Network Reliability Research Articles

Production overrun optimization considering supply chain network reliability

Production overrun is a common practice in manufacturing to meet the demand by increasing the number of qualified products and compensating for manufacturing defects. While production overrun can improve Supply Chain Network Reliability (SCNR), it leads to higher material costs. In this paper, a model is proposed to evaluate SCNR by incorporating the reliabilities of inbound logistics, operations, and outbound logistics. Based on the proposed SCNR model, we study the optimal production overrun of each manufacturing site in a supply chain network and identify the production plan that satisfies the reliability requirement with minimum total production overrun penalty. By analyzing the monotonicity of the objective and constraint functions of the formulated problem, an algorithm based on linearization is developed to solve this optimization problem. Numerical examples across various scales are presented to illustrate the developed model and method. The impact of the penalty coefficient of the production overrun on the result is investigated. The results from the numerical examples provide managerial insights on allocating resources in the entire supply chain network and improving the supply chain reliability and competitiveness.

Energy-efficient and Fault-Tolerant Routing Mechanism for WSN using Optimizer based Deep Learning Model

Fault tolerance is the network's capacity to continue operating normally in the event of sensor failure. Sensor nodes in wireless sensor networks (WSNs) may fail due to various reasons, such as energy depletion or environmental damage. Sensor battery drain is the leading cause of failure in WSNs, making energy-saving crucial to extending sensor lifespan. Fault-tolerant protocols use fault recovery methods to ensure network reliability and resilience. Many issues can affect a network, such as communication module breakdown, battery drain, or changes in network architecture. Our proposed FT-RR protocol is a WSN routing protocol that is both reliable and fault-tolerant; it attempts to prevent errors by anticipating them. FT-RR uses Bernoulli's rule to find trustworthy nodes and then uses those pathways to route data to the base station as efficiently as possible. When CHs have greater energy, they construct these pathways. Based on the simulation findings, our approach outperforms the other protocols concerning the rate of loss of packet, end-to-end latency, and network lifespan.

Generalizable 5G RAN/MEC Slicing and Admission Control for Reliable Network Operation

Generalizable 5G RAN/MEC Slicing and Admission Control for Reliable Network Operation

Advancements in Wireless Communication Technology: A Comprehensive Analysis of 4G to 7G Systems

This paper presents a comprehensive analysis of the advancements in wireless communication technology from 4G to 7G systems, focusing on critical aspects such as network architecture, spectrum allocation, data rates, security, and coverage. The evolution of wireless communication technology has been remarkable, with each generation introducing significant improvements to meet the growing demands of users and applications in an increasingly connected world. From the early adoption of 4G technology to the anticipated rise of 7G systems, sweeping changes have reshaped the landscape of wireless communications and changed the way we communicate, work and interact with our surroundings we are surrounded by communication. These changes signal a shift towards more efficient, high-speed and high-capacity systems designed to meet the needs of a variety of industries. Furthermore, the need to strengthen security measures and expand network services highlights the importance of ensuring reliability and security in networks in the face of competition and threats Continued research and innovation are essential and for further developments in this area and to realize and enable new work capabilities and services to benefit people, businesses and businesses This article examines the proposed mobile communication features from 4G to 7G in. Each generation brings unique spectrum allocation, usage and operational challenges. Each generation presents unique spectrum allocation, utilization, and efficiency challenges. Furthermore, this paper investigates network architecture aspects, data rates, and throughput, emphasizing latency and reliability improvements. Advancements in spectral efficiency, MIMO scheme usage, cell size, used frequency bands, and coverage enhancement strategies are considered. Additionally, the integration of energy efficiency with economic and social sustainability is analyzed. Security challenges, including privacy protection, new services, access control models, and security monitoring, are classified for each generation. One of the main objectives of this study is to identify the primary applications, impact areas, and overall impact of 4G to 7G technologies.

A Framework for Mitigating Gray Hole Attack On Mobile Ad Hoc Networks

posing a significant challenge to network performance and reliability. In a gray hole attack, a malicious node selectively drops packets while forwarding others, leading to increased packet loss and latency. This paper presents a novel Gray Hole Prevention algorithm designed to detect and mitigate the effects of such attacks, thereby enhancing the security and efficiency of MANETs. The proposed algorithm integrates existing techniques, specifically the Secure Detection Prevention and Elimination Gray Hole (SDPEGH) approach, with proactive measures to improve detection accuracy and minimize the risk of blacklisting legitimate nodes. Extensive simulations were conducted using the NS-2 simulation tool, comparing the performance of the proposed algorithm against traditional methods. The results demonstrate a significant improvement in network performance, with the GRAY-HP algorithm achieving a PDR of 92%, compared to 78% for the SDPEGH algorithm, representing an increase of 18%. Additionally, the throughput improved by 25%, reaching 85 kbps, while the energy consumption was reduced by 15%, indicating a more efficient use of resources. The routing overhead was also minimized by 20%, showcasing the algorithm's effectiveness in maintaining network stability. This research contributes to the field of network security by providing a robust solution to gray hole attacks, thereby enhancing the reliability and performance of MANETs. The findings underscore the importance of proactive security measures in dynamic and decentralized environments, paving the way for future advancements in secure communication protocols for mobile networks.

Enhancing Network Management Practices in East Africa: A Comprehensive Review

Network management is an integral component of the region’s rapid urbanisation, huge population growth, and technological advancements in East Africa. However, the smooth operation of East African networks is hindered by inadequate infrastructure, complex rules, and cyber-security issues. Both conventional and contemporary technologies are included in the regional network architecture, with mobile networks and fibre optic cables predominating. Among the difficulties are issues with spectrum distribution and regulatory discrepancies, and network management methods differ greatly between carriers. Strategies such as Kenya’s National Optic Fibre Backbone Infrastructure and Rwanda’s Smart Kigali programme are successful initiatives that demonstrate effective ways to develop infrastructure and urban management. Particularly inventive models such as Uganda’s Village Phone programme and Kenya’s M-PESA platform create digital inclusion and economic power. Public-private partnerships facilitate infrastructure development and technology adoption. Network management strategies for Eastern Africa could include the development of rural connectivity projects, policy change for regulatory harmonisation, the use of advanced technologies, capacity-building programmes, and cross-border interactions. This review examines the situation of mobile networks, internet connections, and data centres, as well as other telecommunication network infrastructure, focusing on their existing strong points, weaknesses, and possibilities for further enhancement. Analysing systems of regulations: We used all recently published material (2004–2014) from various highly reliable databases. The findings suggest that adequate measures of network management are required to propel economic growth, digital inclusion, and deeper regional cooperation in East Africa. East African nations may utilise several strategies such as infrastructure development, legislative reforms, technology adoption, capacity building, and collaboration to produce reliable and inclusive communication networks, which will contribute to sustainable development. Keywords: Enhancing, Network, Management, Communication, Cybersecurity, Innovation, East Africa

Impact of atmospheric conditions on the flash-over voltage of the transmission line insulators using central composite design

The insulators of overhead power lines play a crucial role in maintaining the reliability of transmission and distribution networks. Because they are exposed to harsh and dynamic environmental conditions, it is essential to investigate the impact of environmental parameters such as pollution, inclined angle with the cross arm, and temperature on the dielectric performance of the insulators of overhead lines. Conventionally, the effect of such parameters can be investigated through experimental measurements of the insulator flashover voltage. However, this approach is costly and time-consuming and calls for the isolation of the lines to conduct the test, causing interruption to the entire grid. As such, there is an essential need to develop a new methodology to quantify the flashover voltage of overhead insulators operating under various environmental conditions, which is the main aim of this paper. The Central Composite Design is employed to develop a mathematical correlation between the insulator flash over voltage as a dependent variable and three environmental parameters: pollution level, inclined angle, and temperature as independent variables. The robustness of the developed equation is validated through extensive experimental measurements of the insulator’s flash overvoltage under various conditions. Results reveal a good agreement between the actual and predicted flashover voltage using the developed correlation, as the absolute error for all investigated samples is less than 6%.

A Network Reliability Analysis Method for Complex Real-Time Systems: Case Studies in Railway and Maritime Systems

A Network Reliability Analysis Method for Complex Real-Time Systems: Case Studies in Railway and Maritime Systems

Network Intrusion Detection based on LSTM

Network intrusion detection, as an important means of ensuring daily network security, its accuracy and response speed are crucial for defending against network attacks. This article explores and implements deep learning based network intrusion detection techniques, particularly the application of Long Short Term Memory (LSTM) networks in detecting network intrusion behavior. The aim is to solve the problems of gradient vanishing and exploding in traditional RNNs, improve the emergency response capability of network systems, and enhance the reliability and security of networks. The study used the KDD99 dataset to demonstrate the effectiveness of the LSTM model in network intrusion detection. The experimental results show that the constructed LSTM model achieves an accuracy of 88.01% in network intrusion detection tasks, demonstrating high accuracy and feasibility.

Trust and Affinity Based Clustering for Deterministic Multicast Routing Using Honey Badger Algorithm

Wireless Ad hoc networks (WANETs) and mobile ad hoc networks (MANETs) are forms of wireless networks with no central controller. In contrast to traditional networks based on routers and access points, ad hoc networks do not rely on pre-existing infrastructure. Recently, multicast routing protocols in MANETs have effectively managed group communications. The multicast routing protocol can greatly increase the reliability of the MANET network. This protocol aims to minimize the energy consumption (EC) and network lifetime of the MANET to address these issues. This paper presents a novel Affinity based Clustering for Deterministic Multicast Routing (ACD-MR). The overall process is carried out in three stages: cluster head selection (CHs), trust management, and routing. In the initial phase, the cluster head (CH) is selected and utilizes affinity propagation based on node degree, distance, energy, and lifetime. In the second phase, the data are encrypted using the enhanced AES algorithm, and the trust value of each node is calculated employing direct and indirect trust. In the third phase, the Honey Badger algorithm is employed to select an optimal route. The proposed ACD-MR model’s performance is calculated utilizing various parameters, including the network lifetime, average end-to-end delay, EC, packet delivery ratio (PDR), and detection ratio. The proposed model increased the network lifetime by 17.24%, 19.45%, and 22.34% better than MLLR, CS-MAODV, and DQN, respectively.

A Novel Hybrid FSO-mm Wave System for Enhanced Mobile Network Capacity and Reliability

The ever-growing demand for high-bandwidth communication in mobile networks necessitates innovative solutions that provide exceptional capacity and reliability. This paper introduces a novel dual hop hybrid system model that leverages the complementary strengths of free space optics (FSO) and millimeter-wave (mm W) communication, while incorporating a direct mm W link for added robustness. This paper analyzes the comprehensive performance of the proposed hybrid system using a single threshold selection combining. We consider a scenario where the channel state for the FSO link adheres to a Log-Normal distribution under conditions of weak turbulence. For the millimeter wave link, we assume it follows a Nakagami-m distribution, which encompasses a broad range of commonly encountered radio frequency (RF) fading distributions. The proposed system's performance is analyzed by deriving analytical expressions for average bit error rate (ABER) and outage probability (OP). The theoretical results of the proposed system, which are verified using Monte-Carlo simulations, unveil that under turbulence and adverse weather conditions, the performance of the proposed system over a standalone FSO system is improved significantly due to mm W backup link.

Enhancing Network Fault Detection with Precision Predictive AI

Traditional methods for managing and predicting faults must be revised in today's complex network landscape. Predictive Artificial Intelligence (AI) offers a proactive solution, using advanced algorithms and machine learning to analyze vast data, detect patterns, and prevent issues before they escalate. This approach significantly enhances network reliability, reduces downtime, improves operational efficiency, and has transformative potential in network management. This white paper explores this potential, providing real-world examples and integration strategies. We also discuss its benefits and challenges, highlighting its promise for ensuring stable and resilient network operations.

Protecting unauthenticated messages in LTE/5G mobile networks: A two-level Hierarchical Identity-Based Signature (HIBS) solution

As an essential public infrastructure, the security and reliability of mobile networks have a profound impact on people’s production and life. Although the security of LTE/5G networks has been improved a lot with the evolution of standards, there are still some unprotected messages being transmitted between the cellular network and device due to the symmetric key-based security architecture and the trade-off between security and other criteria like network availability. By exploiting these messages, various security attacks have been proposed and demonstrated against commercial mobile networks and devices in existing literature, such as user location tracking, bidding-down, and DoS attacks. To address this security issue, in this paper, we aim to protect these unauthenticated messages in mobile networks using digital signatures. Based on the idea of Hierarchical Identity-Based Signature (HIBS) in existing work, we analyse and design a two-level HIBS solution in detail in terms of different aspects such as keys generation and provisioning procedures, replay mitigation, and cell selection. Unlike previous work, our proposed solution also supports the protection of individual vulnerable RRC and NAS layer signalling in addition to authenticating the base station. We evaluated the efficiency and feasibility of several existing HIBS schemes and implemented the most efficient one in the 5G standalone network setup using open-source software. The implementation results further proved the feasibility of the solution in practice.

Hybrid optimization-based secured routing in mobile ad-hoc network

Mobile Ad hoc Networks (MANETs) pose significant routing challenges due to their decentralized and dynamic nature. Node mobility results in frequent changes in network topology, leading to unsFIG connectivity and link quality. Traditional RPs designed for static networks are inadequate for MANETs. To deal with these issues, a secure routing approach is proposed using the Red Panda-Lyrebrid Optimization (RePLO) algorithm, which combines the advantages of the Red Panda Optimization (RPO) and Lyrebird Optimization Algorithm (LOA) algorithms. The proposed approach consists of five steps: (i) configuring the system model, (ii) developing the energy model, (iii) creating the mobility model, (iv) selecting cluster heads using the RePLO algorithm, and (v) routing using the RePLO algorithm. The RePLO algorithm optimizes cluster head selection and routing while considering specific constraints such as delay, distance, energy, & security for Cluster Head (CH) selection, and link quality and enhanced trust for routing optimization. The effectiveness of the proposed approach is evaluated using various metrics to demonstrate its efficiency in MANET routing. By integrating multiple optimization techniques and considering critical constraints, the RePLO algorithm offers a systematic and secure solution for MANET routing. The evaluation results confirm the efficacy of the proposed approach in improving network performance, reliability, and security. Overall, the RePLO algorithm presents a promising approach to tackle the routing issues inherent in MANETs, paving the way for more robust and efficient communication in mobile ad hoc networks.

Information protection and cyber security in the public and financial sectors

The public sector faces increasing challenges in the field of cyber security and information protection, stimulated by the rapid development of digital technologies. A significant increase in the need for reliable protection of state data and information systems requires state institutions to implement complex information protection measures. The purpose of the study is to analyze modern approaches and technologies in the field of cyber security and the use of encryption, cryptography, and the use of cloud technologies that can be used in the public sector for effective information protection. The research methodology involves consideration of strategies that allow to increase the level of security and ensure the protection of critical infrastructure. The results of the study emphasize the need to adapt to the rapidly changing digital environment and involve modern technological solutions to create a reliable information network system in the public sector. The findings of the study draw attention to the importance of global cooperation and increased investment in the field of countering digital attacks, demonstrating examples of effective international cooperation and support in this direction. The need for the integration of international standards and practices in order to create a global digital space has been revealed. A promising area of research is the analysis of the impact of the latest technologies on data storage strategies, which contributes to the identification of ways for further innovations in the industry and the creation of flexible information network systems capable of effectively responding to changing conditions and challenges of modern cyberspace.

Enhanced seagull optimization for enhanced accuracy in CUDA-accelerated Levenberg–Marquardt backpropagation neural networks for earthquake forecasting

Hyperparameter tuning is crucial for enhancing the accuracy and reliability of artificial neural networks (ANNs). This study presents an optimization of the Levenberg–Marquardt backpropagation neural network (LM-BPNN) by integrating an improved seagull optimization algorithm (ISOA). The proposed ISOA-LM-BPNN model is designed to forecast earthquakes in the Caribbean region. The study further explores the impact of data and model parallelism, revealing that hybrid parallelism effectively mitigates the limitations of both. This leads to substantial gains in throughput and overall performance. To address computational demands, this model leverages the compute unified device architecture (CUDA) framework, enabling hybrid parallelism on graphics processing units (GPUs). This approach significantly enhances the model’s computational speed. The experimental results demonstrate that the ISOA-LM-BPNN model achieves a 20% improvement in accuracy compared to four baseline algorithms across three diverse datasets. The integration of ISOA with LM-BPNN refines the neural network’s hyperparameters, leading to more precise earthquake predictions. Additionally, the model’s computational efficiency is evidenced by a 56% speed increase when utilizing a single GPU, and an even greater acceleration with dual GPUs connected via NVLink compared to traditional CPU-based computations. The findings underscore the potential of ISOA-LM-BPNN as a robust tool for earthquake forecasting, combining high accuracy with enhanced computational speed, making it suitable for real-time applications in seismic monitoring and early warning systems.

Estimation of the Effect of Electric Vehicles on the Aging of Distribution Transformers Using Fuzzy Logic

Depending on industrialization and technological advancements worldwide, the demand for electrical energy, recognized as clean and dependable energy, is on the rise. Presently, electric energy consumption has notably increased alongside the rise in Electric Vehicles (EVs). The surge in EVs necessitates a thorough examination of the situation, anticipating the widespread adoption of Electric Vehicle Fast Charging Stations (EVFCS) in the near future and the subsequent escalation of their adverse impact on the grid. To mitigate these negative effects on the grid, proactive measures are essential. EVs function as capacitive loads due to their battery composition, and the harmonics produced during their grid connection detrimentally affect the quality of grid electricity, leading to constraints. Furthermore, the escalating EVFCS loads resulting from the rapid growth in EV numbers distribute the burden on distribution networks, posing a threat to network adequacy and reliability. Therefore, integrating EVFCS with distribution and generation units to minimize overloading, additional losses, and voltage fluctuations in the grid will enhance the efficiency of both systems. In addition, each EVFCS is only connected to the distribution transformer assigned to it or to the distribution transformers considered suitable in the city. Depending on the current drawn by one or more EVFCS linked to the feeder of each transformer, it can lead to overloading in transformers and chemical changes in windings and oils, resulting in the aging of transformers. In this context, a FL-based estimation is conducted to assess the impact of EVs' charging loads on transformer aging. The FL method utilizes transformer current load, EVFCS load, transformer temperature, and harmonic power quality data. The data utilized are derived from statistical information about a local distribution network and measured values from a feeder, and the aging effects on EVFCS distribution transformers are examined.

ANALIZA STATYSTYK SIECI KOMPUTEROWYCH W CELU IDENTYFIKACJI PRZEPŁYWÓW INFORMACJI ZAKŁÓCAJĄCYCH STABILNOŚĆ W WOJSKOWYCH SIECIACH LOKALNYCH

The increasing complexity and scope of military computer networks necessitate robust methods to ensure network stability and security. This study presents a comprehensive analysis of computer network statistics in military local networks to develop a method for detecting information flows that disrupt stability. By leveraging advanced statistical techniques and machine learning algorithms, this research aims to enhance the cybersecurity posture of military local networks globally. Military networks are vital for communication, data exchange, and operational coordination. However, the dynamic nature of network traffic and the persistent threat of cyberattacks pose significant challenges to maintaining network stability. Traditional monitoring techniques often fail to meet the unique requirements of military networks, which demand high levels of security and rapid response capabilities. This study employs a multi-faceted approach to detect anomalies in network traffic, utilizing statistical methods such as Z-score analysis, Principal Component Analysis (PCA), and Autoregressive Integrated Moving Average (ARIMA) models. Machine learning techniques, including Support Vector Machines (SVM), Random Forests, Neural Networks, K-means clustering, and Reinforcement Learning, are also applied to identify patterns indicative of stability-disrupting information flows. The integration of statistical and machine learning methods forms a hybrid model that enhances anomaly detection, providing a robust framework for network security. The research problem is formulated as follows: does data collection include comprehensive network traffic data from various segments of military local area networks, including packet flows, transmission rates, and error rates over a specified period? Statistical analysis identifies patterns in the network traffic, which are then used to train machine learning models to classify normal and abnormal traffic. The research hypothesis states that machine learning models achieve high accuracy in detecting stability-disrupting information flows, with a precision rate exceeding 90%. The models identified several instances of stability-disrupting events, correlating these with known security incidents to validate the effectiveness of the detection method. This study underscores the importance of continuous monitoring and analysis of network statistics to ensure stability and security. The proposed method can be integrated with existing network monitoring and intrusion detection systems, providing a comprehensive approach to network security. Future research can build on these findings to develop more sophisticated models and explore additional factors influencing network stability, including the incorporation of advanced machine learning techniques, such as deep learning, and the exploration of other network metrics, like latency and packet loss. This comprehensive approach aims to enhance the security and operational reliability of military local networks.

Revolutionizing Urban Mobility: Smart Transportation & Vehicle-to-Infrastructure Communication – A Review

The burgeoning popularity of IoT technology has significantly altered transportation, with an extensive variety of repercussions. Intelligent Transportation Systems (ITS) are at the vanguard of this change, which has the potential to fundamentally change urban transportation for both individuals and businesses. ITS has expanded considerably in recent years, owing to global urbanization and industrialization advancements. It is regarded as a game-changing technology that improves road safety, improves traffic flow, and improves the entire driving experience. Despite many research efforts on ITS applications and simulation platforms, formal semantic descriptions for intelligent transportation systems have been noticeably lacking. Rising rates of accidents suggest the necessity for a robust Smart Transport System (STS) that prioritizes improving mobility, and reliability, as well as reducing carbon dioxide and methane emissions from automobiles beyond dispersing gains throughout everywhere. Multiple traffic signal management approaches have been designed to enhance real-time traffic movement at intersections, but none have achieved seamless, congestion-free traffic movements. The creation of an interconnected unified framework that integrates and links vehicles with VANET, IoT, and AI technologies will significantly affect the establishment of an innovative reliable mobility network. Internet of Vehicles (IoV) systems will connect several automobiles to share vital information through an IoT-enabled network. Vehicle-to-infrastructure (V2I) communication is essential in developing intelligent transportation and rail traffic management systems, significantly improving road traffic safety and efficiency. A V2I system integrates transmission and analysis capabilities into vehicles and corresponding infrastructure. Wireless sensors are strategically positioned along highways or driving zones in this system. The significance of safe and effective V2X communication between automobiles and facilities expands substantially as intelligent modes of transport progress.

Real-Time Monitoring and Control with Wireless Sensor and Actuator Technology

This paper explores the implementation of a smart monitoring system within a wireless sensor network, with a particular emphasis on developing a robust routing framework using the Routing Protocol for Low-power and Lossy Networks (RPL). This protocol, is designed to address the unique challenges of low-power and lossy environments. Our approach involves using a streamlined version of the Representational State Transfer (REST) architecture, implemented through a binary web service. This setup minimizes overhead and maximizes efficiency, which is critical for resource-constrained networks. Additionally, we use a publish/subscribe model, where each node in the network makes its resources—such as environmental sensors—available to other nodes interested in them. This model enhances the flexibility and responsiveness of the network. A significant part of our research involves a detailed performance evaluation of RPL. We conducted a series of experiments to understand how various parameters of the RPL protocol affect its performance in a smart grid scenario. Our analysis looks at key metrics such as routing efficiency, energy consumption, and overall network reliability. Through these experiments, we aim to provide valuable insights into how different configurations of RPL can impact its effectiveness. Our findings are intended to guide the optimization of RPL for specific applications, offering practical recommendations for deploying smart monitoring systems in similar low-power, lossy environments. This research not only sheds light on RPL’s performance but also contributes to the advancement of more efficient and reliable wireless sensor networks for smart grids and other related applications.