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PRESEP: Cluster Based Metaheuristic Algorithm for Energy-Efficient Wireless Sensor Network Application in Internet of Things

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Abstract

A metaheuristic approach based hybrid data routing algorithm is proposed in this paper for energy-efficient wireless sensor network (WSN) applications in Internet of Things (IoT). High speed generation of network demands more IoT enabled services and platforms. Swarm optimization in route finding is integrated with an energy-efficient heuristic method to obtain an improved data routing algorithm for heterogeneous WSN. Particle swarm optimized Residual Energy Stable Election Protocol (PRESEP) simulation yields prolonged network lifetime, and stable cluster formation with balanced energy utilization. PRESEP protocol minimizes the cluster head (CH) selection cycle as an outcome of reactive protocol benefits of easy global search of swarm optimization for sustainable data routing in energy-efficient cluster based heterogeneous WSN. Enhancing the CH selection of developed residual energy based model with meta heuristic optimization is achieved with the incremented round of operations for varied heterogeneity factor. Support for heterogeneous WSN with various network sizes is evaluated and it outperformed previous heterogeneous algorithm in terms of network lifetime, alive nodes, and reduced energy consumption with a minimum number of repeated CH selection processes.

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References

  1. Misra, S., Roy, S. K., Roy, A., Obaidat, M. S., & Jha, A. (2020). MEGAN: multipurpose energy-efficient, adaptable, and low-cost wireless sensor node for the Internet of Things. IEEE Systems Journal, 14(1), 144–151. https://doi.org/10.1109/JSYST.2019.2920099

    Article  Google Scholar 

  2. Daskalakis, S. N., Goussetis, G., Assimonis, S. D., Tentzeris, M. M., & Georgiadis, A. (2018). A uW backscatter-morse-leaf sensor for low-power agricultural wireless sensor networks. IEEE Sensors Journal, 18(19), 7889–7898. https://doi.org/10.1109/JSEN.2018.2861431

    Article  Google Scholar 

  3. Lv, Y., Liu, Y., & Hua, J. (2019). A study on the application of WSN positioning technology to unattended areas. IEEE Access, 7, 38085–38099. https://doi.org/10.1109/ACCESS.2019.2903820

    Article  Google Scholar 

  4. Giri, P., Ng, K., & Phillips, W. (2019). Wireless sensor network system for landslide monitoring and warning. IEEE Transactions on Instrumentation and Measurement, 68(4), 1210–1220. https://doi.org/10.1109/TIM.2018.2861999

    Article  Google Scholar 

  5. Feng, J., Chen, H., Deng, X., Yang, L. T., & Tan, F. (2021). Confident information coverage hole prediction and repairing for healthcare big data collection in large-scale hybrid wireless sensor networks. IEEE Internet of Things Journal, 8(23), 16801–16813. https://doi.org/10.1109/JIOT.2020.3045024

    Article  Google Scholar 

  6. Vinodha, R., Durairaj, S., & Padmavathi, S. (2022). Energy-efficient routing protocol and optimized passive clustering in WSN for SMART grid applications. International Journal of Communication Systems, 35(1), e5019. https://doi.org/10.1002/dac.5019

    Article  Google Scholar 

  7. Han, S., Liu, X.-M., Huang, H.-Y., Wang, F., & Zhong, Y. H. (2021). Research on energy-efficient routing algorithm based on SWIPT in multi-hop clustered WSN for 5G system. EURASIP Journal on Wireless Communications and Networking, 2021(1), 49. https://doi.org/10.1186/s13638-021-01931-5

    Article  Google Scholar 

  8. Bellavista, P., Cardone, G., Corradi, A., & Foschini, L. (2013). Convergence of MANET and WSN in IoT Urban Scenarios. IEEE Sensors Journal, 13(10), 3558–3567. https://doi.org/10.1109/JSEN.2013.2272099

    Article  Google Scholar 

  9. Shen, J., Wang, A., Wang, C., Hung, P. C. K., & Lai, C. F. (2017). An efficient centroid-based routing protocol for energy management in WSN-assisted IoT. IEEE Access, 5, 18469–18479. https://doi.org/10.1109/ACCESS.2017.2749606

    Article  Google Scholar 

  10. Agarwal, V., DeCarlo, R. A., & Tsoukalas, L. H. (2017). Modeling energy consumption and lifetime of a wireless sensor node operating on a contention-based MAC protocol. IEEE Sensors Journal, 17(16), 5153–5168. https://doi.org/10.1109/JSEN.2017.2722462

    Article  Google Scholar 

  11. Djedouboum, A. C., Abba Ari, A. A., Gueroui, A. M., Mohamadou, A., & Aliouat, Z. (2018). Big data collection in large-scale wireless sensor networks. Sensors. https://doi.org/10.3390/s18124474

    Article  Google Scholar 

  12. Raj, B., Ahmedy, I., Idris, M. Y. I., & Md. Noor, R. (2022). A survey on cluster head selection and cluster formation methods in wireless sensor networks. Wireless Communications and Mobile Computing, 2022, 5322649. https://doi.org/10.1155/2022/5322649

    Article  Google Scholar 

  13. Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: a survey. IEEE Communications Surveys & Tutorials, 15(2), 551–591. https://doi.org/10.1109/SURV.2012.062612.00084

    Article  Google Scholar 

  14. Fedorenko, V., Samoylenko, I., & Samoylenko, V. (2021). Energy-balanced distribution of radio modules with various technical states among positions of nodes in wireless sensor networks. AEU—International Journal of Electronics and Communications, 138, 153849. https://doi.org/10.1016/j.aeue.2021.153849

    Article  Google Scholar 

  15. Al-Qurabat, A. K. M., & Kadhum Idrees, A. (2020). Data gathering and aggregation with selective transmission technique to optimize the lifetime of Internet of Things networks. International Journal of Communication Systems, 33(11), e4408. https://doi.org/10.1002/dac.4408

    Article  Google Scholar 

  16. Mohamed, F. A., Hassan, E. S., Elsafrawey, A. S., & Dessouky, M. I. (2019). Energy-efficient circle zones stable election protocol with helper nodes for heterogeneous WSNs. IET Wireless Sensor Systems, 9(5), 313–322. https://doi.org/10.1049/iet-wss.2018.5200

    Article  Google Scholar 

  17. Maina, R. M., Kibet Lang’at, P., & Kihato, P. K. (2021). Collaborative beamforming in wireless sensor networks using a novel particle swarm optimization algorithm variant. Heliyon, 7(10), e08247. https://doi.org/10.1016/j.heliyon.2021.e08247

    Article  Google Scholar 

  18. Amjad, M., Afzal, M. K., Umer, T., & Kim, B. (2017). QoS-aware and heterogeneously clustered routing protocol for wireless sensor networks. IEEE Access, 5, 10250–10262. https://doi.org/10.1109/ACCESS.2017.2712662

    Article  Google Scholar 

  19. Bhuiyan, M. Z. A., Wang, G., Cao, J., & Wu, J. (2015). Deploying wireless sensor networks with fault-tolerance for structural health monitoring. IEEE Transactions on Computers, 64(2), 382–395. https://doi.org/10.1109/TC.2013.195

    Article  MathSciNet  Google Scholar 

  20. Sharma, S. K., & Chawla, M. (2023). Compatibility issues of wireless sensor network routing in internet of things applications. International Journal of Wireless and Mobile Computing. https://doi.org/10.1504/IJWMC.2023.10057667

    Article  Google Scholar 

  21. Hancke, G., & Leuschner, C (2021) SEER: a simple energy efficient routing protocol for wireless sensor networks.

  22. Nurlan, Z., Zhukabayeva, T., Othman, M., Adamova, A., & Zhakiyev, N. (2022). Wireless sensor network as a mesh: vision and challenges. IEEE Access, 10, 46–67. https://doi.org/10.1109/ACCESS.2021.3137341

    Article  Google Scholar 

  23. Al-Karaki, J. N., & Gawanmeh, A. (2017). The optimal deployment, coverage, and connectivity problems in wireless sensor networks: revisited. IEEE Access, 5, 18051–18065. https://doi.org/10.1109/ACCESS.2017.2740382

    Article  Google Scholar 

  24. Xie, H., Yan, Z., Yao, Z., & Atiquzzaman, M. (2019). Data collection for security measurement in wireless sensor networks: a survey. IEEE Internet of Things Journal, 6(2), 2205–2224. https://doi.org/10.1109/JIOT.2018.2883403

    Article  Google Scholar 

  25. Liu, W., Nishiyama, H., Ansari, N., Yang, J., & Kato, N. (2013). Cluster-based certificate revocation with vindication capability for mobile Ad Hoc networks. IEEE Transactions on Parallel And Distributed Systems, 24(2), 239–249. https://doi.org/10.1109/TPDS.2012.85

    Article  Google Scholar 

  26. Manjeshwar, A., & Agrawal, D. P. TEEN: a routing protocol for enhanced efficiency in wireless sensor networks. In Proceedings 15th International Parallel and Distributed Processing Symposium. IPDPS 2001, 23–27 April 2001 2001 (pp. 2009–2015). https://doi.org/10.1109/IPDPS.2001.925197

  27. Sharma, S. K., & Chawla, M. (2023). Compatibility analysis of cluster-based WSN framework for IoT applications. Wireless Personal Communications, 131(2), 1365–1380. https://doi.org/10.1007/s11277-023-10486-1

    Article  Google Scholar 

  28. Younis, O., & Fahmy, S. (2004). HEED: A hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on Mobile Computing, 3(4), 366–379.

    Article  Google Scholar 

  29. Wang, K., Yu, C. M., & Wang, L. C. (2021). DORA: A destination-oriented routing algorithm for energy-balanced wireless sensor networks. IEEE Internet of Things Journal, 8(3), 2080–2081. https://doi.org/10.1109/JIOT.2020.3025039

    Article  Google Scholar 

  30. Jung, S. M., Han, Y. J., & Chung, T. M. (2007). The concentric clustering scheme for efficient energy consumption in the PEGASIS. In The 9th International Conference on Advanced Communication Technology, (Vol. 1, pp. 260–265). https://doi.org/10.1109/ICACT.2007.358351

  31. Xi-rong, B., Shi, Z., Ding-yu, X., & Zhi-tao, Q. (2010). An energy-balanced chain-cluster routing protocol for wireless sensor networks. In 2010 Second International Conference on Networks Security, Wireless Communications and Trusted Computing, (Vol. 2, pp. 79–84). https://doi.org/10.1109/NSWCTC.2010.155

  32. Chen, K. H., Huang, J. M., & Hsiao, C. C. (2009). CHIRON: An energy-efficient chain-based hierarchical routing protocol in wireless sensor networks. In 2009 Wireless Telecommunications Symposium, (pp. 1–5). https://doi.org/10.1109/WTS.2009.5068960

  33. Kamel, T., & Fouzi, S. (2022). An improvement on LEACH-C protocol (LEACH-CCMSN). Automatic Control and Computer Sciences, 56(1), 10–16. https://doi.org/10.3103/S0146411622010102

    Article  Google Scholar 

  34. Loscri, V., Morabito, G., & Marano, S (2005) A two-levels hierarchy for low-energy adaptive clustering hierarchy (TL-LEACH). In VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, (Vol. 3, pp. 1809–1813). https://doi.org/10.1109/VETECF.2005.1558418

  35. Kim, J. H. (2016). Harmony search algorithm: a unique music-inspired algorithm. Procedia Engineering, 154, 1401–1405. https://doi.org/10.1016/j.proeng.2016.07.510

    Article  Google Scholar 

  36. Tan, H. Ö., & Körpeoǧlu, I. (2003). Power efficient data gathering and aggregation in wireless sensor networks. SIGMOD Record, 32(4), 66–71. https://doi.org/10.1145/959060.959072

    Article  Google Scholar 

  37. Qiu, W., Skafidas, E., & Hao, P. (2009). Enhanced tree routing for wireless sensor networks. Ad Hoc Networks, 7(3), 638–650. https://doi.org/10.1016/j.adhoc.2008.07.006

    Article  Google Scholar 

  38. Won, M., & Stoleru, R. (2015). A hybrid multicast routing for large scale sensor networks with holes. IEEE Transactions on Computers, 64(12), 3362–3375. https://doi.org/10.1109/TC.2015.2409863

    Article  MathSciNet  Google Scholar 

  39. Obad AT, Ilyas M. (2022). Efficient WSN routing using Bootstapped PSO clustering. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), (pp. 1–5). https://doi.org/10.1109/HORA55278.2022.9800019.

  40. Wang, T., Zhang, G., Yang, X., & Vajdi, A. (2018). Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks. Journal of Systems and Software, 146, 196–214. https://doi.org/10.1016/j.jss.2018.09.067

    Article  Google Scholar 

  41. Bayraklı, S., & Erdogan, S. Z. (2012). Genetic algorithm based energy efficient clusters (GABEEC) in wireless sensor networks. Procedia Computer Science, 10, 247–254. https://doi.org/10.1016/j.procs.2012.06.034

    Article  Google Scholar 

  42. Zhou, Y., Wang, N., & Xiang, W. (2017). Clustering hierarchy protocol in wireless sensor networks using an improved PSO algorithm. IEEE Access, 5, 2241–2253. https://doi.org/10.1109/ACCESS.2016.2633826

    Article  Google Scholar 

  43. Kulkarni, R. V., & Venayagamoorthy, G. K. (2011). Particle swarm optimization in wireless-sensor networks: A brief survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 41(2), 262–267. https://doi.org/10.1109/TSMCC.2010.2054080

    Article  Google Scholar 

  44. Xue, X., Shanmugam, R., Palanisamy, S., Khalaf, O. I., Selvaraj, D., & Abdulsahib, G. M. (2023). A hybrid cross layer with harris-hawk-optimization-based efficient routing for wireless sensor networks. Symmetry. https://doi.org/10.3390/sym15020438

    Article  Google Scholar 

  45. Almajidi, A. M., Pawar, V. P., Alammari, A., & Ali, N. S (2020) ABC-Based Algorithm for Clustering and Validating WSNs. In V. K. Gunjan, P. N. Suganthan, J. Haase, A. Kumar, & B. Raman (Eds.), Cybernetics, Cognition and Machine Learning Applications, Singapore, (pp. 117–125): Springer, Singapore.

  46. Sengottuvelan, P., & Prasath, N. (2017). BAFSA: Breeding artificial fish swarm algorithm for optimal cluster head selection in wireless sensor networks. Wireless Personal Communications, 94(4), 1979–1991. https://doi.org/10.1007/s11277-016-3340-7

    Article  Google Scholar 

  47. Rayenizadeh, M., Kuchaki Rafsanjani, M., & Borumand Saeid, A. (2022). Cluster head selection using hesitant fuzzy and firefly algorithm in wireless sensor networks. Evolving Systems, 13(1), 65–84. https://doi.org/10.1007/s12530-021-09405-1

    Article  Google Scholar 

  48. Gupta, G. P., & Jha, S. (2018). Integrated clustering and routing protocol for wireless sensor networks using Cuckoo and Harmony search based metaheuristic techniques. Engineering Applications of Artificial Intelligence, 68, 101–109. https://doi.org/10.1016/j.engappai.2017.11.003

    Article  Google Scholar 

  49. Lee, J. G., Chim, S., & Park, H. H. (2019). Energy-efficient cluster-head selection for wireless sensor networks using sampling-based spider monkey optimization. Sensors. https://doi.org/10.3390/s19235281

    Article  Google Scholar 

  50. Smaragdakis, G., Matta, I., Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. Second International Workshop on Sensor and Actor Network Protocols and Applications (SANPA 2004) (2004).

  51. Qing, L., Zhu, Q., & Wang, M. (2006). Design of a distributed energy-efficient clustering algorithm for heterogeneous wireless sensor networks. Computer Communications, 29(12), 2230–2237. https://doi.org/10.1016/j.comcom.2006.02.017

    Article  Google Scholar 

  52. Hendrarini, N., Asvial, M., & Sari, R. F. (2020). Energy Balanced Threshold Using Game Theory Algorithm for Wireless Sensor Networks Optimization. Paper presented at the Proceedings of the 3rd International Conference on Software Engineering and Information Management, Sydney, NSW, Australia.

  53. Adetunji, K. E., Hofsajer, I. W., Abu-Mahfouz, A. M., & Cheng, L. (2021). Category-based multiobjective approach for optimal integration of distributed generation and energy storage systems in distribution networks. IEEE Access, 9, 28237–28250. https://doi.org/10.1109/ACCESS.2021.3058746

    Article  Google Scholar 

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

    Article  Google Scholar 

  55. Rao, P. C. S., Jana, P. K., & Banka, H. (2017). A particle swarm optimization based energy efficient cluster head selection algorithm for wireless sensor networks. Wireless Networks, 23(7), 2005–2020. https://doi.org/10.1007/s11276-016-1270-7

    Article  Google Scholar 

  56. Wang, J., Gao, Y., Liu, W., Sangaiah, A. K., & Kim, H. J. (2019). An improved routing schema with special clustering using PSO algorithm for heterogeneous wireless sensor network. Sensors. https://doi.org/10.3390/s19030671

    Article  Google Scholar 

  57. Loganathan, S., & Arumugam, J. (2021). Energy efficient clustering algorithm based on particle swarm optimization technique for wireless sensor networks. Wireless Personal Communications, 119(1), 815–843. https://doi.org/10.1007/s11277-021-08239-z

    Article  Google Scholar 

  58. Rawat, P., & Chauhan, S. (2021). Particle swarm optimization-based energy efficient clustering protocol in wireless sensor network. Neural Computing and Applications, 33(21), 14147–14165. https://doi.org/10.1007/s00521-021-06059-7

    Article  Google Scholar 

  59. Mishra, R., & Yadav, R. K. (2023). Energy efficient cluster-based routing protocol for WSN using nature inspired algorithm. Wireless Personal Communications, 130(4), 2407–2440. https://doi.org/10.1007/s11277-023-10385-5

    Article  Google Scholar 

  60. Xiuwu, Y., Zixiang, Z., Wei, P., & Yong, L. (2023). A novel multi-hop clustering routing algorithm based on particle swarm optimization for wireless sensors networks. Wireless Personal Communications, 130(2), 935–956. https://doi.org/10.1007/s11277-023-10314-6

    Article  Google Scholar 

  61. Yadav, R. K., & Mahapatra, R. P. (2022). Hybrid metaheuristic algorithm for optimal cluster head selection in wireless sensor network. Pervasive and Mobile Computing. https://doi.org/10.1016/j.pmcj.2021.101504

    Article  Google Scholar 

  62. Prakash, V., & Pandey, S. (2023). Metaheuristic algorithm for energy efficient clustering scheme in wireless sensor networks. Microprocessors and Microsystems, 101, 104898. https://doi.org/10.1016/j.micpro.2023.104898

    Article  Google Scholar 

  63. Sharma, S. K., & Chawla, M. (2023). RME–SEP: An IoT favorable approach of minimum energy-efficient hybrid SEP for heterogeneous WSN data routing. Arabian Journal for Science and Engineering. https://doi.org/10.1007/s13369-023-08234-5

    Article  Google Scholar 

  64. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, (Vol. 2, pp. 10). https://doi.org/10.1109/HICSS.2000.926982.

  65. Gaspar, A., Oliva, D., Cuevas, E., Zaldívar, D., Pérez, M., & Pajares, G. (2021). Hyperparameter optimization in a convolutional neural network using metaheuristic algorithms. In D. Oliva, E. H. Houssein, & S. Hinojosa (Eds.), Metaheuristics in machine learning: Theory and applications (pp. 37–59). Springer International Publishing.

    Chapter  Google Scholar 

  66. Jiang, S., Mashdoor, S., Parvin, H., Tuan, B. A., & Pho, K.-H. (2021). An adaptive location-aware swarm intelligence optimization algorithm. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 29(02), 249–279. https://doi.org/10.1142/S0218488521500128

    Article  MathSciNet  Google Scholar 

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  1. ECED, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, 131039, India

    Sarvesh Kumar Sharma & Mridul Chawla

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Both authors performed the primary literature review, data collection, experiments, and approved the final manuscript. Mridul Chawla supervised the research and Sarvesh Kumar Sharma drafted the final manuscript.

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Sharma, S.K., Chawla, M. PRESEP: Cluster Based Metaheuristic Algorithm for Energy-Efficient Wireless Sensor Network Application in Internet of Things. Wireless Pers Commun 133, 1243–1263 (2023). https://doi.org/10.1007/s11277-023-10814-5

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