Modern Communication Networks Research Articles

Review of the OSI Model and TCP/IP Protocol Suite on Modern Network Communication

Layered approaches to networking are possible. Network architects categorize protocols in order to simplify their designs. Each layer has its own protocol for talking to the outside world. Every component of the network implements some of the nth layer. Messages are sent back and forth between these parts. Layer n protocol data units [n-PDU] are the official name for these transmissions. All the steps necessary for efficient conversation are covered and these steps are organized into layers for easy comprehension. Layered architecture describes this kind of planning for a communications system. The OSI model is a framework for developing and implementing network-based software applications. It also serves as a basis for the development of new networking protocol hardware, and architectures. This document compares and contrasts the OSI Reference Model seven layers with those of the TCP/IP Model four. There are distinct roles at each successive level. All Internet communication duties may be traced back to the TCP/IP reference model.

Robust Routing Made Easy: Reinforcing Networks Against Non-Benign Faults

With the increasing scale of communication networks, the likelihood of failures grows as well. Since these networks form a critical backbone of our digital society, it is important that they rely on robust routing algorithms which ensure connectivity despite such failures. While most modern communication networks feature robust routing mechanisms, these mechanisms are often fairly complex to design and verify, as they need to account for the effects of failures and rerouting on communication. This paper conceptualizes the design of robust routing mechanisms, with the aim to avoid such complexity. In particular, we showcase simple and generic blackbox transformations that increase resilience of routing against independently distributed failures, which allows to simulate the routing scheme on the original network, even in the presence of non-benign node failures (henceforth called faults). This is attractive as the system specification and routing policy can simply be preserved. We present a scheme for constructing such a reinforced network, given an existing (synchronous) network and a routing scheme. We prove that this algorithm comes with small constant overheads, and only requires a minimal amount of additional node and edge resources; in fact, if the failure probability is smaller than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1/n$</tex-math> </inline-formula> , the algorithm can come without any overhead at all. At the same time, it allows to tolerate a large number of independent random (node) faults, asymptotically almost surely. We complement our analytical results with simulations on different real-world topologies.

Memory-enhanced appearance-motion consistency framework for video anomaly detection

Modern network communication systems extensively utilize video data for various applications, creating a pressing need for efficient Video Anomaly Detection (VAD) mechanisms. The essence of VAD lies in the detection of discrepancies in the appearance-motion dynamics of normal and abnormal events. However, existing methods primarily focus on the isolated analysis of appearance or motion anomalies, thereby overlooking the crucial aspect of appearance-motion consistency semantics. To this end, we introduce a Memory-enhanced Appearance-Motion Consistency (MAMC) framework, which is focused on understanding complex appearance-motion consistency patterns in video data for the discernment of anomalies. The first stage of our model involves the design of an Appearance-Motion Fusion (AMF) module, engineered to generate a robust representation of scene dynamics that captures appearance-motion consistency. This consistency data is then processed through the memory module, augmenting the distinction between normal and anomalous events. Experimental results on three benchmark datasets validate the effectiveness of our approach, which achieves AUCs of 96.7%, 87.6%, and 71.5% on three benchmark datasets (UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets). Additional experiments and visualization analysis confirm the effectiveness of the proposed MAMC framework in anomaly detection scenarios.

Proving Mutual Authentication Property of Industrial Internet of Things Multi-Factor Authentication Protocol Based on Logic of Events

Security protocols are the basis of modern network communication, proving that the security problem of protocols is one of the hot research topics today. The data in industrial IoT are usually transmitted through insecure channels, which brings certain security risks. The Logic of Events is a formal method for proving the security properties of protocols based on event systems. The new theoretical extension is based on the Logic of Events theory, which proposes new event classes Compurte, TimeGap, Construct, and Reconstruct and an axiom AxiomRe and related inference rules for malicious attacks and security privacy issues in emerging protocols, as well as extending the matching descriptions of protocol behaviours in complex cryptographic algorithms and information sharing techniques for applications for the formal analysis of authentication protocols for the Industrial Internet of Things. Finally, formal analysis is carried out using the example of a secure multi-factor authentication protocol for the industrial IoT, which proves the security of the protocol.

Predicting angular-momentum waves based on Yang–Mills equations

As one of the most elegant theories in physics, Yang–Mills (YM) theory not only incorporates Maxwell’s equations unifying electromagnetism, but also underpins the standard model explaining the electroweak and strong interactions in a succinct way. Whereas the highly nonlinear terms in YM equations involving the interactions between potentials and fields retard the resolution for them. In the U(1) case, the solutions of Maxwell’s equations are the electromagnetic waves, which have been applied extensively in the modern communication networks all over the world. Likewise the operator solutions of the YM equations under the assumptions of weak-coupling and zero-coupling predict the SU(2) angular-momentum waves, which is the staple of this work. Such angular-momentum waves are hopefully realized in the experiments through the oscillation of spin angular momentum, such as the “spin Zitterbewegung” of Dirac’s electron.

Quantitative measurement of link failure reaction time for devices with P4-programmable data planes

Quick handling of link failures remains a challenging issue in current communication networks, although it is crucial to many routing algorithms. Link failures are the leading cause of packet losses and delays, therefore, failure recovery is tied to stringent requirements for certain services, such as the sub-50 millisecond completion time for carrier-grade networks, which is sometimes difficult to achieve in traditional routing schemes. For this reason, fast recovery strategies are key pillars of modern communication networks. In this paper, we demonstrate the benefits of the devices with Programmable Data Planes (PDP) for fast reacting to link failures. We first review the link failure detection, reaction and recovery procedures and then we discuss the main fast failure recovery mechanisms employed by different types of devices in current communication networks. In addition, we present a novel method to measure the link failure reaction time of an Intel Tofino switch with PDP, as well as the results obtained when measuring such time using real hardware equipment. Our results show that such hardware devices provide a failure reaction time in the order of microseconds, with an average of 472.88μ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upmu $$\\end{document}s, which poses PDP as a key technology to achieve zero packet loss and zero delay failure recovery.

Application of BIM+IoT technology in the design and operation and maintenance stages of smart buildings

With the continuous emergence of new technologies and the efficient and convenient development of people’s daily life and work, the topic of intelligent building is becoming more and more hot. There are two kinds of data in intelligent building, namely static data and dynamic data. The digital expression of intelligent building is realized through the fusion and interaction of these two kinds of data. The building information model (BIM) stores a large amount of static data, including size, material, color and other data, while the Internet of Things (IoT) collects real-time dynamic data, including temperature, humidity, light and other data, through sensors in every space within the building. The related problems of building data management in the design and operation and maintenance stages mainly include data collection relying on manual work, low data processing efficiency, scattered and disorderly data storage, difficult data interaction, slow speed, and low data visualization. In order to solve these problems, this paper combines the characteristics and advantages of BIM and IoT technology, analyzes the interaction and application value of BIM and IoT technology in the design and operation and maintenance stage of intelligent buildings, and constructs a data management system based on the Internet of things, a 3D visualization technology based on BIM, and an operation and maintenance management platform based on BIM and IoT. The application of BIM and IoT technology in the design and operation and maintenance stage of intelligent buildings has promoted the development of the construction industry. The Internet of Things can provide many functional services, such as real-time monitoring, remote tracking, automatic update, plan management and a series of personalized services and management, to achieve the management, control and detection of various things, and ensure the high-speed and effective operation of the system. The emergence of the building system of modern communication network technology has brought many conveniences to people, and there is still a broad space for development. The combination of new Internet of Things technology and intelligent buildings will certainly be an important development direction.

Optimized Cross-Path Attacks via Adversarial Reconnaissance

While softwarization and virtualization technologies make modern communication networks appear easier to manage, they also introduce highly complex interactions within the networks that can cause unexpected security threats. In this work, we study a particular security threat due to the sharing of links between high-security paths and low-security paths, which enables a new type of DoS attacks, called cross-path attacks, that indirectly attack a set of targeted high-security paths (target paths) by congesting the shared links through a set of attacker-controlled low-security paths (attack paths). While the feasibility of such attacks has been recently demonstrated in the context of SDN, their potential performance impact has not been characterized. To this end, we develop an approach for designing an optimized cross-path attack under a constrained total attack rate, consisting of (i) novel reconnaissance algorithms that can provide consistent estimates of the locations and parameters of the shared links via network tomography, and (ii) efficient optimization methods to design the optimal allocation of attack rate over the attack paths to maximally degrade the performance of the target paths. The proposed attack has achieved a significantly larger performance impact than its non-optimized counterparts in extensive evaluations based on multiple network settings, signaling the importance of addressing such intelligent attacks in network design.

FORMATION OF ENSEMBLES OF SIGNALS WITH TIME DIVISION USING A NEURAL NETWORK

In telecommunications, time-division signals play an extremely important role in the process of information transmission and processing. These signals, which are presented as electromagnetic waves, serve as the main means of transport for the transmission of data from one communication device to another, effectively constituting the circulatory system of modern communication networks. The defining feature that distinguishes these signals is their temporal nature, or more precisely, the changes they undergo over time as they travel over the network. These signals contain a rich spectrum of parameters, covering not only the attributes of amplitude, frequency and phase, but also many other parameters. This article provides a scientific rationale for the feasibility and relevance of researching the process of forming signal ensembles with temporal separation using neural networks. It has been established that one of the key tasks in creating ensembles of complex signals with temporal separation through the application of neural networks is the selection of an optimal neural network architecture. This entails determining the type of neural network that best aligns with a specific research objective. An algorithm for forming signal ensembles with temporal separation using a recurrent neural network (RNN) has been developed. Special attention is devoted to methods for assessing the overall error of signal ensembles with temporal separation using PNN. These analytical methods encompass the utilization of pertinent metrics, the evaluation of which depends on the specific nature of the task, whether it involves regression or classification. The findings of this research will contribute to the further advancement of methods for forming signal ensembles with temporal separation through the utilization of neural networks and enhance their application in contemporary telecommunications systems.

Novel Optical Solitary Wave Structure Solution of Lakshmanan-Porsezian-Daniel Model

The Lakshmanan-Porsezian-Daniel (LPD) model, an extended version of the nonlinear Schrödinger equation, plays a crucial role in comprehending optical solitons—self-reinforcing waves that maintain their form while traveling through a medium. This study employed the G′G2-expansion method to explore the model and present new exact solitary wave solutions with various nonlinearities, including the Kerr, anti-cubic, and parabolic laws. The findings provide solutions for different types of optical solitons, such as bright solitons, dark solitons, rational functions, and periodic solitons. The obtained results offer fresh insights into the complex LPD model, deepening our core understanding of optical soliton dynamics. Importantly, these findings have significant implications for soliton-based applications, specifically in modern communication networks and nonlinear optics, directly impacting optical fiber transmission. Furthermore, the research introduces singular solitons, a unique and highly localized wave phenomenon, through an influential and systematic approach to exact optical solutions for various models. As communication technology and nonlinear optics continue to advance, these findings hold substantial promise for practical applications in the development of cutting-edge optical devices and systems.

Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management

Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each candidate solution. The conventional approach carries out the optimization of resource-allocation parameters for each system configuration, characterized, e.g., by topology and traffic statistics, using global search methods such as Bayesian optimization (BO). These methods tend to require a large number of iterations, and hence a large number of key performance indicator (KPI) evaluations. In this paper, we propose the use of meta-learning to transfer knowledge from data collected from related, but distinct, configurations in order to speed up optimization on new network configurations. Specifically, we combine meta-learning with BO, as well as with multi-armed bandit (MAB) optimization, with the latter having the potential advantage of operating directly on a discrete search space. Furthermore, we introduce novel contextual meta-BO and meta-MAB algorithms, in which transfer of knowledge across configurations occurs at the level of a mapping from graph-based contextual information to resource-allocation parameters. Experiments for the problem of open loop power control (OLPC) parameter optimization for the uplink of multi-cell multi-antenna systems provide insights into the potential benefits of meta-learning and contextual optimization.

Optimization of Delay Using Killer Whale Algorithm (KWA) on NB-IoT

Abstract: NB-IoT is designed to connect IoT devices with low-power, wide-area coverage and efficient costs. Ensuring optimal data transmission delay is a challenge in NB-IoT implementation. Inadequate coverage can hinder IoT adoption. Optimization balances energy saving and delay trade-off. The Killer Whale Algorithm (KWA) optimizes delay by adjusting repetition variables. KWA addresses dimensions, variable limits. Applying KWA in NB-IoT optimizes transmission, enhancing QoS. Optimizing delay involves reducing latency in uplink data transmission using repetition variables. This study applies KWA to optimize NB-IoT delay. Analysis in Table 4 shows non-linear repetition-distance correlation. Interestingly, delay outcomes exhibit a contrasting relationship. Still, delay remains advantageous, remaining under 1 second even at 10 km, specifically 9.2674 ms (0.0092674 seconds). This thesis aims to optimize delay in NB-IoT network transmission using the Killer Whale Algorithm (KWA), crucial for modern communication networks and IoT applications. Leveraging KWA, the research identifies solutions to reduce transmission delay, enhancing efficiency and meeting IoT communication demands for speed and timeliness

Semi-embedded slot waveguide electro-optic modulator.

Electro-optic modulators are essential devices on silicon photonic chips in modern optical communication networks. This paper presents a compact, low-loss electro-optic modulator. The modulation efficiency is greatly improved by embedding the lower half of the slot waveguide into the buried oxide layer and inserting graphene at the junction. The interaction of graphene with an optical field in a waveguide is studied using the finite element method. The functions of phase modulation and absorption modulation are realized by changing the gate voltage to change the chemical potential of graphene. The semi-embedded slot waveguide optical modulator has a length of 50µm. After simulation verification, it can be used as an electro-absorption modulator and can achieve a modulation depth of 26.38dB and an insertion loss of 0.60dB. When used as an electro-refractive modulator, it can be realized with a linear change of phase from zero to π; the total insertion loss is only 0.59dB. The modulator has a modulation bandwidth of 79.6GHz, and the energy consumption as electro-absorption and electro-refraction modulation are 0.51 and 1.92pj/bit, respectively. Compared with common electro-optic modulators, the electro-optic modulator designed in this paper has a higher modulation effect and also takes into account the advantages of low insertion loss and low energy consumption. This research is helpful for the design of higher-performance optical communication network devices.

Enhancing LAN Failure Predictions with Decision Trees and SVMs: Methodology and Implementation

Predicting Local Area Network (LAN) equipment failure is of utmost importance to ensure the uninterrupted operation of modern communication networks. This study explores the use of machine learning algorithms to enhance the accuracy of equipment failure prediction in LAN environments. Using these algorithms to enhance LAN failure predictions involves collecting and analyzing network data, such as packet loss rates and latency, to identify patterns and anomalies. These algorithms can then predict potential LAN failures by recognizing early warning signs and deviations from normal network behavior. By leveraging machine learning, network administrators can proactively address issues, reduce downtime, and improve overall network reliability. In our study, two powerful machine learning algorithms—decision tree and support vector machine (SVM)—are used. To evaluate the effectiveness of the proposed models, a comprehensive dataset comprising various LAN equipment parameters and corresponding failure instances is utilized. The dataset is pre-processed to handle missing values and normalize features, ensuring the algorithms’ optimal performance. Performance metrics, such as accuracy, precision, recall, and F1-score, are employed to assess the predictive capabilities of the models. The excremental results of our study lead to more reliable and stable network operations by allowing early detection of potential issues and preventive maintenance. This leads to reduced downtime, improved network performance, and enhanced overall user satisfaction. They demonstrate the efficacy of both decision tree and SVM algorithms in accurately predicting LAN equipment failure.

Detecting and Real Time Threat Analysis in Smart Grid Networks

The increasing adoption of smart grid systems has revolutionized the power distribution landscape, providing enhanced efficiency, reliability, and sustainability. However, the integration of modern technologies and communication networks has also introduced cybersecurity challenges, exposing the smart grid to potential threats and attacks. To address this critical issue, we propose a novel approach for detecting threats in smart grid systems using a sequential algorithm based on Long Short-Term Memory (LSTM) networks. Our research focuses on leveraging the temporal nature of smart grid data, which is inherently sequential in nature due to the continuous flow of sensor readings and telemetry information. The proposed LSTM-based sequential algorithm is designed to analyze time-series data, allowing it to capture complex patterns and dependencies characteristic of both normal grid behavior and potential security breaches. The methodology involves collecting labeled data from various smart grid sources, including power meters, sensors, communication devices, and control systems. The data undergoes preprocessing to handle missing values, normalization, and feature extraction, enabling effective model training. The LSTM model is then trained on the labeled data, optimizing its architecture and hyperparameters to achieve accurate threat detection. To ensure real-time threat monitoring, the trained model is deployed in the smart grid servers, where it continuously ingests and analyzes incoming data streams. The real-time threat detection system enhances the security and resilience of smart grid networks, safeguarding critical infrastructure and ensuring a stable and reliable power supply. Keywords: Threat detection, Threat analysis, Sequential algorithm, Real time threat monitoring.

Analytical model OFDM-MIMO signal in a radio channel with frequency-time selective fading

One of the main problems in modern wireless electronic communication networks is the transmission and reception of signals in multipath propagation. Due to the imperfect impulse response of the communication channel, frequency-selective fading of the transmitted signal occurs. This can cause waveform distortion and inter-symbol interference, which can seriously degrade the performance of the communication system. Thus, increasing the noise immunity of wireless electronic communication networks in real-world conditions is one of the most important tasks of modern communication technologies. Orthogonal frequency division multiplexing is known for its resistance to multipath fading and is used to reduce the impact of inter-symbol interference. Multi-antenna systems are a key technology for increasing bandwidth and improving communication reliability. The combination of these two technologies in wireless electronic communication networks can reduce the impact of negative phenomena on the transmitted signal in difficult interference conditions. An analytical model of the signal transmitted by a nonstationary frequency-selective radio channel in a wireless electronic communication network with orthogonal frequency multiplexing and a multi-antenna system is proposed. Analytical expressions are presented that allow us to jointly take into account the influence of interchannel and inter-symbol interference (the effect of multipath signal propagation and Doppler shift on the transmitted signal) against the background of fluctuating noise. Taking into account the factors affecting the transmitted signal makes it possible to bring the model closer to the real processes that occur in the vast majority of radio channels of wireless electronic communication networks.

Automated Setup System Security Configuration of Network Equipment

A model of the system for automated setting of network equipment security configuration is proposed. In order to improve the security configuration of network equipment, a set of existing security threats in modern information and communication networks was analyzed. It was determined that with the increase in the level of introduction of information technologies in the life of the individual, society and the state, the level of danger in the cyber space increases. The general trends of ensuring cyber security at all levels are considered. The types of threats to users are systematized and their properties are considered. Based on the results of the study, it was determined that the cloud approach is the most appropriate for infrastructure deployment. Cloud services are considered and approaches to the automation of infrastructure deployment are defined, which provide an opportunity to minimize errors in the configuration of the architecture.

Optimal Device Selection in Federated Learning for Resource-Constrained Edge Networks

Low latency, resource efficiency, and data privacy are some of the crucial requirements in modern communication networks. Federated learning can efficiently address these issues by utilizing the data at the network edge and processing massive amounts of data in parallel at the edge devices, thus ensuring data privacy and low latency. For a large-scale federated learning task spanning many devices, challenges arise due to device heterogeneity, data variability, and limited network resources. Optimal selection of edge devices participating in federated learning is essential to attaining resilient, reliable, and resource-efficient edge networks. In this context, this paper proposes an optimal device selection method to minimize redundant data training and improve network resource utilization without affecting the performance of federated learning over resource-constrained edge networks. The proposed optimal device selection method aims to minimize network resource demands while maximizing data diversity within the aggregated model. The performance of the proposed federated learning framework is evaluated using a publicly available image dataset of handwritten digits, EMNIST (an extended version of the MNIST dataset). Experimental results indicate that the proposed framework can obtain accuracy convergence performance on par with conventional federated learning methods while significantly reducing device usage (up to 50%) and resource utilization (up to 30%) while reaching 99% of achievable accuracy. The proposed method can therefore be effectively applied to resource-constrained edge networks.

Survey on Applications of Machine Learning in Low-Cost Non-Coherent Optical Systems: Potentials, Challenges, and Perspective

Direct Detection (DD) optical performance monitoring (OPM), Modulation Format Identification (MFI), and Baud Rate Identification (BRI) are envisioned as crucial components of future-generation optical networks. They bring to optical nodes and receivers a form of adaptability and intelligent control that are not available in legacy networks. Both are critical to managing the increasing data demands and data diversity in modern and future communication networks (e.g., 5G and 6G), for which optical networks are the backbone. Machine learning (ML) has been playing a growing role in enabling the sought-after adaptability and intelligent control, and thus, many OPM, MFI, and BRI solutions are being developed with ML algorithms at their core. This paper presents a comprehensive survey of the available ML-based solutions for OPM, MFI, and BFI in non-coherent optical networks. The survey is conducted from a machine learning perspective with an eye on the following aspects: (i) what machine learning paradigms have been followed; (ii) what learning algorithms are used to develop DD solutions; and (iii) what types of DD monitoring tasks have been commonly defined and addressed. The paper surveys the most widely used features and ML-based solutions that have been considered in DD optical communication systems. This results in a few observations, insights, and lessons. It highlights some issues regarding the ML development procedure, the dataset construction and training process, and the solution benchmarking dataset. Based on those observations, the paper shares a few insights and lessons that could help guide future research.

(180) Effects of Electromagnetic Radiation Emittted by Smartphones on Sperm Quality: An in Vitro Study

Abstract Introduction In modern times, the widespread use of mobile phones is a notable source of radiofrequency–electromagnetic radiation (RF-EMR) with a potential impact on human health. The advancements in technologies such as Bluetooth earphones have increased the time the cell phone resides inside the pant pockets of men, in close contact with their testicles. As a result, it makes the testicles more prone to RF-EMR exposure for longer. Recent literature has shown that such long exposure is likely to cause damage to the sperm quality and functioning in several ways, including the increase in local body temperature, high oxidative stress, reactive oxygen species, and free radical formation (1,2). However, the causing effect of modern cell phones and wireless communication networks (4G and 5G) on sperm quality has not been explored. Objective The aim of our study was to investigate the effect of cell phone radiations emitted through communication technologies - 4G, 5G and WIFI on sperm motility and viability using an in-vitro approach and further try to shed light if physical barriers or distance ease these effects. Methods Normozoospermic men (n=18) aged 25-35 were recruited whose samples after semen analysis were divided into three parts i) in close contact with sample without cover ii) mobile phone covered with physical barrier, and iii) phone kept at distance from the samples. Standard semen analysis was carried according to WHO laboratory manual for the examination and processing of human semen, 5th ed. Voice calls over the internet were made using 4G, 5G, or WIFI and using these, RF-EMF exposure was given for six hours which was measured by RF-EMR detection meter. In addition, to check for the heating effect, samples were kept inside the incubator at 37°C for samples exposed to WIFI effects. Continuous variables were presented as medians and interquartile ranges [25th - 75th], and comparison between groups was performed using the U Mann Whitney test. SPSS v.28 was used for statistical analysis. Results A decrease in both parameters under study was evident with WIFI (Motility (50% vs. 38%, p = 0.024; viability 60% vs 47%, p = 0.003), while no decrease was noted with 4G or 5G RF-EMR. Samples connected with WIFI were warmer but samples inside the incubator, when observed had decreased progressive motility (40% vs 24%, p= 0.04). On the other hand, the effect of RF-EMR by WIFI on smartphone was mitigated both for sperm motility and viability (p = 0.01, p = 0.01), n = 18) with the use of a case and on increasing the distance of the contact respectively. Conclusions The result from the study suggests that the radiation emitted by WIFI do have an adverse impact on the motility and viability of sperm. It seems plausible that the effect maybe a result of excess heat generation which was most evident with mobile phones connected to WIFI. However, further studies utilizing different phones and different time periods of exposure are warranted to effectively conclude the findings Disclosure No