Data Communication Research Articles

HydroRTC: A web-based data transfer and communication library for collaborative data processing and sharing in the hydrological domain

The exponential growth in data generated by satellites, radars, sensors, and analysis and reanalysis from model outputs for the hydrological domain requires efficient real-time data management and distribution mechanisms. This paper introduces HydroRTC, a web-based data transfer and communication library designed to accelerate large-scale data sharing and analysis. Leveraging next-generation web technologies like WebSockets, WebRTC and Node.js, the library enables seamless peer-to-peer sharing, smart data transmission, and large dataset streaming. Three primary scenarios are presented as use cases, demonstrating the potential of HydroRTC as server-to-peer with intelligent data scheduling and large data streaming, peer-to-peer data sharing, and peer-to-server for data exchange. HydroRTC offers a promising solution for collaborative infrastructures in the hydrological and environmental domain, allowing real-time and high-throughput data sharing and transfer for enhancing research efficiency and collaboration capabilities.

Investigation of the precision of a novel jaw tracking system in recording mandibular movements: A preliminary clinical study

ObjectivesThis preliminary study aimed to clinically assess the precision of a novel optical jaw tracking system (JTS) in registering mandibular movements (MMs) of protrusion and mediotrusion. MethodsTwenty healthy participants underwent recordings using Cyclops JTS (Itaka Way Med) for functional MMs of protrusion and laterotrusion by two trained clinicians. Each subject performed five registrations at different times according to a standardized pattern within one-month period. The angulations of protrusive and mediotrusive functional paths within the first 2 mm from the maximal intercuspal position (MIP) were calculated for each trace, using a data software for angle measurements. Descriptive statistics were used to assess the repeatability of the recordings for each participant and MM. Additionally, inferential statistics were carried out on standard deviation values obtained (α=0.05). ResultsThe overall precision for all the patients was 7.07±3.37° for the protrusion angle, 5.24±2.24° for right laterotrusion and 5.14±3.06° for left laterotrusion angles. The protrusion angle ranged from 3.08° to 13.57°, while the right and left laterotrusion ranged from 1.82° to 9.42° and from 1.58° to 10.59°, respectively. No statistically significant differences were observed between different functional MM types and gender (p > 0.05). ConclusionsRecordings functional MMs of mediotrusion and protrusion using Cyclops JTS showed consistent repeatability, regardless of gender and functional MM type. The results revealed non-negligible variations that may be due to the patients’ abilities to precisely reproduce jaw movements or to the operator's ability to consistently connect the kinesiograph. Clinical significanceCapturing functional MMs digitally and importing the data into dental CAD software is essential for virtual waxing in prosthetic rehabilitations to design a functionalized adapted occlusion. Establishing the repeatability of MM recordings by a JTS is a crucial step in better understanding this novel JTS in the market. This process could facilitate the interpretation of cusp angles, aid in CAD dynamic technical modeling, and enhance clinical data communication between clinicians and technicians in a modern workflow.

Application of neural network algorithm in computer data information processing system

In order to solve the problem that the data processing frequency of the traditional centralized data processing system is low, which leads to the poor feedback effect of massive data, this paper proposes a computer data information processing system based on neural network algorithm. Based on the original system hardware, the system replaces the data processor and increases the total number of the processor, so as to realize the distributed synchronous processing of massive data. In the aspect of software design, the interaction between system units and modules is formed through the protocol to improve the data communication mode of the system; Calculate the Euclidean distance and set the distributed processing mode of the system; According to the definition of cloud computing, the classification function is used to determine the constraints, establish the processing frequency equation, and realize the rapid processing of massive data. The experimental results show that the processing frequency of the system designed in this paper is more than 50 % higher than that of the traditional system, which solves the problem of low processing frequency caused by weak analysis ability and slow response speed in the traditional system. Conclusioncompared with the traditional system, the system designed in this paper has faster processing frequency for massive data and better feedback effect to users.

A Cooperative Communication System With P-LDPC For Internet of Underwater Things

The acoustic communications are extremely challenging in underwater environments due to harsh conditions of this kind of nature. That medium's harshness affects the transmission reliability which in turn crucially requires an adoption of robust channel codes based on cooperative approaches to attain a more reliable data communication. This study proposes a cooperative communication system based on a protograph low-density parity check (P-LDPC) code. The system is elaborated in an Internet of underwater things (IoUT) scenario in which a relay node is acting as a helper that provides side information to destination. Compression and channel coding are offered by the system to cope with requirements of IoT in terms of reliable communication and compressed data between nodes. Furthermore, an algorithm for joint source channel coding is presented based on a modified version of offset min-sum. Moreover, layered decoding is Implemented in order to minimize number of iterations for the P-LDPC code. The results prove that proposed design can achieve a bit error rate (BER) of 5 × 10−6 at signal to noise ratio SNR = 8 dB for a coding rate of 1/3 and block length of 1500 bits. In addition, the proposed system shows a coding gain of 3.6 dB when compared with a non cooperative (no relay) approach. Besides, the proposed cooperative design can offer reduced computations versus the non cooperative system to achieve the same BER at a fixed SNR.

Enhanced stretchability of porous PDMS/CIP composites via weak interfacial bonding and their electromagnetic noise suppression properties

The development of wearable devices operated through massive data communication necessitates the use of lightweight, highly efficient stretchable composites to suppress electromagnetic (EM) noise. In this study, highly stretchable porous polydimethylsiloxane (PDMS)/carbonyl iron powder (CIP) composite-based EM noise suppressors were explored. The porous structure reduced the volume fraction of PDMS in the composites, leading to a reduction in the total weight of the composites. The introduction of CIP to porous PDMS initially degraded stretchability owing to stress concentration at the strongly bonded interfaces between the CIP surface and PDMS. However, reducing the interfacial bonding strength by removing the SiO2 layer on the CIP surface significantly enhanced stretchability, causing the resulting composite to display 1.2–1.6 times higher rupture strain values at the same CIP loading contents than those with high interfacial bonding strength. The EM absorption properties of the porous and nonporous PDMS/CIP composites were characterized, and the porous PDMS/CIP composites exhibited ∼1.5 times higher EM noise suppression performance than the nonporous composites. This improvement is attributed to the vacant space within the porous framework filled with air, which enhances impedance matching and extends the transmission route of EM waves.

Autonomic end-to-end quality-of-service assurance over QKD-secured optical networks

Quantum key distribution (QKD) provides future-proof security for data communications over optical networks. Currently, sophisticated QKD systems are developed and the scale of QKD-secured optical networks (QKD-ONs) becomes larger. Given the complex network conditions and dynamic end-to-end security services in QKD-ONs, autonomic management and control becomes a promising paradigm to support end-to-end quality-of-service (QoS) assurance in an efficient and stable way without requiring human intervention. Hence, to enable and utilize the autonomic functionalities over QKD-ONs for realizing the end-to-end QoS assurance becomes a challenge. This work enhances the software defined networking (SDN) technique to tackle this challenge because SDN can add programmability and flexibility for QKD-ON’s management and control. A new architecture of SDN-based QKD-ONs supporting autonomic end-to-end QoS assurance is designed, where a knowledge engine with autonomic control loops is developed in the SDN controller. We present the autonomic end-to-end QoS assurance procedure, and the cross-layer collaborative QoS assurance (CLC-QA) strategy for implementing the autonomic functionalities in the network level over QKD-ONs. We also establish an experimental testbed of SDN-based QKD-ONs supporting autonomic end-to-end QoS assurance, and perform the numerical simulation to verify our proposed approaches. Experimental results demonstrate that our presented approaches can achieve the millisecond-level overall latency of 337 ms and 618 ms, during the first and second autonomic adjustment without human intervention in case of the autonomic QoS protection. Moreover, the CLC-QA strategy is evaluated under different traffic loads by being compared with the baseline strategy without cross-layer collaboration. It can improve 22.5% protection success ratio and save 5.7% average key consumption.

Preface

With great and successful prestigious history of MAEIE, the 2023 3rd International Conference on Mechanical Automation and Electronic Information Engineering (MAEIE 2023) was held in Nanjing, China during 15th to 17th, December 2023 via virtual format, gathering together about 100 delegates from all over the world.MAEIE 2023 is a great academic platform and is of great significance for disseminating cutting-edge information in disciplines and industry, promoting the transformation of scientific and technological achievements, and condensing academic achievements in the industry at this event.The Conference consisted of 3 plenary keynote speeches addressed by well-known scholars internationally (by Prof. Xiaofang Yuan from Hunan University, China, Assoc. Prof. He Chen from Hebei University of Technology, China, Prof. Aniruddha Bhattacharjya from SGT University, India, respectively), 8 oral and 18 poster presentations, in which a wide range of topics were covered and the most recent significant results were presented. They discussed such topics as Research on Three-dimensional Path Planning Methods for Autonomous Vehicles, Control Research for Underactuated Cranes with State Constraints, Use of Blockchain Technology in IoT Maintaining Cia Triad in Data Communication, among others. Besides, the attending scholars, experts, and researchers exchanged deeply on the latest research achievements and advanced experience in the industry development and shared their unique insights into development trends and research directions of the industry.MAEIE 2023 received submissions from home and abroad, but unfortunately some manuscripts did not pass the final review, which therefore doesn’t appear in this Conference Proceedings. The selected works included in the Proceedings are published in Journal of Physics: Conference Series, covering topics on Automated Instrumentation and Equipment, Molding Manufacturing and Automation, Intelligent Manufacturing Technology, Power Electronics Technology, Electronics and Communication Engineering, among others. They show readers the latest research results and application cases of mechanical automation and electronic information engineering. We firmly believe that by reading these papers, readers will deepen their understanding of these fields and appreciate their enormous significance and potential impact on contemporary society.We wish to acknowledge all the participants, and the support of our sponsors. We would also like to take this opportunity to thank the committee members, as well as the expert reviewers, for their endeavor in making the Conference possible and reviewing the manuscripts submitted. What’s more, we hope that the conference series MAEIE shall continue to the next many sessions and deliver its purpose as the arena for presenting and exchanging the ideas of research in the fields of mechanical automation and electronic information engineering and anything related.The Committee of MAEIE 2023List of Committee Member is available in this pdf.

EBIAS: ECC-enabled blockchain-based identity authentication scheme for IoT device

In the Internet of Things (IoT), a large number of devices are connected using a variety of communication technologies to ensure that they can communicate both physically and over the network. However, devices face the challenge of a single point of failure, a malicious user may forge device identity to gain access and jeopardize system security. In addition, devices collect and transmit sensitive data, and the data can be accessed or stolen by unauthorized user, leading to privacy breaches, which posed a significant risk to both the confidentiality of user information and the protection of device integrity. Therefore, in order to solve the above problems and realize the secure transmission of data, this paper proposed EBIAS, a secure and efficient blockchain-based identity authentication scheme designed for IoT devices. First, EBIAS combined the Elliptic Curve Cryptography (ECC) algorithm and the SHA-256 algorithm to achieve encrypted communication of the sensitive data. Second, EBIAS integrated blockchain to tackle the single point of failure and ensure the integrity of the sensitive data. Finally, we performed security analysis and conducted sufficient experiment. The analysis and experimental results demonstrate that EBIAS has certain improvements on security and performance compared with the previous schemes, which further proves the feasibility and effectiveness of EBIAS.

Research story telling: using the research journey map to communicate information, data, systems, and artifacts

AbstractThe Research Journey Map is introduced to guide researchers on creating engaging, meaningful and impactful presentations and publications. Built on the foundational work of the Hero's Journey by Joseph Campbell, this template helps technical researchers communicate information, data, systems and artifacts that result from research so that audiences grasp and embrace the research findings.

Adaptive sampling artificial-actual control for non-zero-sum games of constrained systems

Considering physical constraints encountered by actuators, this paper addresses the non-zero-sum game of continuous nonlinear systems with symmetric and asymmetric input constraints through aperiodic sampling artificial-actual control. Initially, the artificial system built by the improved Elman dynamic neural networks (EDNNs) has artificial-actual interaction with the physical system, which provides a new perspective for predicting the system state. By constantly learning and adjusting parameters, EDNNs can gradually approximate the dynamic behavior of the real system to achieve more effective control. Aiming at accommodating diverse input constraints, the non-quadratic value function constructed from a smoothly bounded function is devised. Then, the polynomial parameterized adaptive dynamic programming (ADP) is employed to approximate the solution of the coupled Hamilton–Jacobi equation (HJE), deriving optimal control laws for two players. To improve the efficiency of data communication, three adaptive sampling mechanisms including event-triggered mechanism (ETM) with relative threshold, dynamic ETM (DETM) and self-triggered mechanism (STM) are introduced in turn during the iterative learning process of control sequences. DETM further extends sampling intervals by incorporating internal dynamic variables, while STM determines the next trigger time through soft calculation without hardware monitoring. All three trigger modes can ensure the system stability while avoiding the Zeno phenomenon, and relevant proofs are given. Finally, the simulation validates the effectiveness of the designed algorithm and highlights the unique characteristics of each trigger mode.

Intrusion detection in machine learning based E-shaped structure with algorithms, strategies and applications in wireless sensor networks

In the everyday world of computer applications, from the cloud to the Internet of Things, distributed sensor networks are essential (IoT). These computer application devices are often connected to Arduino network connection and microcontrollers such sensors and actuators. Thus, a defensive network with an IDS serves as the need for contemporary networks. The intrusion detection system has unavoidably evolved throughout the years, but despite this, it remains a difficult study topic since the current intrusion detection system uses signature-based approaches rather than anomaly detection. Therefore, improving the current intrusion detection system is challenging since it is difficult to find zero-day attacks in IoT networks when dealing with varied data sources. Filtered Deep Learning Model for Intrusion Detection with a Data Communication Approach is presented in this study. The five steps that make up the suggested model are Initialization of Sensor Networks, Cluster Formation and Head Selection, Connectivity, Attack Detection, and Data Broker. It was discovered that the suggested model for intrusion detection outperformed both the current Deep Learning Neural Net and Artificial Neural Network. In comparison to the most popular algorithms, experimental findings revealed a superior result of 96.12 % accuracy. The E-shaped patch antenna is a brand-new single-patch wide-band microstrip antenna that is presented in this research. A microstrip antenna's patch has two parallel slots built into it to increase its bandwidth. Investigating the behaviour of the currents on the patch allows for the exploration of the wide-band mechanism. A broad bandwidth is achieved by optimising the slot's length, breadth, and location. Finally, a 40.3 % E-shaped patch antenna is developed, made, and tested to resonate at 7.5 and 8.5 GHz for wireless communications. Additionally displayed are the reflection coefficient, VSWR, radiation pattern and directivity.

Micro-Burst Aware ECN in Multi-Queue Data Centers: Algorithm and Implementation

With the development of multi-queue data centers, various efforts have leveraged Explicit Congestion Notification (ECN) to achieve high throughput and low latency for data communication. However, one of the deep-seated problems is that the micro-burst traffic in networks could cause the instantaneous queue length to exceed the ECN threshold, leading to significant mismarkings of ECN. Such mismarkings could further lead to severe network performance degradation. In this paper, we propose Micro-Burst aware ECN (MBECN+) to mitigate this issue. MBECN+ is essentially a novel scheme that decouples ECN threshold setting and ECN marking. First, it finds a more appropriate ECN threshold on a per-queue basis to eliminate the spurious congestion signals caused by micro burst traffic. Second, MBECN+ consists of a double-threshold ECN marking scheme that it marks the packets with ECN in a finer grain. Considering the queue backlog caused by micro-burst traffic, it marks the packets when they are dequeuing, instead of enqueuing. Furthermore, we analyze the feasibility of implementing MBECN+ on commodity layer-3 multi-queue switches. Through testbed experiments and large scale simulations, we demonstrate that MBECN+ can improve the throughput by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 20% and reduce FCT (flow completion time) by up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 40%. The throughput under MBECN+ improves by 1.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 2.4\times$</tex-math></inline-formula> than DCTCP and 1.26 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim 1.35\times$</tex-math></inline-formula> than ECN*.

Performance Degradation Estimation Mechanisms for Networked Control Systems Under DoS Attacks and its Application to Autonomous Ground Vehicle.

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints on the system. Therefore, estimating system performance degradation is of practical importance. By formulating the problem as an ellipsoid-constrained performance error estimation (PEE) problem, we can estimate the system performance degradation caused by DoS attacks. We propose a new Lyapunov-Krasovskii function (LKF) using the fractional weight segmentation method (FWSM) to examine the sampling interval and introduce a relaxed, positive definite constraint to optimize the control algorithm. We also propose a relaxed, positive definite constraint that reduces the initial constraints to optimize the control algorithm. Next, we introduce an alternate direction algorithm (ADA) to solve the optimal trigger threshold and design an integral-based event-triggered controller (IETC) to estimate the error performance of NCSs with limited network resources. Finally, we verify the effectiveness and feasibility of the proposed method using the Simulink joint platform autonomous ground vehicle (AGV) model.

Internet of Things (IoT) Data Communication Challenges, and Solutions

IoT has become an integral part of our lives, and data communication is one of the most important aspects of IoT. IoT devices can exchange data with each other via secure communication channels. This study aimed to review the IoT data communication challenges and solutions This paper attempts to address the key challenges and solutions for effective IoT data communication, starting by exploring the current challenges of IoT data communication related to scalability, reliability, and security, potential solutions to these challenges, such as using a mesh network, centralized servers, and edge computing. In addition, the paper discusses the importance of proper authentication and authorization techniques to ensure secure communication between IoT devices. The potential of IoT data communication is vast and has the power to fundamentally transform our society. It is an exciting time to be involved in this field, and the future looks promising for the advancement of IoT data communication.

Determination of Relationship Between the Application of ICT and Decision-Making Federal Universities of Northern Nigeria

This study examines how Federal Universities of Northern Nigeria use ICT to make decisions. Technology in data storage, analytics, and decision support systems has transformed decision-making, according to the introduction. We examine how ICT improves decision-making through data gathering, analysis, and communication. The hypothesis is that ICT use does not affect decision-making in these universities. The correlational study included 2,114 workers from all Northern Nigerian Federal Universities. The sample procedure selected states from each geopolitical zone and seven federal institutions from each state. Data was collected using a questionnaire on ICT in Effective Planning (QAICTEP). The null hypothesis was tested using Pearson's Product Moment Correlation coefficient (PPMCC). The respondents' age, experience, and qualifications were diverse. Male respondents outnumbered female respondents 75% to 25%. The null hypothesis was rejected since the PPMCC analysis showed a positive correlation (r = 0.609) between ICT application and decision-making. The conclusion shows that ICT use improves decision-making. ICT infrastructure investments, training, enhanced Decision Support System integration, and strong data governance regulations are recommended. at conclusion, this study sheds light on strategic ICT integration and decision-making at Federal Universities in Northern Nigeria, emphasising the need to improve decision-making quality and efficiency.

CuFastTucker: A Novel Sparse FastTucker Decomposition For HHLST on Multi-GPUs

High-order, high-dimension, and large-scale sparse tensors (HHLST) have found their origin in various real industrial applications, such as social networks, recommender systems, bioinformatics, and traffic information. To handle these complex tensors, sparse tensor decomposition techniques are employed to project the HHLST into a low-rank space. In this paper, we propose a novel sparse tensor decomposition model called Sparse FastTucker Decomposition (SFTD), which is a variant of Sparse Tucker Decomposition (STD). The SFTD utilizes Kruskal approximation for the core tensor, and we present a theorem that reduces the exponential space and computational overhead to a polynomial one. Additionally, we reduce the space overhead of intermediate parameters in the algorithmic process by sampling the intermediate matrix. Furthermore, this method guarantees convergence. To enhance the speed of SFTD, we leverage the compactness of matrix multiplication and parallel access through a stochastic strategy, resulting in GPU-accelerated cuFastTucker. Moreover, we propose a data division and communication strategy for cuFastTucker to accommodate data on Multi-GPU setups. Our proposed cuFastTucker demonstrates faster calculation and convergence speeds, as well as significantly lower space and computational overhead compared to state-of-the-art (SOTA) algorithms such as P-Tucker, Vest, GTA, Bigtensor and SGD_Tucker.

Safety Helmet for Miners

Abstract: The safety helmets basically aim at safety of miners reducing the risks of accidents. It also highlights the hazardous environments of mining and potential dangers that the workers face while working in mines. Safety helmets will be containing infrared sensors which keep track of the environment of the miner. It highlights the inherent risks miners face, including poor visibility, potential collisions, and hazardous conditions. The helmets are capable of alerting the miners from dangers. Infrared sensors can sense the heat signatures and obstacles in real-time resulting in spatial awareness. The helmets also consist of Arduino to enable real-time data processing and communication integration. The helmet also consists of LoRa which provide wireless communication between helmets and receiver ends, which would notify them in case of any emergency. The abstract concludes by enhancing the importance of IR-sensors, LoRa and Arduino which interfaces all of the above equipment.

IoT Based Air Pollution Monitoring System

Abstract: In light of the growing concern for air quality and its impact on public health, there is an increasing demand for air pollution monitoring systems that are both effective and efficient. This article introduces an IoT-based Air Pollution Detection System that utilizes the power of Internet of Things (IoT) technology to provide real-time monitoring and analysis of air quality parameters. The system incorporates a network of advanced air quality sensors that can measure particulate matter (PM) concentrations, as well as various gas levels such as carbon dioxide (CO2), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and ozone (O3). These sensors are integrated with a microcontroller that enables data processing and communication capabilities.

Telehealth Monitoring System for Chronic Disease Management

Telehealth Monitoring System for Chronic Disease Management

Correct Understanding and Action on the Transitional Period of EU Carbon Border adjustment Mechanism (CBAM)

Carbon border adjustment mechanism (CBAM) of the European Union has started from October 2023 and the transitional period of it would be from October 2023 to December 2025. CBAM is the first carbon border tax in the world and domestic companies are having difficulties in responding to CBAM among inaccurate information. Thus, this paper is written to deliver accurate information about CBAM based on the EU regulations or documents published by the EU. It is written that CBAM overview, how to respond to the transitional period, and the main concepts and data preparation of CBAM. Domestic companies are classified according to import system in the EU as Type 1, Type 2, and Type 3. The action for each type is presented, and preparation of CBAM report or emissions data communication template is described. Through this paper, it is expected that domestic companies would respond appropriately to CBAM during the transition period and contribute to the export of CBAM goods to the EU.