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Energy-aware disjoint dominating sets-based whale optimization algorithm for data collection in WSNs

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Abstract

One of the major challenges in realizing a reliable wireless sensor network (WSN) that can survive under the emerging applications is the constrained energy of the sensors. Hence, extending the lifetime of WSN is a major concern, which directly impacts the performance of various WSN-based applications. In this regard, various methods have been developed that either investigate the energy consumption or lifetime enhancement of WSN. A promising method to conserve the energy of the sensors is to use sleep–awake scheduling by choosing disjoint groups of nodes called dominating set (DS). By distributing the data collection duties among these DSs, one DS handles these tasks for a specified period of time before being replaced by another group, extending the lifespan of the network. This problem becomes challenging in WSN with heterogeneous energy. Despite the success of the algorithms in determining the DS, none of the existing methods consider the node’s energy while creation or selection of DS. This motivates us to utilize the DSs concept to control and maintain sleep/awake schedule of WSN nodes with heterogeneous energy. Toward this goal, we propose an energy-aware algorithm known as proposed initializer for whale optimization algorithm-based operator (PI-WOA-BO) to construct disjoint DSs that work as collector nodes for data gathering in each round and extend the total WSN lifetime. An energy-aware fitness function is introduced for selecting the best DSs that can maximize the WSN lifetime. Simulation results reveal that PI-WOA-BO exhibits enhanced performance over baseline techniques under various metrics including energy, stability, reliability and lifetime of WSN. PI-WOA-BO outperforms FUZZY-DS-ACO, CDS-FOR, BEE-VBC and CDS-LEACH by (17.4%, 40.1%, 31.1% and 53.6%), (7.7%, 33.5%, 23.4% and 48.5%) and (7.9%, 33.5%, 22.9% and 47.8%) in terms of First, Half and Last node dies, respectively.

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References

  1. Yu W, Chen Y, Wan L, Zhang X, Zhu P, Xu X (2020) An energy optimization clustering scheme for multi-hop underwater acoustic cooperative sensor networks. IEEE Access 11(8):89171–89184. https://doi.org/10.1109/ACCESS.2020.2993544

    Article  Google Scholar 

  2. Faisal A, Alghamdi R, Dahrouj H, Sarieddeen H, Saeed N, Al-Naffouri TY, Alouini MS (2021) Diversity schemes in multi-hop visible light communications for 6G networks. Procedia Comput Sci 1(182):140–149. https://doi.org/10.1016/j.procs.2021.02.019

    Article  Google Scholar 

  3. Arora VK, Sharma V (2021) A novel energy-efficient balanced multi-hop routing scheme (EBMRS) for wireless sensor networks. Peer-to-Peer Netw Appl 14(2):807–820. https://doi.org/10.1007/s12083-020-01039-5

    Article  Google Scholar 

  4. Altowaijri SM (2022) Efficient next-hop selection in multi-hop routing for IoT enabled wireless sensor networks. Fut Intern 14(2):35. https://doi.org/10.3390/fi14020035

    Article  Google Scholar 

  5. Wollschlaeger M, Sauter T, Jasperneite J (2017) The future of industrial communication: automation networks in the era of the internet of things and industry 4.0. IEEE Ind Electron Mag 11(1):17–27. https://doi.org/10.1109/MIE.2017.2649104

    Article  Google Scholar 

  6. Aziz A, Singh K, Osamy W, Khedr AM (2020) An efficient compressive sensing routing scheme for internet of things based wireless sensor networks. Wirel Pers Commun 114(3):1905–1925. https://doi.org/10.1007/s11277-020-07454-4

    Article  Google Scholar 

  7. Aziz A, Singh K, Osamy W, Khedr AM (2019) Effective algorithm for optimizing compressive sensing in IoT and periodic monitoring applications. J Netw Comput Appl 15(126):12–28. https://doi.org/10.1016/j.jnca.2018.10.013

    Article  Google Scholar 

  8. Osamy W, El-Sawy AA, Khedr AM (2020) Effective TDMA scheduling for tree-based data collection using genetic algorithm in wireless sensor networks. Peer-to-Peer Netw Appl 13(3):796–815. https://doi.org/10.1007/s12083-019-00818-z

    Article  Google Scholar 

  9. Mahapatra C, Sheng Z, Kamalinejad P, Leung VC, Mirabbasi S (2016) Optimal power control in green wireless sensor networks with wireless energy harvesting, wake-up radio and transmission control. IEEE Access 28(5):501–518. https://doi.org/10.1109/ACCESS.2016.2644607

    Article  Google Scholar 

  10. Omar DM, Khedr AM, Agrawal DP (2017) Optimized clustering protocol for balancing energy in wireless sensor networks. Int J Commun Netw Inf Secur 9(3):367–375

    Google Scholar 

  11. Osamy W, El-sawy AA, Khedr AM (2019) SATC: a simulated annealing based tree construction and scheduling algorithm for minimizing aggregation time in wireless sensor networks. Wirel Pers Commun 108(2):921–938. https://doi.org/10.1007/s11277-019-06440-9

    Article  Google Scholar 

  12. Khedr AM (2015) Effective data acquisition protocol for multi-hop heterogeneous wireless sensor networks using compressive sensing. Algorithms 8(4):910–928. https://doi.org/10.3390/a8040910

    Article  Google Scholar 

  13. Kumar N, Kumar V, Verma PK (2022) A comparative study of the energy-efficient advanced LEACH (ADV-LEACH1) clustering protocols in heterogeneous and homogeneous wireless sensor networks. In: Cyber Security and Digital Forensics. Springer, Singapore, pp. 433–444. https://doi.org/10.1007/978-981-16-3961-6_36

  14. Qabouche H, Sahel A, Badri A (2021) Hybrid energy efficient static routing protocol for homogeneous and heterogeneous large scale WSN. Wirel Netw 27(1):575–587. https://doi.org/10.1007/s11276-020-02473-2

    Article  Google Scholar 

  15. Aziz A, Osamy W, Alfawaz O, Khedr AM (2022) EDCCS: effective deterministic clustering scheme based compressive sensing to enhance IoT based WSNs. Wirel Netw 25:1–7. https://doi.org/10.1007/s11276-022-02973-3

    Article  Google Scholar 

  16. Zhang Z, Zhou J, Mo Y, Du DZ (2016 ) Performance-guaranteed approximation algorithm for fault-tolerant connected dominating set in wireless networks. In: IEEE INFOCOM 2016—The 35th Annual IEEE International Conference on Computer Communications 2016 Apr 10. IEEE, pp 1–8. https://doi.org/10.1109/INFOCOM.2016.7524456

  17. Wan R, Xiong N (2018) An energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks. HCIS 8(1):1–22. https://doi.org/10.1186/s13673-018-0141-x

    Article  Google Scholar 

  18. Pino T, Choudhury S, Al-Turjman F (2018) Dominating set algorithms for wireless sensor networks survivability. IEEE Access 28(6):17527–17532. https://doi.org/10.1109/ACCESS.2018.2819083

    Article  Google Scholar 

  19. Mirjalili S, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 1(95):51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008

    Article  Google Scholar 

  20. Hassanat A, Almohammadi K, Alkafaween EA, Abunawas E, Hammouri A, Prasath VS (2019) Choosing mutation and crossover ratios for genetic algorithms—a review with a new dynamic approach. Information 10(12):390. https://doi.org/10.3390/info10120390

    Article  Google Scholar 

  21. Guha S, Khuller S (1998) Approximation algorithms for connected dominating sets. Algorithmica 20(4):374–387. https://doi.org/10.1007/PL00009201

    Article  MATH  Google Scholar 

  22. Islam K, Akl SG, Meijer H (2009) Maximizing the lifetime of wireless sensor networks through domatic partition. In: 2009 IEEE 34th Conference on Local Computer Networks 2009 Oct 20. IEEE, pp 436–442. https://doi.org/10.1109/LCN.2009.5355161

  23. Stojmenovic I, Seddigh M, Zunic J (2002) Dominating sets and neighbor elimination-based broadcasting algorithms in wireless networks. IEEE Trans Parallel Distrib Syst 13(1):14–25. https://doi.org/10.1109/71.980024

    Article  Google Scholar 

  24. Wu J, Li H (1999) On calculating connected dominating set for efficient routing in ad hoc wireless networks. In: Proceedings of the 3rd International Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications 1999 Aug 1, pp 7–14

  25. Islam K, Akl SG, Meijer H (2008) A constant factor localized algorithm for computing connected dominating sets in wireless sensor networks. In: 2008 14th IEEE International Conference on Parallel and Distributed Systems 2008 Dec 8. IEEE, pp. 559–566

  26. Mohanty JP, Mandal C, Reade C, Das A (2016) Construction of minimum connected dominating set in wireless sensor networks using pseudo dominating set. Ad Hoc Netw 15(42):61–73. https://doi.org/10.1016/j.adhoc.2016.02.003

    Article  Google Scholar 

  27. Kui X, Zhang S, Wang J, Cao J (2012) An energy-balanced clustering protocol based on dominating set for data gathering in wireless sensor networks. In; 2012 IEEE International Conference on Communications (ICC) 2012 Jun 10. IEEE, pp. 193–197. https://doi.org/10.1109/ICC.2012.6363775

  28. Bezoui M, Bounceur A, Euler R, Lalem F, Abdelkader L (2017) A new algorithm for finding a dominating set in wireless sensor and IoT networks based on the wait-before-starting concept. In: 2017 IEEE Sensors 2017 Oct 29 IEEE, pp. 1–3. https://doi.org/10.1109/ICSENS.2017.8233992

  29. Das B, Sivakumar R, Bharghavan V (1997) Routing in ad hoc networks using a spine. In: Proceedings of Sixth International Conference on Computer Communications and Networks 1997 Sep 22, IEEE, pp 34–39. https://doi.org/10.1109/ICCCN.1997.623288

  30. Yu J, Wang N, Wang G, Yu D (2013) Connected dominating sets in wireless ad hoc and sensor networks—a comprehensive survey. Comput Commun 36(2):121–134

    Article  Google Scholar 

  31. Shi T, Cheng S, Cai Z, Li J (2016) Adaptive connected dominating set discovering algorithm in energy-harvest sensor networks. In: IEEE INFOCOM 2016—The 35th Annual IEEE International Conference on Computer Communications 2016 Apr 10, IEEE, pp 1–9. https://doi.org/10.1109/INFOCOM.2016.7524504

  32. Yu J, Zhang Q, Yu D, Chen C, Wang G (2014) Domatic partition in homogeneous wireless sensor networks. J Netw Comput Appl 1(37):186–193. https://doi.org/10.1016/j.jnca.2013.02.025

    Article  Google Scholar 

  33. Kernighan BW, Lin S (1970) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 49(2):291–307. https://doi.org/10.1002/j.1538-7305.1970.tb01770.x

    Article  MATH  Google Scholar 

  34. Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm Evol Comput 1(48):1–24. https://doi.org/10.1016/j.swevo.2019.03.004

    Article  Google Scholar 

  35. Nasiri J, Khiyabani FM (2018) A whale optimization algorithm (WOA) approach for clustering. Cogent Math Stat 5(1):1483565. https://doi.org/10.1080/25742558.2018.1483565

    Article  MATH  Google Scholar 

  36. Jadhav AR, Shankar T (2017) Whale optimization based energy-efficient cluster head selection algorithm for wireless sensor networks. arXiv preprint arXiv:1711.09389. 2017 Nov 26. https://doi.org/10.48550/arXiv.1711.09389

  37. Ahmed MM, Houssein EH, Hassanien AE, Taha A, Hassanien E (2017) Maximizing lifetime of wireless sensor networks based on whale optimization algorithm. In: International Conference on Advanced Intelligent Systems and informatics 2017 Sep 9. Springer, Cham, pp 724–733. https://doi.org/10.1007/978-3-319-64861-3_68

  38. SureshKumar K, Vimala P (2021) Energy efficient routing protocol using exponentially-ant lion whale optimization algorithm in wireless sensor networks. Comput Netw 9(197):108250. https://doi.org/10.1016/j.comnet.2021.108250

    Article  Google Scholar 

  39. Ahmed MM, Houssein EH, Hassanien AE, Taha A, Hassanien E (2019) Maximizing lifetime of large-scale wireless sensor networks using multi-objective whale optimization algorithm. Telecommun Syst 72(2):243–259. https://doi.org/10.1007/s11235-019-00559-7

    Article  Google Scholar 

  40. Bozorgi SM, Hajiabadi MR, Hosseinabadi AA, Sangaiah AK (2021) Clustering based on whale optimization algorithm for IoT over wireless nodes. Soft Comput 25(7):5663–5682. https://doi.org/10.1007/s00500-020-05563-7

    Article  Google Scholar 

  41. Pham QV, Mirjalili S, Kumar N, Alazab M, Hwang WJ (2020) Whale optimization algorithm with applications to resource allocation in wireless networks. IEEE Trans Veh Technol 69(4):4285–4297. https://doi.org/10.1109/TVT.2020.2973294

    Article  Google Scholar 

  42. Wang RB, Wang WF, Xu L, Pan JS, Chu SC (2022) Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks. Wirel Netw 13:1–8. https://doi.org/10.1007/s11276-022-03048-z

    Article  Google Scholar 

  43. Toloueiashtian M, Golsorkhtabaramiri M, Rad SY (2022) An improved whale optimization algorithm solving the point coverage problem in wireless sensor networks. Telecommun Syst 79(3):417–436. https://doi.org/10.1007/s11235-021-00866-y

    Article  Google Scholar 

  44. Priyanka BN, Jayaparvathy R, DivyaBharathi D (2022) Efficient and dynamic cluster head selection for improving network lifetime in WSN using whale optimization algorithm. Wirel Pers Commun 123(2):1467–1481. https://doi.org/10.1007/s11277-021-09192-7

    Article  Google Scholar 

  45. Mondal S, Ghosh S, Biswas U (2016) A dominating set based data gathering in wireless sensor network using fuzzy logic and ACO. In: Proceedings of the International Conference on Informatics and Analytics 2016 Aug 25, pp 1–8. https://doi.org/10.1145/2980258.2980415

  46. Julie EG, Tamilselvi S (2016) CDS-fuzzy opportunistic routing protocol for wireless sensor networks. Wirel Pers Commun 2(90):903–922. https://doi.org/10.1007/s11277-016-3250-8

    Article  Google Scholar 

  47. Varsa GS, Sridharan D (2019) A balanced energy efficient virtual backbone construction algorithm in wireless sensor networks. AEU-Int J Electron Commun 1(107):110–124

    Article  Google Scholar 

  48. Patra C (2020) Introducing connected dominating set as selection feature of cluster heads in hierarchical protocols of wireless sensor networks. Glob J Comput Sci Technol 20(1):21–26

    Google Scholar 

  49. Osamy W, El-Sawy AA, Salim A (2020) CSOCA: chicken swarm optimization based clustering algorithm for wireless sensor networks. IEEE Access 26(8):60676–60688. https://doi.org/10.1109/ACCESS.2020.2983483

    Article  Google Scholar 

  50. Osamy W, Salim A, Khedr AM, El-Sawy AA (2021) IDCT: intelligent data collection technique for IoT-enabled heterogeneous wireless sensor networks in smart environments. IEEE Sens J 21(18):21099–112. https://doi.org/10.1109/JSEN.2021.3100339

    Article  Google Scholar 

  51. Yilmaz O, Erciyes K (2010) Distributed weighted node shortest path routing for wireless sensor networks. In: International Conference on Wireless and Mobile Networks, 2010 Jun 26. Springer, Berlin. pp 304–314. https://doi.org/10.1007/978-3-642-14171-3_26

  52. Lim Y, Kang S (2013) Intelligent approach for data collection in wireless sensor networks. Int Arab J Inf Technol 10(1):36–42

    Google Scholar 

  53. Silmi S, Doukha Z, Moussaoui S (2021) A self-localization range free protocol for wireless sensor networks. Peer-to-Peer Netw Appl 14(4):2061–2071. https://doi.org/10.1007/s12083-021-01155-w

    Article  Google Scholar 

  54. Sabale K, Mini S (2021) Localization in wireless sensor networks with mobile anchor node path planning mechanism. Inf Sci 1(579):648–666. https://doi.org/10.1016/j.ins.2021.08.004

    Article  Google Scholar 

  55. Lalama Z, Boulfekhar S, Semechedine F (2022) Localization optimization in WSNs using meta-heuristics optimization algorithms: a survey. Wirel Pers Commun 122(2):1197–1220. https://doi.org/10.1007/s11277-021-08945-8

    Article  Google Scholar 

  56. Onat FA, Stojmenovic I (2007) Generating random graphs for wireless actuator networks. In: 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2007 Jun 18, pp 1–12. IEEE. https://doi.org/10.1109/WOWMOM.2007.4351712

  57. Gaber MI, Khalaf AA, Mahmoud II, El-Tokhy MS (2021) Development of information collection scheme in internet of things environment for intelligent radiation monitoring systems. Telecommun Syst 76(1):33–48. https://doi.org/10.1007/s11235-020-00697-3

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Ahmed A. Elsway & Walid Osamy

  2. Computer Science Department, University of Sharjah, Sharjah, 27272, UAE

    Ahmed M. Khedr

  3. Faculty of Science, Zagazig University, Zagazig, Egypt

    Ahmed M. Khedr

  4. Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, UAE

    Oruba Alfawaz

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  1. Ahmed A. Elsway
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Elsway, A.A., Khedr, A.M., Alfawaz, O. et al. Energy-aware disjoint dominating sets-based whale optimization algorithm for data collection in WSNs. J Supercomput 79, 4318–4350 (2023). https://doi.org/10.1007/s11227-022-04814-8

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