Three-tier Network Architecture Research Articles

Intelligent Traffic Flow Prediction and Analysis Based on Internet of Things and Big Data

Nowadays, the problem of road traffic safety cannot be ignored. Almost all major cities have problems such as poor traffic environment and low road efficiency. Large-scale and long-term traffic congestion occurs almost every day. Transportation has developed rapidly, and more and more advanced means of transportation have emerged. However, automobile is one of the main means of transportation for people to travel. In the world, there are serious traffic jams in almost all cities. The excessive traffic flow every day leads to the paralysis of the urban transportation system, which brings great inconvenience and impact to people's travel. Various countries have also actively taken corresponding measures, i.e., traffic diversion, number restriction, or expanding the scale of the road network, but these measures can bring little effect. Traditional intelligent traffic flow forecasting has some problems, such as low accuracy and delay. Aiming at this problem, this paper uses the model of the combination of Internet of Things and big data to apply and analyze its social benefits in intelligent traffic flow forecasting and analyzes its three-tier network architecture model, namely, perception layer, network layer, and application layer. Research and analyze the mode of combining cloud computing and edge computing. From the multiperspective linear discriminant analysis algorithm of the combination method of combining the same points and differences between data and data into multiple atomic services, intelligent traffic flow prediction based on the combination of Internet of Things and big data is performed. Through the monitoring and extraction of relevant traffic flow data, data analysis, processing and storage, and visual display, improve the accuracy and effectiveness and make it easier to improve the prediction accuracy of overall traffic flow. The traffic flow prediction of the system of Internet of Things and big data is given through the case experiment. The method proposed in this paper can be applied in intelligent transportation services and can predict the stability of transportation and traffic flow in real time so as to optimize traffic congestion, reduce manual intervention, and achieve the goal of intelligent traffic management.

Research on Electrical Equipment Monitoring and Early Warning System Based on Internet of Things Technology

With the rapid increase of global population, the power load increases rapidly, and the electrical equipment such as generator, transformer, and power line in the power system is an important basis for production and life. Its safe operation is of great significance because it is related to people’s economic development and life stability. In recent years, with the rapid development of Internet of Things technology, the Internet of Things and electrical equipment safety early warning are combined to use their respective advantages to provide a new way for electrical equipment monitoring and early warning system. Through a variety of key technologies of the Internet of Things, such as sensor technology, network communication technology, and cloud computing technology, data, information exchange, and communication between electrical equipment and Internet technology are carried out according to the agreed protocol, and the safety of the operation status of electrical equipment is monitored in real time, so as to prevent power equipment failure and other problems. Based on this background, this paper studies the Internet of Things technology and electrical equipment monitoring and early warning system and analyzes its three-tier network architecture mode from the Internet of Things technology, namely, perception layer, network layer, and application layer. The combination of cloud computing and edge computing is studied and analyzed to provide theoretical support for the research of electrical equipment monitoring and safety early warning system. The wireless sensor network equipment is also installed on the electrical equipment through the Internet of Things technology to transmit data to the base station, so as to monitor whether the equipment operates safely. The monitoring and early warning system of wireless sensor system based on Internet of Things is given through case experiment. This system realizes relevant intelligent application services, which can not only ensure the stability of information transportation but also real-time monitoring of electrical equipment, early warning, shorten troubleshooting time, reduce the workload of power station staff, and achieve the functions of safety early warning, emergency command, and control. It is of great significance to the monitoring and early warning system of electrical equipment.

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.

Development of computer-based methodology for remote advanced training of medical doctors

Aim. To formulate the methodology for developing the interactive virtual computer simulations with a rating assessment of trainees’ decisions and the capability of remote access.Material and Methods. The methods of knowledge engineering were used to extract and formalize expert knowledge about the structure, significance, and relevance of clinical diagnostic information. The materials for creating virtual computer simulations were based on texts from the archival medical records, laboratory data, and multimedia results of instrumental methods of study. A three-tier network architecture was applied to provide the capabilities of remote access. It was organizationally represented by three components: a client layer, a business logic layer, and a data layer. Data transfer was provided by the Web protocols while microservices represented the infrastructure.Results. The information was formalized and structured after expert analysis and identification of significant diagnostic and prognostic data. The process included defining the domain model, identifying the aggregates and connections between them, and designing software and user interfaces. Possible solutions for trainees are now presented in the form of interactive reference lists. The artifacts of the user’s work are saved in the storage represented by the module for working with the server file system and the object-relational database management system. Each task module contains static and interactive blocks of information. The purpose of static blocks is to provide trainees with the necessary information for making clinical and diagnostic decisions. The interactive blocks allow selecting one or more solutions from the list. The content and sequence of further information presentation are determined by the trainees’ answers to the questions of an interactive block. Trainees’ decision-making competencies are evaluated using the rating system. The final personal rating is calculated as a multiplication of all coefficients related to the trainees’ decisions. This approach integrates a rating system with the trajectory chosen by the trainee for task completion.Conclusions. The distance learning technologies, developed for clinical disciplines in this study, are quite new and innovative. The repository of virtual computer simulations is under development to improve the methodological support of remote clinical training.

Early congenital heart defect diagnosis in neonates using novel WBAN based three-tier network architecture

Medical diagnosis is an emerging area in Wireless Body Area Networks (WBAN). Many research works have been held on WBAN in the perspective of energy efficiency, security, and routing etc. But none of this work conducts medical diagnosis using WBAN. To do this, we proposed a novel WBAN based Three-Ttier architecture for early detection of Congenital Heart Diseases (CHD) in neonates. We adapt IEEE 802.15.6 Media Access Control Address (MAC) to identify the priority level of data. Based on priority level, data is scheduled in time division multiple access (TDMA) manner. Data transmission is secured by using advanced encryption standard (AES) in first tier. Optimal channel selection process is performed using bird swarm optimization (BSO) algorithm in second tier. Here fitness evaluation considers reference signal received quality (RSRQ), signal to noise ratio (SNR), and channel capacity. In third tier, sensed data is classified at diagnosis server to detect CHD in neonates. Neuro-fuzzy algorithm is incorporated in diagnosis server for classification. In diagnosis server, CHD is diagnosed by screening pulse oximetry along with heart beat rate and respiratory rate. Extensive simulation in OMNeT++ shows promising results in delay, average delay, power consumption, and probability of available channels.

Edge Computing-Based Mobile Health System: Network Architecture and Resource Allocation

A cellular network with edge computing and microservice is pervasive to support mobile-health applications anytime and anywhere. Edge computing can potentially enhance the computational capabilities of a network, and microservice can help the network adapt to various mobile-health applications. In such a network, we present the design of its architecture, the allocation of its resources, as well as the management of its data. Specifically, we address a three-tier network architecture for mobile-health applications from the perspectives of communication, computation, as well as service. Also, we discuss the issues of resource allocation within the network in order to optimize its performance. Finally, we address the management of medical data (e.g., video streaming, medical images) in order to efficiently inquire them for diagnosis and medical treatment.

An Analytical Cost-Optimal Cloudlet Placement Framework over Fiber-Wireless Networks with Quasi-Convex Latency Constraint

To meet the low-latency constraints arising from future smart mobile devices, Internet-of-Things, and 5G applications, major interest is currently given to the integration of centralized cloud computing and distributed edge computing infrastructures to deliver higher performance and reliability to edge devices in accessing mobile cloud services. The three-tier network architecture arising from cloud, cloudlet, and edge-devices can handle miscellaneous latency requirements for both latency-sensitive and latency-tolerant applications more efficiently than conventional two-tier networks. In this paper, we primarily focus on the static cloudlet network planning problem and propose an analytical hybrid cost-optimization framework for optimal cloudlet placement. We formulate this problem as a convex optimization problem and solve by using Karush-Kuhn-Tucker (KKT) conditions, and show that this framework can be evaluated without any scalability issues observed with integer programming based frameworks for large datasets. Moreover, we derive user-friendly closed form expressions that provide a first-hand estimation of cloudlet deployment cost depending on a few important network parameters like split-ratio, population density, and network bandwidth. Finally, we also show that the optimal solution of this analytical framework can be considered as a tight lower bound of the optimal solutions of integer programming based frameworks and makes a better cloudlet installation cost estimation compared to other existing frameworks.

Two-stage resource allocation scheme for three-tier ultra-dense network

In 5G Ultra-dense Network (UDN), resource allocation is an efficient method to manage inter-small-cell interference. In this paper, a two-stage resource allocation scheme is proposed to supervise interference and resource allocation while establishing a realistic scenario of three-tier heterogeneous network architecture. The scheme consists of two stages: in stage I, a two-level sub-channel allocation algorithm and a power control method based on the logarithmic function are applied to allocate resource for Macrocell and Picocells, guaranteeing the minimum system capacity by considering the power limitation and interference coordination; in stage II, an interference management approach based on K-means clustering is introduced to divide Femtocells into different clusters. Then, a prior sub-channel allocation algorithm is employed for Femtocells in diverse clusters to mitigate the interference and promote system performance. Simulation results show that the proposed scheme contributes to the enhancement of system throughput and spectrum efficiency while ensuring the system energy efficiency.

Safety barrier warning system for underground construction sites using Internet-of-Things technologies

This paper proposes an Internet-of-Things-based (IoT-based) safety barrier warning system to achieve a safer underground construction site. A focus of this paper is to establish a hazard energy monitoring system and use IoT to generate early warnings and alarms as dynamical safety barriers for hazard energy on underground construction sites. To ensure the performance of the proposed system, the hazard energies and their coupling mechanisms was analyzed to provide safety barrier strategies and scenarios for avoiding unsafe behaviors and unsafe status of construction equipment and workers' environment. The IoT technologies, such as meter-level of RFID-based location and tracking technology, centimeter-level of ultrasonic detection technology, and infrared access technology and so on, were developed in three-tier network architecture to help workers change their risky behaviors and avoid accidents on the changing construction site. The implementation in Yangtze River-crossing Metro Tunnel Construction site has shown the safety performance was improved and the occurrence of accident caused by hazard energy on site could be prevented.

A Recommendation System Based on Regression Model of Three-Tier Network Architecture

The sparsity problem of user-item matrix is a major obstacle to improve the accuracy of the traditional collaborative filtering systems, and, meanwhile, it is also responsible for cold-start problem in the collaborative filtering approaches. In this paper, a three-tier network Architecture, which includes user relationship network, item similarity network, and user-item relationship network, is constructed using comprehensive data among the user-item matrix and the social networks. Based on this framework, a Regression Model Recommendation Approach (RMRA) is established to calculate the correlation score between the test user and test item. The correlation score is used to predict the test user preference for the test item. The RMRA mines the potential information among both social networks and user-item matrix to improve the recommendation accuracy and ease the cold-start problem. We conduct experiment based on KDD 2012 real data set. The result indicates that our algorithm performs superiorly compared to traditional collaborative filtering algorithm.

Data Uploading and Exchange Algorithm for Mobile Sensor Networks in the City Traffic Environment

Mobile sensor networks (MSN) in the city environment have dynamic topology and large amounts of data. In order to overcome the problem of data uploading and exchange, data uploading and exchange algorithm (DUEA) is proposed. In DUEA, the three-tier network architecture composed of vehicle nodes, relay nodes and sink nodes is considered. The link estimator, routing engine, and forwarding engine are modified based on CTP (collection tree protocol). In the link estimator, beacon packet is simplified. In the routing engine, the numbers of elements in the send queue of neighbor nodes, and link quality are weighed. The new calculation formulae of ETX value and selection method of father node are proposed. In the forwarding engine, different snooping methods are used for two types of data named general data and warning data. Simulation results show that compared with CTP, DUEA increases data throughput and transmission rate and reduces energy consumption of packet transmission when data is uploaded. DUEA also reduces average transmission delay when data is exchanged. Physical test results show that DUEA achieves desired effect in the testbed. DUEA is effective and feasible and outperforms CTP in the city traffic environment.

Prediction of Yarn Quality Based on BP Neural Network

As the quality of yarn and the fiber indicators are nonlinear relationship, the traditional mathematical models or empirical formula has been unable to accurately resolve the problem. In view of artificial neural networks do not need to build accurate mathematical models, applicable to solving the problem of yarn quality prediction. In this paper, good nonlinear approximation ability of BP (Back Propagation) neural network be used, the use of neural network toolbox of MATLAB functions for modeling, good results was obtained. Prediction model set a hidden layer, using three-tier network architecture, and take the input layer 4 nodes, hidden layer 8 nodes and output layer 2 nodes. According to forecast results, can ensure the yarn quality effectively, use of raw materials rationally, to achieve optimal distribution of cotton. Meanwhile, the spinning process design can also be provided validation, for the development of new products to provide a theoretical basis.

Wi-Fi based broadband wireless access for users on the road

This paper considers the provision of broadband wireless access (BWA) for users on the road. A Wi-Fi based system, which utilises a three-tier network architecture, is suggested as a readily available way of achieving BWA for road vehicles. However, the use of this approach and technology still poses a number of specific challenges. We identify and examine in greater depth a number of the challenges, and propose possible solutions to overcome them.