Three-tier Architecture Research Articles

A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids

The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.

Data Dissemination for Industry 4.0 Applications in Internet of Vehicles Based on Short-term Traffic Prediction

As a key use case of Industry 4.0 and the Smart City, the Internet of Vehicles (IoV) provides an efficient way for city managers to regulate the traffic flow, improve the commuting performance, reduce the transportation facility cost, alleviate the traffic jam, and so on. In fact, the significant development of Internet of Vehicles has boosted the emergence of a variety of Industry 4.0 applications, e.g., smart logistics, intelligent transforation, and autonomous driving. The prerequisite of deploying these applications is the design of efficient data dissemination schemes by which the interactive information could be effectively exchanged. However, in Internet of Vehicles, an efficient data scheme should adapt to the high node movement and frequent network changing. To achieve the objective, the ability to predict short-term traffic is crucial for making optimal policy in advance. In this article, we propose a novel data dissemination scheme by exploring short-term traffic prediction for Industry 4.0 applications enabled in Internet of Vehicles. First, we present a three-tier network architecture with the aim to simply network management and reduce communication overheads. To capture dynamic network changing, a deep learning network is employed by the controller in this architecture to predict short-term traffic with the availability of enormous traffic data. Based on the traffic prediction, each road segment can be assigned a weight through the built two-dimensional delay model, enabling the controller to make routing decisions in advance. With the global weight information, the controller leverages the ant colony optimization algorithm to find the optimal routing path with minimum delay. Extensive simulations are carried out to demonstrate the accuracy of the traffic prediction model and the superiority of the proposed data dissemination scheme for Industry 4.0 applications.

Design of Project Cost Risk Management System Based on Improved Fuzzy Rule Weight Algorithm

Aiming at the low accuracy of index weight calculation and risk type identification in traditional cost risk management systems, this paper proposes a project cost risk management system based on an improved fuzzy rule weight algorithm. We analyzed the types of project cost risks, adopted a three-tier technical architecture to design the system frame structure and functional organization structure of the cost risk management system, and adopted the B/S architecture for network structure deployment. In the software part, an improved fuzzy rule weight algorithm is used to calculate the weight of project cost risk indicators, thereby improving the effectiveness of risk management. Experimental results show that the designed system works well, can accurately calculate the weights of risk indicators, and can effectively identify multiple risk types.

Controller Placement in SDN with Low Latency Using Meta-heuristic Algorithms

Software-Defined networks (SDNs) are a new generation of computer networks that have eliminated many of the problems of traditional networks. These networks use a three-tier architecture in which the physical layers, controller, and management are located at different levels. This new architecture has made the network very dynamic, and many of the previous problems in the network have been solved. As the size of the network increases, using a controller across the network will cause issues such as increasing the average latency between the switches and the controller, as well as forming a bottleneck in the controller. For this reason, it is recommended to use multiple physical controllers on the control plane. Due to the cost of purchasing and maintaining the controller, it is necessary to solve the mentioned problem with the least controllers. The question is, to achieve a goal such as reducing latency to an acceptable threshold, at least how many controllers are needed, where the controllers should be located, and which switches should be monitored by which controller? Since this is an NP-Hard problem, methods based on meta-heuristic algorithms can be effective in solving it. In this article, we have solved the problem of controller placement in software-based networks to reduce latency using the cuckoo meta-heuristic algorithm. The simulation results show that the efficiency of our proposed method is between 16 to 70 percent better than the method proposed by the PSO algorithm.

Design of College Students’ Career Planning Guidance System Based On B/S Three-Tier Architecture

At present, the guidance mode of college students’ career planning guidance system is imperfect, resulting in too long submission time. A college students’ career planning guidance system based on B/S three-tier architecture is designed. Hardware part: on the basis of obtaining the sensor dynamic compensation filter, expand 6 static memory; Software part: obtain the influencing factors of College Students’ career planning, combined with the external environment, use the B/S three-tier architecture to set the guidance mode, formulate the links of career planning, adopt the hierarchical management mode, and design the role authority management function of the system. Experimental results: the average submission time of the college students’ career planning guidance system designed this time and the other two systems is 11.277s, 16.253s and 15.554s, which proves that the college students’ career planning guidance system integrating the B/S three-tier architecture has a better effect.

A Remote Control System for Emergency Ventilators During SARS-CoV-2.

As COVID-19 began to grip healthcare systems worldwide, worst-case models predicted huge demands for ventilators. The global community sprang to action, producing a large number of emergency “makeshift” ventilator designs. This brought about another problem: a gap between the quantity of new mechanical ventilators and the number of skilled physicians to operate them. New physicians could not complete training at the pace of ventilator production, which threatened to leave patients sitting untreated, next to unusable ventilators. To address this challenge, we developed a universal remote control system for makeshift ventilators that uses low-cost hardware add-on modules to connect to different ventilators, and a three-tier control architecture to interface the ventilators with telemedicine software. We demonstrate system integration with two representative ventilator designs, adding a remote control option that allows caregivers to quickly and easily monitor and control these ventilators remotely.

Daily Information Management System for Postgraduates to Check In and Out of the Dormitory Based on Mobile Edge Computing

In recent years, the enrollment scale of graduate students has been increasing, and tutor resources are relatively scarce. Tutors often need to bring in dozens or even dozens of students, which puts a heavy burden on the tutor’s work. In order to reduce the work pressure of tutors and to manage graduate students in a scientific and standardized manner, this paper has designed a daily information management system for graduate students entering and exiting the dormitory to check in and out, which realizes the management of student attendance, work, and training and is carried out to improve the quality of graduate education. In this paper, based on the actual situation of the student dormitory, we comprehensively analyze user needs, management modes, existing resources, etc. and propose a Web application system divided into fingerprint access control, attendance data manipulation, and B/S architecture. Using design and development ideas for four functional modules on the client and management side and MATLAB simulation, we compare a three-tier and two-tier processing architecture under an average resource allocation scheme in terms of model utility, model power consumption, task delay constraints, and differences in the number of users. The experiment proves that, through the test of LoadRunner software, the experimental result is the average value of multiple tests. After the analysis of the performance test result, the peak number of concurrent users of this system is 1500. The daily information management model designed in this paper for graduate students to enter and exit the dormitory shows that it is stable, easy to operate, and interface-friendly and provides preset functions. It offers a new way for graduate students to manage their daily lives and has high practical value.

A WebGIS-Based Platform for Sewage Treatment Facilities in Villages and Small Towns Near the Lake Dian

Lake Dian is one of “Three-river and Three-lake Project” that focus on water pollution prevention and control in China. Rural non-point source pollution is increasing year by year. In order to effectively control the amount of sewage from villages and small towns entering the lake, a full coverage of treatment facilities has been built near the Lake Dian. Among them, the facilities are scattered in the eight districts and two counties of Kunming City, Yunnan Province. It is so difficult to manage that we have to rely on WebGIS technology for management and analysis. This paper designs a visualization platform for sewage treatment facilities in villages and small towns, which is a three-tier system architecture of B/S model. The system takes the map engine of ArcGIS API for JavaScript and the component development of Vue.js as the technical core, combined with ArcGIS Online service portal and spatial database technology. It finishes function implementation such as the visualization of the treatment situation and data management. The platform can scientifically analyse the problems in the construction, management, and operation of sewage treatment facilities in villages and small towns. It improves the ability to share information, and provide decision-making support to the sewage treatment department of the Lake Dian.

Identification and Authentication in Healthcare Internet-of-Things Using Integrated Fog Computing Based Blockchain Model

The healthcare Internet-of-Things (IoT) offers many benefits including data transmission in real-time mode, the ability to monitor the physiological state of the patient in a different interval of time. Devices such as blood-pressure monitors, glucose meters, heart monitoring implants, Electroencephalography (EEG), Electrocardiogram (ECG), and Electromyography (EMG) wearable devices allow health providers to collect the patient health information locally and make a real-time decision based on the Patient Health Data (PHD). Hospitals have been adopting the IoT for many years and now they have healthcare IoT devices in patients’ rooms and their bodies. However, the medical agencies, hospitals, and companies do not consider the security risk of healthcare IoT devices connected to a Local Area Network (LAN) or Wide Area Network (WAN). The IoT devices can be easily hacked and may lead to several potentially life-threatening risks due to poor authentication and encryption practices. Existing machine learning algorithms and blockchain approach working in the cloud computing environment are unable to meet the Quality of Service (QoS) like reliability, authentication, identification, and security requirements of healthcare IoT devices. Most of the traditional machine learning algorithms and techniques for healthcare IoT lacks the real-world implementation for secure data transmission. Therefore, blockchain is introduced for secure and reliable transaction in healthcare IoT. Whereas Fog Computing (FC) is introduced to extend the services of the cloud at the edge of networks. Integration of FC with blockchain can overcome the issue of healthcare IoT device identification, authentication, and verification for scalable frequent data transmission in a decentralized environment. Hence, a novel solution for the abovementioned problem is proposed using FC and blockchain. It includes an FC-based three-tier architecture, an analytical model, a mathematical framework, and an Advanced Signature-Based Encryption (ASE) algorithm for healthcare IoT device identification, verification, and Patient Health Data (PHD) authentication. The aim is to extend secure data transmission for healthcare IoT and end-users availing the real-time services. The proposed model and algorithm will be able to provide services for transaction and transmission near the edge in a secure manner. By analyzing the generated results from the proposed novel ASE algorithm for throughput, packet error, reliability, and malicious node detection accuracy; it is observed that the ASE algorithm in the FC environment easily outperforms the cloud and the other existing state of the art techniques such as FogBus, Femto cloud, Blockchain Fog-based Architecture Network (BFAN), and BeeKeeper. The malicious node detection accuracy of the ASE algorithm in the FC environment is 91% and in the cloud is 83%. Whereas the reliability percentage of the ASE algorithm in FC is 95% and in the cloud is 87%. The proposed approach is tested on simulators iFogSim (Net-Beans) and SimBlock.

Developing a BIM-enabled building lifecycle management system for owners: Architecture and case scenario

Given the fragmented nature of the architecture, engineering, construction, and operation (AECO) industry, building lifecycle management (BLM) involves bringing together multidisciplinary teams and integrating lifecycle processes of buildings. Despite the increasing use of information technologies and software tools by property owners, several barriers in BLM implementations remain, such as information silos, low data interoperability, poor cross-party coordination, and the mismatch of existing commercial solutions and user requirements. To deal with these practical challenges, this study develops a BIM-enabled BLM system for property owners using a semi-custom approach. For this, a three-tier architecture of the BIM-enabled BLM system is designed. Next, the system is developed based on a real-life project in the Grid State of China. This case scenario verifies the system and further confirmed the proposed three-tier architecture. The developed architecture and system have theoretical and practical implications for property owners' BLM implementations. This proposed semi-custom approach provides guidelines for AECO industry digital transformation.

Hybrid fuzzy interface model of sports rehabilitation activities

At present, the relevant test data and training indicators of athletes during rehabilitation training lack screening and analysis, so it is impossible to establish a long-term longitudinal tracking research system and evaluation system. In order to improve the practical effect of sports rehabilitation activities, this paper successively introduces the matrix normal mixed model and the fuzzy clustering algorithm based on the K-L information entropy regularization and the matrix normal mixed model. Moreover, this paper uses the expectation maximization algorithm to estimate the parameters of the model, discusses the framework, key technologies and core services of the development platform, and conducts certain research on the related technologies of the three-tier architecture. At the same time, according to the actual needs of sports rehabilitation training, this paper designs the functions required for exercise detection and prescription formulation. In addition, this paper analyzes and designs the database structure involved in each subsystem. Finally, this paper designs experiments to verify the performance of the model constructed in this paper. The research results show that the performance of the model constructed in this paper meets the expectations of model construction, so it can be applied to practice.

Development of a cloud SaaS service for a comprehensive research of polymerization processes

The article is devoted to the description of the network architecture of the developed cloud SaaS service for the implementation of a comprehensive study of polymerization processes. The main problem of using local software systems for such tasks is briefly voiced. The main methods and mathematical approaches that allow solving the direct problem of predicting the main molecular characteristics of the produced polymer product for various loading modes of the reaction system and the inverse problem of identifying some of the unknown kinetic parameters are considered. Methods and approaches to the development of algorithms and software are considered separately. As part of the cloud service being developed, a three-tier architecture for accessing data and computing resources is described in detail, the main tasks of each level and the advantages of using this approach are described. The hardware and system requirements for the computing nodes of the developed network application have been written out.

Big Data Analysis of Threat Intelligence Based on IP

This paper first discusses the concept of network threat intelligence and related resource integration, and proposes a three-tier architecture model of threat intelligence analysis system based on IP. Then, it discusses the diversified data acquisition technology, information index extraction technology, data mining and data modeling analysis technology, the key technology of threat intelligence credible intelligence analysis model based on machine learning and privacy protection. Finally, this paper considers and summarizes the legal standardization of threat intelligence analysis.

The design and implementation of data system based on Polymorphic Graphics and text

This paper introduces a set of professional data management system based on polymorphic graphics, which is established according to the requirements of software engineering management. It introduces the key technologies of the system, such as the main function module design, three-tier architecture quick design tool, online visio file and pdf file browsing. The system has strong practicability and good user interface. It can effectively improve the current professional data management mode.

Application of Intelligent Sensing in Power Test System

Smart grid is the development trend of the power industry under the new situation. As an important part of smart grid, smart power consumption directly faces the society and power users, and plays a very important role in the construction of smart grid. With the development of intelligent power construction, the traditional power service can not meet the needs of users to participate in demand side management and interact with the power grid in time. Therefore, the construction of a new intelligent sensing power measurement system to improve the quality and level of intelligent power service has become the focus of current power industry research. Firstly, according to the requirements analysis and design principles of the intelligent sensing power detection system, this paper proposes the function analysis and three-tier architecture design of the intelligent sensing power detection system. The intelligent sensing power detection system is logically divided into business application layer, network communication layer and sensing device layer. Externally, it realizes data integration and interaction with power consumption information collection system, marketing business management system and other intelligent power consumption information systems. Internally, it realizes interconnection with user side perception interactive terminal and perception unit, and realizes data acquisition, information transmission and control functions of power consumption terminal equipment. This paper presents a method of applying intelligent sensing technology to data editing environment of power simulation software, and realizes online error detection of simulation original data, automatic completion of keywords, automatic prompt of data card and intelligent help of data domain. The power simulation data editing software with intelligent sensing function developed in this paper is suitable for primary and advanced users, which can improve work efficiency and reduce error rate. The configurable rule management method applied in the implementation of intelligent sensing technology extends the application of data editor to the developers of models and algorithms, and provides a simple way for the timely upgrade and release of new models and algorithms.

SEEDDUP: A Three-Tier SEcurE Data DedUPlication Architecture-Based Storage and Retrieval for Cross-Domains Over Cloud

Data deduplication has become a promising technique that significantly reduces space utilization of cloud storage servers and optimizes the network bandwidth. A variety of deduplication techniques have been proposed to enhance the cloud server’s storage efficiency. Most of the deduplication schemes do not address global indexing and user privacy issues. This paper elucidates a novel three-tier secure data deduplication architecture (referred to as SEEDDUP) for data storage and retrieval in the cloud. SEEDDUP provides data deduplication with proper indexing and user privacy. In this work, the optimal chunk size, for a given file size and format, is calculated using Cuckoo Search Algorithm. The chunk existence in the cloud is verified by the hash value generated via SHA3-512. To improve data deduplication speed, Merkle Hash Tree based MapReduce (MHTMR) is used for fast index lookup, which improves the performance at data storage and retrieval. In addition, data are encrypted based on its sensitivity level (low and high) using Niederreiter Public Key Cryptosystem. With this new SEEDDUP architecture, only legitimate users can request data successfully for limited times (k) and spurious data requests cannot be processed or handled in the cloud. Experimental results show that the proposed SEEDDUP can reduce latency, achieved good throughput and deduplication efficiency (chunk size variance and security measures) in data deduplication.

Computation Offloading in LEO Satellite Networks With Hybrid Cloud and Edge Computing

Low earth orbit (LEO) satellite networks can break through geographical restrictions and achieve global wireless coverage, which is an indispensable choice for future mobile communication systems. In this article, we present a hybrid cloud and edge computing LEO satellite (CECLS) network with a three-tier computation architecture, which can provide ground users with heterogeneous computation resources and enable ground users to obtain computation services around the world. With the CECLS architecture, we investigate the computation offloading decisions to minimize the sum energy consumption of ground users, while satisfying the constraints in terms of the coverage time and the computation capability of each LEO satellite. The considered problem leads to a discrete and nonconvex since the objective function and constraints contain binary variables, which makes it difficult to solve. To address this challenging problem, we convert the original nonconvex problem into a linear programming problem by using the binary variables relaxation method. Then, we propose a distributed algorithm by leveraging the alternating direction method of multipliers (ADMMs) to approximate the optimal solution with low computational complexity. Simulation results show that the proposed algorithm can effectively reduce the total energy consumption of ground users.

Fuzzy and Mathematical Effort Estimation Models for Web Applications

This paper proposed an Effort Estimation Model for optimizing the deployment of Web Applications Based Fuzzy and Practical Models. Fuzzy logic approach is applied for estimating the development effort, which is compared with practical efforts model in the development process with interpreting the historical data available for the existing functionalities. This paper presented effort estimation model that involves two levels development and requirements for web applications built on three-tier architecture using Microsoft technologies. The first level estimates published by Project Managers and the second level estimates presented by Project Leaders or Developers for any new requirement or enhancements. The model considers the classification of each task as either Low or Medium or High complexity. These tasks pertain to the lowest level parts in bottom-up estimation. Efforts are estimated for designing, coding and unit testing of these tasks and the efforts are summed up to get the effort estimation for the higher level which is a feature to be implemented. The paper also discusses about the application of the effort estimation model by taking a new requirement as a case study. The first level estimates calculated using the effort estimation model has a variance of about 25% when compared with the actual effort. This variance is very much acceptable considering the fact that the first level estimates can be tolerable up to 35%. The proposed effort estimation model would help the project managers to efficiently control the project, manage the resources effectively, and improve the software development process and also trade off analyses among schedule, performance, quality and functionality. Fuzzy logic is used to verify the claims made in efforts estimation. It is proposed a new relation between the number of data and efforts value membership for actual data.

Intelligent anti-corrosion expert system based on big data analysis

PurposeThe paper aims to develop an intelligent anti-corrosion expert system based on browser/server (B/S) architecture to realize an intelligent corrosion management system.Design/methodology/approachThe system is based on Java EE technology platform and model view controller (MVC) three-tier architecture development model. The authors used an extended three-dimensional interpolation model to predict corrosion rate, and the model is verified by cross-validation method. Additionally, MySQL is used to realize comprehensive data management.FindingsThe proposed anti-corrosion system thoroughly considers a full use of corrosion data, relevant corrosion prediction and efficient corrosion management in one system. Therefore, this system can achieve an accurate prediction of corrosion rate, risk evaluation, risk alert and expert suggestion for equipment in petrochemical plants.Originality/valueCollectively, this present study has important ramifications for the more efficient and scientific management of corrosion data in enterprises and experts’ guidance in controlling corrosion status. At the same time, the digital management of corrosion data can provide a data support for related theoretical researches in corrosion field, and the intelligent system also offers examples in other fields to improve system by adding intelligence means.

Cross-phase product configurator for modular buildings using kit-of-parts

One emerging strategy in industrialized construction is the use of mass customization to increase product efficiency without sacrificing design flexibility. Effective implementation of mass customization can be done through a product platform, i.e. a configurator. However, existing configurators often lack integration of knowledge from the downstream supply chain such as manufacturing. This paper proposes a conceptual framework for a configurator unified through a manufacturing kit-of-parts. Kit-of-parts are pre-engineered and pre-designed digital models representing fabrication-ready components. Using production rules and restraints, accurate planning and design representations can be derived and utilized in the configurator. This research develops a configurator prototype using three-tier architecture. The prototype supports a low-to-high level of detail of kit-of-parts. It integrates product platforms and project development across multiple building phases including site planning, floor plan layout, and 3D model generation. Finally, the implementation of a modular building configurator illustrates the benefits of the proposed configurator-based workflow.