An Exploration of Hippopotamus Optimisation Algorithm for Node Localisation in Wireless Sensor Networks
DOI:
https://doi.org/10.26437/wbtt3j22Keywords:
HOA. nature-inspired metaheuristic. node localisation. optimisationAbstract
Purpose: The study explores the application of the newly proposed Hippopotamus Optimisation Algorithm (HOA) for solving the node localisation problem in Wireless Sensor Networks.
Design/Methodology/Approach: In the proposed approach, anchor nodes with predetermined coordinates act as reference points, while HOA calculates the positions of unknown nodes by minimising the error between estimated and actual nodes. Performance is evaluated through simulations and compared with the standard Particle Swarm Optimisation (PSO) and Whale Optimisation Algorithm (WOA) algorithms using metrics such as localisation accuracy, the number of correctly localised nodes, and computational time.
Research Limitation: Despite its strong global search capability, for large-scale Wireless Sensor Networks (WSN), HOA will have a high computational cost because, as a population-based metaheuristic algorithm, it has to evaluate the localisation fitness function for a lot of WSN nodes (which are energy and computation constrained) across multiple iterations.
Findings: Results reveal that HOA has an advantage in strong early random exploration (i.e., peer-based movements and large random defence jumps to escape predators), which makes it very aggressive, thereby placing estimated solutions closer to true node positions very early. However, HOA has a high computational cost due to its heavy structure. Besides, its performance also degrades with more iterations because it does not preserve early-best solutions.
Practical Implication: Real-time or fully distributed localisation is challenging with HOA due to its high computational cost. Thus, it is more suitable for centralised or offline node localisation.
Social Implication: Accurate positioning of sensor nodes is essential for data gathering and efficient network operation because WSN play a key role in application domains such as smart cities, precision farming, environmental monitoring, and defence operations.
Originality/Value: HOA is a recent nature-inspired metaheuristic algorithm that has not been explored sufficiently to solve the node localisation problem in WSN. Thus, this is the first direct adoption and application of HOA to the node localisation problem in WSN.
References
Almuzaini, K. K., & Gulliver, A. (2010). Range-Based Localization in Wireless Networks Using Density-Based Outlier Detection. Wireless Sensor Network, 02(11), 807–814. https://doi.org/10.4236/wsn.2010.211097 DOI: https://doi.org/10.4236/wsn.2010.211097
Amiri, M. H., Mehrabi Hashjin, N., Montazeri, M., Mirjalili, S., & Khodadadi, N. (2024). Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-54910-3 DOI: https://doi.org/10.1038/s41598-024-54910-3
Arora, S., & Singh, S. (2017). Node Localization in Wireless Sensor Networks Using Butterfly Optimization Algorithm. Arabian Journal for Science and Engineering, 42(8), 3325–3335. https://doi.org/10.1007/s13369-017-2471-9 DOI: https://doi.org/10.1007/s13369-017-2471-9
Aspnes, J., Eren, T., Goldenberg, D. K., Morse, A. S., Whiteley, W., Yang, Y. R., Anderson, B. D. O., & Belhumeur, P. N. (2006). A Theory of Network Localization. IEEE Transactions on Mobile Computing, 5(12), 1663-1678. 10.1109/TMC.2006.174 DOI: https://doi.org/10.1109/TMC.2006.174
Cheng, M., Qin, T., & Yang, J. (2022). Node Localization Algorithm Based on Modified Archimedes Optimization Algorithm in Wireless Sensor Networks. Journal of Sensors, 2022. https://doi.org/10.1155/2022/7026728 DOI: https://doi.org/10.1155/2022/7026728
Dao, T. K., Pan, J. S., Nguyen, T. T., Chu, S. C., Tran, H. T., Nguyen, T. D., & Vu, N. T. (2021, July). Node localization in wireless sensor network by ant lion optimization. In Advances in Smart Vehicular Technology, Transportation, Communication and Applications: Proceeding of the Third International Conference on VTCA, 15–18 October 2019, Arad, Romania (pp. 97-109). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-16-1209-1_10 DOI: https://doi.org/10.1007/978-981-16-1209-1_10
Fadel, E., Gungor, V. C., Nassef, L., Akkari, N., Abbas Malik, M. G., Almasri, S., & Akyildiz, I. F. (2015). A survey on wireless sensor networks for smart grid. Computer Communications, 71, 22–33. https://doi.org/10.1016/j.comcom.2015.09.006 DOI: https://doi.org/10.1016/j.comcom.2015.09.006
Goyal, S., & Patterh, M. S. (2014). Wireless sensor network localization based on cuckoo search algorithm. Wireless personal communications, 79(1), 223-234. https://doi.org/10.1007/s11277-014-1850-8 DOI: https://doi.org/10.1007/s11277-014-1850-8
Han, T., Wang, H., Li, T., Liu, Q., & Huang, Y. (2025). MHO: A modified hippopotamus optimization algorithm for global optimization and engineering design problems. Biomimetics, 10(2), 90. https://doi.org/10.3390/biomimetics10020090 DOI: https://doi.org/10.3390/biomimetics10020090
Hao, Z., Dang, J., Yan, Y., & Wang, X. (2021). A node localization algorithm based on Voronoi diagram and support vector machine for wireless sensor networks. International Journal of Distributed Sensor Networks, 17(2). https://doi.org/10.1177/1550147721993410 DOI: https://doi.org/10.1177/1550147721993410
Harikrishnan, R., Jawahar Senthil Kumar, V., & Sridevi Ponmalar, P. (2016). Firefly algorithm approach for localization in wireless sensor networks. Smart Innovation, Systems and Technologies, 44, 209–214. https://doi.org/10.1007/978-81-322-2529-4_21 DOI: https://doi.org/10.1007/978-81-322-2529-4_21
Kanoosh, H. M., Houssein, E. H., & Selim, M. M. (2019). Salp Swarm Algorithm for Node Localization in Wireless Sensor Networks. Journal of Computer Networks and Communications, 2019. https://doi.org/10.1155/2019/1028723 DOI: https://doi.org/10.1155/2019/1028723
Kumari, S., & Tyagi, A. K. (2024). Wireless sensor networks: An introduction. Digital Twin and Blockchain for Smart Cities, 495-528. https://doi.org/10.1002/9781394303564.ch21 DOI: https://doi.org/10.1002/9781394303564.ch21
Lavanya, D., & Udgata, S. K. (2011). Swarm Intelligence Based Localization in Wireless Sensor Networks. In International workshop on multi-disciplinary trends in artificial intelligence (pp. 317-328). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-25725-4_28 DOI: https://doi.org/10.1007/978-3-642-25725-4_28
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008 DOI: https://doi.org/10.1016/j.advengsoft.2016.01.008
Pei, S., Sun, G., & Tong, L. (2025). An improved hippopotamus optimization algorithm based on adaptive development and solution diversity enhancement. PeerJ Computer Science, 11, e2901. https://doi.org/10.7717/peerj-cs.2901 DOI: https://doi.org/10.7717/peerj-cs.2901
Rajakumar, R., Amudhavel, J., Dhavachelvan, P., & Vengattaraman, T. (2017). GWO-LPWSN: Grey Wolf Optimization Algorithm for Node Localization Problem in Wireless Sensor Networks. Journal of Computer Networks and Communications, 2017. https://doi.org/10.1155/2017/7348141 DOI: https://doi.org/10.1155/2017/7348141
Singh, S. P., & Sharma, S. C. (2015). Range Free Localization Techniques in Wireless Sensor Networks: A Review. Procedia Computer Science, 57, 7–16. https://doi.org/10.1016/j.procs.2015.07.357 DOI: https://doi.org/10.1016/j.procs.2015.07.357
Trigka, M., & Dritsas, E. (2025). Wireless sensor networks: From fundamentals and applications to innovations and future trends. IEEE Access. 10.1109/ACCESS.2025.3572328 DOI: https://doi.org/10.1109/ACCESS.2025.3572328
Wang, J., Ghosh, R. K., & Das, S. K. (2010). A survey on sensor localization. Journal of Control Theory and Applications, 8(1), 2–11. https://doi.org/10.1007/s11768-010-9187-7 DOI: https://doi.org/10.1007/s11768-010-9187-7
Win, M. Z., Shen, Y., & Dai, W. (2018). A Theoretical Foundation of Network Localization and Navigation. Proceedings of the IEEE, 106(7), 1136–1165. https://doi.org/10.1109/JPROC.2018.2844553 DOI: https://doi.org/10.1109/JPROC.2018.2844553
Zahia, L., Fouzi, S., & Samra, B. (2023). Node localization optimization in WSNs by using cat swarm optimization meta-heuristic. Automatic Control and Computer Sciences, 57(2), 177-184. https://doi.org/10.3103/S0146411623020104 DOI: https://doi.org/10.3103/S0146411623020104
Zaidi, S., El Assaf, A., Affes, S., & Kandil, N. (2016). Accurate Range-Free Localization in Multi-Hop Wireless Sensor Networks. IEEE Transactions on Communications, 64(9), 3886–3900. https://doi.org/10.1109/TCOMM.2016.2590436 DOI: https://doi.org/10.1109/TCOMM.2016.2590436
Zhang, Q., Wang, J., Jin, C., Ye, J., Ma, C., & Zhang, W. (2008, October). Genetic algorithm based wireless sensor network localization. In 2008 Fourth International Conference on Natural Computation (Vol. 1, pp. 608-613). IEEE Computer Society. 10.1109/ICNC.2008.206 DOI: https://doi.org/10.1109/ICNC.2008.206
Downloads
Published
Issue
Section
License
Copyright (c) 2026 AFRICAN JOURNAL OF APPLIED RESEARCH
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
By submitting and publishing your articles in the African Journal of Applied Research, you agree to transfer the copyright of the Article from the authors to the Journal ( African Journal of Applied Research).
