Internet Of Things Framework Research Articles

A Review on Internet of Things (IoT)-Related Disabilities and Their Implications

The transformative potential of the Internet of Things (IoT) extends to the lives of approximately one billion individuals worldwide living with disabilities, representing around 15% of the global population. This article highlights the importance of addressing challenges related to disability inclusivity and support, recognizing their significant impact on individuals and the global community. Despite inadequate support services, the IoT emerges as a beacon of hope, offering innovative solutions to empower people with disabilities. By integrating smart devices and technologies, the IoT can disrupt the cycle of dependence on families, thereby fostering economic activity and social inclusion. Introducing a proposed IoT framework elucidates its pivotal role in enhancing the quality of life for individuals with disabilities and facilitating their active engagement in societal and economic spheres. The article explores diverse application scenarios to underscore how the IoT can meet the unique needs of this demographic. Furthermore, it addresses critical challenges that must be acknowledged and overcome to implement IoT-based solutions in this context successfully. This study offers a comprehensive overview of IoT solutions tailored to mitigate the challenges encountered by individuals grappling with visual impairments.

QAKA: A novel quantum authentication and key agreement (QAKA) protocol using quantum entanglement for secure communication among IoT devices

AbstractThe constant evolution of the Internet of Things (IoT) concept involves the idea that all elements in the global network communication are reachable without the need for human intervention. In this context, the paramount concern is security, aiming to safeguard IoT communication against unauthorized access. According to the analysis, the security of the IoT communication framework relies on traditional cryptographic schemes like the RSA algorithm. Nevertheless, the latest developments in quantum computing and information processing pose a significant threat to these established schemes. Motivated by this fact, in this article, we proposed a novel quantum authentication and key agreement (QAKA) protocol that provides unconditional security against any classical and futuristic quantum threats. Our protocol ensures secure key agreement and mutual authentication based on quantum hashing with quantum passwords and quantum key distribution. The proposed scheme utilizes quantum teleportation and Greenberger–Horne–Zeilinger states for secure data transfer among entities. Our work is simulated using the Automated Validation of Internet Security Protocols and Applications tool to prove our protocol's formal correctness and security. The Burrows, Abadi, and Needham logic is applied to prove the goal of our protocol. Random oracle model for formal security analysis is presented in our work. Our protocol is also informally analyzed, considering the characteristics such as correctness, security against repudiation and the impossibility of cloning. The proposed protocol is compared with the related protocols in terms of various security features such as replay attacks, man‐in‐middle attacks and futuristic quantum attacks. Finally, the performance of the proposed protocol is presented, revealing superior efficacy when compared with classical authentication schemes and quantum protocols.

Intrusion Detection System based on Chaotic Opposition for IoT Network

The rapid advancement of network technologies and protocols has fueled the widespread endorsement of the Internet of Things (IoT) in numerous domains, including everyday life, healthcare, industries, agriculture, and more. However, this rapid growth has also given rise to numerous security concerns within IoT systems. Consequently, privacy and security have become paramount issues in the IoT framework. Due to the heterogeneous data produced by smart IoT devices, traditional intrusion detection system doesn't work well with IoT system. The massive volume of heterogeneous data has several irrelevant, redundant, and unnecessary features which lead to high computation time and low accuracy of IDS. Therefore, to tackle these challenges, this paper presents a novel metaheuristic-based IDS model for the IoT systems. The chaotic opposition-based Harris Hawk optimization (CO-IHHO) algorithm is used to perform the feature selection of data traffic. The chosen features are subsequently inputted into a machine learning (ML) classifier to detect network traffic intrusions. The performance of the CO-IHHO based IDS model is verified against the BoT-IoT dataset. Experimental findings reveal that CO-IHHO-DT achieves the maximal accuracy of 99.65% for multiclass classification and 100% for binary classification, and minimal computation time of 31.34 sec for multiclass classification and 133.54 sec for binary classification.

ECMT Framework for Internet of Things: An Integrative Approach Employing In-Memory Attribute Examination and Sophisticated Neural Network Architectures in Conjunction With Hybridized Machine Learning Methodologies

ECMT Framework for Internet of Things: An Integrative Approach Employing In-Memory Attribute Examination and Sophisticated Neural Network Architectures in Conjunction With Hybridized Machine Learning Methodologies

Non-Terrestrial Networks for Energy-Efficient Connectivity of Remote IoT Devices in the 6G Era: A Survey.

The Internet of Things (IoT) is gaining popularity and market share, driven by its ability to connect devices and systems that were previously siloed, enabling new applications and services in a cost-efficient manner. Thus, the IoT fuels societal transformation and enables groundbreaking innovations like autonomous transport, robotic assistance, and remote healthcare solutions. However, when considering the Internet of Remote Things (IoRT), which refers to the expansion of IoT in remote and geographically isolated areas where neither terrestrial nor cellular networks are available, internet connectivity becomes a challenging issue. Non-Terrestrial Networks (NTNs) are increasingly gaining popularity as a solution to provide connectivity in remote areas due to the growing integration of satellites and Unmanned Aerial Vehicles (UAVs) with cellular networks. In this survey, we provide the technological framework for NTNs and Remote IoT, followed by a classification of the most recent scientific research on NTN-based IoRT systems. Therefore, we provide a comprehensive overview of the current state of research in IoRT and identify emerging research areas with high potential. In conclusion, we present and discuss 3GPP's roadmap for NTN standardization, which aims to establish an energy-efficient IoRT environment in the 6G era.

A Novel Cloud-Based IoT Framework for Secure Health Monitoring

The growing use of Internet of Things (IoT) technologies in a variety of sectors, including healthcare, has opened up new possibilities for gathering and analyzing patient data. In some cases, the elderly are exposed to significant risk and even mortality as a result of the global aging problem, which has become a burden in recent years. Numerous IoT devices are being created to monitor, track, and record the actions of the elderly to reduce these hazards. This study proposed a novel, dependable, cloud-based remote system patient monitoring framework for IoT health detection. The main distinguished part of this research is that we rarely can find a framework in the literature that is based on real-time systems by considering heartbeat (BPM), blood oxygen (SpO2), and body temperature at the same time. The implementation and testing of this real-time system is classified into six distinctly separated phases for developing both the hardware and software. To verify the performance of the suggested system, data were gathered from BOT-IoT datasets. The outcome enhances patient satisfaction, secure data transmission, and healthcare outcomes by showing that the proposed framework is more efficient than other compared protocols in terms of the decision time, which is 16.3 seconds for 46 features, with 100% accuracy.

A Review on Intrusion Detection in IoT Networks: Existing Challenges

Advancements in computer networks has led to the inter-connectivity of different types of smart devices over the internet. Such a diversified connected network is often termed as internet of things or IoT. Off late, an ancillary of the IoT framework called the fogging or fog computing has gained tremendous prominence. . Fog computing decentralizes the infrastructure without depending on centralizing it, such as with cloud computing. Fog computing is a paradigm proposed that integrates the IoT and the cloud concept to support user mobility, low latency, and location awareness. Due to the de- centralized nature of the Fog architecture, the sharing of data among different smart devices is susceptible to security threats. In this paper, a comprehensive review on fog computing and the allied performance metrics such as coverage, error rate and throughput have been discussed. Moreover, a channel load sensing techniques utilizing the channel state information (CSI) has also been proposed with the aim to enhance the throughput and error rate of the system. Keywords: Internet of Things (IoT), Fog Computing, End Device, Error Rate, Throughput, Channel State Information.

IoT-BIM and blockchain integration for enhanced data traceability in offsite manufacturing

The transformation of dynamic supply chains within the realm of offsite manufacturing presents a unique and compelling opportunity to effectively respond to shifts in market demand. However, this paradigm shift also entails novel challenges and prospects for streamlining processes, augmenting efficiency, and ultimately driving success across production workflows and logistical operations. Dynamic supply chains necessitate unimpeded access to high-quality, dependable data to facilitate real-time, data-driven decision-making. Furthermore, owing to their inherently intricate nature, these supply chains are subject to a heightened vulnerability to disruptions and delays, surpassing those encountered in traditional supply chains. Against this backdrop, this paper proposes a framework that synthesises the convergence of the Internet of Things (IoT), Building Information Modelling (BIM), and blockchain technology (BCT) into a cohesive and integrated system, thereby optimising the efficiency and transparency of supply chain management operations. The proposed framework takes cognisance of the challenges inherent in ensuring information traceability, mitigating fragmented information, enhancing interoperability, and fortifying reliability throughout the design, production, and implementation phases of assets within dynamic supply chains operating in the offsite manufacturing domain. To illustrate the efficacy of our proposition, we present a framework that exemplifies the seamless integration of a permissioned, open-source distributed ledger, which forms the bedrock of our support infrastructure for IoT and BIM integration.Through rigorous analysis, we showcase the effectiveness of the developed framework in the form of a designed Supply Chain Management Model (SCMM-PDO), which harmonises the physical, digital, and operational layers within a robust system architecture (BCT.IoT-BIM). The integration of these technologies provides a decentralised solution, reinforcing supply chains with improved security, reliability, and quality control mechanisms, thus enhancing the efficiency of the supply chain network. It is anticipated that this transformative integration will yield substantial improvements in key performance indicators (KPIs) such as operational efficiency, cost reduction, and the cultivation of trust and accountability across supply chain activities.The research proposes an IoT and BIM conceptual framework that simulates the dynamic supply chain operations, facilitating real-time analysis and prediction of supply chain participants' behaviours from resources, processes, and assets. Furthermore, the integration of IoT-BIM (digital twinning) with a blockchain-based system, such as Hyperledger Fabric, engenders a secure and transparent framework for the diligent tracking of assets and information throughout the supply chain. The findings of this study offer an all-encompassing conceptual framework for the secure and transparent tracking of asset information by integrating Blockchain-based technologies with IoT and BIM. This research endeavour thus expands the existing body of knowledge, establishing a foundation for future investigations within this proliferating field.

Intelligent Intrusion Detection System for Internet of Things using Machine Learning Techniques

The Internet of Things (IoT) is an interconnected framework of computer devices, software, and sensors to work collectively to automate and monitor different tasks and procedures without human intervention. IoTs provide benefits and ease in performing operations for various industries and home and health stakeholders. Still, it faces security threats and challenges that make the IoT framework unreliable regarding security and privacy. Intrusion detection systems have been designed and deployed to overcome these security challenges. Over the past few years, for making the IDS intelligent artificial intelligence has been integrated with them. Besides this, different feature selection techniques are also being used to make the datasets appropriate for Machine Learning (ML) classifiers. This research uses a filter-based feature selection technique to select the important features from two datasets, UNSW-NB15 and NSL-KDD. After that, datasets with selected features are given to the classifiers as input. This work also evaluates different ML classifiers such as Multiple Layers Perceptron (MLP), CatBoost, Light Gradient Boosting (LGBM), Extra-Tree Classifier (ETC), Random Forest (RF), and K-nearest neighbor (KNN). Different metrics, Accuracy, Precision, Recall, F1, Detection Rate, and FAR, have been used for performance analysis. Evaluation results proved the LGBM classifier effective among other classifiers with 98.70% accuracy on the UNSW-NB15 dataset and 99.8% on the NSL-KDD dataset after feature selection with mean absolute difference (MAD) feature selection technique. Results are also compared with existing models to provide the efficiency of the proposed model.

Lightweight authenticated key management mechanism for enhanced data transmission in the Internet of Things framework

The Internet of Things (IoT) system makes it possible for different gadgets to connect to each other and send and receive data without any problems. But it’s not easy to make sure that the data being sent and received within this structure is safe and correct. A Lightweight Authenticated Key Management Mechanism (LAKMM) is suggested as a way to solve this problem. The goal of LAKMM is to improve the security of data transfer in IoT settings while reducing processing waste and resource use, which are important factors for IoT devices that don’t have a lot of resources. LAKMM’s most important parts are its fast key creation, sharing, and identification systems that are designed to work in IoT settings. LAKMM finds a good mix between security and speed by using small cryptographic primitives and speeded-up protocols. It also protects against common attacks like eavesdropping, hacking, and repeat attacks, making sure that the data being sent is kept private and secure. LAKMM also has features for dynamic key changes and revocations, which are important for keeping things safe in IoT settings where devices are always joining and leaving the network. We show that LAKMM works well at offering strong security with little extra work for computers by simulating and analyzing it. So, LAKMM looks like a good way to improve the security of data transfer in the IoT framework.

IoT Educational Framework Case Study: Devices as Things for Hands-on Collaboration

Abstract— As IoT continues to reshape industries and daily life, our research revolves around cultivating a generation of IoTliterate individuals capable of addressing today’s challenges. In the Internet of Things (IoT) education, it is crucial to provide students with the ability to work with the technology and apply it to real-world problems. This creates a demand for teaching methodologies compatible with newly explored networked learning activities, for example, Challenge-Based Education (CBE). This study explores the IoTempower Framework, a versatile pedagogical tool dedicated to enhancing IoT CBE education. Its open-source nature and teaching support make it accessible and adaptable for IoT courses that support hands-on learning and teaching practices. This paper investigates IoTempower Framework's educational applications by analyzing three higher-education study cases where it was deployed. It explores the framework with its associated devices as tools for experiential collective and critical learning. Through a survey of students and participatory observations, the paper evaluates the framework's impact on fostering collaborative engagement in project-driven courses. It introduces the concept of devices as social-material entities or design things and how this approach to device development can aid collaborative teaching and learning experiences. The paper underscores IoTempower Framework as a social-material tool that is significant in shaping effective IoT education by highlighting the interplay between technical and social aspects of its devices as pedagogical tools. Keywords— Internet of Things; Education; IoT Framework; Devices; Design Things; Collaboration.

A decentralised federated learning scheme for heterogeneous devices in cognitive IoT

Cognitive Internet of Things (IoT) technologies typically rely on substantial data collected from edge devices for data analysis and decision-making. However this reliance often leads to the inadvertent exposure of private data from smart edge devices. Federated learning (FL) is a distributed machine learning framework that protects user privacy by performing collaborative training without uploading private data. Nevertheless, applying classical FL to cognitive IoT systems to preserve privacy preservation faces significant challenges, such as central server failure and communication burden. Furthermore, when edge devices with heterogeneous data and systems participate in federated training, the learning process becomes slow and the performance of edge devices is compromised. To address these challenges, we propose a decentralised FL framework for cognitive IoT, termed DFL–MKF. In DFL– MKF, the centralised server is eliminated and each edge device is dynamically connected. We initialised models for edge devices based on computational and storage capabilities to accommodate system heterogeneity. Edge devices learned from the knowledge of multiple neighbours via knowledge transfer, and knowledge fusion was employed to aggregate the knowledge of multiple neighbours, thereby improving the performance of local models, and addressing data heterogeneity. Comprehensive experiments were performed on three image classification tasks. The results of these experiments demonstrate that the proposed method achieved superior performance compared to various baselines and improved communication efficiency.

Neural Networks Based Smart E-Health Application for the Prediction of Tuberculosis Using Serverless Computing.

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serless-based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments.

Resilience and Sustainability plants improvement through Maintenance 4.0: IoT, Digital Twin and CPS framework and implementation roadmap

The Fouth Industrial Revolution is the new innovative wave that touches any sector, bringing great innovation. As the benefits that Industry 4.0 can create are very high, it is mandatory to deeply investigate it and elaborate full applications. Digital technologies require to be faced with a correct pace, beginning with a small pilot, able to generate quick and valuable results. A domain in which Industry 4.0 can bring a great and positive effect is Maintenance, with predictive and prescriptive maintenance. In this research, it is analyzed how Internet of Things (IoT), Digital Twins (DT) and Cyber Physical System (CPS) can enhance maintenance and how they can be integrated to create the Maintenance 4.0 framework, with a clear focus on Resilience and environmental Sustainability, applied with a 6 steps roadmap based on the “starting small” authors’ approach and initial assumption.

Investigation of Machine Learning Analytic-Based Data Classification Frameworks on Iot-Based Infrastructures

The Internet of Things revolutionizes and enables convenient, automated lifestyles for people today. Over the past ten years, advances in computer, connectivity, and application design have led to its development. The Internet of Things has quickly spread around the world, impacting every person on the planet. Everyday IoT devices, such as smartphones, Google Home assistants, smart vehicles, and automated systems in buildings, including smart elevators and temperature control, as well as drones for environmental monitoring and leisure, play crucial roles in our daily lives. Therefore, the purpose of this research is to explore data classification frameworks based on machine learning analytics in Internet of Things-based infrastructures. This study focuses on articles published between 2010 and 2023, utilizing a comprehensive literature review approach. The study entails reading through at least eighty peer-reviewed conference proceedings, industry white papers, and journal articles. An overview of the relationship between machine learning and the Internet of Things (IoT) is given in this study. The approach consists of four standard processes that are used in review papers: gathering articles from Thomson Reuters Web of Science, identifying categories, carrying out descriptive analysis, and assessing the information acquired. A discussion on the categorization of diverse analytics methods for IoT is presented in the paper. Moreover, it introduces and discusses the architectural Framework for IoT and the challenges in IoT Data Classification. Furthermore, Machine Learning Strategies for Identifying and Classifying IoT were presented. Researchers must investigate machine learning analytic-based data classification frameworks for Internet of Things infrastructures. The challenges arising from the unique characteristics of IoT data call for innovative solutions, and current frameworks demonstrate promising results.

A machine learning based framework for IoT devices identification using web traffic.

Identification of the Internet of Things (IoT) devices has become an essential part of network management to secure the privacy of smart homes and offices. With its wide adoption in the current era, IoT has facilitated the modern age in many ways. However, such proliferation also has associated privacy and data security risks. In the case of smart homes and smart offices, unknown IoT devices increase vulnerabilities and chances of data theft. It is essential to identify the connected devices for secure communication. It is very difficult to maintain the list of rules when the number of connected devices increases and human involvement is necessary to check whether any intruder device has approached the network. Therefore, it is required to automate device identification using machine learning methods. In this article, we propose an accuracy boosting model (ABM) using machine learning models of random forest and extreme gradient boosting. Featuring engineering techniques are employed along with cross-validation to accurately identify IoT devices such as lights, smoke detectors, thermostat, motion sensors, baby monitors, socket, TV, security cameras, and watches. The proposed ensemble model utilizes random forest (RF) and extreme gradient boosting (XGB) as base learners with adaptive boosting. The proposed ensemble model is tested with extensive experiments involving the IoT Device Identification dataset from a public repository. Experimental results indicate a higher accuracy of 91%, precision of 93%, recall of 93%, and F1 score of 93%.

NeuroRF FarmSense: IoT-fueled precision agriculture transformed for superior crop care

In light of the ongoing global hunger crisis, it is imperative to improve food production in accordance with Sustainable Development Goal 2.0, which aims to eliminate hunger while promoting sustainable agricultural practices. This research presents a novel Internet of Things (IoT)-driven crop management system, NeuroRF FarmSense, specifically designed for precision agriculture. By utilizing soil sensors and a robust IoT framework, this system enables effective data collection across vast and remote agricultural areas. The study utilizes an extensive Crop Recommendation Dataset obtained from Kaggle, which includes 2,200 entries and seven critical attributes essential for crop selection: phosphorus, humidity, potassium, temperature, nitrogen, pH, and rainfall. This dataset provides a detailed methodology for crop recommendations, revealing more than 22 alternative crops based on varying characteristics. For agricultural forecasting, the NeuroRF FarmSense system employs the NeuroRF Classifier, which integrates neural networks (NN) with the Random Forest Classifier, achieving an unprecedented accuracy of 99.82%, exceeding prior records. This integrative approach harnesses the advantages of NN’s ReLU activation and dropout regularization alongside the robustness of RF. By utilizing NN predictions as input features for RF training and refining RF through grid search with cross-validation, the ensemble model produces highly precise predictions, facilitating strategic crop cultivation for optimal yields across diverse environmental conditions. This innovative methodology signifies a strong solution for classification challenges in precision agriculture. By merging IoT technology with machine learning algorithms, smart farming is poised to enter a transformative phase, providing a scalable response to the pressing issues of global food security. This research aspires to advance precision agriculture in harmony with global sustainability objectives.

Blockchain for Cybersecurity in Edge Networks

The Blockchain is one of the most promising and artistic cybersecurity solutions. It has been practiced in a variety of reinforcements, including healthcare, transportation, and Internet of Things (IoT) applications. However, Blockchain has a colossal scalability challenge, limiting its ability to control services with high transaction volumes. Edge computing, on the other hand, was designed to allow cloud services and resources to be deployed at the network's edge, although it now faces issues in terms of decentralized security and management. The unification of edge computing and Blockchain within one solution jar provides a vast scale of storage systems, database servers, and authenticity computation towards the end in a safe fashion. Despite the potential of interconnected edge computing and Blockchain environments, difficulties such as scalability, resource management, function integration, self-organization, and new security concerns must be addressed before broad adoption. This article provides an overview of the secure IoT framework, paradigms, enablers, and security problems of combining Blockchain and intelligent edge computing. Finally, broader viewpoints for future research directions are investigated.

IoT-Based Framework for COVID-19 Detection Using Machine Learning Techniques

Current advancements in the technology of the Internet of Things (IoT) have led to the proliferation of various applications in the healthcare sector that use IoT. Recently, it has been shown that voice signal data of the respiratory system (i.e., breathing, coughing, and speech) can be processed through machine learning techniques to detect different diseases of this system such as COVID-19, considered an ongoing global pandemic. Therefore, this paper presents a new IoT framework for the identification of COVID-19 based on breathing voice samples. Using IoT devices, voice samples were captured and transmitted to the cloud, where they were analyzed and processed using machine learning techniques such as the naïve Bayes (NB) algorithm. In addition, the performance of the NB algorithm was assessed based on accuracy, sensitivity, specificity, precision, F-Measure, and G-Mean. The experimental findings showed that the proposed NB algorithm achieved 82.97% accuracy, 75.86% sensitivity, 94.44% specificity, 95.65% precision, 84.61% F-Measure, and 84.64% G-Mean.

T-FedHA: A Trusted Hierarchical Asynchronous Federated Learning Framework for Internet of Things

Federated Learning (FL) is a distributed machine learning system designed to effectively address potential data privacy concerns, making it particularly promising for the Internet of Things (IoT). However, challenges arise due to the trust and heterogeneity of IoT devices. FL often assumes devices are trustworthy and curious, which is impractical in the realistic IoT environment where the trustworthiness of participating devices becomes an intractable problem. Additionally, the diverse computational power and communication efficiency across IoT devices impact the efficiency and performance of FL. To address these challenges and enable the effective implementation of FL in the IoT environment, this article proposes a Trusted Hierarchical Asynchronous Federated Learning Framework (T-FedHA). The framework introduces a “Device-Edge-Central” hierarchy and employs a distributed trust management model. This model manages the trust of IoT devices, selecting devices that meet trust requirements for FL training. This enhances the security and trustworthiness of the overall FL system. Furthermore, T-FedHA integrates standard FL methods with a hierarchical asynchronous strategy that includes edge servers. This approach improves model aggregation and update processes, employing an asynchronous technique that considers the staleness of model updates to enhance overall performance and effectiveness. Extensive experiments confirm the efficacy of T-FedHA. The distributed trust management model efficiently calculates and manages IoT device trust, allowing only trusted devices to participate in FL training. The hierarchical asynchronous approach proves more feasible and efficient, as well as robust and flexible, contributing to the reliability of the proposed framework.