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IoT Data Compression and Optimization Techniques in Cloud Storage: Current Prospects and Future Directions

Kaium Hossain, Mizanur Rahman, Shanto Roy

Source Title: International Journal of Cloud Applications and Computing (IJCAC)9(2)

ISSN: 2156-1834|EISSN: 2156-1826|EISBN13: 9781522567776|DOI: 10.4018/IJCAC.2019040103

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MLA

Hossain, Kaium, et al. "IoT Data Compression and Optimization Techniques in Cloud Storage: Current Prospects and Future Directions." IJCAC vol.9, no.2 2019: pp.43-59. http://doi.org/10.4018/IJCAC.2019040103

APA

Hossain, K., Rahman, M., & Roy, S. (2019). IoT Data Compression and Optimization Techniques in Cloud Storage: Current Prospects and Future Directions. International Journal of Cloud Applications and Computing (IJCAC), 9(2), 43-59. http://doi.org/10.4018/IJCAC.2019040103

Chicago

Hossain, Kaium, Mizanur Rahman, and Shanto Roy. "IoT Data Compression and Optimization Techniques in Cloud Storage: Current Prospects and Future Directions," International Journal of Cloud Applications and Computing (IJCAC) 9, no.2: 43-59. http://doi.org/10.4018/IJCAC.2019040103

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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.

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IoT Data Compression and Optimization Techniques in Cloud Storage: Current Prospects and Future Directions

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