Data Transmission In Networks Research Articles

Quick UDP Internet Connections and Transmission Control Protocol in unsafe networks: A comparative analysis

AbstractSecure data transmission and efficient network performance are both key aspects of the modern Internet. Traditionally, Transport Layer Security (TLS)/Transmission Control Protocol (TCP) has been used for reliable and secure networking communications. In the past decade, Quick User Datagram Protocol (UDP) Internet Connections QUIC has been designed and implemented on UDP, attempting to improve security and efficiency of Internet traffic. Real‐world platform investigations are carried out in this paper to evaluate TLS/TCP and QUIC/UDP in maintaining communication, security and efficiency under three different types of popular cyber‐attacks. A set of interesting findings, including delay, loss, server CPU utilisation and server memory usage are presented to provide a comprehensive understanding of the two protocol stacks in performing malicious traffic. More specifically, in terms of the efficiency in achieving short delays and low packet loss rates with limited CPU and memory resources, QUIC/UDP performs better under Denial of Service attacks but TLS/TCP overtakes QUIC/UDP when handling MitM attacks. In terms of security, the implementation of TCP tends to be more secure than QUIC, but QUIC traffic patterns are harder to learn using machine learning methods. We hope that these insights will be informative in protocol selection for future networks and applications, as well as shedding light on the further development of the two protocol stacks.

Fractal Engineering Integration in Antenna Arrays: A Comparative-review for Advanced Transceiver Systems with 5G/IoT Connectivity

The growing demand for space-efficient antennas compatible with portable electronic gadgets inspired the integration of recursively iterated fractal geometries (characterized by self-similarity, space-filling, and multiband/wideband capabilities) in antenna designs. Therefore, this article conducts an extensive survey on fractal-based single-port and multi-port patch antenna designs with multiband and ultrawideband characteristics for their implementation in 5G/IoT (Internet of Things) devices. The survey begins with a basic overview of fractal geometries and the iterated function system (IFS) technique employed for their generation. Further, it highlights the limitation of multipath fading prevalent in single-port antenna arrays that can be overcome by adopting multiple-input multiple-output (MIMO) technology. The MIMO antenna systems are vital for 5G networks to enhance data transmission rate, network capacity, and system reliability. Apart from these admirable attributes, the closely packed antenna elements in the MIMO configuration result in a severe mutual coupling. To address this limitation, various isolation enhancement techniques developed by distinguished researchers are thoroughly discussed in this article, detailing their merits and demerits. At last, this article portrays diverse band-notch structures integrated into the antenna designs to eradicate the existing interfering narrow bands in the ultrawideband (UWB) spectrum. The underlying reason for conducting this survey is to aid antenna designers and academicians with a thorough knowledge of distinct fractal geometries, various isolation improvement approaches, and band-notch techniques on a single platform, desired for designing 5G/IoT-based antenna systems.

Smart Home Automation System

This project introduces the overall framework of an economical wireless home automation system (HAS). Its primary emphasis lies in establishing an Internet of Things (IoT)-centered home automation setup, capable of automatic configuration based on environmental conditions and capable of overseeing multiple devices remotely via the internet. The objective is to design firmware for intelligent control, ensuring automated functionality with minimal human intervention to uphold the well-being of all electrical appliances in the household. Node MCU, a widely recognized open-source IoT platform, has been employed for the automation process. Various transmission modes are implemented across distinct system components to relay user commands from Node MCU to the actual device. The central control system utilizes wireless technology, enabling remote accessibility via a smartphone. The incorporation of a cloud server-based communication system enhances project practicality, providing users unrestricted access to appliances, regardless of their spatial proximity. To fortify automation, a data transmission network has been established. The user-friendly interface, cost-effective construction, and straightforward installation make it an ideal solution for managing household electrical appliances and devices. Both control and appliance status can be monitored through an Android platform. The system's objective is to enhance the living conditions of elderly and disabled individuals by offering necessary support and assistance, ultimately elevating the overall home living experience with the concept of a smart home.

Adaptive Processing of Camera Video Stream with Limitations on the Network Data Transmission Bandwidth

Adaptive Processing of Camera Video Stream with Limitations on the Network Data Transmission Bandwidth

An optimized cooperative data transmission scheme in relay‐enabled cellular communication networks

SummaryCooperative transmission is of great significance in data transmission and resource schedule in mobile cellular communication networks. To attain the desired network transmission performance objectives over the whole cellular network system, we propose an appropriate cooperative data transmission scheme to optimize the data delivery efficiency in terms of the transmission time consumption for each and every transmission group. The proposed transmission optimization scheme includes the tasks of determining the transmission group, allocating the channel rate resource, specifying the cooperative transmission mechanism, and performing resource allocation and schedule as well. Firstly, we propose a utility function based rate allocation scheme to optimize the channel rate resource utilization for all those transmission groups over the cellular communication network. Based on it, we further present a cooperative data transmission scheme to deal with the data delivery and reception for all those subgroups with high efficiency over the whole network system. Particularly, we propose a resource efficiency function‐based coordination scheduling (EFCS) scheme to conduct the cooperative data transmission and resource schedule as well for each transmission group within the network. The EFCS scheme facilitates to determine the subgroups for schedule over each scheduling cycle, which can produce the best data transmission efficiency across both the direct and cooperative transmission hop. Both the theoretical analysis and the simulation evaluation results verify the superior performance of our proposed cooperative data transmission scheme.

Improvement of Teach Protocol for Enhancing Features of WSN

. Wireless Sensor Network (WSN) has several applications such as military, industry. and environment. The importance of WSNs in current applications makes the WSN technology highly relevant and significant to the field of communication and computing. However, WSN's performance deals with number of challenges. Energy consumption is the most considerable because nodes use energy to collect, treat, and send data, but they have restricted energy- For this reason. numerous efficient energy routing protocols have been dev eloped to save the consumption or power. Low energy adaptive clustering hierarchy (LEACH) is considered the most attractive one in WSNs, in this projects we evaluate the LEACH approach effectiveness in the cluster-head (CHJ choosing and in data transmission, then we propose an enhanced protocol. proposed algorithm aims to improve energy Consumption and prolong the lifetime of WSN through selecting CHs depending on the remaining power, balancing the number of nodes in clusters. determining abandoned nudes in order to send their data to the sink. Then CHs choose the optimal path to reach the sink, we propose a new approach to achieve better enhancement of WSN in terms of network lifetime and data transmission time represented by reducing the packet delay time. Then, we compare the simulated result of the proposed algorithm with the basic LEACH protocol.

A blockchain based federated deep learning model for secured data transmission in healthcare Iot networks

The wide use of sensors in healthcare applications has made it necessary to have secure communication in healthcare Internet of Things (IoT) networks. The sensor data is sensitive, and can contain extremely confidential information such as medical diagnosis, clinical records, vital signs and health data of patients. The emergence of blockchain as a technology ensures consensus and trust among systems, and is now considered to be a new trend used to achieve high scalability, data integrity and privacy. Federated learning is a new technology based on distributed learning that exploits the concept of trust. In federated learning, each user builds an individual distributed model to help a central server that is accessible only to a trusted user group. This paper harnesses the potential of these approaches and proposes an attack detection model to discern normal user behaviours from that of adversaries in an IoT network. This model is called the Blockchain enabled Federated Learning model for secured communication in healthcare IoT (BFL-hIoT), to secure data in healthcare IoT networks. This model is trained and tested on a standard dataset and demonstrates the highest classification accuracy of 97.16 % for normal, 0.9546 for backdoors, 0.9618 for XSS etc., outperforming other blockchain and deep learning models.

Artificial self-attention rabbits battle royale multiscale network based robust and secure data transmission in mobile Ad Hoc networks

In a Mobile Ad hoc Network (MANET), the network structure constantly evolves due to the movement of devices, users, or nodes. The Black Hole Attack (BHA) is recognized as the most impactful threat in MANETs, where an attacker node intentionally discards all data traffic, hence minimizing the network performance. Therefore, developing a secure and robust routing protocol is vital for efficient data transmission in a MANET. Although existing solutions have been designed to counteract malicious nodes, these solution presents drawbacks such as the need for additional hardware, notable delivery delays, limited throughput, increased energy consumption, and minimum packet delivery ratio. Thus, this research work introduces a new updated Ad hoc On-demand Distance Vector (AODV) routing protocol that integrates the benefits of the Artificial Rabbits Battle Royale Optimization (ARBRO), and Self-attention Multiscale Network (SMNet). The role of the ARBRO algorithm is to optimize the route by selecting the most appropriate node properties. Then, SMNet identifies the BHA node using these node properties. The proposed scheme is implemented in MATLAB. Experimental results determines that the introduced approach attains a delay of 0.039 s, a throughput of 94.57 Kbps, and a packet delivery ratio of 98.76 %, which are better than the existing techniques. Simulation results demonstrate that the introduced method improves network performance by enhancing the quality of service in the presence of a BHA.

Minimizing the Number of Distrustful Nodes on the Path of IP Packet Transmission

One of the important directions for improving modern Wide Area Networks is efficient and secure packet routing. Efficient routing is often based on using the shortest paths, while ensuring security involves preventing the possibility of packet interception. The work is devoted to improving the security of data transmission in IP networks. A new approach is proposed to minimize the number of distrustful nodes on the path of IP packet transmission. By a distrustful node, we mean a node that works correctly in terms of hardware and software and fully implements its data transport functions, but from the point of view of its organizational subordination, we are not sure that the node will not violate security rules to prevent unauthorized access and interception of data. A distrustful node can be either a transit or an end node. To implement this approach, we modified Dijkstra’s shortest path tree construction algorithm. The modified algorithm ensures that we obtain a path that will pass only through trustful nodes, if such a path exists. If there is no such path, the path will have the minimum possible number of distrustful intermediate nodes. The number of intermediate nodes in the path was used as a metric to obtain the shortest path trees. Routing tables of routers, built on the basis of trees obtained using a modified algorithm, provide increased security of data transmission, minimizing the use of distrustful nodes.

Faulty Nodes Detection for Reliable Data Transmission in Intelligent Wireless Sensor Networks

Faulty Nodes Detection for Reliable Data Transmission in Intelligent Wireless Sensor Networks

Evolution of Energy Efficient Wireless Sensor Network: A Survey on Energy and Clustering Issues

In different fields of human endeavor, wireless sensor networks (WSNs) are being developed to collect, aggregate, fuse, and send data collected by a wide range of sensors in real-time. When developing a wireless network's data transmission routing system, energy consumption is among the most essential components. Designing an energy-efficient (WSN) routing framework is challenging. Several energyefficient routing algorithms exist for effectively transferring data packets between cluster members (CM) and cluster heads (CH). Some suggest the change of state of the nodes that made up the network from active to sleep in the event where the nodes are not sensing of sending data as a method of energy optimisation while others reduce the distance between the nodes because the distance between nodes is directly proportional to the amount of energy use during data transmission. Whatever the method may be, achieving reduced energy consumption in a WSN environment is an enormous task due to the need for both long network life and network efficiency. This paper reviews some research and energy saving techniques that had been carried out on WSN over a period of time.

About projecting and optimization of data transmission networks

About projecting and optimization of data transmission networks

Enhanced Secured and Real-Time Data Transmissions in Wireless Sensor Networks using SFRT Routing Protocol

Enhanced Secured and Real-Time Data Transmissions in Wireless Sensor Networks using SFRT Routing Protocol

Remote Intelligent Medical Monitoring Data Transmission Network Optimization based on Deep Learning

A hospital operating status evaluation data analysis system was established based on the autoencoder's network. The Gibbs sampling method is used to obtain the approximate distribution of RBM. In addition, the Autoencoder neural network can also select feature dimensions that can better characterize the characteristics of financial operation data from a large amount of financial operation data. Deep learning methods are used to study the redundant information elimination method and the generation mechanism of multi-source heterogeneity in multi-source heterogeneous networks. The principle of intrinsic compression is used to reduce the dimensionality of the redundancy in the network and obtain the compression redundancy objective function. This article sets thresholds for information classification on the Internet. The approach was tested using financial data from a medical institution. Use smart encoders to extract 17 financial indicators from financial data that can be used for modeling. The evaluation results are used as the output vector of the model. Comparative experiments show that the AUC value and accuracy of the method proposed in this article can be improved by 0.84 and 83.33% compared with the AUC value of shallow logistic regression and BP neural network. This algorithm has apparent improvements.

Multi-UAV and IRS placement for secure data transmission in NOMA-enabled wireless networks

In this paper, we investigate multi-unmanned aerial vehicle (UAV)-enabled intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) downlink networks for secure data transmission in the presence of an intruder. Since Line of Sight (LoS) communication is obstructed, UAVs are utilized as aerial base stations to transmit confidential data to ground users with the assistance of IRS. The objective is to optimize the placement of UAVs and IRS, phase shift of the IRS, and transmit beamforming of the UAVs in order to maximize the network secrecy rate. The formulated optimization problem is non-convex in nature, posing a challenge in finding the optimal solution using conventional techniques. To tackle this problem, we propose a novel algorithm called balanced grey wolf optimizer and differential evolution (B-GWODE). This algorithm not only leverages the advantages of both the GWO and DE algorithms but also incorporates parameter modifications to mitigate the issue of premature convergence and to strike a suitable balance between exploration and exploitation. This hybrid approach efficiently search the solution space and obtain the optimal configuration that maximizes the network secrecy rate. Simulation results demonstrate the superiority of our proposed B-GWODE algorithm over the benchmark methods.

Research of the optimization problem of structure hierarchical communication network with changing its parameters

The article is devoted to the study of the problem of optimizing the hierarchical structure of a multicommodity communication network with discrete flows when changing its important parameters, such as the capacity of network arcs in transport blocks and the size of the transport block for transportation of the discrete small-lot cargo or of the data transmission in a digital communication network. The network has three levels of hierarchy – a backbone, a zonal and an internal and four types of nodes - backbone nodes of the first, second and third types, forming the backbone and zonal levels of the network, and nodes of the fourth type, which subordinate to each backbone node and forming the internal levels of the network. Types of nodes differ from each other in terms of functionality. The main task of the study is to establish how the structure of the backbone network changes (the number and location of backbone nodes of the first, second, and third types), the flow processing and distribution scheme, and the technical and economic indicators of the network's functioning for different values of its parameters. The principles of organization of sorting and distribution of flows in a three-level network and its mathematical model are given. A mathematical model of the optimization problem of the backbone network structure and flow sorting and distribution scheme is formulated. Algorithms for solving the problem are based on the discrete analogue of the local descent method proposed by the authors earlier, when the neighborhoods of the metric space of possible solutions are chosen based on heuristic considerations, taking into account the specifics of the problem being solved. Computer modeling of the problem on a network containing 10 nodes at the specified change in the both parameters is carried out. The modeling was carried out on the example of the transport network of cargo transportation using a computer program that is part of the instrumental software of the Information and Analytical Decision Support System (IA DSS), which is being developed at the Institute of Telecommunications and Global Information Space of the National Academy of Sciences of Ukraine. An experimental study of solution the problem showed that the structure of the network is weakly dependent on the change in the carrying capacity of the arcs and the size of the transport block. The proposed computer technology for solving problem when the network parameters are changed allows you to interactively modeling various options of a network, changing the topology, hierarchical structure, flows, parameters and constraints of the model and from the set of the obtained solutions to choose a best option, taking into account the selected a goal function and the accepted constraints; calculate preliminary technical and economic indicators of the network's functioning, estimate the cost of additional resources and plan the amount of investment required for the modernization and construction of its structural elements, which ultimately makes it possible to increase the efficiency functioning of the network by optimizing use of its resources and reducing the operating costs for the processing and transportation of flows.

Research on image processing of electric power system terminals based on reinforcement learning and mobile edge computing optimization

AbstractThis research is dedicated to the optimization of power system terminal image processing based on RL and MEC. With the continuous development of power system, the demand for image processing of terminal equipment is increasing day by day. However, traditional image processing methods have the problems of high computing complexity and real‐time and energy consumption. To solve this problem, this study introduces the idea of RL and MEC to improve the efficiency and performance of image processing of power system terminals. By modeling and optimizing the image processing task of the power system terminal equipment, the intelligent adjustment of the processing parameters is realized to adapt to the needs of different scenarios. MEC technology is introduced to move image processing tasks from the central server to the edge device, reducing data transmission delay and network burden, thus improving real‐time performance and reducing energy consumption. The experimental results show that the proposed optimization method based on RL and MEC has a significant performance improvement compared with the traditional method in the power system terminal image processing. The framework our proposed has achieved significant improvement in task completion latency, achieving higher system energy efficiency compared to traditional methods.

Spectrum sensing using deep learning for proficient data transmission in wireless sensor networks for wireless communication

Cognitive radio has been recommended for enhancing wireless spectrum usage, and an important part of cognitive radio is spectrum sensing. Conventional spectrum sensing techniques rely on energy detection and feature extraction from a received signal at a specific position. The advancement of artificial intelligence and deep learning has provided a chance to increase spectrum sensing efficiency. The proposed investigation presented a hybrid model comprising CNN, BILSTM, and Transformer Networks (TN) for spectrum sensing. With the aid of Transformer networks, the proposed model improves sensing accuracy even for lower SNR signals, particularly at −20 dB. The results of the proposed technique show that CNN-BILSTM-TN enhances spectrum sensing in comparison to earlier models studied in this domain. The proposed approach outperforms prior methods in terms of F1 Score and has an improved probability of detection at −20 dB and a lower likelihood of missed detection. Analysis of ten modulation techniques' performance criteria, such as the Jaccard index, Cohen Kappa coefficient, and Matthew's correlation coefficient, demonstrates that the suggested method's spectrum sensing is better even at lower SNRs.

QWLCPM: A Method for QoS-Aware Forwarding and Caching Using Simple Weighted Linear Combination and Proximity for Named Data Vehicular Sensor Network

The named data vehicular sensor network (NDVSN) has become an increasingly important area of research because of the increasing demand for data transmission in vehicular networks. In such networks, ensuring the quality of service (QoS) of data transmission is essential. The NDVSN is a mobile ad hoc network that uses vehicles equipped with sensors to collect and disseminate data. QoS is critical in vehicular networks, as the data transmission must be reliable, efficient, and timely to support various applications. This paper proposes a QoS-aware forwarding and caching algorithm for NDVSNs, called QWLCPM (QoS-aware Forwarding and Caching using Weighted Linear Combination and Proximity Method). QWLCPM utilizes a weighted linear combination and proximity method to determine stable nodes and the best next-hop forwarding path based on various metrics, including priority, signal strength, vehicle speed, global positioning system data, and vehicle ID. Additionally, it incorporates a weighted linear combination method for the caching mechanism to store frequently accessed data based on zone ID, stability, and priority. The performance of QWLCPM is evaluated through simulations and compared with other forwarding and caching algorithms. QWLCPM’s efficacy stems from its holistic decision-making process, incorporating spatial and temporal elements for efficient cache management. Zone-based caching, showcased in different scenarios, enhances content delivery by utilizing stable nodes. QWLCPM’s proximity considerations significantly improve cache hits, reduce delay, and optimize hop count, especially in scenarios with sparse traffic. Additionally, its priority-based caching mechanism enhances hit ratios and content diversity, emphasizing QWLCPM’s substantial network-improvement potential in vehicular environments. These findings suggest that QWLCPM has the potential to greatly enhance QoS in NDVSNs and serve as a promising solution for future vehicular sensor networks. Future research could focus on refining the details of its implementation, scalability in larger networks, and conducting real-world trials to validate its performance under dynamic conditions.

Evaluation on Data Transmission in Wireless Sensor Networks Based on Computer Technology

Traditional network data transmission (DT) has certain limitations in terms of processing, storage, and communication capabilities. Moreover, DT is easily affected by the network environment, which can reduce the real-time performance of DT. In severe cases, network transmission failures, DT interruptions, and other issues may even occur. As a new network, wireless sensor networks (WSN) have been widely used in military, industrial, agricultural, medical and other fields. In this paper, WSN was regarded as an important research method, and in-depth research was carried out around the real-time and energy consumption (for the convenience of the following text, the abbreviation for energy consumption is EC) of WSN DT. This paper focused on the issues of real-time performance, EC, and real-time transmission. It balanced EC and improved real-time performance through the low energy adaptive clustering hierarchy (LEACH) protocol and the power-efficient gathering in sensor information systems (PEGASIS) protocol. The experimental results showed that the real-time rates of LEACH under single reaction nodes were 45.5% and 48.6%, respectively. The lowest and highest real-time rates of PEGASIS were 86.2% and 89.5%, respectively. The real-time rate of PEGASIS was much higher than that of LEACH, proving that the proposed PEGASIS had high real-time performance and reduced DT delay.