Network Resources Research Articles

Wide-Area Protection System for Radial Smart Distribution Networks

The integration of Distributed Energy Resources (DERs) in power distribution networks poses challenges for protection systems due to dynamic bidirectional fault currents. This paper presents a novel wide-area protection scheme for modern Doubly Feed Induction Generator (DFIG)-integrated distribution networks that simplifies fault location and relay coordination. The system employs Artificial Neural Networks (ANNs) for fault detection and a Fault Zone Relay Condition (FZRC) matrix for fault location. The simulation results demonstrate the effectiveness of the proposed approach in fault detection and isolation.

Research on the Fiber-to-the-Room Network Traffic Prediction Method Based on Crested Porcupine Optimizer Optimization

In order to solve the problem of traffic burst due to the increase in access points and user movement in an FTTR network, as well as to meet the demand for a high-performance network, it is necessary to rationally allocate network resources, and accurate traffic prediction is very important for dynamic bandwidth allocation in such a network. Therefore, this paper introduces a novel traffic prediction model, named CPO-BiTCN-BiLSTM-SA, which integrates the Crested Porcupine Optimizer (CPO), bidirectional temporal convolution (BiTCN), and bidirectional long short-term memory (BiLSTM) networks. BiTCN extends the traditional TCN by incorporating bidirectional data information, while BiLSTM enhances the network’s capability to learn from long sequences. Moreover, self-attention (SA) mechanisms are utilized to emphasize the crucial segments in the data. Subsequently, the BiTCN-BiLSTM-SA model is optimized by CPO to obtain the best network hyperparameters, and model training prediction is performed to achieve multi-step predictions based on single-step prediction. To evaluate the model’s generalization ability, two distinct datasets are employed for traffic prediction. Experimental findings demonstrate that the proposed model surpasses existing models in terms of the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). In comparison with the traditional XGBoost model, the proposed model has an average reduction of 29.50%, 25.43%, and 25.00% in RMSE, MAE, and MAPE, respectively, with a 6.70% improvement in R2.

Slice admission control in 5G cloud radio access network using deep reinforcement learning: A survey

SummaryThe emergence of 5G networks has increased the demand for network resources, making efficient resource management crucial. Slice admission control (SAC) is a process that governs the creation and allocation of virtualized network environments, known as “network slices,” which can be tailored to meet specific user requirements. However, traditional SAC methods face dynamic and heterogeneous challenges in wireless networks, especially in cloud radio access networks (C‐RANs). To address this issue, machine learning (ML) techniques, particularly deep reinforcement learning (DRL), have been proposed as powerful tools for optimizing SAC. DRL‐based approaches enable SAC systems to learn from previous interactions with the network environment and dynamically adapt to changing network conditions. This review article comprehensively explains the current state‐of‐the‐art DRL‐based SAC, focusing on C‐RANs. The article identifies key challenges and future research directions and highlights the potential benefits of using DRL for SAC, including improved network performance and efficiency. However, deploying these systems in real‐world scenarios presents several challenges and trade‐offs that need to be carefully considered. Further research and development are required to address these challenges and ensure the successful deployment of DRL‐based SAC systems in wireless networks.

Streamlining ICT Network Resources, Processes and Operations through a Centralized Management System: A Development and Evaluation

This study aims to provide an inventory and monitoring system for the ICT Network and Telecommunication Department at Lyceum of the Philippines University Cavite. As it streamlines the management and documentation of network equipment and IP addresses under a single, centralized system, it incorporates integrated management features to carry out and improve network resource management operations. The development of the system was presented with the use of the Agile Model, which illustrates the client's workflow, allowing for changes and discussions based on the client's feedback; the model involves planning, design, implementation, testing, and evaluation, allowing continuous improvement, and providing high-quality systems that met the client's requirements. In addition, the system's components were tested for unit functionality and browser compatibility by the technical adviser, (2) administrators, and (5) IT experts. Furthermore, the system also underwent an evaluation process based on ISO 25010 to measure its Compatibility, Usability, Reliability, Functional Suitability, Security, Portability, Maintainability, and Performance Efficiency, participated by (1) a technical adviser, (2) administrators, (10) IT Experts, and (10) end users. The findings revealed that the system was fully operational and could carry out its designated functions, with an average result of 98.93% passing rate. No component failed during testing in the presence of the specified test respondents. From the responses of the determined evaluators, all aspects of the system are deemed “Highly Acceptable.” The implications of these findings indicate that the ICT Network Resource Management System for LPU Cavite has met the requirements and expectations set forth. The system's compliance with ISO 25010 requirements, successful test results, and high-end user acceptability highlight its potential to enhance the management and monitoring processes, leading to more efficient and effective management for the university’s ICT network and telecommunication department. Keywords: ICT, Network Management System, ICT Network Resources, Streamlining ICT Resources Received Date: April 12, 2024Revised Date: May 17, 2024Accepted Date: May 26, 2024

Scalable Career & Educational Growth Transitions: Developing a Mentoring Network & Engaging with an Online Mentoring Platform.

The National Research Mentoring Network's (NRMN) goal is to increase diversity and provide mentoring, networking, and professional development resources to underrepresented populations in STEMM. The virtual, scalable MyNRMN platform includes 15,136 mentees, 7,998 mentors, 3,937 institutions, and 11,832 connections. Our goal was to understand mentee growth and "transitions" alongside engagement with mentoring and networking resources in MyNRMN. In this study, we define transition as the act of a person making a change to their MyNRMN profile that includes one of the following profile fields: institution, education level, degree, or career. We hypothesized that diverse networks and engagement in MyNRMN would be significantly associated with positive transitions. We assessed 7,209 user profiles that indicated an education/career change during the study period. Connections between racially homogenous users were associated with decreased positive transitions (b=-0.107, p<0.05). Connections with heterogeneous ethnicities were associated with increased positive transitions (b=0.105, p<0.05). Longevity within MyNRMN platform was associated with increased positive transitions (b=0.424, p<0.05).

AdaBoost‐powered cloud of things framework for low‐latency, energy‐efficient chronic kidney disease prediction

AbstractThe United Nations' sustainable development agenda has set an ambitious goal of reducing premature mortality from non‐communicable diseases by 33% by 2030. Among these diseases, chronic kidney disease (CKD) is a significant contributor to both morbidity and mortality. Integrating the Internet of Things (IoT) and cloud computing in healthcare has gained momentum, particularly in remote patient monitoring. However, it is essential to acknowledge that cloud computing has limitations, particularly in handling vast volumes of Big Data, mainly due to scalability and latency concerns. This article proposes a novel framework, AdaBoostCoTCKD, to mitigate latency issues, minimize response times, reduce power consumption, and optimize network resources for predicting CKD. The framework leverages the synergy between the AdaBoost machine learning technique and fog computing paradigms to enhance the precision and efficiency of CKD prediction methods. In addition, it introduces an auxiliary cloud‐based database, enriching the pool of future insights and facilitating prospective database infrastructure expansions. This augmentation is expected to impact predictive accuracy positively. We conducted comprehensive experiments to demonstrate the effectiveness of our approach. Our model achieved an impressive training accuracy of 99.928% and testing accuracy of 99.975%, while the fog environment reduced latency by 31% and energy consumption by 75% compared to traditional cloud‐based solutions. Our proposed system enables early CKD detection and offers advantages over cloud‐only solutions, providing a robust and efficient platform for healthcare IoT applications with significant clinical value. These promising results underscore the potential of combining fog computing and the AdaBoost machine learning technique to advance healthcare by addressing latency, response time, power consumption, and network resource optimization challenges in CKD prediction.

Towards the automatic network resource management of OPC UA in 5G private networks

In the realm of Network Function Virtualization (NFV) and Software Defined Networking (SDN), the shift of network functions to the edge presents both challenges and opportunities across various industries. This paper investigates the utilization of Network Resource Management (NRM) within the Third Generation Partnership Project (3GPP) Service Enabler Architecture Layer (SEAL) framework for network operations in different sectors. Leveraging the Open Platform Communications (OPC) Unified Architecture (UA) Publish Subscribe (PubSub) pattern, we enhance edge networking by automating NRM within SEAL, introducing custom Message Queuing Telemetry Transport (MQTT) brokers and a mapping node. To validate our approach, we establish a SEAL NRM test server and implement a translator mechanism for interoperability. We address the integration of non-adaptive applications into the NRM domain and demonstrate our proposal with a practical Robot Operating System (ROS) Digital Twin (DT) application, showcasing dynamic NRM’s potential for optimizing network resources in scenarios like 3D printing.

Intent-Driven CoX Resource Management for Space-Terrestrial Networks

While network resources are scarce and there are a variety of network mission demands in the space-terrestrial network, low utilization efficiency due to weak resource management makes multiple missions unachievable. The intentdriven networks present a possible way to guarantee autonomous and reliable services according to users' declarative intents, so that users no longer need to focus on how to implement the management and configuration details. In this article, we define coordination, collaboration, and cooperation as CoX, and propose an intent-driven CoX scheme to match resource agents and mission agents. It focuses on satisfying the requirements of complex missions and providing on-demand network services by exploring and exploiting the capabilities of intent-driven networks. To be specific, the intent-driven CoX scheme consists of the coordination intent decomposition, the collaboration intent management, and the cooperation resource management. To demonstrate the main features and performance, we also provide a proof of concept implementation of the intent-driven CoX scheme.

Integrated production simulation of renewable energy and transportation consumption

Abstract In order to improve the reserve adequacy of high proportion new energy power systems and realize the efficient consumption of large-scale new energy, the source of reserve resources is expanded to the source network and load storage, and the operating reserve level that can be provided by the source network and load storage flexible regulating resources is quantified in terms of the direction of regulation and the mode of participation. Then, according to the regulation idea of “synchronous units bear the responsibility during regular hours and multiple types of resources share the responsibility during special hours”, a coordinated and participatory operating reserve capacity allocation strategy of source, network, load, and storage is proposed, and a reserve distribution optimization model incorporating multiple types of heterogeneous resources is constructed to achieve the optimization of reserve distribution of each type of reserve resource in the whole network. The optimization model of standby distribution is constructed to incorporate multiple types of heterogeneous resources and realize the step-by-step refinement of standby resources distribution among the whole grid - resources of the same type - units/stations. A case study is carried out based on a provincial power grid, and the effectiveness of the proposed strategy is verified by comparing it with the existing operational reserve allocation guidelines.

Integration flexibility of renewable distributed energy resources in active distribution networks: A two-module data-driven characterization method

Characterizing the integration flexibility of renewable distributed energy resources (RDERs), which describes the feasible region of RDER accommodations with which the operational constraints of an active distribution network (ADN) can be respected, facilitates the ADN operations. However, traditional characterization methods face the challenge of concurrently dealing with inevitable AC power flow and numerous RDERs. To resolve this issue, this paper develops a novel characterization method that contains two interconnected modules: learning and updating modules. The two modules jointly and iteratively calculate the parameters to characterize the integration flexibility of RDERs (IFR). In the learning module, a new loss function is designed to train the IFR parameters using a training data set. In the updating module, two optimization problems are developed based on the optimization-based performance estimation of the trained parameters to generate more data points for performance improvement in the learning module. The effectiveness of the proposed method is corroborated in the IEEE 33-bus and IEEE 136-bus test systems.

Enhancing 5G Core Network Performance through Optimal Network Fragmentation and Resource Allocation

Enhancing 5G Core Network Performance through Optimal Network Fragmentation and Resource Allocation

Toward efficient resource utilization at edge nodes in federated learning

Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. This is accomplished by devices computing local, private model updates that are then aggregated by a server. However, computational resource constraints and network communication can become a severe bottleneck for larger model sizes typical for deep learning (DL) applications. Edge nodes tend to have limited hardware resources (RAM, CPU), and the network bandwidth and reliability at the edge is a concern for scaling federated fleet applications. In this paper, we propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices, as well as the load on the server and network in each global training round. For each local model update, we randomly select layers to train, freezing the remaining part of the model. In doing so, we can reduce both server load and communication costs per round by excluding all untrained layer weights from being transferred to the server. The goal of this study is to empirically explore the potential trade-off between resource utilization on devices and global model convergence under the proposed strategy. We implement the approach using the FL framework FEDn. A number of experiments were carried out over different datasets (CIFAR-10, CASA, and IMDB), performing different tasks using different DL model architectures. Our results show that training the model partially can accelerate the training process, efficiently utilizes resources on-device, and reduce the data transmission by around 75% and 53% when we train 25%, and 50% of the model layers, respectively, without harming the resulting global model accuracy. Furthermore, our results demonstrate a negative correlation between the number of participating clients in the training process and the number of layers that need to be trained on each client’s side. As the number of clients increases, there is a decrease in the required number of layers. This observation highlights the potential of the approach, particularly in cross-device use cases.

5G-enabled UAVs for energy-efficient opportunistic networking

The article explores the potential of 5G-enabled Unmanned Aerial Vehicles (UAVs) in establishing opportunistic networks to improve network resource management, reduce energy use, and boost operational efficiency. The proposed framework utilizes 5G-enabled drones and edge command and control software to provide energy-efficient network topologies. As a result, UAVs operate edge computing for efficient data collecting and processing. This invention enhances network performance using modern Artificial Intelligence (AI) algorithms to improve UAV networking capabilities while conserving energy.An empirical investigation shows a significant improvement in network performance measures when using 5G technology compared to older 2.4 GHz systems. The communication failure rate decreased by 50 %, from 12 % to 6 %. The round-trip time was lowered by 58.3 %, from 120 Ms to 50 Ms. The payload efficiency improved by 13.3 %, dropping from 15 % to 13 %. The data transmission rate increased significantly from 1 Gbps to 5 Gbps, representing a 400 % boost. The numerical findings highlight the significant impact that 5G technology may have on UAV operations.Testing on a 5G-enabled UAV confirms the effectiveness of our technique in several domains, including precision agriculture, disaster response, and environmental monitoring. The solution seriously improves UAV network performance by reducing energy consumption and using peripheral network command-and-control software. Our results emphasize the versatile networking capacities of 5G-enabled drones, which provide new opportunities for UAV applications.

A discernment of round-robin vs SD-WAN load-balancing performance for campus area network

Efficient load balancing is crucial for optimizing network performance and ensuring seamless connectivity in modern campus area networks (CANs). With the proliferation of data-intensive applications and the increasing reliance on cloud-based services, organizations are seeking effective load-balancing solutions to distribute network traffic evenly across available resources. The continuous improvement of devices, tools, and techniques to cater a large amount of network traffic, started to be employed on different campuses. Understanding the best approach to maximize the utilization of the network resources is crucial in order to stabilize and maintain the network. The study aims to discern the round-robin and software defined-wide area network (SD-WAN) techniques based on defined metrics and conducted with a predefined payload for commonly used application conditions. The analysis shows that SD-WAN delivers a much superior performance than round-robin based on the criteria. The local area network (LAN) test shows difference between the two types of technology for the three given metrics. The WAN test shows that the round-robin has higher packet loss, latency, and jitter than the SD-WAN technology. While round-robin may suffice for small-scale deployments with relatively homogeneous traffic patterns, SD-WAN offers more sophisticated capabilities for larger CANs with diverse application workloads and distributed locations.

Remote and controlled quantum teleportation network of the polarization squeezed state

Quantum teleportation is a building block in quantum computation and quantum communication. The continuous-variable polarization squeezed state is a key resource in quantum networks, offering advantages for long-distance distribution and direct interfacing of quantum nodes. Although polarization squeezed state has been generated and distributed between remote users, it is a long-standing goal to implement controlled quantum teleportation of the polarization squeezed state with multiple remote users. Here, we propose a feasible scheme to teleport a polarization squeezed state among multiple remote users under control. The polarization state is transferred between different remote quantum networks, and the controlled quantum teleportation of the polarization state can be implemented in one quantum network involving multiple remote users. The results show that such a controlled quantum teleportation can be realized with 36 users through about 6-km free-space or fiber quantum channels, where the fidelity of 0.352 is achieved beyond the classical limit of 0.349 with an input squeezing variance of 0.25. This scheme provides a direct reference for the experimental implementation of remote and controlled quantum teleportation of polarization states, thus enabling more teleportation-based quantum network protocols.

A Review of Fog Computing Concept, Architecture, Application, Parameters and Challenges

The Internet of Things (IoT) has become an integral part of our daily lives, growing exponentially from a facility to a necessity. IoT has been utilized extensively through cloud computing and has proven an excellent technology for deploying in various fields. The data generated by the IoT devices gets transmitted to the cloud for processing and storage. However, with this approach, there are specific issues like latency, energy, computation resources availability, bandwidth, heterogeneity, storage, and network failure. To overcome these obstacles, fog computing is utilized as a middle tier. Fog computing gathers and processes the generated data closer to the user end before transmitting it to the cloud. This paper aims to conduct a structured review of the current state of fog computing and its architectures deployed across multiple industries. This paper also focuses on the implementation and critical parameters for introducing fog computing in IoT-cloud architecture. A detailed comparative analysis has been carried out for 5 different architectures considering various crucial parameters to identify how the quality of service and quality of experience for end users can be optimized. Finally, this paper looks at the multiple challenges that fog computing faces in a structured six-level approach. These challenges will also lead the way for future research in resource management, green computing, and security.

Improving the Accuracy of DDos Attack Detection by Fine Tuning CNN-LSTM Classifier

Abstract: DDoS attacks also known as distributed denial of service (DDoS) attacks have emerged as one of the most serious and fastest-growing threats on the Internet. Denial-of-service (DDoS) attacks are an example of cyber-attacks that target a specific system or network in an attempt to render it inaccessible or unusable for a period of time. As a result improving the detection of diverse types of DDoS cyber threats with better algorithms and higher accuracy while keeping the computational cost under control has become the most significant component of detecting DDoS cyber threats. DDoS (distributed denial-of-service) attack originates from many sources scattered over multiple network locations. DoS attacks are primarily motivated by the desire to significantly degrade the performance or completely consume a certain resource and a process to exploit a machine defect and cause failure of a processing or exhausting the system resources by exploiting a system flaw. Yet another method of assaulting the target system is to flood the network and monopolies it so preventing anyone else from utilizing it. DDoS attack has a high impact on crashing the network resources making the target servers unable to support the valid users.

Transforming the Creative and Social Entrepreneurial Ecosystem: The Broker Roles of Rural Collaborative Workspaces

Creative social enterprises are increasingly emerging in rural regions, motivated by a desire to take social responsibility through creative approaches. These enterprises integrate entrepreneurial activities with creative social attributes and are sometimes set in rural collaborative workspaces (CWSs) facilitating entrepreneurial activities. Under the frame of entrepreneurial ecosystems (EEs), we argue that CWSs can be seen as brokers, who (1) link resources as liaisons, (2) hold and pass resources as gatekeepers, (3) enhance resource flows as coordinators, and (4) reproduce experiences as representatives. Against this backdrop, this paper presents a case study of two creative social enterprises in a CWS with a cooperative structure in rural Upper Austria by analyzing entrepreneurial biographies about the demand and use of entrepreneurial resources. Through a comparison between before and after the emergence of the CWS, the findings suggest that the EE for creative and social entrepreneurship undergoes two different types of transformation, a radical and a gradual one. The brokerage process of the CWS enhances local resource networks’ transformation of EE and brings the transformation in terms of translocal resources and integral EE of enterprises in the CWS. Additionally, the CWS generates social impacts on the local community through social enterprises. This paper contributes to ecosystem literature by introducing an actor-centric perspective and giving new insights into social entrepreneurship and the transformative power of CWSs as brokers.

Optical routing: link failure detection-aided optimal routing in elastic optical networks using hybrid deep learning with target-based feature pool formation

ABSTRACT The Elastic Optical Network (EON) is a kind of optical network where a large amount of data signals are given into the channel of optical fibre by modulating with the optical signal. One of the most crucial tasks for optical networks is resource allocation and routing, which has a significant impact on resource effectiveness and network capacity. Deep learning-based routing and resource allocation have piqued the interest of researchers to a considerable extent. Thus, this paper aims to develop efficient link failure detection in EONs with hybrid deep learning strategies. Initially, the data aggregation is performed to combine the multiple link information from EONs. The aggregated link data is given into the hybrid detection model named as Enhanced Deep Learning Network (EDLNet), where the One-Dimensional Convolutional Neural Network (1D-CNN) is used for extracting the features and used for generating the target-based feature pool. This feature pool is utilised in the Long Short-Term Memory (LSTM) network for detecting link failures. The parameter optimization is done by the hybrid 1DCNN-LSTM network with the support of the Hybrid Red Deer with Sewing Training-based Optimisation (HRD-STO) to achieve accurate detection results. The detected failed link is mitigated at the time of routing, and only reliable links are used to attain certain objective constraints. These objective constraints include shortest path, link reliability and stability, throughput and delay for enhancing the efficiency of the EONs. The replication analysis reveals the effectiveness of the proposed link failure detection model by comparing it with the conventional models.

Optimizing 6G Network Slicing with the EvoNetSlice Model for Dynamic Resource Allocation and Real-Time QoS Management

This research paper focuses on thoroughly examining the challenges in 6G network slicing. To develop, evaluate performance characteristics for on-demand reallocation and instantaneously changeable QoS EvoNetSlice model. The study employs integrated evolutionary algorithms with artificial intelligence-enabled data analytics and multi-objective optimization to optimize network resources usage under minimum end-to-end delay, high transmission rates and optimal background data management. Firstly, the network resource allocation individuals should be based on the network traffic data, QoD (quality of demand) value for some applications and users’ behaviors. The performance degradation detection and quality of service (QoS) adaptation mechanism combined with a multi-layer objective fitness function for achieving good balance in conflict between conflicting objectives. Results indicate that EvoNetSlice improves the general efficiency of a particular network, adapts according to ever shifting requirements for QoS at any time and provides crucial statistics-focused data on network management. The importance of this work lies in developing the future 6G network’s technology. W the key issues, including resource optimization and real-time adaptation required to support modern 6G services, are considered by EvoNetSlice. Such an exploration is an essential element in developing flexible 6G systems that will define next-generation wireless communication.