##plugins.themes.academic_pro.article.main##

Abstract

With trem

With tremendous growth of Information and Communication Technology (ICT) Wireless sensor network has major contribution in big data gathering. Even if data generated by individual sensor is not significant, the overall data generated by all sensors in the network is contributed for generation of significant portion of big data. Hence, the energy efficient big data gathering is a very challenging task in the densely deployed wireless sensor network. Also cluster formation before data collection from sensors in the network is additional challenge. Recent research addressed these challenges with mobile sink, which in turn raise the challenge of determining the sink nodes trajectory. In this paper we have proposed new solution, M-mobile collector based data gathering with network clustering based on improved Expectation maximization technique. Mobile collectors traverse a fixed path to collect data from cluster centroids and sensors in the clusters. Finally all the collected data is transferred amongst M-collectors to reach to the static sink node. Also we derive optimal number of clusters to minimize the energy consumption.

Keywords: Big data, Wireless Sensor Networks (WSNs), clustering, optimization, data gathering, and energy efficiency.

##plugins.themes.academic_pro.article.details##

Author Biographies

Amruta S. Pattanshetti, PVPIT College, Pune, Maharashtra

ME –II Student

Mr. N. D. Kale, PVPIT College, Pune, Maharashtra

Assistant Professor, Department of Computer Engineering
How to Cite
Pattanshetti, A. S., & Kale, M. N. D. (2015). Big-Data Gathering Using Mobile Collector in Densely Deployed Wireless Sensor Network. International Journal of Emerging Trends in Science and Technology, 2(06). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/734

References

1. Daisuke Takaishi , Hiroki Nishiyama Towards Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks DOI 10.1109/ TETC.2014.2318177, IEEE Transactions on Emerging Topics in Computing.
2. Bisio and M. Marchese, Efficient satellite-based sensor networks for information retrieval, IEEE Systems Journal, vol. 2, no. 4, pp. 464475, Dec 2008.
3. S. Sagiroglu and D. Sinanc, Big data: A review, in International Conference on Collaboration Technologies and Systems (CTS), 2013.
4. R. C. Shah, S. Roy, S. Jain, and W. Brunette, Data MULEs: modeling and analysis of a three-tier architecture for sensor networks, Ad Hoc Networks, vol. 1, no. 2-3, pp. 215 - 233, 2003.
5. W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, Balakrishnan, Energy efficient communication protocol for wireless microsensor networks, in Annual Hawaii International Conference on sytem Sciences, vol. 2, Jan. 2000.
6. Daisuke Takaishi , Hiroki Nishiyama Towards Energy Efficient Big Data Gathering in Densely Distributed Sensor Networks DOI 10.1109/TETC.2014. 2318177, IEEE Transactions on Emerging Topics in Computing, October 2014.
7. S. Katti, H. Rahul, W. Hu, D. Katabi, M. Medard, and J. Crowcroft, XORs in the air: Practical wireless network coding, IEEE/ACM Transactions on Networking, vol. 16, no. 3, pp. 497510, Jun. 2008.
8. C. Intanagonwiwat, R. Govindan, and D. Estrin, Directed Diffusion: a scalable and robust communication paradigm for sensor networks, in MobiCom00 Proceedings of the 6th annual international conference on Mobile computing and networking, 2000.
9. M. Chen, T. Kwon, and Y. Choi, Energy-efficient differentiated directed diffusion (eddd) in wireless sensor networks, Computer Communications, vol. 29, no. 2, pp. 231245, 2006.