Data Transmission In Networks Research Articles

Mitigation of Risks Associated with Distrustful Routers in OSPF Networks—An Enhanced Method

Packet routing in computer networks provides complex challenges in environments with distrustful routers due to security vulnerabilities or potential malicious behaviors. The literature offers solutions to the problem designed for different types of networks. This paper introduces a novel method to mitigate risks associated with distrustful routers by constructing secure and efficient routing paths in Open Shortest Path First (OSPF) networks. Networks in which routing is carried out based on OSPF protocols are currently the most widespread, hence ensuring the security of data transmission in such networks is urgently needed. In turn, distrustful routers can degrade the overall security and performance of the network, creating vulnerabilities that can be used for malicious purposes. The proposed method is based on the Dijkstra algorithm which is enhanced to identify and mitigate the risk connected with potential distrustful network nodes. Analysis of the proposed method shows its ability to build efficient routes exclusively through trusted routers if such paths exist. As a criterion for effectiveness, a metric such as the channel weight is used. The proposed method is validated using applications across networks of varying topologies and sizes, including large-scale networks. For networks containing post-distrustful routers to which there is no path without distrustful nodes, the proposed method is able to build the shortest paths that are marked as not secure but have a minimum number of distrustful nodes on their path. In scenarios with multiple compromised routers with different locations in the network, the proposed method significantly increases network resilience.

Strengthening security in IoT-based smart cities utilizing cycle-consistent generative adversarial networks for attack detection and secure data transmission

Strengthening security in IoT-based smart cities utilizing cycle-consistent generative adversarial networks for attack detection and secure data transmission

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.

Implementation of data transmission mechanism in a Bi-directional optical fiber network

Abstract Passive optical network (PON) has become the leader in delivering broadband high-speed connectivity. The paper introduces a detailed description of PON, its working principle, key features, and their potential applications. In addition, we discuss the types of PON architectures including GPON, EPON, and XG-PON with merits and demerits. Finally, we discuss the technical considerations of PONs in data transmission mechanisms, network design, and component selection. This paper further demonstrates the use of PON technology via a case study on the design and implementation of a bidirectional optical fiber network. Within this project, we went ahead to demonstrate the use of PON components including optical splitters, transmitters, receivers, and power controllers towards the development of an efficient and functional network structure. It mainly covered the results of the project since the PONs appeared to be a promising solution for obtaining high-performing and reliable broadband services.

Dynamic-Based Cluster Routing Algorithm for Data Transmission in WSN

Wireless Sensor Network has been used for collection of information from the non-approachable areas.WSN use sensor nodes for sensing the information from the environment. Sensor nodes have been powered using energy source that is battery connection. Battery source of WSN nodes can’t be charged from any external device so that energy efficient utilization of the nodes for sensing and transmitting information is the major concern. Various approaches had been introduced in the past researches that provide better energy efficient protocol for enhancement of network lifetime and data transmission. In this paper various approaches have been implemented for WSN. Clustering based approaches have been utilized and assumed to be efficient approaches for data collection and data transmission over the network. In this paper we have reviewed DEEC, LEACH, EIP-LEACH and comparison has been done with proposed approach based on various parameter i.e Network lifetime. Experimental results of above defined approaches have been demonstrated.

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.

Optimization of data transmission in sensor networks for enhanced control of ozonator efficiency

The main object of the research is the efficiency of real-time ozonator control based on sensor networks. The study addressed the issue of low efficiency in ozonator control systems and the lack of reliability and speed in real-time data transmission. The research revealed that changes in pressure and temperature have a direct impact on ozone concentration. This finding made it possible to increase the ozonator's productivity by 15 %, reduce energy consumption by 10 %, and improve system reliability by 20 %. The key features of the results include the ability to monitor ozone levels in real-time, maintaining the stability of the ozonator, and optimizing its performance. Additionally, sensor networks ensured fast and accurate data delivery, enhancing the energy efficiency and reliability of the system. These results were explained based on experimental data that demonstrated how changes in pressure and temperature affect ozone concentration. The use of sensor networks contributed to increased system stability, reduced energy consumption, and improved control accuracy. The obtained results can be applied to ozonator systems and other fields requiring real-time environmental monitoring and control. The methods proposed in the study provide opportunities for optimizing industrial processes, reducing costs, and achieving sustainable development goals

Optimized cooperative data transmission in two-tier NB-IoT networks

Optimized cooperative data transmission in two-tier NB-IoT networks

Spatial-division multiplexing self-homodyne coherent transmission over 19-/24-core fibers assisted by homemade fan-in/fan-out 3D photonic devices.

The self-homodyne detection (SHD) is a promising solution to achieve low-cost and low-power-consumption fiber-optic communications. In this work, we propose and demonstrate a high-capacity spatial-division multiplexing (SDM) system with SHD technology by employing single-mode multi-core fibers (SM-MCFs), where the fan-in/fan-out (FIFO) 3D photonic devices are designed and fabricated based on the femtosecond laser direct writing technique, enabling high-efficiency coupling between single-mode fibers (SMFs) and SM-MCFs. The FIFO 3D photonic devices, serving as the SDM (de)multiplexer, facilitate superior performance of low insertion loss and low inter-channel crosstalk. Using a low-cost MHz linewidth distributed feedback (DFB) laser, we experimentally demonstrate the SDM-SHD transmission system of 102-Gbaud dual-polarization (DP) 16-ary quadrature amplitude modulation (16QAM) signals per channel over a 2.7-km 24-core SM-MCF and 22-Gbaud DP-64QAM signals per channel over a 1-km 19-core SM-MCF. Without using wavelength-division multiplexing (WDM), the total data rate per channel reaches 816 Gbit/s, with an aggregate data throughput of 18.7 Tbit/s. The obtained results indicate that, by extending SHD technology to SDM communication systems, one can achieve low-cost, low-power-consumption, and scalable high-capacity data transmission in next-generation fiber-optic communication networks.

A Bootstrapped Volatile Key Management Scheme for Efficient and Secured Data Transmission in WSNs

The recent era has witnessed several advances in Wireless Sensor Networks (WSNs), with few resources presents unprecedented challenges for secured data transmission in an ever-unsecured broadcast network. The situation is more challenging when adversaries are equipped with sophisticated resources having direct access to sensor nodes. Nevertheless, there exists several cryptographic techniques used for node authentication, authorization, data confidentiality, integrity, and other security related services. This paper contributes to novel network security strategies to prevent network compromise. The proposed strategies attempt to boost data integrity, reliability and secure data transmissions. The proposal identifies the strategically important nodes (SIN) in implementing network security measures using volatile symmetric-key management scheme (KMS).The scheme involves four phases: The first phase involves strategically important nodes (SIN) designation using Grey Wolf Optimization (GWO). Usually, hackers find it difficult to guess and access the keys that are selected on a random basis. To further increase the key protection, the scheme introduces a limited lifespan master key (LLMK). Secondly, to secure the KMS, this phase utilizes SHA for LLMK distribution, and paired node prediction. Next phase performs secret key generation with a gradient approach and the key sharing is performed using Supersingular Isogeny Diffie-Hellman algorithm. The last phase is responsible for the dynamic S-Box generation using the Blowfish algorithm. The generated S-Box are subsequently shuffled to enhance the cryptographic process offering immunity towards attacks.To evaluate the effectiveness of this approach, the proposed KMS is compared with relevant methods on various performance metrics. The results demonstrate that the proposed scheme achieves notable performance improvements over other methods.

Block Chain Based Automated Multiresolution Channel Allocation of IoT

This research includes the growing popularity of the Internet of Things (IoT) in recent years. Internet of Things (IoT) security and privacy remain a major challenge, mainly due to IoT networks' massive scale and distributed nature. Blockchain-based approaches provide decentralized security and privacy, yet they involve significant energy, delay, and computational overhead unsuitable for most resource-constrained IoT devices. The Internet of Things is a real-time application for high data rates, and blockchain technology is real-time data storage technology. It is an effective way to transfer data from the communication node to the system in the IoT network using blockchain technology, the most recent each IoT node discovers and adjusts for better communication. It is necessary to have the confidence to integrate/maximize the channel's output of the wireless sensor networks' features. The change can be implemented. However, from time to time the channel for the information does not learn to transmit to the protocol. Changing the channel characteristics in these networks like IoTs for achieving and increasing the data transmission and network throughput of wireless sensor networks. Therefore, this research recommends a completely computerized nature-understanding and by-origin procedure designed which may completely automate data transfer communication between multiusers by completely applying channel time spectral and time attributes of wireless network and store the data through blockchain technology then fetch data from any node when you want. For this purpose, designed Protocol channel swapping, data storage from time to time through blockchain technology, and following the shortest path for communication. The planned designed protocol of this WSN is rare as it is understanding and proposed the issue to maximize the WSN intensity established upon the WSN network metrics. This network also permits every node inside that IoT to regularly identify the adjoining network station characteristics so that they will exchange networks which means swapping adjoining channels to realize maximum data transfer.

EVM-Aware relay path decision with hierarchical modulation for reliable data transmission in cooperative relay networks

This paper presents a novel approach of integrating an error vector magnitude with hierarchical modulation in which HM dynamically adjusts modulation schemes to optimize spectral efficiency under varying channel conditions. Relay nodes are strategically selected based on Signal-to-Noise Ratio (SNR), with a focus on prioritizing high-SNR relays to ensure reliable data forwarding. To further enhance the reliability of the communication system, an EVM-aware relay path decision is proposed which selects the most suitable path based on EVM, considering the quality of the transmitted signal. By considering both SNR-based relay selection and EVM-aware path selection, the proposed system aims to minimize errors and optimize the overall performance of the network. The effectiveness of the proposed system is evaluated through extensive simulations in MATLAB, demonstrating that the proposed adaptive modulation scheme significantly outperforms fixed schemes, particularly under fluctuating channel conditions, ensuring robust and reliable communication across different SNR levels. This shows the potential of the proposed protocol to enhance performance in dynamic, making it particularly relevant for applications where reliable data transfer is critical.

Energy efficient mobile sink driven data collection in wireless sensor network with nonuniform data

In wireless sensor network, mobile sink is used to collect data from sensor nodes by periodically traversing the network to prevent hotspot problem. However, when sensor nodes generate data non-uniformly, the efficiency of data collection is constrained by rendezvous points and network topology. It becomes more challenging under network energy consumption constraint. Thus, this paper investigates non-uniform data generation in wireless sensor network and proposes an innovative approach: Optimal Clustering and Network Topology for Mobile Sink-Driven Data Collection, called OCNTMS. It focuses on determining optimal rendezvous points and their associated clusters. Through innovative methods such as weight-balanced clustering, cost function optimization, load-balanced link construction, and node forwarding selection, the OCNTMS can efficiently construct link sets within clusters and accurately plan the mobile sink’s traversal of rendezvous points. Simulation results show that the OCNTMS reduces energy consumption by 18% and increases network lifetime by 40% compared with existing approaches under the constraint of non-uniform data generation. This greatly improves the network energy efficiency and data transmission efficiency.

Quantum Cryptography for Secure Data Transmission in IoT Networks

The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, especially in safeguarding sensitive data transmitted across vast networks. Traditional cryptographic methods, though widely used, face increasing vulnerabilities in light of advancements in quantum computing. This research explores the application of quantum cryptography, particularly Quantum Key Distribution (QKD), to secure data transmission in IoT networks. Quantum cryptography offers a revolutionary approach by utilizing the principles of quantum mechanics, ensuring communication channels are resistant to eavesdropping and quantum attacks. Despite its promise, integrating quantum cryptography into resource-constrained IoT environments presents several technical challenges. This paper examines current quantum cryptographic techniques, their feasibility for IoT applications, and proposed solutions for overcoming integration hurdles. Case studies of real-world implementations and experimental results are discussed to highlight the effectiveness of these methods. The findings suggest that quantum cryptography, while still in its infancy for IoT, has the potential to provide robust security solutions in future networks.

Overview of Space-Based Laser Communication Missions and Payloads: Insights from the Autonomous Laser Inter-Satellite Gigabit Network (ALIGN)

This paper examines the growing adoption of laser communication (lasercom) in space missions and payloads for identifying emerging trends and key technology drivers of future optical communications satellite systems. It also presents a comprehensive overview of commercially available and custom-designed lasercom terminals, outlining their characteristics and specifications to meet the evolving demands of global satellite networks. The analysis explores the technical considerations and challenges associated with integrating lasercom terminals into LEO constellations and the Inter-satellite communications service provision in LEO due to their power, size, and weight constraints. By analyzing advancements in CubeSat lasercom technology designed to cater for the emergence of future mega constellations of interacting small satellites, the paper underscores its promising role in establishing high-performance satellite communication networks for future space exploration and data transmission. In addition, a brief overview of our ALIGN planned mission is provided, which highlights the main key operational features in terms of PAT and link budget analysis.

A Survey of the Real-Time Metaverse: Challenges and Opportunities

The metaverse concept has been evolving from static, pre-rendered virtual environments to a new frontier: the real-time metaverse. This survey paper explores the emerging field of real-time metaverse technologies, which enable the continuous integration of dynamic, real-world data into immersive virtual environments. We examine the key technologies driving this evolution, including advanced sensor systems (LiDAR, radar, cameras), artificial intelligence (AI) models for data interpretation, fast data fusion algorithms, and edge computing with 5G networks for low-latency data transmission. This paper reveals how these technologies are orchestrated to achieve near-instantaneous synchronization between physical and virtual worlds, a defining characteristic that distinguishes the real-time metaverse from its traditional counterparts. The survey provides a comprehensive insight into the technical challenges and discusses solutions to realize responsive dynamic virtual environments. The potential applications and impact of real-time metaverse technologies across various fields are considered, including live entertainment, remote collaboration, dynamic simulations, and urban planning with digital twins. By synthesizing current research and identifying future directions, this survey provides a foundation for understanding and advancing the rapidly evolving landscape of real-time metaverse technologies, contributing to the growing body of knowledge on immersive digital experiences and setting the stage for further innovations in the Metaverse transformative field.

Globally exponentially mean‐square stabilization on disturbed networked switched systems using optimized event‐triggered scheme

AbstractThis article is concerned with the globally exponentially mean‐square stabilization problem for uncertain disturbed networked switched systems (NSSs) subject to mixed network attacks and multiple asynchronous switching by exploring an optimized event‐triggered scheme. First, we consider a new challenge of multiple asynchronous switching stemmed from the varying switching delays within the event‐triggered scheme and controller. Second, assume that the data transmission network is subject to attacks consisting of deception attacks and aperiodic DoS attacks. Third, an optimized resilient dynamic event‐triggered scheme (ORD‐ETS) with switching behavior and external noises is proposed to intelligently reduce the inessential released data. For further improving the system performance, the updated ORD‐ETS (UORD‐ETS) is designed by introducing some fixed signals‐released points. Then, based on the mode‐dependent multiple Lyapunov functionals, integral inequality, incorporated switching signal technique and proposed asynchronous analysis method, some criteria are attained to ensure the globally exponentially mean‐square stable of the uncertain disturbed NSSs with a weighted performance. Further, the co‐design of the desired controller and the event‐triggered scheme is achieved. Ultimately, a numerical simulation with comparative analysis and a practical simulation are conducted to substantiate the validity of the obtained results.

Retraction Note: Design and analysis of photovoltaic solar based longer transmission of data in ADHOC networks

Retraction Note: Design and analysis of photovoltaic solar based longer transmission of data in ADHOC networks

Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks

Structured Reinforcement Learning for Delay-Optimal Data Transmission in Dense mmWave Networks

The methods and tools of ensuring information security of networks

Currently, there is an increase in the number of information threats that lead to unstable operation of data networks. This is due to the mass use, the complication of the hierarchy of area networks, the increase in the heterogeneity of software tools, and the complication of the functionality of network services. In such cases, the development and improvement of methods for determining information security in data transmission networks has a great importance. The article discusses ways to increase the information security of the data network by increasing the reliability of detecting deviations in the operation of the main nodes of the data network and reducing the number of false starts when automating the detection of threats. Experimental verification and evaluation of the effectiveness of the methods were considered, it was shown that the developed methods allow skipping the anomaly of the first type and minimizing errors of the second type. The methods were implemented software in the form of a prototype access detection system (ADS).