Distributed Network Environment Research Articles

Day-Ahead Economic Dispatch Strategy for Distribution Networks with Multi-Class Distributed Resources Based on Improved MAPPO Algorithm

In the context of the global response to climate change and the promotion of an energy transition, the Internet of Things (IoT), sensor technologies, and big data analytics have been increasingly used in power systems, contributing to the rapid development of distributed energy resources. The integration of a large number of distributed energy resources has led to issues, such as increased volatility and uncertainty in distribution networks, large-scale data, and the complexity and challenges of optimizing security and economic dispatch strategies. To address these problems, this paper proposes a day-ahead scheduling method for distribution networks based on an improved multi-agent proximal policy optimization (MAPPO) reinforcement learning algorithm. This method achieves the coordinated scheduling of multiple types of distributed resources within the distribution network environment, promoting effective interactions between the distributed resources and the grid and coordination among the resources. Firstly, the operational framework and principles of the proposed algorithm are described. To avoid blind trial-and-error and instability in the convergence process during learning, a generalized advantage estimation (GAE) function is introduced to improve the multi-agent proximal policy optimization algorithm, enhancing the stability of policy updates and the speed of convergence during training. Secondly, a day-ahead scheduling model for the power distribution grid containing multiple types of distributed resources is constructed, and based on this model, the environment, actions, states, and reward function are designed. Finally, the effectiveness of the proposed method in solving the day-ahead economic dispatch problem for distribution grids is verified using an improved IEEE 30-bus system example.

Multi-Source Information Fusion-Based Localization in Wireless Sensor Networks

In wireless sensor networks, accurate node localization is essential for ensuring the precision of data collection. The DV-Hop algorithm, a popular range-free localization method, estimates distances between nodes by multiplying hop counts with average hop distances obtained through distance vector routing. However, this algorithm often experiences localization errors in randomly distributed network environments due to considerable inaccuracies in average hop distance calculations and the approximation of actual paths by straight-line paths. This paper introduces an enhanced DV-Hop localization algorithm, which constructs a mathematical model to optimize the mean square error of the average hop distance for any anchor node. This optimization corrects the average hop distance across the network, bringing it closer to the actual value, thus reducing errors and enhancing accuracy. Simulation results indicate that with 150 nodes, a 30% beacon node ratio and a 100-m communication range, the localization error of the improved FuncDV-Hop model decreased from 0.3916 to 0.1705, and the Root Mean Square Error (RMSE) decreased from 24.78[Formula: see text]m to 14.39[Formula: see text]m, thereby improving localization accuracy by 56.46%.

Distribution network line loss analysis method based on improved clustering algorithm and isolated forest algorithm

The long-term loss of distribution network in the process of distribution network development is caused by the backward management mode of distribution network. The traditional analysis and calculation methods of distribution network loss can not adapt to the current development environment of distribution network. To improve the accuracy of filling missing values in power load data, particle swarm optimization algorithm is proposed to optimize the clustering center of the clustering algorithm. Furthermore, the original isolated forest anomaly recognition algorithm can be used to detect outliers in the load data, and the coefficient of variation of the load data is used to improve the recognition accuracy of the algorithm. Finally, this paper introduces a breadth-first-based method for calculating line loss in the context of big data. An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out. And the findings revealed that the error of the enhanced fuzzy C-mean clustering algorithm was on average − 6.35, with a standard deviation of 4.015 in the situation of partially missing data. The area under the characteristic curve of the improved isolated forest algorithm subjects in the case of the abnormal sample fuzzy situation was 0.8586, with the smallest decrease, based on the coefficient of variation, and through the refinement of the analysis, it was discovered that the feeder line loss rate is 7.62%. It is confirmed that the suggested technique can carry out distribution network line loss analysis fast and accurately and can serve as a guide for managing distribution network line loss.

Transparency, reproducibility, and replicability of pharmacoepidemiology studies in a distributed network environment.

Our objective is to describe how the U.S. Food and Drug Administration (FDA)'s Sentinel System implements best practices to ensure trust in drug safety studies using real-world data from disparate sources. We present a stepwise schematic for Sentinel's data harmonization, data quality check, query design and implementation, and reporting practices, and describe approaches to enhancing the transparency, reproducibility, and replicability of studies at each step. Each Sentinel data partner converts its source data into the Sentinel Common Data Model. The transformed data undergoes rigorous quality checks before it can be used for Sentinel queries. The Sentinel Common Data Model framework, data transformation codes for several data sources, and data quality assurance packages are publicly available. Designed to run against the Sentinel Common Data Model, Sentinel's querying system comprises a suite of pre-tested, parametrizable computer programs that allow users to perform sophisticated descriptive and inferential analysis without having to exchange individual-level data across sites. Detailed documentation of capabilities of the programs as well as the codes and information required to execute them are publicly available on the Sentinel website. Sentinel also provides public trainings and online resources to facilitate use of its data model and querying system. Its study specifications conform to established reporting frameworks aimed at facilitating reproducibility and replicability of real-world data studies. Reports from Sentinel queries and associated design and analytic specifications are available for download on the Sentinel website. Sentinel is an example of how real-world data can be used to generate regulatory-grade evidence at scale using a transparent, reproducible, and replicable process.

Defence Against Command Injection Attacks in a Distributed Network Environment

Defence Against Command Injection Attacks in a Distributed Network Environment

Simulation of Vehicular Bots-Based DDoS Attacks in Connected Vehicles Networks

Connected vehicles are more vulnerable to attacks than wired networks since they involve rapid mobility, continuous data flow across connected nodes, and dynamic network design in a distributed network environment. Distributed Denial of Service (DDOS) is one of the most common and dangerous security attacks on connected vehicle networks. Attackers can remotely control malicious nodes that are programmed to attack other nodes known. The compromised nodes are known as botnets, which will constantly flood the target nodes with User Datagram Protocol (UDP) packets, disrupting the target nodes data flow and operation. Hence, the goal of this research is to create and simulate a vehicular bot-based Distributed Denial of Service (DDoS) assault in connected vehicle networks. A simulation-based methodology is implemented to observe the impact of the number of bots, DDoS rate, and maximum bulk packet size on network performance. Using the NS-3 network simulator, 73 random mobile vehicle nodes with up to 100 vehicle bots were simulated, and the results are discussed. Regardless of the computational constraints, the findings from this study adds to understanding the risks and problems associated with data transmission by analyzing the impact of vehicular bot-based DDoS attacks on connected vehicle performance. Doi: 10.28991/HIJ-2023-04-04-014 Full Text: PDF

Quality of Service Management Solution Becomes a Software-Defined Network Challenge

The use of Software-Defined Networking (SDN) has created a huge leap in the management of computer networks. SDN offers flexibility and the ability to dynamically manage networks, but along with its advantages, it also brings significant challenges in managing quality of service (QoS). QoS is critical to maintaining performance and user experience in increasingly complex and distributed network environments. The research method uses Solution Development to address a quality of service (QoS) management challenge and solution in Software Defined Networking with the approach of designing and developing practical solutions to address QoS management issues and challenges in SDN environments. Control Separation and Data Plane are centralized network control at the SDN controller, while the data plane reside in the networks hardware, both capable of maintaining QoS and a challenges in SDN with a computer network approach that separates the control layer from the data layer, enabling more flexible and centralized network management. Research conclusions related to SDN Software Defined Networking quality of service management problems and solutions, focusing on the problem of separating management and data levels. The separations of control and data plane concept in SDN has great potential to improve flexibility, scalability and more efficient network management.

Construction of a 3D Distribution Network Environment Based on Multi-source Data Fusion

Traditional distribution networks are designed using two-dimensional drawings. Its expression is abstract, the accuracy of manually measured elevation data could be higher, and complex sections are difficult to measure. To solve this problem, a three-dimensional environment for digital planning and design of the distribution network is proposed by combining laser point cloud and oblique photography modelling. Firstly, a rough registration method based on the main material plane is proposed to improve the registration efficiency. The GICP method is used to accurately register the point cloud and build a refined model of distribution equipment; Then, the UAV tilt photography technology is used to model the distribution line corridor, and the elevation model is obtained at the same time. The experimental results show that the model error after precise registration is 3.75 cm, which meets the requirements of high-precision modelling. The average error between the elevation model obtained by the tilt photography of the distribution line corridor and the measured elevation is 0.112 m, which meets the accuracy requirements of mapping.

ECM: Enhanced confidentiality method to ensure the secure migration of data in VM to cloud environment

Cloud is the technology behind all modern IT paradigms today. All kinds of applications run in the cloud environment and host their data. Enterprises show interest in migrating their data and servers to the cloud to benefit the cloud. The cloud is an open distributed network environment; hence, it is vulnerable to data security attacks. Migration in cloud computing permits the handover of resources, for example, virtual machine (VM) and data from off-premises to on-premises. During the migration, the data needs to be secured. This paper proposes an enhanced confidentiality technique (ECM) to ensure data security when migrated to the cloud. The proposed enhancement is on the advanced encryption standard (AES) to strengthen the protection level of the AES. The standard AES runs for rounds with four unique stages of data processing: Substitute bytes, add round key, shift rows and mix columns. The proposed ECM enhances the substitute bytes stage with dynamic substitution boxes for each round of the AES. The proposed ECM is evaluated for its efficiency by the avalanche effect. The result of the ECM improves data protection when the data is migrated to the cloud.

Research on Volt/Var Control of Distribution Networks Based on PPO Algorithm

In this paper, a model free volt/var control (VVC) algorithm is developed by using deep reinforcement learning (DRL). We transform the VVC problem of distribution networks into the network framework of PPO algorithm, in order to avoid directly solving a large-scale nonlinear optimization problem. We select photovoltaic inverters as agents to adjust system voltage in a distribution network, taking the reactive power output of inverters as action variables. An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment. OPENDSS is used to output system node voltage and network loss. This method realizes the goal of optimal VVC in distribution network. The IEEE 13-bus three phase unbalanced distribution system is used to verify the effectiveness of the proposed algorithm. Simulation results demonstrate that the proposed method has excellent performance in voltage and reactive power regulation of a distribution network.

A Fuzzy-Based Duo-Secure Multi-Modal Framework for IoMT Anomaly Detection

With the advancement in the Internet of Medical Things (IoMT) infrastructure, network security issues have become a serious concern for hospitals and medical facilities. For this, a variety of customized network security tools and frameworks are used to distract several generalized attacks such as botnet-based distributed denial of services attacks (DDoS) and zero-day network attacks. Thus, it becomes difficult to operate routine IoMT services and tasks in between the under-attack scenario. This paper discusses a novel approach named Duo-Secure IoMT framework that uses multi-modal sensory signals’ data to differentiate the attack pattern and routine IoMT devices’ data. The proposed model uses a combination of two techniques such as dynamic Fuzzy C-Means clustering along with customized Bi-LSTM technique that processes sensory medical data securely along with identifying attack patterns within the IoMT network. As a case study, we are using a dataset to evaluate heart disease which consists of 36 attributes and 18940 instances. The performance evaluation shows that the proposed model evaluates a) prediction of heart issues and b) identification of network malware with an individual accuracy of 92.95% and multi-modal joint accuracy of 89.67% in the IoMT-based distributed network environment.

Power restoration model for distribution networks after extreme rainstorm disasters based on electric vehicle travel characteristics

Improving the resilience of the distribution grid to extreme rainstorms can effectively reduce the resulting outage losses. Based on the massive number of electric vehicles (EV) with objective capacity in the distribution network environment, it is essential to study the discharge of EVs to make up for the shortage load in the distribution network’s post-disaster recovery. In this paper, we first establish a coupling network integrating the distribution grid and road network, simultaneously coupling information from different levels. Then establish a model describing the characteristics of EV travel behavior based on the travel chain and probability distribution function to portray the spatial distribution of EVs in the time dimension. Then establish a model of EVs in this area and cross-area EV participating in discharge response based on the area after EV clustering. Also, establish the extreme storm weather affected EV discharge willingness model to describe the uncertainties of EVs. Finally, two faults set in the IEEE33 node are simulated and verified, and the model description and analysis of the influencing factors of EV participation in discharge are given.

A resource scheduling method for reliable and trusted distributed composite services in cloud environment based on deep reinforcement learning.

With the vigorous development of Internet technology, applications are increasingly migrating to the cloud. Cloud, a distributed network environment, has been widely extended to many fields such as digital finance, supply chain management, and biomedicine. In order to meet the needs of the rapid development of the modern biomedical industry, the biological cloud platform is an inevitable choice for the integration and analysis of medical information. It improves the work efficiency of the biological information system and also realizes reliable and credible intelligent processing of biological resources. Cloud services in bioinformatics are mainly for the processing of biological data, such as the analysis and processing of genes, the testing and detection of human tissues and organs, and the storage and transportation of vaccines. Biomedical companies form a data chain on the cloud, and they provide services and transfer data to each other to create composite services. Therefore, our motivation is to improve process efficiency of biological cloud services. Users’ business requirements have become complicated and diversified, which puts forward higher requirements for service scheduling strategies in cloud computing platforms. In addition, deep reinforcement learning shows strong perception and continuous decision-making capabilities in automatic control problems, which provides a new idea and method for solving the service scheduling and resource allocation problems in the cloud computing field. Therefore, this paper designs a composite service scheduling model under the containers instance mode which hybrids reservation and on-demand. The containers in the cluster are divided into two instance modes: reservation and on-demand. A composite service is described as a three-level structure: a composite service consists of multiple services, and a service consists of multiple service instances, where the service instance is the minimum scheduling unit. In addition, an improved Deep Q-Network (DQN) algorithm is proposed and applied to the scheduling algorithm of composite services. The experimental results show that applying our improved DQN algorithm to the composite services scheduling problem in the container cloud environment can effectively reduce the completion time of the composite services. Meanwhile, the method improves Quality of Service (QoS) and resource utilization in the container cloud environment.

Blockchain: Basics, Applications, Benefits, and Opportunities

Blockchain has played a major role in financial, sales, medical, cryptocurrency, gaming, and other areas or fields. blockchain comes into focus mainly due to the bitcoin cryptocurrency. and now blockchain has been used in the development of various applications to help make records such as files, audio, video, paintings, gaming nfts secured and provide greater transparency, improved traceability, efficiency, and speed, and reduced cost. in this paper, we will be looking at what blockchain is, what is blockchain consists of, its structure, framework, and how blockchain works in a distributed network environment.

Quality of Forecasts as the Factor Determining the Coordination of Logistics Processes by Logistic Operator

(1) Background: Currently, logistics operators are struggling with the problem of acquiring new areas to create added value. One of such challenges is acquiring a new function in the form of demand forecasting and coordination skills. The increase in the need to forecast demand is related to the increasing complexity of the distribution network, omnichannel systems and turbulent environment. It is necessary to have a comprehensive approach to the distribution network and to develop competences related to coordination. For the authors, one of the most important coordination factors is the quality of forecasts, especially in relation to modern logistics operators. (2) Methods: In literature studies, the authors combine prognostic abilities with a predisposition to coordinate logistic processes in distribution networks. The aim of the research is to develop a forecasting model that will allow a logistic operator who aspires to coordinate logistics processes to create forecasts in the distribution system. The tool was developed in the R programming language and allows for forecasting based on data from the Warehouse Management System; (3) Results: The quality of forecasts is correlated with the characteristics of the products, the relationship between the manufacturing company and the logistics operator, and the configuration of the distribution network in individual chains. The results of the forecasts were compared with selected features of each of the 5 distribution networks that were tested. In parallel, the attributes of a company capable of forecasting in the distribution network were analyzed. These attributes were also compared to those of the logistics operator. (4) Conclusions: The authors proved that a logistic operator with a set of defined features is capable of generating demand in the entire distribution network.

Distributed Authentication Model for Secure Network Connectivity in Network Separation Technology.

Considering the increasing scale and severity of damage from recent cybersecurity incidents, the need for fundamental solutions to external security threats has increased. Hence, network separation technology has been designed to stop the leakage of information by separating business computing networks from the Internet. However, security accidents have been continuously occurring, owing to the degradation of data transmission latency performance between the networks, decreasing the convenience and usability of the work environment. In a conventional centralized network connection concept, a problem occurs because if either usability or security is strengthened, the other is weakened. In this study, we proposed a distributed authentication mechanism for secure network connectivity (DAM4SNC) technology in a distributed network environment that requires security and latency performance simultaneously to overcome the trade-off limitations of existing technology. By communicating with separated networks based on the authentication between distributed nodes, the inefficiency of conventional centralized network connection solutions is overcome. Moreover, the security is enhanced through periodic authentication of the distributed nodes and differentiation of the certification levels. As a result of the experiment, the relative efficiency of the proposed scheme (REP) was about 420% or more in all cases.

Multi-Step Detection of Simplex and Duplex Wormhole Attacks over Wireless Sensor Networks

Detection of the wormhole attacks is a cumbersome process, particularly simplex and duplex over the wireless sensor networks (WSNs). Wormhole attacks are characterized as distributed passive attacks that can destabilize or disable WSNs. The distributed passive nature of these attacks makes them enormously challenging to detect. The main objective is to find all the possible ways in which how the wireless sensor network’s broadcasting character and transmission medium allows the attacker to interrupt network within the distributed environment. And further to detect the serious routing-disruption attack “Wormhole Attack” step by step through the different network mechanisms. In this paper, a new multi-step detection (MSD) scheme is introduced that can effectively detect the wormhole attacks for WSN. The MSD consists of three algorithms to detect and prevent the simplex and duplex wormhole attacks. Furthermore, the proposed scheme integrated five detection modules to systematically detect, recover, and isolate wormhole attacks. Simulation results conducted in OMNET++ show that the proposed MSD has lower false detection and false toleration rates. Besides, MSD can effectively detect wormhole attacks in a completely distributed network environment, as suggested by the simulation results.

Information Security of New Media Art Platform of Distributed System Based on Blockchain Technology

New media art is different from ready-made art, installation art, body art, and land art. New media art is a new art subject category with “optical” media and electronic media as the basic language. And, because of the continuous development of computer networks, distributed database management systems are becoming more and more popular. This article aims to study the information security control problem in the distributed new media system. Through comprehensive access control, reliable support, and many-to-many random mutual encryption, it solves the security requirements of supporting mobile computing in a distributed network environment, communication, and hierarchical grouping. Group key management and other issues have studied some key security technologies for building secure distributed information systems. This article proposes covering the behavior-based access control model ABAC (operation-based access control model), the architecture of the new Trusted Platform Module, the architecture of multithreaded crypto chips, and their service methods, as well as distributed information systems and key management solutions. Approximately obeying the chi-square distribution with 2 degrees of freedom, when the significance level is taken as 5%, the chi-square value for the skin is 5.99. Different initial values are selected for the chaotic sequence, 200 groups of chaotic sequences with a length of 2000 are selected for sequence inspection, and the pass rate is 97.5%. It can be seen that the autocorrelation and cross-correlation characteristics of the improved spatiotemporal chaotic sequence are still relatively ideal, so the usability of the platform is relatively high.

A Review of Blockchain-Based Access Control for the Industrial IoT

The rapid development and wide application of industrial Internet of things (IoT) have made network security issues such as identity authentication, access control, and data privacy more and more important in the distributed network environment, while blockchain technology benefiting from the advantages of decentralization, high confidence, and difficulty tampering are able to solve the trust problem existing in traditional access control technology. In this paper, firstly the basic concepts of access control and blockchain are explained, and the existing access control model and method based on blockchain in the industrial IoT scenario are summarized. Next, according to different implementation methods of blockchain, two technical routes are innovatively divided, i.e., the blockchain acts as a trusted entity (combination blockchain mode) in combination with the traditional access control technology, and the access control model constructed by completely utilizing the brand-new mode (full blockchain mode) of the distributed and non-tampering characteristics of the blockchain. From the angle of these two technical routes, the unique advantages of applying the blockchain to the IoT access control field are analyzed. Then, according to the key issues in the application of blockchain, the current research progress is summarized from the aspects of dynamic access control, space optimization on the chain, privacy data protection, etc. Finally, the further research direction is provided combined with the current challenges of access control mechanism based on industrial IoT.

S-DPS: An SDN-Based DDoS Protection System for Smart Grids

Information Communication Technology (ICT) environment in traditional power grids makes detection and mitigation of DDoS attacks more challenging. Existing security technologies, besides their efficiency, are not adequate to cater to DDoS security in Smart Grids (SGs) due to highly distributed and dynamic network environments. Recently, emerging Software Defined Networking- (SDN-) based approaches are proposed by researchers for SG’s DDoS protection; however, they are only able to protect against flooding attacks and are dependent on static thresholds. The proposed approach, i.e., Software Defined Networking-based DDoS Protection System (S-DPS), is efficiently addressing these issues by employing light-weight Tsallis entropy-based defense mechanisms using SDN environment. It provides early detection mechanism with mitigation of anomaly in real time. The approach offers the best deployment location of defense mechanism due to the centralized control of network. Moreover, the employment of a dynamic threshold mechanism is making detection process adaptive to the changing network conditions. S-DPS has demonstrated its effectiveness and efficiency in terms of Detection Rate (DR) and minimal CPU/RAM utilization, considering DDoS protection focusing smurf attacks, socket stress attacks, and SYN flood attacks.