Risala Tasin Khan

Work place: Institute of Information Technology, Jahangirnagar University, Dhaka, 1342, Bangladesh

E-mail: risala@juniv.edu


Research Interests: Information Security, Network Architecture, Network Security


Risala T. Khan has completed her B.Sc. (Hons.) in Computer Science and Engineering from Jahangirnagar University, Savar, Dhaka in 2006 and M.Sc. in Computer Science and Engineering from the same University. She worked as a lecturer in the Department of Computer Science and Engineering, Daffodil International University, Dhaka since 2006 to 2009. She is now working as an Associate Professor at Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh. Her research field is network traffic and network security.

Author Articles
Efficient Sensor-Cloud Communication using Data Classification and Compression

By Md. Tanvir Rahman Md. Sifat Ar Salan Taslima Ferdaus Shuva Risala Tasin Khan

DOI: https://doi.org/10.5815/ijitcs.2017.06.02, Pub. Date: 8 Jun. 2017

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.

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