Network Devices Research Articles

Edge Computing-Enabled Secure Forecasting Nationwide Industry PM2.5 with LLM in the Heterogeneous Network

The heterogeneous network formed by the deployment and interconnection of various network devices (e.g., sensors) has attracted widespread attention. PM2.5 forecasting on the entire industrial region throughout mainland China is an important application of heterogeneous networks, which has great significance to factory management and human health travel. In recent times, Large Language Models (LLMs) have exhibited notability in terms of time series prediction. However, existing LLMs tend to forecast nationwide industry PM2.5, which encounters two issues. First, most LLM-based models use centralized training, which requires uploading large amounts of data from sensors to a central cloud. This entire transmission process can lead to security risks of data leakage. Second, LLMs fail to extract spatiotemporal correlations in the nationwide sensor network (heterogeneous network). To tackle these issues, we present a novel framework entitled Spatio-Temporal Large Language Model with Edge Computing Servers (STLLM-ECS) to securely predict nationwide industry PM2.5 in China. In particular, We initially partition the entire sensor network, located in the national industrial region, into several subgraphs. Each subgraph is allocated an edge computing server (ECS) for training and inference, avoiding the security risks caused by data transmission. Additionally, a novel LLM-based approach named Spatio-Temporal Large Language Model (STLLM) is developed to extract spatiotemporal correlations and infer prediction sequences. Experimental results prove the effectiveness of our proposed model.

Enhancing multi-plane light conversion orbital angular momentum multiplexer performance via error analysis

We present what we believe to be a novel orbital angular momentum (OAM) multiplexing apparatus capable of handling topological charges ranging from l = 0 to ±3 through multi-plane light conversion (MPLC) across four phase planes. Nevertheless, the fabricating process of MPLC devices is prone to errors that cannot be avoided. Our investigation primarily delves into the repercussions of misalignment and etching inaccuracies on the device's phase, with the assistance of a spatial light modulator. The scrutiny of fabrication errors associated with the device offers valuable insights for refining the fabricating of MPLC devices. The OAM multiplexing device converts the phase of MPLC onto a glass substrate through four etching steps, corresponding to a depth of 0-775 nm. OAM multiplexing/demultiplexing crosstalk based on MPLC is less than -20 dB and -18 dB, respectively. The insertion loss of the OAM mode generated by the OAM multiplexing device coupled to the few-mode fiber is less than 7 dB. In a communication experiment, we demonstrated multiplexed three OAM channels carrying 10 Gbit/s OOK signals over a 5 km few-mode fiber using two MPLC devices. Both the bit error rate curve and constellation diagram demonstrate the excellent performance of MPLC-based OAM multiplexing devices in communication networks.

Deep reinforcement learning based controller placement and optimal edge selection in SDN-based multi-access edge computing environments

Multi-Access Edge Computing (MEC) can provide computility close to the clients to decrease response time and enhance Quality of Service (QoS). However, the complex wireless network consists of various network hardware facilities with different communication protocols and Application Programming Interface (API), which result in the MEC system's high running costs and low running efficiency. To this end, Software-defined networking (SDN) is applied to MEC, which can support access to massive network devices and provide flexible and efficient management. The reasonable SDN controller scheme is crucial to enhance the performance of SDN-assisted MEC. At First, we used the Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM) model to predict the network traffic to calculate the load. Then, the optimization objective is formulated by ensuring the load balance and minimizing the system cost. Finally, the Deep Reinforcement Learning (DRL) algorithm is used to obtain the optimal value. Based on the controller placement algorithm ensuring the load balancing, the dynamical edge selection method based on the Channel State Information (CSI) is proposed to optimize the task offloading, and according to CSI, the strategy of task queue execution is designed. Then, the task offloading problem is modeled by using queuing theory. Finally, dynamical edge selection based on Lyapunov's optimization is introduced to get the model solution. In the experiment studies, the assessment method evaluated the performance of two sets of baseline algorithms, including SAPKM, the PSO, the K-means, the LADMA, the LATA, and the OAOP. Compared to the baseline algorithms, the proposed algorithms can effectively reduce the average communication delay and total system energy consumption and improve the utilization of the SDN controller.

A Real-Time Resource Dispatch Approach for Edge Computing Devices in Digital Distribution Networks Considering Burst Tasks

Edge computing technology can effectively solve huge challenges posed by the large number of terminal devices accessing and massive data processing in digital distribution networks. Burst tasks, such as faults and data requests from the cloud, can occur at any time for edge computing devices in distribution networks. These tasks are unpredictable and usually hold the highest priority and must be completed as soon as possible. Although resources can be reserved partially at each period in the pre-scheduled operation plan, they may still be insufficient to handle burst tasks adequately. A real-time resource dispatch approach for burst tasks is developed in this study to address the above problems. The concept of flexibility for edge computing devices is presented, determining the real-time dispatch duration. Real-time resource dispatch and task handling processing are analyzed in detail, considered as task real-time dispatch models, computation process real-time dispatch constraints, and resource limitation constraints. The proposed real-time resource dispatch approach takes full advantage of the transferable characteristics for partial original plan tasks to adjust the pre-scheduled operation plan and release flexible resources for immediate processing of the burst task, completing burst tasks quickly and minimizing the impact for previous planned tasks on the edge computing device. The capability of the proposed method to efficiently deal with the burst tasks is also verified by the case study.

Real-time coordination of integrated transmission and distribution systems: Flexibility modeling and distributed NMPC scheduling

This paper proposes a real-time distributed operational architecture to coordinate integrated transmission and distribution systems (ITD). At the distribution system level, the distribution system operator (DSO) calculates the aggregated flexibility of all controllable devices by power-energy envelopes and provides them to the transmission system operator (TSO). At the transmission system level, a distributed nonlinear model predictive control (nmpc) approach is proposed to coordinate the economic dispatch of multiple TSOs, considering the aggregated flexibility of all distribution systems. The subproblems of the proposed approach are associated with different TSOs and individual time periods. In addition, the aggregated flexibility of controllable devices in distribution networks is encapsulated, re-calculated, and communicated through the power-energy envelopes, facilitating a reduction in computational complexity and eliminating redundant information exchanges between TSOs and DSOs, thereby enhancing privacy and security. The framework’s effectiveness and applicability in real-world scenarios are validated through simulated operational scenarios on a summer day in Germany, highlighting its robustness in the face of significant prediction mismatches due to severe weather conditions.

Application of the Multi-Threading Method and Python Script for the Automate of Network

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decision- making process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multi- thread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multi- thread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so.

Navigating latency hurdles: an in-depth examination of a cloud-powered GNSS real-time positioning application on mobile devices

A growing dependence on real-time positioning apps for navigation, safety, and location-based services necessitates a deep understanding of latency challenges within cloud-based Global Navigation Satellite System (GNSS) solutions. This study analyses a GNSS real-time positioning app on smartphones that utilizes cloud computing for positioning data delivery. The study investigates and quantifies diverse latency contributors throughout the system architecture, including GNSS signal acquisition, data transmission, cloud processing, and result dissemination. Controlled experiments and real-world scenarios are employed to assess the influence of network conditions, device capabilities, and cloud server load on overall positioning latency. Findings highlight system bottlenecks and their relative contributions to latency. Additionally, practical recommendations are presented for developers and cloud service providers to mitigate these challenges and guarantee an optimal user experience for real-time positioning applications. This study not only elucidates the complex interplay of factors affecting GNSS app latency, but also paves the way for future advancements in cloud-based positioning solutions, ensuring the accuracy and timeliness critical for safety–critical and emerging applications.

Optimizing OSPF Cost Value for Best Path Selection Using Metric-Type Variation

Open Shortest Path First (OSPF) is a multi-vendor routing standard. Any routing protocol's goal is to discover the optimum path between source and destination addresses inside a computer network. Other routing protocols include Routing Information Protocol (RIP), Border Gateway Protocol (BGP), Enhanced Interior Gateway Routing Protocol (EIGRP), and IS-IS. which are also used in computer networking. Each routing protocol has different set of rules through which they calculate the best path selection. OSPF is often used with the combination of these routing protocol because of its flexible design. This research focuses on the implementation of OSPF network cost (metric) value for best path calculation using network simulation tool like GNS3 for getting correct outcome just like a real networking device working in a live production environment. This research would use the different combinations of OSPF cost calculation using metric-types for external routes learned from different routing protocols like RIP. Being able to accurately identify cost (metric) values obtain by testing for OSPF when used with other routing protocols, just like a real-world environment.

Troubleshooting solution for traffic congestion control

The Internet has existed since the 1970s as a means of data exchange between network devices in small networks. In the early stage, there was a small number of devices, but today there is an ever-increasing number of devices, leading to congestion in the network. Therefore, congestion control has attracted so much attention in the academic community and the industry for the past 30 years. Recently, Google has developed BBR (Bottleneck Bandwidth and Round-Trip Time), a rate-based congestion control algorithm. BBR controls transmission rates based on delivery rate and round-trip time (RTT). However, such a static congestion control algorithm (e.g., BBR, etc.) cannot achieve high performance in various network conditions (e.g., low bandwidth, etc.). Concretely, these static algorithms cannot adapt to the dynamic changes of the network environment. Therefore, in this paper, we propose an adaptive algorithm (called ABBR) for congestion control in next-generation networks. ABBR takes into account the reinforcement learning algorithm to learn relevant policies to change the transmission rate corresponding to each congestion control algorithm to optimize long-term performance. The experimental results show that our proposal can achieve good performance in terms of throughput, RTT, and fairness compared to the benchmarks.

Development of an intrusion detection system using mayfly feature selection and artificial neural network algorithms

Protecting the privacy and confidentiality of information and devices in computer networks requires reliable methods of intrusion detection. However, effective intrusion detection is made more difficult by the enormous dimensions of data available in computer networks. To boost intrusion detection classification performance in computer networks, this study developed a feature selection mode for the classification task. The proposed model utilized the Mayfly feature selection algorithm and ANN as the classifiers. The model was also tested without a mayfly algorithm. The model's efficacy was determined through a comparison of its accuracy, specificity, precision, sensitivity, and F1 score. The experimental outcomes revealed that the proposed model is more efficient than existing models based on the performance evaluation and the CIC-IDS 2017 dataset employed in this research. Accuracy scores of 99.94% (using Data+mayfly+ANN) and 90.17% (using Data+ANN) were attained after experimentation. In comparison to existing models, the proposed model yielded better results in terms of accuracy, sensitivity, specificity, and F1-score metrics. The model's sturdiness can be attributed to the use of mayfly techniques, which harness the strength in PSO, GA and FA for selecting optimal feature subsets. The results of this research provide a reliable dimensionality reduction model that may be used in the field of computer networks for intrusion detection and enhancement of security in computer networking environments.

Synthetic and privacy-preserving traffic trace generation using generative AI models for training Network Intrusion Detection Systems

Network Intrusion Detection Systems (NIDS) are crucial tools for protecting networked devices from cyberattacks. Recent development in the field of Artificial Intelligence (AI) has provided tremendous advantages in implementing NIDSs able to monitor network traffic and block cyberattacks in real-time. In the literature, it is widely recognized that the effective training of a NIDS requires a large quantity of labeled traffic, representative of attacks. Nonetheless, the availability of public and abundant datasets remains remarkably restricted due to the cost of gathering and labeling real traffic traces and privacy concerns for sharing them. To tackle these challenges, in this paper we present a generative AI model capable of synthesizing anonymized traffic traces from real ones, thus dealing with privacy, abundance, and representativeness. The proposal is based on a Conditional Variational Autoencoder (CVAE) and a preprocessing procedure specifically designed for the generation of new traffic traces. To validate our solution, we conduct an extensive empirical study leveraging three recent and publicly-available datasets, containing benign and malicious traffic. The validation is carried out from both the perspectives of classification performance of a robust NIDS and the quality of synthetic data, in comparison to the utilization of real data. We compare our CVAE with two state-of-the-art AI-based traffic data generators and prove that, trained with traces emitted by our generative model, a NIDS has a limited F1-score loss compared to training on real data; competing models instead struggle or fail to generate traces that are as effective for NIDS training and as statistically similar to the original. We make the synthetic datasets available in both PCAP and tabular formats, to facilitate the reproducibility of our findings and encourage further exploration in the field of generative AI for networking.

A high-performance deep reservoir computer experimentally demonstrated with ion-gating reservoirs

While physical reservoir computing is a promising way to achieve low power consumption neuromorphic computing, its computational performance is still insufficient at a practical level. One promising approach to improving its performance is deep reservoir computing, in which the component reservoirs are multi-layered. However, all of the deep-reservoir schemes reported so far have been effective only for simulation reservoirs and limited physical reservoirs, and there have been no reports of nanodevice implementations. Here, as an ionics-based neuromorphic nanodevice implementation of deep-reservoir computing, we report a demonstration of deep physical reservoir computing with maximum of four layers using an ion gating reservoir, which is a small and high-performance physical reservoir. While the previously reported deep-reservoir scheme did not improve the performance of the ion gating reservoir, our deep-ion gating reservoir achieved a normalized mean squared error of 9.08 × 10−3 on a second-order nonlinear autoregressive moving average task, which is the best performance of any physical reservoir so far reported in this task. More importantly, the device outperformed full simulation reservoir computing. The dramatic performance improvement of the ion gating reservoir with our deep-reservoir computing architecture paves the way for high-performance, large-scale, physical neural network devices.

Enabling traffic forecasting with cloud-native SDN controller in transport networks

Network bandwidth is a scarce resource that network operators monitor to cope with future traffic demands and plan more transceiver and fibre deployments. The inclusion of Machine Learning permits the usage of traffic forecasting methods to predict future link usage. Typically, traffic analysis is performed offline due to the high computational load and difficulty of obtaining real-time data directly from the underlying network devices. To overcome these limitations, this paper presents and evaluates an architecture for SDN-controlled packetoptical transport networks to allow real-time traffic monitoring in the transport SDN controller. The presented SDN controller is based on a micro-service-based architecture, which facilitates the ease of deployment of the proposed solution. Four forecasting methods are proposed and evaluated against two topologies to select the most precise and the fastest among them.The algorithm random forest seems to be the most accurate forecasting future link usage with 79.98 % and 95.88 % accuracy and a reasonable fast speed when implemented it into two different topologies

Ensemble learning based anomaly detection for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis

The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other connected devices with little to no human intervention. IoT enables data aggregation and analysis on a large scale to improve life quality in many domains. In particular, data collected by IoT contain a tremendous amount of information for anomaly detection. The heterogeneous nature of IoT is both a challenge and an opportunity for cybersecurity. Traditional approaches in cybersecurity monitoring often require different kinds of data pre-processing and handling for various data types, which might be problematic for datasets that contain heterogeneous features. However, heterogeneous types of network devices can often capture a more diverse set of signals than a single type of device readings, which is particularly useful for anomaly detection. In this paper, we present a comprehensive study on using ensemble machine learning methods for enhancing IoT cybersecurity via anomaly detection. Rather than using one single machine learning model, ensemble learning combines the predictive power from multiple models, enhancing their predictive accuracy in heterogeneous datasets rather than using one single machine learning model. We propose a unified framework with ensemble learning that utilises Bayesian hyperparameter optimisation to adapt to a network environment that contains multiple IoT sensor readings. Experimentally, we illustrate their high predictive power when compared to traditional methods.

Построение онтологии для систематизации характеристик сети Интернета вещей

A formalization of the Internet of Things network model designed for monitoring technological premises with tele-communications equipment at the Federal Research Center "Krasnoyarsk Scientific Center SB RAS" is presented. The network includes measuring devices, a telecommunications environment, data collection servers, and application soft-ware. For information interaction, a “publisher-subscriber” scheme and a lightweight protocol with a low load on com-munication channels are used. An ontology has been created that describes the network architecture and the properties of devices that collect, transmit, store, and process data. The ontology contains classes representing the concepts of the subject area, relationships, data properties, ranges of their changes, and critical values that limit the attributes of ontol-ogy elements. Ontology objects have their own digital representation in databases, including measurement results ob-tained by Internet of Things network sensors, precedents of anomalous data, and their statistical and frequency char-acteristics. This formalization made it possible to identify implicit dependencies between objects, connect them with the characteristics of processes observed by Internet of Things network devices, and solve practical tasks. The problem of selecting characteristics that influence changes in information interaction patterns is considered. A survey of experts was carried out, and a Kano model was built to prioritize the characteristics that influence decision-making on the or-ganization of an information interaction scheme in the Internet of Things network.

Finding influential nodes in complex networks based on Kullback–Leibler model within the neighborhood

As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. If we regard these distributed network devices and connection relationships as a complex network, we can identify the influential nodes to find the crucial points for optimizing the imbalance of the reliability of devices in federated learning system. This paper will analyze the advantages and disadvantages of existing algorithms for identifying influential nodes in complex networks, and propose a method from the perspective of information dissemination for finding influential nodes based on Kullback–Leibler divergence model within the neighborhood (KLN). Firstly, the KLN algorithm removes a node to simulate the scenario of node failure in the information dissemination process. Secondly, KLN evaluates the loss of information entropy within the neighborhood after node removal by establishing the KL divergence model. Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. To validate the performance of KLN, this paper conducts an analysis and comparison of its results with those of 11 other algorithms on 10 networks, using SIR model as a reference. Additionally, a case study was undertaken on a real epidemic propagation network, leading to the proposal of management and control strategies for daily protection based on the influential nodes. The experimental results indicate that KLN effectively evaluates the importance of the removed node using KL model within the neighborhood, and demonstrate better accuracy and applicability across networks of different scales.

Implementation and testing of a KNS topology in an InfiniBand cluster

The InfiniBand (IB) interconnection technology is widely used in the networks of modern supercomputers and data centers. Among other advantages, the IB-based network devices allow for building multiple network topologies, and the IB control software (subnet manager) supports several routing engines suitable for the most common topologies. However, the implementation of some novel topologies in IB-based networks may be difficult if suitable routing algorithms are not supported, or if the IB switch or NIC architectures are not directly applicable for that topology. This work describes the implementation of the network topology known as KNS in a real HPC cluster using an IB network. As far as we know, this is the first implementation of this topology in an IB-based system. In more detail, we have implemented the KNS routing algorithm in the OpenSM software distribution of the subnet manager, and we have adapted the available IB-based switches to the particular structure of this topology. We have evaluated the correctness of our implementation through experiments in the real cluster, using well-known benchmarks. The obtained results, which match the expected performance for the KNS topology, show that this topology can be implemented in IB-based clusters as an alternative to other interconnection patterns.

An Incident Management System Design to Protect Critical Infrastructures from Cyber Attacks

In recent years, there has been a noticeable trend toward targeted threats to information security, where companies are now leveraging vulnerabilities and risks associated with widely used services in order to generate financial gain. Additionally, they implement numerous precautions and consistently carry out their tasks. One item that requires precautionary measures is the network devices utilized. Network devices in computer networks possess the capability to log events. These logs enable the identification of security events on the network and facilitate the implementation of precautionary measures. Various security measures can be implemented to handle such data. One of these measures is Security Information and Event Management (SIEM). It is a system that gathers and analyzes data from networks and security devices. SIEM is a technique employed to consolidate critical information within a cohesive structure. It allows for the correlation of events from different security devices, thereby improving the monitoring capabilities of cybersecurity operations centers. This study extensively covers the critical infrastructure-SIEM relationship, current studies, critical infrastructure, cyber security policies, and SIEM. Our system design was developed using the UNSW\_NB15 dataset, a widely recognized dataset in cybersecurity due to its comprehensive and realistic representation of cyber threats. This dataset consists of data obtained from network traffic, various attack activities, and real-life modern normal scenarios, making it particularly relevant to our study. With the studies, a total of 10 different categories were analyzed, with the category consisting of nine types of attacks, namely Analysis, Backdoor, DoS, Exploits, Fuzzers, Generic, Reconnaissance, Shellcode, and Worms and Normal activities. The study is divided into two as the basic structure. The first step was carried out on Google Collaboratory, and then some experimental studies were carried out in Weka. Classifications were made using several methods, including Logistic Regression (LR), Extra Trees (XT), Support Vector Machines (SVM), Random Forest (RF), and Decision Trees (DT). These methods were chosen for their proven effectiveness in similar studies. In the application developed with Google Colabratory, we achieved 98.62\% in Random Forest, 99.10\% in Decision Trees, 98.87\% in Logistic Regression, 95.13\% success in Extra Trees and 99.12\% success in Support Vector Machines. As a result of the studies and experiments carried out in Weka, we achieved 92.05\% in Random Forest, 100\% in Decision Trees, 100\% in k-Nearest Neighbours, 100\% in J48, 99.19\% in Naive-Bayes and 99.35\% in BayesNet achievements.

The Influence of Brand Image Brand Awareness and Promotion on Purchase Decisions for Ubiquiti Brand IT Network Devices in Indonesia

The background of this research is the increasing demand and competition in the wireless network device industry. Ubiquiti, as one of the major players in the industry, needs to understand how brand image, brand awareness, and promotional activities influence consumers' purchasing decisions. This study aims to analyze the influence of brand image, brand awareness, and promotion variables on purchasing decisions on Ubiquiti brand IT network products. Data were collected from 97 respondents, and analysis was conducted on the demographics of respondents, product use, and the influence of independent variables on purchasing decisions. Research analysis method using SPSS. The results showed that the majority of product users were men aged 25-29 years who worked as employees and used the product for less than 1 year. Statistical analysis revealed that the three variables of brand image, brand awareness, and promotion significantly influence purchasing decisions. Brand awareness has been shown to have the most influence, followed by brand image and promotion. This finding is reinforced by brand awareness's largest adjusted R square value (96.8%). The main conclusion is that brand awareness has the most significant influence on consumer purchasing decisions Ubiquiti brand IT network products, followed by brand image and promotion. The implication of this study is the importance of companies in designing effective marketing and branding strategies to improve consumer purchasing decisions. The research provides valuable insights for Ubiquiti and other companies in the wireless networking industry to better understand the factors driving consumer buying behavior, so they can develop targeted marketing strategies and increase their market share.

Learning Framework for Joint Optimal Node Placement and Resource Management in Dynamic Fog Environment

Background: With recent improvements in fog computing, it is now feasible to offer faster response time and better service delivery quality; however, the impending challenge is to place the fog nodes within the environment optimally. A review of existing literature showcases that consideration of joint problems such as fog node placement and resource management are less reported. Irrespective of different available methodologies, it is noted that a learning scheme facilitates better capability to incorporate intelligence in the network device, which can act as an enabling technique for superior operation of fog nodes. Objective: The prime objective of the study is to introduce simplified and novel computational modelling toward the optimal placement of fog nodes with improved resource allocation mechanisms concerning bandwidth Methods: Implemented in Python, the proposed scheme performs predictive operations using the Deep Deterministic Policy Gradient (DDPG) method. Markov modelling is used to frame the model. OpenAI Gym library is used for environment modelling, bridging communication between the environment and the learning agent. Results: Quantitative results indicate that the proposed scheme performs better than existing schemes by approximately 30%. Conclusion: The prime innovative approach introduced is the implementation of a reinforcement learning algorithm with a Markov chain towards enriching the predictive analytical capabilities of the controller system with faster service relaying operations a.