Network Service Providers Research Articles

Analysis of voltage limit‐induced barrier for connecting inverter‐based distributed generators to medium voltage networks: Australian case studies

AbstractInverter‐based distributed generators (IBDGs), mainly solar photovoltaic, connected in medium‐voltage (MV) networks cause challenges, such as voltage limit violations, for distribution network service providers (DNSPs), and require advanced network management strategies to mitigate these challenges. A theoretical analysis of the voltage limit‐induced barrier to IBDG connection and their export limits due to the change in network characteristics is imperative for developing new strategies. The authors formulated a relationship between the network equivalent impedance and the IBDG's connection point in the network and further explored the link between the network equivalent impedance and voltage magnitude due to the IBDG connection point. The authors also assessed the voltage limit‐induced barrier to IBDG connections in MV networks and proposed solutions to overcome issues with the dynamic export limit of IBDGs. Four representative Australian MV networks are analysed in DIgSILENT PowerFactory under different scenarios, such as variation in IBDG location and static and dynamic export limits. The authors found that an IBDG connected at the end of the network can achieve better performance in supporting the network voltage. An IBDG with a dynamic export limit can export three times more energy than the static export limit, which benefits both the DNSPs and IBDG owners.

An online integrated satellite–terrestrial IoT task offloading and service deployment strategy

As a core component of 6G networking, low earth orbit (LEO) satellite networks are considered a promising solution for providing network services in remote areas and have received considerable attention in recent years. Meanwhile, with the rapid development of IoT terminals and applications, an increasing emphasis has been placed on task offloading for real-time tasks. However, most existing works on task offloading for integrated satellite–terrestrial IoT (IST-IoT) applications are based on the assumption that the global task and network information is known, an assumption that is obviously not met in online real-time task offloading. In addition, current studies rarely consider the tradeoff among the task offloading volume, satellite endurance and wholesale cost of terrestrial resources, which are precisely the holistic issues that satellite network operators must confront. To address such issues, this study considers task offloading, service deployment and resource allocation in highly dynamic online task offloading scenarios with the goal of maximizing the average task offloading volume. Moreover, virtual queues are constructed in this study to account for the impacts of long-term satellite endurance and terrestrial network resource costs on task offloading. We first decouple the original problem to obtain a single-time-slot optimization problem within the Lyapunov optimization framework, and we then propose a 2-stage optimization approach based on deep reinforcement learning (DRL) to solve the complex single-time-slot mixed integer nonlinear programming (MINLP) problem. Simulation experiments demonstrate that our proposed algorithm can yield near-optimal solutions that show superior performance compared to existing results.

Trusted Sharing of Computing Power Resources: Benefit-Driven Heterogeneous Network Service Provision Mechanism

Trusted Sharing of Computing Power Resources: Benefit-Driven Heterogeneous Network Service Provision Mechanism

Detection and analysis of cellular network traffic anomalies and SMS spammers

The unfolding usage of mobile users inclusive of both 4G and 5G creates huge accumulation of data in cellular network. The network service providers need to ensure proper management of resource in terms of uninterrupted service with cost-effectiveness. The detection of cellular traffic and Short Message Service (SMS) spammers is very challenging. In this paper a novel method is proposed to analyse and detect the traffic anomalies and SMS spammers. To achieve this, Call Detail Record (CDR) issued by service provider is used. The CDR is pre-processed to convert into machine understandable format using mean-normalization technique. K-means clustering elbow method proves to be the best tool in identifying the traffic clusters in the network that detects both high and low traffic in the network. The novelty of the proposed work is the detection of low traffic clusters which usually is misled as sleeping cell or cell outage. The paper also presents a model designed to predict whether the message is spam SMS or ham SMS. The proposed model is suitable to run different classifiers like Logistic Regression, Multi nominal Naive Bayes, Support Vector Machine (SVM), Random Forest Classifier. The model gives the highest accuracy rate of 98.277% with SVM in detecting SMS spam.

DDPG 및 연합학습 기반 5G 네트워크 자원 할당과 트래픽 예측

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

A Model-Free Deep Reinforcement Learning-Based Approach for Assessment of Real-Time PV Hosting Capacity

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization.

PERANCANGAN E - TRANSAKSI PEMBAYARAN INTERNET SERVICE PROVIDER PADA PT DIGITAL NETWORK ANTANUSA (DNA.NET) KOTA JAMBI

PT Digital Network Antanusa (DNA.Net) Jambi City is an internet service provider network service company in the city of Jambi. DNA.Net operates using cable and wireless network technology (wireless). DNA.Net is not a cellular or satellite service, but something new, much better and much faster to connect to the internet and provide to homes or companies. However, there are still problems with processing, paying and checking internet bills, therefore errors and files are often lost when processing payment transaction data. These include frequent errors in internet registration data information and lost documents, delays in transactions or transactions taking too long, and even making payment reports is still not neat. This method used is the UML (Unified Modeling Language) method which describes the system model including use cases, use case specifications, activities and class diagrams and the designs used by researchers are prototypes. Therefore, the researcher aims to analyze and design an internet service provider payment transaction information system so that a computerized payment system is more flexible and efficient so that problems at PT Digital Network Antanusa can be resolved so that the obstacles currently faced can be resolved and run very well. . The final result of the analysis and design produces a maturity system. This is done to make the system more effective and efficient and of course to make the bill payment process easier

Digital finance: a developing country perspective with special focus on gender and regional disparity

PurposeEven if digital financial services have a positive impact on financial inclusion, it creates a digital as well as gender divide within and across countries, creating regional disparity even within developing nations. Though pandemic has initiated digitalization of various services, there has been scanty research on whether digital transfer of income can improve digital financial inclusion in post-pandemic era, especially in developing countries. The purpose of the current study is to explain the regional disparity within developing countries from three regions East Asia Pacific, South Asia and Sub-Saharan Africa, using latest World Findex data, 2021.Design/methodology/approachThe author takes an instrumental variable approach to run bivariate probit model to find the factors that motivate the users to make digital payments.FindingsThe study observes that electronic transfer of wages, government transfers and remittances can motivate individuals to make use of digital mode of transactions and mobile. The practice of formal saving and borrowings are the prerequisites. However, this mechanism holds good for East Asia Pacific and not for South Asia and Sub-Saharan Africa, which are poor in information and communication technology infrastructure. Women are lagging behind men, but digital transfer of wages motivate them to make digital transaction.Practical implicationsDigitalization of all government services and provision of affordable mobile network and internet services are necessary for regions like South Asia and Sub-Saharan Africa. In East Asia Pacific region, data protection, data governance and better regulatory framework are required. Higher female labor force participation with digital transfer of wages and empowerment with smartphones are key to reducing the Gender gap.Originality/valueThe current study corrects for the possible endogeneity issue, which the extant literature has not paid attention to, and provides region-specific and gender-specific policy recommendations for an improved digital inclusion.

Has benchmarking improved the performance of the Australian electricity distribution utilities? A meta-frontier model

This research evaluates the effects of performance benchmarking for public utilities using the Australian experience, providing empirical support for the significance of mechanism design in implementing revenue-cap regulation. We estimate the efficiency and productivity of Australian electricity distribution providers by utilizing the meta-frontier framework with panel data covering the period from 2007 to 2021. Our findings indicate that introducing benchmarking has facilitated technological progress and reduced efficiency gaps among companies. However, there is no statistically significant difference in the average efficiency between the periods before and after the reform, potentially due to strategic behavior exhibited by network service providers.

Decriminalization Path of Net Neutrality Assisting Actions: A Perspective from Aiding and Abetting Cybercrimes

Network neutrality assistance actions pertain to the provision of technical or service aid in an online environment to third parties, which may result in harm to others or facilitate unlawful conduct. However, when attempting to regulate such online assistance activities, traditional complicity theories encounter numerous challenges. This is primarily due to the inherent differences between online actions and conventional criminal behaviors, rendering the traditional notions of complicity difficult to apply directly. Analyzing from the perspective of aiding and abetting crimes, the decriminalization path determination of network neutrality assistance actions also confronts issues, such as defining responsibility boundaries and discerning behavioral intent. To address this, this paper adopts the "Restricted Punishment Theory" for the decriminalization path, rationally weighing the benefits and risks of network neutrality assistance actions. Utilizing empirical research, case study analysis, and comparative research methodologies, and based on the overlap analysis of the constitutive elements of aiding and abetting crimes and the refusal to fulfill cyber security management obligations, it's suggested to categorize network service providers. This provides a clearer and more rational basis for their decriminalization.

Routing Optimization With Deep Reinforcement Learning in Knowledge Defined Networking

Traditional routing algorithms cannot dynamically change network environments due to the limited information for routing decisions. Meanwhile, they are prone to performance bottlenecks in the face of increasingly complex business requirements. Some approaches, such as deep reinforcement learning (DRL) have been proposed to address the routing problems. However, they hardly utilize the information about the network environment fully. The Knowledge Defined Networking (KDN) architecture inspires us to develop new learning mechanisms adapted to the dynamic characteristics of the network topology. In this paper, we propose an effective scheme to solve the routing optimization problem by adding a graph neural network (GNN) structure to DRL, called Message Passing Deep Reinforcement Learning (MPDRL). MPDRL uses the characteristics of GNN to interact with the network topology environment and extracts exploitable knowledge through the message passing process of information between links in the topology. The goal is to achieve the load balance of network traffic and improve network performance. We have conducted experiments on three Internet Service Provider (ISP) network topologies. The evaluation results show that MPDRL obtains better network performance than the baseline algorithms.

Privacy by design. User-held data model as a basis for Privacy and Personal Data protection in social networks

The article reveals the concept and features of privacy by design approach. An analysis of the legislation of the European Union, which regulates the obligation to comply with mentioned concept, namely the provisions of the General Data Protection Regulation, was carried out. Within the framework of the concept of privacy by design, the concepts and pecularities of using user-held data model are studied, which aims to change the legal regulation and technical support of information security as well as the security of personal data in social networks.
 In order to achieve the goal of the research, the need to introduce the specified model of data protection was analyzed – namely practical problems of personal data protection within the peculiarities of the market of social network service providers. The need of the interpretation of personal data as an object of property right was outlined, as well as the interpretation of the purpose of the meta data itself as personal information about a person that can be used to recognize or track a person separately or in combination with other information, which is directly stated in the OMB Act No. A-130, however, is neither interpreted similarly in the Data Protection Regulation nor in the legislation of Ukraine. The main advantages of implementing a user-held data model are presented, namely the advantages for data owners, businesses, the state, and cloud service providers for providing and maintaining the model.
 Legislation in the field of personal data protection in the EU, in particular in the field of social networks, is more complex at the supranational and national level. It is provided with a separate regulatory and organizational mechanisms, which are different from the one that exists today in Ukraine. The mechanism for the protection of personal data in the European Union provides the creation of independent public authority that is responsible for the implementation and compliance with the requirements of the General Data Protection Regulation. The article highlights the need to create a single separate body in the field of personal data protection in Ukraine, which will be able to perform the function of monitoring and ensuring compliance with the requirements of the relevant legislation, in particular, compliance with privacy by design.

Lightweight Transformer Model for Mobile Application Classification.

Recently, realistic services like virtual reality and augmented reality have gained popularity. These realistic services require deterministic transmission with end-to-end low latency and high reliability for practical applications. However, for these real-time services to be deterministic, the network core should provide the requisite level of network. To deliver differentiated services to each real-time service, network service providers can classify applications based on traffic. However, due to the presence of personal information in headers, application classification based on encrypted application data is necessary. Initially, we collected application traffic from four well-known applications and preprocessed this data to extract encrypted application data and convert it into model input. We proposed a lightweight transformer model consisting of an encoder, a global average pooling layer, and a dense layer to categorize applications based on the encrypted payload in a packet. To enhance the performance of the proposed model, we determined hyperparameters using several performance evaluations. We evaluated performance with 1D-CNN and ET-BERT. The proposed transformer model demonstrated good performance in the performance evaluation, with a classification accuracy and F1 score of 96% and 95%, respectively. The time complexity of the proposed transformer model was higher than that of 1D-CNN but performed better in application classification. The proposed transformer model had lower time complexity and higher classification performance than ET-BERT.

User trust and evaluation mechanism based on community classification calculation in opportunistic social networks

With the development of Internet of Things technology, 5G communication has gradually entered people’s daily lives. The number of network users has also increased dramatically, and it has become the norm for the same user to enjoy the services provided by multiple network service providers and to complete the exchange and sharing of a large amount of information at the same time. However, the existing opportunistic social network routing is not sufficiently scalable in the face of large-scale network data. Moreover, only the transaction information of network users is used as the evaluation evidence, ignoring other information, which may lead to the wrong trust assessment of nodes. Based on this, this study proposes an algorithm called Trust and Evaluation Mechanism for Users Based on Opportunistic Social Network Community Classification Computation (TEMCC). Firstly, communication communities are established based on community classification computation to solve the problem of the explosive growth of network data. Then a trust mechanism based on the Bayesian model is established to identify and judge the trustworthiness of the recommended information between nodes. This approach ensures that more reliable nodes can be selected for interaction and complete data exchange. Through simulation experiments, the delivery rate of this scheme can reach 0.8, and the average end-to-end delay is only 190 ms.

New Research on Veterans Treatment Courts: An Overview of the Community Participatory Research on Veterans in Specialized Programming Project

ABSTRACT Justice-involved veterans return to civilian life with a variety of mental and physical health challenges that often go untreated and increase their risk for self-harm and involvement in the criminal-legal system. Veterans Treatment Courts (VTC) were created to respond to the unique problems of justice-involved veterans by attempting to coordinate services and support with the U.S. Department of Veterans Affairs (VA), local treatment providers, and the VTC. Our research has two distinct phases. In Phase 1, we conducted qualitative interviews with VTC team members in twenty (20) VTCs from each USA region; in each VTC, we gained the perspectives of team members – judges, prosecutors, defense attorneys, VJOs, VTC program coordinators, mentors, probation officers, and treatment providers – on the operation of VTCs, with a focus on how to improve service provision for justice-involved veterans. A total of 145 interviews were conducted. We begin by describing the unique problems and treatment needs of justice-involved veterans, and briefly summarize the findings from previous research on the implementation and impact of VTCs. We then present our research study protocol and highlight findings from our phase 1 qualitative interviews with VTC team members. In addition, we describe phase 2 of our project, which will include focus groups with VTC graduates, and quantitative analyses of the service provision networks of three VTCs.

Incentivizing Prosumer Voltage Regulation for Unbalanced Radial Distribution Networks

Voltage regulation in distribution networks is becoming extremely challenging with the increased penetration of rooftop photovoltaic (PV) generation. Many recent studies demonstrate the effectiveness of power control of smart inverters for voltage regulation purposes. However, how to fully incentivize demand-side participation in voltage regulation is yet to be answered. This paper seeks to address this challenge by designing a comprehensive voltage regulation incentive scheme for customers to control their smart inverters in unbalanced radial distribution networks. A three-phase residential network from a Distributed Network Service Provider (DNSP) in Australia is modelled in the open-source distribution system simulator OpenDSS with real load and PV data, and an control algorithm to optimize the households' smart inverters in a distributed fashion is developed using game theory. The proposed algorithm is implemented in MATLAB, which is guaranteed to approach the optimal solution. Simulation results show that the proposed scheme achieves better voltage regulation performance as the widely adopted localized voltage-var droop control method and, at the same time, with lower costs to the prosumers.

A Two-Dimensional Hybrid Federated Learning Framework for Secure Data Cooperation of Multiple Network Service Providers

A Two-Dimensional Hybrid Federated Learning Framework for Secure Data Cooperation of Multiple Network Service Providers

Performance Evaluation of Mobile Network Service Delivered Within Federal Polytechnic Bali and Its Environs; With a Focus on 2G and 3G Technologies

Monitoring the QoS (Quality of Service) of mobile network service providers is key if end users must derive maximum satisfaction from the numerous mobile services provided to them. In this research, data was collected via drive test, using mobile phones, Test Mobile System (TEMS) software a technology used by telecom operators to measure, analyze, and optimize their mobile networks, and Mapinfo software for post processing. For 2G technology, four parameters were analyzed which are Rx Lev (Received Signal Level), and Rx Qual (Received Signal Quality), while for 3G technology three parameters were analyzed which are RSCP (Received Signal Code Power), Ec/Io, and downlink throughput. The serving cells, Broadcast control channels as well as the Primary scrambling codes for all mobile network operators were recorded. The data collected was for MTN, Airtel and GLOBACOM (Glo) Networks, and the area of study comprised Bali metropolis, Maihula, Gazuba and Garbabi. It was deduced that the Rx_Lev range of negative 10dBm to negative 65dBm covered just 4.53%, 4.31% and 6.62% of the total route driven for MTN, Airtel and Glo respectively, which falls below the 75% requirement by the Nigerian Communication Commission (NCC). It was recommended amongst others the physical optimizations be carried out for PSC 19, 46, 70, 205, 313, 368 in order to improve coverage, quality and downlink throughput. The results of this study have practical implications for all mobile network operators within the area of study as it provides valuable information enabling them to enhance their network infrastructure and services in order to meet set key performance indicators (KPIs) by the regulator, as well as ensure user derive satisfaction.

Blockchain-based Logistics 4.0: enhancing performance of logistics service providers

PurposeTechnology is the lifeline for the logistics industry, and it has been immensely disrupted by the emerging blockchain technology. This paper has two main objectives. The first is to explore how the current blockchain technology can be implemented in the logistics industry with the aim of improving logistic services amongst the network of logistics service providers (LSPs). The second is to propose the development of a blockchain model for the small and medium logistics service supply chain.Design/methodology/approachA prototype blockchain-based logistics system has been created and tested in a case study with a real logistics company. The primary technologies for developing a blockchain model on the Hyperledger platform as well as how the system is designed based on the logistics service flow are explained.FindingsThe study has resulted in the successful implementation of the proposed prototype blockchain-based logistics system. In particular, the case company has managed to fully utilise the developed tracking and tracing system. Whilst utilising the prototype, the participants have been able to fulfil their responsibilities in an effective manner. The performance of LSPs has improved following the World Bank Logistics Performance Index (LPI) criteria.Originality/valueThis paper contributes to current research in the application of blockchain technologies in the domain of logistics and the supply chain to progress LSPs towards Logistics 4.0. The current frameworks for Logistics 4.0 and how blockchain as a disruptive technology revolutionises logistic services are reviewed. In addition, this paper highlights the benefits of blockchain technology that LSPs can leverage to further improve their performance based on the LPI criteria.

Exploring the Privacy Paradox in Social Network Users: A Double-Entry Mental Accounting Theory Perspective

Social networking service (SNS) users often express great concern for their personal privacy, yet continue to disclose personal information on these platforms. This privacy paradox between privacy concerns and disclosure behavior has drawn widespread academic attention. In this study, we use the double-entry mental accounting theory to construct a theoretical model and conduct an in-depth analysis of the privacy paradox phenomenon and its causes through empirical verification. Our research shows a significant positive correlation between perceived benefits and users’ intention to disclose privacy, while perceived risks and users’ intention to disclose privacy are significantly negatively correlated. The double-entry mental accounting theory plays a crucial role in mediating the relationship between perceived values and users’ intention to disclose privacy. Furthermore, we found that information sensitivity negatively regulates the relationship between perceived risks, the pleasure attenuation coefficient α, the pain buffering coefficient β, and the intention to disclose privacy. Our study provides theoretical and empirical information on the reasons for the privacy paradox and offers insights for social networking service providers to optimize their services.