Open Network Research Articles

Distributed Online Resource Allocation in Open Networks

Distributed Online Resource Allocation in Open Networks

Comprehensive study on blockchain dynamic learning methods

Blockchain technology has been growing at a substantial growth rate over the last decade. Blockchain, originally developed to support the cryptocurrency ecosystem, has recently been used in various other fields to achieve extraordinary levels of security. Blockchain technology has applications beyond cryptocurrencies, including risk management, healthcare facilities, financial and social services, and more. Blockchain has been used in the healthcare industry for several purposes including secure data logging, transactions, and maintenance using smartcontracts. The healthcare sector integrating blockchain into various aspects of this digital age. Its features such as micro-transactions, decentralized exchanges, consensus mechanisms, and smart contracts allow for securing the privacy of the health data of patients who are key stakeholders in the healthcare domain. The User Interface (UI) of the HealthChain system highlights the development of blockchain peers with background data on blockchain network construction. By digitally linking patients, payers, and providers over an open network, Health Chain is building a data- driven healthcare community. The modular Hyperledger fabric architecture, which promotes secrecy, scalability and security in health informatics, is a major asset of HealthChain which is powered by Blockchain. The smartcontracts guarantees that the right people are authorised and given the rights on its network of permissions. This review's primary goal is to offer an extensive look and assessment of blockchain technology. The intention is to provide readers with a comprehensive understanding of blockchain technology, its present status, and its possible effects on a range of industries, including supply chain management, healthcare, banking, and more.

IMAGE ENCRYPTION TECHNIQUE BASED ON A BINARY COMBINATION OF MULTIPLE CHAOTIC MAPS AND DNA SEQUENCE OPERATIONS

The huge advance of digital communication and networks has led to enormous storage and transmission of information over public networks. Nevertheless, the assurance of information security remains incomplete across these unsecured networks. Digital images are the primary means for sharing information over open networks. Consequently, the confidentiality of digital images during storage and transmission has become a crucial concern, particularly when sharing sensitive information. Image encryption has emerged as a solution to this problem. This paper presents an image encryption technique based on multiple one-dimensional chaotic maps and DNA coding. The method employs three one-dimensional chaotic maps, including the Logistic map, Tent map, and piecewise map, multiple times to produce 18 random sequences with different initial values and parameters. SHA-512 hash function is used to indicate the initial values of chaotic maps. For encrypting images, the binary elements from various sequences of chaotic maps are amalgamated to alter the pixel intensities of the image in the diffusion process. Dynamic DNA coding is performed through a random selection of DNA rules and operations (XOR, XNOR, and Addition) to each pixel in the image. The technique is enforced using circular rotations which are applied randomly to each key. The proposed technique is evaluated using many standard images. Different performance metrics have been measured. The empirical findings illustrate the security and resilience of the suggested method and its ability to resist statistical and differential attacks.

Recent developments in urban freight analytics for collaborative city logistics

This paper presents recent developments in urban freight analytics for collaborative city logistics. City logistics provides innovative solutions using emerging technologies such as Information and Communication Technologies (ICT), Intelligent Transport Systems (ITS), Internet of Things (IoT), big data, Artificial Intelligence (AI), autonomous vehicles and robots. Sharing data between public and private entities is important for modelling, planning and evaluating policy measures related to city logistics. Mathematical models such as multi-agent models can replicate the behaviour of stakeholders in city logistics to help make a better decision considering logistics costs, the environment and energy consumption. Autonomous robots and drones can be used for urban deliveries to minimise the risk of infections during the pandemic. Hyperconnected city logistics combines the concepts of the Physical Internet and city logistics to create more sustainable urban distribution systems by promoting more collaborative, integrated and open networks. Digital twins aim to provide a virtual system for replicating physical systems and their processes that allow the dynamic status of the environment and physical elements in the real world to be updated using various sources of information. Warehouse sharing systems are logistics platforms where the available capacity of warehouses can be matched with the shippers/factories looking to temporarily store their goods before further distribution.

User-Centric Access Network (UCAN) for 6G: Motivation, Concept, Challenges and Key Technologies

A universal and flexible network architecture is required to meet the demands for optimal user experience and all coverage types including wide area coverage and ultra-dense coverage in 6G. In this paper, a powerful network architecture, the user-centric access network (UCAN), is proposed to meet the multi-dimensional demands of 6G. After analyzing the motivations and requirements, we introduce the concept, architecture, and networking scenarios of UCAN. UCAN has four distinct features including deeply customized network, elastic reconfigurable network, open and compatible network, and intelligent adaptive network for user services. In deployment, flexible cell can be constructed and updated for a user with the suitable surrounding access points. The challenges and corresponding key technologies to achieve UCAN are then illustrated to include user-centric access and connection management, dynamic AP management, unified management of interference and resource, and lightweight security. Then a simulation result is given to verify the effectiveness of UCAN. Finally, future works are discussed to indicate the research directions for academia and industry.

Secure State Estimation for Artificial Neural Networks With Unknown-But-Bounded Noises: A Homomorphic Encryption Scheme.

This article is concerned with the secure state estimation problem for artificial neural networks (ANNs) subject to unknown-but-bounded noises, where sensors and the remote estimator are connected via open and bandwidth-limited communication networks. Using the encoding-decoding mechanism (EDM) and the Paillier encryption technique, a novel homomorphic encryption scheme (HES) is introduced, which aims to ensure the secure transmission of measurement information within communication networks that are constrained by bandwidth. Under this encoding-decoding-based HES, the data being transmitted can be encrypted into ciphertexts comprising finite bits. The emphasis of this research is placed on the development of a secure set-membership state estimation algorithm, which allows for the computation of estimates using encrypted data without the need for decryption, thereby ensuring data security throughout the entire estimation process. Taking into account the unknown-but-bounded noises, the underlying ANN, and the adopted HES, sufficient conditions are determined for the existence of the desired ellipsoidal set. The related secure state estimator gains are then derived by addressing optimization problems using the Lagrange multiplier method. Lastly, an example is presented to verify the effectiveness of the proposed secure state estimation approach.

Problems and prospects of electronic scientific publishing "The Tale of Bygone Years"

The article discusses the problems of developing electronic scientific publications of sources and creating electronic standards based on them. As a sample for such a publication, M. Yu. Andreicheva suggests turning to the "Tale of Bygone Years" – a narrative monument that, on the one hand, can best demonstrate the features of a whole complex of sources (chronicles), and on the other hand, has been studied well enough to show using his example, a detailed study of the linguistic, source study and textual capabilities of the created electronic publishing model. In his work, the author introduces the image of a portal dedicated to the electronic edition of the initial chronicle. The basis of the publication should be the hypertext of the Tale, that is, a text that includes a system of internal hyperlinks that make it possible to visually represent its lists, translations and original handwritten form, its stratification in textological stems, as well as textual and semantic intersections with other monuments of the era being studied. The electronic scientific publication of "The Tale of Bygone Years" will appear in the form of an open semantic network, the content of which will be updated as the monument is further studied. In the future, an indexed electronic scientific journal may be created on its basis, in which works devoted to the study of PVL and the history of Ancient Rus' will be published. Ultimately, electronic scientific publishing around the world has become a full-fledged scientific platform that takes the study of chronicle text to a new research and technological level. After creating a working model, it can be tested on other types of sources. The result of work on the project may be the creation of a designer for electronic publications of various levels (scientific, popular science, etc.).

Fractional-Order Sliding Mode Load Frequency Control and Stability Analysis for Interconnected Power Systems With Time-Varying Delay

The universal use of open communication networks in interconnected power systems has changed the constant delay in the formerly dedicated channels into time-varying delay. The delay will deteriorate the dynamic performance of the load frequency control and lead to system frequency instability. This paper presents a fractional-order sliding mode controller for interconnected power systems with time-varying delay and performs stability analysis based on the Lyapunov direct method. Firstly, fractional-order integral sliding mode control is used to improve the stability of interconnected power systems containing time-varying delay. This expands the delay margin by taking advantage of the scalability of the fractional-order to the sliding mode surface. Then, the Riemann-Liouville fractional-order Lyapunov functional is introduced to stability analysis. This reduced the conservatism of the stability analysis by adjusting the upper limit of the functional integral. Lastly, combining Lyapunov's theory and integral inequality to obtain the stability conditions of the time-varying delay power system described by linear matrix inequality, and bring the stability margin of the system. The simulation results show that the designed controller can guarantee that the maximum delay time of the system is less than a predefined upper limit. The proposed control strategy obtains a larger stability margin than the other four scenarios. Additional simulation study was also conducted to verify its robustness to load variations and wind fluctuations.

Forecasting of soil moisture using machine learning in smart agriculture systems

Growing crops in modern conditions is a complex task and practically combines the practices of experience and the latest methods, including information technology, which has become part of the concept of "smart farming". An important factor in the stable predicted yield is the level of soil moisture, which is the result of changes in climatic factors such as air temperature, soil temperature, intensity of solar radiation, rainfall, wind speed, etc. A methodology for processing real historical indicators of climate change in a certain geographical area with subsequent training and application of machine learning models to predict soil moisture is proposed. To build a machine learning model, the following algorithms were selected and studied: the algorithm of regression trees, random forest, linear regression, M5P algorithms and the K* algorithm. The data source for training the models is the open information resource International Soil Moisture Network (ISMN) from ismn.earth/en. , which provides data on soil moisture and temperature, air temperature, and rainfall. Other data was used from the Open Meteo information service, which provides a free API and allows you to get historical data and weather forecast in specified coordinates during specified days. A data structure was developed to train the model for further prediction of soil moisture. An architecture has been developed and a software system for predicting soil moisture based on machine learning algorithms has been created using the Spring Framework, the WEKA library and Java FX with the ability to select and study the appropriate algorithms. Experiments have been carried out and the results of the duration of model training have been presented, while the algorithms of regression trees and linear regression require the least training time. A comparison of algorithms is made according to the following criteria: learning speed, cross-testing speed, prediction speed, testing performance indicators for real historical data. Based on the results of the study, conclusions are drawn about individual algorithms, the feasibility of using them to predict soil moisture based on climatic indicators. The obtained results will make it possible to evaluate and select the best models of machine learning in the design of the information and analytical system "smart agriculture" for forecasting soil moisture.

A novel multi-objective stochastic model and a novel hybrid metaheuristic for designing supply chain network under disruption risks to enhance supply chain resilience

Supply chains are vulnerable to various disruption risks that can adversely affect their overall performance and objectives. This study addresses the challenge of designing a resilient and environmentally sustainable mixed open and closed-loop supply chain network that can withstand both operational and disruption risks. The research employs a bi-objective stochastic mathematical model to examine the balance between environmental sustainability and profitability within the SC. To mitigate the impact of disruptions, several resilience strategies are incorporated into the model, significantly reducing their adverse effects. Due to the inherent complexity of the problem, the study introduces a novel hybrid metaheuristic algorithm that combines ant colony optimization with teaching and learning-based optimization, named ACO-TLBO. Additionally, two other enhanced hybrid metaheuristics are proposed. The performance of these solution methods is assessed through various test problems, using specific performance metrics for comparison. Results reveal that the ACO-TLBO algorithm excels in generating high-quality, non-dominated solutions. The model's practical applicability is demonstrated through a case study in the tire industry, validating its effectiveness. The findings indicate that the proposed resilience strategies are crucial for minimizing the negative impacts of disruptions on SC objectives. Furthermore, the results underscore the importance of resilience in maintaining both sustainability and profitability within supply chains.

On-Demand Coordinated Spectrum and Resource Provisioning Under an Open C-RAN Architecture for Dense Small Cell Networks

Mobile networks have seen more than a thousand times traffic increases in the past decade. Network operators face an ever increasing demand and cost to deploy denser networks in order to maintain the quality of services. In view of this paradigm shift, an open cloud radio access network (C-RAN) service model is studied in this work. Integrating the cross-layer functions of spectrum resource sharing, channel and power allocation, and interference management in a C-RAN architecture, we explore the feasibility of providing a scalable yet efficient broadband wireless service with dense small cell networks (DSN). The proposed methods and architecture can be applied to DSN that support the functions of (further enhanced) inter-cell interference cancelation (feICIC/ICIC) techniques of 3GPP standard, and have the potential to provide a cost-effective solution for high-quality DSN. Simulation results across an area of 100 km <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> show that the proposed scheme can offer an aggregate downlink throughput of 21 Gbps over a maximum channel bandwidth of 20 MHz, which is 5 times the throughput with a typical ICIC method. Moreover, the service satisfaction degree can reach 85% under the proposed C-RAN architecture, making it a promising and cost-effective solution for future broadband wireless services.

Techniques and technology improvement of cleaning open drainage networks in Bukhara region

Techniques and technology improvement of cleaning open drainage networks in Bukhara region

Open Networking Engine (ONE): An Orchestration Tool for Open Optical Line System

Open Networking Engine (ONE): An Orchestration Tool for Open Optical Line System

P4flow: A software-defined networking approach with programmable switches for accounting and forwarding IPv6 packets with user-defined flow label tags

A comprehensive analysis of the HEP (High Energy Physics) experiment traffic across LHCONE (Large Hadron Collider Open Network Environment) and other networks, is essential for immediate network optimisation (for example by the NOTED project) and highly desirable for long-term network planning. Such an analysis requires two steps: tagging of network packets to indicate the type and owner of the traffic being carried and then processing of the tagged packets. The RNTWG (Research Network Technical Working Group) has defined a specification to identify the experiment and the application that originates a given network flow, named scitags (Scientific Network Tags) that is applied to the flow label field of the IPv6 header. We report here on the second step: processing of packets tagged according to this specification. We developed P4flow as a software-defined networking approach by using P4 (Programming Protocol-Independent Packet Processors), a language for programming the data plane of network devices to accounting and process IPv6 packets with a scitags-based stamp in the flow label field, to understand the network utilisation and the applications used by the WLCG (Worldwide Large Hadron Collider Computing Grid) sites. With P4flow, and exploiting the control plane capabilities provided by RARE/FreeRtr (an Open Source Network Operating System developed by the GÉANT community), we cannot only generate statistics concerning the traffic per experiment and application but can also, using an Intel Tofino P4-programmable ASIC Ethernet Switch, decide how to forward traffic matching defined flow labels. This latter capability is particularly interesting as we prepare for a future where LHC experiments will be sharing network links with other major science collaborations such as SKA and ITER.

A novel strategy to construct hydrogels with anti-swelling and water-retention abilities by covalent surface modification.

Hydrogels have been widely used in various fields due to their diverse properties and flexible preparation methods. However, limited by the open network structure, hydrogels inevitably lose water in air or absorb water in aqueous solution, resulting in the loss of intrinsic functions, which severely hinders their practical applications. To address this issue, a general strategy was developed by subsequently modifying the surface of hydrogels with branched polyethyleneimine (PEI) and (3-(methacryloxy)propyl)trimethoxysilane (MPS) to covalently construct a dense cross-linked siloxane layer on the hydrogel surface. As a proof of concept, poly(2-(dimethylamino)ethyl methacrylate)/sodium alginate (PDMAEMA/SA) hydrogels were chosen as the model hydrogels to verify the feasibility of this strategy. The hydrogels adsorbed PEI to form amino-rich surfaces through hydrogen bonding, followed by covalently grafting MPS through rapid and catalyst-free mutual chemical reactions between acrylates and amine groups, as well as the hydrolysis of MPS. After modification, robust hydrophobic surfaces were successfully fabricated on the hydrophilic hydrogels. The modified hydrogels exhibited extraordinary anti-swelling and water-retention abilities. As the most typical intrinsic properties of hydrogels, the conductivity and sensing performance were well preserved. The strategy reported here provides a new insight into the construction of hydrogels with anti-swelling and water-retention abilities.

Coalitional Game Strategy and TSSM for Efficient Load Balancing in Software Defined Networking

Using distributed SDN control, software defined networking (SDN) delivers additional flexibility to network management, and it has been a significant breakthrough in network innovation. Switch migration is often used for distributed controller workload balancing. The time-sharing switch migration (TSSM) scheme proposed a strategy in which multiple controllers are allowed to share the workload of a switch via time sharing during an overloaded condition, resulting in reduced ping-pong controller difficulty, fewer overload occurrences, and improved controller efficiency. However, it requires more than one controller to accomplish, it has greater migration costs and higher controller resource usage during the TSSM operating time. As a result, we presented a coalitional game strategy that optimizes controller selection throughout the TSSM phase depending on flow characteristics. The new TSSM method reduces migration costs and controller resource usage while still providing TSSM benefits. For the sake of practicality, the proposed strategy is implemented using an open network operating system. The experimental findings reveal that, as compared to the typical TSSM system, the proposed technique reduces migration costs and controller resource usage by approximately 18%.

Future of Digital Commerce with Online Network for Digital Commerce

Open Network for Digital Commerce (ONDC) is a revolutionizing and ground-breaking initiative first announced in 2020 that is aimed to forever change the world of digital commerce by using new concepts like openness and inclusivity. It is designed to provide safe and secure interoperable digital transactions that go beyond geographical boundaries, enabling users to participate in businesses of all sizes and economies. It also provides standard formats and APIs to ensure cross-compatibility between different platforms, marketplaces, and logistics support. ONDC also puts a great amount of care and emphasis on data privacy, security, robust transactions, and encryption and also encourages user empowerment and allows an individual to manage their data and information to produce maximum trust and transparency between participants. This platform also aims to bring new opportunities by supporting merchants of all levels despite their scale and size and helping them get on online platforms. It will allow both consumers and sellers to engage and trade online regardless of the platform they are using. It is an initiative provided by the Department for Promotion of Industry and Internal Trade (DPIIT) under the Ministry of Commerce and Industry. It was implemented and registered as a non-profit company on December 31, 2021.

PlantBiCNet: A new paradigm in plant science with bi-directional cascade neural network for detection and counting

Deep learning is increasingly popular for precise plant detection and counting in computer vision applications. Despite the rising cross-disciplinary research in this field, an important issue has been neglected: due to the inherent constraints imposed by the natural growth patterns of plants, the costly and time-consuming process of collecting plant image data, resulting in a scarcity of real-world available data. Consequently, researchers are often constrained to exploring large-scale data representations or limited applications. Currently, still no generalized network architecture excels in demanding environments. To address this, we introduce two new bi-directional cascade neural network paradigms, PlantBiCNet and its lightweight variant, PlantBiCNet-Lite. Unlike previous research, we place greater emphasis on the network decoding process itself rather than the encoder. Our bi-directional cascade and weight fusion decoding approach maximizes high-level semantics and low-level spatial data, enhancing the localization signal in a way that respects gradient diversity. We evaluate PlantBiCNet and PlantBiCNet-Lite on five public plant detection and counting datasets and a novel plant dataset. The average precision AP50 on these six small-scale datasets reached 0.905, with a high coefficient of determination R2 of 0.935, while the reported frame rate was 196 FPS. These results significantly outperform state-of-the-art computer vision methods. Moreover, we found that the introduction of the Dropout mechanism markedly enhances the model's performance and generalization ability. Further analysis supports our experiments and provides valuable insights into network optimization. Overall, our organization's open datasets and network modeling details provide a solid foundation for future research in the field of plant science.

Review on various image protection methods

The security of information exchange over email and other web-based means is rather low, and there is the risk of interception when it comes to confidential information (e.g., credit card info). Therefore, online users should be ensured of the security and privacy of their web interactions. Despite the remarkable benefits of the Internet in the modern era and its contribution to digital communication, the information security of open networks has become an important issue due to its heavy costs. Several approaches have been used to address this issue, particularly through information hiding and encryption in system security strategies. Cryptography is the process of information encryption, and steganography refers to information hiding. These methods have proven quite effective in maintaining information security. Cryptography involves making changes to confidential information, which prevents their readability by eavesdroppers, which could also be considered a technique of original text to cipher text. On the other hand, steganography entirely hides confidential information from unauthorized users using multiple carrier formats, including audio, video, protocol, and images. Given the availability of digital images, they are often employed as carrier files online. Other approaches to image steganography are also common and have specific limitations and advantages. The present study aimed to review different methods of image cryptography and steganography and compare the studies in this regard so as to determine which the best, specific and accurate methods of information security.

Dampak Penggunaan Teknologi Internet terhadap Perkembangan Kognitif dan Perilaku Anak dalam Keluarga

The internet is an open global communications network that connects thousands of networks throughout the world. Internet use can now be accessed by all ages, from children, teenagers to adults. As a result, children use the internet more often at home than playing outside the home. The aim of this research is to determine the impact of excessive use of internet technology on the cognitive development and behavior of children in the family. The research method used is literature. Through this method, the author collects various national and international journal literature to study the concepts and theories contained therein. The research results show that excessive use of the internet, especially in multitasking media activities, can affect children's cognitive development in the form of poor focus performance in carrying out their tasks and easily distracted attention. Apart from that, the use of internet technology without supervision from parents has an impact on children's freedom of internet activities, which allows children to be exposed to content that is not appropriate for their age, such as cyber bullying, violent content and others. This can cause children to behave deviantly