Service Layer Research Articles

A Novel Secured Multi-Access Edge Computing based VANET with Neuro fuzzy systems based Blockchain Framework

In vehicle ad-hoc networks, the progression of wireless communication technology to 6G, overcomes storage, processing, privacy, and power limits to create an efficient and intelligent next generation transportation system. Vehicular ad hoc network may now offer remarkable availability, reliability, and throughput using 6G technology. However, the VANET system’s data should be protected. This paper proposes an effective batch authentication and key exchange technique to avoid contact with hostile vehicle users. Moreover, three types of systems are proposed: PKI, ID-based, and MAC-based. The neuro-fuzzy inference technique was used to predict VANET security ratings. The Homogeneous Discrete-Time Markov Chain model is used to secure data transit. Additionally, this research examined the work from a blockchain perspective combined with MEC. There are 3 level to comprise the architecture: perception, edge computing, and services. Throughout the blockchain transmission process, the first layer make certain the security of VANET data. The perception layer makes use of edge computing and cloud services on the edge. The service layer protects data by using both traditional cloud storage and blockchain technology. The lowest layer of the system architecture is dedicated to the throughput and quality of service requirements of MEC users. The primary challenge is achieving consensus across blockchain nodes while maintaining the MEC system’s and blockchain’s performance. To simulate the joint optimization problem, a Markov decision process with reward function is utilized. The simulation results are conferred to illustrate the validity of study assertions.

A Privacy Protection Scheme for Cross-Chain Transactions Based on Group Signature and Relay Chain

Recently, with the rapid development of blockchain technology, the information interaction and value transfer problems between different blockchains have become the focus of research. The cross-chain technology is to solve the cross-chain operation problems of assets and data between different chains. However, the existing cross-chain technology has the problem of identity privacy leakage. Therefore, this article proposes a cross-chain privacy protection scheme for consortium blockchains based on group signature, certificate authority and relay chain. The scheme is divided into three cross-chain service layers, called the management layer, the transaction layer, and the group layer. The management layer is responsible for the forwarding of cross-chain transactions, the transaction layer includes the blockchains that actually participate in cross-chain transactions, and the group layer is responsible for group signature related work. Through this scheme, the identity privacy of both parties to the transaction can be protected during the cross-chain transaction process.

VenusAI: An artificial intelligence platform for scientific discovery on supercomputers

Since the machine learning platform can provide one-stop artificial intelligence (AI) application solutions, it has been widely used in the industrial and commercial internet fields in recent years. Based on the heterogeneous accelerator cards, scientific discovery using large-scale computation and massive data is a significant tendency in the future. However, building a platform for scientific discovery remains challenging, including large-scale heterogeneous resource scheduling and support for massive multi-source data. To free researchers from tedious resource management and environmental configuration, we propose a VenusAI platform for large-scale computing scenarios in scientific research, based on heterogeneous resources scheduling framework. This paper firstly illustrates the VenusAI platform architecture design scheme based on the supercomputers and elaborates on the virtualization and containerization of the underlying hardware resources. Next, a technical framework for heterogeneous resource aggregation and scheduling is proposed. A unified resource interface in the application service layer is introduced. Considering the core three parts of the AI scenario: data, model, and computing power, modularized service decoupling is carried out. Furthermore, three types of experiments are evaluated on the supercomputers and show that the performance of the scheduling framework on virtual clusters is better than that on common clusters. Finally, three scientific discovery applications deployed on VenusAI, i.e., new energy forecasting, materials design, and unmanned aerial vehicle planning, demonstrate the advantages of the platform in solving practical scientific problems.

A Secured Industrial Internet-of-Things Architecture Based on Blockchain Technology and Machine Learning for Sensor Access Control Systems in Smart Manufacturing

In this paper, a layered architecture incorporating Blockchain technology (BCT) and Machine Learning (ML) is proposed in the context of the Industrial Internet-of-Things (IIoT) for smart manufacturing applications. The proposed architecture is composed of five layers covering sensing, network/protocol, transport enforced with BCT components, application and advanced services (i.e., BCT data, ML and cloud) layers. BCT enables gathering sensor access control information, while ML brings its effectivity in attack detection such as DoS (Denial of Service), DDoS (Distributed Denial of Service), injection, man in the middle (MitM), brute force, cross-site scripting (XSS) and scanning attacks by employing classifiers differentiating between normal and malicious activity. The design of our architecture is compared to similar ones in the literature to point out potential benefits. Experiments, based on the IIoT dataset, have been conducted to evaluate our contribution, using four metrics: Accuracy, Precision, Sensitivity and Matthews Correlation Coefficient (MCC). Artificial Neural Network (ANN), Decision Tree (DT), Random Forest, Naive Bayes, AdaBoost and Support Vector Machine (SVM) classifiers are evaluated regarding these four metrics. Even if more experiments are required, it is illustrated that the proposed architecture can reduce significantly the number of DDoS, injection, brute force and XSS attacks and threats within an advanced framework for sensor access control in IIoT networks based on a smart contract along with ML classifiers.

A constrained optimization model for the provision of services in a 5G network with multi-level cybersecurity investments

In this paper, we present a multi-tiered network-based optimization model describing the provision of services by network slices of 5G-Service providers (e.g. through Unmanned Aerial Vehicles (UAVs) organized as Flying Ad hoc Networks (FANET)), taking into account the security levels of each provider. The three levels of the network consist of the infrastructure layers, which contain resources needed to execute a service, the slices layer, where services are served for the services layer, which represents the upper layer of the network and consists of services or applications required by users or devices. The objective of the proposed model is to establish the optimal flows between network layers and the optimal security levels in order to maximize the providers’ profits, given by the difference between the revenues obtained by the sale of services and the rental of their resources and the costs. Numerical experiments are performed and solved with a new nature-inspired genetic algorithm adapted to the optimization 5G network problem.

Research on Multi-party Data Secure Interaction Technology for Energy Internet

Aiming at the problems of the energy Internet interaction process, which involves more users and intermediate links, and each participant is independent of each other, information asymmetry, and participants do not trust each other, this paper proposes a blockchain-based energy Internet multiparty data interaction model to build a data circulation architecture, including four layers, which are data sensing layer, network transmission layer, data processing layer and application service layer, to realize identity association, cross-domain access, It can realize the basic functions such as identity association, cross-domain access, trust evaluation and privacy protection, and effectively guarantee the secure interaction of multi-party data in the energy Internet.

Construction of E-Commerce Economic Management Platform Based on Data Visualization Technology

In the age of information explosion, users are exposed to overwhelmingly visual information every day. The raw unprocessed data are not attractive enough to the human brain. Thankfully, humans are extremely sensitive to graphic image perception and can understand the meaning behind numbers from graphic images. For this reason, data visualization is becoming the best way to communicate with people. In the context of big data, how to display complex data efficiently, what kind of drawing technology to use, and how to improve user interaction with data are urgent problems to be solved by data visualization technology. On the other hand, researchers’ understanding of data visualization is not consistent; there is no clear standard; and it is relatively confusing. Researchers engaged in data visualization and executives engaged in related data visualization industries have different interpretations of it, and the core theoretical basis and research methods are still in the exploration stage. Firstly, I systematically introduce the design principles of data visualization, software development related technologies, data visualization research methods, basic charts, and characteristics of data visualization. Secondly, based on the software development process, I analyzed the key issues of data visualization for the e-commerce system, gave the objectives of the design plan, and designed the whole architecture and development framework of the project. In particular, I designed the class diagram and timing diagram of the control layer, service layer, and data access layer for the visualization of user purchase preferences in the data visualization and analysis module as an example. Then, with the structure and characteristics of the data in the visualization studied, the business logic layer and the front-end visualization layer are implemented, and a good user interaction function is provided for the users. The data visualization is loaded asynchronously and on demand to improve the performance of data visualization and user interaction experience. Finally, the system data is presented with the analysis of the user population’s region, the analysis of user preferences, and the analysis of the product price sales relationship, and the product category tree diagram and the results of the data visualization run are given, providing a comprehensive platform for shopping, data analysis, and data visualization for the next generation e-commerce system.

The Industrial Internet of Things (IIoT): An Anomaly Identification and Countermeasure Method

Networked devices benefit enterprises to gain far-reaching control over their industrial processes, which encourages them to conduct routine operations in a smart manner. Rapidly expanding interconnected sensor devices are eligible to aggregate, process and disseminate wide-ranging data. This paper proposed an extended anomaly discovery and response framework. We argued the prospective security anomalies to the IoT equipped industrial-floor and examined the numerous attacks that are conceivable on the modules in the Industrial Internet of Things (IIoT) architecture. IIoT service layer architecture was designed in consideration of high-volume device connectivity, management and security enforcement. Collection of geospatial service and device data aided the proposed framework to bridge the gap between anomaly identification and context-aware node behavior. Framework evaluation considered design principles such as node interpretability, decentralization, real-time data relay, modularity and required service alignment. Emulation outcomes specify that the malware discovery performance is better if the anomaly recognition model used the applied utility for the yield layer.

APPLICATION OF SOLAR ENERGY TO MEASURE PHOTOVOLTAIC CAPACITY AND BATTERY OPTIMIZATION

This study uses the Markov Decision Model (MDP) to implement battery degradation and optimize battery use in Photovoltaic and the battery system model created. The battery optimization scheme for home loads uses the application of solar energy to optimally measure photovoltaic and battery capacity against each other. The different qualities of the standard used in this study are described starting from system characteristics and charge settings to an analysis of MDP and battery degeneration. Various systems undergo a list of analyses to implement awareness reasoning although developing battery volume and photovoltaic for the current system. The parametric span of cosmic and battery central tariff, the tariff of power worn taken away the framework, tariff of battery degeneration, time of year, photovoltaic generator size, battery size, and Health Status (SoH) of batteries were carried out to determine the optimal volume estimate and analyze the trade-offs essential in a mix scheme. This is then used to treasure trove the minimum amount of fee of the scheme with photovoltaic and battery application.
 This study support decision of the essential sizing deliberation for photovoltaic and battery-managed home loads linked to the services grid. Insightful that the battery can be used more destructively, also it can be formed lower and run at a greater C speed. This study analyzes actual fog computing research tools and storage composition algorithms for fog computing and develops a fog computing monitoring framework to provide data for fog computing storage composition algorithms. The framework proposed in this study provides granular container virtual hardware resource information and black box monitoring of service layer information associated with microservices. Framework usefulness on Raspberry Pis and CPU overhead of framework tested.
 The results of this study present the framework proposed could be used on single-chip microcomputers with relatively inadequate computational performance. In addition, a minimal effect on the battery degeneration system on the MDP decision due to the low system C-rate limit for the battery and interesting behavior of total fee and demand is also found. For future research, testing different maximum C levels should be considered to determine the photovoltaic size and battery system affected. Various battery optimization systems can be proved to check the benefit and disbenefits in the microgrid system case study. Lastly, collecting a scheme for actual-time reproduction to know how nice the operation is performing is the next stage of implementing MDP for battery management and system development.

A Secure Interoperable API Wrapper Tunnel for Integration of GIS-Based ICS-IIoT and Digital Twins in Industry 4.0 Clouds

Industry 4.0 is the most disruptive revolution engulfing all the digital transformation from sensors nodes to cloud applications. A sustainable and secure Industry 4.0 ecosystem is a core challenge for stakeholders and state agencies addressed in this work by layered API tunnel interoperability instead of conventional cybersecurity methods and techniques. In this work, an interoperable application programmer interface (API) wrapper is being proposed to manage heterogeneous cloud architectures using Eucalyptus, Apache CloudStack, and OpenNebula. A comprehensive design and implementation plan from things layer to user interface has been accomplished in this work. Infrastructure as a Service (IaaS) is being automated using Ansible from Web Servers Layers through Common Gateway Interface (CGI) scripting. The Web Servers are being managed using WebSockets by micro–Web Servers Gateway Interface (uWSGI) from cloudstack managers (CSM) API. The Platform as a Service (PaaS) for big data, web servers, and machine learning platforms is accomplished using uWSGI from CSM. The public and private clouds are being managed using individual APIs. The wrapper, proposed in this work enables the big challenge of Industry 4.0 for realizing interoperability, scalability, heterogeneity, and automated cloud integration by functioning at the Software as a Service (SaaS) Layer.

A Web-Based Auxiliary Teaching System for College English

In order to optimize the application quality of the English auxiliary teaching system, shorten the system response time and CPU utilization, design a Web-based college English auxiliary teaching system, and test its application effect. Using the B/S mode in the Web, install the developed auxiliary teaching system on the server of the control center. The B/S function modules in the system software include the presentation layer, application service layer, and resource layer. Based on this, design the user management module and college English auxiliary teaching functions and allocate bandwidth to the platform resource transmission rate to maximize network utility, reduce platform data transmission failures, and optimize the quality of English auxiliary teaching. The experimental results demonstrate that the system based on the B/S multilayer structure passed all of its tests, showing that it is relatively robust and capable of meeting the needs of college English teaching aids for students and teachers while also achieving the desired outcomes. The teaching information communication delay and response time of the designed system are shorter, and the central processing unit (CPU) utilization rate is also lower.

Internet of things-Enabled technologies as an intervention for childhood obesity: A systematic review.

Childhood obesity is one of the most serious public health challenges of the 21st century, with consequences lasting into adulthood. Internet of Things (IoT)-enabled devices have been studied and deployed for monitoring and tracking diet and physical activity of children and adolescents as well as a means of providing remote, ongoing support to children and their families. This review aimed to identify and understand current advances in the feasibility, system designs, and effectiveness of IoT-enabled devices to support weight management in children. We searched Medline, PubMed, Web of Science, Scopus, ProQuest Central and the IEEE Xplore Digital Library for studies published after 2010 using a combination of keywords and subject headings related to health activity tracking, weight management, youth and Internet of Things. The screening process and risk of bias assessment were conducted in accordance with a previously published protocol. Quantitative analysis was conducted for IoT-architecture related findings and qualitative analysis was conducted for effectiveness-related measures. Twenty-three full studies are included in this systematic review. The most used devices were smartphone/mobile apps (78.3%) and physical activity data (65.2%) from accelerometers (56.5%) were the most commonly tracked data. Only one study embarked on machine learning and deep learning methods in the service layer. Adherence to IoT-based approaches was low but game-based IoT solutions have shown better effectiveness and could play a pivotal role in childhood obesity interventions. Researcher-reported effectiveness measures vary greatly amongst studies, highlighting the importance for improved development and use of standardised digital health evaluation frameworks.

FogChain: A Blockchain-Based Peer-to-Peer Solar Power Trading System Powered by Fog AI

Microgrids, gaining traction from rising distributed generation for carbon reduction, demand novel solutions to regulate on- and off-grid operations, as well as both energy and monetary transfers between the microgrid and the central grid and among different microgrid participants. This research aims to develop and validate an intelligent microgrid management system to secure the competitiveness of Singapore’s energy market, by leveraging the inherent synergy between two emerging technologies, i.e., blockchain for Peer-to-Peer (P2P) solar power trading and fog computing for grid infrastructure management. For this vision, we have developed FogChain, an integrative, cost-effective, and scalable microgrid operating system (MGOS), consisting of three technical service layers: 1) a novel microgrid information infrastructure based on the fog-computing paradigm (i.e., intelligence on edge); 2) a blockchain-based microgrid service layer, providing smart contract and decentralized control capabilities for grid application development; and 3) a microgrid application layer (i.e., P2P energy trading) over the blockchain-based grid service. This MGOS would fundamentally transform how solar power is traded among participating electricity prosumers, leading to potentially new operational and business models. We have implemented the FogChain system and conducted extensive experiments to verify its performance advantages. Our results demonstrate that FogChain can efficiently process energy auction among 1000 participants with 1.1 s delay on average, reduce transmission cost up to 20% under the loss-aware trading mechanism, and reduce the solar yield prediction error to 0.11. Our system prototype suggests that FogChain provides a promising solution for efficient decentralized energy trading and intelligent distributed control for microgrids.

Coordinated use of Health Level 7 standards to support clinical decision support: Case study with shared decision making and drug-drug interactions

BackgroundDespite advances in interoperability standards, it remains challenging and often costly to share clinical decision support (CDS) across healthcare organizations. This is due in part to limited coordination among CDS components. To improve coordination of CDS components, Health Level 7 (HL7) has developed a suite of interoperability standards with Fast Health Interoperability Resources (FHIR) specification as a common information model. Evidence is needed to determine the feasibility of implementing these CDS components; therefore, the objective of this study was to investigate the coordination of emerging HL7 standards with modular CDS architecture components. MethodsWe used a modular, standards-based architecture consisting of four components: data, logic, services, and applications. The implementation use-case was an application to support shared decision making in the context of drug-drug interactions (DDInteract). ResultsDDInteract uses FHIR as the data representation model, Clinical Quality Language for logic representation, CDS Hooks for the services layer, and Substitutable Medical Apps Reusable Technologies for application integration. DDInteract was first implemented in a sandbox environment and then in an electronic health record (Epic®) test environment. DDInteract can be integrated in clinical workflows through on-demand access from a menu or through CDS Hooks upon opening a patient’s record or placing a medication order. ConclusionIn the context of drug interactions, DDInteract is the first application to leverage a full stack of emerging interoperability standards for each component of modular CDS architecture. The demonstrated feasibility of interoperable components can be generalized to other modular CDS applications.

Application of a Nursing Data-Driven Model for Continuous Improvement of PICC Care Quality.

A PICC catheter maintenance network was established and managed to monitor the maintenance of catheters in placed patients throughout the process, providing homogeneous PICC catheter continuity of care for patients. Model-driven thinking is an idea for simulation system development. Model-driven architecture (MDA) is a design methodology that implements model-driven thinking and is widely used in simulation system development. Based on the requirements of nursing, the data-driven model is mainly divided into interface layer and functional service layer; this study adopts MDA technology which can detach the functions of the system from the platform, based on domain knowledge, and the metamodel adopts XSD-based data model to generate the PIM model, which is stored in the model library. The results showed that the number of nurses at maintenance sites increased from 79 to 232, the PICC placement rate for oncology patients increased from 35.0% to 76.0%, the nurse maintenance operation pass rate increased from 53.9% to 88.4%, and the maintenance default rate decreased from 40.0% to 10.9%.

Accessibility-oriented performance evaluation of high-speed railways using a three-layer network model

High-speed railways, as dominant transportation patterns, constitute a large-scale complex network. Network performance is critical for high-level transportation services in transportation management. However, existing studies generally adopt pure topological indicators to evaluate performance in a single network and without considering the specific characteristics of rail transportation. This paper modeled the high-speed railway as a three-layer network including topological, functional, and service layers and assessed the integrated network performance from the view of transportation accessibility. In addition, the rail stations were prioritized and their performances in different layers were compared to analyze whether the total integrated performance was restricted by its connection abilities, functional capacities, and transportation service level. Finally, a case study of the high-speed rail network in China and comparisons with other traditional evaluation methods were conducted to verify the feasibility and superiority of the model. The findings of this study enable to provide management implications for rail transportation planning, organization, and operations.

Design and implementation of a smart infrastructure digital twin

There is a critical need to make infrastructure systems more efficient, resilient, and sustainable. Infrastructure systems provide the basis for everyday life and enable the flow of goods, information, and services within urban and regional settings. Providing data-centric solutions to improve this flow is essential. This can only be achieved if we manage to transform passive infrastructure assets into cyber-physical systems. Digital twins bring the opportunity to turn passive infrastructure assets into data-centric systems of systems.This article aims to provide a summary of existing digital twin architectures and exemplify a digital twin design and implementation. To this end, a literature review of digital twin architecture is presented in addition to a case study of a digital twin implementation in smart infrastructure. The case study focuses on a digital twin implementation of a bridge and describes in detail the physical, cyber, integration, and service layers of this implementation. Later in the article, we discuss the learnings from this case study under three main categories – systems perspective, information perspective, and organisational perspective. The findings show the importance of acquiring a systems perspective when designing digital twins today to enable interoperable systems of systems in the future. Furthermore, the findings highlight the vital necessity of data and information management while also considering the multidisciplinary aspects of digital twin design and implementation.

A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics

Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy.

A Method of the Active and Passive Event Service Based on the Sensor Web

The intelligent information system constructed by the sensor web can monitor all kinds of sudden abnormal events, improve the ability of event discovery and rapid disposal, and promote the development of smart city and the construction of the Internet of Things (IoT). In this paper, we consider the problem of complex integration and poor expansibility in the large-scale full-network operation and maintenance system construction and propose an active and passive event service (APES) method based on the sensor web. In the APES, a system framework with the perception layer, data service layer, event service layer, and user layer is firstly defined and constructed. Secondly, a middleware with the ability to active and passive event service (APES) is designed and implemented based on the system framework. Finally, taking abnormal weather and fire warning as examples, the performance of the proposed event service middleware is tested, respectively. Experimental results show that the proposed APES model in this paper has the advantages of high precision, stable operation, and strong practicability and solves “Information Island” and low reusability in the whole network operation and maintenance system. This is an attempt at the structural design of a similar intelligent information system.

Dynamic Analysis of Alternative Elements in an Automated Packaging System Based on 5G Internet of Things

The arrival of 5G will usher in an era of “Internet of Everything.” Massive Internet of Things data contains great value in the dynamic analysis of alternative elements of automated packaging systems. From the perspective of the realization of personalized customization functions, this article solves the problem of dynamic analysis of alternative elements in the automated packaging system. We analyze the connection mechanism and interaction method between the cloud service system layer and the mobile terminal service layer, and carry out the corresponding software design. From the perspective of the realization of the intelligent production of the system in this paper, this topic mainly studies the information interaction mechanism and production control mechanism of the cloud service system and the manufacturing system. Based on the hardware of the manufacturing system layer, a flexible production implementation mechanism is formulated to make it the basis for the implementation of intelligent production of the system. Based on the massive data processing capabilities of the cloud service system, the information processing mechanism and the production planning decision-making mechanism are formulated for it, so as to realize the intelligent adjustment of the manufacturing system layer in the production process. For the connection scenario of IoT group paging, based on the application of NB-IoT technology in the next-generation mobile communication network, the focus of network optimization is to ensure the random access performance of IoT devices as much as possible. To this end, this paper proposes a random access optimization strategy for IoT group paging based on time slot scattering. We establish a mathematical model based on queuing theory for the connection scenario of the Io T group paging, then use the mathematical formula to derive the number of IoT devices scattered to each time slot in the initial state, thereby deriving the specific time slot scattering algorithm. This paper establishes a list of credit nodes, changes the participation mode of consensus nodes from static to dynamic, and supports voting to select trusted nodes. We designed a credit evaluation mechanism as a basis for consensus node elections to improve system’s fault tolerance rate. The algorithm process was simplified, and the PBFT algorithm process was simplified from a 3-phase protocol to a 2-phase protocol to further reduce communication bandwidth overhead and algorithm time. Simulation analysis shows that, compared with the PBFT algorithm, the proposed algorithm improves node flexibility and fault tolerance while reducing communication bandwidth overhead by about 45%, packaging throughput by about 4%, and latency by about 3%.