The Internet of things is a current major developing technology, which is a network of everyday physical objects that enhances the quality of lifestyle. Application of the internet of things encounters dealing with huge amount of data. One of the directions of big data is this huge amount of data with respect to the internet of things. As the name implies, big data refers to the data that cannot be analyzed by traditional data processing software. The key challenge of this phenomenon is to use a proper way to analyse, which can provide useful features from the data absorbed by the perception layer of the internet of things in order to provide feedback to end users, which helps them in better decision making and improves the performance of the corresponding internet of things network. Analysis of big data in theinternetof things is obviously a hard task. Data storages are distributed and there should be parallel data processing. Transmission of the data across the network can slow down because of the massive amount of data. In this regard, this paper focuses on how to analyze the massive and heterogeneous data of the internet of things in a proper way. At first, the internet of things and the big data are discussed separately with architectures, applications, challenges etc. Since these two technologies are interrelated, data analysis in the internet of things is discussed with various methodologies and challenges. Finally, the study discusses a proper framework that can analyze the big data in the internet of things (IOT) in an efficient way.

Internet of Things, machine learning, cloud data, forecasting, load

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