International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Cluster Head Election in Wireless Sensor Network: A Comprehensive Study and Future Directions


Affiliations
1 Department of Computer Science and Applications, Maharishi Markandeshwar (Deemed to be University), Mullana (Ambala), India
2 Department of Computer Science and Engineering, Maharishi Markandeshwar (Deemed to be University), Mullana (Ambala), India
 

Due to the advancement of wireless communication interchanges, electronic technology, and micro-electromechanical devices, Wireless Sensor Network (WSN) has got advanced as a promising zone of research. WSN consists of a collection of sensor nodes having a little calculative capability, limited memory, and constrained energy assets. Clusters are formed from the collection of sensor nodes whose leader node (Cluster head) can send the sensed information from hubs to the BS. To condense the power consumption and boost group longevity, the cluster head executes data accumulation. This paper discusses many algorithms based on deterministic, probabilistic, adaptive, fuzzy logic, and Multi-attribute decisionmaking techniques for clustering and cluster head election. Existing algorithms enhance the network lifetime and energy efficiency but fail to provide a better quality of service and security. So many issues and challenges have been laid down and it is concluded that when computational intelligence is combined with network intelligence then QoS and security both can be provided along with the network longevity and energy efficiency in homogeneous as well as a heterogeneous environment.

Keywords

Wireless Sensor Network (WSN), Deterministic Schemes, Adaptive, Schemes, Probabilistic Schemes, Multi- Attribute Decision Making Schemes (MADM), Fuzzy Based Cluster Head Election Schemes.
User
Notifications
Font Size

  • Y. Al-obaisat and R. Braun, “On Wireless Sensor Networks : Architectures, Protocols, Applications , and Management Routing Protocols for WSNs,” in Proceedings of the 2nd international conference on wireless broadband and ultra wideband communication (AusWireless), 2007, pp. 1–11.

  • T. M. Behera, S. K. Mohapatra, U. C. Samal, M. S. Khan, M. Daneshmand, and A. H. Gandomi, “Residual Energy Based Cluster-head Selection in WSNs for IoT Application,” IEEE Internet Things J., vol. 6, no. 3, pp. 5132–5139, 2019.

  • B. K. Debroy, M. S. Sadi, and M. Al Imran, “An efficient approach to select cluster head in Wireless Sensor Networks,” J. Commun., vol. 6, no. 7, pp. 529–539, 2011, doi: 10.4304/jcm.6.7.529-539.

  • P. K. Batra and K. Kant, “LEACH-MAC: a new cluster head selection algorithm for Wireless Sensor Networks,” J. Wirel. Networks, vol. 22, no. 1, pp. 49–60, 2016, doi: 10.1007/s11276-015-0951-y.

  • A. Abed, A. Alkhatib, and G. S. Baicher, “Wireless Sensor Network Architecture,” in International Conference on Computer Networks and Communication Systems, 2012, vol. 35, no. Cncs2012, pp. 11–15.

  • A. Zeb et al., “Clustering Analysis in Wireless Sensor Networks: The Ambit of Performance Metrics and Schemes Taxonomy,” Int. J. Distrib. Sens. Networks, vol. 12, no. 7, pp. 1–24, 2016, doi: 10.1177/155014774979142.

  • S. R. Jino Ramson and D. Jackuline Moni, “Applications of Wireless Sensor Networks - A survey,” Proc. IEEE Int. Conf. Innov. Electr. Electron. Instrum. Media Technol. ICIEEIMT 2017, vol. 2017-Janua, no. July, pp. 325–329, 2017, doi: 10.1109/.2005.1467103.

  • A. A. Abbasi and M. Younis, “A survey on clustering algorithms for wireless sensor networks,” Comput. Commun., vol. 30, no. 14–15, pp. 2826–2841, 2007, doi: 10.1016/j.comcom.2007.05.024.

  • S. Verma, N. Sood, and A. K. Sharma, “Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network,” Appl. Soft Comput. J., vol. 85, p. 105788, 2019, doi: 10.1016/j.asoc.2019.105788.

  • L. Xu, R. Collier, and G. M. P. O’Hare, “A Survey of Clustering Techniques in WSNs and Consideration of the Challenges of Applying Such to 5G IoT Scenarios,” IEEE Internet Things J., vol. 4, no. 5, pp. 1229–1249, 2017, doi: 10.1109/JIOT.2017.2726014.

  • S. Aslam, N. U. Hasan, J. W. Jang, and K. G. Lee, “Optimized energy harvesting, cluster-head selection and channel allocation for IoTs in smart cities,” Sensors (Switzerland), vol. 16, no. 12, 2016, doi: 10.3390/s16122046.

  • P. Porambage, C. Schmitt, P. Kumar, A. Gurtov, and M. Ylianttila, “PAuthKey: A Pervasive Authentication Protocol and Key Establishment Scheme for Wireless Sensor Networks in Distributed IoT Applications,” Int. J. Distrib. Sens. Networks, vol. 2014, 2014, doi: 10.1155/2014/357430.

  • 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. 1, pp. 1–10, 2000, doi: 10.1109/HICSS.2000.926982.

  • S. V. Manikanthan and T. Padmapriya, “An efficient cluster head selection and routing in mobile WSN,” Int. J. Interact. Mob. Technol., vol. 13, no. 10, pp. 56–70, 2019, doi: 10.3991/ijim.v13i10.11303.

  • A. A. Baradaran and K. Navi, “HQCA-WSN: High-quality clustering algorithm and optimal cluster head selection using fuzzy logic in wireless sensor networks,” Fuzzy Sets Syst., vol. 389, pp. 114–144, 2020, doi: 10.1016/j.fss.2019.11.015.

  • X. Yuan, M. Elhoseny, H. K. El-Minir, and A. M. Riad, “A Genetic Algorithm-Based, Dynamic Clustering Method Towards Improved WSN Longevity,” J. Netw. Syst. Manag., vol. 25, no. 1, pp. 21–46, 2017, doi: 10.1007/s10922-016-9379-7.

  • M. Elhoseny, X. Yuan, Z. Yu, C. Mao, H. K. El-Minir, and A. M. Riad, “Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm,” IEEE Commun. Lett., vol. 19, no. 12, pp. 2194–2197, 2015, doi: 10.1109/LCOMM.2014.2381226.

  • M. Wu, H. Liu, and Q. Min, “Lifetime enhancement by cluster head evolutionary energy efficient routing model for WSN,” in 6th International Conference on Instrumentation and Measurement, Computer, Communication and Control, IMCCC 2016, 2016, pp. 545–548, doi: 10.1109/IMCCC.2016.86.

  • K. N. Dattatraya and K. R. Rao, “Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN,” J. King Saud Univ. - Comput. Inf. Sci., no. xxxx, 2019, doi: 10.1016/j.jksuci.2019.04.003.

  • P. L. Rajarajeswari and N. K. Karthikeyan, “Hyper-geometric energy factor based semi-Markov prediction mechanism for effective cluster head election in WSNs,” J. Intell. Fuzzy Syst., vol. 32, no. 4, pp. 3111–3120, 2017, doi: 10.3233/JIFS-169254.

  • A. E. Fawzy, A. Amer, M. Shokair, and W. Saad, “Proposed intermittent Cluster Head selection scheme for efficient energy consumption in WSNs,” in 34th National Radio Science Conference (NRSC), 2017, no. NRSC, pp. 275–283, doi: 10.1109/NRSC.2017.7893486.

  • S. P. Dongare and R. S. Mangrulkar, “Optimal Cluster Head Selection Based Energy Efficient Technique for Defending against Gray Hole and Black Hole Attacks in Wireless Sensor Networks,” Phys. Procedia, vol. 78, no. December 2015, pp. 423–430, 2016, doi: 10.1016/j.procs.2016.02.084.

  • G. Indranil, D. Riordan, and S. Srinivas, “Cluster-head election using fuzzy logic for wireless sensor networks,” in Proceedings of Annual Communication Networks and Services Research Conference, 2005, pp. 255–260, doi: 10.1109/CNSR.2005.27.

  • N. A. Torghabeh, M. R. A. Totonchi, and M. H. Y. Moghaddam, “Cluster head selection using a two-level fuzzy logic in wireless sensor networks,” in CCET 2010 - 2010 International Conference on Computer Engineering and Technology, Proceedings I, 2010, vol. 2, pp. 357–361, doi: 10.1109/ICCET.2010.5485483.

  • H. Kavandi, M. Meghdadi, and M. Bayat, “A Method for Optimizing of the Energy Consumption in Wireless Sensor Networks By Dynamic Selection of Cluster Head Using Fuzzy Logic,” Indian J. Sci. Res., vol. 7, no. 1, pp. 1018–1025, 2014.

  • M. Taheri and Y. S. Kavian, “Energy Efficient Algorithm for Wireless Sensor Networks using Fuzzy Logic,” Int. J. Comput. Appl., vol. 89, no. 14, pp. 1–5, 2014, doi: 10.5120/15696-4055.

  • S. Gupta and N. Marriwala, “Improved distance energy based LEACH protocol for cluster head election in wireless sensor networks,” in 4th IEEE International Conference on Signal Processing, Computing and Control, ISPCC 2017, 2017, pp. 91–96, doi: 10.1109/ISPCC.2017.8269656.

  • D. Izadi, J. Abawajy, and S. Ghanavati, “A new energy efficient cluster-head and backup selection scheme in WSN,” in Proceedings of the 2013 IEEE 14th International Conference on Information Reuse and Integration, 2013, pp. 408–415, doi: 10.1109/IRI.2013.6642500.

  • E. Thenmozhi and S. Audithan, “Energy Efficinet Cluster Head Selection and Data Convening in Wireless Sensor Networks,” Indian J. Sci. Technol., vol. 9, no. 15, pp. 1–6, 2016, doi: 10.17485/ijst/2016/v9i15/77749.

  • D. Jia, H. Zhu, S. Zou, and P. Hu, “Dynamic Cluster Head Selection Method for Wireless Sensor Network,” IEEE Sens. J., vol. 16, no. 8, pp. 2746–2754, 2016, doi: 10.1109/JSEN.2015.2512322.

  • D. Agrawal and S. Pandey, “FUCA: Fuzzy-based unequal clustering algorithm to prolong the lifetime of wireless sensor networks,” Int. J. Commun. Syst., vol. 31, no. 2, pp. 1–18, 2018, doi: 10.1002/dac.3448.

  • P. Neamatollahi and M. Naghibzadeh, “Distributed unequal clustering algorithm in large-scale wireless sensor networks using fuzzy logic,” J. Supercomput., vol. 74, no. 6, pp. 1–24, 2018, doi: 10.1007/s11227-018-2261-5.

  • P. S. Mehra, M. N. Doja, and B. Alam, “Fuzzy based enhanced cluster head selection (FBECS) for WSN,” J. King Saud Univ. - Sci., vol. 32, no. 1, pp. 390–401, 2018, doi: 10.1016/j.jksus.2018.04.031.

  • H. Natarajan and S. Selvaraj, “A Fuzzy Based Predictive Cluster Head Selection Scheme for Wireless Sensor Networks,” Proc. 8th Int. Conf. Sens. Technol. Liverpool, UK A, pp. 560–566, 2014.

  • J. Kim, S. Park, Y.-J. Han, and T. Chung, “CHEF: cluster head election mechanism using fuzzy logic in wireless sensor networks,” in 10th international conference on Advance Communication Technology, 2008, vol. 1, pp. 654–659, doi: 10.1109/ICACT.2008.4493846.

  • A. Krishnakumar and V. Anuratha, “An Energy-Efficient Cluster Head Selection of LEACH Protocol for Wireless Sensor Networks,” in InNextgen Electronic Technologies: Silicon to Software (ICNETS2), 2017 International Conference on, IEEE., 2017, pp. 57–61.

  • S. A. Sahaaya Arul Mary and J. B. Gnanadurai, “Enhanced Zone Stable Election Protocol based on Fuzzy Logic for Cluster Head Election in Wireless Sensor Networks,” Int. J. Fuzzy Syst., vol. 19, no. 3, pp. 799–812, 2017, doi: 10.1007/s40815-016-0181-1.

  • S. B. S and S. V. U, “Super Cluster Head Selection and Energy Efficient Round Robin Load Balancing Technique in Wireless Sensor Networks,” Int. J. Eng. Sci. Comput., vol. 7, no. 4, pp. 10065–10072, 2017.

  • A. Al-Baz and A. El-Sayed, “A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks,” Int. J. Commun. Syst., vol. 31, no. 1, pp. 57–61, 2017, doi: 10.1002/dac.3407.

  • K. Ramesh, “Improved Fair-Zone Technique Using Mobility Prediction In WSN,” Int. J. Adv. Smart Sens. Netw. Syst., vol. 2, no. 2, pp. 23–32, 2012, doi: 10.5121/ijassn.2012.2203.

  • K. Somasundaram, S. Saritha, and K. Ramesh, “Enhancement of network lifetime by improving the leach protocol for large scale WSN,” Indian J. Sci. Technol., vol. 9, no. 16, 2016, doi: 10.17485/ijst/2016/v9i16/92242.

  • K.Ramesh and K.Somasundaram, “a Comparative Study of Clusterhead Selection Algorithms in Wireless Sensor Networks,” Int. J. Comput. Sci. Eng. Surv., vol. 2, no. 4, pp. 153–164, 2011, doi: 10.1002/fut.

  • A. Al-Baz and A. El-Sayed, “A new algorithm for cluster head selection in LEACH protocol for wireless sensor networks,” Int. J. Commun. Syst., vol. 31, no. 1, pp. 1–13, 2018, doi: 10.1002/dac.3407.

  • A. M. E. Tamizharasi, J. J. S. M. E, A. K. Priya, and R. Maarlin, “Energy Aware Heuristic Approach for Cluster Head Selection in Wireless Sensor Networks,” Bull. Electr. Eng. Informatics, vol. 6, no. 1, pp. 70–75, 2017, doi: 10.11591/eei.v6i1.598.

  • Z. Siqing, T. Yang, and Y. Feiyue, “ScienceDirect ScienceDirect Fuzzy Logic-Based Clustering Algorithm for Multi-hop Wireless Fuzzy Logic-Based Clustering Algorithm for Multi-hop Wireless Sensor Networks Sensor Networks,” Procedia Comput. Sci., vol. 131, pp. 1095–1103, 2018, doi: 10.1016/j.procs.2018.04.270.

  • W. I. S. W. Din, S. Yahya, R. Jailani, M. N. Taib, A. I. M. Yassin, and R. Razali, “Fuzzy logic for cluster head selection in wireless sensor network,” in AIP Conference Proceedings, 2016, vol. 1774, p. 050006, doi: 10.1063/1.4965093.

  • A. Yan and B. Wang, “An adaptive WSN clustering scheme based on neighborhood energy level,” in 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), 2017, pp. 1170–1173.

  • K. A. Prasath and T. Shankar, “RMCHS: Ridge method based cluster head selection for energy efficient clustering hierarchy protocol in WSN,” 2015 Int. Conf. Smart Technol. Manag. Comput. Commun. Control. Energy Mater. ICSTM 2015 - Proc., no. May, pp. 64–70, 2015, doi: 10.1109/ICSTM.2015.7225391.

  • The, P. T., Manh, V. N., & Hung, T. C. (2018, February). Improving network lifetime in wireless sensor network using fuzzy logic based clustering combined with mobile sink. In 2018 20th International Conference on Advanced Communication Technology (ICACT) (pp. 113-119). IEEE.

  • P. Nayak and A. Devulapalli, “A Fuzzy Logic-Based Clustering Algorithm for WSN to Extend the Network Lifetime,” IEEE Sens. J., vol. 16, no. 1, pp. 137–144, 2016, doi: 10.1109/JSEN.2015.2472970.

  • S. Murugaanandam and V. Ganapathy, “Reliability-based cluster head selection methodology using fuzzy logic for performance improvement in wsns,” IEEE Access, vol. 7, pp. 87357–87368, 2019, doi: 10.1109/ACCESS.2019.2923924.

  • R. Ranganathan, B. Somanathan, and K. Kannan, “Fuzzy-Based Cluster Head Amendment (FCHA) Approach to Prolong the Lifetime of Sensor Networks,” Wirel. Pers. Commun., vol. 110, no. 3, pp. 1533–1549, 2020, doi: 10.1007/s11277-019-06800-5.

  • S. Biswas, J. Saha, T. Nag, C. Chowdhury, and S. Neogy, “A Novel Cluster Head Selection Algorithm for Energy-Efficient Routing in Wireless Sensor Network,” 2016 IEEE 6th Int. Conf. Adv. Comput., pp. 588–593, 2016, doi: 10.1109/IACC.2016.114.

  • M. Patil and C. Sharma, “Energy efficient cluster head selection to enhance network connectivity for wireless sensor network,” in Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE International Conference on, 2016, pp. 175–179.

  • D. Sharma, S. Verma, and K. Sharma, “Network Topologies in Wireless Sensor Networks : A Review,” Int. J. Electron. Commun. Technol., vol. 4, pp. 93–97, 2013, doi: 2230-7109.

  • Y. Hu, H. Liu, and J. Liang, “Cluster-Head Election Using Fuzzy Logic Systems in Radar Sensor Networks,” in Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems, 2016, pp. 171–179.

  • P. Azad and V. Sharma, “Clusterhead selection using multiple attribute decision making (MADM) approach in wireless sensor networks,” Lect. Notes Inst. Comput. Sci. Soc. Telecommun. Eng. LNICST, vol. 115, pp. 141–142, 2013.

  • F. Hamzeloei and M. K. Dermany, “A TOPSIS Based Cluster Head Selection for Wireless Sensor Network,” Procedia Comput. Sci., vol. 58, pp. 8–15, 2016, doi: 10.1016/j.procs.2016.09.005.

  • H. Farman et al., “Analytical network process based optimum cluster head selection in wireless sensor network,” PLoS One, vol. 12, no. 7, pp. 1–28, 2017.

  • B. Muhammad, R. Bilal, and R. Young, “Fuzzy-TOPSIS based Cluster Head selection in mobile wireless sensor networks,” J. Electr. Syst. Inf. Technol., pp. 1–16, 2017, doi: 10.1016/j.jesit.2016.12.004.

  • P. Rajpoot and P. Dwivedi, “Multiple Parameter Based Energy Balanced and Optimized Clustering for WSN to Enhance the Lifetime Using MADM,” Wirel. Pers. Commun., vol. 106, no. 2, pp. 829–877, 2019, doi: 10.1007/s11277-019-06192-6.

  • P. T. Karthick and C. Palanisamy, “Optimized cluster head selection using krill herd algorithm for wireless sensor network,” Automatika, vol. 60, no. 3, pp. 340–348, 2019, doi: 10.1080/00051144.2019.1637174.

  • V. Pal, G. Singh, and R. P. Yadav, “SCHS: Smart Cluster Head Selection Scheme for Clustering Algorithms in Wireless Sensor Networks,” Wirel. Sens. Netw., vol. 04, no. 11, pp. 273–280, 2012, doi: 10.4236/wsn.2012.411039.

  • X. Kuang, L. Liu, Q. Liu, and L. Xiang, “A clustering approach based on convergence degree chain for wireless sensor networks,” Secur. Commun. Networks, vol. 5, no. June 2011, pp. 422–437, 2015, doi: 10.1002/sec.

  • A. Lipare, D. R. Edla, and R. Dharavath, “Energy Efficient Routing Structure to Avoid Energy Hole Problem in Multi-Layer Network Model,” Wirel. Pers. Commun., vol. 112, no. 4, pp. 2575–2596, 2020, doi: 10.1007/s11277-020-07165-w.


Abstract Views: 326

PDF Views: 0




  • Cluster Head Election in Wireless Sensor Network: A Comprehensive Study and Future Directions

Abstract Views: 326  |  PDF Views: 0

Authors

Rekha
Department of Computer Science and Applications, Maharishi Markandeshwar (Deemed to be University), Mullana (Ambala), India
Rajeev Gupta
Department of Computer Science and Engineering, Maharishi Markandeshwar (Deemed to be University), Mullana (Ambala), India

Abstract


Due to the advancement of wireless communication interchanges, electronic technology, and micro-electromechanical devices, Wireless Sensor Network (WSN) has got advanced as a promising zone of research. WSN consists of a collection of sensor nodes having a little calculative capability, limited memory, and constrained energy assets. Clusters are formed from the collection of sensor nodes whose leader node (Cluster head) can send the sensed information from hubs to the BS. To condense the power consumption and boost group longevity, the cluster head executes data accumulation. This paper discusses many algorithms based on deterministic, probabilistic, adaptive, fuzzy logic, and Multi-attribute decisionmaking techniques for clustering and cluster head election. Existing algorithms enhance the network lifetime and energy efficiency but fail to provide a better quality of service and security. So many issues and challenges have been laid down and it is concluded that when computational intelligence is combined with network intelligence then QoS and security both can be provided along with the network longevity and energy efficiency in homogeneous as well as a heterogeneous environment.

Keywords


Wireless Sensor Network (WSN), Deterministic Schemes, Adaptive, Schemes, Probabilistic Schemes, Multi- Attribute Decision Making Schemes (MADM), Fuzzy Based Cluster Head Election Schemes.

References





DOI: https://doi.org/10.22247/ijcna%2F2020%2F205319