Internet System Research Articles

Framework for detecting and resisting cyberattacks on cyber-physical systems in internet of things

Framework for detecting and resisting cyberattacks on cyber-physical systems in internet of things

Lightweight authentication scheme for edge control systems in Industrial Internet of Things

Lightweight authentication scheme for edge control systems in Industrial Internet of Things

Adaptive scheduling for Internet of Vehicles using deconfounded graph transfer learning

With the rapid development of Internet of Things technology, the Internet of Vehicles has emerged as a significant application. The Internet of Vehicles connects vehicles with traffic infrastructure and other vehicles, enabling real-time information sharing and vehicle dispatching. However, in the Internet of Vehicles environment, traffic scheduling systems face challenges such as sparse raw data and environmental confounding bias, which limit the effectiveness of traditional graph-based reinforcement learning methods. In this paper, we propose a Cross-City Federated Continual Learning Framework for Spatiotemporal Graph Transfer Learning called CCFTL, which removes confounding effects on reinforcement learning and enhances few-shot transfer learning in data-scarce cities. Our approach aims to significantly improve the adaptability and scalability of cross-city vehicle networking technologies in ever-changing environments. This study acts as an innovative research of the deconfounding strategy with spatiotemporal graph meta-knowledge learning, which enables optimization of cross-city meta-knowledge transfer through a federated continual learning approach. This approach effectively reduces learning bias caused by regional differences, which enhances the model’s generalization and adaptability to complex, heterogeneous urban scenarios. Experimental results show that our framework significantly outperforms traditional methods, especially in improving the efficiency and accuracy of traffic dispatch systems in Internet of Vehicles environments with scarce data and the presence of confounding factors.

Fall Detection Systems for Internet of Medical Things Based on Wearable Sensors: A Review

Fall Detection Systems for Internet of Medical Things Based on Wearable Sensors: A Review

Аналіз номенклатури мікроконтролерів для побудови елементів домашньої автоматизації

The subject of study and research in this article is the list of modern microcontrollers and technologies of the programming for creation elements of home automation, embedded systems, and components of Internet of Things. The goal is to analyze the product range and nomenclature of modern microcontrollers for creation of components of IoT systems and home automation considering an available resources and hardware implementation of interfaces. The tasks: to analyze a significant parameters of microcontroller for practical use; to analyze the product range and a modern manufacturers list of the most widely used microcontrollers; to analyze the main features of AVR microcontrollers; to perform an analysis of the advantages of STM32 microcontrollers; to analyze the capabilities of ESP32 microcontrollers; to perform the comparison of hardware resources of these three groups of microcontrollers. According to the tasks, the following results were obtained. Fourteen leading manufacturers of microcontrollers are analyzed with taking into account the bit length and a set of series and families of devices. The main characteristics and requirements for the practical application of microcontrollers for both the creation of embedded systems and for training and prototyping are highlighted. The reasons of the great popularity of the use of AVR microcontrollers by the community for training and learning of programming are identified. The capabilities of hardware resources and the advantages of using for embedded systems of the set of models of STM32 microcontrollers series are analyzed. The reasonability of the use of ESP32 microcontrollers for building of components of IoT systems and devices with Internet access is concluded. The comparison of the hardware resources of microcontrollers of three manufacturers is performed with taking into account the available size of program memory, the number of interfaces and pins. Conclusions. The main contribution and scientific novelty of the obtained results is that the performed analysis allows to simplify the process of a decision making about the selection of the required product range of modern microcontrollers for the creation of the components of the systems of home automation and Internet of Things with taking into account the available hardware resources, the number of pins, and the presence of hardware interfaces. This reduces the required efforts and costs at the initial stages of prototyping such systems, as well as during the porting to new microcontrollers and integrated environments of an already existing project.

Optimized neural network models for low power elderly monitoring system in Internet of things

This paper proposes a low power consuming system for monitoring elderly people’s activities and their health conditions. The proposed system has two activity recognition modules: smartphone sensor-based wearable module; infrared grid sensor-based remote module. The two activity recognition modules work in a coordinated way. The fraction of the time the person is detected by the infrared sensor, the smartphone remains idle. As a result, energy consumption in the smartphone is reduced significantly, and hence the battery lifetime is increased. In the smartphone, a Feed-forward Neural Network (FNN) based activity recognition algorithm is implemented using fixed-point computation to further reduce energy consumption. A Convolutional Neural Network is used in the infrared sensor-based activity recognition module. The proposed system also has real-time health monitoring capability, which is based on ECG signal classification. A FNN leveraging fixed-point operation is used for ECG signal classification on an embedded ARM processor. Proposed fixed-point implementations of the FNNs are faster than floating-point implementation and require 50% less memory to store the neural network model parameters without loss of classification accuracy.

Improving Localization in Wireless Sensor Networks for the Internet of Things Using Data Replication-Based Deep Neural Networks.

Localization is one of the most challenging problems in wireless sensor networks (WSNs), primarily driven by the need to develop an accurate and cost-effective localization system for Internet of Things (IoT) applications. While machine learning (ML) algorithms have been widely applied in various WSN-based tasks, their effectiveness is often compromised by limited training data, leading to issues such as overfitting and reduced accuracy, especially when the number of sensor nodes is low. A key strategy to mitigate overfitting involves increasing both the quantity and diversity of the training data. To address the limitations posed by small datasets, this paper proposes an intelligent data augmentation strategy (DAS)-based deep neural network (DNN) that enhances the localization accuracy of WSNs. The proposed DAS replicates the estimated positions of unknown nodes generated by the Dv-hop algorithm and introduces Gaussian noise to these replicated positions, creating multiple modified datasets. By combining the modified datasets with the original training data, we significantly increase the dataset size, which leads to a substantial reduction in normalized root mean square error (NRMSE). The experimental results demonstrate that this data augmentation technique significantly improves the performance of DNNs compared to the traditional Dv-hop algorithm at a low number of nodes while maintaining an efficient computational cost for data augmentation. Therefore, the proposed method provides a scalable and effective solution for enhancing the localization accuracy of WSNs.

DIGITAL MENTAL HEALTH: THE FUTURE IS NOW

Over a decade ago, we foresaw the imminent arrival of the digital health era. Yet, it wasn't until the outbreak of the COVID-19 pandemic that this future truly took shape. The pandemic became a catalyst, seamlessly integrating a spectrum of digital tools into clinical practice, fundamentally transforming the way mental health services were delivered. The inclusion of digital health technologies in our arsenal is not merely an addition; it's a revolutionary shift. These tools are pivotal in enhancing care accessibility and significantly augment our capabilities in diagnosis, treatment, and healthcare delivery, transcending national borders. Telemedicine and clinical systems enabled by the Internet are widely accessible and are beginning to affect the dynamics between doctors and patients, with this trend expected to continue in the future. Telemedicine consultations have become so ubiquitous that they are now routinely conducted via broadband Internet systems hosted in the cloud. This has enabled professionals from various fields within mental health (MH), including psychiatrists, psychologists, marriage and family therapists, and career counselors, to offer e-therapy services.

Smart Farm Control System Using IoT

This paper presents a way to do smart agriculture by using the advances in technology and information today, especially the Internet of Things (IoT) to help control the environment to change according to the needs of plants automatically. Which this research used the method of making a closed house or Greenhouse size 6m x 9m x 4m in number of 2 houses and using various types of sensor equipment. To detect parameters that affect the environment such as: Temperature sensor, Humidity sensor, Soil moisture sensor, pH sensor and Light sensor. To bring those values ​​forward to the microcontroller to process and order the electrical equipment to work, such as solenoid valves or water pumps; As well as send the parameter value to display the results to the application on the smartphone and the website through the wireless Internet system (WiFi) using the Blynk platform or Blynk cloud. From the results of the design and experiment, it was found that the system can control electrical equipment in the farm system, such as solenoid valves, water pump, and read various values ​​according to the set goals and objectives. We can order control Turn on and off the water system or the fog system anywhere at any time according to the conditions of the internet connection or direct control through the control box at any place or choose to have the system work automatically such as watering and misting according to the conditions we want and the system can also monitor or check the temperature, touch moisture, soil moisture, light intensity and pH value of the water at any time through the platform of the brand that can be used on both smartphones and computers.

E-CARGO-based dynamic weight offload strategy with resource contention mitigation for edge networks

With the widespread use of Mobile Edge Computing (MEC) in smart manufacturing systems in Industrial Internet of Things (IIoT) and 5G networks, determining how to efficiently offload computing tasks has become a hot research area. The Role-Based Collaboration (RBC) Environments-Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is introduced to comprehensively manage MEC servers and user computation tasks in edge network environments, thereby improving the effectiveness and performance of task offloading in smart manufacturing systems. To begin with, latency and energy consumption are important indicators for evaluating the offloading effect. A pre-allocation algorithm based on user latency tolerance is proposed to dynamically adjust the latency-energy consumption weighting factor to optimize system resource allocation for real-time adjustment of offloading decisions. Second, the Group Role Assignment of Agent Role Conflicts (GRACAR) model based on E-CARGO is extended, along with a dynamic weighting of the GRACAR (GRACAR-DW) model and formal modeling. By introducing resource contention constraints, the resource contention caused by excessive task data offloading to the same MEC server is proactively mitigated. Finally, a Gurobi solution based on Mixed-Integer Linear Programming (MILP) is developed to help validate and synthesize the proposed model. Simulation results show that the strategy considerably enhances the MEC system's overall performance in terms of latency and energy consumption while also providing new ideas and technological support for offloading decisions in edge networks.

A comprehensive survey on deep learning‐based intrusion detection systems in Internet of Things (IoT)

AbstractThe proliferating popularity of Internet of Things (IoT) devices has led to wide‐scale networked system implementations across multiple disciplines, including transportation, medicine, smart homes, and many others. This unprecedented level of interconnectivity has introduced new security vulnerabilities and threats. Ensuring security in these IoT settings is crucial for protecting against malicious activities and safeguarding data. Real‐time identification and response to potential intrusions and attacks are essential, and intrusion detection systems (IDS) are pivotal in this process. However, the dynamic and diverse nature of the IoT environment presents significant challenges to existing IDS solutions, which are often based on rule‐based or statistical approaches. Deep learning, a subset of artificial intelligence, has shown great potential to enhance IDS in IoT. Deep learning models can identify complex patterns and characteristics by utilizing artificial neural networks, automatically building hierarchical representations from data. This capability results in more precise and efficient intrusion detection in IoT‐based systems. The primary aim of this survey is to present an extensive overview of the current research on deep learning and IDS in the IoT domain. By examining existing literature, discussing mainstream datasets, and highlighting current challenges and potential prospects, this survey provides valuable insights into the prevailing scenario and future directions for using deep learning in IDS for IoT. The findings from this research aim to enhance intrusion detection techniques in IoT environments and promote the development of more effective antimalware solutions against cyber threats targeting IoT device systems.

Physical and internet medical system: Service quality and management mode analysis

In an era of social progress and policy changes, some hospitals have diversified their services by incorporating internet hospitals. However, since the development of internet hospitals has been delayed and their management model remains ambiguous, this paper employs game theory to establish an internet medical system. By categorizing patients based on their severity and their acceptance of telemedicine, and by devising the centralized and decentralized management models, the paper assesses the quality of service and management practices of internet hospitals. The findings reveal that under decentralized management, internet hospitals can enhance the quality of service in physical hospitals. Nevertheless, the disparity in quality of physical hospitals in the two medical systems under the centralized management mode requires clarification. In addition, the paper delves into hospital revenue and patient utility, indicating that while the establishment of internet hospitals may not consistently increase healthcare system revenue, it can significantly improve patient utility, especially in remote areas. These results provide valuable insights into the management and expansion of internet hospitals.

Retraction Note: Application of real-time data processing system of internet of things based on blockchain technology in the financial field of Yangtze River Delta urban agglomeration

Retraction Note: Application of real-time data processing system of internet of things based on blockchain technology in the financial field of Yangtze River Delta urban agglomeration

EXPERIMENTAL LABORATORY SET VIA INTERNET SYSTEM ON ELECTRICAL INSTRUMENT AND MEASUREMENT COURSE OF HIGHER VOCATIONAL CERTIFICATE PROGRAM OF RAJAMANGALA UNIVERSITY OF TECHNOLOGY ISAN, SAKON NAKHON CAMPUS

Development qualities, efficiency, and user satisfaction of the experimental laboratory set via internet system are electrical instrument and measurement course of the Higher Vocational Certificate Program of the Rajamangala University of Technology Isan, Sakon Nakhon campus. The research sample group consists of 21 first-year students in the Electronics Engineering Department, Higher Vocational Certificate Program, Rajamangala University of Technology Isan, Sakon Nakhon campus, and the choose a specific type. The research tools were the experimental laboratory set via internet system on electronic measurement tools course of Higher Vocational Certificate Program of Rajamangala University of Technology Isan, Sakon Nakhon campus., content and media production technical quality evaluation forms, academic achievement test and practice evaluation form, and user satisfaction evaluation form. Statistics used in this research were mathematical mean (x̄), standard deviation (SD), and percentage. The research results found that the experimental laboratory set via the internet system on electronic measurement tools course of Higher Vocational Certificate Program of Rajamangala University of Technology Isan, Sakon Nakhon campus have the content quality was good level (x̄ = 3.96, SD = 0.79) and the media production technical quality was good level (x̄ = 4.20, SD = 0.66).

Design of intelligent manufacturing monitoring system for internet of things based on encryption technology and intrusion detection technology

With the rapid development of intelligent manufacturing, energy consumption and safety issues have gradually attracted widespread attention. Heat loss not only leads to energy waste, but also may affect the efficiency of equipment operation and production safety. Therefore, this study aims to design an efficient iot intelligent manufacturing monitoring system, focusing on the synergistic effect of heat loss energy management and intrusion detection to improve energy utilization efficiency and optimize security monitoring. The system uses a sensor network to monitor the heat loss in the manufacturing environment in real time, and evaluates the energy efficiency performance combined with data analysis algorithms. Through intrusion detection technology, real-time monitoring of system security, using machine learning models to identify abnormal behavior. Integrate heat loss management and security monitoring to build an intelligent manufacturing platform based on the Internet of Things. The experimental results show that the monitoring system can effectively reduce the energy heat loss and improve the accuracy of intrusion detection. The implementation of the system significantly improves the energy efficiency and safety of the production line, reducing potential safety hazards. Therefore, the iot intelligent manufacturing monitoring system designed in this paper effectively integrates heat loss energy management and intrusion detection technology, and provides an efficient solution for intelligent manufacturing.

Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things

ABSTRACT Nowadays, the network intrusion and cyberattack have emerged as the two main issues with Internet of Things (IoT) applications. The existing methods for preventing and detecting intrusions are limited in many ways, making it impossible to accurately identify any kind of attack occurring within network traffic. A number of machine learning-based methods that attains poor performance in the multiple class categorization accuracy are provided by the researchers. This research presents Data-Driven Intrusion Detection System in Internet of Things utilizing Optimized Bayesian Regularization-Back Propagation Neural Network (DIDS-BRBPNN-BBWOA-IoT) to overcome these issues. The input data is taken from TON_IoT Dataset. The data balancing of the training dataset is enhanced using Class decomposition and synthetic minority class oversampling method (CDSMOTE). Then, the data is pre-processed using Variational Bayesian-based Maximum Correntropy Cubature Kalman Filtering (VBMCCKF) of noise removal and data enhancement. The preprocessed output is given into feature extraction to extract features by using Dual-Tree Biquaternion Wavelet Transform (DTBWT). The extracted features are fed into Bayesian Regularization-Back Propagation Neural Network (BRBPNN) which detects the intrusion as Ransomware, Password attack, Scanning, Denial of Service (DoS), Distributed Denial of Service (DDoS), Data injection, Backdoor, Cross-Site Scripting (XSS), and Man-In-The-Middle (MITM). In general, BRBPNN does not show any optimization adaption methods to determine the optimal parameter for appropriate detection. Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. The proposed DIDS-BRBPNN-BBWOA-IoT method is implemented using Python. The performance of the DIDS-BRBPNN-BBWOA-IoT approach is examined using performance metrics like accuracy, precision, recall, f1-score, specificity, error rate; computation time, and ROC. The proposed SAPVAEGAN-LCC-IR approach attains 18.44%, 26% ,and 29% greater accuracy; 26.55%, 24.12%, and 27.22% greater recall compared with existing MIDS-MIoT, AID-SDN-IoT, and IID-LW-IoT techniques.

Technologies of Data Protection and Institutional Decisions for Data Sovereignty

This paper aims to propose innovative actions of advanced technological solutions and consequent necessary institutional decisions to achieve in a reasonable time the definitive confidential data protection and data sovereignty, based on available scientific results. Confidential data protection is a fundamental and strategic issue in next-generation Internet systems to guarantee data sovereignty and the respect of human rights as stated in the foundation of the United Nations. Even if presently many international regulations are decisive steps to guarantee data protection within normative contexts, they are not adequate to face new technologies, such as facial recognition, automatic profiling, position tracking, biometric data, AI applications, and many others in the future, as they are implemented without any awareness by the interested subjects. Therefore, a new approach to data protection is mandatory based on innovative and disruptive technological solutions. A recent OECD report highlighted the need for the so-called Privacy-Enhancing Technologies (PETs) for the effective protection of confidential data, even more urgent for the coexistence of privacy and data sharing in international contexts. A common feature of these technologies is the use of software methodologies that can run on currently available microprocessors and their present immaturity. More effective and definitive protection can be achieved with another methodological approach based on the paradigm of ‘Data Usage Control’. This new concept guarantees data protection policy by default and initial design and it requires a new architecture of the data and a new HW&SW architecture of the computers. This contribution has a two-fold objective: first, to clarify why regulations alone and present technological proposals are not adequate for the effective and definitive protection of data and, second, to indicate the new necessary technological approach and the simultaneous institutional actions required to achieve the definitive protection and sovereignty of data in reasonable times, based on the results already available in the scientific literature.

A novel ECG compression algorithm using Pulse-Width Modulation integrated quantization for low-power real-time monitoring

Cardiac monitoring systems in Internet of Things (IoT) healthcare, reliant on limited battery and computational capacity, need efficient local processing and wireless transmission for comprehensive analysis. Due to the power-intensive wireless transmission in IoT devices, ECG signal compression is essential to minimize data transfer. This paper presents a real-time, low-complexity algorithm for compressing electrocardiogram (ECG) signals. The algorithm uses just nine arithmetic operations per ECG sample point, generating a hybrid Pulse Width Modulation (PWM) signal storable in a compact 4-bit resolution format. Despite its simplicity, it performs comparably to existing methods in terms of Percentage Root-Mean-Square Difference (PRD) and space-saving while significantly reducing complexity and maintaining robustness against signal noise. It achieves an average Bit Compression Ratio (BCR) of 4 and space savings of 90.4% for ECG signals in the MIT-BIH database, with a PRD of 0.33% and a Quality Score (QS) of 12. The reconstructed signal shows no adverse effects on QRS complex detection and heart rate variability, preserving both the signal amplitude and periodicity. This efficient method for transferring ECG data from wearable devices enables real-time cardiac activity monitoring with reduced data storage requirements. Its versatility suggests potential broader applications, extending to compression of various signal types beyond ECG.

Research on Sustainable Development Strategy of Energy Internet System in Xiongan New Area of China Based on PEST-SWOT-ANP Model

Research on Sustainable Development Strategy of Energy Internet System in Xiongan New Area of China Based on PEST-SWOT-ANP Model

Ultra‐low power consumption flexible sensing electronics by dendritic bilayer MoS2

AbstractTwo‐dimensional transition metal dichalcogenides (2D TMDs) are promising as sensing materials for flexible electronics and wearable systems in artificial intelligence, tele‐medicine, and internet of things (IoT). Currently, the study of 2D TMDs‐based flexible strain sensors mainly focuses on improving the performance of sensitivity, response, detection resolution, cyclic stability, and so on. There are few reports on power consumption despite that it is of significant importance for wearable electronic systems. It is still challenging to effectively reduce the power consumption for prolonging the endurance of electronic systems. Herein, we propose a novel approach to realize ultra‐low power consumption strain sensors by reducing the contact resistance between metal electrodes and 2D MoS2. A dendritic bilayer MoS2 has been designed and synthesized by a modified CVD method. Large‐area edge contact has been introduced in the dendritic MoS2, resulting in decreased the contact resistance significantly. The contact resistance can be down to 5.4 kΩ μm, which is two orders of magnitude lower than the conventional MoS2 devices. We fabricate a flexible strain sensor, exhibiting superior sensitivity in detecting strains with high resolution (0.04%) and an ultra‐low power consumption (33.0 pW). This study paves the way for future wearable and flexible sensing electronics with high sensitivity and ultra‐low power consumption.image