A Middleware-Based Network Architecture for the Web of Things

2013 ◽  
Vol 756-759 ◽  
pp. 2332-2338
Author(s):  
Jing Sha ◽  
Ji Yang
Keyword(s):  
Network Architecture ◽  
Smart Home ◽  
Large Scale ◽  
Sensors And Actuators ◽  
Web Of Things ◽  
Dynamic Attributes ◽  
The Web

Web of Things has been proposed to facilitate the integration and composition of everyday device like sensors and actuators with existing standards and blueprints. However, the existing researches do not address all the problems such as integrating legacy devices with constrained capabilities, managing the resources with dynamic attributes. In addition, considering the large-scale deployment of devices in WoT, the deployment expense is supposed to be controlled. In this paper, we propose a middleware-based network architecture for the web of things. Within the architecture, we deal with the practical challenges in deployment. A SMART HOME system is developed as the implementation to verify and discuss the proposed architecture.

A Data Collecting and Caching Mechanism for Gateway Middleware in the Web of Things

2013 ◽  
Vol 756-759 ◽  
pp. 2157-2162
Author(s):  
Xu Chao Chang ◽  
Chun Hong Zhang ◽  
Li Sun
Keyword(s):  
Smart Home ◽  
The Internet ◽  
Effective Solution ◽  
Application Layer ◽  
Web Of Things ◽  
Heterogeneous Devices ◽  
Data Collecting ◽  
Caching Mechanism ◽  
The Internet Of Things ◽  
The Web

The Web of Things (WoT) is a refinement of the Internet of Things by integrating heterogeneous devices not only into the Internet (the network), but into the Web (the application layer). With the expansion of applications and devices, WoT Gateway Middleware is bearing more and more pressure from data processing, and therefore the WoT gateway middleware takes more time to respond to the requests of applications. A data collecting and caching mechanism is an effective solution to this problem. In this paper, we begin by describing the gateway middleware architecture with the focus on data collecting and caching functionality. Then the data collecting and caching (DCC) functionality module is discussed in detail. The DCC module supports three kinds of data collecting and caching modes: fundamental mode, configurable mode and adaptive mode. We finally demonstrate how these three modes work. A SMART HOME system is developed as the implementation to verify and discuss the proposed mechanism.

scholarly journals Constructing the Web of Events from Raw Data in the Web of Things

2014 ◽  
Vol 10 (1) ◽  
pp. 105-125 ◽  
Author(s):  
Yunchuan Sun ◽  
Hongli Yan ◽  
Cheng Lu ◽  
Rongfang Bie ◽  
Zhangbing Zhou
Keyword(s):  
Large Scale ◽  
Physical World ◽  
Smart Devices ◽  
Raw Data ◽  
Web Of Things ◽  
Critical Data ◽  
Large Scale Data ◽  
Smart Things ◽  
Monitoring Devices ◽  
The Web

An exciting paradise of data is emerging into our daily life along with the development of the Web of Things. Nowadays, volumes of heterogeneous raw data are continuously generated and captured by trillions of smart devices like sensors, smart controls, readers and other monitoring devices, while various events occur in the physical world. It is hard for users including people and smart things to master valuable information hidden in the massive data, which is more useful and understandable than raw data for users to get the crucial points for problems-solving. Thus, how to automatically and actively extract the knowledge of events and their internal links from the big data is one key challenge for the future Web of Things. This paper proposes an effective approach to extract events and their internal links from large scale data leveraging predefined event schemas in the Web of Things, which starts with grasping the critical data for useful events by filtering data with well-defined event types in the schema. A case study in the context of smart campus is presented to show the application of proposed approach for the extraction of events and their internal semantic links.

The Smart Home meets the Web of Things

2011 ◽  
Vol 7 (3) ◽  
pp. 145 ◽  
Author(s):  
Andreas Kamilaris ◽  
Andreas Pitsillides ◽  
Vlad Trifa
Keyword(s):  
Smart Home ◽  
Web Of Things ◽  
The Web

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Mapping global water projects: improving access to donor investment information on the web

Water Policy ◽  
2003 ◽  
Vol 5 (3) ◽  
pp. 203-212
Author(s):  
J. Lisa Jorgensona
Keyword(s):  
Water Resources ◽  
Web Sites ◽  
Large Scale ◽  
Technical Assistance ◽  
Water Withdrawal ◽  
Small Scale ◽  
Water Projects ◽  
Essential Information ◽  
Water Project ◽  
The Web

This paper discusses a series of discusses how web sites now report international water project information, and maps the combined donor investment in more than 6000 water projects, active since 1995. The maps show donor investment:  • has addressed water scarcity,  • has improved access to improvised water resources,  • correlates with growth in GDP,  • appears to show a correlation with growth in net private capital flow,  • does NOT appear to correlate with growth in GNI. Evaluation indicates problems in the combined water project portfolios for major donor organizations: •difficulties in grouping projects over differing Sector classifications, food security, or agriculture/irrigation is the most difficult.  • inability to map donor projects at the country or river basin level because 60% of the donor projects include no location data (town, province, watershed) in the title or abstracts available on the web sites.  • no means to identify donor projects with utilization of water resources from training or technical assistance.  • no information of the source of water (river, aquifer, rainwater catchment).  • an identifiable quantity of water (withdrawal amounts, or increased water efficiency) is not provided.  • differentiation between large scale verses small scale projects. Recommendation: Major donors need to look at how the web harvests and combines their information, and look at ways to agree on a standard template for project titles to include more essential information. The Japanese (JICA) and the Asian Development Bank provide good models.

Providing a Smart Industrial Environment with the Web of Things and Cloud Computing

2016 ◽  
Author(s):  
Marlon C. Domenech ◽  
Leonardo P. Rauta ◽  
Marcelo Dornbusch Lopes ◽  
Paulo H. Da Silva ◽  
Rodrigo C. Da Silva ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Industrial Environment ◽  
Web Of Things ◽  
The Web

scholarly journals Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 5108
Author(s):  
Navin Ranjan ◽  
Sovit Bhandari ◽  
Pervez Khan ◽  
Youn-Sik Hong ◽  
Hoon Kim
Keyword(s):  
Traffic Congestion ◽  
Network Architecture ◽  
Road Network ◽  
Large Scale ◽  
Pattern Analysis ◽  
Image Data ◽  
Urban Life ◽  
Prediction Algorithm ◽  
Rapid Urbanization ◽  
Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

scholarly journals IoT Traffic: Modeling and Measurement Experiments

IoT ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 140-162
Author(s):  
Hung Nguyen-An ◽  
Thomas Silverston ◽  
Taku Yamazaki ◽  
Takumi Miyoshi
Keyword(s):  
Smart Home ◽  
Large Scale ◽  
Traffic Modeling ◽  
The Novel ◽  
Network Behavior ◽  
Performance Accuracy ◽  
Traffic Generator ◽  
Multiple Devices ◽  
Iot Devices ◽  
The Internet Of Things

We now use the Internet of things (IoT) in our everyday lives. The novel IoT devices collect cyber–physical data and provide information on the environment. Hence, IoT traffic will count for a major part of Internet traffic; however, its impact on the network is still widely unknown. IoT devices are prone to cyberattacks because of constrained resources or misconfigurations. It is essential to characterize IoT traffic and identify each device to monitor the IoT network and discriminate among legitimate and anomalous IoT traffic. In this study, we deployed a smart-home testbed comprising several IoT devices to study IoT traffic. We performed extensive measurement experiments using a novel IoT traffic generator tool called IoTTGen. This tool can generate traffic from multiple devices, emulating large-scale scenarios with different devices under different network conditions. We analyzed the IoT traffic properties by computing the entropy value of traffic parameters and visually observing the traffic on behavior shape graphs. We propose a new method for identifying traffic entropy-based devices, computing the entropy values of traffic features. The method relies on machine learning to classify the traffic. The proposed method succeeded in identifying devices with a performance accuracy up to 94% and is robust with unpredictable network behavior with traffic anomalies spreading in the network.

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