Data Transmission Model Research Articles

Routing Protocol for Underwater Wireless Sensor Networks Based on a Trust Model and Void-Avoided Algorithm

Underwater wireless sensor networks have a wide range of application prospects in important fields such as ocean exploration and underwater environment monitoring. However, the influence of complex underwater environments makes underwater wireless sensor networks subject to many limitations, such as resource limitation, channel openness, malicious attacks, and other problems. To address the above issues, we propose a routing scheme for underwater wireless networks based on a trust model and Void-Avoided algorithm. The proposed scheme establishes a trust model, evaluates the behavior of underwater nodes through direct trust, indirect trust, and environmental trust, and finds malicious nodes while taking into account evaluation of the channel, which provides support for the next data transmission event. The proposed scheme prioritizes the total cabling distance and introduces a two-hop availability checking model for data transmission, checking the nodes for voids and avoiding the void areas, to find the transmission path with the lowest energy consumption and lowest latency as much as possible. In this study, simulation experiments were conducted on the proposed scheme, and the results showed that the target scheme can effectively detect malicious nodes through anomalous behaviors and outperforms existing work in terms of malicious node detection rate, energy consumption, and end-to-end latency, and network performance.

Design and Performance of Privacy Protection Model for Big Data Transmission Based on Mixed Encryption

With the advent of the big data era, data security and privacy protection have become particularly important. Big data has advantages such as large scale and diverse types, but it also brings risks of personal privacy leakage and data abuse. However, the symmetric or asymmetric encryption techniques alone have limitations in big data security and privacy protection. Therefore, a privacy protection model for big data transmission based on mixed encryption is proposed and experimentally validated. The research results indicated that the asymmetric encryption algorithm used had an encryption time of less than 20 ms, and the key space occupation was only 0.031 Kb to 0.063 Kb. After improving the symmetric encryption algorithm, it achieved a lower correlation of 0.16 within 18 ms and increased the number of ciphertext transformations to an average of 82 bits. In the performance verification of mixed encryption technology for 60 MB data packets, the proposed mixed encryption technology took 273.1 ms, the decryption took 254.7 ms, the correlation was as low as 0.12, and the average resistance time in resisting violent attacks exceeded 100 s. The model proposed in the study improves the encryption and decryption speed while ensuring data security, which has important practical application value and theoretical significance for data privacy protection in big data environments.

Lightweight Cryptography Based Secured Data Transmission Model for Quality of Service in Internet of Things-A Comparative Analysis

Lightweight Cryptography Based Secured Data Transmission Model for Quality of Service in Internet of Things-A Comparative Analysis

Research on the design and optimal control of the power take-off (PTO) system for underwater eel-type power generators

Wave energy and ocean current energy are considered stable, reliable, and highly predictable renewable energy sources. The development of ocean current energy conversion technology is crucial in addressing power shortages. However, the low velocity of most ocean currents worldwide, typically less than 1.5 m/s, poses a challenge for traditional ocean current energy capture devices. Current estimations of power generation from underwater devices often significantly differ from actual results. This study presents an eel-type power generation device designed for underwater use, investigates the design and optimization of a hydraulic power take-off (PTO) system suitable for such devices, and examines key components of the hydraulic PTO system like variable hydraulic motors and generators. Analytical research was conducted to understand its operational characteristics, with a focus on components such as the accumulator, speed control valve, and energy storage flywheel. The article also delves into the hydrodynamic and energy conversion characteristics of the device under shallow water wave and current conditions, enhancing the hydraulic PTO system model and establishing an integrated calculation model for real-time data transmission during the calculation process. Through evaluations of pitch angle and power generation under varying sea conditions, the study explores the impact of hydraulic motor displacement and damping coefficient on the hydrodynamic and power generation characteristics of the device, further validated through sea trials. The study findings indicate a high level of parameter adaptation among the components of the hydraulic system. The addition of an accumulator to the system results in smoother output response curves, suggesting that the accumulator absorbs impacts and stabilizes the hydraulic power transmission system. The proposed comprehensive calculation method enables a more precise prediction of the performance of the eel power generation device in real marine environments, capturing its movement behavior and power generation characteristics. Adjusting the motor displacement or damping coefficient under specific conditions can optimize the total damping of the hydraulic PTO system for maximum power output. The optimal power point of the hydraulic power generation system varies for different sea conditions, with a potential power generation efficiency of up to 80.3% under certain circumstances.

Energy efficient reliable data transmission for optimizing IoT data transmission in smart city

The rapid proliferation of the internet of things (IoT) technology has significantly transformed urban landscapes, giving rise to smart city frameworks that leverage interconnected devices for enhanced efficiency and functionality. In these environments, vast amounts of data are generated by diverse sensors and devices, necessitating advanced strategies for effective data collection and transmission. This paper introduces a novel approach to address data collection and transmission challenges in IoT-enabled smart city frameworks. The proposed design integrates IoT-Cloud for efficient data collection and employs the energy efficient reliable data transmission (EERDT) model, optimizing IoT data transmission. The enhanced dragonfly routing algorithm, incorporating the firefly algorithm, enhances data routing efficiency. Experimental results demonstrate EERDT's superiority over energy-aware iot-routing (EAIR) and location-centric energy-harvesting aware-routing (LCEHAR), revealing significant improvements in communication overhead, data processing latency, and network lifetime. The EERDT exhibits substantial reductions in communication overhead, enhancing overall network performance. The EERDT model showcases lower data processing latency and energy consumption, highlighting its potential for resource-efficient IoT data transmission. This work contributes an innovative solution for smart city IoT networks, emphasizing performance enhancements and resource efficiency.

A new Sylvester-type quaternion matrix equation model for color image data transmission

A new Sylvester-type quaternion matrix equation model for color image data transmission

Research on Wireless Multimedia Big Data Transmission Model Based on Non-linear Algebraic Equation with the Adaptive Scheduling Algorithm

With the rapid development of computer technology, image processing and automatic book recognition has become a very important and practical field, and this discipline has a broad prospect and great potential. The digital vision system is a multi-plane information analysis method based on the human brain, which is applied to library identification problems by studying several regular changes in the distribution of objects themselves in two-dimensional space and three-dimensional position, in order to improve book quality and reduce costs. In addition, digital reading devices have become an essential part of people's daily learning life, and multimedia retrieval methods have also undergone radical changes, image processing is becoming more and more complex, and algorithms must become more efficient and reliable as the amount of computing increases to a certain level, so as to adapt to this fast and powerful data storage and query needs. Therefore, the trend is to improve the speed and reliability of computer systems. In this research, we provide a nonlinear model of optimal allocation employing an ant colony management strategy, a genetic operator, and an adaptive scheduling algorithm based on topology (dB) to discover the best scheduling scheme and test its feasibility via simulation. It proposes a viable idea for tackling the practical wireless multimedia massive data transmission model problem by applying the method to the real-time processing process in other sectors with high importance and resilience.

Federated Learning-based neural networks for autonomous driving

An emerging technology with the capacity to revolutionize the transportation sector is autonomous driving, offering the promise of heightened safety, efficiency, and convenience. However, the widescale deployment of autonomous vehicles presents a multitude of challenges, notably the necessity for robust and adaptable machine learning (ML) models capable of handling a wide array of dynamic real-world scenarios. Enter Federated Learning (FL), a decentralized ML approach that has gained recognition as a potential solution to these challenges. This paper delves into the primary advantages of FL within the context of autonomous driving. It highlights FL's capacity to seamlessly adapt to edge devices, respond to localized changes, and continually enhance safety and performance. The document substantiates these advantages through numerous case studies and empirical evidence, demonstrating how FL can potentially elevate the vision, decision-making, control systems, data transmission, and learning model capabilities of autonomous vehicles. By harnessing the collective intelligence of autonomous vehicles while preserving data privacy and security, FL holds the potential to propel us closer to a future where safe, efficient, and autonomous transportation becomes an attainable reality.

Footstone of Metaverse: A Timely and Secure Crowdsensing

Recently, the metaverse has been a hot research topic that fuses multiple cutting-edge techniques, including virtual reality (VR), augmented reality (AR), artificial intelligence (AI), Internet of Things (IoT), and 5th generation (5G) networks. Supported by these powerful cornerstones, people can establish a hyper spatiotemporal virtual world that seamlessly links to the real world and create personal activities such as art creation, concert display, and stock trading. Virtual-reality mapping demands enormous information transmission between two worlds without sensible latency since delayed data could severely impact user experiences. Besides, intrinsic flaws in fundamental technologies also aggravate the vulnerabilities in the metaverse due to real-time interaction. Hence, this paper first proposes a timely and secure data collection framework based on crowdsensing for metaverse modeling. In our proposed Data encryption, Transmission and Perception optimization (DTP) model, the metaverse servers can recruit workers to gather environmental parameters and user information through five layers architecture (i.e., perception layer, link layer, transmission layer, operation layer, and application layer). Each layer takes responsibility for data computation and encryption, which can stimulate data dissemination in a fast and reliable manner for the metaverse. Furthermore, we present a healthcare use case that introduces a bidirectional mapping of patient data between physical and virtual space. This use case illustrates the prospects of the DTP model. Finally, we conclude this paper and summarize several significant future research directions.

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

The emerging fog networks in the internet of things (IoT) applications provide flexibility and agility for service providers. The combination of fog nodes and edge nodes enable them to deliver a given network service. However, the selection of suitable edge and fog nodes and their scheduling still remain a research challenge. Finding a globally optimal scheduling of oversized data transmission over IoT applications for industrial requirements is crucial. Optimal batch scheduling has been regarded as a viable way to achieve optimal scheduling in other contemporary network models. This manuscript has projected an Optimum Batch Scheduling Model (OBSM) for Quality aware Delay Sensitive Data Transmission over Fog Enabled IoT Networks. A novel clustering technique has been proposed in this manuscript to group the transmission nodes (fog or edge nodes) and data packets, which further pairs each group of data with one of the corresponding node group to achieve delay sensitivity and other quality factors such as energy efficiency. The data scheduling between data and node group is drawn from the previous contribution -"Quality aware Energy Efficient Scheduling Model (QEESM) for Fog Enabled IoT Network". The simulation results have shown that, in terms of average make span rate, average round trip time, and energy consumption, the batch scheduling model OBSM performs noticeably better than the contemporary scheduling models. The OBSM scheduling model's average make-span rate, roundtrip time, as well as energy consumption per make span are 23.3 7.03, 17.8 5.2, and 11.33 6.9 joules, respectively, which conclusively demonstrate that the OBSM model outperforms the existing models. A novel batch scheduling algorithm has been proposed using a unique unsupervised learning approach that suggested to cluster the transmission requests and transmission channels in to multiple clusters.

Research on wireless multimedia big data transmission model based on non-linear algebraic equation with the adaptive scheduling algorithm

With the rapid development of computer technology, image processing and automatic book recognition has become a very important and practical field, and this discipline has a broad prospect and great potential. The digital visión system is a multi-plane information análisis method basad on the human brain, which is applied to library identification problems by studying several regular changes in the distribution of objects themselves in two-dimensional space and three-dimensional position, in order to improve book quality and reduce costs. In addition, digital Reading devices have become an essential part of people’s daily learning life, and multimedia retrieval methods have also undergone radical changes, image processing is becoming more and more complex, and algorithms must become more efficient and reliable as the amount of computing increases to a certain level, so as to adapt to this fast and powerful data storage and query needs. Therefore, the tren is to improve the speed and reliability of computer systems. This paper discusses and proposes measures for the analysis and extraction, classification and sorting of library images.

Retracted: IOT and Blockchain-Based Cloud Model for Secure Data Transmission for Smart City

Retracted: IOT and Blockchain-Based Cloud Model for Secure Data Transmission for Smart City

A model for data transmission and its optimization

A model for data transmission and its optimization

5G network model development in AnyLogic environment

Problem statement. The development of mobile communication networks does not stop for a minute, new standards and various new technologies are emerging. The opportunities provided by mobile technologies have long gone beyond voice services. The sharp growth of Internet traffic in networks around the world is explained by the widespread use of devices connected to mobile networks. With the development of mobile networks, new and diverse requirements are being imposed on them. Technology development is aimed at increasing productivity and expanding opportunities. Therefore, it becomes necessary to study the influence of the characteristics of additional parameters (delays, station loads) on the quality of data packet processing in order to optimize the operation of 5G networks and ensure more efficient use of resources. Many scientists who conducted research on 5G networks using modeling did not take into account various variants of queue processing algorithms, did not consider how the values of data transmission delays and the number of lost packets would change with an increase or decrease in the number of base stations, models consisting of several base stations were not considered. Based on this, we will conclude that it is advisable to develop a 5G network model, with the help of which various studies can be carried out, which will take into account the above parameters. The goal is to investigate the resource model of data transmission in 5G networks, to analyze the operation of the 5G network model in the AnyLogic environment. Results. During the study of traffic maintenance in the 5G network, the optimal system parameters were determined, allowing the most efficient use of network resources. It turned out that the use of methods of dynamic resource allocation and automatic change of base station parameters reduces the load on the network and increases its performance. The developed model allows us to assess the reliability of the system and its ability to process user requests, because the probability of losing an application or non-fulfillment of an application is high, this can lead to user dissatisfaction and a decrease in the quality of service. Practical significance. The developed model can be used in practice to study and evaluate the parameters of the 5G network, as well as to analyze various characteristics that affect the quality of the network in order to improve it. This can help telecom operators optimize their networks and ensure more efficient use of resources.

Joint Caching and Computing Offloading Strategies for Power Supply Chain Services Under Cloud Edge Collaborative Computing Environment

Aiming at the problem of long task calculation delay of traditional edge computing offloading strategy (COS), a joint caching and COS for power supply chain services under cloud edge collaborative computing environment is proposed by introducing the master-slave game model. First, for the network of information interaction between power grid system and electrical equipment suppliers, the corresponding overall model of cloud edge collaborative network is constructed by combining cloud computing, edge computing, supplier terminal communication and remote cloud. Then, the cloud-side collaborative network model is analyzed and solved by building the service function cache model, task unloading and data transmission model and power business data calculation model, respectively. Finally, by introducing the master-slave game model, the whole system is divided into three different interests of power service providers, communication providers and communication network terminal providers and the joint caching and COS for power supply chain services is given. Through simulation experiments, the proposed computing unloading strategy and the other three methods are compared and analyzed under the same experimental conditions. The results show that the service cache hit rate of the proposed method is the highest, reaching 96.35%. Compared with the other three comparison methods, it increased by 14.96%, 11.24% and 4.85%, respectively. In addition, its task computing delay is the least affected by agent computing power and agent computing resources, and its performance is better than the other three comparison methods.

A comparative analysis: optimal node selection in large data block transmission in VANET using various node relay optimization algorithms

VANETs (Vehicular Ad-hoc Networks) are a kind of Mobile Ad-hoc Networks (MANETs) with increased mobility as well as occasional geographic changes. Clustering is used in VANETs to divide networks into groups of adaptable vehicles to optimize routing, information gathering, as well as routing. As large data block transmission contains several stages and inside those, one of the important stages such as optimal model selection was these selections of quintessential features helps in a further stage called data transmission much more effective. This stage will also bring energy optimization and also improves the effectiveness of the optimal path for data transmission. This paper brings a comparative analysis of this optimal relay node selection. Here, we use Linear Scaling based Red Colobuses Monkey technique and are contrasted with other state-of-the-art models over various measures. Analysis proves that the proposed optimal relay node selection technique boosts the data transmission model.

Driver behaviour prediction and enhanced ad hoc on‐demand distance vector routing protocol in VANET

SummaryMobility pattern recognition is a complex task in vehicle ad hoc networks (VANET) because the driving state of each vehicle is different. An intelligent transportation system on VANET is used for traffic control and accident prevention. For this reason, human driver behaviour is first analysed to identify mobility patterns. A novel driver behaviour prediction model using a Siamese deep learning architecture is proposed to achieve the goal. Here, an image‐based behaviour prediction model is performed to achieve the highly accurate driving state of the driver. A warning message is forwarded to the neighbouring vehicles based on the driver's behaviour. Due to the dynamic properties of real‐time vehicle mobility, a faster data transmission model is achieved using the ad hoc on‐demand distance vector routing protocol. To achieve faster data transmission and nullified retransmission, here a weighted location‐based routing model is framed. The optimization problem in the location‐aided routing protocol is solved using the vector algorithm's weighted mean. As a result, the proposed method improved the throughput of ASHLOSR to 8.1% and AODV to 7.6%.

Model of Message Transmission across Parallel Route Groups with Dynamic Alternation of These Groups in a Multichannel Steganographic System

The subject of this study is steganographic systems that are able to hide not only the content of the transmitted information but also the fact of its transmission. In this paper, we present a novel data transmission model within such systems, combining a modified Dijkstra’s algorithm with the dynamic utilization of multiple transmission routes. Consequently, this allows the simultaneous transmission of messages through parallel groups of routes with the dynamic alternation of these groups during the information exchange process between the sender and the receiver in the steganographic network. Thus, the speed, reliability, and security of message transmission are improved. Moreover, to increase the security level, the proposed model uses a route–time matrix that is available only to the sender and receiver. Additionally, the data transmission model has been tested on synthetic data and data obtained from real devices. The evaluation of the effectiveness of the proposed model on the example of various structured steganographic systems shows the significant effectiveness of our model. As a result, in comparison with existing similar methods, the secrecy index was increased five times and the violation index was decreased four times.

Reconstruction of Communication Signal in Wireless Networks Based on Perturbation Compression Perception

<p>With the maturity and development of new generation communication technology, the demand for wireless network communication quality is getting higher and higher. However, the existing wireless network data transmission models are greatly affected by the signal quality in the data transmission process, which seriously affects the quality of data transmission. To address the problem of signal perturbation affecting communication quality in wireless network communication, this study designs an improved perturbation compression reconstruction algorithm that can be used to process block-structured data based on the block sparse structure in the original signal, and constructs a single-user wireless network communication data transmission model based on this algorithm. The performance simulation test of the model shows that the MSE values of the model are lower than those of the conventional and perfect schemes used as comparisons under the same noise conditions and the transmitted compressed data length conditions. When the variance of Gaussian white noise is 0.125, the MSEs of the research design model, the conventional scheme and the perfect scheme are 0.031, 0.048, and 0.047, respectively. Experimental data show that the improved disturbance compressed sensing reconstruction algorithm can better reconstruct the compressed information and reduce the information loss caused by the disturbance in the compression process on the premise of removing the signal disturbance. This research result has reference significance for improving the signal quality of wireless network communication.</p> <p> </p>

A novel secure cryptography model for data transmission based on Rotor64 technique

A novel secure cryptography model for data transmission based on Rotor64 technique