Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs

  • Misbahuddin Misbahuddin Universitas Indonesia http://orcid.org/0000-0002-5025-7686
  • Anak Agung Putri Ratna Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, 16424, Indonesia
  • Riri Fitri Sari Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, 16424, Indonesia

Abstract

In multi-hop routing, cluster heads close to the base station functionaries as intermediate nodes for father cluster heads to relay the data packet from regular nodes to base station. The cluster heads that act as relays will experience energy depletion quicker that causes hot spot problem. This paper proposes a dynamic multihop routing algorithm named Data Similarity Aware for Dynamic Multi-hop Routing Protocol (DSA-DMRP) to improve the network lifetime, and satisfy the requirement of multi-hop routing protocol for the dynamic node clustering that consider the data similarity of adjacent nodes. The DSA-DMRP uses fuzzy aggregation technique to measure their data similarity degree in order to partition the network into unequal size clusters. In this mechanism, each node can recognize and note its similar neighbor nodes. Next, K-hop Clustering Algorithm (KHOPCA) that is modified by adding a priority factor that considers residual energy and distance to the base station is used to select cluster heads and create the best routes for intra-cluster and inter-cluster transmission. The DSA-DMRP was compared against the KHOPCA to justify the performance. Simulation results show that, the DSA DMRP can improve the network lifetime longer than the KHOPCA and can satisfy the requirement of the dynamic multi-hop routing protocol.

Author Biography

Misbahuddin Misbahuddin, Universitas Indonesia
Dept. Electrical Engineering,Faculty Of Engineering,Universitas Indonesia

References

[1] Afsar, M. M.; Tayarani, M. H. (2014); Clustering in sensor networks: A literature survey, Journal of Network and Computer Applications, 46, 198-226, 2014.
https://doi.org/10.1016/j.jnca.2014.09.005

[2] Ahmed, N.; Kanhere, S.; Jha, S. (2010); Experimental Evaluation of Multi-hop Routing Protocols for Wireless Sensor Networks, In Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks, 416–417, New York, NY, USA, 2010.

[3] Amin, R.; Islam, S. H.; Biswas, G. P.; Khan, M. K.; Kumar, N. (2016); A robust and anonymous patient monitoring system using wireless medical sensor networks, Future Generation Computer Systems, 2016.

[4] Bagci, H.; Yazici, A. (2013); An energy aware fuzzy approach to unequal clustering in wireless sensor networks, Applied Soft Computing, 13(4), 1741-1749, 2013.
https://doi.org/10.1016/j.asoc.2012.12.029

[5] Bodik, P., Hong, W., Guestrin, C., Madden, S., Paskin, M., and Thibaux, R., Intel Lab Data. Retrieved from http://db.csail.mit.edu/labdata/labdata.html.

[6] Brust, M. R.; Frey, H.; Rothkugel, S. (2008); Dynamic Multi-hop Clustering for Mobile Hybrid Wireless Networks, Proceedings of the 2Nd International Conference on Ubiquitous Information Management and Communication, 130-135. New York, NY, USA: ACM, 2008

[7] Gajjar, S.; Talati, A.; Sarkar, M.; Dasgupta, K. (2015); FUCP: Fuzzy based unequal clustering protocol for wireless sensor networks, 39th National Systems Conference (NSC), 2015.

[8] Gardner, M. (1970); Mathematical Games: The Fantastic Combinations of Jhon Conway's New Solitaire Game "Life", City, 1970.

[9] Ghaddar, A.; Razafindralambo, T.; Simplot-Ryl, I.; Tawbi, S.; Hijazi, A. (2010); Algorithm for data similarity measurements to reduce data redundancy in wireless sensor networks, World of Wireless Mobile and Multimedia Networks (WoWMoM), 2010 IEEE International Symposium on, 1-6, 2010.

[10] Gupta, R.; Sultania, K.; Singh, P.; Gupta, A. (2013); Security for wireless sensor networks in military operations, Computing, Communications and Networking Technologies (ICCCNT), 2013 Fourth International Conference on, 1-6, 2013.

[11] Haibin, D.; Qinan, L. (2015); New progresses in swarm intelligence-based computation, Int. J. Bio-Inspired Computation, 7(1), 26-35, 2015.
https://doi.org/10.1504/IJBIC.2015.067981

[12] Heinzelman, W. R.; Chandrakasan, A.; Balakrishnan, H. (2000); Energy-efficient communication protocol for wireless microsensor networks, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, 3005-3014, 2000.

[13] Heinzelman, W. B.; Chandrakasan, A. P.; Balakrishnan, H. (2002); An application-specific protocol architecture for wireless microsensor networks, IEEE Transactions on Wireless Communications, 2002.

[14] Horita, F. E. A.; Albuquerque, J. P. de; Degrossi, L. C.; Mendiondo, E. M.; Ueyama, J. (2015); Development of a spatial decision support system for flood risk management in Brazil that combines volunteered geographic information with wireless sensor networks, Computers and Geosciences, 80, 2015.

[15] Jia, J.G.; He, Z.W.; Kuang, J.M;, Mu, Y.H. (2010); An Energy Consumption Balanced Clustering Algorithm for Wireless Sensor Network, 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), 2010.

[16] Kang, S. H.; Nguyen, T. (2012); Distance Based Thresholds for Cluster Head Selection in Wireless Sensor Networks, IEEE Communications Letters, 16(9), 1396-1399, 2012.
https://doi.org/10.1109/LCOMM.2012.073112.120450

[17] Kollam, M.; Shree, S. R. B. S. (2011); Zigbee Wireless Sensor Network for better Interactive Industrial Automation, Advanced Computing (ICoAC), 2011 Third International Conference on, 304-308, 2011.

[18] Li, C.; Ye, M.; Chen, G.; Wu, J. (2005); An energy-efficient unequal clustering mechanism for wireless sensor networks, IEEE International Conference on Mobile Adhoc and Sensor Systems Conference, 597-608, 2005.

[19] Lu, K.; Zhou, R.; Li, H. (2016); Event-triggered cooperative target tracking in wireless sensor networks, Chinese Journal of Aeronautics, 29(5), 1326-1334, 2016.
https://doi.org/10.1016/j.cja.2016.08.010

[20] Misbahuddin, M.; Sari, R. F. (2016); Data Similarity Based Dynamic Node Clustering Using Bio-Inspired Algorithm for Self-Organized Wireless Sensor Networks, In P. Novais and S. Konomi (Eds.), Intelligent Environments, 318-327, London, UK: IOS Press, 2016.

[21] Purkait, R.; Tripathi, S. (2015); Fuzzy based unequal energy aware clustering with multihop routing in wireless sensor network, 2015 IEEE Workshop on Computational Intelligence: Theories, Applications and Future Directions (WCI), 2015.

[22] Rahman, M.; Rahman, S.; Mansoor, S.; Deep, V.; Aashkaar, M. (2016); Implementation of ICT and Wireless Sensor Networks for Earthquake Alert and Disaster Management in Earthquake Prone Areas, Procedia Computer Science, 85, 92-99, 2016.
https://doi.org/10.1016/j.procs.2016.05.184

[23] Ross, T. J. (2010); Fuzzy Logic with Engineering Applications, Mexico, USA: Jhon Wiley & Sons, 2010.
https://doi.org/10.1002/9781119994374

[24] Sabor, N.; Abo-Zahhad, M.; Sasaki, S.; Ahmed, S. M. (2016); An Unequal Multi-hop Balanced Immune Clustering protocol for wireless sensor networks, Applied Soft Computing, 43, 372-389, 2016.
https://doi.org/10.1016/j.asoc.2016.02.016

[25] Saleem, M.; Di Caro, G. A.; Farooq, M. (2011); Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions, Information Sciences, 181(20), 4597-4624, 2011.
https://doi.org/10.1016/j.ins.2010.07.005

[26] Tuan Dinh, L.; Dat Ho, T. (2015); Design and deploy a wireless sensor network for precision agriculture. In Information and Computer Science (NICS), 2015 2nd National Foundation for Science and Technology Development Conference on, 294-299, 2015.

[27] Xin-She, Y. (2008); Cuckoo Search and Firefly Algorithm Xin-She Yang Editor Theory and Applications. (X.-S. Yang, Ed.). London, UK: Springer. http://doi.org/10.1007/978-3-319- 02141-6, 2008.

[28] Wang, H.; Wang, W.; Zhou, X.; Sun, H.; Zhao, J.; Yu, X.; Cui, Z.(2017); Firefly algorithm with neighborhood attraction, Information Sciences, 382-383, 374-387, 2017.
https://doi.org/10.1016/j.ins.2016.12.024

[29] Zahedi, Z.M.; Akbari, R.; Shokouhifar, M.; Safaei, F.; Jalali, A. (2016); Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks, Expert Systems with Applications, 55, 313-328, 2016.
https://doi.org/10.1016/j.eswa.2016.02.016
Published
2018-02-12
How to Cite
MISBAHUDDIN, Misbahuddin; PUTRI RATNA, Anak Agung; SARI, Riri Fitri. Dynamic Multi-hop Routing Protocol Based on Fuzzy-Firefly Algorithm for Data Similarity Aware Node Clustering in WSNs. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 13, n. 1, p. 99-116, feb. 2018. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3088>. Date accessed: 22 sep. 2020. doi: https://doi.org/10.15837/ijccc.2018.1.3088.

Keywords

clustering, data similarity, multi-hop routing, fuzzy system, firefly algorithm, Wireless Sensor Networks (WSNs)