SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted Enhancing Robustness and Energy Efficiency of IoT-Coupled Machine Learning Applications(University of Georgia, 2025) AlShehri, Yousef; Ramaswamy, LakshmishMachine learning (ML) applications continue to revolutionize many domains. In recent years, there has been considerable research interest in building novel ML applications for a variety of Internet of Things (IoT) domains, such as precision agriculture, smart cities, and smart manufacturing. However, IoT characteristics pose several fundamental challenges to designing and implementing effective ML applications at the edge, specifically, inference-time data incompleteness and quality induced by sensor failures and the energy management of such applications. This dissertation comprises four components aimed at addressing the aforementioned challenges to enhance IoT ML-based systems’ robustness, energy efficiency, and reliability against sensor failures at inference time. First, to tackle the sudden and concurrent missing data of sensors at the inference time of the ML application, it introduces an ensemble of a few sub-models, each trained with distinct subsets of sensors chosen based on correlation to make ML robust against missing data. This ensemble-based approach maintains the accuracy of the IoTMLapplication during the occurrence of missing data at inference time via performing predictions using sub-model(s) that are built using the correlated sensors to the faulty sensors, excluding the faulty sensors. The second component involves an extensive study to identify energy bottlenecks for operating our ensemble-based approach, SECOE, on a resource-constrained edge device and find the number of ensemble models that further maintain the ML application’s performance while optimizing energy consumption. The third component introduces an energy-aware ensemble of models with enhanced energy management capabilities for handling data incompleteness during inference time to fit resource-constrained IoT devices. Finally, our research offers an innovative autoencoder model for correcting inaccurate/faulty sensor readings caused by the malfunctioning of multiple sensors simultaneously at inference time. This model treats different faulty readings (e.g., drift and bias) as one type via a correlation-based masking mechanism. The experimental results have demonstrated that our methods have effectively alleviated the impact of IoT sensor failures on the performance of theMLapplication during the inference time while optimizing its energy consumption, thereby decisively outperforming existing methods and significantly improving the overall reliability of ML coupled IoT applications.14 0Item Restricted OPTIMIZING INTRUSION DETECTION IN IOT NETWORK ENVIRONMENTS THROUGH DIVERSE DETECTION TECHNIQUES(Florida Atlantic University, 2025-03-11) Al Hanif, Abdulelah; Ilyas, MohammadThe rapid proliferation of Internet of Things (IoT) environments has revolutionized numerous areas by facilitating connectivity, automation, and efficient data transfer. However, the widespread adoption of these devices poses significant security risks. This is primarily due to insufficient security measures within the devices and inherent weaknesses in several communication network protocols, such as the Message Queuing Telemetry Transport (MQTT) protocol. MQTT is recognized for its lightweight and efficient machine-to-machine communication characteristics in IoT environments. However, this flexibility also makes it susceptible to significant security vulnerabilities that can be exploited. It is necessary to counter and identify these risks and protect IoT network systems by developing effective intrusion detection systems (IDS) to detect attacks with high accuracy. This dissertation addresses these challenges through several vital contributions. The first approach concentrates on improving IoT traffic detection efficiency by utilizing a balanced binary MQTT dataset. This involves effective feature engineering to select the most important features and implementing appropriate machine learning methods to enhance security and identify attacks on MQTT traffic. This includes using various evaluation metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, demonstrating excellent performance in every metric. Moreover, another approach focuses on detecting specific attacks, such as DoS and brute force, through feature engineering to select the most important features. It applies supervised machine learning methods, including Random Forest, Decision Trees, k-Nearest Neighbors, and Xtreme Gradient Boosting, combined with ensemble classifiers such as stacking, voting, and bagging. This results in high detection accuracy, demonstrating its effectiveness in securing IoT networks within MQTT traffic. Additionally, the dissertation presents a real-time IDS for IoT attacks using the voting classifier ensemble technique within the spark framework, employing the real-time IoT 2022 dataset for model training and evaluation to classify network traffic as normal or abnormal. The voting classifier achieves exceptionally high accuracy in real-time, with a rapid detection time, underscoring its efficiency in detecting IoT attacks. Through the analysis of these approaches and their outcomes, the dissertation highlights the significance of employing machine learning techniques and demonstrates how advanced algorithms and metrics can enhance the security and detection efficiency of general IoT network traffic and MQTT protocol network traffic.29 0Item Restricted Efficient Deep Learning for Plant Disease Classification in Resource Constrained Environment(The University of Georgia, 2024) Alqahtani, Ola; Ramaswamy, LakshmishDeep Neural Networks (DNNs) have been widely used in today’s applications. In many applications such as video analytics, face recognition, computer vision, and classification problems like plant disease classification, etc. DNN models are constrained by efficiency constraints (e.g., latency). Many deep learning applications require low inference latency, which must fall within the parameters set by a service level objective. The prediction of the inference time of DNN models raises another problem which are the limited resources of Internet of Things devices. These devices need an effective way to run DNN models on them. One of the most widely discussed technological developments since the Internet of Things is edge machine learning (Edge ML), and with good reason. Edge Machine Learning is a fast-growing well-known technological improvement since the existence of the Internet of Things (IoT). Edge ML allows smart devices to use machine learning and deep learning techniques to analyze data using servers locally or at the device level, which reduces the need for cloud networks. This is caused by a variety of issues, including poor internet access, expensive cloud resources, low-resource edge devices, and a high failure rate of Internet of Things (IoT) devices, either because of battery or connection issues. Finding a way to effectively run the DNN models locally on IoT devices is crucial.34 0Item Restricted Detecting Makeup Activities using Internet-of-Things(University of Maryland Baltimore County, 2019-07-30) Alqurmti, Fatimah; Roy, NirmalyaThe make-up market is one of the most furnished fashion markets in product retailing and training demands. Each of the makeup activities involves very delicate movements of hands and requires good amount of training and practice for perfection. The available choices in the make-up training industry depends on practical workshops by professionalinstructors, and still evaluating the perfection of makeup activities lacks certainty. In this work, we introduced a novel application for human activity recognition using sensors’ data and a supervised machine learning approaches for rendering make-up activities. We considered five make-up activities in our work, such as, applying cream, lipsticks, blusher, eyeshadow, and mascara and collected data from ten participants. We built supervised make-up activity recognition using different predictive machine learning algorithms i.e. Naïve Bayes, Simple Logistic, k-nearest neighbors’, and the random forest algorithms. We investigated the models' performance for detecting five make-up activities with or without instructions. Our results show that shallow machine learning algorithms achieve up to 92% accuracy in detecting make-up activities.20 0Item Restricted COMPREHENSIVE SYSTEM ARCHITECTURE FOR HOUSEHOLD REPLENISHMENT SYSTEM: SIMULATION OPTIMIZATION FOR INVENTORY REPLENISHMENT POLICY CONSIDERING QUALITY DEGRADATION & STOCHASTIC DEMAND(Binghamton University, 2024) Almassar, Khaled; Khasawneh, MohammadFood wastage because of the lack or incompletion of a Household Replenishment System is an essential topic to be addressed. Appropriately using the Internet of Things (IoT) and Artificial Intelligence (AI) technologies with particular components is needed to design a smart Household Replenishment System to reduce food waste. This dissertation develops a unified framework and conceptual system architecture for the implementation of a Household Replenishment System, presents an object recognition framework to identify labeled and unlabeled items inside a smart refrigerator, and showcases a low-cost installation model for a smart refrigerator. It develops and validate a simulation optimization model of perishable items inside a smart refrigerator for an optimal replenishment policy. To accomplish those goals, this dissertation initially provides comprehensive analyses and a summary of the literature using IoT and AI tools for perishable items storage compartments, as they are always full of items that need to be monitored. This comprehensive research followed the PRISMA search strategy, which was conducted to point out the approaches, contributions, components used, and limitations of the reviewed papers in developing a unified framework for a household replenishment system. More specifically, 70 papers were examined in chronological order starting from 2000 when LG Electronics invented the first smart refrigerator, and research on technology involvement in food storage compartments increased. The analysis found 43 approaches using IoT technology, 27 using AI, and in the past couple of years, the use of AIoT has been trending. The future directions for researchers were acquired from the limitations of the reviewed papers, and they could enhance the household replenishment system by adding the features to smart food storage compartments v The comprehensive research helps fulfill an objective of this dissertation, system architecture framework The system architecture acts as a road map for developers to implement a Household Replenishment System. It sheds more light on one of the most important techniques of AI, object recognition. A framework of object recognition is developed. The developed object recognition provides insight into the type of information about the stored items that could be obtained by the Household Replenishment System. A practical example of a cost model is presented. The developed cost model minimizes the total installation cost of the smart refrigerator based on household preferences using linear programming, adoption of capital budgeting, and multidimensional knapsack problems. The object recognition framework presented is conceptual. Therefore, several assumptions were used to develop a simulation optimization model of the Household Replenishment System. The simulation optimization model uses discrete-event simulations and a periodic policy considering the review period, minimum stock level, and maximum stock level (T, s, S). The simulation optimization model finds the optimal replenishment policy to minimize the total Household Replenishment Systems’ inventory cost. The simulation optimization model considers holding, shortage, wastage, and order costs as components in the objective function, which accounts for stochastic demand, variation in life span, and quality degradation rates of stored items. The simulation optimization model was tested on single and multiple items, with different scenarios for the multipleitem cases. Experimental runs of the simulation optimization model were completed, validated, and analyzed. The design of the experiment and sensitivity analyses were applied. The simulation optimization model successfully generated a set of top five optimal replenishment policies for the household to choose from. Further investigation into smart home appliances would lead to extensive approaches like smart shops, vi industries, and eventually smart cities. Future work for this dissertation could be achieved by enlarging the scope of research to involve patents, dissertations, and theses that used Artificial Intelligence of Things (AIoT) technologies to improve the Household Replenishment System.22 0Item Restricted Understanding and Improving the Usability, Security, and Privacy of Smart Locks from the Perspective of the End User(University of North Carolina at Charlotte, 2024) Hazazi, Hussein; Shehab, MohamedOver the past two decades, the Internet of Things (IoT) has seen a significant expansion in both the sophistication and variety of its applications. These applications span several domains, including enhancing and automating services in healthcare, advancing smart manufacturing processes, and elevating home living standards through smart home technologies. These technologies empower individuals with greater control over their home appliances. Smart locks are smart home devices that were introduced as replacements for traditional locks. Smart locks, designed to go beyond the basic functionality of traditional locks by offering additional features, have seen a surge in market growth and competitiveness. According to the Statista Research Department, it is projected that the global market for smart locks will surpass four billion dollars by 2027. A number of studies have examined end users' concerns, needs, and expectations regarding smart homes in general. However, little research has been conducted to examine these aspects of the smart lock in particular. To address this gap, we conducted a series of user studies that aim to elucidate how smart locks are integrated and interact within smart home environments, focusing on user interactions both with the locks themselves and when they are part of broader automation scenarios. This dissertation contributes to a deeper understanding of smart lock technology from a user-centric viewpoint. It offers insights into user motivations, concerns, and preferences regarding smart lock usage and automation. It also highlights the importance of balancing convenience and security, the pivotal role of trust, and the complexities of integrating smart locks into broader smart home systems.34 0Item Restricted THE IMPACT OF POPULATION DENSITY AND CYBERSECURITY CHALLENGES IN SMART CITY CREATION(ProQuest, 2024-01-05) Bafail, Ghayda Abdullah; Schaeffer, DonnaWhile the population is growing at a rapid rate worldwide, many people are moving from rural areas to cities when general economic conditions change, looking for good opportunities, better jobs, education, easy life, and better infrastructure. The majority of the population is expected to live in smart cities over the next thirty years. This research contains two parts: quantitative and qualitative. The quantitative part measures the impact of population density on smart-city creation, analyzing thirty-nine countries that have invested in information, communication, and technology (ICT) and ICT goods export for twenty-one years to assess the relationship between population density and smart-city development. The qualitative part briefly discusses the collision of policy, privacy, and ethics in smart cities, which are a top priority in building and developing the smart city, and the main issues policymakers should address when designing smart cities with respect to cybersecurity issues.38 0Item Restricted ENHANCING IOT DEVICES SECURITY: ENSEMBLE LEARNING WITH CLASSICAL APPROACHES FOR INTRUSION DETECTION SYSTEM(Saudi Digital Library, 2023-11-15) Alotaibi, Yazeed; Ilyas, MohammadThe Internet of Things (IoT) refers to a network of interconnected nodes constantly engaged in communication, data exchange, and the utilization of various network protocols. Previous research has demonstrated that IoT devices are highly susceptible to cyber-attacks, posing a significant threat to data security. This vulnerability is primarily attributed to their susceptibility to exploitation and their resource constraints. To counter these threats, Intrusion Detection Systems (IDS) are employed. This study aims to contribute to the field by enhancing IDS detection efficiency through the integration of Ensemble Learning (EL) methods with traditional Machine Learning (ML) and deep learning (DL) models. To bolster IDS performance, we initially utilize a binary ML classification approach to classify IoT network traffic as either normal or abnormal, employing EL methods such as Stacking and Voting. Once this binary ML model exhibits high detection rates, we extend our approach by incorporating a ML multi-class framework to classify attack types. This further enhances IDS performance by implementing the same Ensemble Learning methods. Additionally, for further enhancement and evaluation of the intrusion detection system, we employ DL methods, leveraging deep learning techniques, ensemble feature v selections, and ensemble methods. Our DL approach is designed to classify IoT network traffic. This comprehensive approach encompasses various supervised ML, and DL algorithms with ensemble methods. The proposed models are trained on TON-IoT network traffic datasets. The ensemble approaches are evaluated using a comprehensive metrics and compared for their effectiveness in addressing this classification tasks. The ensemble classifiers achieved higher accuracy rates compared to individual models, a result attributed to the diversity of learning mechanisms and strengths harnessed through ensemble learning. By combining these strategies, we successfully improved prediction accuracy while minimizing classification errors. The outcomes of these methodologies underscore their potential to significantly enhance the effectiveness of the Intrusion Detection System.23 0