Efficient Data Transmission Research Articles

Multi-block Directed Radiation Routing Algorithm for Optimized Wireless Sensor Network

In recent years, the axial radiation model has emerged as a pivotal framework for Wireless Sensor Networks (WSN), particularly in enhancing intelligent sensing platforms. This study delves into the WSN structured around the axial radiation model, encompassing critical aspects like model reconstruction, node deployment, and routing optimization. Our focus is on evaluating the performance metrics that influence the responsiveness of these intelligent platforms. We introduce the Multi-block Directed Radiation (MBDR) routing algorithm, designed to extend system operational time and boost data transmission efficiency. Comprehensive experimental analyses demonstrate the significant advancements of MBDR in survivability, data transmission rates, and regional balance within central axis radiation model environments.

Research on the integration of MEMS and reliable transmission of deep space networks based on time-sensitive networking

With the continuous deepening of human space exploration, deep space networks far away from Earth have emerged. Unlike traditional ground networks, they have the characteristics of frequent link interruptions and time extensions. Traditional data transmission mechanisms cannot be well applied in deep space networks. We propose a data transmission technology that integrates time-sensitive networking and artificial intelligence to address the contradiction between deterministic delay and differentiated service quality assurance in deep space networks and construct a micro electromechanical system (MEMS). Considering the differences in service quality due to different business requirements, data transmission in deep space networks is transformed into a mixed integer programming problem that minimizes transmission delay and maximizes link utilization and solved using artificial intelligence imitation learning. Experimental results have shown that the proposed algorithm has fast convergence, strong applicability, and can achieve reliable and efficient data transmission while meeting the requirements of higher priority data transmission. It can also significantly improve throughput.

A group scheduling algorithm for massive heterogeneous data in the “dual carbon” digital intelligence monitoring center considering time-varying characteristics and priorities

In view of the fact that the data of the “double carbon” digital intelligent monitoring center has the characteristics of constantly changing with time and that there are key tasks with high real-time requirements in the massive heterogeneous data, a “double carbon” that takes into account time-varying characteristics and priorities is proposed. The massive heterogeneous data grouping scheduling algorithm of the digital monitoring center schedules data according to the urgency of the task. The functional data analysis (FDA) method is used to convert the massive multi-source heterogeneous data of the “double carbon” digital intelligence monitoring center into continuous functions to solve the problem of frequency inconsistency and unify the data format; through the CNN–LSTM based on the Attention mechanism. The model extracts time-varying features from the data that eliminates heterogeneous characteristics, and implements data grouping in the “dual carbon” digital intelligence monitoring center; by setting differentiated priorities for different groups of data, it combines the data scheduling demand estimation model and delayed response time (RTT) factor and congestion factor, calculate the data priority-oriented data scheduling link similarity (DPLS), allocate the data to be scheduled to the scheduling link with the highest DPLS value for transmission, and realize the “double carbon” digital intelligence monitoring center data group scheduling. Experimental results show that this algorithm can unify heterogeneous data to the form of functional expression and improve data consistency. The absolute value of the Pearson correlation coefficient of data grouping reaches 0.953, and the grouping effect is good. High-priority data can be scheduled to the best transmission link to improve the efficiency and reliability of data transmission and realize the optimal allocation of resources.

TxCocket: an innovative solution for efficient cross-node data transmission enabled by CXL-based shared memory

TxCocket: an innovative solution for efficient cross-node data transmission enabled by CXL-based shared memory

Distributed Filtering for State‐Saturated Systems With Switching Nonlinearities via Rayleigh Fading Channels: An Adaptive Event‐Triggered Case

ABSTRACTThe distributed filtering (DF) problem is investigated for state‐saturated systems (SSSs) with switching nonlinearities under the adaptive event‐triggered mechanism (AETM) and Rayleigh fading channels over sensor networks, where the data is transmitted between nodes through the Rayleigh fading channel. In addition, the AETM is introduced to save communication resources and improve data transmission efficiency. First, a distributed filter is designed incorporating the information of state saturation, switching nonlinearity, Rayleigh fading channel and AETM. Second, the upper bound (UB) on the filtering error covariance (FEC) is derived by the mathematical induction method, and the filter gain is obtained by minimizing the trace of the UB. Subsequently, the boundedness of UB on the FEC is shown through mathematical analysis. Finally, the effectiveness of the filtering scheme designed in this article is demonstrated through a numerical simulation example and a practical example, in which the influence of different Rayleigh parameters to the filtering performance and the superiority of using the AETM are discussed.

A Gray Image Quantum Encryption using GNEQR Representation

In the digital era, characterized by extensive online data exchange, information security has become a priority. While traditional encryption methods have proven effective in protecting data transfers, the advent of advanced quantum computing has increased susceptibility to security breaches. Quantum encryption provides a revolutionary solution to this problem by using quantum mechanics principles to establish algorithms that are impermeable to decryption. Using these quantum properties, cryptographic protocols are developed to provide superior security, unlike traditional encryption methods. The image plays an important role in transmitting information in all areas. Therefore, quantum image encryption methods are specifically designed to counter the potential risks posed by quantum computers, which can compromise conventional encryption protocols. This ensures the preservation of data security despite advances in quantum computing technology. In addition, quantum image encryption improves data transmission efficiency by establishing secure communication channels using quantum stats, thereby reducing the need for bandwidth and improving transmission speed. This paper proposes a new method of quantum encryption based on GNEQR representation and the modification of pixel values and positions in an image. After converting the image into a quantum form, we applied an algorithm to modify the values and positions of the pixels using a succession of quantum gates. We concluded this study with a statistical analysis showing the robustness of our quantum image encryption method.

A neural coding method based on feature sensing

AbstractThe novel network contains many sensors, which greatly heightens data transmission burdens. Some networks require the data perceived by sensors for a period to make decisions. Drawing inspiration from the human neural conduction mechanism, a waveform data encoding method called feature sensing neural coding (FSNC) is proposed to enhance network data transmission efficiency. It involves feature decomposition of information and subsequent non‐linear encoding of feature coefficients for data transmission. This approach exploits the unique neuronal responses to diverse stimuli and the inherent non‐linear characteristics of human neural coding. Finally, taking the speech signal and seismic wave signal as examples, the effectiveness of FSNC is verified by simulating the auditory nerve conduction process with frequency as a feature according to the mechanism of travelling wave motion of the basilar membrane in the cochlea. Moreover, experiments on seismic waveform signals have demonstrated the wide applicability of FSNC. Compared with traditional speech coding schemes, the FSNC bit rate is only 6.4 kbps, which greatly reduces the amount of data transmitted. Not only that, FSNC also has a certain fault tolerance, and parallel transmission can also greatly increase the transmission rate. This research provides new ideas for efficient data transmission over new networks.

Исследование способов диспетчеризации видео на множестве конечных устройств в контексте языка программирования Rust

Abstract. The modern world is actively moving towards digitalization, which requires efficient distribution of video content on a variety of end devices. The problem is that different usage scenarios impose different requirements on security, quality, and latency when transmitting video. The aim of the study is to identify the most suitable protocols for video dispatching in educational and real-world applications using the Rust programming language, known for its security and performance. Protocols such as HTTPS (TCP), BitTorrent, HLS, WebRTC, SRT and DASH have been investigated and analyzed. The results show that HLS and DASH have advantages in adaptive streaming in a changing network environment, while SRT and WebRTC provide low latency and high reliability for applications requiring real-time operation. The practical significance of these findings is confirmed by successful integration into educational systems, which ensures stable work even with a change in workload. Discussion: in the future, it is recommended to explore the combined use of several protocols, such as HLS and SRT, to improve the overall efficiency and reliability of video data transmission, as well as integration with CDN to improve quality and reduce load.

Large-scale acceleration algorithms for a deep convective physical parameterization scheme on GPU.

Early warning of geological hazards requires monitoring extreme weather conditions, such as heavy rainfall. Atmospheric circulation models are used for weather forecasting and climate simulation. As a critical physical process in atmospheric circulation models, the Zhang-McFarlane (ZM) deep convective physical parameterization scheme involves computationally intensive calculations that significantly impact the overall operational efficiency of the model. However, many of these calculations are independent and can be computed in parallel. Therefore, this paper proposes a GPU-based acceleration algorithm for the ZM scheme. Based on the computation characteristics of the ZM scheme, we propose its one-demensional and two-demensional acceleration algorithms based on GPU. These algorithms are implemented using CUDA C and compared against a single Kunpeng-920 (Dual Socket) CPU core and the OpenMP version on multi-core CPUs. In the absence of I/O transmission, the proposed algorithm achieves a speedup of 413.6×. Experimental results demonstrate the significant acceleration effect of the proposed algorithms and methods. It is of great significance for the development of deep convective parameterization schemes and their further generalization in climate models. Additionally, we propose a performance optimization method utilizing the CUDA streaming technology to improve data transmission efficiency between CPU and GPU. In the presence of I/O transmission, the proposed algorithm achieves a speedup of 350.1× on A100 GPU.

Developing power control algorithms for dynamic multi-connection industrial wireless sensor networks

In this paper, we propose a Dynamic Multi-Connection Industrial Wireless Sensor Network (DMC IWSN) model to improve data transmission efficiency in a factory workshop. We introduce a new power control algorithm called Window-Selection (WS), which adjusts sensor power based on predefined upper and lower limits. The algorithm reduces power when it exceeds the upper limit, increases power when it falls below the lower limit, and maintains power within these bounds. We compare the performance of the WS algorithm with the Max-Min (MM) algorithm, and a scenario no power control in terms of the weakest sensor capacity and total capacity in uplink transmission. Simulation results show that the WS algorithm achieves the highest capacities, while the MM algorithm moderately improves system performance in the DMC IWSN model.

THE EFFECT OF DIFFERENT COLOUR LEDS ON DATA TRANSMISSION

Visible light communication (VLC) is a communication method where light is utilized as a medium of communication for data transmission. VLC is also regarded as a viable technique for indoor applications, assisting Wi-Fi to handle the spectrum crunch. This study aims to investigate the effect of different LED colours towards the distance and efficiency of data transmission with solar panels as a receiver. The experiment entailed gathering and inspecting components such as Arduino UNOs, solar panels, and LEDs, as well as replacing damaged or poorly kept ones to ensure correct operation and efficient debugging. A program was uploaded to the Arduino UNO boards to handle data transmission, including noise cancellation and ensuring synchronisation. Red, yellow and green LEDs are used as a transmitter light source. The experiment was conducted in a dark room as minimum ambient light noise is required to ensure accurate results. The study concluded that red colour LED could transmit the data at a longer distance than the other two colours but with low efficiency. It shows that light intensity and light sensitivity of the solar panel play a crucial role in data transmission performance.

Optimizing FSO systems for 6G network using particle swarm optimization

Abstract Free Space Optics (FSO) is an innovative optical communication technology that leverages light propagation in free space for direct line-of-sight interactions. This technology is characterized by several benefits, including secure transmission, high bandwidth, and rapid data transfer rates, making it a promising candidate for integration into emerging 6G networks. However, the efficiency of FSO systems is adversely affected by various weather conditions, such as rain, temperature fluctuations, snow, and fog, as well as atmospheric disturbances. This paper presents a Particle Swarm Optimization (PSO)-based approach to optimize the divergence angle of FSO systems. PSO is a type of artificial intelligence that excels at managing complex settings and adapting to changes in the environment, thus making FSO systems more robust against such challenges. By optimizing this critical parameter, the proposed method significantly improves the reliability and efficiency of data transmission in FSO systems, supporting the advanced performance demands of future 6G networks. This optimization ensures FSO systems can achieve faster and more reliable connectivity, essential for the next generation of wireless communication.

Energy-efficient artificial fish swarm-based clustering protocol for enhancing network lifetime in underwater wireless sensor networks

Underwater wireless sensor networks (UWSNs) face significant challenges, such as limited energy resources, high propagation delays, and harsh underwater environments. Efficient clustering can help address these challenges by grouping nearby nodes to minimize network fragmentation and balance energy consumption. However, placing gateways near the sink node can result in increased communication overhead and higher energy consumption in regions with concentrated data flow. To address these issues, we propose an energy-efficient artificial fish swarm-based clustering cognitive intelligence protocol (EAFSCCIP). EAFSCCIP leverages the collective behavior of artificial fish within a Bees algorithm framework, using a combination of heuristic and metaheuristic approaches for optimal cluster-head (CH) selection in each round. The protocol focuses on reducing energy consumption and extending network lifetime by considering real-time energy levels and the proximity of nodes for CH selection. Simulations have been executed in NS3 to validate and compare the performance of the proposed algorithm with the existing clustering protocols. The results indicate that EAFSCCIP significantly enhances the packet delivery ratio (PDR) by an average of 5.33% over existing methods and improves network lifetime by 6.54% compared to traditional protocols. It also reduces energy consumption by 25.6% and decreases packet loss by 50.5%, while achieving 20.4% higher throughput at the initial stage. These improvements make EAFSCCIP a promising solution for applications like acoustic monitoring in UWSNs, providing a balance between energy efficiency and reliable data transmission.

Hybrid Optimization Approach for Handoff Strategy–Based Spectrum Allocation in Cognitive Radio Network

ABSTRACTDevice‐to‐device (D2D) transmission is essential for enhancing the functionality of fifth‐generation (5G) networks. This paper addresses the need for effective power allocation and resource management for D2D users, who operate as secondary users (SUs) alongside primary users (PUs). Ensuring that D2D operations do not disrupt PU interactions is crucial. Traditional bandwidth distribution approaches rely on complete channel state information (CSI) from the base station (BS), leading to uncertainty fin resource allocation. To get rid of these challenges, this paper proposes a novel handoff strategy based on spectrum allocation in cognitive radio networks (CRNs). The data priority–based channel allocation is carried out using proposed hybrid optimization cuttlefish updated dwarf mongoose optimization (CUDMO). It is the combination of both cuttle fish algorithm (CFA) and dwarf mongoose optimization (DMO) algorithms. This optimization considers constraints such as coverage, signal strength, distance, bandwidth and improved strategy like signal‐to‐noise ratio–channel usability (SNR‐CU). Furthermore, an improved fuzzy logic–based proactive handoff mechanism, the fuzzy‐induced modified rules for channel selection (FIMRCS), is introduced. This scheme optimally selects channels, minimizing service interruption during handoff. In the dynamic multichannel selection (DMCS) scheme, parameters like channel rank, channel transmission, and channel usability are considered as the constraints while selecting the channel. They are evaluated against a set of 27 defined rules, ensuring efficient data transmission through the chosen channels. Finally, the performance of proposed CUDMO algorithm is contrasted over state‐of‐the‐art models in terms of various constraints. The CUDMO for Device 450 generated a bandwidth of 1.175 bps, surpassing the lower bandwidth achieved by conventional strategies.

POWER LINE COMMUNICATION SYSTEM

Power Line Communication System (PLCS) is an emerging technology that utilizes existing electrical power infrastructure for data transmission. Devices can exchange data and access communication via the power grid thanks to its ability to provide communication across power lines. This approach has many benefits, including as cost-effectiveness, quick implementation, and broad availability without out the need for additional cabling. Smart grid management, home automation, industrial control systems, and Internet of Things connectivity are just a few of the fields in which PLC has found use This abstract provides an overview of PLCS, highlighting its benefits, challenges, and key technologies. It also discusses the potential of PLCS in transforming power distribution net-works into intelligent and interconnected systems. Additionally, emerging trends and future directions for PLCS research and development are explored, emphasizing the potential for enhanced reliability, speed, and efficiency in data transmission over power lines. This paper proposed a two-way communication from source to load sides using BPSK (Binary phase-shift keying) and QPSK (Quadrature phase-shift keying) modulation techniques. Monte Carlo simulation is used to predict the transformer theoretical channel AWGN (Additive White Gaussian Noise) and compare the efficiency of the proposed methods. To compare the performance curve, randomly chosen data with sizes ranging from 10 k to 50 k are employed. Key Words: Transformer, Transmission line, Arduino nano, ATmega328p

Power Line Communication System

Power Line Communication System (PLCS) is an emerging technology that utilizes existing electrical power infrastructure for data transmission. Devices can exchange data and access communication via the power grid thanks to its ability to provide communication across power lines. This approach has many benefits, including as cost-effectiveness, quick implementation, and broad availability without out the need for additional cabling. Smart grid management, home automation, industrial control systems, and Internet of Things connectivity are just a few of the fields in which PLC has found use This abstract provides an overview of PLCS, highlighting its benefits, challenges, and key technologies. It also discusses the potential of PLCS in transforming power distribution net-works into intelligent and interconnected systems. Additionally, emerging trends and future directions for PLCS research and development are explored, emphasizing the potential for enhanced reliability, speed, and efficiency in data transmission over power lines. This paper proposed a two-way communication from source to load sides using BPSK (Binary phase-shift keying) and QPSK (Quadrature phase-shift keying) modulation techniques. Monte Carlo simulation is used to predict the transformer theoretical channel AWGN (Additive White Gaussian Noise) and compare the efficiency of the proposed methods. To compare the performance curve, randomly chosen data with sizes ranging from 10 k to 50 k are employed

Optimizing System Performance with Adaptive Algorithms for Signal Processing in Wireless Communication Networks

In modern wireless communication networks, the need for high-speed, reliable, and efficient data transmission is paramount. Signal processing algorithms play a critical role in optimizing network performance, especially under dynamic conditions such as varying network loads and environmental interference. This paper presents an in-depth study of adaptive signal processing algorithms designed to improve the performance of wireless communication systems. We examine various techniques, including adaptive filtering, channel estimation, and interference cancellation, and assess their effectiveness in optimizing signal quality and network throughput. Through simulation and real-world case studies, the paper demonstrates how these algorithms can be applied to mitigate signal distortion and reduce data transmission errors in wireless networks. The results show that adaptive algorithms significantly enhance the overall system performance, particularly in environments with fluctuating signal conditions. The paper concludes with recommendations for further development in adaptive algorithms for future wireless communication standards, particularly 5G and beyond.

Simulation and Modeling of Data Transmission Process in Boreholes Using Intelligent Drill Pipe for a Laboratory Experiment

Currently, most oil and gas wells are drilled by continuously transmitting downhole measured information (directional and geological information) in real-time to the surface to monitor and steer the well along a pre-defined path. The intelligent drill pipe method can transmit data over longer distances and at a higher rate than other methods, such as mud pulse telemetry, acoustic telemetry, and electromagnetic telemetry. Nevertheless, it is expensive and requires boosters along the drill string. In the available literature, academic research rarely addresses the data transmission process in boreholes using intelligent drill pipes. Furthermore, there is a need for an effective and validated model to study various controllable parameters to enhance the efficiency of the intelligent drill pipe telemetry without the need to develop several physical lab or field prototypes. This paper presents the development of a model based on MATLAB Simulink to simulate the process of data transmission in boreholes utilizing intelligent drill pipes. Laboratory experimental prototype measurements have been used to test the model’s effectiveness. A good correlation is found between the measured lab data and the model’s predictions for the signals transmitted contactless through intelligent drill pipes with a correlation coefficient (R2) above 0.9. This model can enhance data transmission efficiency via intelligent drill pipes, study different concepts, and eliminate the need to develop several unnecessarily expensive and time-consuming physical lab prototypes.

Estimation of Link Margin and Doppler Shift of Ground Sensor Terminal for Nanosatellite Payload

Abstract The primary objective of this study is to develop and validate a method for estimating Link Margin and Doppler Shift to enhance the performance of the Ground Sensor Terminal (GST) for nanosatellite communication systems. The Link Margin estimation considers key factors such as Antenna Gain and losses, Atmospheric Effect, System Noise Temperature (Tsys), Margin (M), and Received Signal Strength (Pr). The methodology includes calculating the uplink budget using both the EB/No and SNR methods, with parameters extracted from the AMSAT IARU Link Model. Additionally, Doppler Shift is analysed as the change in frequency of the signal due to the relative motion between the satellite and the ground station. Mitigation strategies, including frequency management, adaptive modulation and coding, predictive tracking algorithms, and real-time feedback control systems, were explored as effective means to counteract the Doppler effect’s influence on signal integrity. The results indicate that for a satellite positioned at an altitude of 400 km, with a frequency of 145.825 MHz, the maximum Doppler shift at 0 degrees elevation is observed to be 4 kHz. Communication can be established with a link margin of 11.3 dB, starting from an elevation of 0 degrees, using the EB/No method, significantly improving the reliability and efficiency of data transmission in nanosatellite missions.

An Approach of Modified IDEA with 1024 Bits Key to Enhance Security and Efficiency of Data Transmission in the Healthcare Sector

An Approach of Modified IDEA with 1024 Bits Key to Enhance Security and Efficiency of Data Transmission in the Healthcare Sector