Network Transmission Research Articles

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

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

Synchronisation of complex nonlinear dynamical networks with coupling delays via dual hybrid-triggered control design

The synchronisation problem for a class of complex nonlinear dynamical networks (CNDNs) that are subject to time-varying coupling delays and nonlinearities is explored in this work. This is accomplished by designing a dual hybrid-triggered communication technique based on the dynamic output feedback controller which is the main intent of this study. In contrast to the existing hybrid-triggered mechanism, a dual hybrid-triggered technique is being employed with the utilisation of outputs of considered CNDNs and the dynamic output feedback controller. To be precise, with an aim to effectively lower the network transmission rate in both the sensor-to-controller and controller-to-actuator channels, the triggering mechanism is implemented to both the outputs. Later, the Lyapunov–Krasovskii functional is utilised to establish the stability criterion and the necessary conditions for ensuring the synchronisation of chosen CNDNs are derived in terms of linear matrix inequalities. Eventually, two numerical examples, including the aircraft model have been rendered to reveal the potency of acquired theoretical results.

Optimal Power Allocation for Secure Transmission in Untrusted Relay Networks with Alamouti Space-Time Block Coding

Optimal Power Allocation for Secure Transmission in Untrusted Relay Networks with Alamouti Space-Time Block Coding

Self-organized efficient transmission in dynamic networks

Effective protocols are needed to ensure successful transmission of signals, messages, or parcels through a network of intermediaries. Existing studies often overlook real-world constraints such as limited resources. We investigate the design of efficient and robust transmission strategies in dynamic networks subject to dissipation and resource limited node activity, as in wireless sensor networks. We find that dissipation and randomness can enhance transmission efficiency and network resilience. For a broad family of protocols, a self-organized dynamics assures global connectivity, even in the limit of low density of links, where random networks fail.

Variable speed limit control strategy considering traffic flow lane assignment in mixed-vehicle driving environment

To accurately predict traffic flow and optimize the operations of freeway bottleneck areas in a mixed-vehicle driving environment, this paper proposes a traffic prediction model and a variable speed limit (VSL) cooperative control strategy. Firstly, a lane-level short-term traffic prediction model, physics informed Transformer and cell transmission model (PIT-CTM), is constructed by combining the Transformer neural network and lane-level cell transmission model (CTM) based on the physics-informed deep learning framework. On this basis, the accuracy and transferability of PIT-CTM are analysed. Secondly, a lane assignment decision model is presented, which enables the dynamic planning of the optimal traffic distribution across lanes. Furthermore, a lane-level VSL control model is constructed based on the model predictive control (MPC) framework. The model induces vehicles to change lanes earlier by setting the speed limit difference between lanes. By regulating the input flow in the bottleneck area of the freeway, it reduces conflicts between mainline vehicles and ramp vehicles. Finally, the feedback regulation between the lane assignment decision model and the lane-level VSL control model promotes the cooperative optimisation of the lateral and longitudinal flows and adapts the control strategy to the dynamic traffic characteristics. A three-lane freeway merging zone is selected, the numerical experiment is conducted and compared with differential lane-level VSL. The results show that the strategy can effectively optimise the mixed-vehicle traffic state and maintain better control performance under any connected and autonomous vehicle (CAV) penetration rates.

Dual-Network Layered Network: A Method to Improve Reliability, Security, and Network Efficiency in Distributed Heterogeneous Network Transmission

This article delves into the routing architecture and reliable transmission service framework of dual-network layered networks, with a focus on analyzing their core design ideas and implementation strategies. In the context of increasing network complexity today, traditional single-network architectures are unable to meet diverse application needs. Therefore, dual-network layered networks, as an innovative solution, are gradually receiving attention from both academia and industry. This article first analyzes the key technical elements in the dual-network layered network architecture, including the optimization of routing algorithms, distributed consensus, reliability assurance mechanisms for packet transmission, and dynamic allocation strategies for network resources. Through in-depth research on these technologies, this article elaborates on the important role of dual-network layered networks in building efficient and stable transmission environments, providing important theoretical foundations and technical support for the construction and optimization of future network systems.

Rate adaptive compressed sampling based on region division for wireless sensor networks

Image acquisition and transmission in wireless sensor networks (WSN) are core issues for some resource-deficient multimedia sensing applications. Reducing sampling rates and data transmission lowers sensor node costs and energy, addressing communication bottlenecks. Block compressed sampling (BCS) can meet the above requirements. For BCS, the sparsity or smoothness of the block signal is a crucial parameter, which determines the setting range of the sampling rate. For the sampling side of the sensor node, we cannot directly obtain the complete digital signal. Therefore, it becomes difficult to perform adaptive rate compressed sampling. In this paper, a novel adaptive sampling rate allocation scheme based on region division is proposed. First, we use a simple auxiliary vector to determine the complex and smooth regions of the current image. For the smooth region, we use a mean vector to divide each block into a residual block and a mean value block. Then the proposed prior probability sparsity estimation model is used to estimate the sparsity order of each residual block, while each mean value block requires only one measurement to restore losslessly. For the complex region, we first set a higher baseline sampling rate for it, and then adaptively allocate the remaining supplementary sampling rate based on the statistical characteristics of each block itself. Experiment results show that the proposed scheme can allocate an appropriate sampling rate to each block, reduce the total sampling rate, and significantly improve the signal reconstruction quality simultaneously.

A TOPSIS based multi-objective optimal deployment of solar PV and BESS units in power distribution system electric vehicles load demand

The growing concerns regarding the depletion of fossil fuels, CO2 emissions, and the effects of climate change prompt the usage of plug-in electric vehicles (PHEVs) all over the world in a big way. The increased electrical demand brought on by the charging of electric vehicles puts a burden on the distribution network parameters like energy loss, voltage profile and thermal limits. Recently, renewable energy-based distribution generation (RDGs) units are firmly integrated with the transmission and distribution system networks to lower the carbon footprint generated due to conventional thermal power plants. In addition, Battery Energy Storage Systems (BESS) are used to enhance grid operation and lessen the consequences of the high intermittency nature of RDGs power. In this work, two charging methods of PHEVs are considered: charging electric vehicles at home during night-time and charging electric vehicles at public fast charging stations (PFCS). The uncertain nature of arrival time and trip distance of PHEVs are addressed using probability density functions (PDFs). The 33-bus test system consists of commercial, industrial and residential buses is taken to implement the proposed methodology. In this work, 500 PHEVs are taken into consideration. The aforementioned charging methods produce a 24-h electric demand for PHEVs, which is then placed on the corresponding distribution system buses. The effect of PHEVs on technical distribution system metrics, including voltage profile and energy loss, is investigated. To improve the above metrics, optimal planning of inverter-based non-dispatchable PV units and dispatchable PV-BESS units in the distribution network by the inclusion of PHEVs electric load demand is addressed. The Pareto-based meta-heuristic multi-objective chaotic velocity-based butterfly optimization method (MOCVBOA) is chosen for optimization of desired objectives. The results of the MOCVBOA optimization algorithm are compared with those of the other optimization algorithms, NSGA-II & MOBOA, frequently described in the literature to assess its effectiveness.

Deep joint source-channel coding empowered two-way relay networks for wireless image transmission

Compared with the traditional uni-directional relaying, two-way relay networks provide important enhancements and optimizations to modern communication systems. However, with the increasing requirements of artificial intelligence applications for image data transmission, relay-assisted communication technologies are reaching the theoretical limit in terms of bandwidth, which hinders the further development of AI applications. To address this issue, we propose a deep joint source-channel coding empowered two-way relay network (DeepJSCC-TWRN) to help image transmission. Specifically, in the DeepJSCC-TWRN, a DeepJSCC is employed to improve image transmission quality of the TWRN from the perspective of visual semantic information, and each source can achieve optimal performance by being trained in a uniform deep learning framework. For measuring the performance of the proposed DeepJSCC-TWRN, we employ the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) as performance metrics. Simulation results show that DeepJSCC-TWRN outperforms the baseline method, demonstrating the ability to preserve visual semantic information.

Network Traffic Classification Analysis on Differentiated Services Code Point Using Deep Learning Models for Efficient Deep Packet Inspection

Network Traffic Classification Analysis on Differentiated Services Code Point Using Deep Learning Models for Efficient Deep Packet Inspection

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.

Learning uplinks and downlinks transmissions in RF-charging IoT networks

This paper considers uplink and downlink transmissions in a network with radio frequency-powered Internet of Things sensing devices. Unlike prior works, for uplinks, these devices use framed slotted Aloha for channel access. Another key distinction is that it considers uplinks and downlinks scheduling over multiple time slots using only causal information. As a result, the energy level of devices is coupled across time slots, where downlink transmissions in a time slot affect their energy and data transfers in future time slots. To this end, this paper proposes the first learning approach that allows a hybrid access point to optimize its power allocation for downlinks and frame size used for uplinks. Similarly, devices learn to optimize (1) their transmission probability and data slot in each uplink frame, and (2) power split ratio, which determines their harvested energy and data rate. The results show our learning approach achieved an average sum rate that is higher than non-learning approaches that employed Aloha, time division multiple access, and round-robin to schedule downlinks or/and uplinks.

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.

Retraction Note: A hybrid QL ANN model designed to improve the Quality of Transmission of optical communication network

Retraction Note: A hybrid QL ANN model designed to improve the Quality of Transmission of optical communication network

Advanced Artificial Intelligence Drivers and IoT Network Systems for Data Collection and Network Transmission in the Industry 5.0 Era

The primary bases of Industry 5.0, the Internet of Things (IoT), artificial intelligence (AI), and big data (BD) may serve as a basis for applying the concepts of smart manufacturing, innovative goods and services, and predictive maintenance (PDM) into practice. Assessing the remaining useful life of components using failure data and state monitoring information constitutes the AI techniques for predictive maintenance (PDM), which are critical in Industry 5.0. However, it is sometimes challenging to identify the annotation of component usage, creating an open problem for obtaining accurately labeled information and understanding it. This problem needs to be rectified if Industry 5.0 progress is proceeded with. In order to solve the PDM job, this study presents a new data-driven decision system (DDS) based on AI that overcomes the earlier issue. It does by concentrating on an actual industrial use scenario that involves modern processing and monitoring machines. Specifically, the most fundamental components of the recommended DDS include cloud-based storage, data interpretation, predictive modeling, recognition of features using Principal Component Analysis (PCA), and data collecting. An efficient prediction model called the unique moth flame search-tuned light gradient boosting machine (MFS-LGBM) technique is presented in this work. By enabling the ongoing gathering of identified samples and offering a data analysis environment to assist the maintainer/operator, the suggested approach resolves the PDM task. The research findings showed that when compared to the previous approaches, the suggested approach accomplishes the greatest conceivable balance between comprehension, computational effort, and forecasting accuracy in terms of quality estimation. Based on the suggested methodology, the performance is examined in terms of RMSE (600.10), MAE (390.60), [Formula: see text] (0.790) and MAPE (25.70) measures. By incorporating it into the cloud structure, the suggested technique minimizes maintenance expenses and enhances production efficiency by improving machining processes, assisting maintainers, and delivering current operating risk alerts.

Research on cloud dynamic public key information security based on elliptic curve and primitive Pythagoras

In order to solve the problems of key redundancy, transmission and storage security and the difficulty of realizing the one-time-secret mode in image encryption algorithm, this paper proposes a cloud dynamic public key information security scheme based on elliptic curve and primitive Pythagoras. The 6-D Lorentz hyperchaos system is used as the key generator, and the generated key is stored in the cloud key management center, which supports automatic update and dynamic change to further ensure the security of the key. The encryption key is selected by elliptic curve and primitive Pythagorean triplet. In the scrambling algorithm, according to the parity of random number and the parity of image pixel value coordinates, elliptic curve is used to reset the plaintext image. In the diffusion algorithm, the three-side rotation characteristic of the Rubik’s cube is simulated, the binary sequence of pixel values is stored at eight vertices of the cube, the cube is rotated according to the key value, and the changed 8 new binary numbers are XOR operated with the random sequence to obtain the new pixel value. The algorithm in this paper is applied to network transmission, which can effectively prevent data eavesdropping, tampering and forgery, ensure communication security.

Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition.

The integration of communication and sensing (ICAS) in optical networks is an inevitable trend in building intelligent, multi-scenario, application-converged communication systems. However, due to the impact of nonlinear effects, co-fiber transmission of sensing signals and communication signals can cause interference to the communication signals, leading to an increased bit error rate (BER). This paper proposes a noncoherent solution based on the alternate polarization chirped return-to-zero frequency shift keying (Apol-CRZ-FSK) modulation format to realize a 4 × 100 Gbps dense wavelength division multiplexing (DWDM) optical network. Simulation results show that compared to traditional modulation formats, such as chirped return-to-zero frequency shift keying (CRZ-FSK) and differential quadrature phase shift keying (DQPSK), this solution demonstrates superior resistance to nonlinear effects, enabling longer transmission distances and better transmission performance. Moreover, to meet the transmission requirements and signal sensing and recognition needs in future optical networks, this study employs the Inception-ResNet-v2 convolutional neural network model to identify three modulation formats. Compared with six deep learning methods including AlexNet, ResNet50, GoogleNet, SqueezeNet, Inception-v4, and Xception, it achieves the highest performance. This research provides a low-cost, low-complexity, and high-performance solution for signal transmission and signal recognition in high-speed optical networks designed for integrated communication and sensing.

Quantum Advantage: A Single Qubit's Experimental Edge in Classical Data Storage.

We implement an experiment on a photonic quantum processor establishing efficacy of the elementary quantum system in classical information storage. The advantage is established by considering a class of simple bipartite games played with the communication resource qubit and classical bit (c bit), respectively. Conventional wisdom, supported by the no-go theorems of Holevo and Frenkel-Weiner, suggests that such a quantum advantage is unattainable when the sender and receiver share randomness or classical correlations. However, our results reveal a quantum advantage in a scenario devoid of any shared randomness. Our experiment involves the development of a variational triangular polarimeter, enabling the realization of positive operator value measurements crucial for establishing the targeted quantum advantage. Beyond showcasing a robust communication advantage with a single qubit, our work paves the way for immediate applications in near-term quantum technologies. It provides a semi-device-independent certification scheme for quantum encoding-decoding systems and offers an efficient method for information loading and transmission in quantum networks.

Robust image encryption algorithm based on oscillated substitution and effective confusion module with novel chaining permutation and pixel mutation

To ensure secure network transmission of multimedia content, it is crucial to protect it from unauthorized access. Encryption techniques are de facto protection techniques against hacking attempts. Different methods, including mathematical encoding, neural networks, DNA encoding, and chaos-based techniques, have been developed for this purpose. However, these methods often overlook the impact of input variations, which can enhance encryption robustness by amplifying confusion. We propose an encryption technique that demonstrates how effectively leveraging input variations can enhance the complexity and security of encrypted information. We designed an oscillated substitution technique, a multi-state method that adjusts the substitution pattern based on changes in the key or input. Additionally, chaotic methods are employed to generate noise codes for bit mutation and chaining-based permutation. Experimental results indicate that our technique significantly improves security indicators and outperforms recent encryption methods.

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.