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Exploring Innovation| ISSN:2347-6389(Online)| Reg. No.:15318/BPL/13| Published by BEIESP| Impact Factor:3.76
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Volume-1, Issue-8 July 18, 2014
25
Volume-1, Issue-8 July 18, 2014
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S. No

Volume-1 Issue-8, July 2014, ISSN: 2347-6389 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Awan Dhawan

Paper Title:

Energy Efficient Protocols for Wireless Sensors Network

Abstract: In wireless sensors networks (WSN), huge number of nodes are deployed randomly in area for analysing the environment conditions at that time like temperature, light, earthquake, humidity, sound etc. & transmit their sensed or measured data to sink nodes by means of multi hopping data transmission process. The sensor nodes relay on limited battery life where as sink nodes are always rich power because they are connected at back end network. During the data transmission, the sensor nodes which are closer to sink nodes use up their energy earlier than the nodes which are away because they relay more data packets. It means some sensor nodes are burn out and some are alive. This cause to energy imbalance in between the sensor nodes, and leads to connectivity holes and coverage holes, and finally there is whole network failure.

Keywords:
Wireless sensor networks, Clustering, Simulation for WSN.


References:

1.        M. C. Vuran, Ö. B. Akan, and I. F. Akyildiz, “Spatio-temporal correlation: theory and applications for wireless sensor networks,” Computer Networks, vol. 45, no. 3, pp. 245–259, 2004.
2.        Q. Huang, C. Lu, and G. Catalin, “Spatio-temporal multicast in sensor networks,” in Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys '03), pp. 38–45, 2003.

3.        M. A. Batalin, “Spatio-temporal multicast in sensor networks,” in Proceedings of the 2nd ACM International Conference on Embedded Networked Sensor Systems (SenSys '04), pp. 25–38, Baltimore, Md, USA, November 2004.

4.        A. M. Turing, “The chemical basis of morphogenesis,” The Philosophical Transactions of the Royal Society B, vol. 237, no. 641, pp. 37–72, 1952.

5.        J. D. Murray, Mathematical Biology, Springer, Berlin, Germany, 1989.

6.        Q. Ouyang and H. L. Swinney, “Transition from a uniform state to hexagonal and striped turing patterns,”Nature, vol. 352, no. 6336, pp. 610–612, 1991.

7.        H. Gutowitz, Cellular Automata: Theory and Experiment, The MIT Press, Cambridge, Mass, USA, 1991.

8.        W. M. Shen, P. Will, A. Galstyan, and C. M. Chuong, “Hormone-inspired self-organization and distributed control of robotic swarms,” Autonomous Robots, vol. 17, no. 1, pp. 93–105, 2004.

9.        W. M. Shen, B. Salemi, and P. Will, “Hormone-inspired adaptive communication and distributed control for CONRO self-reconfigurable robots,” IEEE Transactions on Robotics and Automation, vol. 18, no. 5, pp. 700–712, 2002.

10.     E. Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence from Natural to Artificial Systems, Oxford University Press, New York, NY, USA, 1999.

11.     H. van Dyke Parunak and S. Brueckner, “Entropy and self-organization in multi-agent systems,” inProceedings of the 5th International Conference on Autonomous Agents, pp. 124–130, Montreal, Canada, June 2001.

12.     D. Payton, R. Estkowski, and M. Howard, “Entropy and selforganization in multiagent systems,” inProceedings of the International Conference on Autonomous Agents, pp. 23–28, Montreal, Canada, 2002.

13.     H. van Dyke Parunak, “Making swarming happen,” in Proceedings of the Swarming Network Enabled C4ISR Conference, pp. 170–179, Tysons Corner, Va, USA, 2003.


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