Efficient Sensor-Cloud Communication using Data Classification and Compression

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Author(s)

Md. Tanvir Rahman 1,* Md. Sifat Ar Salan 2 Taslima Ferdaus Shuva 1 Risala Tasin Khan 3

1. Department of Computer Science and Engineering, Daffodil International University, Dhaka, 1207, Bangladesh

2. Department of Statistics, Jahangirnagar University, Dhaka, 1342, Bangladesh

3. Institute of Information Technology, Jahangirnagar University, Dhaka, 1342, Bangladesh

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2017.06.02

Received: 21 Jun. 2016 / Revised: 4 Oct. 2016 / Accepted: 28 Jan. 2017 / Published: 8 Jun. 2017

Index Terms

Wireless Sensor Network, Cloud Computing, Classification, Compression, Sensor-Cloud Communication

Abstract

Wireless Sensor Network, a group of specialized sensors with a communication infrastructure for monitoring and controlling conditions at diverse locations, is a recent technology which is getting popularity day by day. Besides, cloud computing is a type of high-performance computing that uses a network of remote servers which simultaneously provides the service to store, manage and process data rather than a local server or personal computer. An architecture called sensor-cloud is also providing good services by combining the capabilities from both ends. In order to provide such services, a large volume of sensor network data needs to be transported to cloud gateway with a high amount of bandwidth and time requirement. In this paper, we have proposed an efficient sensor-cloud communication approach that minimizes the enormous bandwidth and time requirement by using statistical classification based on machine learning as well as compression using deflate algorithm with a minimal loss of information. Experimental results describe the overall efficiency of the proposed method over the traditional and related research.

Cite This Paper

Md. Tanvir Rahman, Md. Sifat Ar Salan, Taslima Ferdaus Shuva, Risala Tasin Khan, "Efficient Sensor-Cloud Communication using Data Classification and Compression", International Journal of Information Technology and Computer Science(IJITCS), Vol.9, No.6, pp.9-17, 2017. DOI:10.5815/ijitcs.2017.06.02

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