A Storage Solution for Massive IoT Data Based on NoSQL

Storage is an important research direction of the data management of the Internet of Things. Massive and heterogeneous data of the Internet of Things brings the storage huge challenges. Based on the analysis of the IoT data characteristics, this paper proposed a storage management solution called IOTMDB based on NoSQL as current storage solutions are not well support storing massive and heterogeneous IoT data. Besides, the storage strategies about expressing and organizing IoT data in a uniform manner were proposed, some evaluations were carried out. Furthermore, we not only just concerned about descripting the data itself, but also cared for sharing of the data, so a data sharing mechanism based on ontology was proposed. Finally, index was studied and a set of query syntaxes to meet the needs of different kinds of IoT queries based NoSQL was given.

[1]  Wei Hong,et al.  TinyDB: an acquisitional query processing system for sensor networks , 2005, TODS.

[2]  He Yuhua,et al.  Preliminary Study on Data Management Technologies of Internet of Things , 2011, 2011 International Conference on Intelligence Science and Information Engineering.

[3]  Diego Klabjan,et al.  Warehousing and Analyzing Massive RFID Data Sets , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[4]  Rick Cattell,et al.  Scalable SQL and NoSQL data stores , 2011, SGMD.

[5]  Zhi-Ming Ding,et al.  A Database Cluster System Framework for Managing Massive Sensor Sampling Data in the Internet of Things , 2012 .

[6]  Stefan Jablonski,et al.  NoSQL evaluation: A use case oriented survey , 2011, 2011 International Conference on Cloud and Service Computing.

[7]  T.A.M.C. Thantriwatte,et al.  NoSQL query processing system for wireless ad-hoc and sensor networks , 2011, 2011 International Conference on Advances in ICT for Emerging Regions (ICTer).

[8]  Yang Liu,et al.  RNS-A Public Resource Name Service Platform for the Internet of Things , 2012, GreenCom.

[9]  Jiawei Han,et al.  Data Mining: Concepts and Techniques, Second Edition , 2006, The Morgan Kaufmann series in data management systems.

[10]  Wu Hai,et al.  A Massive Data Storage and Management Strategy for Online Computer-Assisted Audit System , 2006 .

[11]  Yongxuan Lai,et al.  Research on Cloud Databases , 2012 .

[12]  José Pereira,et al.  Assessing NoSQL Databases for Telecom Applications , 2011, 2011 IEEE 13th Conference on Commerce and Enterprise Computing.

[13]  Adam Dunkels,et al.  A database in every sensor , 2011, SenSys.

[14]  Tongrang Fan,et al.  A scheme of data management in the Internet of Things , 2010, 2010 2nd IEEE InternationalConference on Network Infrastructure and Digital Content.

[15]  Florian Waas.  Beyond Conventional Data Warehousing - Massively Parallel Data Processing with Greenplum Database - (Invited Talk) , 2008, BIRTE.

[16]  Kyoung-Don Kang,et al.  Adaptive Data Replication for Load Sharing in a Sensor Data Center , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems Workshops.

[17]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

[18]  Madoka Yuriyama,et al.  Sensor-Cloud Infrastructure - Physical Sensor Management with Virtualized Sensors on Cloud Computing , 2010, 2010 13th International Conference on Network-Based Information Systems.

[19]  Shuigeng Zhou,et al.  Achieving optimal data storage position in wireless sensor networks , 2010, Comput. Commun..

[20]  Liu Yong.  A Data Storage Method Suitable for WSN , 2011 .

[21]  Yang Xu,et al.  Ontology Based Service Discovery Method for Internet of Things , 2011, 2011 International Conference on Internet of Things and 4th International Conference on Cyber, Physical and Social Computing.