Data Transmission Techniques Research Articles

FPGA Implementation of 256-Bit Key AES Algorithm

Abstract: The most popular network security technique for secure data transmission between two end users, particularly over a public network, is the AES algorithm, also known as the Rijndael algorithm. It may be found in all kinds of wired and wireless digital communication networks AES, a network security technique, uses 256 bits on an FPGA. An authorized cryptographic technique that can be used to secure electronic data is the Advanced Encryption Standard technique (AES), which is specified by the National Institute of Standards and Technology. Because they provide faster and more flexible solutions, reprogrammable devices like Field Programmable Arrays (FPGA) are a very appealing alternative for hardware implementation of the encryption algorithm AES. The effective FPGA implementation of advanced encryption standards (AES) is proposed in this research. The encryption code is written in the Verilog programming language. The AES Rijndael algorithm performs better than any other when implemented on FPGA. Other works are covered by this implementation to demonstrate its effectiveness. The block and key sizes in the design are 256, and an iterative looping technique is used. Bits, S-box implementation using lookup tables. This results in a low-complexity design that can readily achieve high throughput and low latency

LiFi Technology Application and Prospect in the Field of Medical

Light Fidelity (LiFi) technology utilizes visible light as a transmission medium with a large spectrum and resistance to electromagnetic interference. Due to its unique characteristics, the use of light emitted by light-emitting diodes (LED) to transmit data makes it very safe when transmitting data. LiFi technology plays an integral role in many fields, such as aerospace, underwater communications, defense and security, and medicine. This paper introduces the basic working principle of LiFi. LiFi technology is used as a modulation technique for data transmission, and the current status of LiFi technology in the medical field is briefly analyzed, along with the challenges and difficulties encountered in this field. Examples include a medical monitoring system based on LiFi technology and the transmission of images in the operating theater using LiFi technology. In the medical field, LiFi technology also faces challenges and difficulties, such as ensuring the stability and reliability of communication and achieving efficient signal processing and data transmission. The potential of multiple-input multiple-output (MIMO) technology for in-depth research is described.

Exploring FSO link performance in varied atmospheric conditions to optimize 5G communication with a polarized quasi-diffuse transmitter

Abstract Free space optical (FSO) communication is an innovative technology that holds immense promise for numerous applications thanks to its high data transmission rates, rapid scalability, cost-effectiveness, superior security, and comprehensive capacity access techniques for Gbps data transmission. FSO communication relies on the Earth’s atmosphere as the medium, which introduces atmospheric disturbances that have driven the development of spatial diversity techniques aimed at enhancing the technology’s performance. Our proposed system employs a polarized quasi-diffused system with a fork as a spatial diversity scheme, which eliminates the need for multiple transmitters. We have developed a model that integrates both simulation designs to achieve optimal results. The proposed model has demonstrated excellent performance under various atmospheric turbulences, including rain, fog, and haze, exhibiting a very high maximum quality factor, improved received power, and better bit error rate (BER). Finally, we have obtained, analyzed, and extensively discussed the simulation results to provide a comprehensive understanding of the proposed model’s potential benefits. Our simulations show a maximum quality factor of 23 compared to existing models in much better, a 34 % increase in received power, and a 31 % decrease in BER compared to existing models. These results highlight the potential benefits of the proposed model for FSO communication systems.

An Underwater Source Location Privacy Protection Scheme Based on Game Theory in a Multi-Attacker Cooperation Scenario.

Ensuring source location privacy is crucial for the security of underwater acoustic sensor networks amid the growing use of marine environmental monitoring. However, the traditional source location privacy scheme overlooks multi-attacker cooperation strategies and also has the problem of high communication overhead. This paper addresses the aforementioned limitations by proposing an underwater source location privacy protection scheme based on game theory under the scenario of multiple cooperating attackers (SLP-MACGT). First, a transformation method of a virtual coordinate system is proposed to conceal the real position of nodes to a certain extent. Second, through using the relay node selection strategy, the diversity of transmission paths is increased, passive attacks by adversaries are resisted, and the privacy of source nodes is protected. Additionally, a secure data transmission technique utilizing fountain codes is employed to resist active attacks by adversaries, ensuring data integrity and enhancing data transmission stability. Finally, Nash equilibrium could be achieved after the multi-round evolutionary game theory of source node and multiple attackers adopting their respective strategies. Simulation experiments and performance evaluation verify the effectiveness and reliability of SLP-MACGT regarding aspects of the packet forwarding success rate, security time, delay and energy consumption: the packet delivery rate average increases by 30%, security time is extended by at least 85%, and the delay is reduced by at least 90% compared with SSLP, PP-LSPP, and MRGSLP.

Intelligent Resource Allocation Scheme Using Reinforcement Learning for Efficient Data Transmission in VANET.

Vehicular ad hoc networks (VANETs) use multiple channels to communicate using wireless access in vehicular environment (WAVE) standards to provide a variety of vehicle-related applications. The current IEEE 802.11p WAVE communication channel structure is composed of one control channel (CCH) and several service channels (SCHs). SCHs are used for non-safety data transmission, while the CCH is used for broadcasting beacons, control, and safety. WAVE devices transmit data that alternate between CCHs and SCHs, and each channel is active for a duration called the CCH interval (CCHI) and SCH interval (SCHI), respectively. Currently, both intervals are fixed at 50 ms. However, fixed-length intervals cannot effectively respond to dynamically changing traffic loads. Additionally, when many vehicles are simultaneously using the limited channel resources for data transmission, the network performance significantly degrades due to numerous packet collisions. Herein, we propose an adaptive resource allocation technique for efficient data transmission. The technique dynamically adjusts the SCHI and CCHI to improve network performance. Moreover, to reduce data collisions and optimize the network's backoff distribution, the proposed scheme applies reinforcement learning (RL) to provide an intelligent channel access algorithm. The simulation results demonstrate that the proposed scheme can ensure high throughputs and low transmission delays.

Simulation Studies of 3-Stage Clos Switching Network with Prioritization Mechanism Used in Flexible Wavelength-Division Multiplexing Network Nodes

Given the escalating need for greater bandwidth, there is a growing interest in implementing mechanisms that ensure reliable and optimal service levels for specific traffic classes, especially during periods of heavy network traffic. One such mechanism is the prioritization mechanism, where certain portions of resources are exclusively allocated to predefined services. In the context of contemporary flexible WDM networks that employ advanced data transmission techniques, efforts have been made to create a simulation program capable of assessing the traffic characteristics of nodes within flexible WDM networks. This article presents a simulator of flexible WDM nodes with an implemented mechanism for prioritizing call classes. The simulator allows us to determine the loss probability for individual traffic classes in a switching network with point-to-point selection. In the simulator, the Clos structure was adopted as the structure of the flexible WDM network node due to its popularity in many studies and applications.

Enhancing secure multi-group data sharing through integration of IPFS and hyperledger fabric.

Data sharing is increasingly important across various industries. However, issues such as data integrity verification during sharing, encryption key leakage, and difficulty sharing data between different user groups have been identified. To address these challenges, this study proposes a multi-group data sharing network model based on Consortium Blockchain and IPFS for P2P sharing. This model uses a dynamic key encryption algorithm to provide secure data sharing, avoiding the problems associated with existing data transmission techniques such as key cracking or data leakage due to low security and reliability. Additionally, the model establishes an IPFS network for users within the group, allowing for the generation of data probes to verify data integrity, and the use of the Fabric network to record log information and probe data related to data operations and encryption. Data owners retain full control over access to their data to ensure privacy and security. The experimental results show that the system proposed in this study has wide applicability.

Combined transmission of PPM and WSK modulated optical signal over free space optical link enabling physical layer security

This work proposes a novel technique for transmission of data from two different sources by using a combination of wavelength shift keying (WSK) and pulse position modulation (PPM) over a free space optical (FSO) link. It has been shown that the proposed technique has potential of improving the physical layer security of FSO links, which is a growing concern due to considerable interest in the employment of FSO for high data rate applications. The proposed technique transmits at a combined data rate of 20 Gbps over a single pulse per bit slot. The wavelength and position in time of the pulse is varied according to the combination of bits received from the two different data sources. Gamma–Gamma channel model is used to simulate the FSO link whose length has been varied from 0.5 km to 3 km to observe the bit error rate (BER) performance. The BER has also been observed for different time delays induced over the optical pulse for implementing the PPM technique. It has been shown that the proposed technique can work under different time delays, hence making it difficult for an eavesdropper to sample the optical pulses at the correct instant for information extraction. Furthermore, this scheme allows us to perform balanced detection, resulting in wider eye opening at the receiver.

Performance Evaluation of IoT Sensors in Urban Air Quality Monitoring: Insights from the IoT Sensor Performance Test

In this paper, we report on extensive experiments conducted to evaluate Internet of Things (IoT) sensor performance in monitoring urban air quality. As certified sensors showed a considerably reduced air quality measurement error of 4.3% compared to uncalibrated sensors at 8.5%, our results highlight the crucial function of sensor calibration. The performance of sensors was impacted by environmental factors; higher temperatures produced better accuracy (3.6%), while high humidity levels caused sensors to react more quickly (2.3 seconds). The average air quality index (AQI) recorded by inside sensors was 45, but outside sensors reported an AQI of 60. This indicates that the positioning of the sensors had a substantial influence on the air quality data. Additionally, the methods of data transmission were examined, and it was found that Wi-Fi-transmitting sensors had lower latency (0.6 seconds) and data loss (1.8%) than cellular-transmitting sensors. These results emphasize the significance of environmental factors, sensor placement strategy, sensor calibration, and suitable data transmission techniques in maximizing IoT sensor performance for urban air quality monitoring, ultimately leading to more accurate and dependable air quality assessment.

Architecture of intercomponent interaction of a microprocessor-powered sustainable relay protection system based on open standards IoT

Nowadays, the problem of the coordination of relay protection systems during faults becomes widespread, as the trip of the circuit breaker must be fast. One of the solutions is the application of the Internet of Things. The object of this research is a relay protection system architecture, which uses elements of the Internet of Things and is based on open standards. The research aims to address the efficiency of using the Internet of Things in the relay protection systems, as well as the relevance of the proposed architecture. This architecture unifies processes encompassing relay protection, data collection, analysis, real-time communication protocols, and secure data transmission techniques. Apart from the increased quality of relay protection, it provides a well-organized framework for the precise and efficient acquisition, transmission, and analysis of data. As a result of the research, it was derived that the Internet of Things can be used in relay protection applications along with the proposed architecture if certain conditions are met.

Application of IoT network for marine wildlife surveillance

Abstract Every day of the week, wireless communication is almost all around us. The Internet of Things (IoT) is a standard protocol used to describe the rapidly advancing technology in which almost every electronic device is or may be connected to the Internet. These electronic gadgets constantly provide data signals to the gateways, which satellites such as those in Low Earth Orbit may transmit. Because of these networks’ limited resources and the IoT, these transactions must be completed with the least amount of latency and data loss possible. We also analyze the performance implications of implementing RF-based powering for such a network. The techniques presented in this paper may benefit the scientific community and industry in general when it comes to the dynamic distributed parameter allocation methodology for IoT network devices. We will also discuss how research on animals and the natural environment has been impacted by IoT breakthroughs, in particular, animal sensors’ limits and incapacity to broadcast from everywhere. Our analysis illustrates the most effective data transmission technique and establishes the bounds of these restrictions. Furthermore, the physics of the RF channel plays a critical role in the uncertainty of the channel as well as the amount of energy harvested. By employing simulation based on the physics of the RF channel, the article shows the performance of the system considering both the uncertainty of data arrival as well as the variability of the channel. The findings of the simulation show that the devices consume less energy overall as the signal-to-noise ratio rises. Furthermore, a timing factor of 10–15% is shown to be effective in maintaining a constant mean rate and increasing the energy efficiency of the system.

An Innovative Approach to Cardiovascular Disease Prediction: A Hybrid Deep Learning Model

The increasing prevalence of cardiovascular disorders has created an imperative need for accurate diagnoses. Despite the emergence of numerous techniques for disease classification and secure data transmission, a prevailing shortcoming is the lack of precision in decision-making. This study aimed to address this critical issue by introducing an innovative disease prediction model that uses a hybrid classifier. The proposed hybrid classifier combined deep Bidirectional Long-Short-Term Memory (deep Bi LSTM) and deep Convolutional Neural Network (deep CNN).To further improve its performance, the proposed approach employed hybridized swarm optimization to fine-tune fusion parameters and optimize the learning model for enhanced accuracy. This study focused on heart disease as its central concern, strengthening data security through the implementation of Diffi-Huffman based on Elliptic Curve Cryptography (ECC) during data transmission. The resulting automatic disease prediction model adopted the hybrid deep classifier, which was born from the amalgamation of two components: the interactive hunt-deep CNN classifier and the WoM-deep Bi LSTM. The proposed hybrid learning model achieved impressive accuracy, F-measure, sensitivity, and specificity of 97.716%, 97.848%, 98.021%, and 97.807%, respectively, marking a significant advance in the realm of cardiovascular disease prediction.

A Multi-Phase SMO-Based MAC Protocol for IoT-Enabled WBAN Systems

This paper proposes an optimised Medium Access Control (MAC) protocol to minimise the number of channel access collisions and to improve the bandwidth efficiency of IoT-based Wireless Body Area Networks (WBANs). The frame structure of the proposed MAC protocol consists of three different phases, namely Contention Phase (CP), Transmission Phase (TP), and Inactive Phase (IP). During CP, the proposed protocol prioritises the life-sensitive data. Based on allocated priority, the communicating sensor nodes (SNs) compete for channel access. During TP, the successful SNs transmit their data. We use TDMA mechanism for data transmission and Spider Monkey Optimisation (SMO) technique to improve the bandwidth efficiency of the system by selecting the best possible TDMA time slot according to the data packet size of the SNs. During IP, the SNs go into sleep mode to save energy. We simulate the scenario on MATLAB simulator. The simulation results show that the proposed SMO-MAC improves the throughput by 22.23%, 14.00%, and 17.62%, residual energy by 60.76%, 81.17%, and 124.34%, communication reliability by 70.32%, 52.60%, and 52.60%, and data packet transmission delay by 80.74%, 70.10%, and 77.40% as compared to DRT-MAC, PSO-MAC, and GA-MAC, respectively.

Performance Evaluation of Single-Carrier and Orthogonal Frequency Divison Multiplexing-Based Autoencoders in Comparison with Low-Density Parity-Check Encoder

Recently, the growing demands for ultra-high speed applications require more advanced and optimal data transmission techniques. Wireless autoencoders have gained significant attention since they provide global optimization of the transceiver structure. This article explores the application of autoencoders to enhance the performance of wireless communication systems. It provides the performance evaluation of the systems using single-carrier and OFDM-based autoencoders under the conditions of AWGN and fading channels. Then, in terms of the BLER metric, the wireless systems with autoencoders are compared with conventional systems using LDPC coding and quadrature amplitude modulation for various configurations. Simulation results indicate that for high-modulation orders (QAM-64 or QAM-256), communication systems employing autoencoders provide superior performance compared to systems using LDPC channel encoding in regions with a low signal-to-noise (SNR) ratio. Specifically, a gain of 1–2 dB in signal power is obtained for single-carrier autoencoders and 0.3–2 dB is obtained for OFDM-based autoencoders. Therefore, wireless communication systems utilizing autoencoders can be considered as a promising candidate for future wireless communication systems.

Modeling of Burst Impulse Noise Errors in an In-House M-QAM-Based Power Line Communications Channel Using the Fritchman-Markov Model.

Within the power line communication (PLC) network, a large number of electronic devices are connected, and environmental factors can cause unusual behavior, leading to high-amplitude impulse noise in the received signal and, as a result, packet losses and burst errors in the data that are sent. Burst errors make it difficult to send data over power line channels efficiently and accurately. Analyzing error patterns with intelligent techniques can provide valuable insights into data transmission efficiency, enhance transmission quality, and optimize PLC systems. This research proposes a three-state Fritchman-Markov chain-based power line communication error model and develops a software-defined PLC system. The goal is to analyze and model the system's statistical error process. The PLC system's fundamental error pattern is deduced from the transmission and reception of data on our software-defined (SD) PLC platform. The system is designed with multi-state quadrature amplitude modulation (M-QAM) data transmission and reception techniques. An error pattern consisting of 50,000 bits is obtained by comparing the bits transmitted with those received using the in-house M-QAM-based PLC transceiver system. The error characteristics of the newly developed M-QAM SD-PLC system are precisely modeled using the error model. Examining the burst error statistics of the reference error sequences of the SD-PLC system and the three-state Fritchman-Markov error model reveals striking similarities. According to the results, the error model accurately represents the error characteristics of the developed M-QAM SD-PLC system. The proposed three-state Fritchman-Markov chain-based error model for PLC has the potential to provide a comprehensive understanding of the error process in PLC. Additionally, it can assess error control strategies with less computational complexity and a shorter simulation time.

Spectrum-Efficient Analog Pulse Index Modulation for High-Volume Wireless Data Telemetry

Spectrum-efficient, high-density, and high-speed data transmission are essential to store and exchange sensitive information through Internet-of-Things (IoT) networks. However, the resource-constrained IoT devices and the limited existing frequency spectrum pose a significant challenge to employing the more efficient technique for the next-generation technology. This paper presents an innovative pulse index modulation (PIM) architecture with a randomization technique for analog pulse-based packet-less high-volume data transmission. The pulse-based compression simplifies the encoding and decoding schemes for short-range wireless telemetry. The pulse randomization technique creates the pulse blinding by reordering the pulse positions and adds an extra level of physical layer security. This paper describes the design, simulation, and prototype development of PIM to validate the feasibility and compatibility of pulse-based data telemetry using the standard architecture. Simulation results show that the proposed PIM increases the data compression ratio by 2.61 times and improves the processing time by 5.56 time compared to the Huffman coding for a 3-bit system. Test results show that the BER is 7.5×10-4 at 10.0 dB SNR, which satisfies the lower bound for data communication and increases the transmission speed k-times by supporting 2k*k bits data using 2k number of pulses, where k is a non-negative integer number. The proposed PIM has the potential to improve data rate with added security to support the next-generation IoT networks.

A Internet of Things Improvng Deep Neural Network Based Particle Swarm Optimization Computation Prediction Approach for Healthcare System

Internet of Things (IoT) systems tend to generate with energy and good data to process and responding. In internet of things devices, the most important challenge when sending data to the cloud the level of energy consumption. This paper introduces an energy-efficient abstraction method data collection in medical with IoT-based for the exchange. Initially, the data required for IoT devices is collected from the person. First, Adaptive Optimized Sensor-Lamella Zive Welch (AOSLZW) is a pressure sensing prior to the data transmission technique used in the process. A cloud server is used data reducing the amount of data sent from IoT devices to the AOSLZW strategy. Finally, a deep neural network (DNN) based on Particle Swarm Optimization (PSO) known as DNN-PSO algorithm is used for data sensed result model make decisions based as a predictive to make it. The results are studied under distinct scenarios of the presented of the performance for AOSLZW-DNN-PSO method, for that simation are studied under different sections. This current pattern of simalation results indicates that the AOSLZW-DNN-PSO method is effective under several aspects.

Compressed Sensing Approach for Physiological Signals: A Review

The immense progress in physiological signal acquisition and processing in health monitoring allowed a better understanding of patient disease detection and diagnosis. With the increase in data volume and power consumption, effective data compression, signal acquisition, transmission, and processing techniques are essential, especially in telemonitoring healthcare applications. An emerging research area focuses on integrating compressed sensing (CS) with physiological signals to deal with a massive amount of physiological data, transmission bandwidth, and power-saving purposes. A review of CS for physiological signals is presented in this article, including electroencephalography (EEG), electrocardiography (ECG), electromyography (EMG), and electrodermal activity (EDA), focusing on the pros and cons of CS in treating such signals and the suitability of CS for hardware implementation. Furthermore, we emphasize performance matrices, such as compression ratio (CR), signal-to-noise ratio (SNR), Percentage Root-mean-square Difference (PRD), and processing time to evaluate the performance of CS. We also investigate the current practices, challenges, and opportunities of using CS in healthcare applications.

Enhanced algorithmic modelling and architecture in deep reinforcement learning based on wireless communication Fintech technology

Financial technology” or “FinTech” refers to use of information technologies to derive financial solutions. FinTech is now widely regarded as a hotly debated blend of financial services and information technology. A combination of AI and IoT approaches will significantly increase the extraction of valuable financial data as well as provide better services to customers. This research proposed a novel protocol in data transmission using fuzzy rule-based secure transmission with data optimization technique using deep reinforcement neural network by wireless communication. This technique for data transmission will reduce the credit risk and enhances the data optimization by fuzzy rule-based protocol with DRNN. The logic utilized is fuzzy logic, which is a multi-valued logic with truth values for variables ranging from 0 to 1. When truth values range from entirely true to completely false, it is used. The end user's searching experience is improved by fuzzy logic-based semantic search, which finds and retrieves exact matching files for relevant search files provided by user. When exact matches aren't possible, method leverages semantic similarities to determine most relevant matches. A grading method is utilized to minimize number of false positives. For this, proposed technique was used, which may decide on a collection of suitable storage servers on which the data must be saved and resulting in a reduction in execution time while maintaining a higher level of security. The experimental results show the execution time of 54 %, network performance of 96 %, the overall complexity of 71 %, data optimization of 95 % and end-end delay of 67 %.

Media kontra terabajty informacji, czyli rzecz o kompresji danych

Data compression and related techniques play a fundamental role in the present media world. Technological progress and common digitalization observed nowadays in media coverage exploit a great range of algorithms and techniques of data compression and transmission. Large number of data and digital information, being stored, processed or transferred requires advanced computer technologies derived from the field of information and communication theory, as well as related to various protocols necessary to deliver information. The quality of processed and delivered data is another important issue.