Distributed Network Architecture Research Articles

Underwater Multi-Channel MAC with Cognitive Acoustics for Distributed Underwater Acoustic Networks.

The advancement of underwater cognitive acoustic network (UCAN) technology aims to improve spectral efficiency and ensure coexistence with the underwater ecosystem. As the demand for short-term underwater applications operated under distributed topologies, like autonomous underwater vehicle cluster operations, continues to grow, this paper presents Underwater Multi-channel Medium Access Control with Cognitive Acoustics (UMMAC-CA) as a suitable channel access protocol for distributed UCANs. UMMAC-CA operates on a per-frame basis, similar to the Multi-channel Medium Access Control with Cognitive Radios (MMAC-CR) designed for distributed cognitive radio networks, but with notable differences. It employs a pre-determined data transmission matrix to allow all nodes to access the channel without contention, thus reducing the channel access overhead. In addition, to mitigate the communication failures caused by randomly occurring interferers, UMMAC-CA allocates at least 50% of frame time for interferer sensing. This is possible because of the fixed data transmission scheduling, which allows other nodes to sense for interferers simultaneously while a specific node is transmitting data. Simulation results demonstrate that UMMAC-CA outperforms MMAC-CR across various metrics, including those of the sensing time rate, controlling time rate, and throughput. In addition, except for in the case where the data transmission time coefficient equals 1, the message overhead performance of UMMAC-CA is also superior to that of MMAC-CR. These results underscore the suitability of UMMAC-CA for use in challenging underwater applications requiring multi-channel cognitive communication within a distributed network architecture.

Revolutionizing Data Management through Cloud-Based Data Fusion Platforms in Distributed Network Architectures

Data management is developing rapidly, and we need solutions that can handle massive volumes of diverse data. Especially for cloud-based data fusion and global network designs. Our research offers a fresh solution. Each difficult formula in this manner improves the system. Standardizing, matching, translating, and merging data from several sources is the fundamental strategy for data integration and management. We found that this alternative is superior to standard data management systems for growing, working fast, consistently, securely, and accurately integrating data, as well as cost-effectiveness. Data's visual presentation enhances the method's advantages and shows its potential. This research proves the technique works and illustrates how it may be utilized to advance the field. Supporting today's sophisticated data systems is a major advance. It's a solid, scalable data management solution that can evolve.

Design of Edge-IoMT Network Architecture with Weight-Based Scheduling.

Population health monitoring based on the Internet of Medical Things (IoMT) is becoming an important application trend healthcare improvement. This work aims to develop an autonomous network architecture, collecting sensor data with a cluster topology, forwarding information through relay nodes, and applying edge computing and transmission scheduling for network scalability and operational efficiency. The proposed distributed network architecture incorporates data compression technologies and effective scheduling algorithms for handling the transmission scheduling of various physiological signals. Compared to existing scheduling mechanisms, the experimental results depict the network performance and show that in analyzing the delay and jitter, the proposed WFQ-based algorithms have reduced the delay and jitter ratio by about 40% and 19.47% compared to LLQ with priority queueing scheme, respectively. The experimental results also demonstrate that the proposed network topology is more effective than the direct path transmission approach in terms of energy consumption, which suggests that the proposed network architecture may improve the development of medical applications with body area networks such that the goal of self-organizing population health monitoring can be achieved.

A Software-Defined Distributed Architecture for Controlling Unmanned Swarm Systems

An unmanned swarm is usually composed of a group of homogeneous or heterogeneous hardware platforms, software control systems, and interfaces for human–computer interaction that operate collectively to achieve a specific goal by information interaction. They exhibit robustness and fault tolerance when facing complex missions, making it crucial in military, transportation, intelligent traffic, and other fields. However, the coupling between the hardware and software of a heterogeneous unmanned swarm can indeed have significant implications for system flexibility, software development and deployment, and hardware maintenance. Over the years, there has been a significant shift from traditional hardware-focused control systems to a greater emphasis on the core software layer. In this paper, a distributed network architecture is proposed to solve this problem, in which hardware resources are abstracted and represented to accomplish standardization and unification by defining a consistent and uniform set of data formats, and a resource pool of hardware data is constructed to realize the function that the number and scale of platforms is irrelevant, the task module can be plug-and-play at any time, and the software can be configured on demand. The resource scheduling of a single platform is achieved through process and thread communication using shared memory, while the resource scheduling of a cross platform is achieved through a network using request and response and subscription and notification. As a result, it can satisfy the development of functional modules in a software-defined mode and gradually improve the intelligence capability of an unmanned swarm. Based on the above architecture, the overall framework of the autonomous navigation system and the collaborative control system has been successfully established. Finally, a hardware-in-the-loop simulation environment is constructed, and the integration and verification of the proposed distributed architecture is carried out by the cooperative formation experiment, which proves the feasibility of this proposal.

Rate-stabilized and load-balanced handover management for subway hybrid LiFi and WiFi networks

In this paper, a subway interchange mobility (SIM) model is first proposed to simulate the interchange behavior of users in the subway scenario. Then, an event-triggered distributed network architecture is presented and employed in the subway hybrid light-fidelity (LiFi) and wireless-fidelity (WiFi) networks (HLWNets). Next, a rate-stabilized and load-balanced handover management scheme for subway HLWNets is developed. By using the event triggering mechanism, the proposed architecture can significantly reduce the measurement reports uploaded by users. By the proposed handover method, not only the network load can be balanced with very low complexity, but also the data rate of users is stabilized without compromising network throughput. Finally, by simulation, the effectiveness of the proposed method has been demonstrated. Simulation results show that the proposed architecture reduces up to 92.67% in the number of measurement reports compared to the conventional periodical structure. Moreover, the proposed handover method decreases the fluctuation index by 13.5% and 25%, respectively, compared to the standard and separate optimization algorithm (SOA)-based handover methods. Besides, the proposed method also has significant advantages in terms of Jain's fairness index and user satisfaction.

Research on Voltage Flexibility Analysis and Governance Measures for Distribution Networks with High Proportion

In response to issues such as reduced voltage flexibility caused by the integration of a large number of high-proportion distributed photovoltaic (HPDP) and new energy electric vehicles (NEEV) into the distribution networks, this article proposes a method for evaluating the voltage flexibility (VF) of distributed PV distribution networks with high proportions and corresponding governance measures, which can effectively improve the VF of distribution networks and the absorption rate of photovoltaic (PV) systems. Firstly, we build an integrated system model for PV, electrochemical energy storage (EES), and distributed charging piles (DCP). Then, from a spatial perspective, a VF evaluation method for HPDP distribution networks is proposed. We analyze the impact of HPDP integration on the VF of each node in the distribution network and propose a governance measure that combines EES and DCP to enhance the VF of the distribution network. Finally, we take a typical 10 kV distribution network architecture in Jilin Province as an example for numerical analysis, so the effectiveness of the proposed method is verified.

SOREC: Self-organizing and resource efficient clustered blockchain network

With the emergence of Industry 4.0, the features of the technologies that are utilized in broad range of areas are obliged to change according to the new necessities. Especially, the network requirements have to be adopted accordingly in terms of efficiency, transparency, faster pay-outs and asset security. The blockchain technology promises to fulfill the requirements these key aspects of networking impose with the advantages it provides. However, the technology has certain limitations regarding its scalability and its transaction throughput rate. The advantages in security provided by blockchain comes at a cost in terms of scalability and transaction throughput rate. In this paper, a novel decentralized and distributed network architecture is presented. With its novel approach the proposed architecture aims to address these issues with network scalability and to provide an increased transaction throughput rate. Without introducing any centralization with its clusters, the proposed architecture allows the network to utilize its resources much more efficiently and effectively, which allows the participants to focus their efforts on mining, increasing the performance of the network. The architecture also proposes a novel communication handling promoting bulk communications and random workload distribution over the entire network to reduce the bottlenecking that occurs on the peers. A comparison to other state-of-the-art works, namely Bitcoin [1] and Community Clustering [2], is also presented in this paper. The comparison of the collected data shows that the proposed architecture in this paper is able to reduce the overall network latency and provide an increase in the transaction throughout of the network.

A multi-dimensional trust model for misbehavior detection in vehicular ad hoc networks

Communication security is one of the focus issues of vehicular ad hoc networks (VANETs). Trust model is commonly used to identify malicious attackers or filter false messages in the network, which effectively guarantees secure communication between vehicles. Constrained by the distributed network architecture of VANETs, trust evaluation results may be biased due to uneven data distribution and lack of reference. By colluding with the partners, malicious vehicle launches imbalance attack and zigzag attack to bypass the detection of the trust model. To address these new intelligent attacks, we propose a multi-dimensional trust model (MDT) in this paper, which makes a combined evaluation of different trust attributes from vehicles. In MDT, data collection and trust calculation are deployed in vehicles and trust authority (TA), respectively. Since the model dynamically adjusts the corresponding weight coefficient according to the data fluctuations of each trust indicator by adopting entropy weight method and filters anomalous evaluation results by using the median absolute deviation (MAD) algorithm, it can effectively deal with complex and variable intelligent attacks. The legitimate identity of a vehicle will be revoked from the network by TA once its trust value is less than the threshold. We have conducted extensive experimental studies to evaluate the performance of MDT. The experimental results show that the MDT model can accurately and effectively calculate the global trust of vehicles and detect malicious attacks in the network under different scenarios and tests.

Multilayer self‐organized aggregation control for large‐scale unmanned aerial vehicle swarms

SummaryThe cooperative control of large‐scale unmanned aerial vehicle (UAV) swarms to establish a complex configuration is one of the most challenging goals. The poor scalability of the network topology, inability to self‐organize the desired configuration and the separation of swarm configuration are the main obstructions that hinder the attainment of the goal. This paper studies the self‐organized aggregation control of large‐scale UAVs by designing a scalable distributed network architecture. We present a novel centerless and scalable multilayer graph model to solve the nonextensible problem of the traditional network topology. A large number of UAVs are organized with several interconnected and globally covered layers, and each layer consists of many undifferentiated clusters. Afterwards, a self‐organized aggregation control law using the potential function method is designed for UAVs to realize autonomous configuration and configuration swarming under the consideration of collision and obstacle avoidance.

Industrial Internet Sensor Node Construction and System Construction Based on Blockchain Technology

The present industrial Internet platform (IIP) is subjected to numerous complex security issues, and thus technologies that focus on achieving data security of the IIP has become top priority. The data security crisis of IIP gets mainly reflected in the malicious deletion and theft of data, the random access of terminal devices, and the low security of traditional security authentication methods. Blockchain technology adopts distributed network architecture, wherein through asymmetric encryption mechanism, it helps to solve problems associated with single point of downtime and privacy data leakage of central management of industrial Internet. In addition, considering the accept of random access and deletion of industrial interconnection terminal equipment, this paper uses the role access control mechanism based on Hyperledger, thereby regulating user access according to the user’s role in the platform and management of terminal equipment through chain code. The paper includes the following contributions: (1) firstly, it expounds the research background and significance of the IIP and introduces the research status of the industrial Internet and blockchain at home and abroad. (2) Secondly, the IIP architecture is designed based on blockchain technology and implements a sensor node management and monitoring system. (3) Thirdly, the paper uses the gateway to preprocess the collected data, thereby reducing the data transmission delay and improving the real‐time and effectiveness of the data. The paper finally implements neural network to evaluate the construction quality of industrial Internet sensor nodes and obtains promising test results.

Electric Vehicle Charging Capacity of Distribution Network Considering Conventional Load Composition

At present, the large-scale access to electric vehicles (EVs) is exerting considerable pressure on the distribution network. Hence, it is particularly important to analyze the capacity of the distribution network to accommodate EVs. To this end, we propose a method for analyzing the EV capacity of the distribution network by considering the composition of the conventional load. First, the analysis and pretreatment methods for the distribution network architecture and conventional load are proposed. Second, the charging behavior of an EV is simulated by combining the Monte Carlo method and the trip chain theory. After obtaining the temporal and spatial distribution of the EV charging load, the method of distribution according to the proportion of the same type of conventional load among the nodes is adopted to integrate the EV charging load with the conventional load of the distribution network. By adjusting the EV ownership, the EV capacity in the distribution network is analyzed and solved on the basis of the following indices: node voltage, branch current, and transformer capacity. Finally, by considering the 10-kV distribution network in some areas of an actual city as an example, we show that the proposed analysis method can obtain a more reasonable number of EVs to be accommodated in the distribution network.

Marketing data security and privacy protection based on federated gamma in cloud computing environment

This study aims to address the existing data security and user privacy vulnerabilities in the “cloud” environment, which is essential for ensuring the safety and integrity of data in the era of big data and cloud computing. To achieve this purpose, we propose a novel approach that combines the logit link function with a longitudinal joint learning framework for the gamma regression model. This approach enhances the application of the model and the loss function, providing a robust solution for data security and user privacy in cloud-based systems. While cloud computing technology has greatly improved the convenience of work and life, it has also introduced significant challenges related to data security and user privacy. This study leverages semantic web technology and blockchain technology to establish a distributed and credit-guaranteed product quality and safety traceability application. By designing a concept verification system and ensuring data integrity at each stage of the product supply chain, this approach addresses these challenges effectively. The distributed network architecture employed in our technical design ensures overall system stability, reliability, and sustainability, with no single point of failure.

Blockchain-Based Secure and Trusted Distributed International Trade Big Data Management System

As an object of rapid development of Internet technology in recent years, network security has attracted more and more attention from people from all walks of life and has gradually become a topic in international trade activities. The application of blockchain is also extremely extensive, involving many aspects, such as the development of the metaverse game engine and bank financing. With the launch of the Belt and Road strategy, trade exchanges have become more and more frequent, and at the same time, cross-border e-commerce, a new type of industry that applies Internet information technology, has emerged. The application of blockchain technology to international trade big data processing can not only improve the utilization rate of massive transaction information resources shared by enterprises, governments, financial institutions, and other institutions, but also reduce enterprise costs. The use of blockchain improves the efficiency of data storage, reduces the degree of encryption, and reduces the amount of database access and server resource occupation. This paper mainly studied the application of blockchain in international trade big data system, introduced its secure and trusted distributed network architecture, and analyzed its performance. The experimental results showed that the performance of the decentralized manager was better than that of the centralized manager when processing a large amount of data. The peak throughput of the decentralized manager was about 450 pcs/sec and the peak throughput of the centralized manager was about 150 pcs/sec. And the performance of the storage system in the local area network was better than that in the public network.

Energy router interconnection system: A solution for new distribution network architecture toward future carbon neutrality

Energy router interconnection system: A solution for new distribution network architecture toward future carbon neutrality

Blockchain Technologies

Blockchain technology has proven to be particularly effective at securely processing distributed transactions. They may be used to handle bitcoin coins and smart contracts, among other things. Blockchain has lately being investigated for data science applications. This study looks at blockchain technology and how it can be used in data science and cyber security. Blockchain allows for the transmission of value at a minimal cost, allowing data from smart devices to be used to generate economic value. The goal of this study is to create a high-performance blockchain platform that uses technologies such distributed network architecture, intelligent device node mapping, and the PBFT-DPOC consensus algorithm to achieve intelligent device decentralisation. The impact of network delay on blockchain forking behavior, as well as probable violations of the six confirmations convention for transaction approval, are investigated in this research. We reduce the blockchain's data structure to speed up our simulations and avoid the massive processing necessary in proof-of-work systems (POW). We demonstrate that the six confirmations standard is sensitive to peer-to-peer network delay through simulation, as well as how quickly it is violated with a lower difficulty of POW mining.

Cognitive intelligence based 6G distributed network architecture

5G is envisioned to guarantee high transmission rate, ultra-low latency, high reliability and massive connections. To satisfy the above requirements, the 5G architecture is designed with the properties of using service-based architecture, cloud-native oriented, adopting IT-based API interfaces and introduction of the Network Repository Function. However, with the wide commercialization of 5G network and the exploration towards 6G, the 5G architecture exposes the disadvantages of high architecture complexity, difficult inter-interface communication, low cognitive capability, bad instantaneity, and deficient intelligence. To overcome these limitations, this paper investigates 6G network architecture, and proposes a cognitive intelligence based distributed 6G network architecture. This architecture consists of a physical network layer and an intelligent decision layer. The two layers coordinate through flexible service interfaces, supporting function decoupling and joint evolution of intelligence services and network services. With the above design, the proposed 6G architecture can be updated autonomously to deal with the future unpredicted complex services.

PGCE: A distributed storage causal consistency model based on partial geo-replication and cloud-edge collaboration architecture

The current causal consistency model has trouble facing the high synchronization overheads and response delays found in cloud storage systems. This paper proposes a causal consistency model for distributed storage based on partial geographical replication and Cloud-Edge collaboration structure (PGCE). The model is based on the distributed network architecture of Cloud-Edge collaboration, and the cloud dataset is divided into multiple subsets by a hash function to store these subsets in the edge nodes that are close to the user network to realize partial geo-replication. At the same time, the timestamp stabilization mechanism and metadata processing service are set up to implement data consistency between nodes on the premise of ensuring causality, reducing the overhead of metadata processing and data synchronization. The client interacts directly with the edge nodes, which reduces the response delay of interacts with the cloud DC. An evaluation of the PGCE compared with existing models shows that it has better performance in response latency and throughput.

Cloud Network Data Acquisition Challenges

The challenge and problem for network investigators is that many of the data repositories are now virtualized and Cloud distributed. This paper reviews the extraction of evidence from virtualized RAM in the Cloud context on two virtual machines. Such evidence informs network system fault correction, and attack diagnosis. The contribution of this research is to promote an awareness of valuable evidence held in Cloud virtual machines, where it is located, and the extraction tools kits required. A challenge for network investigators is the variation in distributed network architecture and protocols. There is little consistency in the Cloud environment beyond proprietary dominance of Cloud services, and vendor virtualization provisions. This exploratory research takes up this challenge and demonstrates a working solution to the extraction of data in Cloud distributed networks.

A fast physical layer security-based location privacy parameter recommendation algorithm in 5G IoT

The 5G IoT (Internet of Things, IoT) is easier to implement in location privacy-preserving research. The terminals in distributed network architecture blur their accurate locations into a spatial cloaking region but most existing spatial cloaking algorithms cannot work well because of man-in-the-middle attacks, high communication overhead, time consumption, and the lower success rate. This paper proposes an algorithm that can recommend terminal's privacy requirements based on getting terminal distribution information in the neighborhood after cross-layer authentication and therefore help 5G IoT terminals find enough collaborative terminals safely and quickly. The approach shows it can avoid man-in-the-middle attacks and needs lower communication costs and less searching time than 520ms at the same time. It has a great anonymization success rate by 93% through extensive simulation experiments for a range of 5G IoT scenarios.

Controllable entangled-state distribution in a dual-rail reconfigurable optical network

The authors introduce a new quantum distribution network architecture enabling control of the propagation direction of an entangled state using linear-optical devices and phase shifters and offering reconfigurable connections between multiple quantum nodes.