Enhancing Energy Efficient in Fault Node Recovery for a Wireless Sensor Network

International Journal of Computer Science and Engineering
© 2015 by SSRG - IJCSE Journal
Volume 2 Issue 4
Year of Publication : 2015
Authors : S.Shanmadhi, K.Sekar, T.Dheepa

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Citation:
MLA Style:

S.Shanmadhi, K.Sekar, T.Dheepa, "Enhancing Energy Efficient in Fault Node Recovery for a Wireless Sensor Network" SSRG International Journal of Computer Science and Engineering 2.4 (2015): 13-16.

APA Style:

S.Shanmadhi, K.Sekar, T.Dheepa, (2015). Enhancing Energy Efficient in Fault Node Recovery for a Wireless Sensor Network. SSRG International Journal of Computer Science and Engineering 2.4, 13-16.

Abstract:

In Wireless Sensor Network, the sensor nodes forms a cluster and each cluster will have a cluster head. The cluster head is selected on the basis of battery level. The cluster head collect the data from the sensors and transmit the data to the sink node. Since the cluster head is transmitting more amount of data compare with other nodes, so it will drains the battery. Due to this the cluster head is losing the energy very fastly and shuts down. The battery drained cluster head is known as sensor fault. The sensor fault will leads to data loss. So, the sensor fault is re placed by using FNR algorithm. Even though, the sensor faults has been replaced; the cluster head have to transmit more data and loses its energy. To minimize the risk of sensor faults, an efficient method is used to compress the data.

References:

[1] Intanagonwiwat, R. Govindan, D. Estrin, J. Heidemann and F. Silva, ― Directed Diffusion for wireless sensor networking‖, IEEE/ACM Trans. Netw., vol. 11, no. 1,pp2-16, Feb.2003. 
[2] CelalettinKarakus, Ali CaferGurbuz and BulentTavli, ― Analysisof Energy Efficiency of Compressive Sensing in Wireless Sensor Networks‖, IEEE SENIORS JOURNAL, vol. 13, no. 5, May 2013, pp.1999-2008. 
[3] D. Baron, M. B. Walkin, M. F. Duarte, S. Sarvotham and R. G. Barniuk, ― Distributed compressed sensing‖, Rice Univ., Dept. Electr. Comput. Eng., Tech. Rep. TREE-0612,2006. 
[4] E. Candes and T. Tao, ―Near-Optimal Signal Recovery from Random Projections: Universal Encoding Strategies?‖ IEEE Trans. Information Theory, vol. 52, no. 12, pp.5406-5425, Dec. 2006. 
[5] F. Fael, M. Fazel and M. Stojanovic, ―Random access compressed sensing for energy-efficient underwater sensor networks‖, IEEE I. Sel. Areas Commun., vol.29, no. 8, Sep. 2011, pp.1660-2008. 
[6] G. Cao, F. Yu and B. Zhang,‖ Improving network lifetime for wireless sensor network using compressive sensing‖, in Proc. IEEE Int. Conf. High Perform.Comput.Commun. (HPCC), pp.448-454, Sep.2011. 
[7] Hong-Chi Shis, Jiun-Huei Ho, Bin-Yih Liao and Jeng-Shyang Pan, ―Fault Node Recovery Algorithm for a Wireless Sensor Network‖, IEEE SENIORS JOURNAL, vol. 13, No, 7, pp. 2683- 2689, July 2013. 
[8] K.Akkaya and M.Younis, ―A survey on routing protocols for wireless sensor networks‖, Ad Hoc Netw., vol. 3, no. 3, pp.325- 349, May 2005. 
[9] T. H. Liu, S. C. Yi, and X. W. Wang, ― A fault management protocol for low-energy and efficient wireless sensor networks‖, J. Inf. Hiding Multimedia Signal Process., vol. 4, no. 1, pp.34- 45,2013. 
[10] T. P. Hong and C. H. Wu, ―An improved weighted clustering algorithm for determination of application nodes in heterogeneous sensor networks,‖ J. Inf. Hiding Multimedia Signal Process., vol. 2, no. 2,pp. 173–184, 2011. 
[11] Hong-Chi Shih, Jiun-Huei Ho, Bin-Yih Liao and Jeng –Shyang Pan, ―Fault Node Recovery Algorithm for a wireless sensor network.‖IEEE Sensors Journal, Vol-13, No. 7, July2013. 
[12] R. Jurdak, X. R.Wang, O. Obst, and P. Valencia, Wireless Sensor Network Anomalies:‖ Diagnosis and Detection Strategies, Intelligence-Based Systems engineering‖, vol. 10, Springer, Berlin-Heidelberg, Germany, 2011. 
[13] R. V. Kulkarni, A. F¨orster, and G. K. Venayagamoorthy, ―Computational Intelligence in Wireless Sensor Networks‖: A Survey, Journal of IEEE Communications Surveys &Tutorials, vol. 13, no. 1,pp. 68-96, 2011. 
[14] E.Candes and J.Romberg, l1- magic:Recovery of Sparse Signals via Convex Programming, http://www.acm.caltech.edu/l1magic/, 2013. 
[15] W. H. Liao, Y. Kao, and C. M. Fan, ―Data aggregation in wireless sensor networks using ant colony algorithm‖, Netw.Comput.Appl., vol. 31, no. 4, pp.387-401, 2008.

Key Words:

Grade Diffusion algorithm, Genetic Algorithm, Compact Sensing Theory, Wireless Sensor Network (WSN).