Management Scheme For Networks 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.

DER Control and Management Strategies for Distribution Networks: A Review of Current Practices and Future Directions

DER Control and Management Strategies for Distribution Networks: A Review of Current Practices and Future Directions

INTELLIGENT HOME IOT DEVICES: AN EXPLORATION OF MACHINE LEARNING-BASED NETWORKED TRAFFIC INVESTIGATION

In the rapidly evolving landscape of smart homes powered by Internet of Things (IoT) devices, the twin specters of safety and privacy loom large, exacerbated by pervasive security vulnerabilities. Confronted with a heterogeneous array of devices each with unique Value of Service (QoS) requirements, devising a singular network management strategy proves untenable. To mitigate these risks, device categorization emerges as a promising avenue, wherein rogue or vulnerable devices are identified and network operations are automated based on device type or function. This novel approach not only fortifies IoT security but also streamlines network management, offering a multifaceted solution to the burgeoning challenges. Recognizing the burgeoning interest in leveraging machine learning for traffic analysis in IoT environments, this study delves deep into the potential and pitfalls of such techniques. Beginning with a comprehensive framework for categorizing IoT devices, the research meticulously examines methodologies and remedies across every stage of the workflow. Key focal points include the categorization of public datasets, nuanced analysis of IoT traffic data collection methodologies, and the exploration of feature extraction techniques. Through a rigorous evaluation of machine learning algorithms for IoT device classification, the study elucidates emerging trends and highlights promising avenues for future exploration. The culmination of this investigation manifests in meticulously crafted taxonomies, offering insights into prevailing patterns and informing future research trajectories. Moreover, the study identifies and advocates for uncharted territories within this burgeoning domain, propelling the discourse forward and catalyzing innovation in IoT security and management.

INTELLIGENT HOME IOT DEVICES: AN EXPLORATION OF MACHINE LEARNING-BASED NETWORKED TRAFFIC INVESTIGATION

In the rapidly evolving landscape of smart homes powered by Internet of Things (IoT) devices, the twin specters of safety and privacy loom large, exacerbated by pervasive security vulnerabilities. Confronted with a heterogeneous array of devices each with unique Value of Service (QoS) requirements, devising a singular network management strategy proves untenable. To mitigate these risks, device categorization emerges as a promising avenue, wherein rogue or vulnerable devices are identified and network operations are automated based on device type or function. This novel approach not only fortifies IoT security but also streamlines network management, offering a multifaceted solution to the burgeoning challenges. Recognizing the burgeoning interest in leveraging machine learning for traffic analysis in IoT environments, this study delves deep into the potential and pitfalls of such techniques. Beginning with a comprehensive framework for categorizing IoT devices, the research meticulously examines methodologies and remedies across every stage of the workflow. Key focal points include the categorization of public datasets, nuanced analysis of IoT traffic data collection methodologies, and the exploration of feature extraction techniques. Through a rigorous evaluation of machine learning algorithms for IoT device classification, the study elucidates emerging trends and highlights promising avenues for future exploration. The culmination of this investigation manifests in meticulously crafted taxonomies, offering insights into prevailing patterns and informing future research trajectories. Moreover, the study identifies and advocates for uncharted territories within this burgeoning domain, propelling the discourse forward and catalyzing innovation in IoT security and management.

SAKMS: A Secure Authentication and Key Management Scheme for IETF 6TiSCH Industrial Wireless Networks Based on Improved Elliptic-Curve Cryptography

SAKMS: A Secure Authentication and Key Management Scheme for IETF 6TiSCH Industrial Wireless Networks Based on Improved Elliptic-Curve Cryptography

Enhancing Efficiency and Security in MTC Environments: A Novel Strategy for Dynamic Grouping and Streamlined Management

This study presents a new strategy to improve security and efficiency in Machine-Type Communication (MTC) networks, addressing the drawbacks of the existing Adaptive Hierarchical Group-based Mutual Authentication and Key Agreement (AHGMAKA) protocol. The AHGMAKA protocol, crucial for securing communication within groups of devices with limited resources, has been found to cause significant operational delays and inefficiencies. Our proposed solution integrates advanced cryptographic methods, including an optimized Authenticated Message Authentication Code (AMAC) and lightweight encryption, sophisticated optimization algorithms for dynamic grouping, and an efficient, lightweight group management protocol. It also introduces adaptive network management strategies to customize performance according to the needs of MTC networks. The effectiveness of this approach has been validated through empirical analysis, showing considerable improvements in operational performance and energy efficiency. These improvements mark a significant step toward achieving an optimal balance between efficiency and security for MTC networks. However, the research acknowledges ongoing challenges, including the trade-off between security and efficiency and the issue of compatibility with older devices, suggesting these as areas for future study. The paper outlines potential research paths, including using machine learning for better group management, adopting post-quantum cryptographic methods, applying hardware acceleration, and pushing to standardize these technologies. This work significantly advances the field of secure and efficient communication in MTC, a critical component of the growing Internet of Things (IoT) landscape, setting the stage for future breakthroughs.

A Hierarchical Network Management Strategy for Distributed CIIoT With Imperfect CSI

A Hierarchical Network Management Strategy for Distributed CIIoT With Imperfect CSI

Design of an Iterative Method for Dynamic Resource Management in 5G Networks with IoT Integration Operations

In the realm of fifth-generation (5G) wireless networks, the escalating demands for high-speed, reliable, and efficient communication are paramount, especially with the widespread deployment of Internet of Things (IoT) devices & scenarios. Despite the advancements in 5G technologies, existing network management strategies often fall short in addressing the dynamic nature of network conditions, the heterogeneity of IoT device requirements, and the need for stringent privacy measures. These limitations underscore the necessity for innovative approaches that can adapt in real-time to varying demands while ensuring optimal network performance and user privacy. This paper introduces a suite of machine learning models designed to enhance the efficiency and reliability of 5G networks, catering specifically to the diverse needs of IoT applications. At the forefront, DynamicSlicerNet, a deep reinforcement learning-based model, dynamically slices 5G network resources tailored to IoT devices' requirements, addressing device mobility, application demands, and network congestion. This model demonstrates a substantial reduction in latency by up to 30% and improvement in reliability by up to 20%, outperforming static resource allocation methods. Further enhancing edge computing capabilities, FedEdgeAI leverages federated learning to train models directly on edge devices, a move that not only slashes latency by minimizing data transmission to centralized servers but also fortifies data privacy. Experimental evaluations highlight FedEdgeAI's efficacy in maintaining model accuracy while halving communication overhead. PredictiveNetCare, employing time-series analysis and anomaly detection, anticipates network failures, facilitating preemptive maintenance strategies. This predictive approach has shown a marked precision over 90% in identifying potential disruptions, significantly reducing maintenance-related downtime by 30% and bolstering network reliability by 15%. OptiAllocRL and AdaptiveQoSDL, both harnessing reinforcement and deep learning techniques, respectively, optimize resource allocation and manage Quality of Service (QoS) parameters adaptively. OptiAllocRL's strategy results in a 40% latency reduction and a 25% throughput increase, while AdaptiveQoSDL minimizes packet loss by up to 50% and enhances end-to-end delay by up to 35%, ensuring high QoS levels under fluctuating network conditions. This comprehensive approach sets a new benchmark for future 5G network management, paving the way for a more connected, efficient, and secure digital world.

Connection patterns and resilience simulation of innovation network in the context of technology transfer: A case study of strategic emerging industries in the Guangdong-Hong Kong-Macao Greater Bay Area

Technology transfer is crucial for the flow and reconfiguration of regional innovation elements. Besides, leveraging network methodologies effectively uncovers regional knowledge exchange and transfer patterns. Combining the “Buzz-Gatekeeper-Pipeline” model, this study introduces a comprehensive analytical framework that utilizes a patent transfer database to characterize innovation network types, identify connection patterns, and enable resilience loss simulation, elucidating regional technology transfer types and connection patterns from a network perspective, delineating the evolution and potential causes of network structural resilience loss, and finally establishing a list of differentiated and hierarchical innovation network management strategies. Focusing on the Guangdong-Hong Kong-Macao Greater Bay Area, this empirical study reveals the following: 1) Technology transfer primarily occurs between different enterprises. However, on average, knowledge products from colleges/universities and research institutions boast relatively high technical contribution efficiency to enterprises. 2) The innovation network is driven by the “Technological Gatekeeper” as the main connector. Four types of innovation communities—isolated, insider, seeker, and networked—are formed based on different combinations of “Community Buzz” and “Regional Pipeline”. 3) A significant positive correlation exists between innovation network connection patterns and network structural resilience. The impact of different types of “Regional Pipelines” on network structural resilience varies significantly.

Evolution of Project-Based Collaborative Networks for Implementing Prefabricated Construction Technology: Case Study in Shanghai

Prefabricated construction (PC) is considered to be a low-carbon construction method. Implementing prefabricated construction projects (PCPs) requires multiple industry organizations to participate and collaborate. As different PCPs are initiated and implemented, industry organizations will gradually gather into a complex and evolving collaborative network at the industry level based on specific collaboration relationships with different project backgrounds. The evolution of the collaborative network is related to how industry organizations interact with each other, and how PC-related knowledge and innovation has spread among organizations in the long term. However, the laws of network evolution and the micro effects that drive network evolution are still unknown. This study analyzes 236 prefabricated construction projects (PCPs) in Shanghai during 2015–2023, using the stochastic actor-oriented models (SAOM) method to explore how the macro structure of project-based inter-organizational collaborative networks for prefabricated construction (PC) technology implementation evolves over time, and how micro effects jointly support the evolution of the networks. The macro-level descriptive analysis of the network indicates that the collaborative network has become increasingly dense over time and continues to show a core–peripheral structure, with a small number of super-connected organizations. The micro-level SAOM analysis further reveals that the evolution of the collaborative network structure is driven by structure-based preferential attachment and geographic proximity effects, as well as attribute-based ownership similarity effect. This exploratory effort applies a network dynamics model to investigate the micro mechanism of the evolution of inter-organizational collaboration. The research results provide theoretical guidance and decision-making references for PC industry organizations to develop efficient network action strategies. In addition, it can help industry managers to formulate appropriate network management strategies.

Do network management and trust matter for network outcomes? A meta-analysis and research agenda

ABSTRACT Collaborative and network governance assume that network management and trust matter for network outcomes. We test this assumption by conducting a meta-analysis of public administration studies investigating the correlation between network management and network outcomes (50 effect sizes), and trust and network outcomes (28 effect sizes). While both matter for achieving network outcomes across countries, trust matters most. Trust is particularly important for achieving process outcomes and multiple network management strategies combined are more effective than separate single strategies. A research agenda centred on complex modelling, comparative research and using mixed, multisource, experimental and longitudinal data is stipulated in conclusion.

Optimization of Ship Power Supply Network and Intelligent Energy Management Strategy Under Multiple Energy Modes

<p>Purpose: This article aims to address the issues of incomplete management models and slow convergence speed of optimization models in energy management in ship multi energy systems, and to construct a comprehensive dynamic optimization objective model. Method: Firstly, establish an optimization design model with the objective functions of energy storage system cost, grid power fluctuation smoothing, and energy supply and demand balance; Then, in the optimization process of the objective function, a bus voltage coordination control strategy is adopted, and for the parameter optimization problem in the strategy, a cuckoo search algorithm based on population feature feedback is used for optimization. Result: Through simulation experiments, the method proposed in this article provides effective guidance for capacity configuration and energy management of multi energy ship microgrids, improving quality and efficiency.</p> <p> </p>

Comparative Analysis of Time Series Prediction Algorithms on Multiple Network Function Data of NWDAF

With the emergence and vigorous development of 5G technology, there is a significant surge in network usage and traffic, resulting in heightened complexity within network and IT environments. This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). This function grants network operators the flexibility to either employ their own data analytics methodologies based on machine learning (ML) and deep learning (DL) into their networks. In this paper, we present a dataset named “NWDAF-NFPP” for network function performance time series prediction, collected from a laboratory at China Telecom. The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. The availability of this dataset provides valuable resources for researchers to conduct time series prediction research on network element performance. Following data collection, a total of six models were employed for network element performance prediction, encompassing both machine learning and deep learning approaches. This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. This comparative analysis will provide valuable insights into the strengths and limitations of each model, aiding in informed decision-making for network optimization and management strategies in the future.

Stochastic model for active distribution networks planning: An analysis of the combination of active network management schemes

The widespread adoption of distributed generation (DG) is changing the operation of distribution networks. To increase the control of power flows and avoid the detriments of DG intermittency, active distribution networks (ADN) emerge as a feasible solution. In this paper, we propose a convex model for ADN expansion planning. Four active network management (ANM) schemes are considered: DG active power curtailment, DG reactive power control, on-load tap changer (OLTC) tap adjustment, and shunt compensation device control. The problem is formulated as a mixed integer second-order cone programming problem (MISOCP) using the distflow equations and second-order cone relaxations (SOCR). By scenario analysis, we address the uncertainty associated with power demand, renewable generation, and electricity prices. The proposed model is used to study the benefits of different combinations of ANM schemes to a modified IEEE 33-bus system and compare them with the individual benefits of each scheme. The results indicate that ANM schemes can reduce total network costs, reduce losses, delay network reinforcement, and improve the voltage profile. In addition, some combinations of schemes develop synergies in which the combined economic benefits are greater than the sum of the individual benefits.

"You Shouldn't Need to Share Your Data": Perceived Privacy Risks and Mitigation Strategies Among Privacy-Conscious Smart Home Power Users

Fueled by Internet-of-Things technologies and spanning a wide range of sensors, speakers, and cameras, smart homes promise to make our lives easier and automate routine tasks. From speakers to security cameras, smart home devices (SHDs) answer our questions, monitor our home environment, and conserve energy. They also collect significant data, ranging from on/off commands to audio and video data, and they do this in some of our most private spaces. In this paper, we explore the privacy risks associated with SHDs by focusing on privacy-conscious smart home power users--those who spend significant time and money to research, install, and integrate devices throughout their homes and engage in advanced device and network management strategies to mitigate privacy concerns. Drawing on data from 10 focus groups with 32 privacy-conscious power users, we identify the key privacy risks they perceive from this technology, as well as how they mitigate those risks through increasingly complex strategies. Our findings reveal that navigating the technical landscape that makes up the smart home environment--including what data is collected, what options are available for managing or restricting data flows, and who has access to data collected by SHDs--is complex and often confusing, even for people who spend significant time researching devices and integration options. We use these findings to argue for further development of tools that are transparent, easy to use, and aligned with the privacy needs of a diverse userbase.

Network management schemes for IoT environment towards 6G: A comprehensive review

Usage of smart devices, expansion of Internet of Things (IoT) network, and its adoption in variety of applications grows exponentially day-by-day. It leads to various issues such as network privacy, security management, unreliable network management (NM) scheme, resource utilization, energy utilization, decentralization, transparency, and data interoperability. Development of the NM platform/framework for secure data dissemination in IoT networks is tedious due to heterogeneity and imbalanced IoT protocol stack. Recently, traditional network scheduling methods, Software Defined Networks (SDN), Network Function Virtualization (NFV), and hybrid approaches are widely used for managing the IoT network. But, most of these approaches are controlled by a centralized server, which may not be appropriate due to the heterogeneity of the IoT devices. Motivated from the aforementioned discussion, In this paper, we present a systematic survey on various NM schemes used to secure data dissemination for the IoT environment. We present a detailed taxonomy of all the existing NM schemes for IoT networks. Pros and cons of the traditional NM methods applicable for IoT environment are discussed in detail. The paper discusses a state-of-art survey on integrating blockchain (BC) technology in the existing IoT NM techniques. In this paper, our prime focus is to understand the potential of BC for enabling secured IoT management. Finally, a case study on BC implication for Unmanned Aerial Vehicle (UAV) network using 6G is also discussed along with future directions.

Blockchain-Assisted Privacy-Preserving Traffic Route Management Scheme for Fog-Based Vehicular Ad-Hoc Networks

Traffic route management is essential for reducing traffic jams and enhancing driving safety becauese of the growing number of vehicles and frequent occurrence of traffic accidents. However, in vehicular ad-hoc networks (VANETs), real-time messages are transmitted via wireless channels, which can result in security and privacy concerns. Existing proposals for traffic route management exist security vulnerabilities, as well as high calculation and communication costs. Encouraged by this fact, we design a lightweight traffic route management scheme for fog-based VANETs. In this scheme, vehicles utilize homomorphic encryption to encrypt their driving routes and then send the encrypted information to a fog node. The traffic management center (TMC) decrypts the received ciphertexts that are aggregated by the fog node and performs traffic management according to the decrypted data, without knowing individual route of each vehicle. Furthermore, blockchain is used in the scheme to conduct public keys management of vehicles. Our detailed security proof and analysis indicate that our proposal can meet the security objectives of VANETs. Further, to demonstrate the feasibility of the scheme, we also implement it in the Ethereum test network (i.e.,Rinkeby). Significantly, the performance analysis demonstrates that our proposal achieves a better performance than other relevant representative schemes.

New key management scheme lattice-based for clustered wireless sensor networks.

Aiming at the quantum algorithm which can solve the problem of large integer decomposition and discrete logarithm in polynomial time, an anti-quantum computing key management scheme for clustered sensor networks is proposed in this paper. The lattice-based cryptosystem is used to achieve the anti-quantum performance of the key management scheme, and the security of the network is further improved through the mutual authentication of sensor network nodes. Due to the limited storage space of sensor nodes, this paper adopts the cluster management of wireless sensor networks, and most sensor nodes only need a small amount of storage space, thus reducing the deployment cost. Cluster management is suitable for medium and large-scale deployment of sensor networks. Because the data traffic is much larger than that of mutual authentication, the sensor nodes in wireless sensor networks use symmetric keys to communicate with each other after mutual authentication, which can effectively improve the communication efficiency in the case of frequent data communication. Experiments show that the authentication scheme based on lattice cryptosystem proposed in this paper will not improve with the continuous improvement of the security level, and its authentication scale will maintain a relatively stable state, while the algorithm scheme based on RSA will increase the authentication cost with the continuous improvement of the security level, so the scheme proposed in this paper is more suitable for application in the environment with high security level. This scheme can effectively reduce the cost of mutual authentication of sensor nodes, is conducive to the expansion of the network, and can ensure the security of authentication between sensor nodes even in the post-quantum era.

Multiple Probability Ecological Network and County-Scale Management

Ecological networks are threatened by rapid urbanization; the implementation of ecological network management and maintenance strategies is essential in the county units of urban agglomerations. This study focused on the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA) and objectively identified and evaluated the regional ecological networks. Connectivity distance thresholds were determined using goodness-of-fit experiments. The Probability of becoming an Ecological Source (PES) was constructed to investigate the ecological networks at different probabilities, and the network weight in each county was analyzed based on the gravity model. The results show that: (1) The distance thresholds were 2500, 2700, and 2300 m in 2000, 2010, and 2020, respectively. Source degradation and corridor disconnection were most significant at a PES > 50%. (2) At a PES > 50%, the high gravitational value increased from 39,972 to 31,4642, owing to fragmentation. Fourteen counties received weights, and fluctuations were most significant at a PES > 90%. (3) The trends in the PES and gravitational value were not always synergistic, and the negative impact of source degradation on the gravitational value was significantly less than the positive impact of the reduced distance between sources. The gravity center movement under the high PES determined the ecological network status of neighboring counties. The findings can quantify ecological information flow and provide a basis for county ecological management and restoration.

Optimal planning and forecasting of active distribution networks using a multi-stage deep learning based technique

This paper presents a comprehensive methodology for long-term planning in distribution networks to address the challenges associated with integrating renewable energy resources (RERs) and battery energy storage systems (BESSs). The methodology incorporates active network management (ANM) schemes, coordinated voltage control (CVC), and demand side management (DSM) to optimize the sizing and siting of RERs and BESS. Deep learning-based long-term forecasting techniques are utilized to capture the hourly variations in load demand, solar irradiance, and wind speed throughout the year. The objective is to minimize overall network costs, considering investment and operational costs, while planning over a 5-year horizon with a 3% annual load growth. The model includes tie-lines as a decision variable for network expansion investments, ensuring efficient network flexibility. The linearized mathematical model is developed as a mixed integer linear programming (MILP) problem to enhance scalability. A case study is conducted on the UKGDS 16-bus system to verify the optimization model, and further validation is performed on the IEEE 69-bus system to assess scalability. The results demonstrate the effectiveness of the proposed method in achieving accurate forecasting and efficient sizing and siting decisions for RERs and BESSs, including tie-line investments, providing valuable insights for real-world distribution network scenarios.