334 192
Full Length Article
Volume 1 , Issue 1, PP: 33-46 , 2020

Title

Type 2 Fuzzy Logic based Unequal Clustering algorithm for multi-hop wireless sensor networks

Authors Names :   A. Sariga   1 *     J. Uthayakumar   2  

1  Affiliation :  Department of Computer Science, Pondicherry University, Puducherry, India

    Email :  sarikaaut@gmail.com


2  Affiliation :  Department of Computer Science, Pondicherry University, Puducherry, India

    Email :  uthayresearchscholar@gmail.com



Doi   :  10.5281/zenodo.3825847


Abstract :

Wireless sensor network (WSN) is an integral part of IoT and Maximizing the network lifetime is a challenging task. Clustering is the most popular energy efficient technique which leads to increased lifetime stability and reduced energy consumption. Though clustering offers several advantages, it eventually raises the burden of CHs located in proximity to the Base Station (BS) in multi-hop data transmission which makes the CHs near BS die earlier than other CHs. This issue is termed as hot spot problem and unequal clustering protocols were introduced to handle it. Presently, some of the clustering protocols are developed using Type-2 Fuzzy Logic (T2FL) but none of them addresses hot spot problem. This paper presents a Type-2 Fuzzy Logic based Unequal Clustering Algorithm (T2FLUCA) for the elimination of hot spot problem and also for lifetime maximization of WSN. The proposed algorithm uses residual energy, distance to BS and node degree as input to T2FL to determine the probability of becoming CHs (PCH) and cluster size. For experimentation, T2FLUCA is tested on three different scenarios and the obtained results are compared with LEACH, TEEN, DEEC and EAUCF in terms of network lifetime, throughput and average energy consumption. The experimental results ensure that T2FLUCA outperforms state of art methods in a significant way.

Keywords :

Decision making; Fuzzy logic; Hot spot problem; Unequal clustering; IoT; Energy efficient communication.

References :

[1]       D. Estrin, J. Heidemann, S. Kumar, and M. Rey, “Next Century Challenges: Scalable Coordination in Sensor Networks,” in Proceedings of the 5th annual ACM, 1999, pp. 263–270.

[2]       K. Sohraby, D. Minoli, and T. Znati, Wireless Sensor Networks: Technology, Protocols, and Applications. Hoboken, New Jersey: John Wiley & Sons, 2007.

[3]       I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless sensor networks: a survey,” Comput. Networks, vol. 38, no. 4, pp. 393–422, 2002.

[4]       C. S. Raghavendra, K. M. Sivalingam, and T. Znati, Wireless Sensor Networks, 1st ed. US: Springer US, 2004.

[5]       W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient communication protocol for wireless microsensor networks,” Proc. 33rd Annu. Hawaii Int. Conf. Syst. Sci., vol. 0, no. c, pp. 3005–3014, 2000.

[6]       X. Liu, “A survey on clustering routing protocols in wireless sensor networks sensors,” Sensors Journal, IEEE, vol. 12, no. 8, pp. 11113–11153, 2012.

[7]       S. Soro and W. B. Heinzelman, “Prolonging the lifetime of wireless sensor networks via unequal clustering,” Proc. - 19th IEEE Int. Parallel Distrib. Process. Symp. IPDPS 2005, vol. 2005, 2005.

[8]       A. Sariga and P. Sujatha, “A survey on unequal clustering protocols in Wireless Sensor Networks,” J. King Saud Univ. - Comput. Inf. Sci., 2017.

[9]       D. Wu, “Two Differences Between Interval Type-2 and Type-1 Fuzzy Logic Controllers : Adaptiveness and Novelty.”

[10]     Z. Molay, R. Akbari, M. Shokouhifar, and F. Safaei, “Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks,” Expert Syst. Appl., vol. 55, pp. 313–328, 2016.

[11]     R. G, Z. H, and G. S, “Improving on LEACH protocol of wireless sen- sor networks using fuzzy logic.,” J. Inf. Comput. Sci, vol. 7, pp. 767–775, 2010.

[12]     M. Singh, Gaurav, V. Kumar, and S. Soni, “Clustering using fuzzy logic in wireless sensor network,” in Computing for Sustainable Global Development (INDIACom), 2016 3rd International Conference on, 2016.

[13]     H. Bagci and  a Yazici, “An energy aware fuzzy unequal clustering algorithm for wireless sensor networks,” 2010 IEEE Int. Conf. Fuzzy Syst. (FUZZ), , pp. 1–8, 2010.

[14]     S. Mao, C. Zhao, Z. Zhou, and Y. Ye, “An Improved Fuzzy Unequal Clustering Algorithm for Wireless Sensor Network,” Mob. Networks Appl., no. 2009, pp. 206–214, 2012.

[15]     R. Logambigai and A. Kannan, “Fuzzy logic based unequal clustering for wireless sensor networks,” Wirel. Networks, vol. 22, no. 3, pp. 945–957, 2016.

[16]     B. Baranidharan and B. Santhi, “DUCF: Distributed load balancing Unequal Clustering in wireless sensor networks using Fuzzy approach,” Appl. Soft Comput. J., vol. 40, pp. 495–506, 2016.

[17]     S. Gajjar, M. Sarkar, and K. Dasgupta, “FAMACRO : Fuzzy and Ant Colony Optimization based MAC / Routing Cross-layer Protocol for Wireless Sensor Networks,” vol. 0, pp. 235–247, 2014.

[18]     P. Nayak and B. Vathasavai, “Energy Efficient Clustering Algorithm for Multi Hop Wireless Sensor Network Using Type-2 Fuzzy Logic,” no. c, 2017.

[19]     J.C.Cuevas-Martinez, A.J.Yuste-Delgado, and A.Triviño-Cabrera, “Cluster head enhanced election Type-2 fuzzy algorithm for wireless sensor networks,” IEEE Commun. Lett., vol. 21, no. 9, pp. 2069–2072, 2017.

[20]     A. A. A. Ari, A. Gueroui, B. O. Yenke, and N. Labraoui, “Energy efficient clustering algorithm for Wireless Sensor Networks using the ABC metaheuristic,” in Computer Communication and Informatics (ICCCI), 2016 International Conference on, 2016.