Smart Network Research Articles

Light responsive self-healable carbon nanotube/polyurethane smart networks with precisely remote-controlled shape-changing properties

Smart materials can dynamically react to environmental stimuli and thus change at the micro or macro scale. However, when exposed to harsh conditions, damage can occur to them to varying degrees, thus affecting their performance and service life. In this study, a recyclable shape memory polyurethane material with self-healing properties was prepared using polycaprolactone (PCL) and polytetrahydrofuran diol (PTMG) as raw materials and isofuranone diisocyanate (IPDI) as a cross-linking agent. Dynamic cross-linking networks are constructed by introducing reversible disulfide bonds to enhance the self-healing and recyclable properties of the materials. Hydroxylated multiwalled carbon nanotubes (MWCNTs-OH), as photothermal particles, impart a remote response ability to the material, making the material temperature variable under near-infrared radiation (NIR). The shape memory recovery of CNT-PCL-PTMG can be triggered remotely and precisely controlled the irradiated NIR area. The results of tensile tests, scanning electron microscopy (SEM) and optical microscopy (OM) show that 3%CNT-5PCL-5PTMG can achieve efficient and rapid self-healing. Additionally, the synergistic effect of transesterification and the dynamic exchange of disulfide bonds gives materials reprocessability for recycling use. The material is promising as an alternative material for soft robotics and smart sensors.

IoT applications and challenges in smart cities and services

AbstractInternet of Things (IoT) is a revolutionary and novel platform where a smart network connects to the large number of electronic devices via internet through available communication systems for reliable and real time connectivity, sensing thus acquiring data from sensors, computing and actuating devices. A review of the current status of IoT features, architecture, communication infrastructure and applications is presented here. Number of IoT applications and challenges in the development of smart cities are covered about this innovative technological platform in this review along with communication protocols characteristics and applications. IoT aided smart city services, health applications, transport applications and smart grid improvements are also discussed. Many challenges and issues related to security, communication, applications and system architecture along with future directions of IoT technology are elaborated in detail.

The Promotion of 5G+VR to Science Teaching in Primary Schools – Taking “The Passage of Time” in the First Volume of the Fifth Grade of the Education and Science Edition as an Example

With the increasing attention paid to 5G-assisted teaching, there is a growing need to explore the practical application of 5G+VR in science education. This paper uses the lesson “Sun Clock” in the “The Passage of Time” section of the fifth-grade education and science textbook as an example to investigate the use of 5G+VR technology in science teaching. By taking advantage of the school’s 5G network environment and smart education platform, this study aims to improve students’ learning experience and solve the problems encountered in experimental teaching. Through the use of VR technology, students were able to independently explore and understand how light and shadow are used to tell time with the help of the “Sun Clock” experiment. Students were able to observe the relationship between light and shadow and learn how to use them to tell time by combining the explanation in VR glasses and multi-angle observation of the sundial on the equipment. The study found that the use of 5G+VR technology significantly improved students’ understanding of the content and increased their sense of self-efficacy and on-the-spot experience. The implementation of 5G+VR technology in science education has solved the problems of safety concerns, limitations of experimental materials and site, and improved students’ problem-solving abilities in practical situations. The findings of this study have important implications for the integration of technology in primary school science education.

Research on the Factors Affecting the Adoption of Smart Aged-Care Products by the Aged in China: Extension Based on UTAUT Model.

With the rapid development of network technology and smart technology, smart aged-care products are becoming increasingly valued for their ability to help the aged actively cope with the challenges of aging. However, seniors face challenges in using smart aged-care products for many reasons, which reduces their willingness to adopt them. As a result, the sustainable development of smart aged-care products is constrained. This study combined the unified theory of technology acceptance and use, perceived risk theory and perceived cost theory, and reconstructed a research model that investigated the adoption of smart aged-care products by the elderly in China. Questionnaires were given to older Chinese adults in this study, and 386 valuable responses were received. The findings of the structural equation model (SEM) analysis are as follows: (1) performance expectancy, effort expectancy, and social influence were positively related to the behavioral intention of seniors to use smart aged-care products; (2) perceived cost and perceived risk were negatively related to the behavioral intention of seniors to use smart aged-care products; (3) perceived risk indirectly affected use behavior through behavioral intentions; (4) facilitating conditions did not have a significant impact on the use behavior of seniors in adopting smart aged-care products. Based on the empirical results, this study sought to improve the use behavior of the aged in relation to the adoption of smart aged-care products, and provided suggestions to improve the overall service quality and sustainability of those products.

The impact of the thermal distribution network operating temperature and system design on different communities' energy profiles

The use of heat pumps to increase the flexibility of heating networks is critical for overcoming the intermittent nature of renewable energy. This article introduces a novel thermal network operation strategy, referred to as “Smart Network with Peak Control,” which entails adjusting the network temperature to control the peak electricity demand created by the electrification of heating. A comparison between four different operating scenarios—namely, low-temperature (fourth-generation), ultra-low temperature (fifth-generation), smart network (hybrid of low and ultra-low temperatures), and smart network with peak control—is conducted for different sites of different energy profiles. For heating-dominant sites, the application of the smart network and smart network with peak control resulted in 10% lower emissions than the other scenarios. While the peak electricity demand in the smart network scenario was double that of the conventional system, the smart network with peak control scenario was able to counter this effect by adjusting the network temperature. The effects of integrating combined heat and power with different sizes and operating schedules based on the hours of natural-gas peaking generators in mixed electrical grids were also investigated. The developed model was applied to 1139 sites in Canada, revealing significant carbon emissions reductions of up to 73%.

Machine learning-based jamming attack classification and effective defense technique

The fourth industrial revolution has resulted in the intelligent Internet of Things being widely used for home networking applications and smart infrastructure. Consequently, wireless connectivity has become essential in industrial and daily-life applications. Wireless communication is a continuously evolving technology that satisfies high-speed and ultra-low latency requirements. However, as multiple users utilize a single channel by sharing frequency and time, the service quality cannot be ensured owing to the interference from a congested network. Additionally, malicious attackers can compromise communication availability or destroy data integrity through jamming attacks, threatening human life and safety. Conventional jamming attack detection and response technology respond to attacks without detecting the type of jammer, exhibiting certain limitations in detecting and defending against an intelligent attack. This study proposes a novel jammer classification and effective defense (JCED) algorithm that can classify jamming attack types using machine learning (ML) and provide differential responses based on the jamming types. Depending on the jammer type, the JCED algorithm can adaptively select various response methods, ranging from simple retransmission to active battery-draining attacks. The experimental results verify that JCED exhibits 24.9% higher effective throughput and 23.4% lower energy consumption than the countermeasure detection and consistency algorithm (CDCA). Moreover, JCED improves the effective throughput by an average of approximately three times compared to CDCA in an environment with integrity violation attacks. Thus, the JCED is an effective defense mechanism against jamming attacks, ensuring digital information safety and high throughput.

Lithium-ion battery state of charge prediction based on machine learning approach

With the extensive utilization of lithium ion batteries as renewable energy source in electronics devices, smart network and electric vehicles, supplementary enhancements in the performance of lithium-ion batteries and accurate prediction of state of charge (SOC) are still a great challenge to battery research and innovation community. Machine learning (ML), which is one of the essential tools of artificial intelligence, is promptly changing many areas with its capability to learn from provided data and solve multifaceted tasks, and it has emerged as a new method used to solve research issues in the area of lithium ion batteries. In this paper, we investigate the relationship between input factors including current, voltage and temperature, and predicted SOC of lithium ion battery. The effectiveness of three ML models — linear regression, Gaussian process regression (GPR) and support vector machine (SVM) were assessed and compared. It was found that the predictions made by these models accurately matched the data from experiments.

Novel Composable and Scaleout Architectures Using Compute Express Link

Compute Express Link (CXL) is an open industry standard interconnect offering caching and memory semantics on top of PCI-Express. In addition to providing high-bandwidth and low-latency connectivity between host processor and accelerators, smart network interface card, and memory expansion devices, it also enables resource pooling across multiple systems for scalable, power-efficient, and cost-effective computing. This article delves into a novel composable and scaleout architecture to enable large-scale systems at rack level and beyond with pooled and shared heterogeneous memory and heterogenous compute resources using CXL interconnect.

Compute Express Link (CXL): Enabling Heterogeneous Data-Centric Computing With Heterogeneous Memory Hierarchy

Compute Express Link is an open industry standard interconnect offering caching and memory semantics on top of peripheral component interconnect-Express, with resource pooling and fabric capabilities. In addition to providing high-bandwidth and low-latency connectivity between host processor and accelerators, smart network interface cards, and memory expansion devices, it also enables resource pooling across multiple systems for scalable, power-efficient, and cost-effective computing. This article delves into the microarchitectural design to deliver power-efficient performance based on our experience designing a Xeon central processing unit and field-programmable gate array with this technology with demonstrated silicon interoperability.

Modeling wax deposition of crude oils using cascade forward and generalized regression neural networks: Application to crude oil production

Crude oil is made of different compositions and materials, namely hydrocarbons, oxygen, nitrogen, sulfur, and various metals. Deposition of heavy components can lead to a restriction of flow paths and decrease the oil production rate during production processes. Deposition of wax is a crucial issue that appears in the transfer equipment and surface facilities and decreases the inner diameter of pipelines. Wax deposition leads to loss of effective diameter and an increase in roughness, which will increase the pressure drop in the oil reservoir. Deposition of wax particles appears in low temperatures which can lead to a blockage of pipelines. When the oil's temperature is less than the wax appearance temperature (WAT), which is called the cold flow regime, oil composition changes because wax crystals precipitate. This research generally focuses on developing accurate intelligent models to predict wax deposition using the pour point temperature and °API of the crude oil sample as input variables. Various smart networks, including multilayer perceptron (MLP), radial basis function (RBF), cascade forward neural network (CFNN), and generalized regression neural network (GRNN) are utilized for this purpose. Four optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno quasi-Newton backpropagation (BFGS), Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) are used for training the models. The results showed that the developed GRNN smart model could obtain a superior performance providing predictions for the amount of wax deposited with a root mean squared error (RMSE) of 0.845, an average absolute percent relative error (AAPRE) of 10.36%, and a coefficient of determination (R2) of 0.975. The trend analysis indicated that increasing pour point temperature leads to a wax deposition increase. Oppositely, increasing oil °API gravity reduces wax deposition. Lately, outlier detection was carried out by applying the Leverage approach to identify the outlier data points in a data bank. It detected only 4 data points (out of 173) as outliers.

MATHEMATICAL MODEL OF A FLEXIBLE MICRO GRID INTEGRATED INTO THE COUNTRY GRID

The functioning of local electric networks ('Micro Grid') is described, which due to the development of Smart Network technologies, distributed generation and electricity storage systems received an incentive for development. The structural decomposition of the market model is suggested for the 'Top-Level Market' and 'Micro Grid' local systems to reflect the interaction of the 'Micro Grid' with the wholesale electricity market. Load regulation instruments are described, which ensure the flexibility of the Micro Grid. A mathematical model of the 'Micro Grid' system is proposed, which formulated as a Unit commitment problem. The computational experiment shows the adequacy of the results of modelling of 'Micro Grid' systems. References 9, figures 4, tables 2.

Towards a Smart Environment: Optimization of WLAN Technologies to Enable Concurrent Smart Services

In this research paper, the spatial distributions of five different services-Voice over Internet Protocol (VoIP), Video Conferencing (VC), Hypertext Transfer Protocol (HTTP), and Electronic Mail-are investigated using three different approaches: circular, random, and uniform approaches. The amount of each service varies from one to another. In certain distinct settings, which are collectively referred to as mixed applications, a variety of services are activated and configured at predetermined percentages. These services run simultaneously. Furthermore, this paper has established a new algorithm to assess both the real-time and best-effort services of the various IEEE 802.11 technologies, describing the best networking architecture as either a Basic Service Set (BSS), an Extended Service Set (ESS), or an Independent Basic Service Set (IBSS). Due to this fact, the purpose of our research is to provide the user or client with an analysis that suggests a suitable technology and network configuration without wasting resources on unnecessary technologies or requiring a complete re-setup. In this context, this paper presents a network prioritization framework for enabling smart environments to determine an appropriate WLAN standard or a combination of standards that best supports a specific set of smart network applications in a specified environment. A network QoS modeling technique for smart services has been derived for assessing best-effort HTTP and FTP, and the real-time performance of VoIP and VC services enabled via IEEE 802.11 protocols in order to discover more optimal network architecture. A number of IEEE 802.11 technologies have been ranked by using the proposed network optimization technique with separate case studies for the circular, random, and uniform geographical distributions of smart services. The performance of the proposed framework is validated using a realistic smart environment simulation setting, considering both real-time and best-effort services as case studies with a range of metrics related to smart environments.

From Coherent Systems Technology to Advanced Fiber Sensing for Smart Network Monitoring

Coherent technology developed in the past decade for transmission over core optical networks has leveraged the fiber capacity with the introduction of polarization diversity I/Q modulation along with advanced signal processing. Meanwhile, Distributed Acoustic Sensing (DAS) over telecom infrastructure has recently gained interest, and its introduction in deployed optical networks for in-depth supervision becomes a desirable feature for telecom operators. This paper intends to show that the combination of coherent solutions and digital processing techniques implemented today in current transceiver systems can strongly benefit DAS technology: key performance parameters as sensitivity threshold and sensing range, and coexistence with data traffic can be significantly improved compared with the figures achieved by initially introduced conventional solutions.

A Survey on Virtual Network Functions for Media Streaming: Solutions and Future Challenges

Media services must ensure an enhanced user’s perceived quality during content playback to attract and retain audiences, especially while the streams are distributed remotely via networks. Thus, media streaming services rely heavily on good and predictable network performance when delivered to a large number of people. Furthermore, as the quality of media content gets high, the network performance demands are also increasing, and meeting them is challenging. Network functions devoted to improving media streaming services become essential to cope with the high dynamics of network performance and user mobility. Furthermore, new networking paradigms and architectures under the 5G networks umbrella are bringing new possibilities to deploy smart network functions, which monitor the media streaming services through live and objective metrics and boost them in real-time. This survey overviews the state-of-the-art technologies and solutions proposed to apply new network functions for enhancing media streaming.

LoRa-Based IoT Network Assessment in Rural and Urban Scenarios.

The implementation of smart networks has made great progress due to the development of the Internet of Things (IoT). LoRa is one of the most prominent technologies in the Internet of Things industry, primarily due to its ability to achieve long-distance transmission while consuming less power. In this work, we modeled different environments and assessed the performances of networks by observing the effects of various factors and network parameters. The path loss model, the deployment area size, the transmission power, the spreading factor, the number of nodes and gateways, and the antenna gain have a significant effect on the main performance metrics such as the energy consumption and the data extraction rate of a LoRa network. In order to examine these parameters, we performed simulations in OMNeT++ using the open source framework FLoRa. The scenarios which were investigated in this work include the simulation of rural and urban environments and a parking area model. The results indicate that the optimization of the key parameters could have a huge impact on the deployment of smart networks.

Assessment of the Gross, Technical and Economic Potential of Region’s Solar Energy for Photovoltaic Energetics

A great number of factors determining the development of photovoltaics are associated with the assessment of possible volumes of solar energy use in correlation with the technical and economic characteristics of photovoltaic equipment. An appropriate assessment of solar energy potential that applies universally to any subsequent use option still remains a crucial task. This work is devoted to the assessment and analysis of the gross, technical and economic potentials of solar energy for photovoltaics. The smart analysis includes the basic program working in the context of connection to databases and to the programs used for determining required initial data or, as a limited option, in the context of full or partial initial data input by the user. Therefore, optimally, a smart network is formed, which for the purposes of obtaining the values of potentials, uses the most up-to-date values of initial data and other required information. This work sets out the tried and tested assessment program for the potentials of solar energy available in large and medium areas. The proposed approach to the analysis of solar energy potential in a region makes it possible to secure a high degree of assessment reliability which can be used for more detailed calculations, including the potentials analysis for a specific point on the ground or a specific type of PV system.

Energy-Aware Next-Generation Mobile Routing Chains with Fog Computing for Emerging Applications

The Internet of Things (IoT) provides robust services to connected sensors in a distributed manner, and maintains real-time communication using wireless standards. The smart network has offered many autonomous smart systems to collect information from remote nodes, and share it by exploring the network layer. Researchers have recently offered a variety of ways to increase the effectiveness of emerging applications using trustworthy relaying systems. However, there are still many issues with route reformulation due to frequent disconnections of mobile devices and resource limitations. Furthermore, most of the existing methods for IoT systems are unable to utilize network resources, which lowers the performance of green networks. Thus, providing a foolproof solution for the autonomous system with energy efficiency is a challenging task. Therefore, this paper presents an algorithm for the mobile network using fog computing to reduce network disconnectivity. Furthermore, using security services, the proposed algorithm efficiently explores the characteristics of the device, and avoids malicious traffic to drain the additional energy consumption of the network. The main aspects of the proposed algorithm are as follows: (i) using the adjustable transmission power, the proposed algorithm offers a fault-tolerant solution to transmit the aggregated data over the unpredictable wireless system; (ii) with the support of fog nodes, the data load is reduced among devices with the offering of a secured authentication scheme. Using simulations, the proposed algorithm is tested, and its significance is demonstrated against other related studies.

Combined Heat and Power Economic Dispatching within Energy Network using Hybrid Metaheuristic Technique

Combined heat and power (CHP) plants have opportunities to work as distributed power generation for providing heat and power demand. Furthermore, CHP plants contribute effectively to overcoming the intermittence of renewable energy sources as well as load dynamics. CHP plants need optimal solution(s) for providing electrical and heat energy demand simultaneously within the smart network environment. CHP or cogeneration plant operations need appropriate techno-economic dispatching of combined heat and power with minimising produced energy cost. The interrelationship between heat and power development in a CHP unit, the valve point loading effect, and forbidden working regions of a thermal power plant make the CHP economic dispatch’s (CHPED) objective function discontinuous. It adds complexity in the CHPED optimisation process. The key objective of the CHPED is operating cost minimisation while meeting the desired power and heat demand. To optimise the dispatch operation, three different algorithms, like Jaya algorithm, Rao 3 algorithm, and hybrid CHPED algorithm (based on first two) are adopted containing different equality and inequality restrictions of generating units. The hybrid CHPED algorithm is developed by the authors, and it can handle all of the constraints. The success of the suggested algorithms is assessed on two test systems; 5-units and 24-unit power plants.

Video coding deep learning-based modeling for long life video streaming over next network generation.

Availability is one of the primary goals of smart networks, especially, if the network is under heavy video streaming traffic. In this paper, we propose a deep learning based methodology to enhance availability of video streaming systems by developing a prediction model for video streaming quality, required power consumption, and required bandwidth based on video codec parameters. The H.264/AVC codec, which is one of the most popular codecs used in video steaming and conferencing communications, is chosen as a case study in this paper. We model the predicted consumed power, the predicted perceived video quality, and the predicted required bandwidth for the video codec based on video resolution and quantization parameters. We train, validate, and test the developed models through extensive experiments using several video contents. Results show that an accurate model can be built for the needed purpose and the video streaming quality, required power consumption, and required bandwidth can be predicted accurately which can be utilized to enhance network availability in a cooperative environment.

ІНВЕСТИЦІЙНИЙ КЛІМАТ УКРАЇНИ В ПОВОЄННІЙ ВІДБУДОВІ

The article defines the importance of a favorable investment climate and the main prerequisites for attracting funds to the domestic economy in post-war reconstruction. Attention is focused on the results of investment impact on certain sectors of the economy, and which sectors require priority capital investments. The Index of Investment Attractiveness of our country and the problems affecting the formation of this Index have been analyzed. The directions of development of the investment climate in Ukraine during the war and in the post-war period have been highlighted. It has been found out about the creation of a unique electronic platform Advantage Ukraine for the purpose of attracting foreign investors to the Ukrainian economy, which collects more than 500 investment projects and opportunities in ten sectors of the economy. Among the possibilities that have been discussed are: the construction of "smart networks", the introduction of energy balancing capacities, gas extraction using advanced technologies, "green" metallurgy using direct recovery iron – these are things that can be implemented as a priority. The issue of guaranteeing the security of investments in war and post-war times remains the most painful issue today, which is one of the most restraining factors for investors. In addition, assumptions have been made about restoring the production potential of Ukraine's economy and creating a favorable business climate for attracting financial resources from abroad with the help of partner countries. The importance of restoring the inflows of direct foreign investments to overcome the consequences of Russian military aggression has been highlighted. The influence of macroeconomic factors on the investment activity of foreign investors has been outlined on the basis of a correlation-regression analysis of the influence of these factors on the inflow of foreign direct investment into the national economy. Directions for increasing the investment attractiveness of Ukraine's economy in modern conditions of development have been given. Based on the conducted research, ways of increasing the investment attractiveness of Ukraine in modern conditions have been proposed.