Save Energy in Wireless Sensor Networks Routing Using Fuzzy Approach and Biogeography Based Optimization

Save Energy in Wireless Sensor Networks Routing Using Fuzzy Approach and Biogeography Based Optimization

Authors

  • Yasser Kareem AlRikabi University of Sumer

DOI:

https://doi.org/10.24297/ijmit.v12i2.6344

Keywords:

Biogeography Based Optimization (BBO) algorithm, fuzzy approach, network lifetime, routing, wireless sensor networks (WSNs).

Abstract

Extending the lifetime of the energy constrained wireless sensor networks is a crucial challenge in wireless sensor networks (WSNs) research. When designing a WSN infrastructure Resource limitations have to be taken into account. The inherent problem in WSNs is unbalanced energy consumption, characterized by multi hop routing and a many-to-one traffic pattern. This uneven energy dissipation can significantly reduce network lifetime. This paper proposes a new routing method for WSNs to extend network lifetime using a combination of a fuzzy approach and Biogeography Based Optimization (BBO) algorithm which is capable of finding the optimal routing path form the source to the destination by favoring some of routing criteria and balancing among them to prolong the network lifetime. To demonstrate the effectiveness of the proposed method in terms of balancing energy consumption and maximization of network lifetime, we compare our approach with the BBO search algorithm and fuzzy approach using the same routing criteria. Simulation results demonstrate that the network lifetime achieved by the proposed method could be increased by nearly 25% more than that obtained by the BBO algorithm and by nearly 20% more than that obtained by the fuzzy approach.

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References

REFERENCES
[1] I. F. Akyildiz, S. Weilian, S. Yogesh, and C. Erdal. ”A survey on sensor networks”, Communications magazine, IEEE , Vol. 40, No. 8,pp. 102-114, 2002.
[2] C. Y. Chong, and P. K. Srikanta, “Sensor networks: evolution, opportunities, and challenges”, Proceedings of the IEEE, Vol. 91, No. 8, pp. 1247-1256 , 2003.
[3] H. Zhang, and S. Hong, “Balancing energy consumption to maximize network lifetime in data-gathering sensor networks”, Parallel and Distributed Systems, IEEE Transactions, Vol. 20, No. 10, pp. 1526-1539, 2009.
[4] V. A. Kottapalli, S. K. Anne, P. L. Jerome, E. D. Carryer, W. K. Thomas, H. L. Kincho, and L. Ying, "Two-tiered wireless sensor network architecture for structural health monitoring." In Smart Structures and Materials, pp. 8-19, 2003..
[5] J. N. Al-Karaki, and E. K. Ahmed, “Routing techniques in wireless sensor networks: a survey”, Wireless communications, IEEE, Vol. 11, No. 6, pp. 6-28, 2004.
[6] I. S. AlShawi, Y. Lianshan, P.Wei, and L. Bin, “Lifetime enhancement in wireless sensor networks using fuzzy approach and A-star algorithm”, Sensors Journal, IEEE, Vol. 12, No. 10, pp. 3010-3018, 2012.
[7] F. Ren, Z. Jiao, H. Tao, L. Chuang, and K. R. Sajal, “EBRP: energy-balanced routing protocol for data gathering in wireless sensor networks”, Parallel and Distributed Systems, IEEE Transactions,Vol. 22, No. 12,pp. 2108-2125, 2011.
[8] M. Patil, and C. B. Rajashekhar, “A survey on routing protocols in wireless sensor networks”, In Networks (ICON)”,18th IEEE International Conference, pp. 86-91, 2012.
[9] A. Jamshidi, and N. K. Masoumeh, “Performance analysis of transmitter-side cooperation–receiver-side-relaying schemes for heterogeneous sensor networks”, Vehicular Technology, IEEE Transactions, Vol. 57, No. 3, pp. 1548-1563 2008.
[10] E. Jain, and L. Qilian, “Sensor placement and lifetime of wireless sensor networks: theory and performance analysis”, In Global Telecommunications Conference, IEEE, vol. 1, pp. 5-pp, 2005.
[11] H. Shu, L. Qilian, and G. Jean, “Wireless sensor network lifetime analysis using interval type-2 fuzzy approach systems, Fuzzy Systems”, IEEE Transactions, Vol. 16, No. 2, pp. 416-427, 2008.
[12] S. Priyankara, K. Kazuhiko, T. Hideki, and M. Koso, “A clustering/multi-hop hybrid routing method for wireless sensor networks with heterogeneous node types”, In GLOBECOM Workshops (GC Wkshps), IEEE, pp. 207-212, 2010.
[13] Y. Lin, Z. Jun, S. H. C. Henry, H. Ip, Wai, L. Yun, and H. S. Yu, "An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks,” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions, Vol.42, No. 3,pp. 408-420 ,2012.
[14] J. Park, and S. Sartaj, “An online heuristic for maximum lifetime routing in wireless sensor networks”, Computers, IEEE Transactions, Vol. 55, No. 8, pp.1048-1056, 2006.
[15] C. Wu, Y. Ruixi, and Z. Hongchao, “A novel load balanced and lifetime maximization routing protocol in wireless sensor networks”, In Vehicular Technology Conference, VTC Spring, IEEE, pp. 113-117, 2008.
[16] J. Anno, B. Leonard, D. Arjan, X. Fatos, and K. Akio, “Performance evaluation of two fuzzy-based cluster head selection systems for wireless sensor networks”, Mobile Information Systems, Vol. 4, No. 4, pp. 297-312,2008.
[17] W. Pedrycz, and G. Fernando, “An introduction to fuzzy sets: analysis and design”, Mit Press, 1998.
[18] B. E. Van, and D. B. Bernard, “Fast and accurate center of gravity defuzzification of fuzzy system outputs defined on trapezoidal fuzzy partitions”, Fuzzy Sets and Systems, Vol. 157, No. 7,pp. 904-918,2006.
[19] J. S. Lee, and L. C. Wei, “Fuzzy-logic-based clustering approach for wireless sensor networks using energy predication”, Sensors Journal, IEEE, Vol. 12, No. 9, pp. 2891-2897, 2012.
[20] M. H. Yin, and X. T. Li, “A hybrid bio-geography based optimization for permutation flow shop scheduling”, Sci. Res. Essays, Vol. 6, No. 10, pp. 2078-2100, 2011.
[21] X. Li, W. Jinyan, Z. Junping, and Y. Minghao, “A perturb biogeography based optimization with mutation for global numerical optimization”, Applied Mathematics and Computation, Vol. 218, No. 2, pp. 598-609, 2011.
[22] X. Li, and Y. Minghao, “Hybrid differential evolution with biogeography-based optimization for design of a reconfigurable antenna array with discrete phase shifters”, International Journal of Antennas and Propagation, 2011.
[23] G. Wang, G. Lihong, D. Hong, L. Luo, and W. Heqi, “Dynamic deployment of wireless sensor networks by biogeography based optimization algorithm”, Journal of Sensor and Actuator Networks, Vol.1, No. 2, pp. 86-96, 2012.
[24] L. A. Zadeh, “Soft computing and fuzzy approach”, IEEE software, vol. 11, No. 6, pp. 48-56, Nov., 1994.
[25] R. V. Kulkarni, F. Anna, and K. V. Ganesh, “Computational intelligence in wireless sensor networks: A survey”, Communications Surveys & Tutorials, IEEE, Vol. 13, No. 1, pp. 68-96, Feb.,2011.
[26] K. Y. Cai, and Z. Lei, “Fuzzy reasoning as a control problem”, Fuzzy Systems, IEEE Transactions, Vol. 16, No. 3, pp. 600-614 ,2008.
[27] D. Simon, “Biogeography-based optimization”, IEEE Transactions, Vol. 12, No. 6, pp. 702-713, 2008.
[28] T. Runkler, “Selection of appropriate defuzzification methods using application specific properties”, Fuzzy Systems, IEEE Transactions, Vol. 5, No. 1, pp. 72-79, 1997.
[29] W. R. Heinzelman, C. Anantha, and B. Hari, “Energy-efficient communication protocol for wireless microsensor networks”, In System sciences, Proceedings of the 33rd annual Hawaii international conference, IEEE, pp. 10-pp. 2000.
[30] N. A. Latiff, C. T. Charalampos, and S. S. Bayan, “Performance comparison of optimization algorithms for clustering in wireless sensor networks”, In Mobile Adhoc and Sensor Systems, MASS 2007. IEEE International Conference, pp. 1-4. IEEE, 2007.
[31] M. R. Minhas, G. Sathish, and L. Victor, “An online multipath routing algorithm for maximizing lifetime in wireless sensor networks”, In Information Technology: New Generations, ITNG'09. Sixth International Conference, IEEE, pp. 581-586, 2009.
[32] M. A. Azim, and J. Abbas, “Performance evaluation of optimized forwarding strategy for flat sensor networks”, In Global Telecommunications Conference, GLOBECOM'07. IEEE, pp. 710-714, 2007.

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Published

2017-11-30

How to Cite

AlRikabi, Y. K. (2017). Save Energy in Wireless Sensor Networks Routing Using Fuzzy Approach and Biogeography Based Optimization: Save Energy in Wireless Sensor Networks Routing Using Fuzzy Approach and Biogeography Based Optimization. INTERNATIONAL JOURNAL OF MANAGEMENT &Amp; INFORMATION TECHNOLOGY, 12(2), 3167–3178. https://doi.org/10.24297/ijmit.v12i2.6344

Issue

Vol. 12 No. 2 (2017)

Section

Articles

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Creative Commons License All articles published in Journal of Advances in Linguistics are licensed under a Creative Commons Attribution 4.0 International License.

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