An energy efficient cluster head selection in priority region aware wireless sensor networks using metaheuristic algorithms
Year 2025,
Issue: 062, 71 - 89, 30.09.2025
Osman Gökalp
,
Dogan Aydın
,
Aybars Uğur
Abstract
The cluster head (CH) selection problem is one of the challenges posed by wireless sensor network (WSN) design, where nodes assume leadership roles. The primary objective of this problem is energy conservation, as becoming a CH requires high energy consumption. Therefore, optimizing the CH selection process is crucial. Despite numerous attempts to solve this problem, existing algorithms do not consider area prioritization, where certain regions such as industrial facilities with hazardous zones and military surveillance areas require special attention. This work first describes the standard CH selection problem in non-priority environments and then introduces priority region-aware WSNs. It then presents how energy-efficient CH selection using metaheuristics, with a priority- and energy-aware fitness function developed in this study, can be performed in such networks for the first time in the literature. The findings from comprehensive simulation-based experiments demonstrate the superior performance of both classical and state-of-the-art metaheuristic-driven approaches compared to the baseline Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm. Specifically, the Adaptive Differential Evolution with Optional External Archive (JADE) algorithm improves the performance of LEACH by up to 16% in terms of the total priority of transferred packets. Additionally, it can extend the lifetime of nodes in high-priority regions by up to 27% to 44%.
References
-
[1] M. A. Jan, P. Nanda, X. He, and R. P. Liu, "A Sybil attack detection scheme for a forest wildfire monitoring application", Future Gener. Comput. Syst., vol. 80, pp. 613–626, 2018, doi: 10.1016/j.future.2016.05.034.
-
[2] N. Dey et al., "Developing residential wireless sensor networks for ECG healthcare monitoring", IEEE Trans. Consum. Electron., vol. 63, no. 4, pp. 442–449, 2017, doi: 10.1109/TCE.2017.015063.
-
[3] S. Li et al., "Survey on high reliability wireless communication for underwater sensor networks", J. Network Comput. Appl., vol. 148, p. 102446, 2019, doi: 10.1016/j.jnca.2019.102446.
-
[4] E. Felemban, "Advanced border intrusion detection and surveillance using wireless sensor network technology", Int. J. Commun., Netw. and Syst. Sci., vol. 148, no. 5, pp. 251–259, 2013, doi: 10.4236/ijcns.2013.65028.
-
[5] O. Gökalp, "Optimizing Connected Target Coverage in Wireless Sensor Networks Using Self-Adaptive Differential Evolution", Balkan J. Electr. Comput. Eng., vol. 8, no. 4, pp. 325–330, 2020, doi: 10.17694/bajece.624527.
-
[6] J. Dai et al., "Cluster head selection method of multiple UAVs under COVID-19 situation", Comput. Commun., vol. 196, pp. 141–147, 2022, doi: 10.1016/j.comcom.2022.09.026.
-
[7] W. B. Heinzelman, A. P. Chandrakasan, and H. Balakrishnan, "An application-specific protocol architecture for wireless microsensor networks", IEEE Trans. Wireless Commun., vol. 1, no. 4, pp. 660–670, 2002, doi: 10.1109/TWC.2002.804190.
-
[8] D. Karaboga, S. Okdem, and C. Ozturk, "Cluster based wireless sensor network routing using artificial bee colony algorithm", Wireless Netw., vol. 18, no. 7, pp. 847–860, 2012, doi: 10.1007/s11276-012-0438-z.
-
[9] 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 Netw., vol. 23, no. 7, pp. 2005–2020, 2017, doi: 10.1007/s11276-016-1270-7.
-
[10] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, "Energy-efficient communication protocol for wireless microsensor networks", in Proc. of the 33rd Annu. Hawaii Int. Conf. Syst. Sci., 2000, pp. 10–pp, doi: 10.1109/HICSS.2000.926982.
-
[11] A. C. Çabuker, M. N. Almalı, and İ. Parlar, "Evaluation of controller parameters on the twin rotor multiple input multiple output system using butterfly-based particle swarm optimization", JSR-A Trans, no. 052, pp. 174–189, 2023, doi: 10.59313/jsr-a.1198441.
-
[12] P. C. S. Rao and H. Banka, "Novel chemical reaction optimization based unequal clustering and routing algorithms for wireless sensor networks", Wireless Netw., vol. 23, no. 3, pp. 759–778, 2017, doi: 10.1007/s11276-015-1148-0.
-
[13] D. Karaboga, "An idea based on honey bee swarm for numerical optimization", Technical report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
-
[14] K. V. Price, "Differential evolution", in Handb. Optim.: From Classical to Mod. Approach, Springer, 2013, pp. 187–214, doi: 10.1007/978-3-642-30504-7_8.
-
[15] J. Kennedy and R. Eberhart, "Particle swarm optimization", in Proc. ICNN'95 – Int. Conf. on Neural Networks, vol. 4, 1995, pp. 1942–1948, doi: 10.1109/ICNN.1995.488968.
-
[16] N. Hansen, "The CMA evolution strategy: a comparing review", in Towards a New Evol. Comput.: Adv. Estimation of Distrib. Algorithms, Springer, 2006, pp. 75–102, doi: 10.1007/3-540-32494-1_4.
-
[17] J. Zhang and A. C. Sanderson, "JADE: adaptive differential evolution with optional external archive", IEEE Trans. Evol. Comput., vol. 13, no. 5, pp. 945–958, 2009, doi: 10.1109/TEVC.2009.2014613.
-
[18] R. Tanabe and A. Fukunaga, "Success-history based parameter adaptation for differential evolution", in 2013 IEEE Congr. on Evol. Comput., 2013, pp. 71–78, doi: 10.1109/CEC.2013.6557555.
-
[19] J. Xu et al., "Improvement of LEACH protocol for WSN", in 2012 9th Int. Conf. on Fuzzy Syst. Knowl. Discovery, 2012, pp. 2174–2177, doi: 10.1109/FSKD.2012.6233907.
-
[20] M. B. Yassein, Y. Khamayseh, and W. Mardini, "Improvement on LEACH protocol of wireless sensor network (VLEACH)", J. Digit. Content Technol. its Appl., 2009.
-
[21] V. Loscri, G. Morabito, and S. Marano, "A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH)", in IEEE Veh. Technol. Conf., vol. 62, no. 3, pp. 1809, 2005.
-
[22] S. Lindsey and C. S. Raghavendra, "PEGASIS: Power-efficient gathering in sensor information systems", in Proc., IEEE Aerosp. Conf., vol. 3, pp. 3–3, 2002, doi: 10.1109/AERO.2002.1035242.
-
[23] O. Younis and S. Fahmy, "HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks”, IEEE Trans. Mob. Comput., vol. 3, no. 4, pp. 366–379, 2004, doi: 10.1109/TMC.2004.41.
-
[24] E. Alimohammadi, S. Haghzad Klidbary, and M. Javadian, "Energy-aware clustering method for cluster head selection to increasing lifetime in wireless sensor network”, J. Supercomput., vol. 81, no. 1, p. 2, 2025, doi: 10.1007/s11227-024-06474-2.
-
[25] J. Tillett, R. Rao, and F. Sahin, "Cluster-head identification in ad hoc sensor networks using particle swarm optimization”, in 2002 IEEE Int. Conf. on Pers. Wireless Commun., 2002, pp. 201–205, doi: 10.1109/ICPWC.2002.1177277.
-
[26] S. M. Guru, S. K. Halgamuge, and S. Fernando, "Particle swarm optimisers for cluster formation in wireless sensor networks”, in 2005 Int. Conf. on Intell. Sens., Sensor Networks and Information Processing, 2005, pp. 319–324, doi: 10.1109/ISSNIP.2005.1595599.
-
[27] N. M. A. Latiff, C. C. Tsimenidis, and B. S. Sharif, "Energy-aware clustering for wireless sensor networks using particle swarm optimization”, in 2007 IEEE 18th Int. Symp. on Pers., Indoor and Mobile Radio Commun., 2007, pp. 1–5, doi: 10.1109/PIMRC.2007.4394521.
-
[28] B. Singh and D. K. Lobiyal, "A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks”, Hum.-centric Comput. Inf. Sci., vol. 2, no. 1, pp. 1–18, 2012, doi: 10.1186/2192-1962-2-13.
-
[29] H. Banka, P. K. Jana et al., "PSO-based multiple-sink placement algorithm for protracting the lifetime of wireless sensor networks”, in Proc. Second Int. Conf. on Comput. Commun. Technol., 2016, pp. 605–616, doi: 10.1007/978-81-322-2517-1_58.
-
[30] T. E. Kalayci and A. Uğur, "Genetic algorithm-based sensor deployment with area priority”, Cybern. Syst., vol. 42, no. 8, pp. 605–620, 2011, doi: 10.1080/01969722.2011.634676.
-
[31] E. Ateş, T. E. Kalayci, and A. Uğur, "Area-priority-based sensor deployment optimisation with priority estimation using K-means”, IET Commun., vol. 11, no. 7, pp. 1082–1090, 2017, doi: 10.1049/iet-com.2016.1264.
-
[32] J. Huang, H. Wang, Y. Qian, and C. Wang, "Priority-based traffic scheduling and utility optimization for cognitive radio communication infrastructure-based smart grid”, IEEE Trans. Smart Grid, vol. 4, no. 1, pp. 78–86, 2013, doi: 10.1109/TSG.2012.2227282.
-
[33] J. Huang and B. H. Soong, "Priority-aware hybrid scheduling for fast, energy-efficient max function computation in single-hop networks”, IET Commun., vol. 10, no. 18, pp. 2606–2612, 2016, doi: https://doi.org/10.1049/iet-com.2016.0311.
-
[34] S. Say, H. Inata, J. Liu, and S. Shimamoto, "Priority-based data gathering framework in UAV-assisted wireless sensor networks”, IEEE Sens. J., vol. 16, no. 14, pp. 5785–5794, 2016, doi: 10.1109/JSEN.2016.2568260.
-
[35] P. K. Agarwal and C. M. Procopiuc, "Exact and approximation algorithms for clustering," Algorithmica, vol. 33, no. 2, pp. 201–226, 2002, doi: 10.1007/s00453-001-0110-y.