Communication Data Transmission Research Articles

Health Community 4.0: An Innovative Multidisciplinary Solution for Tailored Healthcare Assistance Management.

This paper presents a co-designed solution aimed at improving community healthcare assistance management. To enhance patients' self-awareness of their health status, encouraging active participation in care process, and to support nurses in delivering patient-centered care, we have developed an innovative platform with a highly customized app. This platform was designed using a multi-disciplinary, bottom-up approach. Patient data collection and processing facilitate the automation of care timeline planning, based on real-time patients' needs and the available resources. To achieve this goal, different components have been considered: real-time health data collection and processing, patient care planning, decision support for nurses, secure communication for data transmission, and user-friendly interfaces to ensure easy access to platform functionalities.

Reconfigurable Intelligent Surface-Based Backscatter Communication for Data Transmission

Data transmission is one of the critical factors in the future of the Internet of Things (IoT). The techniques of a reconfigurable intelligent surface (RIS) and backscatter communication (BackCom) are in need of a solution of realizing low-power sustainable transmission, which shows great potential in wireless communication. Hence, this paper introduces an RIS-based BackCom system, where the RIS receives energy from a base station (BS) and sends information by backscattering the signals from the BS. To maximize the sum rate of all IoT devices (IoTDs), we jointly optimized the time allocation, the RIS-reflecting phase shifts and the transmit power of the BS by exploiting an alternative optimization algorithm. The simulation results illustrate the effectiveness and the feasibility of the proposed wireless communication scheme and the proposed algorithm in IoT networks.

A Wireless Ad Hoc Network Communication Platform and Data Transmission Strategies for Multi-Bus Instruments

As automatic test technology advances, the number of programmable instruments in a single test system increases. Traditional wired communication methods have a limited range and involve complex cable layouts. Single-function wireless converters provide a viable alternative, but they have limitations. These include complicated configuration, issues with multi-system collaboration, and data blocking. This paper proposes a wireless ad hoc network platform for multi-bus instruments based on a low-cost ESP-12H WiFi module. The platform supports GPIB, RS232, RS485, and CAN bus interface instrument access. It features easy configuration, ad hoc networking, and self-repairing capabilities. A relay multi-hop network with a tree topology expands capacity and coverage. Additionally, a dynamic window-receiving mode and an improved multi-priority queue ensure data transmission integrity. The experimental results show that the platform’s networking time is less than 10 s, and the coverage range reaches 50 m in complex indoor environments. It also shows good stability when running for a long time. However, due to hardware and software design limitations, the actual upload speeds fall short of the theoretical values. For example, RS232 and RS485 are about 10% slower than the theoretical values, and GPIB is about 80% slower. Further optimization is required in the future.

A new PPP one-way timing approach via BDS-3 PPP-B2b and short-message communication service

In the realm of time-frequency, precise point positioning (PPP) technology has been widely used, the current PPP time transfer or timing necessitates network communication for data transmission, which consequently leads to a reduction in the reliability of PPP timing. Based on the BDS-3 PPP service (PPP-B2b) and the global short message communication (GSMC) service, this study proposes a novel PPP one-way timing method that is not subject to distance limitations or the requirements of network communication. Users can directly obtain the difference with UTC(k). Then, the proposed PPP timing method was validated using four stations equipped with external high-performance atomic clocks, based on a dataset of eight days. According to the findings, the suggested PPP one-way timing technique might reach a timing accuracy of roughly 0.5 ns in environments without a network. In terms of frequency stability, the proposed PPP timing method exhibits a stability of approximately 1.4 × 10−13 at 960 s and a stability of about 3 × 10−14 at 15360 s. Further, even under GSMC communication delays of 1 min, 5 min, and 10 min, the proposed PPP one-way timing accuracy can still achieve better than 0.5 ns.

Improving the efficiency of channel evaluation during high-speed data transmission in underwater acoustic communication with OFDM

Problem statement. The organization of noise-resistant wireless underwater acoustic communication is not a simple and solved task. One of the main problems is the multipath propagation of a sound acoustic wave as a result of re–reflections from the seabed surface and a change in the frequency of signal vibrations when the receiver and transmitter move relative to each other. Thus, finding a channel evaluation solution that can improve the quality of communication is an urgent task for developers of underwater acoustic communication systems. Purpose. To consider the possibility of developing effective methods for processing underwater acoustic communication signals with high noise immunity by evaluating the communication channel. Results. The possibility of using an adaptive filter with a sliding window to evaluate the channel during high-speed data transmission in underwater acoustic communication is shown. The simulation results showed that the proposed adaptive filter with a sliding window surpasses the normalized adaptive filters in terms of the BER bit error and wins over additive filters with an exponential window in reducing the number of mathematical operations. When modeling wireless acoustic communication systems with OFDM, error-free data transmission with a spectral efficiency of 0.33 bits/s/Hz was achieved. Practical significance. The obtained results can be used to improve the wireless transmission of data in underwater acoustic communication due to the proposed algorithm of the adaptive filter.

Secure Encrypted Transmission of Network Data in Cloud Computing Technology Environment

In order to solve the problem of communication data theft in conventional network communication data transmission methods and ensure the security of network communication data transmission, it is necessary to design new network communication data security transmission methods based on cloud computing technology, formulate network communication data security transmission agreements, construct a network communication data security transmission model based on cloud computing technology, and design a network communication data security transmission scheme, implement secure transmission of network communication data. The experimental results show that after using the designed network communication data secure transmission method, the amount of stolen communication data is less than that of conventional methods. This proves that the designed network communication data secure transmission method has high transmission security, good transmission effect, and reliability, and can be used as a reference for subsequent network communication data encryption transmission.

Energy efficient data dissemination in wireless sensor network enabled IoT using domain‐adaptive message passing graph neural network

SummaryIn the past few years, restricted wireless sensor networks (WSNs) enabled the Internet of Things (IoT) have attracted significant attention and expansion to enhance service delivery and resource efficiency. Dissemination is a service offered by WSN that uses radio transmission and over‐the‐air programming for updating the deployed sensor nodes through online. The centralized data dissemination methods are replaced by the distributed approaches because they affect the drawbacks of a single point of failure, no scalability, and insecurity. Therefore, an Energy Efficient Protocol for Data Dissemination in Wireless Sensor network‐enabled IoT using Domain‐Adaptive Message Passing Graph Neural Network (EEP‐WSN‐IoT‐DMPGNN) is proposed in this paper. The nodes are formed as clusters utilizing the Deep Fuzzy Curriculum Clustering (DFCC) technique that rewards nodes belonging to a given cluster. By using the Crayfish Optimization Algorithm (COA), the Cluster Head (CH) selection optimally chose the ideal CH and satisfies the multiple objective functions, such as energy, delay, traffic density, and distance. Afterward, domain‐adaptive Message Passing Graph Neural Network (DMPGNN) based routing protocol is developed, the input given to the routing protocol includes a sink, action history, future node, and maximum‐distance node, which attains enhanced data transfer in the chosen path. The proposed technique attains a lower no. of dead nodes, lower energy consumption, and higher Network Lifetime while analyzed with existing techniques, such as routing technique depending on deep learning for effectual data transmission in 5G WSN communication (DL‐RPDT‐WSN), Reinforcement‐Learning base energy effectual optimized routing protocol in WSN (RL‐EERP‐WSN), and Energy‐efficient intellectual routing method for IoT‐enabled WSN (EIR‐IoT‐WSN), respectively.

A Deep Learning Approach for Accurate Path Loss Prediction in LoRaWAN Livestock Monitoring.

The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of LoRa link performance in spatial dimensions and ascertain the extent of reliable communication coverage achievable by gateways in mountainous environments. Additionally, an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model's precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5 dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes.

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.

Performance analysis of routing protocols for an efficient data transmission in 5G WSN communication

In WSN structures the routing scheme the usage of the sensor nodes are carried out in between group of specific clusters. The nodes are working for information aggregation from these supply nodes they also performs statistics dissemination and community management and activities sensing and records gathering in the neighborhood. Many clustering topology are proposed in recent years to localize the route inside the cluster. In this paper we have reviewed and in contrast these topologies to locate out the network mechanism which are less difficult to control and scalable for getting excessive satisfactory response with recognize to dynamics of the environment.

A Portable Tool for Spectral Analysis of Plant Leaves That Incorporates a Multichannel Detector to Enable Faster Data Capture

In this study, a novel system was designed to enhance the efficiency of data acquisition in a portable and compact instrument dedicated to the spectral analysis of various surfaces, including plant leaves, and materials requiring characterization within the 410 to 915 nm range. The proposed system incorporates two nine-band detectors positioned on the top and bottom of the target surface, each equipped with a digitally controllable LED. The detectors are capable of measuring both reflection and transmission properties, depending on the LED configuration. Specifically, when the upper LED is activated, the lower detector operates without its LED, enabling the precise measurement of light transmitted through the sample. The process is reversed in subsequent iterations, facilitating an accurate assessment of reflection and transmission for each side of the target surface. For reliability, the error estimation utilizes a color checker, followed by a multi-layer perceptron (MLP) implementation integrated into the microcontroller unit (MCU) using TinyML technology for real-time refined data acquisition. The system is constructed with 3D-printed components and cost-effective electronics. It also supports USB or Bluetooth communication for data transmission. This innovative detector marks a significant advancement in spectral analysis, particularly for plant research, offering the potential for disease detection and nutritional deficiency assessment.

Wireless and battery-free wearable biosensing of riboflavin in sweat for precision nutrition

Nutrition assessment is crucial for dietary guidance and prevention of malnutrition. Recent endeavors in wearable biochemical sensors have enabled real-time, in situ analysis of nutrients in sweat. However, the monitoring of riboflavin, an indispensable vitamin B involved in energy metabolism, remains challenging due to its trace level and variations in the sweat matrix. Herein, we report a wireless, battery-free, and flexible wearable biosensing system for the in situ monitoring of sweat riboflavin. Highly sensitive and selective electrochemical voltammetric detection is realized based on the synergistic effect of electrodeposited reduced graphene oxide (rGO) and platinum nanoparticles (PtNPs) with a low detection limit of 1.2 nM. The fully integrated system is capable of sweat sampling with the microfluidic patch, real-time riboflavin analysis and pH calibration with the flexible electrode array, as well as wirelessly simultaneous near field communication (NFC) energy harvesting and data transmission with the flexible circuit and a smartphone. On-body human sweat analysis demonstrates high accuracy cross-validated with gold-standard measurements, and reveals a strong correlation between sweat and urine riboflavin levels. The proposed wearable platform opens up attractive possibilities for noninvasive nutrient tracking, providing strong potential for personalized dietary guidance towards precision nutrition.

A secure and efficient UAV network defense strategy: Convergence of blockchain and deep learning

Unmanned Aerial Vehicles (UAVs) are highly versatile and efficient tools utilized across diverse industries for data collection. However, they are vulnerable to wireless communication and data exchange risks, including unauthorized access, data theft, and network attacks. To address these problems, we introduce a secure and reliable UAV network service architecture that incorporates blockchain and deep learning to provide more secure and efficient network services for UAVs. We propose a UAV cluster identity management module by combining blockchain, encryption algorithms, and digital signatures to enhance the security of UAV communication data transmission. Then, based on machine learning, deep learning, and malicious process detection technology, we propose a real-time secure situational awareness system for UAV cluster terminal devices to enhance the security of the operating environment for UAVs. Finally, we propose a data-trustworthy interconnection platform based on blockchain, smart contracts, and consensus algorithms to realize secure and efficient sharing and transmission of terminal data. The results of the experiments demonstrate the feasibility and effectiveness of our UAV network service architecture.

An ADO‐OFDM with integrated precoding, companding and optimized power allocation methods for high‐speed data transmission in visible light communication

AbstractVisible light communications (VLC) has received a lot of attention in recent studies because of its benefits over radio‐frequency (RF) communications. It is a short‐range optical wireless communication system that uses light‐emitting diodes (LEDs) as transmitters. Optical orthogonal frequency division multiplexing (OFDM) is an auspicious technology for VLC high‐speed data transfer. The use of OFDM in a VLC system raises the system's peak‐to‐average power ratio (PAPR). The intrinsic non‐linearity of LED is a key concern in an asymmetrically clipped DC‐biased optical OFDM (ADO‐OFDM) system due to its high PAPR. Also, conventional ADO‐OFDM modulation scheme cannot be used for accommodating different demands of services in downlink multiple access due to the restriction of direct current biased optical OFDM (DCO‐OFDM) and asymmetrically clipped optical (ACO‐OFDM) transmission on odd and even subcarriers. To tackle this issue, a modified ADO‐OFDM (MADO‐OFDM) is proposed that adjusts the numbers of subcarriers required for the transmission of ACO‐OFDM and DCO‐OFDM adaptively based on the requests of services in downlink multiple access. Also, the proposed MADO‐OFDM is integrated with a discrete Hartley Matrix transform (DisHMT) precoder and Generalized Piecewise Linear Compander (GPLD) to provide high‐speed data transmission with less PAPR. In addition, the power is allocated to the proposed MADO‐OFDM system optimally by maximizing the channel capacity based on the Aquila optimizer algorithm. The simulation results reveal that the suggested system's PAPR is reduced by 2.4 dB and 0.8 dB, respectively, compared to conventional ADO and hybrid ADO‐OFDM. It also confirms that the suggested generalized PLC can greatly increase BER without affecting the PAPR performance.

A Hybrid Optical-Acoustic Modem Based on MimoOfdm for Reliable Data Transmission in Green Underwater Wireless Communication

A Hybrid Optical-Acoustic Modem Based on MimoOfdm for Reliable Data Transmission in Green Underwater Wireless Communication

Representation of data by their residual images during transmission and reception of messages in wireless information and telecommunication systems under interference conditions

The article is devoted to the development of new methods used in information technologies for monitoring the results of telemetry in conditions of various types of interference, the need for which is most acute in the development of automated systems for monitoring the technical condition of complex complexes and systems. To resolve the contradictions, a new non-traditional approach is proposed that uses diagnostic methods for obtaining estimates of measurement results, the basis of which is the non-traditional representation of measurement data by their residual images during data transmission and reception in wireless information communications.

Interactive low delay music and speech communication via network connections (OVBOX)

The “OVBOX” is a tool for low-delay network audio communication and generic data transmission between multiple clients. Acoustic end-to-end delays of about 30 ms can be achieved, assuming a good internet connection and a distance between clients of less than about 1500 km. For optimal spatial perception, e.g., when using multiple instruments of similar timbre, an individual 3-dimensional room acoustic simulation based on physical modelling is applied in each client. The system is optimised for headless operation using a dedicated single-board computer (Raspberry Pi 4B), but desktop clients are also available for better integration with other audio software. A client-server system allows remote configuration and automatic traversal of network address translation routers and firewalls. With the low latency that can be achieved, the “OVBOX” is used for music applications such as distributed rehearsals or concerts. Other applications include hearing research to achieve interactive speech communication with low delay transmission of head movements for real-time control of virtual reality, and transmission of other biophysical data for online analysis or central data logging. The tool is fully open source.

Synthesis and optical wireless communication application of high efficiency extreme blue CsPbBr3 nanoplates

Extreme blue 435 nm emission, 97% PLQY CsPbBr3 nanoplates and up to 150 Mbps optical wireless communication data transmission rate.

Learning-enabled recognition of LG beams from multimode fiber specklegrams

The use of orbital angular momentum (OAM) beams, like Laguerre Gaussian (LG) beams is a significant advancement in optical communications, enhancing versatility in data encoding. In optical communications, especially OAM multiplexing, previous studies have successfully recognized LG beams in free space. However, free space propagation presents challenges including power loss, beam distortion, wandering, and physical obstacles. To overcome these hurdles, we introduce a novel method using speckle patterns (specklegrams) within a multimode fiber (MMF) for LG beam recognition through deep learning. Our study focuses on recognizing 15 LGp|l| beams (0≤p,|l|≤3; excluding LG00) generated with a spatial light modulator (SLM) and launched into a standard silica MMF with a 200 µm core diameter, producing corresponding speckle patterns. We utilize the widely-used deep convolutional neural network (CNN) model, AlexNet, to train these specklegram images, achieving an impressive 91 % recognition accuracy. Furthermore, we assess the recognition accuracy of 15 LGp(-l) modes, obtaining an 86.5 % accuracy rate. We also explore the impact of external perturbations, such as transverse weights (ranging from 0 to 3 kg in 1 kg increments) and temperature variations (ranging from 50 °C to 150 °C in 10 °C steps) on a section of MMF. In both scenarios, recognition accuracies are > 80 %. These findings highlight the effectiveness of our proposed approach, promising high-capacity data transmission in optical communications.

On data-induced CO[formula omitted] emissions of vehicle automation: An overlooked emission source

CO2 emission of vehicles and its influence on climate change is a widely discussed topic already for many years. New CO2 emission norms for vehicles have been introduced based on the propulsion of the vehicle, to reduce future CO2 emissions. Automated vehicles (AVs) have potential in reducing emissions by optimizing routes and speed profiles. However, they also generate extra emissions due to large data involved. Whether the norms can be met with these extra data-induced emissions of AVs remains an open question. This paper provides an approach to determine the CO2 emissions of these data related aspects. The approach dissects data-induced emissions stemming from energy consumption of the sensing components, the computing platform, disks inside the vehicle, wireless communication networks and data centers. We apply the approach to estimate CO2 emissions for varying scenarios of technology composition and energy mix. Sensitivity analysis shows that the energy intensity of wireless communication networks and the data transmission rate from vehicle to data center have the strongest influence on the resulting CO2 emissions. The energy mix also significantly affects whether the norms can be met. For high amounts of data transmission, compliance with the norms seem to be difficult in most scenarios. We recommend that the energy consumption of wireless communication networks and data transmission from vehicle to data center should be further optimized. Future work should focus on empirical evidence to validate/falsify the key assumptions in this paper, which will lead to a more accurate estimate of automation-induced emissions.