Quality Of Data Transmission Research Articles

Pengembangan Mesh Network Sebagai Ekspansi Protokol LoRaWAN di Politeknik Negeri Malang

In realizing a smart campus, Politeknik Negeri Malang (Polinema) has implemented digital technology in its learning and administrative processes with the goal of improving efficiency, speed, and operational ease. One of the key areas being developed is the Internet of Things (IoT), which is expected to function autonomously to support various campus activities. However, the main challenges in IoT implementation are the limitations in communication range and device power consumption, which cause issues in sensor and actuator data transmission, especially when data cannot be optimally received between nodes. To address these challenges, Polinema is ex-ploring the application of LoRaWAN technology. Although LoRaWAN is effective, it experiences a decline in data transmission quality when the sender and receiver are located in multi-story buildings, which can lead to delays, packet loss, or other disruptions. As a solution, the use of a Mesh Network is proposed to enhance the range and stability of data transmission. This study collected data using RSSI, SNR, and delay parameters in the civil engineering building at Polinema, with sensor data visualized through Grafana. The results show that the system can be well-integrated without conflicts between WiFi and LoRa. The average transmission time was approximately 29 seconds, with no packet loss detected. Additionally, changing the transmission method to confirmed uplink was necessary to maintain data integrity, while adjusting transmis-sion intervals was crucial to avoid scheduling issues. These findings indicate that implementing a Mesh Network as an extension of the LoRaWAN protocol can significantly improve the perfor-mance of IoT systems in Polinema’s indoor environment.

Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things

Many real-world networks for the Industrial Internet of Things (IIoT) have diverse connectivity characteristics and real-time constraints imposed by industrial processing. In the context of digital twin networks (DTNs), a large number of IIoT devices may access the network and have a tremendous volume of data. A crucial element of these IIoT devices is mobility, which cannot be effectively solved because the number of IIoT devices connected is extremely large. IIoT devices in DTNs suffer from poor data transmission and link quality because of their mobility. In this paper, device-to-device (D2D) communication-based mobility-assisted digital twin networks are proposed, where edge computing is introduced to design an efficient mapping between the physical and virtual space. Then, we propose the architecture of data transmission for the D2D network to maximize the data rate for reliable connectivity among multiple mobile nodes based on IIoT. A Markov decision process (MDP) is formulated to maximize the data rate for multiple mobile nodes while maintaining the D2D communication link quality. The simulation results demonstrate the superiority of the proposed scheme over other comparable models.

Effective Patient Scheduling For Public Health Events Based On Deep Reinforcement Learning

Amidst the current worldwide pandemic of the novel coronavirus, there are still issues about the efficient allocation of emergency supplies and shortcomings in recovery protocols. To enhance individuals' health and overall welfare, it is imperative to enhance and refine the emergency patient scheduling framework for health emergencies while upholding the idea of a society with an inherent future for humanity. The paper proposed an Efficient Patient Scheduling for Public Health Emergencies (EPS-PHE) using Multiagent Reinforcement Learning (MRL). Originally intended to solve the PHE and improve EPS, the technology was conceived as a distributed multiagent system. MRL has proposed a decentralized incentive system evaluation technique for the EPS-PHE input. An incentive type called a Direct Incentive (DI) is used to show the relationships between individuals or details about two closely connected events. The quality of data transmission in EPS-PHE or the efficacy of the emergency network is measured using a Collective Incentive (CI). Simulation research has shown the value of the Deep Learning (DL) - MRL approach in EPS-PHE framework optimization.

Asynchronous Code Division Multiplexing-Based Visible Light Positioning and Communication Network Using Successive Interference Cancellation Decoding.

In the evolving landscape of sixth-generation wireless communication, the integration of visible light communication (VLC) and visible light positioning (VLP), known as visible light positioning and communication (VLPC), emerges as a pivotal technology. This study addresses the challenges of asynchronous code division multiplexing (ACDM) in VLPC networks, focusing on the enhancement of data transmission quality and positioning accuracy. Firstly, we propose an orthogonal pseudo-random code (OPRC) for ACDM-based VLP systems. Leveraging its excellent correlation properties, VLP signals preserve orthogonality even amidst asynchronous transmissions, achieving sub-centimeter average positioning errors. Next, by combining OPRC with successive interference cancellation decoding (SICD), we propose an enhanced ACDM-based VLPC system that utilizes OPRC for improved signal orthogonality and SICD for progressive elimination of multiple access interference (MAI) among VLPC signals. The results show substantial improvements in bit-error rate (BER) and positioning error (PE), approaching the performance levels observed in synchronized VLPC systems. Specifically, the SICD-OPRC scheme reduces average BER to 4.3 × 10-4 and average PE to 2.7 cm, demonstrating its robustness and superiority in complex asynchronous scenarios.

Studying the influence of correction codes on coherent reception of M-PSK signals in the presence of noise and harmonic interference

Objectives. Signals with multiple phase shift keying (M-PSK) exhibiting good spectral and energy characteristics are successfully used in many information transmission systems. These include satellite communication systems, GPS, GLONASS, DVB/DVB-S2, and a set of IEEE 802.11 wireless communication standards. In radio communication channels, the useful signal is affected by various interferences in addition to noise. One of these is harmonic interference. As a result, high intensity harmonic interference practically destroys the reception of M-PSK signals. One of the important requirements for the quality of data transmission is the system error tolerance. There are different ways of improving the quality of information transmission. One of these is the use of corrective encoding technology. The aim of the paper is to assess the noise immunity of a coherent demodulator of M-PSK signals using Hamming codes (7,4) and (15,11), and convolutional encoding with Viterbi decoding algorithm (7,5) when receiving M-PSK signals under noise and harmonic interference in the communication channel.Methods. The methods of statistical radio engineering, optimal signal reception theory and computer simulation modeling were used.Results. Experimental dependencies of the bit error rate on the signal-to-noise ratio and on the intensity of harmonic interference of coherent reception of M-PSK signals in a channel with noise and harmonic interference were obtained using computer simulation modeling. This was done without using correction codes and with Hamming code (7.4) and (15.11) and convolutional encoding with Viterbi decoding algorithm (7,5).Conclusions. It is shown that the application of the correction codes effectively corrects errors in the presence of noise and harmonic interference with lower intensity. The correction is ineffective in the presence of high intensity interference. These results can provide important guidance in designing the reliable and energy efficient system.

Evaluating PAM-4 Data Transmission Quality Using Multi-Dimensional Mapping of Received Symbols

Evaluating PAM-4 Data Transmission Quality Using Multi-Dimensional Mapping of Received Symbols

Ultra High multi channel clock frequency(THz, PHz, EHz, ZHz,YHz,XHz,WHz) baud rate speed PRBS Transceiver HDL RTL ASIC Design for Ultra High long Distance Wireless Communication Engineering Smart Computing Products/ Applications

The Key Objective is HDL RTL Design Architecture of Ultra high multi Clock Frequency Speed Rate ( MHz, GHz, THz, PHz, EHz, ZHz, etc) Bits Per Second Baud Rate ) P.R.B.S(Pseudo Random Binary Sequence) Transceiver Soft A.S.I.C IP Core product for identification of the property of Different Pseudo Random Binary Sequence Patterns (Seed Words) of 2e 7 -1, 2e10 -1, 2e 15 -1, 2e 23 -1, 2e 31 -1 tapped elements as per C.C.I.T.T-I.T.U Standards and IEEE-754 Single and Double Data Rate Data Precision Standards (32 bit & 64 Bit Data Width ) suited for Very Advanced Futuristic Hi-tech Smart High Speed Long Distance Wireless Digital Communication A.S.I.C Products / Applications like Space/Satellite, Aerospace and Large Data Processing and computing like High Speed Internet and Cloud Computing based Hi-Fi Industrial Data Automation Standard Ultra High Speed Wireless Communication A.S.I.C products and Applications-3G,4G,5G etc. The Multi channel PRBS Transceiver consists Transmitter and receiver of different PRBS patterns- 2e7 -1, 2e10 -1, 2e15 -1, 2e23 -1, 2e31 -1 NRZ. The data input transmitted and received serially in the form bit by bit. These different pattern sequences are Designated as per CCITT ITU 0.151/O.152/O.153 & AT&T Standards. The Aim and purpose of invention of Ultra high multichannel Clock frequency PRBS Transceiver is for transmit and receive data serially by using Tx_in and Tx_out, rxin, rxout signals and The Transceiver is processed the low frequency signal input with different high speed carrier wave frequencies in the form of different PRBS pseudo random binary sequence seed word bit/byte patterns -2e7 -1, 2e10 -1, 2e15 -1, 2e23 -1, 2e31 -1 NRZ and also on receiver side processed with the above patterns. Materials and Methods: Transmission and reception of Data serially based on the Deterministic random seed word pattern methods of different PRBS 2e7 -1, 2e10 -1, 2e15 -1, 2e23 -1, 2e31 -1 tapped sequence Elements and All these PRBS are purely synchronized with Ultra high clock frequency(MHz, GHz, THz, PHz, EHz, ZHz, YHz, XHz, WHz). The Soft IP Core Designed by System Verilog HDL/ Verilog HDL. RTL Design Simulation done by Synopsys VCS 2020.1 software and Altera Model-Sim Software and Logic Design Flow & Synthesis done by Xilinx ISE and Altera Quartus EDA Tools. Results: Generation of Simulation Display and waveform for Ultra High Clock frequency(MHz, GHz, THz, PHz, EHz, ZHz, YHz, XHz, WHz) Synchronized Pseudo Random Binary Sequence (PRBS) Register Transfer Level (RTL) Generators, Transceiver for efficient and effective High Quality Data transmission and Reception of Various tapped sequence patterns for Identification of property of different PRBS patterns2e7 -1, 2e10-1, 2e15-1, 2e23 -1, 2 31 -1 NRZ of Ultra high speed Long distance wireless engineering Applications/ products.

A energy efficiency optimization routing processing method for Linear Wireless Sensor Networks

Linear wireless Sensor networks (LWSNs) are extensively utilized to monitor linear infrastructure such as railways, bridges, mines, and borders. In the large-scale deployment of sensor networks, extending network life and improving network energy balance remains a significant challenge. The existing routing protocols adopt the path planning method based on the routing information database, which can effectively transmit data packets. However, the methods consume additional energy when establishing and maintaining routing tables, significantly reducing the nodes’ lifespan and leading to unstable data transmission quality. This paper proposes a dynamic random multipath routing method (DRMRM) for LWSNs. The technique combines the node depth and residual energy models to select the optimal next-hop relay node without relying on the transmission routing table. At the same time, we designed a data loss retransmission mechanism and a data loop retreat mechanism to prevent data packets from reaching a dead end. The experimental results demonstrate that our routing method is superior to existing energy consumption balance and network lifespan protocols.

ReLeC-based clustering and multi-objective optimization for efficient energy optimization in IoT networks

In response to the escalating demand for energy-efficient wireless sensor networks (WSNs) within the expanding Internet of Things (IoT) landscape, we introduce ReLeC-MO, a novel protocol that integrates the ReLeC clustering algorithm with multi-objective optimization. Leveraging reinforcement learning-based clustering, ReLeC optimizes network topology to enhance energy efficiency. Multi-objective optimization further refines this process by identifying non-dominated solutions on the Pareto front, facilitating a balanced trade-off between network lifetime, energy consumption, and data transmission quality. Our comprehensive simulations reveal the remarkable performance improvements achieved by ReLeC-MO over existing techniques. Specifically, ReLeC-MO demonstrates a 39% reduction in delay, a 50% decrease in energy consumption, and a 25% increase in throughput, showcasing its efficacy in enhancing both energy efficiency and network performance. It also increases network lifetime by 20%, surpassing the latest existing model. Furthermore, its implementation in MATLAB ensures ease of replication and adaptation across diverse IoT applications.

Analysis of Ultra-Dense Wavelength Division Multiplexing (UDWDM) in a Passive Optical Network (PON)

Passive Optical Networks (PONs) are essential in optical communications to meet the increasing demand for network capacity and connected users, ensuring reliable and adaptable connections for data transmissions. These networks also simplify infrastructures and efficiently utilize energy by eliminating the need for active devices. In this context, Ultra- Dense Wavelength Division Multiplexing (UDWDM) is one of the most prominent solutions for data transmission. This technology uses the narrow separation between channels from 25GHz and even as small as 6.25GHz to increase the transmission capacity. This document analyzes the performance of UDWDM considering the transmission of three simultaneous channels in a PON network for which three different scenarios have been considered for the analysis with the following parameters: i) transmission speed from 10 Gbps to 17 Gbps, ii) the distance from 10 km to 20 km and iii) the separation between channels of 15GHz, 20GHz, and 25GHz. The performance metrics for the analyzed scenario are the bit error rate and the eye diagram. To ensure the reception of the transmitted channel, an analysis of raised cosine and Gauss filters is also discussed. Including these filters allows for verifying whether their use enhances the performance of the channels compared to transmission without a filter. This is crucial for understanding how filters can optimize the quality and reliability of data transmission in a PON, which is of great importance in environments where high efficiency and connection quality are required.

A Mobile AD Hoc Network Relay Method based on OLSR Protocol

Aiming at the reliability and performance problems of data transmission in the complex mobile AD hoc network environment, a relay method is designed and implemented in the unstable network environment with complex dynamics such as mobile, multi-hop and no center according to practical needs. By introducing a prior link-state routing protocol, OLSR is introduced. Using range value judgment, sliding window statistics and improved multi-point relay selection technology, it can effectively reduce the redundancy of relay node set, reduce the forwarding frequency of service data in the network, save the network overhead, and ensure the data transmission delay and quality in the large scale and dense mobile AD hoc network.

Design and Development of a Quality Evaluation and Monitoring System for the Short-term Weather Forecast in Gansu Province

In order to further improve the development of the meteorological observation quality management system in Gansu Province, this article takes the organic integration of quality management and meteorological observation as the concept, and combines practical business applications to develop a scoring model to quantitatively evaluate the quality of short-term warning signals and observation data transmission in Gansu Province. At the same time, a visual model is developed to achieve a personalized monitoring and analysis system for the entire process of “monitoring warning service”. System users can monitor the actual situation of various meteorological disasters, analyze disasters, and assess risks in the system, and display and apply decision information, thereby improving the scientific and standardized level of meteorological observation business management.

Advanced channel coding schemes for B5G/6G networks: State‐of‐the‐art analysis, research challenges and future directions

SummaryAs a consequence of continuing demand for additional capacity and higher quality data transmission, channel coding as a key component of a digital communication network has progressed from a classical single pair, point‐to‐point information theoretic application to a class of coding techniques with diverse parameters. The heterogeneous requirements of B5G/6G networks technology have made flexibility of code parameters the main desired feature while designing channel codes. Owing to this, channel coding techniques have evolved to support enabling applications that depend on different factors such as latency, reliability, energy efficiency and throughput. We review and discuss the application of new techniques, modifications in the design and construction of modern channel coding schemes, including block, convolutional, rateless and space‐time codes. Further, the error correction performance of mainstream channel decoders for LDPC, polar and turbo codes is compared and analysed for their attainable error rate. The aim of this study is to highlight the adaptability of the discussed coding schemes for the forthcoming cellular network landscape. This article also addresses the implementational challenges of high throughput, low latency and machine type communication scenarios. Moreover, some recent studies exploring the role of machine learning for optimisation of B5G/6G networks are classified and discussed. Concluding, research areas pertaining to the scalability of error control coding for next generation networking are addressed.

Decentralized Protocol for Organizing Sustainable Interaction between Subscribers in Networks with High Dynamics of Topology Changes

Emergency services often have to carry out rescue and liquidation operations in the absence of centralized communications. The inability to ensure stable communication between members of the rescue unit significantly reduces the quality of work. Moreover, in modern realities, stable communication means not only voice exchange, which can be provided by shortwave radio transmitters, but also intensive exchange of large volumes of traffic. The use of standard solutions based on standard network equipment (Wi-Fi, satellite communications, etc.) and existing algorithms for ensuring quality of service in the conditions under consideration does not allow quickly ensuring information exchange between heterogeneous subscribers. Moreover, operation in high-Hz bands can be very difficult in the presence of obstacles, which reduces the overall coverage area and the quality of data transmission. We propose a network layer routing protocol designed to organize decentralized communication in an emergency service department, where subscribers have different degrees of mobility and types of transmitted traffic. This protocol includes algorithms for connecting to the network, detecting optimal and alternative communication routes, and transmitting and balancing traffic along the found routes. The original route search algorithm analyzes the performance of communication channels and determines all possible paths for transmitting traffic between subscribers. Using the route evaluation function based on gradient boosting of decision trees, optimal and alternative communication routes are formed, and when transmitting data, traffic balancing is performed based on the received information. An experimental study of the proposed protocol showed an improvement in the speed of deployment and quality of service in scenarios with varying degrees of subscriber mobility.

Machine learning-based methods for MCS prediction in 5G networks

In the ever-evolving landscape of wireless communication systems, including fifth-generation (5G) networks and beyond (B5G), accurate Modulation and Coding Scheme (MCS) prediction is crucial for optimizing data transmission efficiency and quality of service. Traditional MCS selection methods rely on predefined rules and heuristics, offering transparency and control but lacking adaptability in changing wireless conditions. The emergence of Machine Learning (ML) has brought transformative capabilities, particularly in MCS prediction. ML leverages data-driven models, promising improved accuracy and adaptability in dynamic wireless environments. This paper marks a novel endeavor in this domain, as it explores and evaluates a range of machine learning (ML) techniques for predicting MCS in orthogonal frequency-division multiplexing (OFDM) systems, representing the first such investigation in this field. Additionally, it introduces a specialized Deep Neural Network (DNN) architecture with two hidden layers for MCS prediction, guided by performance metrics such as accuracy, precision, recall, and F1-score. The examined ML methods include Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF), and Bagging with k-NN (B-kNN). These methods undergo thorough training and evaluation using a dataset generated from simulations of non-standalone 5G networks. The study incorporates physical layer measurements and employs a ray-tracing path loss prediction model for comprehensive environmental characterization. Also, advanced data mining techniques preprocess raw data, addressing model underfitting and overfitting challenges. Finally, performance evaluation results reveal that the ANN with two hidden layers achieves the highest accuracy at 98.71%, while RF and B-kNN methods attain the lowest accuracy, below 88.65%. SVM and ANN models, with one and four hidden layers, respectively, demonstrate comparable MCS prediction accuracy, ranging from 97.02 to 97.30%.

Analysis of Wireless Multimedia Big Data Transmission Based on Adaptive Scheduling Algorithm

The increasing demand for wireless multimedia applications has prompted the need for efficient data transmission strategies that can adapt to dynamic network conditions and diverse Quality of Service (QoS) requirements. This paper analyzes wireless multimedia big data transmission using an innovative Adaptive QoS-Aware Scheduling Algorithm (AQOS-ASA). The proposed algorithm aims to optimize the transmission of multimedia data by dynamically adjusting the scheduling based on QoS considerations and real-time network conditions. Through adaptive scheduling, the algorithm ensures that high-priority multimedia data receives optimal transmission treatment, balancing the quality preferences set by users. The algorithm dynamically allocates time slots based on the changing network conditions, allowing for efficient utilization of the available bandwidth. The paper analyses the algorithm's performance in different wireless network scenarios through simulations and evaluations, highlighting its effectiveness in adapting to varying QoS requirements and network conditions. The results demonstrate that the proposed algorithm significantly enhances the quality and efficiency of wireless multimedia big data transmission, making it well-suited for applications with diverse multimedia content and dynamic network environments. The findings contribute valuable insights to wireless multimedia communication and provide a foundation for further advancements in adaptive scheduling algorithms for improved Quality of Service.

The Latency Performance Analysis and Effective Relay Selection for Visible Light Networks.

In visible light communication (VLC), the precise latency evaluation of wireless access networks and the efficient forwarding strategy of core networks are the crux for end-to-end reliability provisioning. Leveraging martingale theory, an elegant latency-bounded reliability analysis framework is studied for the VLC network. Considering the characteristic that VLC links are easy to block, the martingale of the service process is constructed. Based on the time shift features, the martingale process related to latency is proposed for the VLC system, which models the influence of entanglement between aggregate arrivals and random service on latency. A stopping time event about latency is defined. Renting the stopping time theory, a tight upper bound of the unreliability with regard to latency is derived. In the core network, a dynamic forward backhaul framework is proposed, which uses the relay selection algorithm based on back-pressure theory to improve data transmission quality. The theoretical latency-bounded reliability matches the simulation results well, which verifies the effectiveness of the proposed analysis framework, and the proposed relay selection algorithm can also improve network performance under data-intensive transmission.

Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers.

Recently, there has been increased interest in adopting novel sensing technologies for continuously monitoring structural systems. In this respect, micro-electrical mechanical system (MEMS) sensors are widely used in several applications, including structural health monitoring (SHM), in which accelerometric samples are acquired to perform modal analysis. Thanks to their significantly lower cost, ease of installation in the structure, and lower power consumption, they enable extensive, pervasive, and battery-less monitoring systems. This paper presents an innovative high-performance device for SHM applications, based on a low-noise triaxial MEMS accelerometer, providing a guideline and insightful results about the opportunities and capabilities of these devices. Sensor nodes have been designed, developed, and calibrated to meet structural vibration monitoring and modal identification requirements. These components include a protocol for reliable command dissemination through network and data collection, and improvements to software components for data pipelining, jitter control, and high-frequency sampling. Devices were tested in the lab using shaker excitation. Results demonstrate that MEMS-based accelerometers are a feasible solution to replace expensive piezo-based accelerometers. Deploying MEMS is promising to minimize sensor node energy consumption. Time and frequency domain analyses show that MEMS can correctly detect modal frequencies, which are useful parameters for damage detection. The acquired data from the test bed were used to examine the functioning of the network, data transmission, and data quality. The proposed architecture has been successfully deployed in a real case study to monitor the structural health of the Marcus Aurelius Exedra Hall within the Capitoline Museum of Rome. The performance robustness was demonstrated, and the results showed that the wired sensor network provides dense and accurate vibration data for structural continuous monitoring.

Bio-Inspired algorithms for secure image steganography: enhancing data security and quality in data transmission

The proliferation of data sharing over the Internet has given rise to pressing concerns surrounding data security. Addressing these concerns, steganography emerges as a viable mechanism to safeguard data during transmission. It involves concealing messages within other media, such as images, exchanged over networks. In this research, we propose an innovative image steganography approach by harnessing the capabilities of bio-inspired algorithms. A central challenge in steganography revolves around the inherent pixel correlations within cover images, which may inadvertently leak sensitive information to potential intruders. To tackle this challenge head-on, we harness the potential of bio-inspired algorithms, which have exhibited promise in efficiently mitigating these vulnerabilities. This paper introduces a steganography strategy rooted in a fusion model that seamlessly integrates diverse bio-inspired algorithms. Our novel embedding approach ensures the production of robust and high-quality cover images and disrupts bit sequences effectively, thereby enhancing resistance against potential attacks. We meticulously evaluate the performance of our method using a comprehensive dataset encompassing grayscale and JPEG color images. Our particular emphasis on color images arises from their superior capacity to conceal a greater volume of information. The results vividly demonstrate our approach's effectiveness in achieving secure and efficient data concealment within images.

Particle-Filter-Based State Estimation for Delayed Artificial Neural Networks: When Probabilistic Saturation Constraints Meet Redundant Channels.

In this brief, the state estimation problem is investigated for a class of randomly delayed artificial neural networks (ANNs) subject to probabilistic saturation constraints (PSCs) and non-Gaussian noises under the redundant communication channels. A series of mutually independent Bernoulli distributed white sequences are introduced to govern the random occurrence of the time delays, the saturation constraints, and the transmission channel failures. A comprehensive redundant-channel-based communication mechanism is constructed to attenuate the phenomenon of packet dropouts so as to enhance the quality of data transmission. To compensate for the influence of randomly occurring time delays, the corresponding occurrence probability is exploited in the process of particle generation. In addition, an explicit expression of the likelihood function is established based on the statistical information to account for the impact of PSCs and redundant channels. By virtue of the modified operations of particle propagation and weight update, a particle-filter-based state estimation algorithm is proposed with mild restriction on the system type. Finally, an illustrative example with Monte Carlo simulations is provided to demonstrate the effectiveness of the developed state estimation scheme.