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Internet of Things and data analytics: A current review

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Abstract With the advent of Internet and computing, we entered into an era with more people exchanging information over the Internet using devices like desktops, laptops, tablets, mobile phones, and similar data transmitting and receiving gadgets. This was a host centric communication approach. Internet of Things (IoT) is the next stage of technological advancement in computation, networking and communication with physical objects around the world getting connected to the network and exchanging data. This is an information centric approach. Thus it can be defined as an expanding physical network of dynamically increasing physical objects. The objects share information derived from their environments, reliably, and securely over the communication medium leveraging multiple protocols. The protocols involved are getting standardized to address the compatibility and interoperability issues. Each object connected is uniquely identified and controlled in the network. IoT finds its applications in many fields as environment monitoring, logistics, health care, automobile, controlled industrial environment, smart cities, and many more. As the devices, their cardinality and alignment, the data type, the data rate and specifics of the domain of the IoT applications vary; there is also a need to define architectures that incorporate the devices, communication mediums, storage, and analysis capabilities and the consumers of the derived output. Advancements in data analytics, machine learning, and digital technologies offer possibilities of derive meaningful intelligence for actionable output and in creating useful and context aware applications. This article is categorized under: Algorithmic Development > Web Mining Application Areas > Internet Application Areas > Data Mining Software Tools Application Areas > Industry Specific Applications
Big Data and Internet of Things industries market share. Courtesy: Finos ()
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Core factors in determining the application framework
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Relation between Internet of Things and data analysis. Courtesy: Tsai, Lai, Chiang, and Yang ()
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Machine learning techniques classification deep learning
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Machine learning techniques classification
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Application Areas > Industry Specific Applications
Algorithmic Development > Web Mining
Application Areas > Data Mining Software Tools

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