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A Hybrid approach to cluster head selection in space-air-ground integrated networks: leveraging SMC and OOA for optimal performance

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Abstract

Energy efficiency and prolonging network lifetime of Internet of Things (IoT) sensor node management in the context of Space-Air-Ground Integrated Networks (SAGINs) has high importance. This work presents an Energy-Efficient Cluster Head Selection using the Osprey Optimization Algorithm (EECHOOA), utilizing clustering as the first layer in SAGINs that clusters IoT sensor nodes into clusters to optimize data aggregation and communication. Existing methods, such as ZFO-SHO, PUAG, NCOGA and MMABC, often struggle with limited adaptability to dynamic network conditions and suboptimal energy efficiency. The proposed EECHOOA address these shortcomings by introducing dynamic cluster head (CH) selection and scalable clustering techniques optimized for dense IoT environment. At the individual node level, each sensor consumes energy. A CH controls the process and relays information to the upper layers, minimizing total energy consumption. We further enhance the clustering process with the osprey optimization algorithm (OOA) for intelligent CH selection. The OOA employs the unique behaviors of ospreys to identify optimal CHs dynamically as a function of node energy levels and proximity. Simulation results indicate that our proposed clustering approach integrated with OOA for CH selection achieves 56.25\(-\)76.35% energy savings and extends network lifetime by 50 to 100% compared to the state of the art, such as ZFO-SHO, PUAG, NCOGA and MMABC. This study demonstrates that clustering techniques can be utilized in conjunction with skilled optimization algorithms in SAGINs, to allow for more sustainable and effective IoT-based networks that are able to be responsive to a variety of applications.

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The data that supports this study, along with the software application or custom code used to solve for the proposed methods in this study are available from the corresponding author upon request.

References

  1. Lu Y, Wen W, Igorevich KK, Ren P, Zhang H, Duan Y, Zhu H, Zhang P (2023) UAV ad hoc network routing algorithms in space-air-ground integrated networks: challenges and directions. Drones 7:448

    Article  MATH  Google Scholar 

  2. Abdulzahra SA, Idrees AK (2022) Two-level energy-efficient data reduction strategies based on sax-lzw and hierarchical clustering for minimizing the huge data conveyed on the internet of things networks. J Supercomput 78:17844–17890

    Article  MATH  Google Scholar 

  3. Chen Q, Guo Z, Meng W, Han S, Li C, Quek TQ (2024) A survey on resource management in joint communication and computing-embedded Sagin, IEEE Communications Surveys & Tutorials

  4. Idrees AK, Abou Jaoude C (2020) Data reduction and cleaning approach for energy-saving in wireless sensors networks of iot. In: 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), vol 2020. IEEE, pp 1–6

  5. Tay M, Senturk A (2022) A new energy-aware cluster head selection algorithm for wireless sensor networks. Wireless Pers Commun 122:2235–2251

    Article  MATH  Google Scholar 

  6. Idrees AK (2018) Distributed data aggregation and selective forwarding protocol for improving lifetime of wireless sensor networks. J Eng Appl Sci 13:4644–4653

    MATH  Google Scholar 

  7. Saeedi IDI, Al-Qurabat AKM (2021) A systematic review of data aggregation techniques in wireless sensor networks. In: Journal of Physics: Conference Series vol 1818. IOP Publishing, p 012194

  8. Jabar MK (2021) Human activity diagnosis system based on the internet of things. In: Journal of Physics: Conference Series vol 1879. IOP Publishing, p 022079

  9. Saeedi IDI, Al-Qurabat AKM (2022) An energy-saving data aggregation method for wireless sensor networks based on the extraction of extrema points. In: AIP Conference Proceedings, vol 2398. AIP Publishing

  10. Singh M, Soni SK (2021) Network lifetime enhancement of WSNS using correlation model and node selection algorithm. Ad Hoc Netw 114:102441

    Article  MATH  Google Scholar 

  11. Bayrakdar ME (2020) Energy-efficient technique for monitoring of agricultural areas with terrestrial wireless sensor networks. J Circuits Syst Comput 29:2050141

    Article  MATH  Google Scholar 

  12. Saeedi IDI, Al-Qurabat AKM (2022) Perceptually important points-based data aggregation method for wireless sensor networks. Baghdad Sci J 19:0875–0875

    Article  Google Scholar 

  13. Al-Baz A, El-Sayed A (2018) A new algorithm for cluster head selection in leach protocol for wireless sensor networks. Int J Commun Syst 31:e3407

    Article  MATH  Google Scholar 

  14. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN’95-International Conference on Neural Networks, vol 4. IEEEE, pp 1942–1948

  15. Karaboga D, Basturk B (2007) Artificial bee colony (abc) optimization algorithm for solving constrained optimization problems. In: International fuzzy systems association world congress. Springer, pp 789–798

  16. Chopra N, Ansari MM (2022) Golden jackal optimization: a novel nature-inspired optimizer for engineering applications. Expert Syst Appl 198:116924

    Article  Google Scholar 

  17. Dehghani M, Montazeri Z, Trojovská E, Trojovskỳ P (2023) Coati optimization algorithm: a new bio-inspired metaheuristic algorithm for solving optimization problems. Knowl-Based Syst 259:110011

    Article  MATH  Google Scholar 

  18. Faramarzi A, Heidarinejad M, Mirjalili S, Gandomi AH (2020) Marine predators algorithm: a nature-inspired metaheuristic. Expert Syst Appl 152:113377

    Article  Google Scholar 

  19. Maratha P, Gupta K (2023) Linear optimization and fuzzy-based clustering for wsns assisted internet of things. Multimedia Tools Appl 82:5161–5185

    Article  MATH  Google Scholar 

  20. Idrees AK, Abou Jaoude C (2020) Dictionary-based dpcm method for compressing iot big data. In: International Wireless Communications and Mobile Computing (IWCMC), vol 2020. IEEE, pp 1290–1295

  21. Jawad GAM, Idrees AK (2022) Maximizing the underwater wireless sensor networks’ lifespan using BTC and mnp5 compression techniques. Ann Telecommun 77:703–723

    Article  Google Scholar 

  22. Abdulzahra SA (2024) Fonic: an energy-conscious fuzzy-based optimized nature-inspired clustering technique for iot networks. J Supercomput 1–53

  23. Omidi A, Mohammadshahi A, Gianchandani N, King R, Leijser L, Souza R (2024) Unsupervised domain adaptation of mri skull-stripping trained on adult data to newborns. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp 7718–7727

  24. Abbood IK, Idrees AK (2024) Data reduction techniques for wireless multimedia sensor networks: a systematic literature review. J Supercomput 80:10044–10089

    Article  MATH  Google Scholar 

  25. Nedham WB (2023) A comprehensive review of clustering approaches for energy efficiency in wireless sensor networks. Int J Comput Appl Technol 72:139–160

    Article  MATH  Google Scholar 

  26. Salman HM, AaR Finjan (2021) Bigradient neural network-based quantum particle swarm optimization for blind source separation. IAES Int J Artific Intell 10:355

    MATH  Google Scholar 

  27. Lu Y, Miao Z, Sahraeian P, Balasundaram B (2023) On atomic cliques in temporal graphs. Optim Lett 17:813–828

    Article  MathSciNet  MATH  Google Scholar 

  28. Abdulzahra SA, Idrees AK (2021) Energy conservation approach of wireless sensor networks for IoT applications. Karbala Int J Modern Sci 7:9

    Article  MATH  Google Scholar 

  29. Mohammad HM, Abdullah AA (2023) Ddos attack mitigation using entropy in sdn-iot environment. In: AIP Conference Proceedings, vol 2591. AIP Publishing

  30. Al Essa HA, Bhaya WS (2024) Ensemble learning classifiers hybrid feature selection for enhancing performance of intrusion detection system. Bull Electr Eng Inf 13:665–676

    MATH  Google Scholar 

  31. Choupanzadeh R, Zadehgol A (2023) A deep neural network modeling methodology for efficient emc assessment of shielding enclosures using meca-generated rcs training data, IEEE Transactions on Electromagnetic Compatibility

  32. Vikhyath K et al (2023) Optimal cluster head selection in wireless sensor network via multi-constraint basis using hybrid optimization algorithm: Nmjsoa. Int J Electr Electron Res 11:1087–1096

    Article  MATH  Google Scholar 

  33. Sankar S, Ramasubbareddy S, Dhanaraj RK, Balusamy B, Gupta P, Ibrahim W, Verma R (2023) Cluster head selection for the internet of things using a sandpiper optimization algorithm (SOA). J Sens 2023:3507600

    Article  Google Scholar 

  34. Kusla V, Brar GS (2023) A technique for cluster head selection in wireless sensor networks using african vultures optimization algorithm, EAI Endorsed Transactions on Scalable Information Systems 10

  35. Chaurasia S, Kumar K, Kumar N (2023) Mocraw: a meta-heuristic optimized cluster head selection based routing algorithm for wsns. Ad Hoc Netw 141:103079

    Article  MATH  Google Scholar 

  36. ROBERTS MK, Ramasamy P, Dahan F (2024) An innovative approach for cluster head selection and energy optimization in wireless sensor networks using zebra fish and sea horse optimization techniques, Journal of Industrial Information Integration. 100642

  37. Sefati SS, Abdi M, Ghaffari A (2023) Qos-based routing protocol and load balancing in wireless sensor networks using the Markov model and the artificial bee colony algorithm. Peer-to-Peer Netw Appl 16:1499–1512

    Article  Google Scholar 

  38. Heidari E, Movaghar A, Motameni H, Barzegar B (2022) A novel approach for clustering and routing in wsn using genetic algorithm and equilibrium optimizer. Int J Commun Syst 35:e5148

    Article  MATH  Google Scholar 

  39. Cherappa V, Thangarajan T, Meenakshi Sundaram SS, Hajjej F, Munusamy AK, Shanmugam R (2023) Energy-efficient clustering and routing using asfo and a cross-layer-based expedient routing protocol for wireless sensor networks. Sensors 23:2788

    Article  Google Scholar 

  40. Abu Salem AO, Shudifat N (2019) Enhanced leach protocol for increasing a lifetime of wsns. Pers Ubiquit Comput 23:901–907

    Article  MATH  Google Scholar 

  41. Abraham R, Vadivel M (2023) An energy efficient wireless sensor network with flamingo search algorithm based cluster head selection. Wireless Pers Commun 130:1503–1525

    Article  MATH  Google Scholar 

  42. Cherappa V, Thangarajan T, Meenakshi Sundaram SS, Hajjej F, Munusamy AK, Shanmugam R (2023) Energy-efficient clustering and routing using asfo and a cross-layer-based expedient routing protocol for wireless sensor networks. Sensors 23:2788

    Article  Google Scholar 

  43. Sankar S, Ramasubbareddy S, Luhach AK, Alnumay WS, Chatterjee P (2022) Nccla: new caledonian crow learning algorithm based cluster head selection for internet of things in smart cities. J Ambient Intell Humaniz Comput 13:4651–4661

    Article  Google Scholar 

  44. Muhammed EB, Calhan A (2017) Performance evaluation of tdma medium access control protocol in cognitive wireless networks. Comput Sci J Moldova 73:21–43

    MATH  Google Scholar 

  45. Zachariah UE, Kuppusamy L (2022) A hybrid approach to energy efficient clustering and routing in wireless sensor networks. Evol Intel 15:593–605

    Article  MATH  Google Scholar 

  46. Yue Y, You H, Wang S, Cao L (2021) Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks. Int J Distrib Sens Netw 17:15501477211018140

    Article  MATH  Google Scholar 

  47. Arunachalam N, Shanmugasundaram G, Arvind R (2021) Squirrel search optimization-based cluster head selection technique for prolonging lifetime in wsn’s. Wireless Pers Commun 121:2681–2698

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Networks, College of Information Technology, University of Babylon, Babylon, Hillah, 51002, Iraq

    Iman Dakhil Idan Saeedi

  2. Department of Cyber Security, College of Sciences, Al-Mustaqbal University, Babylon, Hillah, 51001, Iraq

    Ali Kadhum M. Al-Qurabat

  3. Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Hillah, 51002, Iraq

    Ali Kadhum M. Al-Qurabat

Authors
  1. Iman Dakhil Idan Saeedi
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Contributions

Iman Dakhil Idan Saeedi and Ali Kadhum M. Al-Qurabat contributed equally to this work, with all being involved in Conceptualization, Methodology, Data Curation, Formal Analysis, Software Development, and Writing - Original Draft Preparation.

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Correspondence to Ali Kadhum M. Al-Qurabat.

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Saeedi, I.D.I., Al-Qurabat, A.K.M. A Hybrid approach to cluster head selection in space-air-ground integrated networks: leveraging SMC and OOA for optimal performance. J Supercomput 81, 526 (2025). https://doi.org/10.1007/s11227-025-06978-5

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