Network Transmission Research Articles

Demonstration of a three-node wavelength division multiplexed hybrid quantum-classical network through multicore fiber

This paper presents the practical development of a hybrid quantum-classical network through multiple links of four- and seven-core industrial jacketed multicore fiber. The network utilizes dense wavelength division multiplexing to propagate the C-band quantum and classical information in the same core, making full use of the unidirectional nature of quantum key distribution. Total network transmission of 1.16 Tbps is achieved with a total network secret key rate of approximately 7.4 kbps through a combination of 1 and 2 km links of multicore fiber. The deployment configurations presented are independent of the classical modulation format, and the maximal transmission rate for an operational hybrid network is found to be dependent only on classical optical power occupying the quantum channel wavelength. A model was developed to estimate the impact classical optical channels will have on coexisting quantum channels, which may allow engineers to quickly validate hybrid network designs.

All-optical perception based on partially coherent optical neural networks

In the field of image processing, optical neural networks offer advantages such as high speed, high throughput, and low energy consumption. However, most existing coherent optical neural networks (CONN) rely on coherent light sources to establish transmission models. The use of laser inputs and electro-optic modulation devices at the front end of these neural networks diminishes their computational capability and energy efficiency, thereby limiting their practical applications in object detection tasks. This paper proposes a partially coherent optical neural network (PCONN) transmission model based on mutual intensity modulation. This model does not depend on coherent light source inputs or active electro-optic modulation devices, allowing it to directly compute and infer using natural light after simple filtering, thus achieving full optical perception from light signal acquisition to computation and inference. Simulation results indicate that the model achieves a highest classification accuracy of 96.80% and 86.77% on the MNIST and Fashion-MNIST datasets, respectively. In a binary classification simulation test based on the ISDD segmentation dataset, the model attained an accuracy of 94.69%. It is estimated that this system’s computational inference speed for object detection tasks is 100 times faster than that of traditional CONN, with energy efficiency approximately 50 times greater. In summary, our proposed PCONN model addresses the limitations of conventional optical neural networks in coherent light environments and is anticipated to find applications in practical object detection scenarios.

Against Mobile Collusive Eavesdroppers: Cooperative Secure Transmission and Computation in UAV-Assisted MEC Networks

Against Mobile Collusive Eavesdroppers: Cooperative Secure Transmission and Computation in UAV-Assisted MEC Networks

آلية إدارة مفتاح RSA المحسّنة لحماية حزم التحكم في شبكات تبديل الاندفاع البصري

Optical Burst Switch (OBS) network is a typical technology currently proposed, as an infrastructure to deal with most highly demanded communication services. OBS architecture consists of Data Burst (DB) as a payload and Burst Header Packet (BHP) as a control packet. BHP carries important information about path reservation and its corresponding DB. However, the information security (Privacy, Reliability, Confidentiality, Integrity, Availability, Authentication, and Authorization) of OBS network communication has been represented as the current issues, which affect the network performance in terms of data loss and data transmission delay. This paper focuses on security weaknesses of BHP transmission in OBS network against Data Burst Redirection (DBR) Attack. The current paper was conducted to develop a protection mechanism to ensure the confidentiality and authentication of BHP. In OBS, RSA public-key encryption algorithm has been enhanced and integrated. Moreover, the Self-Controlling key distribution technique has been implemented to ensure a high security level of key transmission between each pair of OBS nodes. Three different OBS environments have been designed and implemented. These environments were established on the bases of three different concepts; OBS Topology without Security Measures and without Security Attacks, OBS Topology under Security Attacks without Security Measures, and OBS Topology under Security Attacks with Security Measures. The obtained results are based on Burst Loss Ratio, Throughputs, and Average Delay Ratio. Such a result has successfully proved the trustworthiness and efficiency of Control Packet Protection Technique (CPPT-OBS) to prevent DBR attack. Keywords: OBS , DB, BHP, DBR, RSA, CPPT

Particle swarm optimization for beamforming design in a cognitive radio

Beamforming is essential for improving transmission in wireless sensor networks (WSNs), particularly in cognitive radio networks (CRNs) with several secondary users (SU) equipped with transmitting antennas. Optimizing beamforming while minimizing interference with primary users (PU) is of great interest. This study proposes an improved particle swarm optimization (PSO) algorithm to enhance beamforming performance. This approach aims to maximize the power of the beam directed to the SU receiver while controlling interference in the PU protection region. The results show that this algorithm constantly improves beam focus and signal-to-noise ratio to effectively optimize beamforming. Firstly, beam focusing becomes narrower as the number of antenna elements increases, generating optimal transmission conditions. Secondly, the algorithm achieves a considerable improvement in signal-to-noise ratio as the number of antenna elements increases. Furthermore, optimization performance improves as the number of antenna elements increases, as shown by the best fitness values. The simulations also illustrate the performance of the proposed method.

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

Centralized Fusion Estimation in Networked Systems: Addressing Deception Attacks and Packet Dropouts with a Zero-Order Hold Approach

Article Centralized Fusion Estimation in Networked Systems: Addressing Deception Attacks and Packet Dropouts with a Zero-Order Hold Approach Caballero-Águila Raquel 1,*, and Linares-Pérez Josefa 2 1 Departamento de Estadística e I.O., Universidad de Jaén, Campus Las Lagunillas, 23071 Jaén, Spain 2 Departamento de Estadística e I.O., Universidad de Granada, Campus Fuentenueva, 18071 Granada, Spain * Correspondence: raguila@ujaen.es Received: 26 August 2024 Accepted: 11 October 2024 Published: 24 December 2024 Abstract: This paper addresses the centralized fusion estimation problem in networked systems with stochastic uncertainties characterized by random parameter matrices together with multiplicative and additive noises. To reflect real-world engineering situations, it is further assumed that the network transmissions are simultaneously subject to random packet dropouts and deception attacks which randomly alter real measurements by replacing them with noises. A novel approach is proposed that avoids the need for a specific state equation, relying instead only on the mean and covariance functions of the processes involved. The additive noises in the sensor measurements are considered to be time-correlated and packet dropouts are managed through a zero-order hold compensation strategy that attenuates the effect of data loss on the estimation process. On the basis of the available measurement information, recursive fusion filtering and smoothing algorithms are developed using an innovation-based methodology. The proposed approach is validated by numerical simulations, demonstrating its feasibility and correctness. Comparative results show the superior performance of the proposed fusion estimation scheme over existing filters in the literature, highlighting its effectiveness in mitigating the impact of deception attacks and packet dropouts in networked systems.

OPTIMIZATION OF THE SCATTERING MATRIX OF FREQUENCY -DETUNED ADD/DROP FILTERS FOR MULTIPLEXERS BUILT ON SYSTEMS OF OPTICAL DIELECTRIC RESONATORS WITH WHISPERING GALLERY MODES

Background. A significant increase in the speed of information transmission in fiber-optic communication networks is determined by the strict requirements imposed on the elemental base of receiving and transmitting devices. One of the important components of such devices is diplexers built on different notch and bandpass filters, which are often performed on dielectric resonators (DR) with whispering gallery mode (WGM) oscillations. Calculation and optimization of the parameters of multilink filters and diplexers built on DR is impossible without further development of the theory of their design. The development of the theory of diplexers today is often based on electrodynamic modelling, which is built on preliminary calculations of filter scattering parameters in various transmission lines with a complex topology of connections. Objective. The aim of this study is to construct electrodynamics' models of wave scattering on complex multi-connected DR structures with degenerate types of WGM natural oscillations, which contain several frequency-detuned bandpass or notch filters located in one or several transmission lines. To solve the scattering problem, we proposed a system of equations derived from perturbation theory for Maxwell's equations [24], modified to describe the DR fields with whispering gallery oscillations in transmission lines. The construction of such solutions is complicated by the fact that each of the partial optical resonators of the filters, in the case of excitation of azimuthally inhomogeneous WGM in it, has, as a rule, two degenerate types of natural oscillations. Moreover, each such type of oscillation is characterized by different complex values of the coupling coefficients with the line, open space, and also with other resonators. The latter circumstance leads to the fact that the systems of equations for the amplitudes of natural and forced oscillations of resonators are doubled. The signs of the coefficients of mutual coupling are usually different, this leads to the fact that the behaviour of DRs in the system becomes more difficult to predict, therefore the second objective of this work is to study the patterns of the scattering characteristics of line waves on systems of frequency-detuned DRs with degenerate types of oscillations with the possibility of constructing multiplexers for modern optical communication systems. Methods. The methods of technical electrodynamics are used for calculating and analysing scattering matrices. The end result is obtaining new analytical equations and formulas for new complex structures of coupled dielectric resonators with whispering gallery oscillations in the different transmission lines. Results. Frequency dependences of scattering matrices on complex structures of frequency-detuned filters built on coupled optical DR with whispering gallery oscillations located in one or more transmission lines are considered. Electromagnetic models of bandstop and add/drop filters are built, consisting of various optical resonators with degenerate types of natural oscillations. General analytical expressions of vector coefficients and matrices for building systems of equations describing coupled oscillations, as well as forced oscillations of resonators in cases of their use in optical filters with serial and parallel arrangement, are given. General solutions for the scattering field on frequency-detuned resonators located in different optical transmission lines have been found. Examples of calculating frequency dependences of the scattering matrix for the most interesting structures consisting of two different frequency-detuned filters are given. The frequency scattering characteristics of several types of devices are calculated, which consist of two notch filters with different blocking bands, made on detuned DRs in one transmission line. The possibilities of the earlier proposed method are demonstrated in the example of calculating the scattering characteristics of known types of diplexers built on the basis of the use of two add/drop filters with different frequency bandwidths. The frequency dependences of the scattering matrices of the two most common types of devices, located in parallel between two or four regular transmission lines of add/drop filters with different numbers of resonators; laterally coupled add/drop filters; parallel-coupled add/drop filters; twisted double-channel side-coupled integrated space sequence of resonators (SCISSORs), were studied. New scattering models of diplexers consisting of optical resonators of different connection topologies were built: serial, parallel with the use of laterally coupled add/drop filters; parallel-coupled add/drop filters; twisted double-channel SCISSORs. The frequency dependences scattering matrix of the diplexers were also calculated. The characteristics of the designed diplexers obtained from the examined filters of different types were compared. Conclusions. The theory of diplexer construction, which takes place on the simultaneous optimization of the scattering matrix of several filters built on complex systems of dielectric resonators with degenerate types of whispering gallery oscillations is expanded. A calculation method was developed and new analytical relations were found for the scattering matrix coefficients of optical diplexers of various types.

Medical images transmission over MC-MC-CDMA based MRC detection

The increasing demand for high data rate multimedia transmission in wireless networks necessitates robust and spectrally efficient technologies. Multi-code multi-carrier code division multiple access (MC-MC-CDMA) presents a promising solution by combining the advantages of multi-code CDMA and multi-carrier CDMA techniques. This paper comprehensively investigates the MC-MC-CDMA system for medical image transmission applications. A detailed system model is developed, including the transmitter, channel, and maximum ratio combining (MRC) receiver. Analytical expressions for the bit error rate (BER) performance are derived, and comparative studies demonstrate the superior BER performance of MC-MC-CDMA over multi-carrier CDMA and multi-code CDMA systems under varying system parameters. The highlight of this work is the evaluation of the MC-MC-CDMA system for transmitting medical images using MRC detection. Extensive simulations are conducted to analyze the peak signal-to-noise ratio (PSNR) as a measure of received image quality under different signal-to-noise ratio (SNR) conditions. The results reveal that the proposed MC-MC-CDMA system with MRC detection achieves significantly improved PSNR performance compared to equal gain combining (EGC) receivers, enabling reliable and high-quality medical image transmission. The findings underscore the potential of the MC-MC-CDMA system for robust and efficient multimedia data delivery in future wireless healthcare and telemedicine applications.

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.

Performance Analysis of NSUM Estimators in Social-Network Topologies

The Network Scale-up Methods (NSUM) are methods to estimate unknown populations based on indirect surveys in which the participants provide information about aggregated data of their acquaintances. This preserves the privacy and may lead to higher participation. During the last thirty years, new NSUM estimators have emerged. However, conditions related to the design of the experiments and the robustness of the estimators have not been studied in depth, especially in a realistic simulation environment. This study aims to compare nine NSUM estimators under relevant conditions in the literature through simulation experiments. We have analyzed how the NSUM is affected by the network topology, transmission and recall errors, the distribution of the unknown subpopulation, the number and sizes of subpopulations, and sample size. This article shows that some NSUM estimators barely used are better and more robust to some conditions, especially when the network is scale-free or under barrier effects. In addition, some methods are very sensitive to recall errors. In terms of the subpopulations configuration, we observe that the number of known subpopulations usually employed is quite large and that the most common NSUM is robust to the number and sizes of the subpopulations.

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.

RSMA-Enhanced Secure Transmission in IRS-Assisted Networks Against Internal and External Eavesdroppers

RSMA-Enhanced Secure Transmission in IRS-Assisted Networks Against Internal and External Eavesdroppers

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

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

The network traffic classification is essential in identifying and categorizing the network traffic data packets in the network transmission. The network traffic transmission is effectively managed and prioritized using Quality of Service (QoS). The Differential Services Code Point within the Differentiated Service (DiffServ) field is primarily used inside the Layer 3 encapsulated network IP packets. Since the user generated data is growing rapidly with variety in data such as, streaming, VoIP, online gaming etc. There is a need to have effective prioritization and classification of IP packets for routers to enable the forwarding of such packets including packets having critical data efficiently and with a lower drop rate. This study develops and analyze using neural network-based models for effective classification of data packets using the DSCP header field. The data was gathered using real-time packet capturing tools which were then processed and moved with model development using different deep learning algorithms such as, LSTM, MLP, RNN and Autoencoders. Most of the algorithms got promising results and classify packets based on DSCP accurately. This study will help to advance network packet classification within the network transmission by network administrators to monitor network more efficiently and to avoid malicious activities within the network environment.