scholarly journals A microservice architecture for real-time IoT data processing: a reusable web of things approach for smart ports

2021 ◽  
pp. 103604
Author(s):  
Guadalupe Ortiz ◽  
Juan Boubeta-Puig ◽  
Javier Criado ◽  
David Corral-Plaza ◽  
Alfonso Garcia-de-Prado ◽  
...  
2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.


2011 ◽  
Vol 65 ◽  
pp. 295-298 ◽  
Author(s):  
Fan Yang ◽  
Cai Li Zhang

Considering the insufficient ability of data processing existed in configuration software, a scheme integrated both advantages of advanced programming language and configuration software is provided. In this scheme real-time data acquisition and complex processing are achieved by advanced programming language, the human-computer interface and other functions of the monitoring system are achieved by configuration software. Configuration software achieves the purpose of expanding data processing ability by data communications between advanced programming language and configuration software based on OLE technology. The practical application result indicates that the data processing ability of configuration software can be effectively expanded based on OLE technology, which has well stability and real-time, and can play significant performance in complex parameters and data processing related monitoring system.


2021 ◽  
Vol 92 (6) ◽  
pp. 063523
Author(s):  
K. C. Hammond ◽  
F. M. Laggner ◽  
A. Diallo ◽  
S. Doskoczynski ◽  
C. Freeman ◽  
...  

2014 ◽  
Vol 23 (01) ◽  
pp. 27-35 ◽  
Author(s):  
S. de Lusignan ◽  
S-T. Liaw ◽  
C. Kuziemsky ◽  
F. Mold ◽  
P. Krause ◽  
...  

Summary Background: Generally benefits and risks of vaccines can be determined from studies carried out as part of regulatory compliance, followed by surveillance of routine data; however there are some rarer and more long term events that require new methods. Big data generated by increasingly affordable personalised computing, and from pervasive computing devices is rapidly growing and low cost, high volume, cloud computing makes the processing of these data inexpensive. Objective: To describe how big data and related analytical methods might be applied to assess the benefits and risks of vaccines. Method: We reviewed the literature on the use of big data to improve health, applied to generic vaccine use cases, that illustrate benefits and risks of vaccination. We defined a use case as the interaction between a user and an information system to achieve a goal. We used flu vaccination and pre-school childhood immunisation as exemplars. Results: We reviewed three big data use cases relevant to assessing vaccine benefits and risks: (i) Big data processing using crowd-sourcing, distributed big data processing, and predictive analytics, (ii) Data integration from heterogeneous big data sources, e.g. the increasing range of devices in the “internet of things”, and (iii) Real-time monitoring for the direct monitoring of epidemics as well as vaccine effects via social media and other data sources. Conclusions: Big data raises new ethical dilemmas, though its analysis methods can bring complementary real-time capabilities for monitoring epidemics and assessing vaccine benefit-risk balance.


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