Saudi Cultural Missions Theses & Dissertations

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The Application of IoT in Predictive Maintenance for Railway Systems: A Systematic Literature Review
(University of Nottingham, 2024-09) Alghefari, Abdulrahman; Chesney, Thomas
This research explores the implementation of IoT-based predictive maintenance within railway systems, focusing on the technologies, cost implications, reliability, safety, and barriers identified in the literature. The study systematically reviews 30 peer-reviewed journals to assess the current state of IoT applications in the railway sector. Critical IoT technologies such as sensors, wireless sensor systems, and edge processing are examined in their role in enhancing predictive maintenance practices. The research highlights significant long-term cost savings associated with IoT adoption, despite high initial implementation costs. Furthermore, the study evaluates how IoT technologies contribute to improved reliability and safety by enabling real-time monitoring and predictive analysis. However, several barriers to widespread adoption are identified, including technical integration challenges, financial constraints, regulatory hurdles, and organisational resistance. The findings underscore the need for a strategic approach that will help tackle all obstacles by realising the benefits of IoT-predictive maintenance in the railway sector. This study offers significant insights for stakeholders, offering a deep understanding of the challenges of IoT-based predictive maintenance in railways. Future research directions are suggested, emphasising the importance of long-term studies, holistic approaches, and the integration of emerging technologies to address the identified barriers.
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Simplifying IoT Application Deployment in Edge Computing Environment
(newcastle university, 2019) Daghistan, iIsmail; Ranjan, Rajiv
Currently, deploying IoT Application in Edge Computing Environment is facing many challenges. For instance, the limited capabilities of Edge device alongside distributed nature of its Environment where all Edge devices needs to be programmed separately. As a result, edge Environment deployment tools needs to be lightweight and able to orchestrate the deployment effectively. Therefore. I decided to deploy Edge Computing Environment by using docker. Docker proved to be the most suitable tool. This is because Docker is lightweight and ability to run on the Edge . Then, I decided to simplify the deployment by using scripting, to allow the deployment to take place in on device rather than programming several devices. Finally, I will use REST api to capture the user’s requirements to generate and run a script based the that requirements. This will the user to deploy the Edge Environment with a single URL request.
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A Simulation Framework for Evaluating the Performance of Blockchain-based IoT Ecosystems
(Newcastle University, 2024-09-05) Albshri, Adel; Solaiman, Ellis
Recently, it has been appealing to integrate Blockchain with IoT in several domains, such as healthcare and smart cities. This integration facilitates the decentralized processing of IoT data, enhancing cybersecurity by ensuring data integrity, preventing tampering, and strengthening privacy through decentralized trust mechanisms and resilient security measures. These features create a secure and reliable environment, mitigating potential cyber threats while ensuring non-repudiation and higher availability. However, Blockchain performance is questionable when handling massive data sets generated by complex and heterogeneous IoT applications. Thus, whether the Blockchain performance meets expectations will significantly influence the overall viability of integration. Therefore, it is crucial to evaluate the feasibility of integrating IoT and Blockchain and examine the technology readiness level before the production stage. This thesis addresses this matter by extensively investigating approaches to the performance evaluation of Blockchain-based IoT solutions. Firstly, it systematically reviews existing Blockchain simulators and identifies their strengths and limitations. Secondly, due to the lack of existing blockchain simulators specifically tailored for IoT, this thesis contributes a novel blockchain-based IoT simulator which enables investigation of blockchain performance based on adaptable design configuration choices of IoT infrastructure. The simulator benefits from lessons learnt about the strengths and limitations of existing works and considers various design requirements and views collected through questioners and focus groups of domain experts. Third, the thesis recognises the shortcomings of blockchain simulators, such as support for smart contracts. Therefore, it contributes a middleware that leverages IoT simulators to benchmark real blockchain platforms' performance, namely Hyperledger Fabric. It resolves challenges related to integrating distinctive environments: simulated IoT models with real Blockchain ecosystems. Lastly, this thesis employs Machine Learning (ML) techniques for predicting blockchain performance based on predetermined configurations. Contrariwise, it also utilises ML techniques to recommend the optimal configurations for achieving the desired level of blockchain performance.
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INTO THE DIGITAL ABYSS: EXPLORING THE DEPTHS OF DATA COLLECTED BY IOT DEVICES
(Johns Hopkins University, 2024-02-22) Almogbil, Atheer; Rubin, Aviel
The proliferation of interconnected smart devices, once ordinary household appliances, has led to an exponential increase in sensitive data collection and transmission. The security and privacy of IoT devices, however, have lagged behind their rapid deployment, creating vulnerabilities that can be exploited by malicious actors. While security attacks on IoT devices have garnered attention, privacy implications often go unnoticed, exposing users to potential risks without their awareness. Our research contributes to a deeper understanding of user privacy concerns and implications caused by data collection within the vast landscape of the Internet of Things (IoT). We uncover the true extent of data accessible to adversarial individuals and propose a solution to ensure data privacy in precarious situations. We provide valuable insights, paving the way for a more informed and comprehensive approach to studying, addressing, and raising awareness about privacy issues within the evolving landscape of smart home environments.
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Testing Privacy and Security of Voice Interface Applications in the IoT Era
(Temple University, 2024-04-04) Shafei, Hassan Ali; Tan, Chiu C.
Voice User Interfaces (VUI) are rapidly gaining popularity, revolutionizing user interaction with technology through the widespread adoption in devices such as desktop computers, smartphones, and smart home assistants, thanks to significant advancements in voice recognition and processing technologies. Over a hundred million users now utilize these devices daily, and smart home assistants have been sold in massive numbers, owing to their ease and convenience in controlling a diverse range of smart devices within the home IoT environment through the power of voice, such as controlling lights, heating systems, and setting timers and alarms. VUI enables users to interact with IoT technology and issue a wide range of commands across various services using their voice, bypassing traditional input methods like keyboards or touchscreens. With ease, users can inquire in natural language about the weather, stock market, and online shopping and access various other types of general information. However, as VUI becomes more integrated into our daily lives, it brings to the forefront issues related to security, privacy, and usability. Concerns such as the unauthorized collection of user data, the potential for recording private conversations, and challenges in accurately recognizing and executing commands across diverse accents, leading to misinterpretations and unintended actions, underscore the need for more robust methods to test and evaluate VUI services. In this dissertation, we delve into voice interface testing, evaluation for privacy and security associated with VUI applications, assessment of the proficiency of VUI in handling diverse accents, and investigation into access control in multi-user environments. We first study the privacy violations of the VUI ecosystem. We introduced the definition of the VUI ecosystem, where users must connect the voice apps to corresponding services and mobile apps to function properly. The ecosystem can also involve multiple voice apps developed by the same third-party developers. We explore the prevalence of voice apps with corresponding services in the VUI ecosystem, assessing the landscape of privacy compliance among Alexa voice apps and their companion services. We developed a testing framework for this ecosystem. We present the first study conducted on the Alexa ecosystem, specifically focusing on voice apps with account linking. Our designed framework analyzes both the privacy policies of these voice apps and their companion services or the privacy policies of multiple voice apps published by the same developers. Using machine learning techniques, the framework automatically extracts data types related to data collection and sharing from these privacy policies, allowing for a comprehensive comparison. Next, researchers studied the voice apps' behavior to conduct privacy violation assessments. An interaction approach with voice apps is needed to extract the behavior where pre-defined utterances are input into the simulator to simulate user interaction. The set of pre-defined utterances is extracted from the skill's web page on the skill store. However, the accuracy of the testing analysis depends on the quality of the extracted utterances. An utterance or interaction that was not captured by the extraction process will not be detected, leading to inaccurate privacy assessment. Therefore, we revisited the utterance extraction techniques used by prior works to study the skill's behavior for privacy violations. We focused on analyzing the effectiveness and limitations of existing utterance extraction techniques. We proposed a new technique that improved prior work extraction techniques by utilizing the union of these techniques and human interaction. Our proposed technique makes use of a small set of human interactions to record all missing utterances, then expands that to test a more extensive set of voice apps. We also conducted testing on VUI with various accents to study by designing a testing framework that can evaluate VUI on different accents to assess how well VUI implemented in smart speakers caters to a diverse population. Recruiting individuals with different accents and instructing them to interact with the smart speaker while adhering to specific scripts is difficult. Thus, we proposed a framework known as AudioAcc, which facilitates evaluating VUI performance across diverse accents using YouTube videos. Our framework uses a filtering algorithm to ensure that the extracted spoken words used in constructing these composite commands closely resemble natural speech patterns. Our framework is scalable; we conducted an extensive examination of the VUI performance across a wide range of accents, encompassing both professional and amateur speakers. Additionally, we introduced a new metric called Consistency of Results (COR) to complement the standard Word Error Rate (WER) metric employed for assessing ASR systems. This metric enables developers to investigate and rewrite skill code based on the consistency of results, enhancing overall WER performance. Moreover, we looked into a special case related to the access control of VUI in multi-user environments. We proposed a framework for automated testing to explore the access control weaknesses to determine whether the accessible data is of consequence. We used the framework to assess the effectiveness of voice access control mechanisms within multi-user environments. Thus, we show that the convenience of using voice systems poses privacy risks as the user's sensitive data becomes accessible. We identify two significant flaws within the access control mechanisms proposed by the voice system, which can exploit the user's private data. These findings underscore the need for enhanced privacy safeguards and improved access control systems within online shopping. We also offer recommendations to mitigate risks associated with unauthorized access, shedding light on securing the user's private data within the voice systems.
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Adaptive Cyber Security for Smart Home Systems
(Howard University, 2024-04-29) Alsabilah, Nasser; Rawat, Danda B.
Throughout the recent decade, smart homes have made an enormous expansion around the world among residential customers; hence the most intimate place for people becomes connected to cyberspace. This environment attracts more hackers because of the amount and nature of data.Furthermore, most of the new technologies suffer from difficulties such as afford the proper level of security for their users.Therefore, the cybersecurity in smart homes is becoming increas- ingly a real concern for many reasons, and the conventional security methods are not effective in the smart home environment as well. The consequences of cyber attacks’ impact in this environment exceed direct users to society in some cases. Thus, from a historical perspective, many examples of cybersecurity breaches were reported within smart homes to either gain information from con- nected smart devices or exploit smart home devices within botnet networks to execute Distributed Denial of Service (DDoS) as well as others.Therefore, there is an insistent demand to detect these malicious attacks targeting smart homes to protect security and privacy.This dissertation presents a comprehensive approach to address these challenges, leveraging insights from energy consumption and network traffic analysis to enhance cybersecurity in smart home environments.The first objec- tive of this research focuses on estimating vulnerability indices of smart devices within smart home systems using energy consumption data. Through sophisticated methodology based on Kalman filter and Shapiro-Wilk test, this objective provides estimating for the vulnerability indices of smart devices in smart home system. Building upon the understanding that energy consumption is greatly affected by network traffic based on many empirical observations that have revealed alterations in the energy consumption and network behavior of compromised devices, the subsequent objectives as complementary endeavors to the first objective delve into the development of adaptive technique for cyber-attack detection and cyber-behavior prediction using Rough Set Theory combined with XGBoost. These objectives aim to detect and predict cyber threats, thus enhancing the overall security posture of smart home systems.
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LIGHTWEIGHT MUTUAL AUTHENTICATION PROTOCOLS FOR IOT SYSTEMS
(University of Maryland Baltimore County, 2024) Alkanhal, Mona; Younis, Mohamed
The Internet of Things (IoT) refers to the large-scale internetworking of diverse devices, many of them with very limited computational resources. Given the ad-hoc formation of the network and dynamic membership of nodes, device authentication is critical to prevent malicious devices from joining the network and impersonating legitimate nodes. The most popular authentication strategy in the literature is to pursue asymmetric cryptography. Such a solution is costly in terms of computing resources and power consumption and thus is not suitable for IoT devices which are often resource constrained. Moreover, due to the autonomous nature of the IoT nodes, relying on an intermediary server to manage the authentication process induces overhead and consequently decreases the network efficacy. Thus, the authentication process should be geared for nodes that operate autonomously. This dissertation opts to fulfill the aforementioned requirements by developing a library of lightweight authentication protocols that caterers for variant IoT applications. We consider a hardware-based security primitive, namely Physical Unclonable Functions (PUFs). A PUF benefits from the random and uncontrollable variations experienced during the manufacturing of integrated circuits in constructing a device signature that uniquely maps input bits, referred to as challenge, into an output bit(s) that reflects the PUF response. A fundamental issue with distributed authentication using PUFs is that the challenge-response exchange is among IoT nodes rather than the secure server and hence becomes subject to increased vulnerability to attacks. Particularly, eavesdroppers could intercept the inter-node interactions to collect sufficient challenge-response pairs (CRPs) for modeling the underlying PUF using machine learning (ML) techniques. Obfuscating the challenge and response through encryption is not practical since it requires network-wide management of secret keys and diminishes the advantages of PUFs. The dissertation tackles the aforementioned challenges. We first develop a novel authentication mechanism that is based on the incorporation of a PUF in each device. Our mechanism enables the challenge bit string intended by a verifier δy to be inferred by a prover δx rather than being explicitly sent. The proposed mechanism also obfuscates the shared information to safeguard it from eavesdroppers who strive to model the underlying PUF using machine learning techniques. Secondly, we further combine the advantage of PUFs, and the agility and configurability of physical-layer communication mechanisms, specifically the Multi-Input Multi Output (MIMO) method. We devise a protocol that utilizes an innovative method to counter attackers who might intercept the communication between δy and δx and uncover a set of CRPs to model δx’s PUF. Our protocol encodes the challenge bit using MIMO antennas array in a manner that is controlled by the verifier and that varies overtime. Additionally, we derive a two-factors authentication protocol by associating a Radio Frequency (RF) fingerprint with PUF. Such a unique combination obviates the need for traditional identification methods that rely on key storage for authentication. This identification mechanism enables the protocol to obfuscate the PUF response, circumventing the need for the incorporation of cryptographic primitives. Since both the PUF and the RF-fingerprint are based on unintended variations caused by manufacturing, we aim to increase robustness and mitigate the potential effect of noise by applying the fuzzy extractor. Such a protocol does not retain CRPs of a node during the enrollment phase, nor does it incorporate a cryptosystem. All the aforementioned techniques enable mutual authentication of two devices without the involvement of a trusted third party. The experimental results demonstrate the efficacy of the proposed protocols against modeling attacks and impersonation attempts.
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DUAL ENERGY MANAGEMENT AND ENERGY SAVING MODEL FOR THE INTERNET OF THINGS (IOT) USING SOLAR ENERGY HARVESTING (SEH)
(University of Arizona, 2024-01-10) Albalawi, Nasser; Rozenblit, Jerzy W
The Internet of Things (IoT) is a fast-growing internet technology and has been incorporated into a wide range of fields. The optimal design of IoT systems has several challenges. The energy consumption of the devices is one of these IoT challenges, particularly for open-air IoT applications. The major energy consumption takes place due to inefficient medium access and routing, which can be addressed by the energy-efficient clustering method. In addition, the energy harvesting method can also play a major role in increasing the overall lifetime of the network. Therefore, in the proposed work, a novel energy-efficient dual energy management and saving model is proposed to manage the energy consumption of IoT networks. This model is based on dual technologies, i.e., energy-efficient clustering and solar energy harvesting (SEH). The proposed method is implemented for high-density sensor network applications. The dual elbow method is used for efficient clustering and guaranteed QoS. The model is able to manage energy consumption and increase the IoT network’s overall lifetime by optimizing IoT devices’ energy consumption. The protocol was simulated in MATLAB and compared to Fuzzy C-Means (FCM) and Time Division Multiple Access scheduling (TDMA) based Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols based on network lifetime
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Smart Home Cybersecurity Challenges: An Assessment of End-User Knowledge and a Training Solution to Mitigate these Challenges.
(Saudi Digital Library, 2023-11-22) Nusair, Ali; Chipidza, Wallace
As the digital revolution unfolds, individuals are increasingly transforming their traditional homes into smart homes, adopting semi- and fully automated smart devices. This transformative shift, fueled by advancements in information technology, presents vast social and economic opportunities. Despite the burgeoning number of smart devices in the market, a surge in smart home adoption has concurrently given rise to profound security challenges. Predominantly, end-users, often possessing rudimentary knowledge of associated risks, remain vulnerable to breaches of their privacy and security. Given that smart devices, interconnected and internet-enabled, relay substantial data, they are attractive targets for hackers. One fundamental reason for these challenges is the end-users' lack of requisite knowledge to safeguard their smart homes. To address these challenges, there's a pressing need for effective knowledge dissemination. This dissertation introduces two artifacts: a training framework detailing smart home vulnerabilities and best practices for cybersecurity, and an application named "Smart Home Security App". This application prompts users to update their passwords biannually and continuously monitors for potential security breaches. Drawing from an extensive literature review, the two artifacts were developed. To evaluate the framework's effectiveness, a set of 34 survey questions was crafted, reflecting key cybersecurity knowledge areas. Fifteen participants, after providing written consent, responded to these questions. Their initial responses informed the development of the first artifact, and post-training, the same questions were administered. Notably, there was a marked enhancement in the participants' understanding of smart home security post-training. Leveraging the Design Science Research methodology, the artifact's efficacy as a consumer training tool was assessed. Keywords: Smart home, IoT, vulnerabilities, smart devices, cybersecurity, hacking, social engineering, identity theft, Smart Home Security App.
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Acceptance of Internet of Things-based Innovations for Improving Healthcare in Saudi Arabia
(Saudi Digital Library, 2023-09-27) Masmali, Feisal; Miah, Shah
The rapid evolution of Information Systems (IS) has ushered in a new era of digital transformation, with the Internet of Things (IoT) at the forefront. This research delves into the acceptance and integration of IoT-oriented IS applications in the healthcare sector of Saudi Arabia. With the primary objective of understanding the factors influencing the acceptance of IoT innovations in healthcare, this study offers a comprehensive analysis to capture the influence in terms of the potential benefits, challenges, and implications of integrating IoT into healthcare service delivery. Impact of IoT in IS, a paradigm shift in technology, enables essential objects equipped with sensors and software systems to communicate and exchange data over the Internet. This capability has revolutionized various industries, with healthcare being a notable beneficiary. The global healthcare sector has witnessed a surge in the acceptance of IoT innovations, particularly in remote public-healthcare atmospheres where patient monitoring and management are of paramount task. Thus, studying the impact of IoT may bring new insights for both researchers and practitioners that could contribute to create new knowledge in the target body of IS literature. In the country context of Saudi Arabia, the integration of IoT in healthcare systems presents a plethora of opportunities. The Saudi Arabian government has been proactive in endorsing the adoption of IoT technologies, establishing regulations, and fostering public-private partnerships to drive innovation. With the country's robust Internet infrastructure and government-backed initiatives like electronic health records and telehealth services, the potential for the IoT influence in relation to acceptance and integration in healthcare systems practices is immense, however existing studies are limited in this sub-field. A comprehensive study towards full-scale of IoT oriented system adoption in healthcare holds a lot of emerging challenges. This is related to data protection, privacy, infrastructure limitations, compliances and cybersecurity are of critical elements for further exploration. Additionally, issues like interoperability, financial constraints, and talent shortages need to be addressed to ensure seamless integration. Thus, it is imperative to develop new systematic empirical study in this subfield. This research adopts a mixed-methods approach, combining qualitative interviews with quantitative surveys, to investigate the aspects through a holistic viewpoint for developing new understanding to fill the significant knowledge gap in the IS literature. The qualitative phase, conducted with key stakeholders in Saudi Arabia's healthcare sector, offers in-depth insights into the current landscape and potential future trajectories. These findings then inform the quantitative phase, which aims to capture broader trends of IoT acceptance within the sector. These findings are consistent with a combined aspect of the Technology Acceptance Model (TAM) and the Diffusion of Innovation Model (DOI), which suggest that successful adoption and integration of IoT into healthcare systems requires knowledge, familiarity, and recognition of the usage of IoT. The PhD thesis is based on the combined theoretical underpinnings of Information Systems (IS) design, technology acceptance, and diffusion of innovations. In terms of technology acceptance, our study concentrates on the acceptance of Internet of Things-oriented IS applications and various service provisions, especially innovative ones, that practitioners in the healthcare sector can deploy and in terms of the diffusion of innovations it brings the practitioners view on the cutting-edge application of IoT in the aspect of IS design. That is why, the study's theoretical foundation is rooted in the combined framework of the Technology Acceptance Model (TAM) and the Diffusion of Innovation (DOI). This integrated model offers a comprehensive perspective on the multifaceted nature of technology acceptance, encompassing both individual perceptions and broader organizational influences. Key findings from the research highlight the pivotal role of knowledge and awareness in the successful implementation of IoT. The perceived benefits of IoT, such as enhanced patient care, improved communication, and streamlined processes, act as strong motivators for adoption. However, challenges related to system compatibility, training requirements, and financial constraints emerge as potential barriers. The thesis underscores the need for targeted interventions, strategic planning, and resource allocation to overcome these challenges. In summary, the acceptance of IoT in Saudi Arabia's healthcare sector holds the promise of transformative change. While the benefits are manifold, a strategic approach, underpinned by a deep understanding of the challenges, is crucial. This research contributes significantly to the literature on IoT adoption in healthcare, offering valuable insights of new knowledge that may be used as recommendations for policymakers, healthcare professionals, and researchers. The findings serve as a beacon for future endeavours aimed at harnessing the power of IoT for a healthier and more efficient healthcare system design in Saudi Arabia.
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