Large-scale Internet Research Articles

Global evaluation of the natural environment and household income for sustainable development

With global economic growth, the burden on the natural environment is severe, and more and more attention is paid to sustainable development that enhances the economic growth and sustainability of the natural environment. This study aims to show the concern of global citizens regarding the natural environment and household income. It used worldwide individual interview data from 149 countries between 2007 and 2021, with 2,450,043 observations (Gallup World Poll surveys) and an original large-scale internet- and face-to-face-survey from 37 nations with 100,956 observations. Using a binary logit regression model, this study also analyzes public sentiment regarding who should bear the financial burden for environmental conservation. We found that 60 % of citizens showed that their current income provides a comfortable life; on the other hand, 49 % of the respondents were dissatisfied with conservation of the natural environment. The satisfaction with household income was much greater than satisfaction with natural environmental conservation, leaving space for improvement in environmental stringency. For citizens’ preferences regarding financial responsibility, the government and companies are expected to afford conservation costs.

Health utility attributable to oral conditions in Japanese adults.

Utility values enable relative comparisons across various health conditions, providing information for efficient allocation of healthcare resources. This study aimed to (1) quantify the utility values attributable to oral health-related quality of life (OHRQoL) in Japanese adults and (2) develop models for converting the 14-item Oral Health Impact Profile (OHIP-14) scores into EuroQoL 5-dimension 5-level (EQ-5D-5L)-based utility values. This was a cross-sectional study. Data from a large-scale Internet survey of Japanese adults conducted in 2022 (n = 28 405; mean age 48.2 years) were analysed. Multiple linear regression models were fitted to investigate the association between OHIP-14 scores and EQ-5D-5L-based utility values, adjusting for confounders. Conversion models were developed using a random half of the participants, and the observed and predicted utility values in the other half were compared to evaluate the model performance. Among the participants, 55.2% scored 0, 20.9% scored 1-5 and 23.9% scored 6-56 on the OHIP-14, corresponding mean utility values of 0.93, 0.90 and 0.84, respectively. A one-point increase in the OHIP-14 score was associated with a lower utility value (coefficient: -0.0053; 95% confidence interval:health-related quality of life -0.0056, -0.0051). The estimated utility value attributable to OHIP-14 was -23.3 per 1000 individuals, greater than that for other prevalent chronic conditions, including hypertension and diabetes (-2.9 and -7.1 per 1000 individuals, respectively). The conversion model incorporated the OHIP-14 total score, age, sex and self-rated health, predicted utility scores on average and captured differences according to the number of teeth lost. However, there was a discrepancy between predicted and observed utility values in the lower utility value groups. OHRQoL substantially impacted utility values at the population level. The OHIP-14 holds the potential as a valuable tool for predicting average utility values based on oral health conditions; however, the prediction performance was relatively low for individuals with a lower health-related quality of life.

Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management.

State-of-the-art smart cities have been calling for economic but efficient energy management over a large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze, and control electric loads of all users in the system. In this study, a non-intrusive load monitoring method was designed for smart power management using computer vision techniques popular in artificial intelligence. First of all, one-dimensional current signals are mapped onto two-dimensional color feature images using signal transforms (including the wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods. Second, a deep neural network with multi-scale feature extraction and attention mechanism is proposed to recognize all electrical loads from the color feature images. Third, a cloud-based approach was designed for the non-intrusive monitoring of all users, thereby saving energy costs during power system control. Experimental results on both public and private datasets demonstrate that the method achieves superior performances compared to its peers, and thus supports efficient energy management over a large-scale Internet of Things network.

An Investigative Study on Fault-Tolerant Design Principles for Future Internet

Fault tolerance is crucial for ensuring uninterrupted service delivery in future internet architectures. This paper presents an investigative study on fault-tolerant design principles tailored to the evolving needs of future internet systems. Beginning with a comprehensive review of existing fault tolerance techniques in current internet architectures, the study identifies the limitations and challenges traditional approaches face in meeting the requirements of future internet environments. Fundamental concepts of fault tolerance are discussed, including fault types, detection mechanisms, and recovery strategies. Essential requirements and challenges for future internet architectures are identified, encompassing scalability, heterogeneity, dynamicity, security, and resource constraints. Building upon this foundation, the paper presents a set of comprehensive fault-tolerant design principles customized for future internet systems. These principles address architectural considerations, fault detection and isolation mechanisms, and recovery strategies. Case studies and real-world examples illustrate the application of these design principles in large-scale internet platforms and distributed systems. The paper outlines future research directions and opportunities for further innovation in fault-tolerant design for future internet architectures.

Efficient Optimization approach in WSN using Multicast Forwarding Scheme in 6LowPAN Environment

The rapidly growing network of devices that are connected known as the Internet of Things, also referred to as IoT, is designed to collect and exchange data. These devices are integrated with sensors, software, and other technologies. An IPv6-based routing system called 6LowPAN was created especially for Internet of Things devices with little resources. However, because multicast packets must be replicated to every multicast group member, the conventional multicast forwarding technique in 6LowPAN may be wasteful. This can result in excessive energy usage and network traffic, particularly in large-scale Internet of Things implementations. An optimization process is presented here using a multicast forwarding scheme. In the proposed forwarding scheme, nodes can send multicast packets up and down at Low Power and Lossy networks over the 6LowPAN environment. Should the sink node be on the root node, it should immediately request that other nodes join the multicast messaging network of things. If otherwise, an ICMPv6 packet will be sent towards the root node in the Destination-oriented Directed-Acyclic Graphs (DODAG) tree, which expands the multicast messages in the first source's interest. In multi-bounce forward straight-line topology, when the DODAG network root is the source of multicast congestion, it exhibits a similar ratio of packet delivery and delay from end to end with a minimal memory overhead. When traffic via multicast originates from the most important rank node in a backward straight-line topology, this paper focuses on the efficiency of the multicast network at 6LowPAN.

A hybrid convolutional neural network-transformer method for received signal strength indicator fingerprinting localization in Long Range Wide Area Network

In recent years, low-power wide area networks (LPWANs), particularly Long-Range Wide Area Network (LoRaWAN) technology, are increasingly being adopted into large-scale Internet of Things (IoT) applications thanks to having the ability to offer cost-effective long-range wireless communication at low-power. The need to provide location-stamped communications to IoT applications for meaningful interpretation of physical measurements from IoT devices has increased demand to incorporate location estimation capabilities into LoRaWAN networks. Fingerprint-based localization methods are increasingly becoming popular in LoRaWAN networks because of their relatively high accuracy compared to range-based localization methods. This work proposes hybrid convolutional neural networks (CNNs)-transformer fingerprinting method to localize a node in a LoRaWAN network. CNNs are adopted to complement the strengths of the Transformer by adding the ability to capture local features from input data and consequently allow the Transformer, through the attention mechanism, to effectively learn global dependencies from the input data. Specifically, the proposed method works by first learning the local location features from the input data using the CNNs and passing the resulting information to the transformer encoder to learn global features from the input data. The output of the transformer encoder is then concatenated with information learned at the local level and then passed through the regressor for the final location estimation. With a localization performance of 290.71 m mean error achieved, the proposed method outperformed similar state-of-the-art works in the literature evaluated on the same publicly available LoRaWAN dataset.

RSSI-based fingerprint localization in LoRaWAN networks using CNNs with squeeze and excitation blocks

The ability to offer long-range, high scalability, sustainability, and low-power wireless communication, are the key factors driving the rapid adoption of the LoRaWAN technology in large-scale Internet of Things applications. This situation has created high demand to incorporate location estimation capabilities into large-scale IoT applications to meaningfully interpret physical measurements collected from IoT devices. As a result, research aimed at investigating node localization in LoRaWAN networks is on the rise. The poor localization performance of classical range-based localization approaches in LoRaWAN networks is due to the long-range nature of LoRaWAN and the rich scattering nature of outdoor environments, which affects signal transmission. Because of the ability of fingerprint-based localization methods to effectively learn useful positional information even from noisy RSSI data, this work proposes a fingerprinting-based branched convolutional neural network (CNN) localization method enhanced with squeeze and excitation (SE) blocks to localize a node in LoRaWAN using RSSI data. Results from the experiments conducted to evaluate the performance of the proposed method using a publicly available LoRaWAN dataset prove its effectiveness and robustness in localizing a node with satisfactory results even with a 30% reduction in both the principal component analysis (PCA) variances on the training data and the size of the original sample. A localization accuracy of 284.57 m mean error on the test area was achieved using the Powed data representation, which represents an 8.39% increase in localization accuracy compared to the currently best-performing fingerprint method in the literature, evaluated using the same LoRaWAN dataset.

Co-Sharding: A Sharding Scheme for Large-Scale Internet of Things Application

Blockchain technology finds widespread application in the management of Internet of Things (IoT) devices. In response to the challenges posed by performance scalability and the convergence of multiple ledgers stemming from an expanding network, this study introduces the concept of Co-Sharding . Within this framework, the ledger maintained by sub-chains overseeing IoT operations in distinct geographic regions is conceptualized as a shard within the Large-scale Internet of Things (LIoT) ledger. Meanwhile, elected nodes within each region assume responsibility for maintaining a coordinating shard, facilitating cross-regional communication and data interaction. Furthermore, our work presents a multi-objective optimization algorithm grounded in the multi-shard paradigm to enact a scheduling strategy that spans various regions. We undertake a series of pertinent experiments and conduct a comparative analysis of scheduling algorithms within the context of a real-world cross-regional agricultural IoT system, utilizing actual operational data. The comparative results demonstrate that, in comparison to intra-sub-region scheduling, the Co-Sharding approach enhances machine utilization rates by approximately 30% and reduces scheduling time by around 18% when confronted with a task count of 12. In terms of performance, Co-Sharding also exhibits the capability to reduce the storage requirements of lightweight nodes within each region by approximately 39% while concurrently improving throughput by approximately 1.5 times when contrasted with a single-chain architecture.

Polarization Insensitive and Thin Metamaterial Absorber Performed in High-Frequency 5G Bands

A variety of fascinating applications, including 5G communication devices, high-speed data transfer, and large-scale Internet of Things (IoT), make life easier with 5G technology. Despite the 5G’s superior features, the percentage of electromagnetic (EM) waves in the environment execute a significant increase, unpleasantly. Broadband metamaterial absorbers are an appealing alternative to gather these unwanted signals. This study aims to numerically investigate a broadband metamaterial absorber (MMA) in the 5G high-frequency spectral range with the metasurface formed with coupled resistors. In addition, the 24.25-27.5GHz frequency range, one of the high-frequency 5G bands used by selected countries such as the European Union and China, was preferred. The minor aim of this study is that the usage of coupled elements as resistors may have the ability to increase the absorption bandwidth and magnitude. Comprehensive simulations were performed using the finite integration technique (FIT) utilized by the CST Microwave Studio program to investigate the absorber performance and other relevant parameters. The unit cell design is created metal-substrate-metal structures as asymmetric, single-layer, and easy to implement. The absorption responses are investigated according to the oblique incidence angle, polarization angle for TE &TM modes. The suggested MMA provided an absorbency response above 87.6% in the frequency range 24.20-27.30GHz under normal incidence. Moreover, to comprehend the physical mechanism on absorption, the top and bottom surfaces of the absorber's electric field and surface current distributions are assessed. The designed MMA resulting in relatively high performance and polarization insensitive is helpful for electromagnetic interference (EMI) shielding of 5G signals in the FR2/mmWave frequency regime.

Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning

With the rapid growth of IoT devices, the threat of botnets is becoming increasingly worrying. There are more and more intelligent detection solutions for botnets that have been proposed with the development of artificial intelligence. However, due to the current lack of computing power in IoT devices, these intelligent methods often cannot be well-applied to IoT devices. Based on the above situation, this paper proposes a distributed botnet detection method based on graph partitioning, efficiently detecting botnets using graph convolutional networks. In order to alleviate the wide range of IoT environments and the limited computing power of IoT devices, the algorithm named METIS is used to divide the network traffic structure graph into small graphs. To ensure robust information flow between nodes while preventing gradient explosion, diagonal enhancement is applied to refine the embedding representations at each layer, facilitating accurate botnet attack detection. Through comparative analysis with GATv2, GraphSAGE, and GCN across the C2, P2P, and Chord datasets, our method demonstrates superior performance in both accuracy and F1 score metrics. Moreover, an exploration into the effects of varying cluster numbers and depths revealed that six cluster levels yielded optimal results on the C2 dataset. This research significantly contributes to mitigating the IoT botnet threat, offering a scalable and effective solution for diverse IoT ecosystems.

Joint Waveform Design and Detection in Symbiotic Ambient Backscatter NOMA Systems

Non-orthogonal multiple access (NOMA) and symbiotic ambient backscatter communications (AmBC) are both considered promising technologies for beyond 5G mobile communication technology by enabling low-powered and spectrum-efficient access in large-scale Internet of Things. In this paper, we investigate the uplink symbiotic communication in AmBC enabled NOMA system. In contrast with existing works, we assume the carrier transmitter (CT) transmits information while providing energy to the backscatter devices (BDs), which improves the energy efficiency. However, the signal power transmitted by the CT to integrated receiver (IR) through the direct link is much larger than the signal reflected by the BD, which can seriously affect the detection of the reflected signal by BD. Therefore, we jointly design the signal waveforms of CT and BDs, and propose a multi-user blind detection algorithm based on interference cancellation at IR. The simulation results demonstrate that the proposed multi-user detection algorithm achieves improved performance even the active BD number is unknown or without direct link.

Modeling Distributed and Configurable Hierarchical Blockchain over SDN and Fog-Based Networks for Large-Scale Internet of Things

Modeling Distributed and Configurable Hierarchical Blockchain over SDN and Fog-Based Networks for Large-Scale Internet of Things

Enabling large-scale low-power LoRa data transmission via multiple mobile LoRa gateways

Low-Power Wide Area Networks (LPWANs) enable large-scale Internet of Things (IoT) applications. LoRa is a promising LPWAN technology, but LoRa nodes are battery-powered, so their lifetime depends on energy efficiency. In practice, LoRa nodes have short battery life because long transmission distances to gateways incur high energy costs. With expensive battery replacement, particularly in large networks, it is crucial to reduce LoRa nodes’ energy consumption. Existing low-power transmission techniques focus on static or single mobile gateways, consuming substantial remote node energy unfit for large-scale networks. This paper proposes integrating LoRa with mobility to minimize node energy use by shortening transmission distances. We design the first large-scale mobile LoRa system, MLoRaDrone, using multiple unmanned aerial vehicles (UAVs) equipped with LoRa gateways flying near nodes. First, we present a low-power mechanism for reliable node sensing and UAV communication. Next, we develop a LoRa transmission parameter assignment strategy encompassing a distributed ADMM-based optimal transmission policy and a 32-approximation channel allocation. Moreover, we propose a UAV trajectory scheduling scheme with a 4-approximation minimizing node energy and UAV flight paths. Evaluations on varied scales verify MLoRaDrone effectiveness for different node counts and UAV paths. Compared to baselines, MLoRaDrone reduces 3000-node energy consumption by up to 35.56× within 25 km2.

Multi-Region Asynchronous Swarm Learning for Data Sharing in Large-Scale Internet of Vehicles

Multi-Region Asynchronous Swarm Learning for Data Sharing in Large-Scale Internet of Vehicles

Explore Deep Reinforcement Learning to Energy-Efficient Data Synchronism in 5G Self-Powered Sensor Networks

Recently, the explosive data traffic growth and large-scale Internet of Things (IoT) equipment connection have brought 5G mobile communication technology many challenges. The communication delay of network services such as augmented reality, smart grid, disaster warning and emergency communication have been relieved by 5G. However, these services are still confronted with the serious challenge in energy conservation. Most of traditional optimization methods usually need complex operations and iterations to get the optimal results, which are not suitable for communication systems with high real-time performance. Based on this argument, this paper has focused on the green communication in Mobile Edge Computing (MEC)-based self-powered sensor network and proposed a novel approach called MIDS (Mobile Intelligent Data Synchronization based on deep reinforcement learning). It exploits Deep Deterministic Policy Gradient (DDPG), continuous interacting with the environment and making tryouts to evaluate the feedback from the environment to optimize future decision-making on the path selection scheme for data transmission as well as achieve the energy efficiency and energy consumption balance of mobile devices with self-powered sensors. We provide experiments to show the capability of our approach in reducing the additional energy consumption of mobile devices and the base station as well as balancing the energy consumption of mobile devices with sensor communication. The superiority of our approach for energy conservation in data synchronization is convincingly demonstrated by comparing with state-of-the-art methods in MEC-based self-powered sensor network.

ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN TECHNOLOGY FOR SECURE DATA AND PRIVACY

Data, which is then processed in order to extract the desirable characteristics, serves as the input for a number of different AI algorithms. However, the facts that can be found on the internet are incredible and tough to authenticate. Given the complexity of the internet, it is quite challenging to validate the data for the consumers. Consequently, in this research, we suggested using SecNet as a solution. An architecture that assists in the protection of data storage, the processing of data, and the sharing of large-scale Internet settings is known as SecNet. The primary objective of this architecture is to enhance the performance of artificial intelligence algorithms across a variety of data sources in order to provide a cyberspace that is more safe and to make use of actual big data. This architecture combines and supplies the following three primary components: 1) The trading of data based on a blockchain is carried out with the ownership of the data being assured. This enables the interchange of accurate data in a wide-ranging environment and contributes to the formation of actual "big data. 2) The protection of an AI-based secure computing platform that is powered by artificial intelligence to develop more astute security standards and contribute to the establishment of a cyberspace that is more reliable. 3) The trustworthy value-sharing Security Service buy Mechanism gives participants a fantastic opportunity to gain Economic Rewards for the provision of data or services, which makes data sharing easier and ultimately leads to improved AI performance".

Intelligent Resource Allocation in Backscatter-NOMA Networks: A Soft Actor Critic Framework

With the use of power domain non-orthogonal multiple access (NOMA) and backscatter communication (BAC), future sixth-generation ultra massive machine-type communications networks are expected to connect large-scale internet of things (IoT) devices. However, due to NOMA co-channel interference, the power allocation to large-scale IoT devices becomes critical. The existing convex optimization-based solutions are highly complex, and therefore it is difficult to find the optimal solution to the resource allocation problem in a highly dynamic environment. To alleviate this problem, this work develops an efficient model-free BAC approach with a NOMA system to assist the base station with complex resource scheduling tasks in a dynamic BAC-NOMA IoT network. The objective is to increase the sum rate of uplink backscatter devices. More specifically, we jointly optimize the transmit power of downlink IoT users and the reflection coefficient of uplink backscatter devices using a reinforcement learning algorithm, namely, the soft-actor critic (SAC) algorithm. With the advantage of entropy regularization, the SAC agent learns to explore and exploit the dynamic BAC-NOMA network efficiently. The proposed algorithm ensures the quality of service (QoS) requirements of downlink users while enhancing the sum rate of uplink backscatter devices. Numerical results reveal the superiority of the proposed algorithm over the conventional optimization (benchmark) approach in terms of the average sum rate of uplink backscatter devices. We show that the network with multiple downlink users obtained a higher reward with respect to a large number of iterations compared to episodes with a lower number of iterations. Moreover, the proposed algorithm outperforms the benchmark scheme and BAC with orthogonal multiple access in terms of the average sum rate with the different number of backscatter devices. Additionally, we show that our proposed algorithm enhances sum rate efficiency with respect to different self-interference coefficients and different noise levels. Finally, we evaluate and show the sum rate efficiency of the proposed algorithm with different QoS requirements and cell radii.

A Cooperative Attack Detection Framework for MANET-Iot Network Using Optimized Gradient Boosting Convolutional Neural Network

The implementation of large-scale Internet of Things (IoT) devices results in smart cities. Using standard mobile ad-hoc networks and IoT, developers establish the communication model for a smart city. The rapid growth of IoT devices based on smart cities poses different Quality of Service (QoS) and security problems. This research presents a novel Modified Elephant Herd Optimization (MEHO) method and a Gradient Boosting Convolutional Neural Network (CNN) strategy to address these issues. The cooperative attacks with varied disruption probabilities are initially assessed at the edge nodes of the IoT network. The MEHO-based Gradient Boosting CNN (MEHO-CNN) approach effectively detects cooperative attacks, ensuring the identification of malicious entities. For traditional cloud access, both bandwidth utilization as well as expected latency are minimized in edge computing. By using the IoT network, the proposed MEHO-CNN model identifies and eliminates malicious nodes. To establish the claimed trustworthy background, the legitimate accusations are based on an examination of trust-based allegations. When compared to existing methodologies, the proposed approach lowers the impact of cooperative attacks, resulting in increased throughput, reduced attack detection rates, lower packet loss ratio, lower packet delivery ratio, and other benefits.

Dental Treatment and Risk of COVID-19 in Japan.

During the Coronavirus Disease 2019 (COVID-19) pandemic, there has been concern about nosocomial infections acquired through dental practice, where machines - such as air turbines - that generate aerosols are used, and where there are many opportunities to come into contact with saliva and blood. Because there is no report to date on whether dental treatment is associated with a risk of SARS-CoV-2 infection in Japan, the aim of the present cross-sectional study was to examine the risk of SARS-CoV-2 infection associated with dental treatment. Cross-sectional data were gathered from the Japan COVID-19 and Society Internet Survey (JACSIS), a large-scale internet survey conducted in 2021 (n=28,175). From September 27, 2021, to October 30, 2021, the questionnaires were distributed to candidates selected from the panelists of a Japanese Internet research company to represent the Japanese population regarding age, sex, and residential prefecture using a simple random sampling procedure. The risk of SARS-CoV-2 infection related to dental treatment was examined and analysed. Multivariable logistic regression analysis showed that younger age, male sex and living alone were statistically significant factors positively associated with SARS-CoV-2 infection, whereas the presence or absence of dental treatment was not statistically significantly correlated with SARS-CoV-2 infection. The present epidemiological study showed that dental treatment is not a positive risk factor for SARS-CoV-2 infection in Japan.

SAV-D: Defending DDoS with Incremental Deployment of SAV

Large-scale Internet Protocol (IP) spoofing distributed denial-of-service (DDoS) attacks is one of the major cyber threats. Current commercial defenses focus on eliminating attacks at the destination end, which raises concerns about the cost of appliances and the impact on quality of service. As complementaries, source-end schemes using source address validation (SAV) can block IP spoofing traffic from entering the backbone. However, their effectiveness is restricted by incremental deployment. This paper proposes SAV-D, an SAV-based honeynet-like distributed defense architecture against IP spoofing DDoS. Each SAV device functions as a honeypot to capture more threat data. By aggregating these data, SAV-D can accurately detect ongoing attacks and generate defense policies. With the policies, both SAV and non-SAV devices can filter malicious traffic. Our simulation results demonstrate that SAV-D can effectively filter out 80% of attack traffic with a modest deployment ratio of only 10%. To enable broader adoption, we also provide some guidance on standardizing SAV-D.