Internet of Things Applications

The technology that lets us control our smart thermostats and wireless door cameras is a part of the Internet of Things (IoT) ecosystem. In order to make everyday objects “smart,” we equip these “things” with sensors, processors and wireless communication capabilities. The IoT sounds like a consumer fantasy or a science fiction come true.



The convenience of turning off the home lights from miles away or leaving the grocery purchase to the refrigerator when milk needs to be replenished sounds technologically interesting. However, there is more to the IoT than the technological lifestyle enhancement by using smart devices. The actual potential of IoT lies on the corporate side, enabling organizations to collect and analyze data from sensors on equipment, pipelines, weather stations, meters, delivery trucks, traffic lights, automobiles, healthcare devices and other types of machinery.

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Choosing IoT analytics solution wisely. Building analytics solutions that can handle the scale of IoT solutions isn’t easy, but the right technology stack makes the challenge less time consuming. These data storage, management and analytic solutions need to be chosen wisely [6]. Basic steps of IoT analytics include:
- Number protocols enable the receipt of events (or transactions) from IoT devices. It doesn’t matter whether a device connects to the network using Beacon, Wi-Fi, Bluetooth, cellular or a hardwire connection, only that it can send a message to a broker using a defined protocol (e.g., Message Queue Telemetry Transport, Constrained Application Protocol, XMPP).
- Once a message is received by a broker such as Mosquito, you can hand that message to the data hosting and analytics system. A best practice is to store the original source data before performing any transformations.
- This unstructured message data can be stored in Hadoop, Hive or Couchbase-type NoSQL document databases, or it can be stored in big SQL databases after transformation. Most of the time, data from devices in their raw form are not directly suited for analytics. Quality and transformation steps need to be followed to clean the data and complete the missing data.
- After transformation, this data needs to be stored in a NoSQL or SQL database for analysis. Apache Storm is explicitly designed for handling continuous streams of unstructured data in a scalable fashion, performing any calculation that you can code over a boundless stream of messages. There is an ongoing debate about using Hadoop type of framework to analyze unstructured data or using Big SQL databases for large relational structured data.
- After data storage and in-memory metric development, analytic tools like Tableau, BIRT, Pentaho, JasperReports or similar tools can be utilized to create any required reports or visualization.

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See more at: analytics-magazine.org