Improve Network Reliability Research Articles

Reliability analysis of the global graphite trade industry chain risk assessment based on percolation theory

Graphite, as a critical strategic mineral resource, holds a significant position in the global high-tech industry, and its reliability is essential for global economic stability. To address the resilience and stability of the graphite trade network, we propose a method based on an improved Pointwise Mutual Information (PMI) and percolation theory. By constructing a directed weighted network reflecting trade dependencies between countries, we evaluate the network’s performance under different risk scenarios. The model employs PMI to quantify trade dependencies and uses percolation theory to simulate both random and targeted disruptions, analyzing the network integrity of various stages of the graphite supply chain in the context of edge removals. Key metrics include the Largest Weakly Connected Component (LWCC) and Critical Link Scores (CLSs), which are used to assess structural robustness and identify critical trade links. The results indicate that network structure and connectivity density are key determinants of its resilience. The upstream network stage is relatively vulnerable due to its dependence on high-weight connections, whereas the midstream and downstream stages exhibit higher resilience due to diversification in trade relationships. Enhancing the maximum critical links is crucial for improving network reliability. This study innovatively proposes a systematic risk mapping and mitigation approach for strategic material networks, providing practical recommendations for international trade policy and supply chain optimization. Additionally, policy recommendations focusing on countries with high CLSs are particularly important, as they can help nations formulate effective response strategies.

A secure and lightweight trust evaluation model for enhancing decision-making in resource-constrained industrial WSNs

The dependability of nodes in an Industrial Wireless Sensor Network (IWSN) is vital for precise decision-making and the overall functioning of the network. Unreliable nodes have the potential to result in incorrect data, which can undermine the reliability of monitoring and control systems. Inaccurate decision-making in IWSNs can result in operational failures, safety risks, inadequate resource utilization, elevated energy consumption, and susceptibility to security vulnerabilities. Trust models tackle these challenges by recognizing and reducing the impact of unreliable nodes, improving the precision of data, and strengthening the security of the IWSNs. To overcome these challenges, this research work introduces a “Novel approach for Trust Utilization and Reliability Enhancement” (NATURE) in IWSNs. The unique approach employed in proposed NATURE model is distinguished by its multi-level clustered model, which improves attack mitigation, reduces communication overhead, and enhances overall network efficiency. This model is particularly effective in industrial environments where the network structure and the nature of data traffic are highly dynamic. The use of a multifactor trust estimation framework allows NATURE to assess the trustworthiness of SNs based on a comprehensive set of criteria, including behavior patterns, energy consumption, and communication reliability. By operating on multiple levels, NATURE can dynamically adjust trust functions to accurately distinguish between trustworthy and faulty SNs, thereby improving network reliability. Moreover, NATURE employs the temporal decay factor to prioritize recent node behaviors, ensuring that obsolete actions have minimal impact. Additionally, it uses the dynamic adjustment factor to balance the influence of negative interactions, encouraging reliable and responsive trust evaluations. Additionally, NATURE integrates a dynamically adjustable logical time window to enhance monitoring precision and adaptability, outperforming fixed-length windows in anomaly detection. NATURE integrates an Optimal Lead Node Election Algorithm (OLNEA) to improve cluster leader selection process. OLNEA considers network density, link quality, and Lead Node (LN), battery life, ensuring competent data aggregation and load balancing. By periodically selecting robust LNs and seamlessly switching to alternatives, NATURE promotes reliability and mitigates the impact of low LN battery levels. Additionally, NATURE employs a trust-based attacks detection algorithm to fortify IWSN security. This algorithm employs keen methods to verify data integrity, monitor energy levels, and ensure message authenticity, effectively safeguarding the IWSN from malevolent attacks and ensuring the safe transmission of data. Experimental results highlight NATURE’s exceptional performance across significant metrics when compared to existing trust schemes. In a WSN of 500 SNs with 30% being malicious, NATURE detects malicious behavior with a 97% accuracy, outperforming other models. Even with 50% malicious SNs, NATURE maintains a high detection accuracy of 91%, again surpassing alternative approaches. Additionally, NATURE significantly reduces energy consumption while achieving efficient throughput rates, underscoring its effectiveness in challenging network conditions.

Improving Network Reliability with Traffic Steering Using Two ISP Links on Mikrotik Routers

Improving Network Reliability with Traffic Steering Using Two ISP Links on Mikrotik Routers

Research on control and management of smart grid optical network based on optical transmission control Protocol (OTN) technology

Smart grids can improve the operational efficiency and reliability of power systems, and optical transmission control protocol (OTN) technology is widely used in the field of power communication. This article focuses on the control and management of smart grid optical networks, aiming to achieve efficient control and management of smart grid optical networks through OTN technology. This article proposes a smart grid OTN management system based on Optical Transmission Control Protocol (OTN) technology. This system achieves the management and optimization of power OTN business by researching functions such as container management and service orchestration, pod and physical node management, application and cluster status monitoring, application logs and cluster logs. In this system, the sources of data are very rich. Data can be obtained from databases, file streams and Flow network. This can ensure that the system receives comprehensive data support for better business analysis and decision-making. This article further studies the optimization methods of optical network control for power OTN services to improve network reliability and reduce risk levels. The experimental results indicate that the use of OTN technology can improve the operational efficiency and reliability of the smart grid, and achieve remote monitoring and control of the smart grid.

Polarization sensing of network health and seismic activity over a live terrestrial fiber-optic cable

Wide-scale sensing of natural and human-made events is critical for protecting against environmental disasters and reducing the monetary losses associated with telecommunication service downtime. However, achieving dense sensing coverage is difficult, given the high deployment overhead of modern sensor networks. Here we offer an in-depth exploration of state-of-polarization sensing over fiber-optic networks using unmodified optical transceivers to establish a strong correlation with ground truth distributed acoustic sensing. To validate our sensing methodology, we collect 85 days of polarization and distributed acoustic sensing measurements along two colocated, 50 km fiber-optic cables in Southern California. We then examine how polarization sensing can improve network reliability by accurately modeling overall network health and preemptively detecting traffic loss. Finally, we explore the feasibility of wide-scale seismic monitoring with polarization sensing, showcasing the polarization perturbations following low-intensity earthquakes and the potential to more than double seismic monitoring coverage in Southern California alone.

Reliability analysis of urban road traffic network under targeted attack strategies considering traffic congestion diffusion

The cascading failures caused by traffic congestion diffusion may deteriorate traffic network reliability. Comprehending urban traffic congestion mechanisms is essential for road network planning and traffic management against cascading failures. To uncover this, the reliability of urban road traffic network (URTN) under cascading failure considering different attack strategies is analyzed. The cascading failure model is established based on the improved nonlinear load-capacity relationship. Five kinds of attack strategies including Strength Attack (SA), Betweenness Centrality Attack (BCA), Eigenvector Centrality Attack (ECA), Closeness Centrality Attack (CCA), and Random Attack (RA) are selected. In particular, the capacity affected by traffic congestion is considered, providing a new perspective for the study of traffic congestion diffusion. A state update equation for networks is proposed to simulate the network congestion diffusion. Finally, a case study is conducted by using the URTN of Shanghai as the background. The results show that the network will experience large-scale congestion when high-importance nodes are attacked. The congestion degree is the highest under CCA strategy, network efficiency is the lowest under ECA strategy, and traffic quality is the poorest under CCA strategy. As the congestion critical failure threshold decreases, the speed and scale of cascading failures caused by traffic congestion diffusion are greater. Maintaining proper traffic management and control capability can largely reduce the cascading effect to a great extent and improve the reliability of the network. The results can provide a research basis for traffic management to improve network reliability.

Greentooth: Robust and Energy Efficient Wireless Networking for Batteryless Devices

Communication presents a critical challenge for emerging intermittently powered batteryless sensors. Batteryless devices that operate entirely on harvested energy often experience frequent, unpredictable power outages and have trouble keeping time accurately. Consequently, effective communication using today’s low-power wireless network standards and protocols becomes difficult, particularly because existing standards are usually designed to support reliably powered devices with predictable node availability and accurate timekeeping capabilities for connection and congestion management. In this article, we present Greentooth, a robust and energy-efficient wireless communication protocol for intermittently powered sensor networks. It enables reliable communication between a receiver and multiple batteryless sensors using Time Division Multiple Access–style scheduling and low-power wake-up radios for synchronization. Greentooth employs lightweight and energy-efficient connections that are resilient to transient power outages, while significantly improving network reliability, throughput, and energy efficiency of both the battery-free sensor nodes and the receiver—which could be untethered and energy constrained. We evaluate Greentooth using a custom-built batteryless sensor prototype on synthetic and real-world energy traces recorded from different locations in a garden across different times of the day. Results show that Greentooth achieves 73% and 283% more throughput compared to Asynchronous Wake-up on Demand MAC and Receiver-Initiated Consecutive Packet Transmission Wake-up Radios, respectively, under intermittent ambient solar energy and over 2× longer receiver lifetime.

Reviewing the application of big data analytics in satellite network management to optimize performance and enhance reliability, with implications for future technology developments

The application of big data analytics in satellite network management has emerged as a transformative approach to optimize performance and enhance reliability in the satellite telecommunications industry. This paper reviews the current state of big data analytics in satellite network management, highlighting its key applications and benefits. By analyzing large volumes of data generated by satellite networks, big data analytics enables satellite telecommunications companies to gain valuable insights into network performance, identify potential issues, and take proactive measures to ensure optimal performance. One of the key applications of big data analytics in satellite network management is predictive maintenance. By analyzing historical data and equipment performance metrics, companies can predict when equipment is likely to fail and take preventive measures to avoid downtime. This not only improves network reliability but also reduces maintenance costs and improves overall operational efficiency. Another important application is network optimization. Big data analytics can analyze network traffic, weather conditions, and other factors to optimize satellite beam coverage, frequency allocation, and routing. This helps companies maximize bandwidth utilization, reduce interference, and improve service quality. The implications of big data analytics for future technology developments in satellite network management are significant. As the volume of data generated by satellite networks continues to grow, there is a need for advanced analytics tools and techniques to process and analyze this data efficiently. Future technology developments in areas such as AI, machine learning, and data visualization are expected to play a key role in enhancing the capabilities of big data analytics in satellite network management. In conclusion, the application of big data analytics in satellite network management offers significant benefits in terms of optimizing performance and enhancing reliability. By leveraging the insights provided by big data analytics, satellite telecommunications companies can improve operational efficiency, reduce costs, and deliver better services to their customers. Future technology developments will further enhance the capabilities of big data analytics, paving the way for more efficient and reliable satellite network management.

A comprehensive review of strategic management practices in satellite telecommunications, highlighting the role of data analytics in driving operational efficiency and competitive advantage

Satellite telecommunications play a crucial role in modern connectivity, enabling global communication, broadcasting, and internet access. This paper presents a comprehensive review of strategic management practices in the satellite telecommunications industry, with a particular focus on the role of data analytics in enhancing operational efficiency and achieving competitive advantage. The satellite telecommunications sector is characterized by rapid technological advancements, evolving market dynamics, and increasing demand for bandwidth-intensive services. To thrive in this competitive landscape, companies in the industry must adopt strategic management practices that enable them to effectively leverage their resources, capabilities, and market opportunities. One of the key strategic management practices in satellite telecommunications is the integration of data analytics into business operations. Data analytics encompasses the collection, analysis, and interpretation of large volumes of data to extract valuable insights and inform decision-making. By harnessing the power of data analytics, satellite telecommunications companies can optimize their network performance, enhance customer experience, and drive innovation. Data analytics can also help companies in the satellite telecommunications industry to identify new market trends, customer preferences, and competitive threats. By analyzing data from various sources, including satellite sensors, customer feedback, and market research, companies can gain a deeper understanding of their operating environment and make informed strategic decisions. Furthermore, data analytics can enable satellite telecommunications companies to enhance their operational efficiency by optimizing resource allocation, minimizing downtime, and improving network reliability. By using predictive analytics, companies can anticipate equipment failures and proactively address potential issues, thereby reducing maintenance costs and improving service quality. In conclusion, this paper highlights the importance of strategic management practices in the satellite telecommunications industry, with a focus on the role of data analytics in driving operational efficiency and competitive advantage. By adopting data-driven decision-making processes, satellite telecommunications companies can position themselves for long-term success in a rapidly evolving industry landscape.

Enhancing electricity distribution efficiency in Pakistan: A framework for progress and action

Pakistan's power distribution utilities (DISCOs) face significant challenges, including outdated infrastructure, theft, and financial constraints, leading to inefficiencies and higher costs. A new framework proposes a strategy for operational enhancement and sector reforms, focusing on modernizing management, increasing capacity, improving network reliability, and implementing advanced metering and automated solutions. It aims to bolster transactional accuracy, safety, and inventory management, recalibrate subsidies, and ensure rigorous IT oversight for loss reduction and maintenance optimization. The framework promotes sustained investment and professional development, striving for financial stability and accountability while empowering DISCOs with the autonomy to maintain efficiency and engage consumers more effectively.

Harnessing the Power of Supercomputers: Solving Complex Mathematical Problems and Queueing Theory

Supercomputers represent the pinnacle of computational power, offering unparalleled capabilities to tackle complex mathematical problems across various domains. This paper provides a comprehensive overview of the ways supercomputers are utilized in solving intricate mathematical issues, ranging from understanding the nature of these problems to exploring the challenges and future directions in the field. Supercomputers play a critical role in the practical applications of queueing theory across various industries by enabling the analysis and optimization of complex systems that involve waiting lines and service processes. In telecommunications networks, for instance, supercomputers are used to model and optimize traffic flow, ensuring efficient data transmission and minimal latency. By simulating network conditions under different traffic loads and service protocols, supercomputers help identify optimal resource allocations and configurations, significantly improving network reliability and performance. This capability is crucial for managing the ever-increasing data demands of modern communication systems.

Identifying the critical nodes in multi-modal transportation network with a traffic demand-based computational method

In the last decade, there has been an abundance of research on identifying critical nodes in single-modal transportation network, but less for multi-modal transportation network (MTN). In addition, none of them consider the effect of interactions between nodes in a transportation corridor on node importance. For this purpose, a modified weighted k-shell (MWKS) model is proposed for identifying the critical nodes of the MTN, which combines the network topology and traffic demand. It is modified by the differential contribution of the multi-order neighbor nodes embodied by the passenger volume and the transportation corridor idea. Then the effectiveness of MWKS is verified with an example of a real transportation system using susceptible-infected (SI) model and cascading failure model. The analysis proves that MWKS is suitable for identifying critical nodes in the MTN. Furthermore, the proposed contribution attenuation coefficient based on the idea of the transportation corridor outperforms the topology-based and constant. It demonstrates the necessity of capturing the difference in the contribution of each neighbor node based on traffic demand when calculating the node importance. The results can provide a research basis for traffic planning to improve network reliability.

An Empirical Investigation on Employing Machine Learning for Balancing Home Agent Loads in Next Generation IP Mobility

Mobile IPv6 is a key technology to enable the mobility of devices on next-generation internet protocol networks. Home agents provide simple services to registered mobile nodes. In addition, the use of multiple domestic operators for load-balancing devices faces problems of synchronization and signalling overhead. Thus, most of the existing load balancing methods present a significant delay in the transmission of data packets. The machine learning approach provides an efficient hardware storage solution for balancing the load of the home agent by improving the state-work schedule. It provides a method for dynamic load balancing based on the research-consumption system. Thus, the present paper investigates the use of machine learning, specifically reinforcement learning, to enhance home agent load balancing in next-generation IP traffic. The reinforcement learning-based home agent decisions, guided by mobile node speed and registration queries, reduce latency, improve network performance, and provide dynamic solutions. The proposed method significantly reduces the delay for packet delivery by balancing the home agent load with the incorporation of reward parameters. The empirical investigation found a remarkable improvement in network reliability, as manifested by a 2.31% increase in packet delivery ratio compared to systems with existing methods.

Improvement of Network Reliability by Hybridization of thePenalty Technique Based on Metaheuristic Algorithms

In this study, a network created by converting the Petri net into a network for shutdown system, by calculatinga network’s nonlinear reliability polynomial by splitting the network into two, each having a source node and anterminal node. This the procedure converts the system into two parallel-series block diagrams that are connected ina series. The metaheuristic algorithm using the honey badger algorithm (HBA) and Dwarf Mongoose OptimizationAlgorithm (DMO) have been used to improve the problem for the shutdown network hybridizing these algorithmsemploying the penalty method, we will obtained the (PFMHBA, PFMDMO) algorithms. Thereafter, we comparedthe results of the use of the hybridization technique with the results of the process that does not use this technique. Theobjective of this comparison was to ascertain whether the use of hybridization makes the results increasing reliable,reducing cost, and shortening the duration of implementation.

Lightweight model of remote sensing ship classification based on YOLOv7-tiny improvement

A lightweight ship classification and detection approach based on modified YOLOv7-tiny is presented to address the issue that the classification and detection of ships in optical remote sensing images is prone to mistakes and missed detection due to the complicated background in the offshore. Due to the uneven distribution of data set samples, this method applies a lightweight feature extraction module E_DSC to the backbone network to reduce the number of parameters and calculations of the feature extraction network and improve network reliability. On the other hand, the MPDIoU loss function is introduced to improve the network prediction box coverage and improve the network prediction accuracy. On a dataset of remote sensing images of ships, this strategy has been tested. The findings reveal that it improves the network’s average accuracy of ship recognition and classification by 3.3%. The amount of parameters and calculation is also better than YOLOv7-tiny with a 3% reduction in parameters and a 22% reduction in the calculation.

A two-stage SCUC model for distribution networks considering uncertainty and demand response

Demand response (DR) is one of the most effective and economical methods for power operators to improve network reliability in face of uncertainty and emergencies. In this paper, considering the uncertainty of wind power output and the failure of power system components, a two-stage security-constrained unit commitment (SCUC) model is established by optimizing the real-time pricing mechanism to indirectly adjust the DR. Firstly, the uncertainty of wind power output is modeled based on self-organizing map (SOM), and the component failure of the power system is modeled based on Monte Carlo. Then, a pricing scheme is proposed to stimulate users' electricity consumption behavior. Finally, the marine predator algorithm is used to solve the problem. Simulation results on IEEE-RTS system show that the proposed method can reduce the total operating cost by 10%, effectively stimulate the electricity consumption behavior of users, to improve the peak load shifting and valley filling capacity of the power system.

Sizing of community centralized battery energy storage system and aggregated residential solar PV system as virtual power plant to support electrical distribution network reliability improvement

Virtual Power Plants (VPP) have been seen as one of the techniques to integrate more decentralized and distributed variable renewable energy systems into the grid. They will thus facilitate the greater democratization of the energy systems. VPP is an aggregation of renewable energy technologies, such as solar PV. It is usually operated with battery energy storage system (BESS) facilities to solve the variability issue of solar PV. However, BESS used as part of VPP has to be appropriately sized to work in the electrical network. While VPP operated with BESS may provide significant technical and financial advantage, most of the BESS sized to be used as VPP in the literature are large scale, operated with large scale VRE’s in the grid. Some of the literatures also provide the use of BESS sizing with Solar PV but for shared residential community use only. The context of BESS sizing for VPP from a residential community is not prevalent in the literature. In this paper, the context of a sizing BESS for VPP from a residential neighbourhood is studied. It is aimed that by optimally sizing BESS for a residential community with rooftop solar PV can supply power to the community and any excess energy may be used for VPP application for the grid. This paper provides a practical process for evaluating the proper size of a centralized BESS in a community with rooftop Solar PV by considering the energy consumption and peak demand profiles of the residential units in three types of communities with rooftop Solar PV, namely low-cost, medium-cost, and high-end community, by considering the mean and maximum energy consumption and peak demand profiles of each residential unit. Such values are used to allocate for the Solar PV system per residential unit and the centralized BESS at mean, 75% of maximum, maximum, and 125% of maximum energy consumption, to be used for the community and for any excess energy to support the electrical networks reliability improvement. The result shows that using the maximum and 125% of maximum energy consumption can provide more than enough energy capacity for VPP application, particularly electrical networks reliability improvement by supplying for the Energy not Supplied (EnS), EN. The result provides that for a High-end community, the stored energy in the BESS can reach up to 24.578 MW h using 125% of maximum sizing at either mean or maximum energy consumption. While for Medium-cost and Low-cost communities, the stored energy can reach up to 17.549 MW h and 14.527 MW h using 125% of maximum sizing at either mean or maximum energy consumption, respectively. Moreover, the result also shows that to alleviate Energy not Supplied (EnS) and contribute to reliability improvement, more residential units are needed if sizing is based on maximum energy consumption, while a lesser number of residential units if the size is based on 125% of the maximum energy consumption.

분산형 전원이 연결된 배전계통에서의 분리된 상별 인공지능 모델을 이용한 고장종류 및 고장구간 판별 방법

In recent years, the number of distributed generation (DG) in distribution network (DN) has increased. The increasing proportion of DG in DN brings benefits through improved network reliability and reduced transmission power losses. However the connection of DG makes network be more complicated, it changes the unidirectional flow of currents and power to bidirectional flow and thus protection based on over-current relay has limitations of protection coordination. These limitations may occur malfunction or misbehavior of protective devices so it requires new protection methods. This paper proposes a new method using artificial neural network (ANN) can adjust on DN with DG to allocate fault section and to classify fault type as part of solving limitations of protection coordination. This ANN model is separated to each phase and use magnitude and phasor of voltages and currents extracted from each generation sides. The new proposed method is applied to unbalanced distribution network model and verified to be useful in DN through computer simulations.

Schottky-Diode Design for Future High-Speed Telecommunications.

The impact of 5G communication is expected to be widespread and transformative. It promises to provide faster mobile broadband speeds, lower latency, improved network reliability and capacity, and more efficient use of wireless technologies. The Schottky diode, a BN/GaN layered composite contacting bulk aluminum, is theoretically plausible to harvest wireless energy above X-band. According to our first principle calculation, the insertion of GaN layers dramatically influences the optical properties of the layered composite. The relative dielectric constant of BN/GaN layered composite as a function of layer-to-layer separation is investigated where the optimized dielectric constant is ~2.5. To push the dielectric constant approaching ~1 for high-speed telecommunication, we upgrade our BN-based Schottky diode via nanostructuring, and we find that the relative dielectric constant of BN monolayer (semiconductor side) can be minimized to ~1.5 only if it is deposited on an aluminum monolayer (metal side). It is rare to find a semiconductor with a dielectric constant close to 1, and our findings may push the cut-off frequency of the Al/BN-based rectenna to the high-band 5G network.

Postfault optimal islanding of smart grids using a reinforcement learning approach

AbstractThis paper presents a method for optimal reconfiguration of smart grids following the occurrence of short circuit faults. Due to restoration delays, the aim of the proposed approach is to save the maximum possible number of loads by forming stable islands and serving loads with Distributed Energy Resources (DERs). The islanding plan aims to prevent island instability and to help DERs continue supplying the maximum number of loads by the optimal network reconfiguration. Fault isolation is carried out by the protection system and the proposed procedure is commenced right after the fault isolation by controlling the condition of the network remote‐controlled switches. The proposed islanding plan is a novel method by this paper in the management of the postfault conditions of smart grids. Furthermore, a Q‐learning reinforcement approach is presented as the optimization tool because of its great capability and fast response for the determination of optimal reconfiguration. Numerous simulation tests for various fault locations on a 6‐bus and a 33‐bus test networks show the effectiveness of the proposed method in the improvement of postfault network reliability and sustainability.