Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
Browse
20 results
Search Results
Item Restricted Hospital's Agility and Lean in International Supply Chain Management during COVID-19(University of Exeter, 2024) Albadawi, Abdulrahman Ismail; Sam, Abraham JohnThis 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.6 0Item Restricted IoT-Enhanced Vehicular Networks: Simulation Frameworks for Energy Efficiency and Cyber-Security in Smart Cities(Newcastle University, 2025-05) Almutairi, Reham; Graham, Morgan; Giacomo, BergamiThe 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.15 0Item Restricted Deciphering Hand Movement Patterns During Driving Using Smartwatch Signals Without Ground Truth(University of Houston, 2025-02-07) Alghamdi, Huda; Pavlidis, IoannisWe developed a method to identify atypical hand movements in driving, some of which are associated with detachment from the steering wheel and, thus, physical distraction. We performed our data analysis on NUBI { a naturalistic dataset collected from a week-long observation of n=57 Texas drivers. NUBI features data from over 900 trips with a total duration of over 300 hours. Due to a lack of visual ground truth, we employed unsupervised learning methods. Thanks to the GPS data to our avail, we used information about the type of road (highway or city street) and the type of segment (straight or turn) to narrow our search space. In more detail, we extracted features from the drivers' smartwatch motion signals using Temporal Convolutional Autoencoder (TCNAE). Then, we fed these encoded features into a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN produced a main cluster and the remainder. The remainder is consistently associated with behaviors in turns and other atypical scenarios, such as queuing to pick up orders from fast-food dispensing windows. The characteristics of these atypical patterns are so distinct from the typical driving patterns (main cluster) that a random forest classi cation algorithm attained 99% area under the curve (AUC) performance in a ve-fold cross-validation test. Based on the kinematic constraints of the driver's hands, we developed a physics-based formula that associates elbow angles with gravitational acceleration values. We estimated the gravitational acceleration values that correspond to hand detachment from the steering wheel (i.e., extreme elbow angles). Applying these thresholds to the NUBI dataset, we found that such steering wheel detachment values arise just outside the dispensing windows of fast-food chains, where the drivers must pick up their orders. This nding validates our estimation method. In all, our approach not only nds atypical hand-motion patterns in driving but also pinpoints among these atypical patterns the patterns that involve hand detachment from the steering wheel. The latter are associated with physical distractions and crash risk under certain conditions. Notably, our approach achieves all these from smartwatch signals alone without any need to resort to visual ground truth from camera feeds. Given the ubiquity of smartwatches and the unavailability of cameras in car interiors, the practical implications of this development cannot be overestimated.23 0Item Restricted Decoding real world LR-FHSS signals: design, implementation, and approaching the theoretical limit.(Florida State University, 2025-03-26) Bukhari, Jumana; Zhang, ZhenghaoLong Range-Frequency Hopping Spread Spectrum (LR-FHSS) is a new physical layer option added to the LoRa family, promising higher network capacity than the previous versions of LoRa. Since the announcement, LR-FHSS has gathered growing interest. Various studies have attempted to evaluate and enhance its communication range and network capacity, while others have compared its performance with previous version of LoRa. However, the actual network capacity of LR-FHSS and the effectiveness of proposed methods remain unknown, as most existing studies rely on mathematical analysis or simulations with certain simplifying assumptions. Our goal is to reveal the actual capacity of LR-FHSS and develop methods to enhance its performance while evaluating these methods in a setting as close to real-world conditions as possible. In this dissertation, we design and implement a software LR-FHSS receiver from scratch to convert the baseband waveform into bits and pass the Cyclic Redundancy Check (CRC). To the best of our knowledge, this work is the first of its kind that processes signals transmitted by an actual LR-FHSS device while accounting for real-world issues such as frequency estimation errors. Also, we design customized methods to enhance receiver's capacity, including error correction decoding and Successive Interference Cancellation (SIC), which were not mentioned in earlier studies but effectively handle collisions. Furthermore, we develop an analytical bound for the theoretical capacity of LR-FHSS networks. The evaluation of our receiver was based on real-world packet traces collected using an LR-FHSS device and demonstrated through real-world experiments on the POWDER wireless platform, in addition to trace-driven simulations for large networks. Our result shows that LR-FHSS outperforms the previous version of LoRa, meets expectations in communication range, achieves significantly higher network capacity than those reported earlier, and confirms that the capacity of our software receiver is approaching the upper bound of LR-FHSS networks. We overcame a number of challenges such as lack of documentation of LR-FHSS and open-source resources, header acquisition, match reconstructed waveform with the received waveform under heavy collisions, and find a good approximation of the residual power of packets that have been decoded and canceled.7 0Item Restricted The Application of IoT in Predictive Maintenance for Railway Systems: A Systematic Literature Review(University of Nottingham, 2024-09) Alghefari, Abdulrahman; Chesney, ThomasThis 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.16 0Item Restricted A Simulation Framework for Evaluating the Performance of Blockchain-based IoT Ecosystems(Newcastle University, 2024-09-05) Albshri, Adel; Solaiman, EllisRecently, 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.69 0Item Restricted INTO THE DIGITAL ABYSS: EXPLORING THE DEPTHS OF DATA COLLECTED BY IOT DEVICES(Johns Hopkins University, 2024-02-22) Almogbil, Atheer; Rubin, AvielThe 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.15 0Item Restricted 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.31 0Item Restricted 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.14 0Item Restricted LIGHTWEIGHT MUTUAL AUTHENTICATION PROTOCOLS FOR IOT SYSTEMS(University of Maryland Baltimore County, 2024) Alkanhal, Mona; Younis, MohamedThe 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.18 0