Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (11): 310-315.doi: 10.11896/jsjkx.200400045

Special Issue: Internet of Things

• Computer Network • Previous Articles     Next Articles

Design of Temporal-spatial Data Processing Algorithm for IoT

XU He1,2, WU Hao1, LI Peng1,2   

  1. 1 School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    2 Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks,Nanjing 210003,China
  • Received:2020-04-10 Revised:2020-07-09 Online:2020-11-15 Published:2020-11-05
  • About author:XU He,born in 1985,associate professor,master supervisor,is a member of China Computer Federation.His main research interests include Internet of Things (IoT) technology and applications.
    LI Peng,born in 1979,Ph.D,professor,master supervisor,is a member of China Computer Federation.His main research interests include computer communication networks,cloud computing,and information security.
  • Supported by:
    This work was supported by the National Key R&D Program of China (2019YFB2103003,2018YFB1003201),National Natural Science Foundation of P. R. China (61672296,61602261,61872196,61872194,61902196),Scientific and Technological Support Project of Jiangsu Province (BE2017166,BE2019740),Major Natural Science Research Projects in Colleges and Universities of Jiangsu Province (18KJA520008) and Six Ta-lent Peaks Project of Jiangsu Province (RJFW-111).

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Abstract: With the rapid development of Internet of Things (IoT) and 5G technology,there are more and more applications of artificial intelligence based on deep learning,which makes medical imaging,urban security,autonomous driving and other visual fields based on temporal-spatial data become research hotpots in the direction of IoT.At the same time,the video data,picture data,temperature and humidity data and gas concentration data collected by the IoT system also grow rapidly,which eventually makes the processing speed and feedback speed of the IoT system slower and slower.In view of the large amount of temporal-spatial data collected by IoT nodes and the problem of transient anomalies,this paper designs an EPLSN (Exception Processing Long and Short Memory Network) algorithm based on long and short memory network.This paper designs logic structure of the input gate and improves the network model structure,solving the problem of the classification of transient abnormal data and temporal-spatial data,improving the classification accuracy of the IoT temporal-spatial data,and cleaning the abnormal data.According to the characteristics of the temporal-spatial data collected by the IoT sensor,the data is stored in different data blocks.At the same time,a time-series database is used to temporarily store temporal-spatial data,and an IoT search architecture based on temporal-spatial data is proposed.The architecture is suitable for the real-time search system in IoT environment and accele-rates the search speed of the IoT system.

Key words: Abnormal data, Data classification, Data cleaning, Deep learning, Internet of Things, Temporal-spatial data

CLC Number: 

  • TP301.6
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