Saudi Cultural Missions Theses & Dissertations

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A Facial Expression-Aware Edge AI System For Driver Safety Monitoring
(Saudi Digital Library, 2025) Almodhwahi, Maram; Wang, Bin
This dissertation presents a driver monitoring system (DMS) that integrates emotion recognition to address critical issues in road safety. Road safety has become a global concern due to the significant increase in vehicle numbers and the rapid growth of transportation infrastructure. The number one cause of road accidents is human error, with a 90% ratio, with common contributing factors like distraction, drowsiness, panic, and fatigue. Traditional DMS approaches often fall short in identifying these emotional and cognitive states, limiting their effectiveness in accident prevention. To address these limitations, this research proposes a robust, deep-learning-based DMS framework designed to identify and respond to driver emotions and behaviors that may compromise safety. The proposed system utilizes advanced convolutional neural networks (CNN), specifically the inception module and Caffe-based ResNet-10 with a single-shot detector (SSD), to perform efficient facial detection and classification. These chosen model structures helped balance computational efficiency and accuracy. The DMS is trained on an extensive, diverse dataset comprising approximately 198,000 images and 1,600 videos sourced from multiple public and private datasets, ensuring the system’s robustness across a range of emotions and real-world driving scenarios. Emotions of interest include high-risk states such as drowsiness, distraction, and fear, alongside neutral conditions, and the model can perform well in different conditions, including low-light and foggy/blurry environments. Methodologically, the system incorporates essential data preprocessing techniques such as resizing, brightness normalization, pixel scaling, and noise reduction to optimize the model’s performance. On top of that, data augmentation and grayscale conversion improves the dataset’s variability, allowing the decrease of computational costs without sacrificing accuracy. This approach enabled the model to achieve high performance metrics, with an overall accuracy of 98.6% , an F1-score of 0.979, precision of 0.980, and recall of 0.979 across the four primary emotional states. This research contributes to the field by offering a less invasive, real-time solution for monitoring high-risk driver behaviors and providing insights for further advancements in automated driver assistance technologies. Future directions include optimizing the system for microcontrollers with low power consumption and implementing alerts for high-risk states to further mitigate accident risks, as well as including a multi-modal fusion of data from different sources (Infrared Camera, and a Microphone) to increase emotion recognition accuracy, which leads to taking better control and initiating more efficient proactive interventions.
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Enhancing Wi-Fi Sensing Performance Using Advanced Deep Learning Approaches
(Saudi Digital Library, 2025) Alatawi, Lafi; Lin, Cai
Smart sensing technologies are increasingly integrated into our daily lives, creating demand for complementary sensing approaches that are unobtrusive, noninvasive, and capable of operating in complex indoor environments. Wi-Fi sensing has emerged as a promising solution as it leverages existing wireless infrastructure and relies on metrics such as Received Signal Strength (RSS) and Channel State Information (CSI) to capture spatial information and variations in signal propagation patterns. Existing methods can be broadly categorized as model-based or learning-based. Model-based approaches are interpretable but often limited by simplified assumptions, while learning-based approaches can capture complex features yet are hindered by noise, limited data, and environmental dynamics. To overcome these challenges and develop a more robust Wi-Fi sensing framework, it is necessary to both enhance the quality of CSI data and extract meaningful representations from it. In this thesis, we propose a simple yet effective Wi-Fi sensing method that combines Conditional Generative Adversarial Networks (CGANs) for CSI data augmentation with denoising and feature-extraction techniques based on Convolutional Neural Networks (CNNs). The proposed system increases the diversity of the training dataset, reduces the impact of noisy measurements, and improves the discriminative capability of learned features. Experimental evaluations demonstrate that our method achieves high recognition accuracy under limited-data conditions, surpasses baseline deep learning models, and maintains competitive performance on unseen data. These results indicate that integrating generative models with feature-enhanced learning-based techniques offers a promising direction for robust and practical Wi-Fi sensing applications.
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Cold chain logistics: analyse how Almarai company in Saudi Arabia manages temperature-sensitive logistics for dairy and food products
(Saudi Digital Library, 2025) Aljuaid, Muhannad; Agca, Ahmet
Background: This research aims to critically assess cold chain logistics for Almarai Company in managing their temperature-sensitive logistics for transporting food and dairy products in Saudi Arabia. Method: The research adopts a systematic literature review (SLR) guided by specific inclusion and exclusion criteria. Data from 33 relevant studies were analysed through thematic analysis to identify patterns, themes, and gaps, ensuring transparency, validity, and reliability throughout. Findings: Findings reveal cold chain logistics as essential for food safety and quality, influenced by infrastructure, workforce skills, technology adoption, climate, and regulations. Almarai faces high costs, infrastructural gaps, and technological limitations. Effective improvement measures include adopting IoT and RFID systems, workforce training, regulatory support, and infrastructure investment, ensuring sustainability and resilience. Recommendations: The study recommends Almarai enhance technological integration, particularly IoT-enabled monitoring, strengthen workforce training programs, and collaborate with government bodies to improve infrastructure and regulations. Strategic investment in sustainable and energy-efficient cold logistics is vital. Partnerships with logistics firms and adoption of green practices will strengthen operational resilience, compliance, and long-term competitiveness.
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Energy Efficient Sensing for Unsupervised Event Detection in Real-Time
(Saudi Digital Library, 2019) Bukhari, Abdulrahman; Hyoseung Kim
General-purpose sensing offers a flexible usage and a wide range of Internet of Things (IoT) applications deployment. In order to achieve a general-purpose sensing system that is suitable for IoT applications, several design aspects such as performance, efficiency and usability, must be taken into consideration. The work of this thesis is focusing on implementing an energy efficient general-purpose sensing system that is based on unsupervised learning techniques for events labeling and classification. The system clusters raw data collected from a variety of events, like microwave, kettle and faucet running, etc., for classification. During the training phase, the system computes sensing polling periods, based on the rate of change in classes, that are then feed into a dynamic scheduler implemented on the sensor board in order to reduce energy consumption. The system is deployed in a one-bedroom apartment for raw data collection, and system evaluation. The results show that the mean accuracy of event classification is 83%, and sensor data polling is reduced in average by 95%, which translates to 90% energy saving, compared to the fixed polling period in the state-of-art approach.
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Improving Occupant Behavior Understanding And Environmental Awareness In Smart Buildings
(Saudi Digital Library, 2024) Alsafery, Wael; Charith, Perera; Omer, Rana
This thesis explores the integration of sensor technologies and user-centered design to improve building environments, with a focus on enhancing resource efficiency, occupant satisfaction, and sustainable operations. While the study's experimental component was conducted in educational buildings, the findings and insights contribute broadly to various building types. The research employs a mixed-methods approach, combining sensor data analysis with qualitative insights from workshops and interviews to understand occupant behavior and space utilization. In the context of a newly constructed university building, sensor technology, supported by the SpaceSense toolkit, captured patterns of space usage and environmental conditions, while interactions with students and facility managers revealed preferences and operational needs. Follow-up interviews with facility managers further highlighted the effectiveness of sensor-based solutions in improving facility management and occupant well-being. A key innovation in this study is the EcoCube device, designed to bridge communication gaps between building occupants and facility managers. The EcoCube enables users to report issues, provide feedback, and engage in building management processes, fostering a collaborative approach. The findings reveal variations in satisfaction across different spaces and underscore the importance of adaptable, user-centered solutions to meet diverse needs. By integrating real-time environmental monitoring, occupancy data, and direct feedback mechanisms, this thesis demonstrates the potential of combining sensor technology with user-centered design to create responsive and sustainable building environments. Recommendations include refining communication channels, leveraging occupancy data for real-time decision-making, and advancing collaborative building management to support sustainable and user-friendly operations. Future work includes expanding environmental metrics to address a wider range of building types, refining communication technologies like the EcoCube for broader adoption, and employing advanced predictive analytics to enhance proactive facility management strategies.
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Internet of Things (IoT) in Smart Cities: Privacy and Security in Concerns in Saudi Arabia
(Saudi Digital Library, 2025) Alajmi, Abdullah; Ioan Petri
Internet of Things (IoT) technologies have developed at an extremely fast pace.They are drastically changing urban infrastructure and paving the way for smart cities currently being built across the globe.However, as IoT devices weave into the backbone of critical urban sys- tems, these advancements raise pressing privacy and security concerns. In this thesis, I invest- igate the issues of implementing IoT in Saudi Arabias smart cities according to the principle of privacy and security, as per Saudi Arabias vision 2030. Saudi Arabias ambitions for cities such as Riyadh, Jeddah, and NEOM as smart hubs will capitalise on IoT to optimise city func- tions, enhance service delivery, and enhance living standards. This research provides an under- standing of privacy and security vulnerabilities associated with these integrations, and presents capabilities for enabling secure IoT ecosystems through ontology based management and feder- ated learning (FL) technologies. This studys methodology encompasses four key phases. The first phase is an employee perspective case survey of IoT privacy and security in Saudi Arabian cities. The second phase uses a natural language processing (NLP)-based social media analysis to assess public sentiments of IoT-based smart city applications. The third phase is the devel- opment of an ontology for an IoT smart city (ISCO) to structure and manage key components of IoT. The last phase is an implementation of federated long short-term memory (FLSTM) to enhance security through decentralised and privacy preserving learning. The findings show the challenges of IoT adoption in Saudi Arabia related to security and privacy pose significant barriers to adoption because employees are concerned about data misuse and lack of security protocols. Additional sentiment analysis of social media further indicates public apprehension, particularly concerning the application of IoT in traffic monitoring, safety, and environmental monitoring. Using the proposed IoT Smart City ontology, IoT components, including devices, users, and data properties, can be categorised in a secure and privacy friendly fashion, and the management of risks and regulatory compliance are more efficient. The findings also show that the FLSTM model offers 10%20% better threat detection accuracy than centralised models and is capable of adapting to variations in distribution of data, a key requirement for managing IoT data in changing urban environments. For privacy and security issues in smart cities, this thesis provides a comprehensive approach that enables scalable, flexible, and privacy resilient frame- works. Future work will expand the ontology to encompass evolving IoT ecosystems and then explore advanced FL architectures. This research aims to secure robust security and user trust to support Saudi Arabia’s pursuit of smart city excellence, towards a sustainable, secure, and user driven urban environment.
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Cyber Physical Attacks and Detection MEthods in Water Distribution Systems
(The University of Alabama, 2025) Hameed Addeen, Hajar; Yang, Xaio
Modern technologies adopt Internet of Things (IoT) devices to increase water management efficiency and enhance water quality services. However, the limitations of IoT devices, such as small sizes and poor security, weaken the Water Distribution System (WDS) security and many attackers compromise the critical components of WDS. Cyber-physical attacks (CPAs) are considered one of the biggest challenges that decrease the security factors in WDS by disrupting normal operations and tampering with the critical data of the water system. Therefore, this dissertation proposes an anomaly detection method to detect cyber-physical attacks and mitigate their bad impacts on the components of WDS. First, we study the current state-of-art for the common cyber-physical attacks and common detection mechanisms for the WDS. Also, we compare CPA attacks and detection methods with emphasis on ideas, methods, evaluation results, advantages, and limitations. Second, we propose a deep learning model based on a conditional variational autoencoder (CVAE) to detect cyber-physical attacks. The CVAE model shows a highly effective way to maximize the chance of generating the desired output and detecting CPA attacks quickly. We also train CVAE on (BATADAL) real medium-sized water distribution dataset and demonstrate high-efficiency results. Experiment results indicate that our proposed method of CVAE can detect all the listed attacks with high accuracy and reduce false alarm issues. Then, we evaluate the proposed models’ performance using various metrics, including accuracy, precision, recall, and F1 score. In addition, we compare the CVAE model with existing models to detect CPA attacks, and the results show that we reach a high accuracy of 98%. Third, we designed an adversarial attack on our CVAE model to show the security risks of this attack and the negative impact on the model’s accuracy. We apply the Fast Sign gradient method to fool the CVAE model and predict the anomalies as normal data rather than anomalies. Then, we propose our novel defense approach, the CVAE defense model, to detect adversarial attacks. The CVAE defense model consists of adversarial detection and the CVAE defense models. The adversarial detection model adopts CNN and LSTM methods to classify data as adversarial or clean. The CVAE defense model takes the output of the adversarial detection model and passes it to our proposed noise generation method. After that, the noise generation method is produced and passed to the CVAE model and activation function. Finally, we calculate the Euclidean distance between the reconstructed output and input vectors and compare it to the threshold. If the output is less than the threshold, there is no attack. Otherwise, the output should be one, and there is an attack. The results show that our CVAE defense model can detect adversarial attacks and increase the performance to an overall 92%.
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Investigating IoT Adoption in the Face of Organizational Culture and Uncertainty Challenges: The Mediating Role of Information Behaviors on Communication Quality
(Saudi Digital Library, 2025-05-17) ALASMARI, AHMAD; Shabtai, Itamar
Abstract Investigating IoT Adoption in the Face of Organizational Culture and Uncertainty Challenges: The Mediating Role of Information Behaviors on Communication Quality By Ahmad Ali Alasmari Claremont Graduate University: 2025 The successful integration of Internet of Things (IoT) technologies in higher education institutions necessitates agile service deployment, transparent communication, and cultural congruence. Guided by Social Cognitive Theory (SCT) and Diffusion of Innovation (DOI) frameworks, this mixed-methods study examines the interconnections between High Uncertainty Avoidance, Organizational Culture, Information Availability, Information-Seeking Behavior, and IoT Service Agility and their collective impact on Communication Quality at the University of Tabuk in Saudi Arabia. Employing a sequential explanatory design, quantitative data from 373 survey respondents was augmented by qualitative insights from seven in-depth interviews with faculty, administrative staff, and IT management. Findings demonstrate that high uncertainty avoidance significantly diminishes IoT agility and adversely affects communication quality, while a supportive organizational culture enhances communication effectiveness. The relationships among cultural characteristics, IoT agility, and communication quality are mediated by proactive information behaviors, specifically information seeking and availability. The study provides practical strategies to overcome cultural and uncertainty-related barriers, including enhancing information transparency, implementing structured training programs, and fostering proactive leadership engagement. These insights offer valuable guidance for Saudi Arabian higher education institutions and similar contexts aiming to optimize IoT technology adoption.
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Hospital's Agility and Lean in International Supply Chain Management during COVID-19
(University of Exeter, 2024) Albadawi, Abdulrahman Ismail; Sam, Abraham John
This hypothesis stems from the assumption that even though KFSHRC had efficient strategies in place, the highly unforeseen nature of the pandemic illustrated the strategies that still needed to be improved. To do this, the study seeks to reject the following hypothesis: this research will analyze the employees' experiences and compare the efficiency of the approaches used in the hospital. The research aims to fill these gaps and limitations to evaluate KFSHRC precisely during the crisis response and recommend future improvements.
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IoT-Enhanced Vehicular Networks: Simulation Frameworks for Energy Efficiency and Cyber-Security in Smart Cities
(Newcastle University, 2025-05) Almutairi, Reham; Graham, Morgan; Giacomo, Bergami
The Internet of Things (IoT) has rapidly evolved over the past two decades, transforming the way we interact with the environment through a network of interconnected devices. The purpose of this thesis is to explore the integration of IoT with Vehicular Ad-Hoc Net- works (VANETs) in order to enhance intelligent transportation systems (ITS) and smart city infrastructure through the use of IoT. VANETs, characterized by high mobility and dynamic topology, play a crucial role in enhancing traffic safety, efficiency, and vehicu- lar services. They improve traffic safety by enabling real-time communication between vehicles and roadside infrastructure, allowing the sharing of critical information such as accident warnings and road conditions to prevent collisions and enhance emergency re- sponse times. VANETs boost traffic efficiency through intelligent traffic management, optimizing signal timings and route planning based on real-time data to reduce con- gestion and travel times. Additionally, they provide enhanced vehicular services such as infotainment, navigation assistance, and maintenance alerts, thereby improving the overall driving experience and vehicle performance monitoring. This research addresses the significant challenges of simulating VANET environments, particularly the high mobility of vehicles and the need for realistic traffic scenarios. Ex- isting VANET simulators, while advanced, often lack support for new technologies and comprehensive security systems, highlighting the necessity for more comprehensive sim- ulation frameworks. The primary aim of this PhD thesis is to integrate IoT and traffic simulations to accurately evaluate vehicular energy efficiency and overall network perfor- mance. Therefore, this thesis presents multilateral research towards optimization, mod- eling, and simulation of VANET and IoT environments. Several tools and algorithms have been proposed, implemented, and evaluated, considering various environments and applications.
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