A Data Compression and Storage Optimization Framework for IoT Sensor Data in Cloud Storage

Abstract

The paper presents a multi-layered data compression framework that reduces the amount of data before being stored in cloud. At present, Internet of Things (IoT) has gained noticeable attention due to the approaches and advancements towards smart city aspects. With increasing number of devices and sensors connected to the Internet, tremendous amount of data is being generated at every moment which requires volumes of storage space to be stored. However, Data compression techniques can reduce the size of the data and the storage requirement by compressing the data more efficiently. In this article we introduced a two layered compression framework for IoT data that reduces the amount of data with maintaining minimum error rate as well as avoiding bandwidth wastage. In our proposed data compression scheme, we got an initial compression at the fog nodes by 50% compression ratio and in the Cloud storage we have compressed the data up to 90%. We also showed that the error is varied from the original data by 0% to 1.5% after decompression.