IoT Data Management Using Cloud Computing and Big Data Technologies

IoT Data Management Using Cloud Computing and Big Data Technologies

Sangeeta Gupta, Raghuram Godavarti
Copyright: © 2020 |Pages: 9
DOI: 10.4018/IJSI.2020100104
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Abstract

With recent developments in technology, devices like vehicles and home appliances are able to connect to the Internet and communicate, contributing to the Internet of Things. These advancements lead to generation of huge amount of data. This data is needed to derive all the metrics of the IoT devices, which can later be used to make suitable analysis and henceforth take some business decisions. Moreover, these huge amounts of data are very difficult to handle with conventional data warehousing techniques and need a better system. The existing data centers that are located on-site are mostly relational databases which are not scalable to handle increasing needs of storage and compute. These systems are also inefficient to handle different types of data which is mandatory for IoT devices capturing different metrics. In the proposed work, a model is designed to better handle the data generated by IoT devices via Rest API's. Results are presented to depict the functioning of Rest API across all the nodes deployed in a cluster via JSON request. The input to the model is a corresponding JSON payload as a request. The transactions get added to the registered nodes, without a necessity to add payload for the second time. A new batch is created with readings from all the devices. The contents of the entire batch and all systems are obtained while retrieving the results, thus signifying the effectiveness of the proposed work.
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2. Literature Survey

IoT device data is maintained and managed through Relational Databases like Oracle, MySQL, PostgreSQL, etc. This requires a three-tier architecture where every IoT device acts as a client and sends readings to a server. A server is a machine located at a single remote location which then transforms and relays this readings data onto to a huge database. The data in this huge relational data base is modeled and warehoused to analyze and derive insights about the purpose the IoT devices are serving. This architecture is a pure three-tier client server architecture (Abu-Elkheir et al., 2013).

MapReduce at the other end, is a programming template to process large datasets on parallel computing systems. It ensures that all items with the same key will be processed by the same reducer as the mapper all use the same function hash to decide which reducer to send key pairs/value. This programming paradigm is very complicated to use directly, given the number of jobs needed to perform complex data operations and transform their queries into a set of jobs that are run in succession. One of such Big Data analytics tools uses MapReduce concepts that can satisfy above demands that can be deployed on a cloud environment is Apache Spark.

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