECHNIQUES
(Florida Atlantic University, 2025-03-11) Al Hanif, Abdulelah; Ilyas, Mohammad
The rapid proliferation of Internet of Things (IoT) environments has revolutionized numerous areas by facilitating connectivity, automation, and efficient data transfer. However, the widespread adoption of these devices poses significant security risks. This is primarily due to insufficient security measures within the devices and inherent weaknesses in several communication network protocols, such as the Message Queuing Telemetry Transport (MQTT) protocol. MQTT is recognized for its lightweight and efficient machine-to-machine communication characteristics in IoT environments. However, this flexibility also makes it susceptible to significant security vulnerabilities that can be exploited. It is necessary to counter and identify these risks and protect IoT network systems by developing effective intrusion detection systems (IDS) to detect attacks with high accuracy. This dissertation addresses these challenges through several vital contributions. The first approach concentrates on improving IoT traffic detection efficiency by utilizing a balanced binary MQTT dataset. This involves effective feature engineering to select the most important features and implementing appropriate machine learning methods to enhance security and identify attacks on MQTT traffic. This includes using various evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, demonstrating excellent performance in every metric. Moreover, another approach focuses on detecting specific attacks, such as DoS and brute force, through feature engineering to select the most important features. It applies supervised machine learning methods, including Random Forest, Decision Trees, k-Nearest Neighbors, and Xtreme Gradient Boosting, combined with ensemble classifiers such as stacking, voting, and bagging. This results in high detection accuracy, demonstrating its effectiveness in securing IoT networks within MQTT traffic. Additionally, the dissertation presents a real-time IDS for IoT attacks using the voting classifier ensemble technique within the spark framework, employing the real-time IoT 2022 dataset for model training and evaluation to classify network traffic as normal or abnormal. The voting classifier achieves exceptionally high accuracy in real-time, with a rapid detection time, underscoring its efficiency in detecting IoT attacks. Through the analysis of these approaches and their outcomes, the dissertation highlights the significance of employing machine learning techniques and demonstrates how advanced algorithms and metrics can enhance the security and detection efficiency of general IoT network traffic and MQTT protocol network traffic.
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Design, Development and Deployment of Customisable Mobile and IoT Systems to Enhance Mosquito Surveillance
(University College London, 2025-04-10) Aldosery, Aisha; Kostkova, Patty
Mosquito-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.
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Software-based Fault-Tolerant Internet of Things (IoT) Multi-Sensor Device using the BEAM Virtual Machine
(Newcastle University, 2024-08-22) Alghamdi, Abdulrahman; Bystrov, Alex
The 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.
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Adaptive Resilience of Intelligent Distributed Applications in the Edge-Cloud Environment
(Cardiff University, 2024-04) Almurshed, Osama; Rana, Omer
This 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.
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Efficient Deep Learning for Plant Disease Classification in Resource Constrained Environment
(The University of Georgia, 2024) Alqahtani, Ola; Ramaswamy, Lakshmish
Deep 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.
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Detecting Makeup Activities using Internet-of-Things
(University of Maryland Baltimore County, 2019-07-30) Alqurmti, Fatimah; Roy, Nirmalya
The 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.
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COMPREHENSIVE SYSTEM ARCHITECTURE FOR HOUSEHOLD REPLENISHMENT SYSTEM: SIMULATION OPTIMIZATION FOR INVENTORY REPLENISHMENT POLICY CONSIDERING QUALITY DEGRADATION & STOCHASTIC DEMAND
(Binghamton University, 2024) Almassar, Khaled; Khasawneh, Mohammad
Food 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.
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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, Son
The 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.
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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, Martin
Background: 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.
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