Internet System Research Articles

Impact of internet development on transportation carbon emissions: Evidence from China

The development of the internet is of great significance for carbon reduction in the transportation sector. Drawing on panel data from 31 provinces in China spanning 2011 to 2021, we present novel evidence from the transportation sector. We construct an internet development assessment system comprising 18 indicators and compute an internet development index from four dimensions. Employing OLS, mediation analysis, and dynamic threshold panel models, we validate the relationship between the internet development index and carbon emissions in the transportation sector. Results indicate a significant increase in transportation sector carbon emissions with internet development, with a 0.072% rise in emissions for every 1% increase in the internet development index. This is primarily attributed to the influence of internet development on increasing internet shopping, vehicle ownership, and travel scale. Importantly, in regions with higher transportation sector carbon emissions, such as Shanghai, internet development may also lead to emission reductions. Thus, the Chinese government should pay particular attention to strategically employing internet technology in different regions, leveraging its technical advantages to promote carbon emission reduction strategies in the transportation sector.

Experimental study on designing smart antenna sensor networks and modelling management energy consumption for intelligent systems of internet of medical things

ABSTRACT The Internet of Medical Things (IoMT) is revolutionizing healthcare through technologies like artificial intelligence, data analysis, and remote monitoring. However, designing energy-efficient Wireless Body Area Networks (WBANs) remains challenging due to the complex propagation characteristics of on-body communication links, which vary with human body posture and size. This study aims to develop smart wearable antenna sensors and model energy consumption in on-body communication links. We have conducted experimental studies on monopole and patch antennas at 2.4 GHz, focusing on body morphological factors and channel characteristics. Our findings reveal that the path loss exponent of on-body links significantly varies with body posture and size, and the proposed energy consumption model effectively enhances energy efficiency during signal transmission. This research contributes to optimizing WBANs for improved healthcare applications.

Utilizing Multimedia Data Transmission Technology for Interactive Design in Smart Home Products

In the actual production of smart home products, it is difficult to complete the matching design in specific stages. Therefore, the design workload at different stages will have a certain time gap. For the first time in this paper, user groups are divided into two categories, namely users, controllers, buyers and Experiencers. A new smart home system model is designed. Its innovation lies in the integrated system of Internet of things and smart home. Connect the home appliances to the network through the gateway, and communicate with mobile phones and computers. Through analyzing and comparing its characteristics, it is concluded that the controller is the most active response group of smart home products. The effectiveness of the protocol implementation is verified by writing corresponding simulation scenario scripts and general trace file processing scripts. The verification results show that the implementation of the protocol is effective. ZBR algorithm is the complementary advantage of the two routing algorithms.

Effective DDoS attack detection in software-defined vehicular networks using statistical flow analysis and machine learning.

Vehicular Networks (VN) utilizing Software Defined Networking (SDN) have garnered significant attention recently, paralleling the advancements in wireless networks. VN are deployed to optimize traffic flow, enhance the driving experience, and ensure road safety. However, VN are vulnerable to Distributed Denial of Service (DDoS) attacks, posing severe threats in the contemporary Internet landscape. With the surge in Internet traffic, this study proposes novel methodologies for effectively detecting DDoS attacks within Software-Defined Vehicular Networks (SDVN), wherein attackers commandeer compromised nodes to monopolize network resources, disrupting communication among vehicles and between vehicles and infrastructure. The proposed methodology aims to: (i) analyze statistical flow and compute entropy, and (ii) implement Machine Learning (ML) algorithms within SDN Intrusion Detection Systems for Internet of Things (IoT) environments. Additionally, the approach distinguishes between reconnaissance, Denial of Service (DoS), and DDoS traffic by addressing the challenges of imbalanced and overfitting dataset traces. One of the significant challenges in this integration is managing the computational load and ensuring real-time performance. The ML models, especially complex ones like Random Forest, require substantial processing power, which necessitates efficient data handling and possibly leveraging edge computing resources to reduce latency. Ensuring scalability and maintaining high detection accuracy as network traffic grows and evolves is another critical challenge. By leveraging a minimal subset of features from a given dataset, a comparative study is conducted to determine the optimal sample size for maximizing model accuracy. Further, the study evaluates the impact of various dataset attributes on performance thresholds. The K-nearest Neighbor, Random Forest, and Logistic Regression supervised ML classifiers are assessed using the BoT-IoT dataset. The results indicate that the Random Forest classifier achieves superior performance metrics, with Precision, F1-score, Accuracy, and Recall rates of 92%, 92%, 91%, and 90%, respectively, over five iterations.

Cyber policy for monitoring and regulating cyberspace and cyber security measures for combating technologically enhanced crime in South Africa

Advancements in science and technology has resulted in substantial transformations inside society. The telecommunications sector has established cyberspace, a virtual domain for online communication, data exchange, and digital interactions. This platform enables individuals to interact and cultivate relationships worldwide, fostering a sense of proximity despite geographical separation. Nonetheless, these improvements provide concerns, since there has been an increase in cyber victimisation via cyberspace, the internet, computer networks, and online systems. Criminals are progressively utilising these technologies to perpetrate illicit actions, endangering individuals, governments, organisations, and institutions.This study analyses the evaluation of cyber policies for regulating, monitoring, and supervising cyberspace activities, along with the implementation of cybersecurity measures to address technologically advanced crime. A mixed methods strategy, including qualitative and quantitative methodologies, was employed for data collecting. The research revealed that although cyberspace provides avenues for social contact, idea exchange, information dissemination, commercial transactions, and additional activities, it remains susceptible to cyber threats including ransomware, fraud, warfare, terrorism, trafficking, and breaches of privacy. Formulating cyber regulations to regulate actions in cyberspace is crucial for efficient law enforcement, alongside the establishment of a cybersecurity framework to safeguard critical cyber systems and bolster security protocols across several sectors. To safeguard both the public and private sectors against cybersecurity risks, it is essential to create and execute plans. These tactics encompass the regular evaluation and auditing of security systems, investment in cybersecurity training programs, maintenance of current systems, formulation and rehearsal of incident response plans, and dissemination of information among experts and professionals.

Review of spatial layout planning methods for regional multi-station integration

In order to accelerate the high-quality development of China’s infrastructure, it is not only necessary to ensure the continuation and efficiency improvement of the original infrastructure, but also to promote the layout, implementation, improvement and quality improvement of new infrastructure. As an important part of the energy Internet and new power system, “multi-station integration” plays a role in strengthening digital transformation, intelligent upgrading, integration and innovation support, achieving the nearby integration of resources and the coordination and complementarity of operation, reducing redundant design, so that users, enterprises and industries in the region can reduce costs, energy saving and emission reduction, improve quality and efficiency. By combing the spatial layout planning methods, models and influencing factors of traditional single function station and multi-station integration in the region, the influences of the function station itself and other surrounding infrastructure on the function station are analyzed. Combined with the application scenario, load demand and integration mode of multi-station integration, it is proposed to combine common factors and characteristic factors. The objective evaluation method is introduced to improve the practicability and suitability of the layout location. Finally, the three-layer theoretical framework is proposed to improve and supplement the existing layout principles and methods, so as to provide reference suggestions for the scientific location selection and optimization of regional multi-station integration.

Improvement of intrusion detection system in industrial Internet of Things based on deep learning with fog computing capability

Improvement of intrusion detection system in industrial Internet of Things based on deep learning with fog computing capability

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

Adaptive Hybrid Optimized LSTM Models: A Novel Computational Framework for Algorithmic Financial Trading System

In our modern society, with the development of Internet and information system, pre-programing algorithmic trading strategies for online automatic trading has also flourished, especially in the rapidly fluctuating trading market. Using quantitative trading programs that can automatically trade in response to market conditions has attracted the attention of major financial institutions and governments in the financial market. How to use self-adaptive programs to automatically trade in the ever-changing financial market has become a popular research topic for the development and pursuit of all financial markets in recent years. This research proposes an online self-adaptive trading algorithm that can be applied to financial markets such as stock market, currency markets, cryptocurrency markets, futures markets, etc. In the first part of the algorithm, the simplified swarm optimization will be used to optimize the parameters of the newly proposed flexible grid in this research. Then the data will be imported into the artificial neural network model for training in the latter part, helping the trading model automatically select the appropriate parameters for construction flexible grid corresponding to the market conditions. The greatest contribution of the research is to provide a whole new trading algorithm that can adapt to the dynamic trading market, automatically make suitable adjustments to current trading strategy and place real-time orders. The algorithm controlling both profit and risk, which can seem like a balanced trading algorithm that are robust and profitable.

Artificial Intelligence: A Challenge to Scientific Communication.

Recent years have seen formidable advances in artificial intelligence. Developments include a large number of specialised systems either existing or planned for use in scientific research, data analysis, translation, text production and design with grammar checking and stylistic revision, plagiarism detection, and scientific review in addition to general-purpose AI systems for searching the internet and generative AI systems for texts, images, videos, and musical compositions. These systems promise more ease and simplicity in many aspects of work. Blind trust in AI systems with uncritical, careless use of AI results is dangerous, as these systems do not have any inherent understanding of the content they process or generate, but only simulate this understanding by reproducing statistical patterns extracted from training data. This article discusses the potential and risk of using AI in scientific communication and explores potential systemic consequences of widespread AI implementation in this context.

Swarm intelligence for intrusion detection systems in internet of things environments

Swarm intelligence for intrusion detection systems in internet of things environments

Improved Particle Swarm Optimization based Bidirectional-Long Short-Term Memory for Intrusion Detection System in Internet of Vehicle

Improved Particle Swarm Optimization based Bidirectional-Long Short-Term Memory for Intrusion Detection System in Internet of Vehicle

Dynamic hierarchical intrusion detection system for internet of vehicle on edge computing platform

AbstractIn recent days, the Internet of Vehicles (IoV) and its network of connected automobiles have revealed several new security risks. Classical intrusion detection systems face challenges in identifying intrusions due to the growing number of vehicles, the dynamic nature of IoV, and limited resources. A hierarchical clustering method allows dividing the IoV network into clusters. The elements that determine the outcome are the geographical proximity and the traffic density. It is called the Dynamic Hierarchical Intrusion Detection Framework (DHIDF) for the IoV. To protect infrastructure and passengers, an IoV‐specific DHIDF using edge computing has been proposed. Because of this, anomaly detection and localised assessment of danger will become less required. The application of DHIDF on a large scale inside the ecosystem of IoV is not entirely out of the question. The term encompasses several subfields, including intelligent transportation networks (ITNs), smart city infrastructure, fleet management, transportation, and autonomous vehicle systems. The efficacy of DHIDF is assessed through simulations that replicate current and potential future threats, including those related to the Internet of Things. Analysis of key performance parameters, including response time, detection accuracy, asset utilization, and scalability, has been conducted to assess the system's feasibility and durability.

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.

Improving device access efficiency using a device protocol matching model

The connectivity of devices and systems in the Industrial Internet of Things (IIoT) enables interoperability and collaboration between industrial systems. Device access is the pathway to achieve connectivity, while protocol matching is the basis for device access. Protocol matching is a complex task due to the diverse range of device types, numerous protocols, the issues related to protocol privatization, and the reliance on domain knowledge. These complexities result in inefficient device access. To improve the device access efficiency, a Device Protocol Matching Model (DPMM) is proposed in this paper, which uses only the basic device information to find the best-matched protocol, including protocol identification and basic data. The DPMM adopts a two-stage strategy, consisting of an ontology creation stage and a protocol matching stage. In the ontology creation stage, a simplified device ontology is built to enable the uniform expression of device information and the representation of domain knowledge. In the protocol matching stage, a protocol matcher based on the Two-layer Cooperative Iteration (TCI) algorithm is designed to find the best-matched protocol. In the TCI, to achieve the global optimization of protocol matching efficiency, a penalty mechanism-based weight update method and learning-based matcher evolution are designed. Experiments in two scenarios: a communication base station and a copper smelting production line, are conducted to validate the effectiveness of the DPMM. The experimental results demonstrate that the DPMM can achieve automatic protocol matching with an average matching index of 80.3% and an average hit rate of 35.1%. Moreover, it significantly reduces network resource consumption by up to 96.7%, and increases the hit rate by up to 12.1 times compared with the existing methods.

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

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

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.