A Review on Nature Inspired Artificial Bee Colony in Wireless Sensor Networks and Future Enhancements

M. Shiny*
Department of Electronics & Communication Engineering, DMI College of Engineering, Aralvaimozhi, Tamil Nadu, India.
Periodicity:July - December'2022


Wireless Sensor Networks (WSN) have expanded significantly over the past few decades as a result of recent developments in Wireless Communication technologies. Many innovative architectures, protocols, algorithms, and applications have been proposed and implemented. Routing protocols that directly affect the lifetime of a network have a significant impact on the efficiency of these networks. One of the most commonly used and preferred methods for routing operations is Clustering. Swarm Optimization techniques such as the Nature inspired Artificial Bee Colony (ABC) algorithm have been successfully used to solve a variety of Optimization problems in Wireless Sensor Networks. The ABC algorithm is discussed in this paper, along with its various applications, and some improvements to the ABC algorithm are also reviewed.


Clustering, Wireless Sensor Network, Routing, Artificial Bee Colony, Swarm Optimization.

How to Cite this Article?

Shiny, M. (2022). A Review on Nature Inspired Artificial Bee Colony in Wireless Sensor Networks and Future Enhancements. i-manager’s Journal on Wireless Communication Networks, 11(1), 32-40.


[1]. Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14-15), 2826-2841. https://doi.org/10.1016/j.comcom.2007.05.024
[2]. Anastasi, G., Falchi, A., Passarella, A., Conti, M., & Gregori, E. (2004, October). Performance measurements of motes sensor networks. In Proceedings of the 7th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems, (pp. 174-181). https://doi.org/10.1145/1023663.1023695
[3]. Babu, M. S. P., & Rao, N. T. (2010). Implementation of artificial bee colony (ABC) algorithm on garlic expert advisory system. International Journal of Computer Science and Research, 1(1), 69-74.
[4]. Bonabeau, E., Dorigo, M., & Theraulaz, G. (1999). Swarm Intelligence: From Natural to Artificial Intelligence. Oxford University Press.
[5]. Crossbow Technology, Inc. (n.d.). MICAz Wireless Measurement System. http://courses.ece.ubc.ca/494/files/MICAz_Datasheet.pdf
[6]. Cui, L., Li, G., Zhu, Z., Lin, Q., Wen, Z., Lu, N., ... & Chen, J. (2017). A novel artificial bee colony algorithm with an adaptive population size for numerical function optimization. Information Sciences, 414, 53-67. https://doi.org/10.1016/j.ins.2017.05.044
[7]. Deng, X. (2013). An enhanced artificial bee colony approach for customer segmentation in mobile ecommerce environment. International Journal of Advancements in Computing Technology, 5(1), 139-148.
[8]. Dorigo, M., & Di Caro, G. (1999, July). Ant colony optimization: a new meta-heuristic. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 2, 1470-1477. https://doi.org/10.1109/CEC.1999.782657
[9]. Fister, I., Fister, I. J., Brest, J., & Žumer, V. (2012, June). Memetic artificial bee colony algorithm for large-scale global optimization. In 2012, IEEE Congress on Evolutionary Computation, (pp. 1-8). IEEE. https://doi.org/10.1109/CEC.2012.6252938
[10]. Gao, W. F., Liu, S. Y., & Huang, L. L. (2014). Enhancing artificial bee colony algorithm using more informationbased search equations. Information Sciences, 270, 112- 133. https://doi.org/10.1016/j.ins.2014.02.104
[11]. Gao, W., & Liu, S. (2011). Improved artificial bee colony algorithm for global optimization. Information Processing Letters, 111(17), 871-882. https://doi.org/10.1016/j.ipl.2011.06.002
[12]. Gherbi, C., Aliouat, Z., & Benmohammed, M. (2016). An adaptive clustering approach to dynamic load balancing and energy efficiency in wireless sensor networks. Energy, 114, 647-662. https://doi.org/10.1016/j.energy.2016.08.012
[13]. Goldsmith, A. J., & Wicker, S. B. (2002). Design challenges for energy-constrained ad hoc wireless networks. IEEE Wireless Communications, 9(4), 8-27. https://doi.org/10.1109/MWC.2002.1028874
[14]. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications, 1(4), 660-670. https://doi.org/10.1109/TWC.2002.804190
[15]. Kang, F., Li, J., & Xu, Q. (2009). Structural inverse analysis by hybrid simplex artificial bee colony algorithms. Computers & Structures, 87(13-14), 861-870. https://doi.org/10.1016/j.compstruc.2009.03.001
[16]. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Retrieved from https://abc.erciyes.edu.tr/pub/tr06_2005.pdf
[17]. Karaboga, D., & Akay, B. (2009). A comparative study of artificial bee colony algorithm. Applied Mathematics and Computation, 214(1), 108-132. https://doi.org/10.1016/j.amc.2009.03.090
[18]. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In IEEE International Conference on Neural Networks, 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968
[19]. Latiff, N. A., Tsimenidis, C. C., & Sharif, B. S. (2007, October). Performance comparison of optimization algorithms for clustering in wireless sensor networks. In 2007, IEEE International Conference on Mobile Adhoc and Sensor Systems, (pp. 1-4). IEEE. https://doi.org/10.1109/MOBHOC.2007.4428638
[20]. Liang, Y., & Yu, H. (2005, December). PSO-based energy efficient gathering in sensor networks. In International Conference on Mobile Ad-Hoc and Sensor Networks, (pp. 362-369). Springer, Berlin, Heidelberg. https://doi.org/10.1007/11599463_36
[21]. Luo, J., Wang, Q., & Xiao, X. (2013). A modified artificial bee colony algorithm based on convergeonlookers approach for global optimization. Applied Mathematics and Computation, 219(20), 10253-10262. https://doi.org/10.1016/j.amc.2013.04.001
[22]. Manjeshwar, A., Zeng, Q. A., & Agrawal, D. P. (2002). An analytical model for information retrieval in wireless sensor networks using enhanced APTEEN protocol. IEEE Transactions on Parallel and Distributed Systems, 13(12), 1290-1302. https://doi.org/10.1109/TPDS.2002.1158266
[23]. Ozturk, C., & Karaboga, D. (2011, June). Hybrid artificial bee colony algorithm for neural network training. In 2011, IEEE Congress of Evolutionary Computation (CEC), (pp. 84-88). IEEE. https://doi.org/10.1109/CEC.2011.5949602
[24]. Prabha, M. S., & Vijayarani, S. (2011). Association rule hiding using artificial bee colony algorithm. International Journal of Computer Applications, 33(2), 41-47.
[25]. Qing, L., Zhi, T., Yuejun, Y., & Yue, L. (2009). Localized Structural Health Monitoring Using Energy-Efficient Wireless Sensor Networks. IEEE Sensors Journal, 9(11), 1596–1604. https://doi.org/10.1109/JSEN.2009.2019318
[26]. Sharma, T. K., & Pant, M. (2013). Enhancing the food locations in an artificial bee colony algorithm. Soft Computing, 17(10), 1939-1965. https://doi.org/10.1007/s00500-013-1029-3
[27]. Song, X., Yan, Q., & Zhao, M. (2017). An adaptive artificial bee colony algorithm based on objective function value information. Applied Soft Computing, 55, 384-401. https://doi.org/10.1016/j.asoc.2017.01.031
[28]. Transpire Online. (2019). Artificial Bee Colony (ABC) Algorithm: A Novel Method Motivated From the Behavior of Bees for Optimal Solution. Retrieved from https://transpireonline.blog/2019/08/02/artificial-bee-colonyabc-algorithm-a-novel-method-motivated-from-thebehavior-of-bees-for-optimal-solution/.
[29]. Yi, S., Heo, J., Cho, Y., & Hong, J. (2007). PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks. Computer Communications, 30(14-15), 2842-2852. https://doi.org/10.1016/j.comcom.2007.05.034
[30]. Zhong, Y., Lin, J., Ning, J., & Lin, X. (2011, July). Hybrid artificial bee colony algorithm with chemotaxis behavior of bacterial foraging optimization algorithm. In 2011, Seventh International Conference on Natural Computation, 2, 1171-1174. https://doi.org/10.1109/ICNC.2011.6022147
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