An IoT-Oriented Data Storage Framework in Cloud Computing Platform

TLDR

IoT systems using RFID and wireless sensors generate rapid, voluminous, and heterogeneous data, posing significant storage and processing challenges for cloud platforms. This study proposes a data‑storage framework that efficiently stores massive IoT data while integrating both structured and unstructured information. The framework combines multiple databases with Hadoop, extending it to a distributed file repository that manages diverse sensor and RFID data types. A prototype implementation demonstrates the framework’s effectiveness in handling large‑scale IoT data.

Abstract

The Internet of Things (IoT) has provided a promising opportunity to build powerful industrial systems and applications by leveraging the growing ubiquity of Radio Frequency IDentification (RFID) and wireless sensors devices. Benefiting from RFID and sensor network technology, common physical objects can be connected, and are able to be monitored and managed by a single system. Such a network brings a series of challenges for data storage and processing in a cloud platform. IoT data can be generated quite rapidly, the volume of data can be huge and the types of data can be various. In order to address these potential problems, this paper proposes a data storage framework not only enabling efficient storing of massive IoT data, but also integrating both structured and unstructured data. This data storage framework is able to combine and extend multiple databases and Hadoop to store and manage diverse types of data collected by sensors and RFID readers. In addition, some components are developed to extend the Hadoop to realize a distributed file repository, which is able to process massive unstructured files efficiently. A prototype system based on the proposed framework is also developed to illustrate the framework's effectiveness.