Concepedia

IoT Data Compression and Optimization Techniques in Cloud Storage

Kaium Hossain, Md. Mizanur Rahman, Shanto Roy

International Journal of Cloud Applications and Computing · 2019 · 50 citations · 69 references

TL;DR

The rapid growth of IoT devices in smart cities and homes generates massive data streams, necessitating efficient compression and optimization to support smart decision‑making systems. This survey aims to review current cloud‑based data compression and storage optimization techniques for IoT, and to outline future research directions. The authors examined recent approaches to IoT data management, mining, compression, and optimization, summarizing their outcomes and implications for cloud, fog, and edge computing. The review highlights the effectiveness of various compression and optimization methods, identifies their limitations, and proposes future perspectives for IoT data management across cloud, fog, and mobile edge platforms.

Abstract

This article presents a detailed survey on different data compression and storage optimization techniques in the cloud, their implications, and discussion over future directions. The development of the smart city or smart home systems lies in the development of the Internet of Things (IoT). With the increasing number of IoT devices, the tremendous volume of data is being generated every single day. Therefore, it is necessary to optimize the system's performance by managing, compressing and mining IoT data for smart decision support systems. In this article, the authors surveyed recent approaches with up-to-date outcomes and findings related to the management, mining, compression, and optimization of IoT data. The authors then discuss the scopes and limitations of present works and finally, this article presents the future perspectives of IoT data management on basis of cloud, fog, and mobile edge computing.