Monitoring Data Transmission Research Articles

Multi-Layered Unsupervised Learning Driven by Signal-to-Noise Ratio-Based Relaying for Vehicular Ad Hoc Network-Supported Intelligent Transport System in eHealth Monitoring.

Every year, about 1.19 million people are killed in traffic accidents; hence, the United Nations has a goal of halving the number of road traffic deaths and injuries by 2030. In line with this objective, technological innovations in telecommunication, particularly brought about by the rise of 5G networks, have contributed to the development of modern Vehicle-to-Everything (V2X) systems for communication. A New Radio V2X (NR-V2X) was introduced in the latest Third Generation Partnership Project (3GPP) releases which allows user devices to exchange information without relying on roadside infrastructures. This, together with Massive Machine Type Communication (mMTC) and Ultra-Reliable Low Latency Communication (URLLC), has led to the significantly increased reliability, coverage, and efficiency of vehicular communication networks. The use of artificial intelligence (AI), especially K-means clustering, has been very promising in terms of supporting efficient data exchange in vehicular ad hoc networks (VANETs). K-means is an unsupervised machine learning (ML) technique that groups vehicles located near each other geographically so that they can communicate with one another directly within these clusters while also allowing for inter-cluster communication via cluster heads. This paper proposes a multi-layered VANET-enabled Intelligent Transportation System (ITS) framework powered by unsupervised learning to optimize communication efficiency, scalability, and reliability. By leveraging AI in VANET solutions, the proposed framework aims to address road safety challenges and contribute to global efforts to meet the United Nations' 2030 target. Additionally, this framework's robust communication and data processing capabilities can be extended to eHealth monitoring systems, enabling real-time health data transmission and processing for continuous patient monitoring and timely medical interventions. This paper's contributions include exploring AI-driven approaches for enhanced data interaction, improved safety in VANET-based ITS environments, and potential applications in eHealth monitoring.

GCP: A multi-strategy improved wireless sensor network model for environmental monitoring

Nowadays, smart environmental monitoring devices are widely used in various fields, and one of the most representative tools is the wireless sensor network (WSN). WSNs are easy to deploy and provide real-time information feedback, which is very suitable for environmental monitoring. As we all know, the environmental monitoring network because of the special nature of its work requirements, the need for uninterrupted and real-time transmission of monitoring data, which leads to energy consumption is extremely large, and this cannot meet the needs of its long-term work. Existing traditional routing has problems such as unscientific cluster head election and high redundancy in data transmission, which usually lead to a large amount of energy consumption, which is not conducive to the long-term stable operation of sensor networks. In this paper, we improve the traditional routing protocol and design a cluster head election method based on the genetic algorithm, which proposes a new fitness function in terms of energy, distance, and the number of nodes in the cluster, and performs the selection of cluster head nodes based on this method. In addition, we propose a new grey prediction model, which can realize the real-time update of data queues, and optimize the data transmission process of traditional WSNs based on this prediction model to reduce the amount of intra-cluster data transmission. Combining these improvements, a grey cluster prediction (GCP) model is proposed, and the performance of the model is tested based on real mine and soil data sets. The simulation results show that the model significantly reduces energy loss and extends the network life cycle while ensuring the integrity of data transmission. It can also meet the requirements of long-term stable operation of environmental monitoring equipment.

A novel method of reliable data transmission for Internet of Vehicles based on crowd sensing strategy

SummaryThe Internet of Vehicles (IoV) is a hot research topic in intelligent transportation, and the research on new methods of intelligent data transmission is one of the key contents. Aiming at the application scenario of urban air quality monitoring data collection and transmission based on IoV, a new reliable data transmission method (Vehicular Grouping‐Communicated Data [VGCD]) based on crowd sensing strategy is proposed. This method is designed to utilize the intelligent perception and collaborative monitoring of vehicle to collect data, avoiding redundancy and overload of information; a reliable data transmission minimum delay hybrid routing method is proposed in the data transmission part, which combines encoding mechanism with routing design, integrates routing switching ideas, predicts vehicle adaptive connectivity based on fuzzy logic, and makes probability based routing decisions to minimize delay, achieving reliable and efficient data collection and transmission. The proposed new method has important theoretical significance and practical value for various applications such as dynamic remote perception monitoring based on the IoV in intelligent transportation.

Application and prospects of spatial information technology in CO2 sequestration monitoring

This paper systematically reviews the current applications of various spatial information technologies in CO2 sequestration monitoring, analyzes the challenges faced by spatial information technologies in CO2 sequestration monitoring, and prospects the development of spatial information technologies in CO2 sequestration monitoring. Currently, the spatial information technologies applied in CO2 sequestration monitoring mainly include five categories: eddy covariance method, remote sensing technology, geographic information system, Internet of Things technology, and global navigation satellite system. These technologies are involved in three aspects: monitoring data acquisition, positioning and data transmission, and data management and decision support. Challenges faced by the spatial information technologies in CO2 sequestration monitoring include: selecting spatial information technologies that match different monitoring purposes, different platforms, and different monitoring sites; establishing effective data storage and computing capabilities to cope with the broad sources and large volumes of monitoring data; and promoting collaborative operations by interacting and validating spatial information technologies with mature monitoring technologies. In the future, it is necessary to establish methods and standards for designing spatial information technology monitoring schemes, develop collaborative application methods for cross-scale monitoring technologies, integrate spatial information technologies with artificial intelligence and high-performance computing technologies, and accelerate the application of spatial information technologies in carbon sequestration projects in China.

EARLY LANDSLIDE WARNING SYSTEM

Landslides pose significant threats to both human lives and infrastructure, especially in regions prone to geological instabilities. Timely detection of early signs of potential landslides is crucial for mitigating the risks associated with these natural disasters. This project introduces an innovative approach utilizing the NodeMCU platform, integrated with a suite of sensors including accelerometers, gyroscopes, and moisture detectors, to create an early landslide detection system. By continuously monitoring ground conditions, including soil moisture levels and ground vibrations, the system employs real-time data analysis and anomaly detection algorithms to provide early warnings to relevant authorities and communities. The implementation leverages the versatility and connectivity capabilities of C, ensuring seamless data transmission for remote monitoring and rapid response. This project not only showcases the potential of IoT technology in enhancing natural disaster preparedness but also underscores its significance in safeguarding lives and property. The experimental results and performance evaluation demonstrate the effectiveness of the proposed system in detecting early signs of landslides, offering a promising solution for proactive risk management in landslide-prone areas. Keywords: Embedded based,NodeMCU

Remote Intelligent Medical Monitoring Data Transmission Network Optimization based on Deep Learning

A hospital operating status evaluation data analysis system was established based on the autoencoder's network. The Gibbs sampling method is used to obtain the approximate distribution of RBM. In addition, the Autoencoder neural network can also select feature dimensions that can better characterize the characteristics of financial operation data from a large amount of financial operation data. Deep learning methods are used to study the redundant information elimination method and the generation mechanism of multi-source heterogeneity in multi-source heterogeneous networks. The principle of intrinsic compression is used to reduce the dimensionality of the redundancy in the network and obtain the compression redundancy objective function. This article sets thresholds for information classification on the Internet. The approach was tested using financial data from a medical institution. Use smart encoders to extract 17 financial indicators from financial data that can be used for modeling. The evaluation results are used as the output vector of the model. Comparative experiments show that the AUC value and accuracy of the method proposed in this article can be improved by 0.84 and 83.33% compared with the AUC value of shallow logistic regression and BP neural network. This algorithm has apparent improvements.

Monitoring subsidence of open pit mine slope based on GNSS/CORS technology - case study at the Coc Sau open-pit coal mine

Subsidence value is one of the important parameters to evaluate the stability of open pit mine slope. The subsidence is determined from height values measured at different times using different methods and equipment. When applying GNSS/CORS technology to monitor displacement, landslides, ground subsidence or structures, a specialized monitoring system is needed to ensure the necessary accuracy. This article presents the results of research on subsidence monitoring of open pit mine slope based on CORS technology and a self-developed GNSS receiver. The CORS station is set up with Trimble's Zyphir 2 Geodetic antenna and Stonex GNSS receiver located in Cam Pha city, Quang Ninh. The GNSS receiver for monitoring station is designed and developed based on the Trimble's GNSS BD970 board and integrated with a 4G wifi modem, allowing the transmission of monitoring data in standard NMEA0183 format to the CORS station's host computer. Decoded monitoring data and error filtering have improved the accuracy of height determination achieved at the mm level. Results of simulation experiments and experiments with actual monitoring data at Coc Sau coal mine, Cam Pha, Quang Ninh have proven the effectiveness and reliability of the method. With simulated experimental monitoring results, it shows that the largest subsidence difference is 16 mm and the smallest is 6 mm.

Enhancing security in Wireless Body Area Networks (WBANs) with ECC-based Diffie-Hellman Key Exchange algorithm (ECDH).

Wireless Body Area Networks (WBANs) are integral to modern healthcare systems, providing continuous health monitoring and real-time data transmission. The sensitivity of medical data being transmitted makes security a significant concern in WBANs. This study explores the application of the Elliptic Curve Cryptography (ECC)-based Diffie-Hellman Key Exchange (ECDH) algorithm to enhance security within WBANs. The study investigates the suitability of ECC for this context and evaluates the performance and security implications of implementing ECDH in WBANs. The findings reveal that ECDH provides a robust and computationally efficient solution for secure key exchange in WBANs, addressing inherent vulnerabilities. The adoption of ECC-based ECDH is poised to bolster data confidentiality and integrity in WBANs, promoting trust and widespread use of these networks in healthcare applications. This research contributes to the growing body of knowledge regarding security measures in WBANs and opens new avenues for the secure transmission of sensitive medical information.

Online diagnosis of MVB termination fault based on expert system

As a train communication technology, multifunction vehicle bus (MVB) plays a vital role in the transmission of control and monitoring data. However, the termination fault on MVB bus equipment often occurs, which leads to the increase of bit error rate and packet loss rate, and seriously affects the communication quality and stable operation of the train. The traditional off-line diagnosis method has poor timeliness. Therefore, this paper proposes and implements an online diagnosis method of MVB termination fault based on expert system. Firstly, the obtained physical waveform data is preprocessed, and then the waveform characteristics are extracted. Finally, the expert system algorithm is used for diagnosis, so as to quickly and accurately detect the MVB termination fault. The experimental platform was built for online fault diagnosis test. The experimental results show that the diagnosis method has high accuracy and reliability, which is conducive to improving the diagnosis efficiency.

Automated operational modal analysis for civil engineering structures with compressed measurements

Introducing compressive sensing (CS) to modal analysis enables modal identification approaches to process compressed measurements, which enhances the efficiency in both data transmission and analysis for long-term monitoring of civil engineering structures. Nevertheless, the existing methods normally require users’ judgment in mode selection, and thus they cannot realize automated modal analysis. To bridge this gap, this study proposes an automated CS-based operational modal analysis (OMA) method, namely Automated Sparse Decomposition (ASD). A mode selection strategy is proposed to gradually eliminate noise and false modes and thus realize automated modal identification based on compressed measurements. A search strategy with both extensive and intensive search stages is proposed to realize higher frequency search resolution with low computational costs. Additionally, an automated mode shape identification process is designed to enhance accuracy. To demonstrate the effectiveness of the proposed method, experimental data on an offshore wind turbine model and real-world monitoring data from an in-service cable-stayed bridge are analyzed. The results show that the identification accuracy of the proposed ASD is comparable to state-of-art CS-based methods. Due to its superior performance in both efficiency and accuracy, the proposed method has the potential to become a promising online modal analysis tool.

Address direction finding of ZigBee devices

Statement of problem. The active use of wireless technologies requires the development of means of monitoring devices and data transmission networks and, in particular, ZigBee wireless personal networks. Therefore, the detection and localization of working ZigBee devices is an urgent task of radio monitoring services. Correlation-interferometric direction finders based on two-channel radio receiving equipment cyclically connected to various pairs of elements of a multi-element antenna system can serve as a tool for bearing radio signals. For the direction finding of packet radio signals, the direction finder accumulates time samples from different antenna pairs over long time intervals; joint processing of these data allows determining the direction of arrival of radio signals. An obstacle to the direction finding of ZigBee devices is the fact that many similar devices can simultaneously operate in the same frequency range, generating a chaotic alternation of packets in the air. As a consequence, the accumulation of information from a specific ZigBee device for different antenna pairs of the direction finder becomes problematically. Purpose of the work is to develop a method for joint identification and bearing of detected devices of ZigBee networks. Results. the presented method of address bearing is implemented on the basis of the mobile direction finder ARTICLE-M, used to search for and locate unauthorized sources of radio emissions, and allows detecting signals of the 802.15.4 standard, identifying personal network devices, as well as forming estimates of directions to these sources of radio emissions. Practical importance. the use of address bearing of ZigBee network devices increases the functionality of existing radio monitoring equipment, and also allows the localization of unauthorized radio sources masquerading as Internet of Things devices.

Water Quality Monitoring Data Transmission Network Design

With the continuous advancement of electronic technology and the introduction of new concepts, people's demands on life are increasing and the concept of intelligent automation is gaining popularity. With the continuous consumption of the source of life, people's concern about fresh water resources is also increasing. With the theme of intelligent detection, a water quality online monitoring system that integrates detection and monitoring is designed. The system can monitor temperature, humidity, water level and turbidity in real time, and can transmit monitoring values through wireless module. Users can use mobile applications to view the data detected at this time, which is very convenient. The system hardware design includes STM32 system processor circuit, temperature and humidity sensor module, water level monitoring module, WIFI module, and turbidity monitoring module design.

A Novel Nature-Inspired Routing Scheme for Improving Routing Quality of Service in Power Grid Monitoring Systems

In the process of power grid monitoring, image data are mostly transmitted in segments by wireless image sensor networks (WISNs), and the external attacks or interference on a communication link easily causes the data to be retransmitted. Therefore, high quality of service (QoS) routing is essential to the secure and robust operation of the WISNs. Specifically, the QoS routing optimization in WISN is a nondeterministic polynomial hard problem, which usually cannot be solved by ordinary algorithms. Based on this motivation, a novel heuristic trust scheme (i.e., multi-objective trust routing scheme based on cloned elite slime mould algorithm (CESMA-MTRS)) is proposed to greatly improve the security and QoS of WISN. The scheme designs a novel trust difference test mechanism to enhance the identification of malicious nodes. Furthermore, a new elite archiving strategy and clone optimization strategy are designed to improve the convergence speed of grid monitoring data transmission routing search. The simulation results show that when the network scale is 100 and the attacked node scale is 30%, CESMA-MTRS reduces the delay by 8% and 15% and the packet loss rate by 24% and 27%, respectively, compared with trust-based routing protocol called the On-demand trust-based multicast routing protocol (ODTMRP) and evolutionary selfcooperative trust approach.

Weavable yarn-shaped supercapacitor in sweat-activated self-charging power textile for wireless sweat biosensing

The yarn-based sweat-activated battery (SAB) is a promising energy source for textile electronics due to its excellent skin compatibility, great weavability, and stable electric output. However, its power density is too low to support real-time monitoring and wireless data transmission. Here, we developed a scalable, high-performance sweat-based yarn biosupercapacitor (SYBSC) with two symmetrically aligned electrodes made by wrapping hydrophilic cotton fibers on polypyrrole/poly (3,4-ethylenedioxythiophene):poly (styrenesulfonate)-modified stainless steel yarns. Once activated with artificial sweat, the SYBSC could offer a high areal capacitance of 343.1 mF cm−2 at 0.5 mA cm−2. After 10,000 times of bending under continuous charge-discharge cycles and 25 cycles of machine washing, the device could retain the capacitance at rates of 68% and 73%, respectively. The SYBSCs were integrated with yarn-shaped SABs to produce hybrid self-charging power units. The hybrid units, pH sensing fibers, and a mini-analyzer were woven into a sweat-activated all-in-one sensing textile, in which the hybrid, self-charging units could power the analyzer for real-time data collection and wireless transmission. The all-in-one electronic textile could be successfully employed to real-time monitor the pH values of the volunteers’ sweat during exercise. This work can promote the development of self-charging electronic textiles for monitoring human healthcare and exercise intensity.

Smart Medical System Integrated into the Design of Real-Time Monitoring System for Efficient Medical Students' Physical and Mental Health

An embedded visual remote medical monitoring front-end system is introduced, which has the functions of embedded Web transmission of medical students' mental health monitoring data, voice intercom and high-definition visual monitoring. A survey was conducted among 718 medical students using the Five-Factor Mindfulness Awareness Scale, College Students' Psychological Harmony Scale, Self-Esteem Scale, Interpersonal Interpersonal Diagnosis Scale, Trait Coping Style Questionnaire and Symptom Self-Rating Scale. The construction strategy of the smart medical system aims to provide new ideas and references for the smart medical system to adapt to the ever-expanding medical application needs. SPSS 21.0 is used to test the internal consistency of the questionnaire results.

Wearable Biosensor: How to Improve the Efficacy in Data Transmission in Respiratory Monitoring

Wearable Biosensor: How to Improve the Efficacy in Data Transmission in Respiratory Monitoring

Application of Edge Computing in Structural Health Monitoring of Simply Supported PCI Girder Bridges.

This study proposes an innovative method for structural health monitoring of simply supported PCI girder bridges based on dynamic strain and edge computing. Field static and dynamic load tests were conducted on a bridge consisting of a span with newly replaced PCI girders and numerous spans with old PCI girders. Both the static and dynamic test results showed that the flexural rigidity of the old PCI girders decreased significantly due to deterioration. To improve the efficiency of on-site monitoring data transmission and data analysis, this study developed a smart dynamic strain gauge node with the function of edge computing. Continuous data with a sampling frequency of 100 Hz were computed at the sensor node. Among the computed results, only the maximum dynamic strain data caused by the passage of the heaviest vehicle within 1 min were transmitted. The on-site monitoring results indicated that under routine traffic conditions, the dynamic strain response of the new PCI girder was smaller than that of the deteriorated PCI girder. When the monitored dynamic strain response has a tendency to magnify, attention should be paid to the potential prestress loss or other deterioration behaviors of the bridge.

Dynamic sensing and control system using artificial intelligent techniques for non-uniform indoor environment

Traditional ventilation systems have difficulty in effectively capturing the dynamic and non-uniform distribution of indoor environment, making it difficult to meet the dynamic demand of real-time indoor environment control. This results in a large amount of energy consumption, and the comfort of indoor personnel is not guaranteed. Microsensors are used to monitor indoor air quality for responding to indoor environmental conditions and providing real-time regulation of indoor ventilation system. However, the monitoring by a single microsensor cannot reflect the global non-uniform indoor environment and can easily lead to ineffective control of indoor environment and thermal discomfort for personnel. Increased number of sensors can lead to wasted monitoring resources and inconvenient installation in the room. Therefore, this study developed dynamic ventilation control system based on “limited monitoring - fast prediction - real-time control” method. The system realized online transmission and reception of indoor environment monitoring data through wireless communication technology. Combined with fast prediction model to obtain the optimal air change rate per hour (ACH) value of indoor environment, the real-time regulation and intelligent control of indoor ventilation system can be realized, further promoting a balance between energy consumption (up to 60% ventilation energy savings) and indoor environmental quality. This work can provide important strategies and technologies for the construction and implementation of intelligent ventilation control systems.

Transmission Control Over Satellite Network for Marine Environmental Monitoring System

The transmission of huge amounts of data generated from various sensors is the basis and prerequisite for the analysis and prediction of the marine ecosystem. The special geographical conditions of the ocean determine that its data transmission is different from the common terrestrial data transmission. In this study, a marine environmental monitoring network architecture is proposed to collect environmental data from marine ecosystems using a heterogeneous network constructed by a multi-hop network and a satellite network. Considering the characteristics of satellite networks, such as high bandwidth delay product (BDP), long latency and high bit error rate, this work proposes a transmission scheme that improves the transmission performance of Transmission Control Protocol/Internet Protocol (TCP/IP)-based Internet networks over satellite networks. The improved scheme increases the monitoring data transmission rate, distinguishes between random data loss and congested data loss, rationalizes the threshold in fast recovery, and improves the overall transmission performance of the marine environmental monitoring network. The experimental results show that the proposed scheme can greatly improve the transmission performance of the monitoring network compared with traditional transmission protocols. The network model proposed in this study makes full use of current advanced network technologies, including multi-hop networks, wireless sensor networks, and satellite networks to expand the monitoring area at sea. This study provides a useful reference for the network model and data transmission for ocean monitoring.

Compressed feature reconstruction for localized fault diagnosis with generalized minimax-concave penalty

Compressed sensing can effectively relieve the burden of data storage and transmission for mechanical condition monitoring. However, feature reconstruction from compressed observations has always been a challenge. A novel compressed feature reconstruction method for the impulse response signal caused by localized damage is proposed based on the generalized minimax-concave (GMC) penalty. To improve the efficiency of compression and reconstruction, the fault signal is divided into several segments before compression, and the segmented sparse coefficient is obtained by solving a GMC problem. The fault-caused impulse signal is obtained by combining all the reconstructed segments together. Furthermore, a strategy of regularization parameter selection based on the sparse coefficient proxy is presented. Compared with the commonly used l1 regularization-based reconstruction method, the proposed method can better preserve the amplitudes of impulse features. The effectiveness of the proposed method is further verified by simulation and experimental data.