Network Management Operations Research Articles

The Western Nigeria Security Network Operation and Crime Management in Ondo State, Nigeria

The Western Nigeria Security Network Operation and Crime Management in Ondo State, Nigeria

Research and implementation of non-linear management and monitoring system for classified information network

Abstract In order to better realize the secret-related information monitoring system, an algorithm based on a nonlinear network is proposed and is combined with the traditional algorithm. This article mainly analyzes the theory of nonlinear networks, designs and trains new network parameters according to their own needs, and combines the nonlinear network as a feature extractor with the existing intrusion detection and wandering detection algorithms, which greatly improves the recognition ability of traditional algorithms. The main feature of a nonlinear network is that it can extract the positional features of objects from the network while also extracting object features, that is, positioning and classification are realized in the same network. As a feature extractor, this network can not only have a higher recognition rate than background difference, hog, and other algorithms but also have a greater ability to extract position information than other convolutional neural networks. The successful application of nonlinear production network systems in TV stations at all levels has greatly improved the editing and production capability and efficiency of TV programs. How to ensure the safe, reliable, stable, orderly, and efficient operation of nonlinear production network systems requires vendors and TVS Taiwan technical staff to jointly conduct in-depth research and summarize their findings. In this article, from the perspective of TV users, information components in nonlinear production network systems are analyzed, including class, title management mode, storage space management, material management, security management, and workflow management in nonlinear systems. Make some analysis, discussion, and summaries of network system and operation management problems. The experimental results show that the nonlinear algorithm in this article has a significant advantage over the original tracking algorithm; that is, most tracking algorithms do not have the ability of category recognition during the initial tracking process, which means that these tracking algorithms cannot accurately know what they are tracking. Because the nonlinear network has the ability to output categories, whether it is initial tracking or tracking loss recovery, nonlinearity has fundamentally better advantages than other tracking algorithms. Therefore, it can be predicted that there is a strong recognition ability in the later monitoring and wandering detection. It has been proved that the nonlinear algorithm can be effectively applied to the secret information monitoring system.

Networks Enabling the Alliance’s Command and Control

The Alliance’s wide area networks enabling operational command and control (C2) are under continuous revision in order to facilitate the wide spectrum data exchange between NATO Command Structure (NCS), NATO Force Structure (NFS) elements and other key organisations.The focus is – as always – on the information technology’s researches and network-enabled capability development.It is clear that running the current NATO wide area network has challenges in terms of network management, information security and counter-cyber operations. Therefore, it requires a viable transformation to a wide area network with a higher-level resiliency and scalability.Having supported by NATO Communications and Information Agency (NCIA), the decade’s one of the most important tasks is to re-new, re-design and re-organise the existing classified network domain in support of efficient C2 for the current and future operations.In this scientific article, I will provide with a short historical background of NCIA’s efforts in creation of a more resilient classified domain-net and the needs of core and functional services within the Alliance to introduce the already decided, the viable solution of classified network enhancements.

Risk-averse restoration of coupled power and water systems with small pumped-hydro storage and stochastic rooftop renewables

Modern coupled power and water (CPW) systems exhibit increasing integration and interdependence, which challenges system performance to disasters and makes service restoration complex during post-disruption. Meanwhile, new technologies, such as small pumped-hydro storage (PHS) and rooftop renewables, are being widely installed and further deepen the interdependencies. To capture these features and improve overall performance, this paper proposes a coordinated restoration framework for a CPW system to respond to disruptions. The proposed CPW model comprises physical networks and mechanisms, considering available units, such as water desalination/treatment plants, pump stations and small PHS, in the water system, and rooftop renewables, distributed generators, in power system. The interdependencies are modeled through component-wise connections and consumer behavior, then grouped into three phases: production, distribution, and consumption. Aggregate service loss with respect to different consumer loads and time periods, is chosen as performance metric and to be minimized using network reconfiguration, energy/water dispatching, load curtailment, and operation management of components. A two-stage risk-averse stochastic programming is applied for reliable restoration and manage risks, to tackle the uncertainties in renewable power generations and water/power demands that affect method effectiveness. Finally, the method is implemented on a modified 33-bus/25-node CPW system, and the results demonstrate the effectiveness of the proposed restoration framework.

Distributionally robust transactive control for active distribution systems with SOP-connected multi-microgrids

The increasing demand for distributed energy and active load increases the risk of voltage violations in active distribution systems. This paper proposes a distributionally robust transactive control method for local energy trading and network operation management of active distribution systems with interconnected microgrids. In particular, the emerging SOP technology is used for the flexible connection of the multi-microgrids (MMGs). First, the local energy interactive market between distribution network operator (DNO) and regional microgrids is constructed to simultaneously solve the economic and security problems of distribution networks considering the energy trading scenarios and various operational constraints. Then, the dual relaxation technique is used to transform the two-layer structure model into a single-layer model. Furthermore, the single-layer game model is further transformed into a two-stage distributed robust problem considering the uncertainties of load demand and renewable power outputs, and the second stage of the problem is decomposed into multiple parallel subproblems without dual information. Finally, numerical simulations on IEEE-33 test system verify the advantages and effectiveness of the proposed method.

OCATA: a deep-learning-based digital twin for the optical time domain

The development of digital twins to represent the optical transport network might enable multiple applications for network operation, including automation and fault management. In this work, we propose a deep-learning-based digital twin for the optical time domain, named OCATA. OCATA is based on the concatenation of deep neural network (DNN) modeling of optical links and nodes, which facilitates representing lightpaths. The DNNs model linear and nonlinear noise, as well as optical filtering. Additional DNN-based models are proposed to extract useful lightpath metrics, such as lightpath length, number of optical links, and nonlinear fiber parameters. OCATA exhibits low complexity, thus making it ideal for real-time applications. Illustrative results for the application of OCATA to disaggregated and mixed disaggregated-proprietary optical network scenarios reveal remarkable accuracy.

Avoiding Overconfidence in Predictions of Residential Energy Demand Through Identification of the Persistence Forecast Effect

Forecasting domestic electricity consumption is important for a wide range of modern power system solutions and smart applications that support network operation, grid stability, and demand-side management, most of which depend on robust and accurate predictions. The methods producing these predictions infer future load from statistical regularity in historical data. If such regularity is lacking, predictions then regress towards the most recently observed consumption value used in the input set. Predictions then follow the actual load data one step behind in time, potentially affecting the robustness of predictions and functionality of applications. Current evaluation methods do not detect this behaviour which may result in overconfidence in prediction results. In this study, we I) define and systematically analyse this behaviour, which we label the Persistence Forecast Effect and illustrate its impacts, II) propose a novel method, called 1-Step-Shifting, to detect its presence, and III) analyse and establish the relationship between irregularity in data and the effect. Further, we provide a case study applying state-of-the-art forecasting techniques to a real-world dataset of electricity consumption data from 69 households in order to demonstrate the Persistence Forecast Effect, its implications, and its relationship to statistical regularity in historical data.

Process-based ecosystem models to support management of drained peatland forests

Forested peatlands are globally significant carbon pools, important forest resources and areas for other bioproduction. Management of drained peatland forests has partly contradictory targets, such as economic profit, climate change mitigation and adaptation, and water protection. Balancing between these targets by comparing different management options requires a thorough understanding of the ecosystem processes, as well as modelling tools that are able to represent their complexity. This thesis presents three such tools, which are based on process-based models, and their applications to concrete water table management scenarios in drained peatlands. All three works are built upon a common peat hydrological model. Two of the studies analyze the effect of canal-blocking restoration practices on the water table and carbon dioxide emissions in tropical peatlands. The first work shows that using optimization algorithms to choose the location of a fixed number of canal blocks can lead to sizeable improvements on the amount of peat they rewet. The second work systematically analyzes the impact of canal blocks on tropical peatland water tables, and provides insights about their performance for different weather conditions and peat types. The third study presents a peatland ecosystem model focused on the effect of drainage on nutrient dynamics and forest growth in boreal peatlands. By connecting the relevant hydrological and biogeochemical processes, this ecosystem model enables the study of interconnected phenomena such as the identification of the stand growth limiting factor, and the impact of typical ditch network management operations on the nutrient balance and forest productivity.

Reliability-Based Expansion Planning Studies of Active Distribution Networks With Multiagents

In this paper, a multi-agent framework is proposed to address the expansion planning problem in a restructured active distribution network. In this framework, the objective and techno-economic constraints of participating agents are addressed in the expansion planning of power network and DER assets as well as the network and DERs optimal operation management. The agents include distributed generator owners and load aggregators which participate along with the distribution network operator (DNO) in the active distribution network planning. The proposed framework is formulated as a bi-level optimization problem with multi-lower levels in which the DNO optimizes the network expansion planning and operation in the upper level where the network reliability is also modeled. In the lower level problems, other participating agents optimize the expansion planning and operation of their assets. The model addresses the optimal operation of resources by clearing the local energy market. The uncertainties in the upper and the lower level problems are effectively addressed using a stochastic adaptive robust optimization approach. The proposed model is implemented on the 54-bus and 138-bus distribution networks and the results demonstrate the effectiveness and scalability of the proposed framework.

ETHoS: Energy-Aware Traffic Engineering for Sustainable Hybrid SDN

In this paper, we present a traffic engineering scheme for sustainable hybrid Software-Defined Networks (SDN) to reduce the overall energy consumption of the network and increase the amount of programmable traffic. Hybrid SDN involves both legacy and SDN switches because of the migration from a legacy network to an SDN. The primary reason for this migration is to increase the amount of programmable traffic and add flexibility to network management operations such as network monitoring, load distribution, and energy management. The energy management solutions in SDN include dynamic activation or deactivation of network elements and traffic rerouting. However, there exists a trade-off between energy-aware routing and programmable traffic, as unplanned traffic rerouting may transform programmable traffic into a non-programmable one. In this paper, we propose a scheme for dynamic activation of SDN links and optimal route selection of existing flows. In contrast to the previous works, we focus on reducing energy consumption, while maximizing the programmable traffic, as it serves the primary intent of transforming a legacy network into an SDN. The simulation results show that the proposed scheme, ETHoS, increases the energy savings by <inline-formula><tex-math notation="LaTeX">$28.91\%$</tex-math></inline-formula> compared to SENEtoR, an existing scheme.

Active distribution network operation management integrated with P2P trading

With the steady development of electricity trading market, active distribution networks (ADNs) need to face new challenges in operation management. Meanwhile, the traditional operation management with peer-to-peer (P2P) trading leads to a heavy computational burden due to a great number of prosumers. An operation management model for large-scale distribution networks integrated with P2P trading is proposed in this paper, where network usage fees are specially addressed. To simplify the management of the P2P trading market, the trading agent is introduced into the P2P trading platform. In addition, this model is adaptive to the reconfiguration of ADN, so the operation management system and the corresponding market structure can be automatically updated according to the new network topology. As shown in case studies, the total trading amount in the P2P trading market can increase by about 62MWh by adjusting the unit price of network usage fees, and the computational time can decline by about 77% in a practical-scale 152-bus system. Also, the total trading amount can be doubled approximately, and the computational time can be reduced by about 85% in the IEEE 8500-bus system.

Research and Performance Optimization of Visualization of Panoramic Monitoring in Responsive Distribution Network

As the scale of the power grid continues to expand, the number of power grid devices in the national power system is increasing, the structure of the power grid is becoming more and more complex, and the information construction of the distribution network is improving, but the visualization is relatively low in real time. In terms of informationization, the current information presentation and interaction mode cannot adapt to the rapid expansion of power supply scale and daily distribution network operation and maintenance management. In this paper, we study GIS-based grid panoramic visualization display technology and responsive visualization solutions for multiple terminals. These solutions not only improve the monitoring efficiency of the distribution network, but also reduce the time of finding and overhauling when problems occur. It also enables visualization of topological data and timely access to the required information. The experiments in this paper use clustering methods to optimize the visualization elements as well as to optimize the GIS rendering, which effectively improves the visualization efficiency.

Sixth-Generation (6G) Mobile Cloud Security and Privacy Risks for AI System Using High-Performance Computing Implementation

The exchange of information from one person to another is called communication. Telecommunication makes it possible with electronic devices and their tools. The scientist Alexander Graham Bell has invented the basic telephone in 1876 in the USA. Telephones now have the new format in the form of mobile phones, which are the primary media for communicating and transmitting data. We are using 5th-generation mobile network standards. Still, there are some requirements for the users that are believed to be solved in the 6th-generation mobile network standards. By 2030, all of the people would be using 6G. The computing model in the cloud is not dependent on either the location or any specific device that would provide the service. It is an on-demand computational service-oriented mechanism. Combining these two technologies as mobile cloud computing provides customized options with more flexible implementations. Artificial intelligence is being used in devices in many fields. AI can be used in mobile network services (MNS) to provide more reliable and customized services to the users, such as network operation monitoring, network operation management, fraud detection, and reduction in mobile transactions and security to the cyber devices. Combining cloud with AI in mobile network services in the 6th generation would improve human beings’ lives, such as zero road accidents, advanced level special health care, and zero crime rates in society. However, the most vital needs for sixth-generation standards are the capability to manage large volumes of records and excessive-statistics-fee connectivity in step with gadgets. The sixth-generation mobile network is under development. This generation has many exciting features. Security is the central issue that needs to be sorted out using appropriate forensic mechanisms. There is a need to approach high-performance computing for improved services to the end-user. Considering three-dimensional research methodologies (technical dimension, organizational dimension, and applications hosted on the cloud) in a high-performance computing environment leads to two different cases such as real-time stream processing and remote desktop connection and performance test. By ‘narrowing the targeted worldwide audience with a wide range of experiential opportunities,’ this paper is aimed at delivering dynamic and varied resource allocation for reliable and justified on-demand services.

Active Learning for Network Traffic Classification: A Technical Study

Network Traffic Classification (NTC) has become an important feature in various network management operations, e.g., Quality of Service (QoS) provisioning and security services. Machine Learning (ML) algorithms as a popular approach for NTC can promise reasonable accuracy in classification and deal with encrypted traffic. However, ML-based NTC techniques suffer from the shortage of labeled traffic data which is the case in many real-world applications. This study investigates the applicability of an active form of ML, called Active Learning (AL), in NTC. AL reduces the need for a large number of labeled examples by actively choosing the instances that should be labeled. The study first provides an overview of NTC and its fundamental challenges along with surveying the literature on ML-based NTC methods. Then, it introduces the concepts of AL, discusses it in the context of NTC, and review the literature in this field. Further, challenges and open issues in AL-based classification of network traffic are discussed. Moreover, as a technical survey, some experiments are conducted to show the broad applicability of AL in NTC. The simulation results show that AL can achieve high accuracy with a small amount of data.

ESPAsync Web Server to Control Multiple Sensor Reading: A Review

SDN (Software-Defined Networking) is a new networking architecture that is enterprising, feasible, cost-effective and robust applications. This approach decouples network management and forwarding operations, allowing network control to be directly programmable while the underlying infrastructure is veiled for applications and network services. MQTT is a lightweight publish-subscribe network protocol for sending and receiving messages. It is made for communications with remote places when there are resource limits or network capacity limitations. The ESP8266EX-based NodeMCU development board is a small module that has a microcontroller, integrated Wi-Fi receiver, and transmitter. The findings of the performance evaluation show that IoT devices' computational overhead and energy consumption are decreased, as well as the total time of the handshake.

Blockchain technology empowers telecom network operation

Telecom operators are deploying the fifth generation (5G) networks around the world which promises high information transmission rate, wide network coverage, low communication delay, and easy access to a large number of devices. However, during the construction and operation of 5G, telecom operators face many challenges, such as insufficient frequency resources, low efficiency of network management, information opacity, risks of data interoperability, and network privacy vulnerabilities. As 5G is generally deployed in heterogeneous networks with massive ubiquitous devices, it is quite necessary to provide secure and decentralized solutions. Blockchain is a distributed system maintained by multiple parties with inherit features including decentralization, traceability and tamperproof. These features collectively contribute to a new application ecosystem where transactions are secure and trustworthy. Therefore, blockchain is expected to integrate with 5G networks to build safer and more reliable mobile network infrastructures. This paper firstly discusses the merits of blockchain technology and the benefits of integrating it with 5G networks. Then a variety of applications of blockchain technology in telecom network operation are reviewed, including spectrum sharing, international roaming settlement, network operation management, number selection management and supply chain management. In the end, we show our recently advance in integrating blockchain in 5G by introducing a multi framework based blockchain service network architecture deployed in real environment.

Internet traffic matrix prediction with convolutional LSTM neural network

With the rapid growing trend of Internet, prediction‐based network operation optimization and management has drawn the attention from both the academia and the industry. For predicting Internet traffic, deep learning models have been proven more effective than linear and statistical models. However, some of the previous studies model the Internet traffic prediction as a multivariate time series prediction problem simply, without using the Internet traffic matrix structure. In this letter, the Internet traffic matrix prediction problem is firstly modeled as a video prediction task. Then a ConvLSTM‐based Seq2Seq model named ConvLSTM‐TM is proposed for predicting the traffic matrix in the next time slot. Based on three real‐world traffic matrix datasets, ConvLSTM‐TM outperforms five deep learning baselines with a lower prediction error. For replication, the dataset, code, and results are publicly available in a Github repository.

Method And Implementation of MPLS Tunnel Selection On Nokia Metro-E Devices

&lt;p class="AbstractL-MAG"&gt;Tunneling is an interconnection solution between local networks separated by remote over a public IP. The current issue does not have proper guidelines for using tunneling techniques. Tunneling selection is generally only based on the beliefs and experience of network management operators or following SOP. The brand of tunneling device is widely used by telecommunication operators in Indonesia (especially P. Java) is Nokia. In this study, implementation, testing and analysis of MPLS techniques on Nokia devices to produce recommendations when to use MPLS tunnels according to network topology appropriately. The software used in this study was FTP, Tfgen and Wireshark. FTP and Tfgen as network traffic generators and Wireshark to record throughput, delay and downtime. Analysis of MPLS on Nokia devices is conducted based on recording results for various traffic engineering scenarios including loadless conditions, and various other conditions in case of network disruption. Mpls research results when without load all went well with an average throughput of 1.08 Mbps and delay of 10 ms. When with load has a stability delay of 10 ms, when when network disruption using ICMP get 1 Request Time Out or 4s. MPLS techniques are used on established networks because it can provide alternative density lines.&lt;/p&gt;

Power marketing and distribution network remote visual control system based on big data architecture

As the main power transmission system connecting power supply equipment and power customers, whether the distribution network is safe and reliable in production and operation directly determines whether the work capacity of power supply equipment and power consumption equipment can be improved. The application of big data centralized control system can help us timely and accurately grasp the reasons for the decline of security and reliability of distribution network, and provide safe and stable power energy for power customers while improving the operation efficiency of power supply system. On the basis of previous research results, the author systematically analyzes the role of big data centralized control system in distribution network production and operation management, and puts forward some suggestions on applying big data centralized control system to distribution network production and operation management according to his many years of work experience. The comparative experiment proves that the fault location accuracy of the system designed in this paper is higher than 90%, which effectively increases the fault detection accuracy.

Fine network operation and maintenance management method based on telemetry

Aiming at the disadvantages of high delay, pull mode and affecting device performance when SNMP reads network device data, this paper proposes a refined network operation and maintenance management method with high timeliness based on Telemetry technology, which can collect and analyze data at sub-second level and dynamically adjust network parameters to send data to network devices in a unified manner. Forming a positive feedback closed-loop network tuning mechanism to achieve intelligent and efficient network operation and maintenance.