Reliable Data Collection Model and Transmission Framework in Large-Scale Wireless Medical Sensor Networks

Large-scale wireless sensor networks(WSNs)play a critical role in monitoring dangerous scenarios and responding to medical emergencies.However,the inherent instability and error-prone nature of wireless links present significant challenges,necessitating efficient data collection and reliable transmission services.This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs.The primary goal is to enhance the reliability of data collection and transmission services,ensuring a comprehensive and practical approach.Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability.Additionally,it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops.These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios.Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes.The evaluation spans varying sensor densities,wireless channel conditions,and packet transmission rates,showcasing the protocol’s superiority in ensuring reliable and efficient data transfer.Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs.By prioritizing fairness,reliability,and efficiency,the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services,thereby offering a valuable contribution to the field of medical event-drivenWSNs.

Enabling Efficient Data Transmission in Wireless Sensor Networks-Based IoT Application

The use of the Internet of Things(IoT)is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices.In critical infrastructure domains like oil and gas supply,intelligent transportation,power grids,and autonomous agriculture,it is essential to guarantee the confidentiality,integrity,and authenticity of data collected and exchanged.However,the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques.Consequently,designing a lightweight secure data transmission scheme is becoming essential.In this article,we propose lightweight secure data transmission(LSDT)scheme for IoT environments.LSDT consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement protocol.We design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation costs.Security and performance analysis indicates that the LSDT scheme is secure,suitable for IoT applications,and performs better in comparison to other related security schemes.

Remote wireless transmission and error recovery of log data

Remote transmission of log data is an urgent problem for service companies. Remote transmission technology of log data here refers to both the transmission solution in combination with the CifNet multi-well data management system to automate the transmission, storage, management, and retrieval of log data to reduce turn-over time. It is an applied digital signature technology to implement breakpoint transmission and error recovery and ensure the effectiveness and reliability of log data transmission.

Phase equilibrium data prediction and process optimizationin butadiene extraction process

In response to the lack of reliable physical parameters in the process simulation of the butadiene extraction,a large amount of phase equilibrium data were collected in the context of the actual process of butadiene production by acetonitrile.The accuracy of five prediction methods,UNIFAC(UNIQUAC Functional-group Activity Coefficients),UNIFAC-LL,UNIFAC-LBY,UNIFAC-DMD and COSMO-RS,applied to the butadiene extraction process was verified using partial phase equilibrium data.The results showed that the UNIFAC-DMD method had the highest accuracy in predicting phase equilibrium data for the missing system.COSMO-RS-predicted multiple systems showed good accuracy,and a large number of missing phase equilibrium data were estimated using the UNIFAC-DMD method and COSMO-RS method.The predicted phase equilibrium data were checked for consistency.The NRTL-RK(non-Random Two Liquid-Redlich-Kwong Equation of State)and UNIQUAC thermodynamic models were used to correlate the phase equilibrium data.Industrial device simulations were used to verify the accuracy of the thermodynamic model applied to the butadiene extraction process.The simulation results showed that the average deviations of the simulated results using the correlated thermodynamic model from the actual values were less than 2%compared to that using the commercial simulation software,Aspen Plus and its database.The average deviation was much smaller than that of the simulations using the Aspen Plus database(>10%),indicating that the obtained phase equilibrium data are highly accurate and reliable.The best phase equilibrium data and thermodynamic model parameters for butadiene extraction are provided.This improves the accuracy and reliability of the design,optimization and control of the process,and provides a basis and guarantee for developing a more environmentally friendly and economical butadiene extraction process.

Big Data Application Simulation Platform Design for Onboard Distributed Processing of LEO Mega-Constellation Networks

Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.

Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.

Data processing method for aerial testing of rotating accelerometer gravity gradiometer

A novel method for noise removal from the rotating accelerometer gravity gradiometer(MAGG)is presented.It introduces a head-to-tail data expansion technique based on the zero-phase filtering principle.A scheme for determining band-pass filter parameters based on signal-to-noise ratio gain,smoothness index,and cross-correlation coefficient is designed using the Chebyshev optimal consistent approximation theory.Additionally,a wavelet denoising evaluation function is constructed,with the dmey wavelet basis function identified as most effective for processing gravity gradient data.The results of hard-in-the-loop simulation and prototype experiments show that the proposed processing method has shown a 14%improvement in the measurement variance of gravity gradient signals,and the measurement accuracy has reached within 4E,compared to other commonly used methods,which verifies that the proposed method effectively removes noise from the gradient signals,improved gravity gradiometry accuracy,and has certain technical insights for high-precision airborne gravity gradiometry.

Machine Learning-based Identification of Contaminated Images in Light Curve Data Preprocessing

Attitude is one of the crucial parameters for space objects and plays a vital role in collision prediction and debris removal.Analyzing light curves to determine attitude is the most commonly used method.In photometric observations,outliers may exist in the obtained light curves due to various reasons.Therefore,preprocessing is required to remove these outliers to obtain high quality light curves.Through statistical analysis,the reasons leading to outliers can be categorized into two main types:first,the brightness of the object significantly increases due to the passage of a star nearby,referred to as“stellar contamination,”and second,the brightness markedly decreases due to cloudy cover,referred to as“cloudy contamination.”The traditional approach of manually inspecting images for contamination is time-consuming and labor-intensive.However,we propose the utilization of machine learning methods as a substitute.Convolutional Neural Networks and SVMs are employed to identify cases of stellar contamination and cloudy contamination,achieving F1 scores of 1.00 and 0.98 on a test set,respectively.We also explore other machine learning methods such as ResNet-18 and Light Gradient Boosting Machine,then conduct comparative analyses of the results.

Optimizing Healthcare Big Data Processing with Containerized PySpark and Parallel Computing: A Study on ETL Pipeline Efficiency

In this study, we delve into the realm of efficient Big Data Engineering and Extract, Transform, Load (ETL) processes within the healthcare sector, leveraging the robust foundation provided by the MIMIC-III Clinical Database. Our investigation entails a comprehensive exploration of various methodologies aimed at enhancing the efficiency of ETL processes, with a primary emphasis on optimizing time and resource utilization. Through meticulous experimentation utilizing a representative dataset, we shed light on the advantages associated with the incorporation of PySpark and Docker containerized applications. Our research illuminates significant advancements in time efficiency, process streamlining, and resource optimization attained through the utilization of PySpark for distributed computing within Big Data Engineering workflows. Additionally, we underscore the strategic integration of Docker containers, delineating their pivotal role in augmenting scalability and reproducibility within the ETL pipeline. This paper encapsulates the pivotal insights gleaned from our experimental journey, accentuating the practical implications and benefits entailed in the adoption of PySpark and Docker. By streamlining Big Data Engineering and ETL processes in the context of clinical big data, our study contributes to the ongoing discourse on optimizing data processing efficiency in healthcare applications. The source code is available on request.

Data Analysis Methods and Signal Processing Techniques in Gravitational Wave Detection

Gravitational wave detection is one of the most cutting-edge research areas in modern physics, with its success relying on advanced data analysis and signal processing techniques. This study provides a comprehensive review of data analysis methods and signal processing techniques in gravitational wave detection. The research begins by introducing the characteristics of gravitational wave signals and the challenges faced in their detection, such as extremely low signal-to-noise ratios and complex noise backgrounds. It then systematically analyzes the application of time-frequency analysis methods in extracting transient gravitational wave signals, including wavelet transforms and Hilbert-Huang transforms. The study focuses on discussing the crucial role of matched filtering techniques in improving signal detection sensitivity and explores strategies for template bank optimization. Additionally, the research evaluates the potential of machine learning algorithms, especially deep learning networks, in rapidly identifying and classifying gravitational wave events. The study also analyzes the application of Bayesian inference methods in parameter estimation and model selection, as well as their advantages in handling uncertainties. However, the research also points out the challenges faced by current technologies, such as dealing with non-Gaussian noise and improving computational efficiency. To address these issues, the study proposes a hybrid analysis framework combining physical models and data-driven methods. Finally, the research looks ahead to the potential applications of quantum computing in future gravitational wave data analysis. This study provides a comprehensive theoretical foundation for the optimization and innovation of gravitational wave data analysis methods, contributing to the advancement of gravitational wave astronomy.

A Novel Secure Data Transmission Scheme in Industrial Internet of Things

The industrial Internet of Things(IoT)is a trend of factory development and a basic condition of intelligent factory.It is very important to ensure the security of data transmission in industrial IoT.Applying a new chaotic secure communication scheme to address the security problem of data transmission is the main contribution of this paper.The scheme is proposed and studied based on the synchronization of different-structure fractional-order chaotic systems with different order.The Lyapunov stability theory is used to prove the synchronization between the fractional-order drive system and the response system.The encryption and decryption process of the main data signals is implemented by using the n-shift encryption principle.We calculate and analyze the key space of the scheme.Numerical simulations are introduced to show the effectiveness of theoretical approach we proposed.

L2-LBMT:A Layered Load Balance Routing Protocol for Underwater Multimedia Data Transmission

Providing highly efficient underwater transmission of mass multimedia data is challenging due to the particularities of the underwater environment. Although there are many schemes proposed to optimize the underwater acoustic network communication protocols, from physical layer, data link layer, network layer to transport layer, the existing routing protocols for underwater wireless sensor network(UWSN) still cannot well deal with the problems in transmitting multimedia data because of the difficulties involved in high energy consumption, low transmission reliability or high transmission delay. It prevents us from applying underwater multimedia data to real-time monitoring of marine environment in practical application, especially in emergency search, rescue operation and military field. Therefore, the inefficient transmission of marine multimedia data has become a serious problem that needs to be solved urgently. In this paper, A Layered Load Balance Routing Protocol(L2-LBMT) is proposed for underwater multimedia data transmission. In L2-LBMT, we use layered and load-balance Ad Hoc Network to transmit data, and adopt segmented data reliable transfer(SDRT) protocol to improve the data transport reliability. And a 3-node variant of tornado(3-VT) code is also combined with the Ad Hoc Network to transmit little emergency data more quickly. The simulation results show that the proposed protocol can balance energy consumption of each node, effectively prolong the network lifetime and reduce transmission delay of marine multimedia data.

Blockchain-based data transmission control for Tactical Data Link

Tactical Data Link(TDL)is a communication system that utilizes a particular message format and a protocol to transmit data via wireless channels in an instant,automatic,and secure way.So far,TDL has shown its excellence in military applications.Current TDL adopts a distributed architecture to enhance anti-destruction capacity.However,It still faces a problem of data inconsistency and thus cannot well support cooperation across multiple militarily domains.To tackle this problem,we propose to leverage blockchain to build an automatic and adaptive data transmission control scheme for TDL.It achieves automatic data transmission and realizes information consistency among different TDL entities.Besides,applying smart contracts based on blockchain further enables adjusting data transmission policies automatically.Security analysis and experimental results based on simulations illustrate the effectiveness and efficiency of our proposed scheme.

Fast Web - Based Data Transmission

Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.

Energy Efficiency Based on Joint Data Packet Fragmentation and Cooperative Transmission

Energy efficiency(EE) is a key requirement for the design of short-range communication network.In order to alleviate energy consumption(EC) constraint,a novel layered heterogeneous mobile cloud architecture is proposed in this paper.Based on the proposed layered heterogeneous mobile cloud architecture,we establish an appropriate energy consumption model,and design an energy efficiency scheme based on joint data packet fragmentation and cooperative transmission and analyze the energy efficiency corresponding to different packet sizes and the cloud size.Simulation results show that,when all nodes of the cloud are accessing the same size of data packet fragmentation,the proposed layered heterogeneous mobile cloud architecture can provide significant energy savings.The results provide useful insights into the possible operation of the strategies and show that significant energy consumption reductions are possible.

Power Line Monitoring Data Transmission Using Wireless Sensor Network

The WSN used in power line monitoring is long chain structure, and the bottleneck near the Sink node is more obvious. In view of this, A Sink nodes’ cooperation mechanism is presented. The Sink nodes from different WSNs are adjacently deployed. Adopting multimode and spatial multiplexing network technology, the network is constructed into multi-mode-level to achieve different levels of data streaming. The network loads are shunted and the network resources are rationally utilized. Through the multi-sink nodes cooperation, the bottlenecks at the Sink node and its near several jump nodes are solved and process the competition of communication between nodes by channel adjustment. Finally, the paper analyzed the method and provided simulation experiment results. Simulation results show that the method can solve the funnel effect of the sink node, and get a good QoS.

Enhanced Data Transmission Rate of XCTD Profiler Based on OFDM

An expendable conductivity-temperature-depth profiler(XCTD)is one of the most important instruments used to obtain hydrological data,such as temperature and conductivity,and detect ocean depth in a large area.However,the XCTD channel provides poor time-varying performance,narrowband,and low signal-to-noise ratio(SNR),which severely restricts the data transmission rate.In contrast to conventional single-carrier modulation techniques,such as amplitude-shift keying and differential phase-shift keying,this article provides a new method,based on orthogonal frequency division multiplexing(OFDM)to enhance the data transmission rate of deep-sea abandoned profilers.We apply the OFDM to enhance the SNR of the XCTD,which is achieved by reducing the data transmission rate of each sub-channel.Moreover,the bandwidth utilization may be improved by increasing the number of subcarriers in a given bandwidth,which enhances the data transmission rate.Based on analysis of the XCTD channel model,OFDM with different parameters such as constellation mapping,number of subcarriers,subcarrier spacing,signal period and cyclic prefix are achieved.To verify the effectiveness of the OFDM,this study investigates the influence of different parameters on the data transmission rate at different noise levels,i.e.,-20 dB and-40 d B.

Principle and realization method of tunnel deformation detection based on image recognition and data transmission technology

To detect the deformation of the tunnel structure based on image sensor networks is the advanced study and application of spatial sensor technology. For the vertical settlement of metro tunnel caused by internal and external stress after its long period operation, the overall scheme and measuring principle of tunnel deformation detection system is in- troduced. The image data acquisition and processing of detection target are achieved by the cooperative work of image sensor, ARM embedded system. RS485 communication achieves the data transmission between ARM memory and host computer. The database system in station platform analyses the detection data and obtains the deformation state of tunnel inner wall, which makes it possible to early-warn the tunnel deformation and take preventive measures in time.

Simulation analysis of a method to improve data-transmission performance of Nanshan 26 m Radio Telescope based on Software-Defined Networks

Data Center of Xinjiang Astronomical Observatory(XAO-DC)commenced operating in 2015,and provides services including archiving,releasing and retrieving precious astronomical data collected by the Nanshan 26 m Radio Telescope(NSRT)over the years,and realises the open sharing of astronomical observation data.The observation data from NSRT are transmitted to XAO-DC 100 km away through dedicated fiber for long-term storage.With the continuous increase of data,the static architecture of the current network cannot meet NSRT data-transmission requirements due to limited network bandwidth.To get high-speed data-transmission using the existing static network architecture,a method for reconstruction data-transmission network using Software-Defined Networks(SDN)is proposed.Benefit from the SDN’s data and control plane separation,and open programmable,combined with the Mininet simulation platform for experiments,the TCP throughput(of single thread)was improved by~24.7%,the TCP throughput(of multi threads)was improved by~9.8%,~40.9%,~35.5%and~11.7%.Compared with the current network architecture,the Latency was reduced by~63.2%.

A new method for improving the inductively coupled data transmission rate of mooring buoy based on carrier signal frequency selection

In the inductively coupled data transmission system of the mooring buoy, the carrier signal frequency of the transmission channel is limited due to the inherent characteristics of the system, resulting in limited channel bandwidth. The limited channel bandwidth limits the increase in inductively coupled data transmission rate.In order to improve the inductively coupled data transmission rate of mooring buoy as much as possible without damaging the data transmission performance, a new method was proposed in this paper. The method is proposed to improve the data transmission rate by selecting the appropriate carrier signal frequencies based on the principle of maximizing the amplitude value of amplitude-frequency characteristic curve of the system. Research has been done according to this method as follows. Firstly, according to the inductively coupled transmission mooring buoy structure, the inductively coupled data transmission circuit model was established. The binary frequency shift keying(2FSK) digital signal modulation mode was selected. Through theoretical analysis, the relation between the carrier signal frequency and the data transmission performance, the relation between the carrier signal frequency and the 2FSK signal bandwidth were obtained. Secondly, the performance and the bandwidth of the signal transmission were studied for the inherent characteristics of the actual inductively coupled data transmission system. The amplitude-frequency characteristic of the system was analyzed by experiments. By selecting the appropriate carrier signal frequency parameters, an excellent data transmission performance was guaranteed and a large 2FSK signal bandwidth was obtained. Finally, an inductively coupled data transmission rate optimization experiment and a bit error rate analysis experiment were designed and carried out. The results show that the high-speed and reliable data transmission of the system was realized and the rate can reach 100 kbps.