A Technique for Cluster Head Selection in Wireless Sensor Networks Using African Vultures Optimization Algorithm


  • Vipan Kusla Department of Computer Science and Application, CT University, Ludhiana, Punjab, India
  • Gurbinder Singh Brar Department of Computer Science and Engineering, CT University, Ludhiana, Punjab, India




Wireless Sensor Network (WSN), Cluster Head Selection, Network Lifetime


INTRODUCTION: Wireless Sensor Network (WSN) has caught the interest of researchers due to the rising popularity of Internet of things(IOT) based smart products and services. In challenging environmental conditions, WSN employs a large number of nodes with limited battery power to sense and transmit data to the base station(BS). Direct data transmission to the BS uses a lot of energy in these circumstances. Selecting the CH in a clustered WSN is considered to be an NP-hard problem.

OBJECTIVES: The objective of this work to provide an effective cluster head selection method that minimize the overall network energy consumption, improved throughput with the main goal of enhanced network lifetime.

METHODS: In this work, a meta heuristic based cluster head selection technique is proposed that has shown an edge over the other state of the art techniques. Cluster compactness, intra-cluster distance, and residual energy are taken into account while choosing CH using multi-objective function. Once the CHs have been identified, data transfer from the CHs to the base station begins. The residual energy of the nodes is finally updated during the data transmission begins.

RESULTS: An analysis of the results has been performed based on average energy consumption, total energy consumption, network lifetime and throughput using two different WSN scenarios. Also, a comparison of the performance has been made other techniques namely Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Atom Search Optimization (ASO), Gorilla Troop Optimization (GTO), Harmony Search (HS), Wild Horse Optimization (WHO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA) and Biogeography Based Optimization (BBO). The findings show that AVOA's first node dies at round 1391 in Scenario-1 and round 1342 in Scenario-2 which is due to lower energy consumption by the sensor nodes thus increasing lifespan of the WSN network.

CONCLUSION: As per the findings, the proposed technique outperforms ABC, ACO, ASO, GTO, HS, WHO, PSO, FA, and BBO in terms of performance evaluation parameters and boosting the reliability of networks over the other state of art techniques.


M. A. Matin and M. Islam, "Overview of wireless sensor network," Wireless sensor networks-technology and protocols, vol. 1, no. 3, 2012.

J. Yick, B. Mukherjee, and D. Ghosal, "Wireless sensor network survey," Computer networks, vol. 52, no. 12, pp. 2292-2330, 2008.

N. Kaur and I. K. Aulakh, "An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network," EAI Endorsed Transactions on Scalable Information Systems, vol. 8, no. 31, pp. e6-e6, 2021.

X. Li, L. Xu, H. Wang, J. Song, and S. X. Yang, "A differential evolution-based routing algorithm for environmental monitoring wireless sensor networks," Sensors, vol. 10, no. 6, pp. 5425-5442, 2010.

M. Younis, I. F. Senturk, K. Akkaya, S. Lee, and F. Senel, "Topology management techniques for tolerating node failures in wireless sensor networks: A survey," Computer networks, vol. 58, pp. 254-283, 2014.

K. Kadarla, S. Sharma, and K. Uday Kanth Reddy, "An implementation case study on ant-based energy efficient routing in WSNs," in Soft Computing: Theories and Applications: Springer, 2018, pp. 567-576.

A. A. Abbasi and M. Younis, "A survey on clustering algorithms for wireless sensor networks," Computer communications, vol. 30, no. 14-15, pp. 2826-2841, 2007.

D. Vishwas, M. Gowtham, and H. Gururaj, "Energy efficient Technique for Cluster-head Selection in IoT Network," EAI Endorsed Transactions on Smart Cities, vol. 4, no. 11, pp. e2-e2, 2020.

N. A. Latiff, C. C. Tsimenidis, and B. S. Sharif, "Energy-aware clustering for wireless sensor networks using particle swarm optimization," in 2007 IEEE 18th international symposium on personal, indoor and mobile radio communications, 2007, pp. 1-5: IEEE.

W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-efficient communication protocol for wireless microsensor networks," in Proceedings of the 33rd annual Hawaii international conference on system sciences, 2000, p. 10 pp. vol. 2: IEEE.

S. Lindsey and C. S. Raghavendra, "PEGASIS: Power-efficient gathering in sensor information systems," in Proceedings, IEEE aerospace conference, 2002, vol. 3, pp. 3-3: IEEE.

J.-L. Liu and C. V. Ravishankar, "LEACH-GA: Genetic algorithm-based energy-efficient adaptive clustering protocol for wireless sensor networks," International Journal of Machine Learning and Computing, vol. 1, no. 1, p. 79, 2011.

M. Sharawi, E. Emary, I. A. Saroit, and H. El-Mahdy, "Bat swarm algorithm for wireless sensor networks lifetime optimization," Int. J, vol. 3, no. 5, pp. 654-664, 2014.

V. Gupta and S. K. Sharma, "Cluster Head Selection Using Modified ACO," in Proceedings of Fourth International Conference on Soft Computing for Problem Solving(Advances in Intelligent Systems and Computing, 2015, pp. 11-20.

P. C. S. Rao, P. K. Jana, and H. Banka, "A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks," Wireless Networks, vol. 23, no. 7, pp. 2005-2020, 2016.

P. Sengottuvelan and N. Prasath, "BAFSA: Breeding artificial fish swarm algorithm for optimal cluster head selection in wireless sensor networks," Wireless Personal Communications, vol. 94, no. 4, pp. 1979-1991, 2017.

P. Srinivasa Rao and H. Banka, "Energy efficient clustering algorithms for wireless sensor networks: novel chemical reaction optimization approach," Wireless Networks, vol. 23, no. 2, pp. 433-452, 2017.

G. Yogarajan and T. Revathi, "Improved cluster based data gathering using ant lion optimization in wireless sensor networks," Wireless Personal Communications, vol. 98, no. 3, pp. 2711-2731, 2018.

T. Ahmad, M. Haque, and A. M. Khan, "An Energy-Efficient Cluster Head Selection Using Artificial Bees Colony Optimization for Wireless Sensor Networks," in Advances in Nature-Inspired Computing and Applications(EAI/Springer Innovations in Communication and Computing, 2019, pp. 189-203.

K. N. Dattatraya and K. R. Rao, "Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN," Journal of King Saud University - Computer and Information Sciences, 2019.

S. Prahadeeshwaran and G. M. Priscilla, "A Hybrid Elephant Optimization Algorithm-based Cluster Head Selection to Extend Network Lifetime in Wireless Sensor Networks (WSNs)," EAI Endorsed Transactions on Energy Web, vol. 8, no. 31, pp. e13-e13, 2021.

N. Arunachalam, G. Shanmugasundaram, and R. Arvind, "Squirrel Search Optimization-Based Cluster Head Selection Technique for Prolonging Lifetime in WSN’s," Wireless Personal Communications, vol. 121, no. 4, pp. 2681-2698, 2021.

A. Sarkar and T. Senthil Murugan, "Cluster head selection for energy efficient and delay-less routing in wireless sensor network," Wireless Networks, vol. 25, no. 1, pp. 303-320, 2019.

W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks," IEEE Transactions on wireless communications, vol. 1, no. 4, pp. 660-670, 2002.

C. Cheng, Q. Han, G. Cheng, and S. Zhai, "Heterogeneous wireless sensor network routing protocol for an adaptive gray wolf optimizer," in Proceedings of the 13th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2020, 27-28 August 2020, Cyberspace, 2020.

B. Abdollahzadeh, F. S. Gharehchopogh, and S. Mirjalili, "African vultures optimization algorithm: A new nature-inspired metaheuristic algorithm for global optimization problems," Computers & Industrial Engineering, vol. 158, p. 107408, 2021.

T. Dokeroglu, E. Sevinc, T. Kucukyilmaz, and A. Cosar, "A survey on new generation metaheuristic algorithms," Computers & Industrial Engineering, vol. 137, p. 106040, 2019.

H. Rajabi Moshtaghi, A. Toloie Eshlaghy, and M. R. Motadel, "A comprehensive review on meta-heuristic algorithms and their classification with novel approach," Journal of Applied Research on Industrial Engineering, vol. 8, no. 1, pp. 63-89, 2021.

D. Karaboga, "An idea based on honey bee swarm for numerical optimization," Technical report-tr06, Erciyes university, engineering faculty, computer …2005.

M. Dorigo, M. Birattari, and T. Stutzle, "Ant colony optimization," IEEE computational intelligence magazine, vol. 1, no. 4, pp. 28-39, 2006.

W. Zhao, L. Wang, and Z. Zhang, "Atom search optimization and its application to solve a hydrogeologic parameter estimation problem," Knowledge-Based Systems, vol. 163, pp. 283-304, 2019.

I. Naruei and F. Keynia, "Wild horse optimizer: A new meta-heuristic algorithm for solving engineering optimization problems," Engineering with Computers, pp. 1-32, 2021.

Z. W. Geem, J. H. Kim, and G. V. Loganathan, "A new heuristic optimization algorithm: harmony search," simulation, vol. 76, no. 2, pp. 60-68, 2001.

B. Abdollahzadeh, F. Soleimanian Gharehchopogh, and S. Mirjalili, "Artificial gorilla troops optimizer: a new nature‐inspired metaheuristic algorithm for global optimization problems," International Journal of Intelligent Systems, vol. 36, no. 10, pp. 5887-5958, 2021.

X.-S. Yang, "Firefly algorithm, stochastic test functions and design optimisation," arXiv preprint arXiv:1003.1409, 2010.

Y. Wei and L. Qiqiang, "Survey on particle swarm optimization algorithm," Engineering Science, vol. 6, no. 5, pp. 87-94, 2004.

D. Simon, "Biogeography-based optimization," IEEE transactions on evolutionary computation, vol. 12, no. 6, pp. 702-713, 2008.

A. Chunawale and S. Sirsikar, "Minimization of average energy consumption to prolong lifetime of Wireless Sensor Network," in 2014 IEEE Global Conference on Wireless Computing & Networking (GCWCN), 2014, pp. 244-248: IEEE.

Y. Chen and Q. Zhao, "On the lifetime of wireless sensor networks," IEEE Communications letters, vol. 9, no. 11, pp. 976-978, 2005.

Y. Li, N. Yu, W. Zhang, W. Zhao, X. You, and M. Daneshmand, "Enhancing the performance of LEACH protocol in wireless sensor networks," in 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS), 2011, pp. 223-228: IEEE.




How to Cite

Kusla V, Brar GS. A Technique for Cluster Head Selection in Wireless Sensor Networks Using African Vultures Optimization Algorithm. EAI Endorsed Scal Inf Syst [Internet]. 2023 Jan. 11 [cited 2023 Mar. 28];10(3):e9. Available from: https://publications.eai.eu/index.php/sis/article/view/2680