MongoDB-Based Repository Design for IoT-Generated RFID/Sensor Big Data

• Published in: IEEE sensors journal 2016-01, Vol.16 (2), p.485-497
• Main Authors: Kang, Yong-Shin, Park, Il-Ha, Rhee, Jongtae, Lee, Yong-Han
• Format: Article
• Language: English
• Subjects: Big Data; Data base management systems; Data management; Data models; Data processing; Design engineering; Distributed databases; EPCIS; Internet of Things; IoT; MongoDB; Radio frequency identification; Radiofrequency identification; Relational databases; Repositories; RFID; Sensor; Sensors; Supply Chain
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2015.2483499

Internet of Things (IoT)-generated data are characterized by its continuous generation, large amount, and unstructured format. The existing relational database technologies are inadequate to handle such IoT-generated data due to the limited processing speed and the significant storage-expansion cost. Thus, big data processing technologies, which are normally based on distributed file systems, distributed database management, and parallel processing technologies, have arisen as a core technology to implement IoT-generated data repositories. In this paper, we propose a sensor-integrated radio frequency identification (RFID) data repository-implementation model using MongoDB, the most popular big data-savvy document-oriented database system now. First, we devise a data repository schema that can effectively integrate and store the heterogeneous IoT data sources, such as RFID, sensor, and GPS, by extending the event data types in electronic product code information services standard, a de facto standard for the information exchange services for RFID-based traceability. Second, we propose an effective shard key to maximize query speed and uniform data distribution over data servers. Last, through a series of experiments measuring query speed and the level of data distribution, we show that the proposed design strategy, which is based on horizontal data partitioning and a compound shard key, is effective and efficient for the IoT-generated RFID/sensor big data.