Internet Learning Research Articles

RHRA-DRL: RSU-Assisted Hybrid Road-Aware Routing Using Distributed Reinforcement Learning in Internet of Vehicles

RHRA-DRL: RSU-Assisted Hybrid Road-Aware Routing Using Distributed Reinforcement Learning in Internet of Vehicles

Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey

Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey

A Verifiable Discrete Trust Model (VDTM) Using Congruent Federated Learning (CFL) for Social Internet of Vehicles

A Verifiable Discrete Trust Model (VDTM) Using Congruent Federated Learning (CFL) for Social Internet of Vehicles

Intelligent Transportation Vehicle Road Collaboration and Task Scheduling Based on Deep Learning in Augmented Internet of Things

Intelligent Transportation Vehicle Road Collaboration and Task Scheduling Based on Deep Learning in Augmented Internet of Things

Graph Sparsification-Based Secure Federated Learning for Consumer-Driven Internet of Things

Graph Sparsification-Based Secure Federated Learning for Consumer-Driven Internet of Things

Communication and Computation Efficient Federated Learning for Internet of Vehicles With a Constrained Latency

Communication and Computation Efficient Federated Learning for Internet of Vehicles With a Constrained Latency

Blockchain-Powered Deep Learning for Internet of Things with Cloud-Assisted Secure Smart Home Networks

Blockchain-Powered Deep Learning for Internet of Things with Cloud-Assisted Secure Smart Home Networks

Time-Distributed Feature Learning for Internet of Things Network Traffic Classification

Time-Distributed Feature Learning for Internet of Things Network Traffic Classification

Internet usage and household energy poverty

Abstract Based on the double critical value method and the data of the China Household Panel Survey from 2014 to 2023, this paper measures the multidimensional relative household energy poverty in China. After confirming the impact of income poverty on energy poverty, this paper explores the formation mechanism of energy poverty from the perspective of opportunity acquisition. It explores the occurrence path of “opportunity access-income poverty-energy poverty” of residents by combining the structural equation model. Finally, the impact of Internet use on energy poverty was investigated through multiple regression analysis. According to the study, China’s official poverty line is about 2.0 US dollars, and the energy poverty line has increased by 49.6% in 2022 compared with 197.2 yuan in 2018. In the empirical analysis of access level and energy poverty, the coefficient of Internet learning is 0.311. Further, adding the moderating effect model regression results of the interaction term shows that the Internet penetration rate has a significant positive impact on multidimensional energy poverty. When it is increased by 1%, the multidimensional energy poverty is reduced by 9.25%.

Personalized Fair Split Learning for Resource-Constrained Internet of Things.

With the flourishing development of the Internet of Things (IoT), federated learning has garnered significant attention as a distributed learning method aimed at preserving the privacy of participant data. However, certain IoT devices, such as sensors, face challenges in effectively employing conventional federated learning approaches due to limited computational and storage resources, which hinder their ability to train complex local models. Additionally, in IoT environments, devices often face problems of data heterogeneity and uneven benefit distribution between them. To address these challenges, a personalized and fair split learning framework is proposed for resource-constrained clients. This framework first adopts a U-shaped structure, dividing the model to enable resource-constrained clients to offload subsets of the foundational model to a central server while retaining personalized model subsets locally to meet the specific personalized requirements of different clients. Furthermore, to ensure fair benefit distribution, a model-aggregation method with optimized aggregation weights is used. This method reasonably allocates model-aggregation weights based on the contributions of clients, thereby achieving collaborative fairness. Experimental results demonstrate that, in three distinct data heterogeneity scenarios, employing personalized training through this framework exhibits higher accuracy compared to existing baseline methods. Simultaneously, the framework ensures collaborative fairness, fostering a more balanced and sustainable cooperation among IoT devices.

Harnessing federated generative learning for green and sustainable Internet of Things

The rapid proliferation of devices in the Internet of Things (IoT) has ushered in a transformative era of data-driven connectivity across various domains. However, this exponential growth has raised pressing concerns about environmental sustainability and data privacy. In response to these challenges, this paper introduces One-shot Federated Learning (OSFL), an innovative paradigm that harmonizes sustainability and machine learning within IoT ecosystems. OSFL revolutionizes the traditional Federated Learning (FL) workflow by condensing multiple iterative communication rounds into a single operation, thus significantly reducing energy consumption, communication overhead, and latency. This breakthrough is coupled with the strategic integration of generative learning techniques, ensuring robust data privacy while promoting efficient knowledge sharing among IoT devices. By curtailing resource utilization, OSFL aligns seamlessly with the vision of green and sustainable IoT, effectively extending device lifespans and mitigating their environmental footprint. Our research underscores the transformative potential of OSFL, poised to reshape the landscape of IoT applications across domains such as energy-efficient smart cities and groundbreaking healthcare solutions. This contribution marks a pivotal step towards a more responsible, sustainable, and technologically advanced future.

Distributed Machine Learning for Terrestrial and Non-Terrestrial Internet of Things Networks

Distributed Machine Learning for Terrestrial and Non-Terrestrial Internet of Things Networks

TSGS: Two-stage security game solution based on deep reinforcement learning for Internet of Things

TSGS: Two-stage security game solution based on deep reinforcement learning for Internet of Things

Health monitoring of historic buildings using machine learning in real-time internet of things

<span>In this paper, structural health monitoring (SHM) is used to detect the damage level for the historic building. The damaged level is defined based on the support vector machine (SVM) algorithm to extract the damage feature. Physical checks allow us to detect any damage or structural degeneration. Supervised training machine learning (ML) is used as a tool to examine accelerometer data to ascertain the condition of structures following an occurrence. The three training models, the SVM, the random forest linear classification, and the k-nearest neighbor (KNN) model are tested and compared to classify data. The data obtained from structural health monitoring, teams of responders, and investigators can be used to manage the most vulnerable structures. The accuracy of the SVM algorithm was found up to 94% accurate and precise, at a high level. The internet of things (IoT) architecture is also introduced with SVM learning algorithms for early warning. The proposed system makes use of an SHM system to identify seismic events or accelerations. The IoT system SHM uses real data from the structure, allowing for online damage identification and ongoing monitoring. A dashboard is used to represent the monitoring data and the damage level.</span>

Research on Teaching Reform of Intelligent Transportation System based on Internet Learning

Network learning education teaching method is widely used in the new era, is a new method of teacher teaching, new technology, is a new idea of school teaching management, is an important teaching tool for schools to organize online learning. Network learning has the characteristics of high technology value, high integration, high sharing and high personalization. Network teaching has its unique advantages and promotes the new development direction of teaching. At the same time, because of its own shortcomings, network teaching can not fully replace traditional teaching. This paper aims at the problems existing in intelligent transportation courses, combined with the advantages of network learning, to explore the reform of teaching methods and means, so as to improve the efficiency and quality of education and teaching, and enhance students' interest in learning.

PROFESSIONAL TRAINING OF FUTURE SPECIALISTS IN PHYSICAL CULTURE AND SPORTS IN THE CONDITIONS OF MARITAL STATE IN UKRAINE

The article is devoted to the study of the introduction of blended learning of students of higher education institutions (HEIs) into the educational process. The problems of using information and communication technologies as a means of organizing and implementing the educational process in the conditions of military operations were analyzed, and the experience of introducing mixed learning technologies into the educational process of higher education institutions was investigated. Both positive and negative experiences of the organization of mixed education are revealed, and the attitude of students and teachers to the implemented organization of mixed and classroom education in higher education institutions of Ukraine is revealed. It has been proven that the most important task of domestic institutions of higher education in the conditions of war is to ensure access to the educational process for all its participants ‒ both students and teachers in the form of distance learning or, if possible, mixed learning using the Internet, learning management systems, adaptive learning systems, mass open online courses, etc. Blended learning is a fairly common form of student learning. Students of higher education among traditional, mixed and distance forms of education prefer mixed education, while noting its positive aspects: accessibility, productivity, independence, taking into account individual capabilities and others. Mixed training in physical education is a flexible and diverse form of conducting classes that allows you to use the advantages of traditional face-to-face and distance learning and, on the contrary, avoid or minimize their disadvantages. The use of recreational fitness in physical education classes will increase the effectiveness of the educational process. Blended learning can encompass a wide range of approaches to teaching. ICT technologies expand the possibilities of effective learning. A high-quality combination of classroom and remote forms of education creates conditions for expanding access to education, flexibility of teaching, improvement of pedagogical methods, technologies and methods. In addition, “blended learning can include synchronous and asynchronous online learning and a wide selection of modern technologies and various Internet learning tools”.
 Keywords: Students; blended learning; technologies of blended learning; distance learning; classroom learning; specialty “Physical culture and sport”; physical education.

RFedFW: Secure and trustable aggregation scheme for Byzantine-robust federated learning in Internet of Things

Federated learning is a promising approach in the Internet of Things (IoT) that enables collaborative and distributed machine learning among massive IoT devices without sharing private data, thereby constructing smart IoT applications. However, traditional federated learning approaches are unable to monitor the local training process of devices. Some malicious devices can exploit the vulnerability to conduct Byzantine attacks, which may potentially lead to the failure or compromise of shared global model. In this paper, we propose a new Byzantine-robust federated learning framework called rFedFW, which aims to achieve secure and trustable federated learning in the IoT. Specifically, we propose a dual filtering mechanism to identify and discard malicious gradients. Furthermore, we design an adaptive weight adjustment scheme that dynamically reduces the aggregated weight of potentially malicious gradients, ultimately achieving robust model aggregation. Additionally, we propose a dynamic clipping method to reduce the magnitude of various gradients, and we incorporate an additive model aggregation scheme with momentum to smooth the effects of local gradients and achieve efficient model aggregation. Extensive experimental results on various datasets demonstrate the effectiveness and robustness of rFedFW.

RETRACTED ARTICLE: Three-Periods Optimization Algorithm: A New Method for Solving Various Optimization Problems

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”: Mohammad Dehghani, Pavel Trojovský, Štěpán Hubálovský, Theyab R. Alsenani & Jaswinder Singh (2022) Three-Periods Optimization Algorithm: A New Method for Solving Various Optimization Problems, IETE Journal of Research, DOI: 10.1080/03772063.2022.2052982 Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal’s peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as “Retracted”.

Satellite-assisted edge computing management based on deep reinforcement learning in industrial internet of things

The insufficient edge computing equipment in remote areas cannot meet explosively growing computing needs of industrial Internet of things devices, which undoubtedly leads to unaffordable overheads of the device-side energy and computation timeout. In response to this problem, we propose a collaborative computation offloading architecture based on the satellite-assisted edge computing (SAEC) deployed in low earth orbit ultra-dense satellite networks which hold great promise in the 6G communications benefiting from the low latency, high bandwidth, global coverage, etc. To make the non-differentiable computation offloading problem tractable, we propose an asynchronous advantage actor-critic based SAEC offloading (ASO) deep reinforcement learning (DRL) algorithm to optimize the integrated reward reflecting in latency and energy, and optimally train the action set determining the scale of participating SAEC servers and the distribution of their computational tasks. Numerous simulation results verify that our ASO algorithm can greatly accelerate the convergence and improve the integrated reward compared with contrast methods.

Blockchain-Based Personalized Federated Learning for Internet of Medical Things

The rapid growth of artificial intelligence (AI), blockchain technology, and edge computing services have enabled the Internet of Medical Things (IoMT) to provide various healthcare services to patients, including neural network-based disease diagnosis, heart rate monitoring, and fall detection. Generally, end devices should transmit the collected patient data to a centralized server for further model training, but at the same time, the patient's privacy may be at risk. In addition, due to the diversity of patient conditions, a one-size-fits-all model cannot meet personalized healthcare needs. To address the above challenges, we propose a blockchain-based personalized federated learning (FL) system that enables clients to participate in personalized model training without directly uploading private data. We further realize the decentralized FL by combining blockchain technology, which improves the security level of the system. Finally, we verify the reliable performance of our system on different datasets through simulation experiments.