A new energy-efficient fuzzy cluster-based routing algorithm with a Constant threshold in wireless sensor network

Document Type : Persian Original Article

Authors

1 Department of Electrical and Computer Engineering Imam Reza International University, Mashhad, Iran

2 Department of Electrical and Computer Engineering, Khayyam University, Mashhad, Iran

Abstract

A major challenge in the development of wireless sensor networks is Increase network lifetime. Cluster-based routing protocols are proposed as a solution to improve energy consumption in wireless sensor networks. Clustering in each round and the single-hop transmission to base station is base of many algorithms that have been presented so far. Clustering in each round increases the number of control messages, collision and reduces energy of the network. Multi-hop routing increases the life time of cluster nodes and improves network performance. Proposed algorithm use the benefits of clustering and multi-hop routing, we consider a new fuzzy clustering based routing protocol with fixed threshold. The innovations of proposed paper include clustering nodes in different rounds, consideration of a fixed threshold, using different clustering algorithms, and multi-hop routing by considering the appropriate intermediate node In order to send data from each cluster head to the base station. Fuzzy inference using parameters such as "remaining energy", "neighbors' number", and "distance" of each node. The proposed algorithm has been compared with other algorithms on the field of network lifetime parameters, number of dead nodes per round, first dead node, half dead, and last dead node. The simulation results show that the proposed algorithm increases the network life expectancy of 44.5 percent in compared with other methods.

Keywords

References

   [1]      Tifenn Rault and Abdelmadjid Bouabdallah and Yacine Challal,         ” Energy efficiency in wireless sensor networks: A top-down survey”, Computer Networks, 2014                                                                         
   [2]       I.F. Akyildiz  and  W. Su and Y. Sankarasubramaniam and E. Cayirci “Wireless sensor networks: a survey”. Computer Networks, 2002
 
   [3]       M. MehdiAfsar and Mohammad-H  and Tayarani-N ,” Clustering in sensor networks: Al literature survey”, Journal of Network and Computer Applications, 2016
   [4]      Junaid Ahmed Khan and  Hassaan Khaliq Qureshi and Adnan Iqbal, “Energy management in Wireless Sensor Networks: A survey", Elsevier, Computers and Electrical Engineering, 2015
   [5]      Gaurang Raval and Madhuri Bhavsar and Nitin Patel,” Enhancing data delivery with density controlled clustering in wireless sensor networks”, Microsystem Technologies, 2017
   [6]      Chirihane Gherbi and Zibouda Aliouat and Mohamed Benmohammed, " A Survey on Clustering Routing Protocols in Wireless Sensor Networks ", Sensor Review, 2017
   [7]       Naranjo and P.G.V and Shojafar and  M., Mostafaei and H. et al. J “ P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks”. Supercomputing, 2017
   [8]      Nikolaos A. Pantazis, Stefanos A. Nikolidakis and Dimitrios D. Vergados,” Energy-Efficient Routing Protocols in Wireless Sensor Networks: A Survey”, IEEE Communications Surveys & Tutorials, 2013
   [9]       Huang, J. and Hong Y. and Zhao Z. et al, “An energy-efficient multi-hop routing protocol based on grid clustering for wireless sensor networks”, Springer,Cluster Computing, 2017
[10]      Chirihane Gherbi and  Zibouda Aliouat and Mohamed Benmohammed, “An adaptive clustering approach to dynamic load balancing andenergy efficiency in wireless sensor networks”, Elsevier Energy, 2016
[11]      Wenjing Guo and Wei Zhang,” survey on intelligent routing protocols in wireless sensor networks”, Journal of Network and Computer Applications, 2013
[12]      Ashutosh Kumar Singh and  N. Purohit and  S. Varma, “Fuzzy logic based clustering in wireless sensor networks: a survey”, Taylor & Francis, International Journal of Electronics, 2013
[13]      JSR Jang, CT Sun, E Mizutani, “Fuzzy inference systems, chapter 4”, 1997
[14]      Handy, M. J. and Haase, M. and Timmermann, D. “Low energy adaptive clustering hierarchy with deterministic cluster-head selection”, In Mobile and wireless communications network, 2002
[15]      O. Younis  and S. Fahmy ,” a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks”, IEEE Transactions on Mobile Computing, 2004
[16]      Baranidharan, B. and  Santhi, B., “ DUCF: Distributed load balancing unequal clustering in wireless sensor networks using fuzzy approach”, Applied Soft Computing, 2016
[17]      Seyyit Alper and Sert Hakan and Bagci Adnan Yazici, “MOFCA: Multi-Objective Fuzzy Clustering Algorithm for Wireless Sensor Networks”, Applied Soft Computing , 2015
[18]      Bagci, H. and Yazici, A. “An energy aware fuzzy approach to unequal clustering in wireless sensor networks ” Applied Soft Computing, 2013
[19]      Akila, I.S. and Venkatesan. “A Cognitive Multi-hop Clustering Approach for Wireless Sensor Networks”, R. Wireless Pers Commun, 2016
[20]      Baranidharan Balakrishnan and Santhi Balachandran, “FLECH: Fuzzy Logic Based Energy Efficient Clustering Hierarchy for Nonuniform Wireless Sensor Networks”, Hindawi Wireless Communications and Mobile Computing, 2017
Journal of Soft Computing and Information Technology
Volume 7, Issue 1
December 2018
Pages 87-103

Files

Share

How to cite

Statistics