Current Network Conditions Research Articles

Quantitative analysis of the road network in the city of Constantine

The primary objective of this study is to analyze the national and provincial road network in the city of Constantine, employing a range of quantitative methods commonly used in transportation geography. The aim is to assess the current state of the city's road network, its adequacy, efficiency, and suitability in meeting the needs of the population. This evaluation will be conducted in terms of accessibility, connectivity between network components, and efficiency as measured by curvature and traffic volume. While transportation geography offers a variety of quantitative measurement techniques, this research will focus on four primary methods: (1) Network analysis based on the curvature index: This method will assess the efficiency of the road network by evaluating the degree of curvature of the road segments, (2) Network analysis based on daily traffic volume: This method will examine the capacity of the road network by measuring the volume of traffic on each road segment, (3) Network analysis based on connectivity and road dispersion: This method will assess the level of connectivity between different parts of the network and the overall distribution of roads within the study area, (4) Network analysis based on traffic volume density: This method will evaluate the concentration of traffic on the road network.

The Integrated Development of Wireless Networks and AI

This paper combines the current research status of wireless networks and artificial intelligence, delving into the state of wireless network research and its impacts, and highlights the importance and necessity of the integrated development of wireless networks and AI. The aim is to provide readers with an understanding of the current situation and challenges of wireless networks. With the increasing number of mobile communication users, spectrum resources are becoming increasingly scarce, network security issues are more prominent, and the complexity of network architecture and business processes has significantly increased, making it difficult for traditional operations and maintenance methods to meet these demands. Therefore, leveraging AI to build a new mode of communication network operation has become an inevitable choice. The intelligent evolution of wireless networks is an unstoppable trend. In the future, full intelligence will be achieved through digital infrastructure, knowledge introduction, and digital twins, driving the efficient development of wireless networks.

PDCF-DRL: a contention window backoff scheme based on deep reinforcement learning for differentiating access categories

AbstractIn wireless networks, the Contention Window (CW) is a crucial parameter for wireless channel sharing among numerous stations, directly influencing overall network performance. With the rapid growth and increasing diversity of traffic in wireless networks, differentiating and serving various types of traffic to enhance network performance has become a pressing issue that needs to be addressed. Deep Reinforcement Learning (DRL) optimizes decision-making through the interaction between an agent and its environment, enabling it to handle complex state spaces and high-dimensional data, making it particularly suitable for dynamically adjusting the contention window and adapting to environmental changes in real time. Access Category (AC) is used to differentiate various types of traffic to support different quality of service (QoS) requirements. Therefore, this paper proposes the integration of DRL techniques into the EDCA mechanism, utilizing AC to differentiate network traffic, while the DRL technology adjusts the contention window to adequately ensure QoS for different types of traffic and enhance overall network performance. This paper proposes a CW back off scheme based on DRL for differentiating ACs. The scheme uses DRL technology to observe the channel collision rate and sense the current network conditions, thereby adaptively adjusting the CW size for ACs. In addition, it dynamically adjusts the back off strategy according to the perceived current network conditions to optimize the data transmission process. In the range of 20–120 stations, the scheme was tested in both single AC and multiple AC traffic scenarios, demonstrating excellent performance. The collision rate consistently remained below 18%, while the normalized throughput was maintained above 76%. Additionally, there is a significant improvement compared to existing deep reinforcement learning-based optimization schemes. Experimental results show that this scheme effectively discriminates between different ACs, resulting in lower latency and higher throughput for high-priority traffic. Furthermore, adaptively adjusting the CW size and improving the back off strategy maintains a low collision rate and stable throughput even under heavy load conditions, significantly improving the overall network performance.

Current Situation and Prospect of Geospatial AI in Air Pollution Prediction

Faced with increasingly serious environmental problems, scientists have conducted extensive research, among which the importance of air quality prediction is becoming increasingly prominent. This article briefly reviews the utilization of geographic artificial intelligence (AI) in air pollution. Firstly, this paper conducts a literature metrology analysis on the research of geographical AI used in air pollution. That is, 607 documents are retrieved from the Web of Science (WOS) using appropriate keywords, and literature metrology analysis is conducted using Citespace to summarize research hotspots and frontier countries in this field. Among them, China plays a constructive role in the fields of geographic AI and air quality research. The data characteristics of Earth science and the direction of AI utilization in the field of Earth science were proposed. It then quickly expanded to investigate and research air pollution. In addition, based on summarizing the current status of Artificial Neural Network (ANN), Recurrent Neural Network (RNN), and hybrid neural network models in predicting air quality (mainly PM2.5), this article also proposes areas for improvement. Finally, this article proposes prospects for future research in this field. This study aims to summarize the development trends and research hotspots of the utilization of geographic AI in the prediction of air quality, as well as prediction methods, to provide direction for future research.

The Efficiency Assessment of the Blockchain Networks Adaptive Algorithm as Part of a Multi-Consensus System on Communication Networks

The relevance of the approaches and solutions proposed in the work is due to the desire for distributed computing and decentralization of system modules using blockchain technology in order to increase the autonomy, security and independence of system components. The implementation of blockchain technology for decentralization is hampered by the lack of flexibility in terms of the algorithm for making a single decision in the asynchronous system ‒ consensus, which affects the amount of generated network traffic and the requirements for the hardware component. Due to the changing nature of the transmitted traffic, the load on the communication channel, the time of day of traffic transmission, the equipment, and the network protocols used, the development of a single universal consensus algorithm is not possible, since it depends on a very wide range of variable and specific parameters characteristic of different tasks. The introduction of adaptation mechanisms to current network conditions and a change in the consensus algorithm without losing information and with a satisfactory level of delays would provide sufficient flexibility for further implementation in existing telecommunication systems. The purpose of this paper is to evaluate the effectiveness of the proposed adaptive algorithm for choosing a consensus for blockchain networks.The essence of the presented algorithm is to change the consensus algorithm on a section of the blockchain network upon reaching certain network conditions, which allows regulating the amount of generated load on the communication network to reduce the loss of transaction blocks and the subsequent delay in their processing. The proposed efficiency assessment model is based on mathematical modeling methods, differential equation analysis, queueing theory and network graphs. The analysis of the results showed the efficiency of the proposed model when comparing the results of the analytical calculation and the experiment. The scientific novelty of the proposed approach is to developing an adaptive consensus selection algorithm, as opposed to developing a universal consensus algorithm, and the efficiency assessment model of the proposed algorithm on the communication network, taking into account a number of parameters characterizing the devices and the section of the communication network.The theoretical significance is in the universality of the proposed efficiency assessment model for calculating the synchronization speed of all nodes of the blockchain network with specified network parameters.The practical significance of the proposed algorithm and efficiency assessment method lies in the formation of new approaches and opportunities for integrating blockchain technology into modern communication networks, abstracting from the problem of consensus selection in changing conditions of the communication network section.

Optical Networking Gym: an open-source toolkit for resource assignment problems in optical networks

The dynamic provisioning of optical network services requires algorithms to find a suitable solution given the specific service requirements and the current network state. These algorithms are usually evaluated using a software simulator developed ad hoc, which may require different levels of detail depending on the problem addressed and how realistic the evaluation needs to be. Moreover, to demonstrate they are a significant contribution to the field, these new algorithms must be benchmarked against the best-performing previously proposed solutions. Due to the large set of parameters and their wide range of possible values, benchmarking algorithms from the literature is not straightforward and can quickly become challenging and time-consuming. This work introduces the Optical Networking Gym, an open-source toolkit that simplifies implementing optical resource assignment simulations and benchmarking new solutions against previously published algorithms. The toolkit provides environments modeling relevant optical networking scenarios, common algorithms for solving problems related to these scenarios, and a set of scripts to prepare and execute simulations for various use cases. Currently, four environments are available, with the possibility of increasing this number through contributions from the co-authors and the community. This paper describes the architecture, interface, environments, and scripts included with the toolkit. We adopt the quality of transmission (QoT)-aware dynamic resource allocation of optical services as the network scenario under examination. Three use cases highlight the toolkit’s modularity, flexibility, and performance. The toolkit allows researchers to streamline the process of developing simulation scenarios and algorithms, enhancing their ability to benchmark their algorithms.

Latency Reduction in Immersive Systems through Request Scheduling with Digital Twin Networks in Collaborative Edge Computing

Immersive systems, which offer immersive interactive experiences by tightly coupling the real and virtual worlds, encompassing Augmented Reality (AR) and Virtual Reality (VR), have undergone rapid development in recent years. The inherent latency issue of immersive systems poses considerable challenges for AR/VR applications that are highly sensitive to delay. To address this challenge, we propose utilizing edge collaboration as an efficient means to schedule AR/VR requests, owing to the inherent ability of edge nodes to seamlessly coordinate within the same geographical region and optimally allocate request resources to minimize latency. However, when it comes to edge nodes across different regions, a significant hurdle emerges. These nodes often lack real-time knowledge about cached resources across other regions, making cross-regional edge collaboration for AR/VR request scheduling an extremely challenging problem. We formally define and prove the NP-hardness of this problem. To tackle this complexity, we introduce a Federated Digital Twin Model Construction (FedDT), which establishes a global digital twin network connecting different edge nodes. This network simulates and reflects the resource status and availability of each edge node in real time. Subsequently, we devise an extended Dijkstra algorithm to determine the shortest path between AR/VR requests and edge nodes, taking into account current network conditions to optimize service delay. Both theoretical analysis and experimental results confirm that compared with baseline methods, our proposed solution consistently achieves an average delay reduction of 36.78% across various scenarios, effectively demonstrating its superior performance.

ECONOMIC-STATISTICAL ANALYSIS OF THE DEVELOPMENT OF THE FRUIT AND VEGETABLE SECTOR IN UZBEKISTAN

This article describes the current state and dynamics of the agricultural network of our republic, an economic and statistical analysis of the production of agricultural products in economic entities and promising directions. The activities of farms operating in our country were also carefully analyzed, and with the help of statistical methods, the role and importance of this industry in the development of the industry was revealed. Dynamic changes in statistical indicators of network development over the years are shown. KEY WORDS: agriculture, agricultural products, gross domestic product, gross regional product, agriculture, livestock breeding, structural changes, investments, regional economy, economic and statistical analysis, agrarian reforms, socio-economic processes .

Eight challenges in developing theory of intelligence.

A good theory of mathematical beauty is more practical than any current observation, as new predictions about physical reality can be self-consistently verified. This belief applies to the current status of understanding deep neural networks including large language models and even the biological intelligence. Toy models provide a metaphor of physical reality, allowing mathematically formulating the reality (i.e., the so-called theory), which can be updated as more conjectures are justified or refuted. One does not need to present all details in a model, but rather, more abstract models are constructed, as complex systems such as the brains or deep networks have many sloppy dimensions but much less stiff dimensions that strongly impact macroscopic observables. This type of bottom-up mechanistic modeling is still promising in the modern era of understanding the natural or artificial intelligence. Here, we shed light on eight challenges in developing theory of intelligence following this theoretical paradigm. Theses challenges are representation learning, generalization, adversarial robustness, continual learning, causal learning, internal model of the brain, next-token prediction, and the mechanics of subjective experience.

BPNN‐based flow classification and admission control for software defined IIoT

AbstractFlow admission control (FAC) aims to efficiently manage the service requests while maximizing the network utilization. With multiple connection requests, access delay or even service interruption may occur. This paper proposes a novel FAC approach to reduce the contention between the end nodes and ensure high utilization of the networking resources for software defined IIoT. First, incoming flows are classified into different priorities using back propagation neural network based on selected features representing the current network status. Second, with the designed flow admission policies, bandwidth and buffer size are estimated with stochastic network calculus model. Finally, the thresholds of the proposed FAC scheme are dynamically decided based on the above two parameters. Various flows are admitted or rejected via the proposed FAC to maintain real time processing. Unlike traditional FAC schemes rely on static priority systems, the proposed scheme leverages machine learning technique for dynamic flow prioritization and the stochastic network calculus model for precise estimation. Computer simulation reveals that the proposed scheme accurately classifies the flows, and substantially decreases the transmission delay and improves the network utilization compared to the existing FAC schemes. This highlights the superiority of the proposed scheme meeting the demands of software defined IIoT.

Summary of Lunar Constellation Navigation and Orbit Determination Technology

The Moon is the closest celestial body to the Earth. Its rich unique resources are an important supplement to the Earth’s resources and have a profound impact on the sustainable development of human society. As large-scale exploration missions gradually progress, demands for communication, navigation, surveying and other services of lunar-space probes have significantly increased. Constellation navigation and orbit determination technology will become an indispensable part of future lunar exploration infrastructure. This article systematically analyzes the current status of lunar relay navigation satellite networks at home and abroad, summarizes the technical principles of single-satellite and constellation navigation and orbit determination, discusses the technical difficulties in lunar navigation constellation orbit determination and navigation, and analyzes possible solutions. Finally, the development trend of research on high-precision orbit determination and navigation methods for lunar navigation constellations is proposed.

Secured Computation Offloading in Multi-Access Mobile Edge Computing Networks through Deep Reinforcement Learning

Mobile edge computing (MEC) has emerged as a pivotal technology to address the computational demands of resource-constrained mobile devices by offloading tasks to nearby edge servers. However, ensuring the security and efficiency of computation offloading in multiaccess MEC networks remains a critical challenge. This paper proposes a novel approach that leverages deep reinforcement learning (DRL) for secure computation offloading in multi-access MEC networks. The proposed framework utilizes DRL agents to dynamically make offloading decisions based on the current network conditions, resource availability, and security requirements. The agents learn optimal offloading policies through interactions with the environment, aiming to maximize task completion efficiency while minimizing security risks. To enhance security, the framework integrates encryption techniques and access control mechanisms to protect sensitive data during offloading. The proposed approach undergoes comprehensive simulations to assess its performance in terms of security, efficiency, and scalability. The results demonstrate that the DRL-based approach effectively balances the tradeoffs between security and efficiency, achieving robust and adaptive computation offloading in multi-access MEC networks. This study contributes to advancing the state-of-the-art in secure and efficient mobile edge computing systems, fostering the development of intelligent and resilient MEC solutions for future mobile networks.

CURRENT STATE AND DEVELOPMENT PROSPECTS FOR THE RENEWAL OF SEWERAGE NETWORKS IN KHARKIV CITY

Ensuring the reliable operation of sewerage systems is one of the essential tasks of municipal services in Ukrainian cities. As the research shows, in most cases, their construction involved concrete and reinforced concrete, which are prone to destruction under the influence of many factors, particularly microbiological corrosion. The destruction of sewage collectors is often the reason for wastewater leaking into groundwater and soil, resulting in pollution. The article provides statistical data on the ‘Kharkivvodovidvedennia’ Complex of the ME ‘Kharkivvodokanal’ as of 01.01.2024, which operates the city’s external water drainage networks. The article outlines the most common factors that lead to emergencies in sewage networks, such as aging and wear of infrastructure, insufficient maintenance and repair, mechanical damage, pollution and clogging, increased volume of wastewater, planning problems, chemical reactions, biological corrosion, and connection defects. The relationship between these causes may be that corrosion can weaken pipeline materials, making them more vulnerable to mechanical damage and leakage. According to preliminary calculations, it is necessary to relay or rehabilitate (trenchless pipeline laying) at least 22–25 km of city networks annually to ensure the reliability of sewage networks. The article analyses the work cycles performed on the tunnel collectors’ objects and large-diameter collectors over five years. Major repair and reconstruction work that deserves attention includes work on the repair of mine No. 12 of the tunnel collector of the 761 micro-district (mine depth H = 6.75 m, diameter – 5 m); restoration work of the Oleksiivskyi collector Ø 2000 mm near the viewing chamber No. 33 on the street Lopanska; restoration of the collector of the Northern group of factories Ø 900 mm at Danylivskyi Uzviz. As a result, we carried out an analysis of the current state of the sewage network of the city of Kharkiv. The main factor in the wear and tear of sewerage networks is corrosion, which, in turn, can weaken pipeline materials, making them more vulnerable to mechanical damage and leaks. Despite the difficult economic conditions, work on the repair and operation of networks is carried out. Also worthy of special attention for future research is the survey of mines where restoration work took place over the past ten years using modern methods. Keywords: underground construction, sewerage, renewal, repair, reconstruction, bearing capacity, wear, corrosion.

Reliable multiplex semi-local random walk based on influential nodes to improve link prediction in complex networks

In recent years, the exponential growth of online social networks as complex networks has presented challenges in expanding networks and forging new connections. Link prediction emerges as a crucial technique to anticipate future relationships among users, leveraging the current network state to address this challenge effectively. While link prediction models on monoplex networks have a well-established history, the exploration of similar tasks on multilayer networks has garnered considerable attention. Extracting topological and multimodal features for weighting links can improve link prediction in weighted complex networks. Meanwhile, establishing reliable and trustworthy paths between users is a useful way to create metrics that convert unweighted to weighted similarity. The local random walk is a widely used technique for predicting links in weighted monoplex networks. The aim of this paper is to develop a semi-local random walk over reliable paths to improve link prediction on a multilayer social network as a complex network, which is denoted as Reliable Multiplex semi-Local Random Walk (RMLRW). RMLRW leverages the semi-local random walk technique over reliable paths, integrating intra-layer and inter-layer information from multiplex features to conduct a trustworthy biased random walk for predicting new links within a target layer of multilayer networks. In order to make RMLRW scalable, we develop a semi-local random walk-based network embedding to represent the network in a lower-dimensional space while preserving its original characteristics. Extensive experimental studies on several real-world multilayer networks demonstrate the performance assurance of RMLRW compared to equivalent methods. Specifically, RMLRW improves the average f-measure of the link prediction by 3.2% and 2.5% compared to SEM-Path and MLRW, respectively.

ХАРАКТЕРИСТИКА МАРШРУТНОЙ СЕТИ ОБЩЕСТВЕННОГО ТРАНСПОРТА ГОРОДА БАРНАУЛА

The development of a city is closely related to the state of its transport system and the spatial organization of urban public transport. In general, a number of transport problems can be noted for the city related to the waiting time for transport, the provision of various areas with urban public transport routes, etc. These problems have become particularly relevant in the 21st century and have become very actively discussed. The article is devoted to the analysis of the route network of the city of Barnaul, as well as the number of rolling stock on the routes. Particular attention is paid to routes where there is a shortage of rolling stock. The purpose of the work is to analyze the current state of the route network of the city of Barnaul and identify problems. The main methods were literature analysis, as well as monitoring public transport using the 2GIS service. In general, it can be noted that the city’s public transport runs stably and interruptions occur due to weather conditions (in particular freezing) or traffic situations (congestion in the morning and evening); there is also a dependence of preferential traffic to the city center and from the city center to depending on the time of day.

Nefis: A network coding based flexible device-to-device video streaming scheme

Wireless device-to-device (D2D) communication has empowered efficient and convenient video sharing among neighboring devices. However, the mobility and diversity of user devices, combined with dynamically shifting channel conditions, make these processes susceptible to environmental interference. In this paper, we introduce an adaptive network coding scheme tailored for D2D raw source video streaming in the scalable video coding (SVC) format. Video frames are partitioned into equal-sized fragments and encoded into streaming packets with the addition of certain redundancy. To address the observed deficiency where decoding throughput of coded video frames is generally lower than that of the encoding operation on mobile device platforms, we introduce a parallel decoding algorithm based on LU decomposition. Furthermore, we propose a flexible adjustment mechanism, named Nefis, which dynamically adapts the network coding redundancy and stream resolution based on the statistical model established to predict current network conditions and video streaming quality. Implementation on Android platform demonstrates that Nefis reduce redundancy in bandwidth usage during streaming process and enhance resilience to network dynamics. Experimental results also conclusively demonstrate the feasibility and advantages of network coding technology in D2D streaming applications. Nevertheless, achieving these advantages requires carefully designed encoding and decoding mechanisms.

Intelligent load balancing in data center software‐defined networks

AbstractIn response to the increasing demand for efficient resource utilization in data center networks (DCNs), the development of intelligent load‐balancing algorithms has become crucial. This article introduces the dual double deep Q network (DDQN) algorithm, designed for software‐defined networking (SDN) environments within data centers. By leveraging deep reinforcement learning, DDQN addresses the challenges posed by dynamic traffic patterns, diverse flow requirements, and the coexistence of elephant and mice flows. Our algorithm adopts a comprehensive SDN approach, evaluating the network's status by analyzing switch load and bandwidth utilization. Using convolutional neural networks for elephant and mice flows in DCN, our algorithm enables adaptive learning and training tailored to the specific demands of elephant flows. Employing a double deep Q network architecture (DDQN), DDQN optimizes paths for both elephant and mice flows independently. Real‐time adaptation mechanisms make routing decisions based on the robust learning capabilities of DDQN, enhancing network utilization and reducing packet loss by generating optimal forwarding paths according to the current network state and traffic patterns. Simulations conducted in a Mininet environment with RYU as the controller, utilizing a fat‐tree data center topology, validate the efficacy of DDQN. The results demonstrate its effectiveness in achieving higher throughput, lower latency, and superior load balancing compared to traditional algorithms like equal‐cost multipath and Hedera.

Multi-Agent Deep Reinforcement Learning-Based Fine-Grained Traffic Scheduling in Data Center Networks

In data center networks, when facing challenges such as traffic volatility, low resource utilization, and the difficulty of a single traffic scheduling strategy to meet demands, it is necessary to introduce intelligent traffic scheduling mechanisms to improve network resource utilization, optimize network performance, and adapt to the traffic scheduling requirements in a dynamic environment. This paper proposes a fine-grained traffic scheduling scheme based on multi-agent deep reinforcement learning (MAFS). This approach utilizes In-Band Network Telemetry to collect real-time network states on the programmable data plane, establishes the mapping relationship between real-time network state information and the forwarding efficiency on the control plane, and designs a multi-agent deep reinforcement learning algorithm to calculate the optimal routing strategy under the current network state. The experimental results demonstrate that compared to other traffic scheduling methods, MAFS can effectively enhance network throughput. It achieves a 1.2× better average throughput and achieves a 1.4–1.7× lower packet loss rate.

Comprehensive Assessment of the Use of the Territory of Olkhon Island (Baikal National Park)

Olkhon Island is a business card of the Irkutsk region and one of the most visited sites. Being a source of attraction for tourists, the island began to demand social development of the territory both in terms of individual and collective development, as well as in terms of housing and communal services and road infrastructure. In this regard, a comprehensive assessment of the use of the territory as an object of attraction for an increasing number of people, both for recreation and permanent residence, is required. The article presents an ecological and socio-economic assessment of the use of the territory of Olkhon Island, which is part of the Baikal National Park. The data on the current state, development and capacity of the island's road network are presented. The assessment of the recreational impact on the most popular tourist route of the Baikal National Park “North of Olkhon Island” was carried out. The issues of waste management, both in settlements and on tourist routes, are considered. The problem of cattle grazing on steppe landscapes is considered and analyzed. According to the results of the study, the proportion of anthropogenic impact on the ecosystems of Olkhon Island was determined, the main risks in the complex development of the island's territory were identified and recommendations were given to reduce the impact on the territory

Video quality adaptation using CNN and RNN models for cost-effective and scalable video streaming Services

Video streaming services require adaptive bit rate strategies that optimize video quality based on network conditions and user preferences to provide a cost-effective and scalable solution. In this manuscript, we present a novel architecture that utilizes a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to extract features from the video stream and predict optimal bit rates for each frame. The CNN feature extractor extracts relevant features from the input video stream, which are then passed on to the RNN predictor, where the optimal bit rate is predicted to ensure a personalized viewing experience tailored to the current network conditions and specific user preferences. Our experimental results demonstrate that the proposed architecture achieves significant improvements in video quality while minimizing bandwidth usage and providing a better user experience. Specifically, our results show a 37.1% improvement in average bit rate, indicating that the proposed architecture optimizes the video quality and reduces bandwidth usage by 37.1% on average. We also observed a 16.6% improvement in Quality of Experience (QoE), meaning that users will perceive the video quality as better. Additionally, rebuffering was reduced by 87.5%, indicating that users enjoy smoother video playback without interruptions. Importantly, our architecture is optimized for Dynamic Adaptive Streaming over HTTP (DASH), addressing the need for efficient streaming over the most widely used protocol in the industry.