Saudi Cultural Missions Theses & Dissertations
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Item Restricted Enhancing Trust Modelling for the Internet of Underwater Things(University of Nottingham, 2025) Almutairi, Abeer; Furnell, Steven; Carpent, XavierThe Internet of Underwater Things (IoUT) has gained growing interest from researchers and industry alike, due to its potential for advancing the development of smart cities and underwater intelligent systems. However, the harsh and unpredictable nature of underwater environments, coupled with the inherent limitations of existing technologies, presents significant challenges to establishing a sustainable IoUT. Furthermore, the open nature of such networks renders them highly susceptible to malicious attacks and security threats. Traditional security measures, which are widely implemented in conventional cyber systems, exhibit severe performance constraints in underwater networks, highlighting the urgent need for novel security solutions that meet the unique requirements of underwater networks. Trust modelling has been widely recognised as an effective soft security measure to mitigate the impact of internal attacks. It primarily achieves this by analysing behavioural characteristics between network entities, thereby introducing a layer of defence against malicious activities. In the context of underwater networks, trust establishment between nodes has the potential to significantly enhance overall network security. However, existing Trust Modelling and Management (TMM) often fail to address the complexities of underwater environments, which necessitate new TMM that are lightweight, accurate, and decentralised. In light of these limitations, this thesis investigates and enhances TMM to meet the application requirements of underwater networks while addressing the specific challenges inherent to IoUT. The central research question addressed in this thesis is: To what extent can existing TMM accommodate diverse network topologies within the IoUT and effectively mitigate potential attacks from both the communication and physical domains. In order to answer this question, a comprehensive understanding of the key challenges and potential application requirements for underwater networks is required. To facilitate this investigation, a simulated environment is constructed to analyse the effectiveness of TMM. This study critically evaluates the capabilities of current TMM in detecting malicious activities across various underwater network structures, identifying vulnerabilities, and exposing potential attack vectors. In response to these findings, this thesis proposes a distributed multi-dimensional TMM, referred to as the Mobility-Aware Trust Model (MATMU), designed to enhance the detection of malicious behaviour within the constraints of underwater environments. MATMU expands the metric domain to include mobility-aware metrics, allowing for the assessment of similarities and differences in node movement patterns. Additionally, the model employs a dynamic weighting strategy that integrates metrics from both the communication and physical domains. The performance of MATMU is evaluated through extensive simulations conducted across various underwater scenarios and attack models. The results demonstrate that MATMU effectively mitigates malicious behaviour, exhibiting notable improvements over benchmark models, particularly in terms of faster convergence and enhanced attack detection. These findings underscore the suitability of MATMU for strengthening secure and reliable communication in underwater networks. This thesis also tackles the critical issue of dishonest recommendations within TMM in the IoUT context, which is introduced by malicious entities, aiming to manipulate trust computations by providing false or misleading recommendations, thereby degrading the reliability and stability of the TMM. A novel recommendation evaluation method is introduced, combining filtering and weighting strategies to more effectively detect dishonest recommendations. The proposed model incorporates an outlier detection-based filtering technique and deviation analysis to evaluate recommendations based on both collective outcomes and individual experiences. Furthermore, a belief function is employed to refine recommendations by assigning weights based on criteria such as freshness, similarity, trustworthiness, and trust decay over time. This multi-dimensional approach demonstrates a marked improvement in recommendation evaluation, effectively capturing deceptive behaviours that exploit the complexities of IoUT. The effectiveness of the model is validated through extensive simulations and comparative analyses with existing trust evaluation methods, demonstrating consistently high performance across varying proportions of dishonest recommendations, with the highest accuracy improvement observed when dishonest recommendations constitute up to 45% of the total recommendations. These findings underscore the model’s potential to significantly enhance the reliability and security of IoUT networks.19 0Item Restricted Measuring Human’s Trust in Robots in Real-time During Human-Robot Interaction(Swansea University, 2025) Alzahrani, Abdullah Saad; Muneeb, Imtiaz AhmadThis thesis presents a novel, holistic framework for understanding, measuring, and optimising human trust in robots, integrating cultural factors, mathematical modelling, physiological indicators, and behavioural analysis to establish foundational methodologies for trust-aware robotic systems. Through this comprehensive approach, we address the critical challenge of trust calibration in human-robot interaction (HRI) across diverse contexts. Trust is essential for effective HRI, impacting user acceptance, safety, and overall task performance in both collaborative and competitive settings. This thesis investigated a multi-faceted approach to understanding, modelling, and optimising human trust in robots across various HRI contexts. First, we explored cultural and contextual differences in trust, conducting cross-cultural studies in Saudi Arabia and the United Kingdom. Findings showed that trust factors such as controllability, usability, and risk perception vary significantly across cultures and HRI scenarios, highlighting the need for flexible, adaptive trust models that can accommodate these dynamics. Building on these cultural insights as a critical dimension of our holistic trust framework, we developed a mathematical model that emulates the layered framework of trust (initial, situational, and learned) to estimate trust in real-time. Experimental validation through repeated interactions demonstrated the model's ability to dynamically calibrate trust with both trust perception scores (TPS) and interaction sessions serving as significant predictors. This model showed promise for adaptive HRI systems capable of responding to evolving trust states. To further enhance our comprehensive trust measurement approach, this thesis explored physiological behaviours (PBs) as objective indicators. By using electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), eye blinking rate (BR), and blinking duration (BD), we showed that specific PBs (HR, SKT) vary between trust and distrust states and can effectively predict trust levels in real-time. Extending this approach, we compared PB data across competitive and collaborative contexts and employed incremental transfer learning to improve predictive accuracy across different interaction settings. Recognising the potential of less intrusive trust indicators, we also examined vocal and non-vocal cues—such as pitch, speech rate, facial expressions, and blend shapes—as complementary measures of trust. Results indicated that these cues can reliably assess current trust states in real-time and predict trust development in subsequent interactions, with trust-related behaviours evolving over time in repeated HRI sessions. Our comprehensive analysis demonstrated that integrating these expressive behaviours provides quantifiable measurements for capturing trust, establishing them as reliable metrics within real-time assessment frameworks. As the final component of our integrated trust framework, this thesis explored reinforcement learning (RL) for trust optimisation in simulated environments. Integrating our trust model into an RL framework, we demonstrated that dynamically calibrated trust can enhance task performance and reduce the risks of both under and over-reliance on robotic systems. Together, these multifaceted contributions advance a holistic understanding of trust measurement and calibration in HRI, encompassing cultural insights, mathematical modelling, physiological and expressive behaviour analysis, and adaptive control. This integrated approach establishes foundational methodologies for developing trust-aware robots capable of enhancing collaborative outcomes and fostering sustained user trust in real-world applications. The framework presented in this thesis represents a significant advancement in creating robotic systems that can dynamically adapt to human trust states across diverse contexts and interaction scenarios.18 0Item Restricted ACCEPTANCE OF BLOCKCHAIN TECHNOLOGY BY HIGHER EDUCATION INSTITUTIONS IN THE KINGDOM OF SAUDI ARABIA(Aston University, 2025) Alhumayzi, Mohammed; Batista, Luciano; Benson, VladlenaThe increasing adoption of new technologies in the Higher Education Institutions (HEIs) sector highlights the importance of exploring blockchain acceptance among employees. Adopting blockchain, as an emerging technology, is likely to encounter resistance among employees. This study explores this issue. Specifically, this study aims to determine the drivers and hindrances of blockchain acceptance among employees in the HEIs industry. To address this aim, this study proposes a framework that extends the unified theory of acceptance and use of technology (UTAUT) with individual characteristics. The framework represents factors that explain blockchain acceptance. The identified elements are performance expectancy (PE), effort expectancy (EX), social influence (SI), facilitating conditions (FC), trust, perceived security (SEC) and awareness (AW). The relationships among these factors were examined based on a quantitative approach, where an online questionnaire was employed to collect data from administrative and academic staff working for HEIs in the Kingdom of Saudi Arabia (KSA). Partial Least Squares Structural Equation Model (PLS-SEM) and Multi-group Analysis (MGA) techniques were employed to analyse 394 responses. The findings of this study revealed the direct drivers and hindrances of blockchain acceptance, i.e., PE, FC, SEC and AW. Additionally, this study demonstrated the moderating effects of AW between FC and blockchain acceptance. Furthermore, it determined specific indirect effects of the EX, trust and SEC on blockchain acceptance. Moreover, this research detected significant differences between categories’ subsamples and identified the significant factors of blockchain acceptance per subsample. Finally, this research identified where blockchain needs to be adopted most within the HEIs, i.e., financial exchange, certificate management, and students’ assessment areas. The HEIs industry in KSA could use these findings to develop concise strategies that encourage the adoption of blockchain. Scholars might also employ the proposed framework to investigate the adoption of blockchain.32 0Item Restricted Employing communication for building learned trust in autonomous vehicles, A qualitative pilot study.(Politecnico Di Milano, 2024) Alharbi, Eilaf; Borghetti, Fabio; Bianchini, BeatriceAs autonomous vehicles (AVs) advance towards full automation, trust between passengers and the automated systems becomes a critical factor to their existence. This research presents a pilot study on developing a design framework to help build and maintain a dynamic learned trust during interactions with fully autonomous vehicles by exploring various information types, structures, and communication modalities. The study draws on insights from previous research, semi-structured interviews with 6 users and 3 experts to identify key design elements that influence trust. Key findings suggest that minimizing driving-related information and instead focusing on journey-related details can prevent passengers from feeling the need to monitor the vehicle’s decisions, thereby fostering trust. The concept of "Information on Demand" emerged as a valuable approach to balance transparency and personalization, allowing passengers to request specific information whenever is needed. Additionally, "technical explanations" were identified as effective in restoring trust when errors occur, emphasizing the importance of timely and clear communication. The research also highlighted the limited impact of non-driving tasks, such as entertainment on trust. Furthermore, communication modalities should be tailored to the type of information being conveyed, taking into account various risks and the passengers’ ability to process different communication methods. This pilot study’s results lay the foundations for a larger scale study aiming to examine various factors that influence the dynamic learned trust during the interaction with the automated system in the vehicles.27 0Item Restricted Factors driving Mobile Shopping Apps Loyalty in the UK(University of East Anglia, 2024-08-22) Alkhairallah, Khulud; Alotaibi, DalalThe widespread adoption of digital technologies has substantially transformed many aspects of daily life, especially retail and consumer behaviour. A significant increase in online shopping through mobile applications is anticipated in the UK due to the growing popularity of smartphones, a trend known as mobile shopping (M-shopping) or mobile commerce (M-commerce). This dissertation investigates the factors influencing customer loyalty in M-shopping apps, an area that has not been extensively explored in existing literature. This study, which focuses on the UK market, looks at how user loyalty in mobile shopping applications is affected by four factors namely service quality, perceived usefulness, perceived ease of use, and trust. A questionnaire survey was conducted and 201 valid responses were received. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyse data and to test the research hypotheses. The analysis revealed that all four factors have significant direct influence on M-Shopping loyalty and they account for 61.1% of the variation in mobile shopping loyalty in the UK. The study offers practical insights for companies looking to increase customer loyalty in the ever-changing digital marketplace. It pinpoints strategies for improving these factors. The findings also advance our knowledge of how changing digital interactions and customer expectations affect loyalty in mobile shopping.24 0Item Restricted Leveraging Blockchain for Trust Enhancement in Decentralized Marketplaces: A Reputation System Perspective(Old Dominion University, 2024-07) Aljohani, Meshari; Olariu, Stephan; Mukkamala, RaviCentralized marketplaces provide reliable reputation services through a central authority, but this raises concerns about single points of failure, user privacy, and data security. Decentralized marketplaces have emerged to address these issues by enhancing user privacy and transparency and eliminating single points of failure. However, decentralized marketplaces face the challenge of maintaining user trust without a centralized authority. Current blockchain-based marketplaces rely on subjective buyer feedback. Additionally, the transparency in these systems can deter honest reviews due to fear of seller retaliation. To address these issues, we propose a trust and reputation system using blockchain and smart contracts. Our system replaces unreliable buyer feedback with objective transaction assessments. Performance challenges of blockchain-based systems are tackled through three innovative schemes, resulting in a substantial improvement over the baseline approach. Furthermore, we proposed a decentralized marketplace utilizing blockchain-based smart contracts to address privacy concerns in buyer reviews that arise from the transparency of decentralized marketplaces. This enables buyers to use one-time identities for reviews to promote anonymity. This system ensures that buyers provide reviews by requiring a review fee, which is fully refunded after the review is submitted. Moreover, we proposed a trust and reputation service based on Laplace’s Law of Succession, where trust in a seller is defined as the subjective probability that they will fulfill their contractual obligations in the next transaction. This method accommodates multi-segment marketplaces and time-varying seller performance, predicts trust and reputation far into the future, and discounts older reputation scores. In addition, we propose SmartReview, an automated review system utilizing blockchain smart contracts to generate objective, bias-free reviews. The review module is designed as a smart contract that takes the contract terms and the evidence provided by the buyer and seller as inputs. It employs advanced computer vision and machine learning techniques to produce quantitative and qualitative reviews for each transaction, ensuring objectivity and eliminating reviewer bias. Lastly, we introduce a structured blockchain architecture featuring a layered approach. This architecture includes mechanisms for secure transaction recording and efficient query retrieval through auxiliary indexing, demonstrating significant advancements in decentralized data management.28 0Item Restricted COMPARATIVE OVERVIEW ON THE PROCEEDINGS OF TRANSFERRING OWNERSHIP OF PROPERTY AT DEATH BETWEEN U.S. AND SAUDI ARABIA.(Wake Forest University, 2024-04-26) Alolayan, Mohammed Hammoud; Meazell, ChristopherWhat has been established in the field of rights is that its only source is law. It is the decision for the reasons that create the right and the reasons that acquire and transfer rights. Rights, as is known, vary and diverge. The right may be in kind, original or consequential. The right may be personal. One of the most important original rights in kind is the right of ownership. This right is the way to acquire and obtain it legally. The right of ownership may be directed to a movable or real estate. If ownership is based on a property, the legislator is required in the process of acquiring this property, or transferring it in several stages, until the real estate is transferred from one person to another because the property has a status It is important and vital in the field of social and economic development. Real estate is an important source of profit for the state treasury, through which the tax imposed on the transactions of individuals in their real estate is obtained. Therefore, we find that the legislator has distinguished real estate with several advantages. The most important feature of real estate is to require the legislator in the process of transferring its ownership to a series of stages, starting to document it until its registration, and the process of making it famous. Because this topic is one of the most important topics that should be taken care of and given a great deal of importance as it relates to the property of individuals, whether real or personal property, and because it is one of the most disputed topics, especially after death, specifically in the event of non-division, donation, or even guardianship, in addition to the inadequate qualifications of judges, poor outputs, and the lack of specialized persons in this field, the legislator has decided to make some changes to the current law in order The second portion of the paper offers a historical analysis of how ownership of real estate in the Kingdom is passed down from one generation to the next after a person's passing. Saudi Arabia, first, to describe the Saudi Constitution, and then, regarding this topic, to review the perspectives of the four different schools; In the third segment, we will talk about the transfer of ownership of real estate after death by will in both the Saudi law and the American law. We will compare and contrast the two legal laws with regard to the similarities and differences that exist in this area. This paper will come to a close with the fourth portion.41 0Item Restricted Intelligent Context-aware Fog Node Discovery and Trust-based Fog Node Selection(University of Technology Sydney, 2024) Bukhari, Afnan; Hussain, Farookh KhadeerIn today’s highly advanced technological age, edge devices are widely used. By 2030, Cisco predicts that more than 500 billion edge devices (also known in this research as fog consumers) will be in use [1]. Data from all these devices may experience significant delays when handled, processed and stored through cloud computing. To resolve this issue, fog computing is the best solution. With fog computing, processing, storage, and networking are brought to the edge of the network near fog consumers. This reduces latency, network bandwidth, and response times. Researchers have yet to address the critical challenge of identifying and selecting a reliable and relevant fog node to fog consumers. The existing approaches consider the discovery and selection of fog nodes based on the networking point of view. However, no approach addresses the use of AI-driven mechanisms for intelligent fog node discovery and selection. This research aims to propose an intelligent and distributed framework for context-aware fog node discovery and trust-based fog node selection. This research aims to discover the closest fog nodes in a context-aware manner and select a reliable fog node based on the trust value. The proposed approach is based on the distributed Fog Registry Consortium (FRC) between fog consumers and fog nodes that can facilitate the discovery and selection processes of fog nodes. To ensure that the tasks from the fog consumer are processed in a timely manner, one of the crucial aspects to consider for fog node discovery is the geographic distance between the fog node and the fog consumer as this directly impacts latency, response time, and bandwidth usage for fog consumers. Thus, location-based context awareness is one of the key decision criteria for fog node discovery to ensure that the QoS metrics are satisfied. In this research, we propose the Fog Node Discovery Engine (FNDE) within the Distributed Fog Registry (DFR), within FRC, as an intelligent and distributed fog discovery mechanism which enables a fog consumer to intelligently discover fog nodes in a context-aware manner. In this research, the KNN, K-d tree and brute force algorithms are used to discover fog nodes based on the location-based context-aware criteria of fog consumers and fog nodes. Fog node selection is a crucial aspect in the development of a fog computing system. It forms the foundation for other techniques such as resource allocation, task delegation, load balancing, and service placement. Fog consumers have the task of choosing the most suitable and reliable fog node(s) from the available options, based on specific criteria. This research presents the intelligent and reliable Fog Node Selection Engine (FNSE), which is an intelligent method to assist fog consumers to select appropriate and reliable fog nodes in a trustworthy manner. This intelligent mechanism predicts the trust value of fog nodes to help the user select a reliable fog node based on its trust value. Our selection approach is based on the trust value of the fog node based on the values of the QoS factors. If the fog node has historical information of the QoS factors provided to this fog node, then the Trust Evaluation Engine (TEE) in the FNSE is responsible to carry out the prediction of the trust value. With the trust value of fog nodes, the FNSE will be able to rank the fog node to select the most reliable fog node in the network. We propose three mechanisms: the TEE mechanism based on fuzzy logic, the TEE mechanism based on logistic regression, and the TEE mechanism based on a deep neural network. However, if the QoS values of the fog node are unknown, this means the FNSE is unable to make a meaningful selection of fog nodes. To solve the problem of the cold-start fog node, we propose the Bootstrapping Engine (BE) which is an intelligent trust-based fog node bootstrapping framework. This framework is designed to address the cold-start problem in fog computing environments which enables fog consumers to make informed and trustworthy decisions when selecting fog nodes for their applications. To address this challenge, the BE employs two key modules, namely the QoS prediction module and the reputation prediction module. The QoS prediction module utilizes the k-means clustering and KNN algorithms to predict the initial QoS values of new cold-start fog nodes. Additionally, within the reputation prediction module, we propose three AI methods to achieve the best performance and prediction results, namely fuzzy logic-based reputation prediction, regression-based reputation prediction, and deep learning-based reputation prediction to predict and evaluate the trust value of the new cold-start fog nodes. Finally, we present the simulation of the framework and the evaluation results of each proposed engine which highlight the best performance.26 0Item Restricted Extending the Technology Acceptance Model: Exploring Trust and Perceived Risk in the Adoption of Virtual Assistants within the Context of Saudi Arabia(Nottingham University, 2023-12-13) Altamimi, Bashaer; Muaid, ReemThis research examined, in depth, the factors that impact users' willingness to utilise virtual assistant apps in the Kingdom of Saudi Arabia. The analysis included trust and perceived risk as essential elements, expanding on the technology acceptance model. Drawing from the perspectives of 281 virtual assistant application users in Saudi Arabia, the results of the study confirmed and underscored the significant, positive roles played by both the technology acceptance model and user confidence in shaping users’ propensity to embrace virtual assistant applications. In addition, the study revealed that different dimensions of perceived risk, spanning performance, as well as psychological risks, privacy risks and security risks did not exert any noteworthy influence on the adoption of virtual assistant applications. These findings provide valuable strategic insights for virtual assistant companies, aiding them in devising strategies to attract users and efficiently allocating resources for customer growth and retention. Furthermore, this investigation presents empirical evidence that enhances the theoretical comprehension of users' intents to embrace virtual assistant apps, specifically in the context of Saudi Arabia.21 0Item Restricted Evaluation of the Effectiveness of Media Policy in Protecting Social Media Users in Saudi Arabia from Hate Speech and Discriminatory Content: Transparency, Awareness, Trust, and Future Vision(Saudi Digital Library, 2023-08) Alghannam, Hussain Ali; Boyle, RaymondThis groundbreaking study delves into the effectiveness of media policies in Saudi Arabia in combating hate speech and discriminatory content on social media platforms. Through a comprehensive exploration of Saudi users' perspectives, this research measures exposure levels, evaluates awareness and trust in state-enacted policies, and gauges users' optimism for future developments. The integration of quantitative and qualitative methods provides a nuanced understanding of the data. Results indicate relatively low exposure to harmful content, with 66.48% reporting no encounter, yet 33.52% experienced such content. Qualitative insights reveal a consensus on defining hate speech, aligning with global perspectives. Awareness of policies is high but calls for intensified education emerged, emphasising the correlation between awareness and trust. Remarkably, 88.26% express confidence in Saudi media policies. Optimism about future policy development is widespread, with over 90% expressing positivity. Recommendations for future research include broader inclusion of stakeholders, comparative studies with other cultures, and exploring the dynamics of trust and awareness. This study contributes to media management research, emphasising the importance of effective policies in creating respectful and protective digital spaces. Despite its Saudi focus, the study's implications transcend borders, advocating for global collaboration in mitigating harmful online content.107 0