An Efficient Data Stream Analytics Model for Real Time Internet of Things (Iot) Applications
K. Kranthi Kumar1, E Ramaraj2

1Mr. Kranthi Kumar, Research Scholar, Department of Computer Science, Alagappa University, Karaikudi, Tamil Nadu, India
2Dr. E.Ramaraj, Professor and Head, Department of Computer Science, Alagappa University, Karaikudi, Tamil Nadu, India.
Manuscript received on February 02, 2020. | Revised Manuscript received on February 10, 2020. | Manuscript published on March 30, 2020. | PP: 495-500 | Volume-8 Issue-6, March 2020. | Retrieval Number: F7359038620/2020©BEIESP | DOI: 10.35940/ijrte.F7359.038620

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Abstract: Internet of Things (IoT), data analytics is supporting multiple applications. These numerous applications try to gather data from different environments, here the gathered data may be homogeneous or heterogeneous, but most of the data collected from multiple environments were heterogeneous, the task of gathering, processing, storing and the analysis that is being performed on data are still challenging. Providing security to all these things is also a challenging task due to untrusted networks and big data. Big data management in the ever-expanding network may rise several non-trivial concerns on data collection, data-efficient processing, analytics, and security. However, the above said scenarios depends on large scale sensor deployed. Sensors continuously transmit data to clouds for real time use, which can raise the issue of privacy disclosure because IoT devices may gather data including a kind of sensitive private information. In this context, we propose a two-layer system or model for analyzing IoT data, collected from multiple applications. The first layer is mainly used for gathering data from multiple environments and acts as a service-oriented interface to ingest data. The second layer is responsible for storing and analyses data securely. The Proposed solutions are implemented by the use of open source components.
Keywords: Data, Data Stream, Spark, Analytics, IoT.
Scope of the Article: Data Analytics.