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A butterfly optimization approach for improving the performance of futuristic internet-of-things

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Abstract

Although the fifth generation (5G) is crucial to the current Internet of Things (IoT), the increase of automated IoT systems and data-centric services that need thousands of microseconds of latency, terabytes of data every second, and more than 107 IoT connections per km2 would be beyond the capability of current 5G networks. In order to fulfil the requirements of future IoT networks, this research presents an intelligent clustering and routing approach for IoT networks (ICR-IoT) that minimizes energy consumption and server latency, which are crucial factors in ensuring quality of service to the end users. Load balancing and energy efficiency in IoT-edge computing systems are NP- hard problems. This paper presents a novel approach called ICR-IoT for intelligent clustering and routing in internet of things networks, with the goal of minimizing energy consumption and server latency to ensure quality of service for end users. The approach uses a metaheuristic called the Butterfly Optimization Algorithm (BOA) to solve the NP-hard problems of load balancing and energy efficiency in IoT edge computing systems, and a dynamic routing approach to handle changing network conditions such as node energy. ICR-IoT presents novel parameters, packet uniformity (Pu) and Lifetime uniformity (Lu) of the data aggregators to improve the overall 6G IoT network performance. An experiment set is created to thoroughly assess the performance of the proposed work with various sensor node and gateway configurations. We have implemented our code in Python 3.10 on a 64-bit system having 8 GB RAM and 2.00 GHz processor. The proposed approach was tested and found to perform better than the BPSO approach, with improvements in lifetime and energy uniformity of the network. The lifetime and energy uniformity has been improved by 13.6% and 27.07%, respectively. In general, the ICR-IoT approach has the potential to enhance routing and clustering performance, as well as quality of service and network lifetime, in 6G-IoT networks.

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References

  • Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-Qaness MA, Gandomi AH (2021a) Aquila optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250

    Article  Google Scholar 

  • Abualigah L, Diabat A, Mirjalili S, Abd Elaziz M, Gandomi AH (2021b) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng 376:113609

    Article  MathSciNet  MATH  Google Scholar 

  • Abualigah L, Abd Elaziz M, Sumari P, Geem ZW, Gandomi AH (2022) Reptile search algorithm (RSA): A nature-inspired meta-heuristic optimizer. Expert Syst Appl 191:116158

    Article  Google Scholar 

  • Amutha J, Sharma S, Sharma SK (2022) An energy efficient cluster based hybrid optimization algorithm with static sink and mobile sink node for wireless sensor networks. Expert Syst Appl 203:117334

    Article  Google Scholar 

  • Arora S, Singh S (2019) Butterfly optimization algorithm: a novel approach for global optimization. Soft Comput 23(3):715–734

    Article  Google Scholar 

  • Arya A, Malik A, Kumar S (2015) A routing protocol for detecting holes in wireless sensor networks with multiple sinks. In Proceedings of the Third International Symposium on Women in Computing and Informatics. 103–108

  • Chettri L, Bera R (2020) A comprehensive survey on Internet of Things (IoT) toward 5G wireless systems. IEEE Internet Things J 7(1):16–32

    Article  Google Scholar 

  • Chowdhury MZ, Shahjalal M, Ahmed S, Jang YM (2020) 6G wireless communication systems: applications, requirements, technologies, challenges, and research directions. IEEE Open J Commun Soc 1:957–975

    Article  Google Scholar 

  • Daniel J, Francis SFV, Velliangiri S (2021) Cluster head selection in wireless sensor network using tunicate swarm butterfly optimization algorithm. Wireless Netw 27:5245–5262

    Article  Google Scholar 

  • Du J, Wang L (2011) “Uneven clustering routing algorithm for Wireless Sensor Networks based on ant colony optimization,” In 2011 3rd International Conference on Computer Research and Development 3: 67–71. doi: https://doi.org/10.1109/ICCRD.2011.5764247

  • Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In MHS’95. Proceedings of the sixth international symposium on micro machine and human science. IEEE. 39–43

  • Esmaeeli M, Ghahroudi SAH (2016) Improving energy efficiency using a new game theory algorithm for wireless sensor networks. Int J Comput Appl 136:12

    Google Scholar 

  • Ezugwu AE, Agushaka JO, Abualigah L, Mirjalili S, Gandomi AH (2022) Prairie dog optimization algorithm. Neural Comput Appl 34(22):20017–20065

    Article  Google Scholar 

  • Ezugwu AE, Agushaka JO, Abualigah L (2022) Dwarf mongoose optimization algorithm. Comput Methods Appl Mech Eng 391:114570–8

    Article  MathSciNet  MATH  Google Scholar 

  • Fan X, Jia H, Wang L, Xu P (2017) Energy balance based uneven cluster routing protocol using ant colony taboo for wireless sensor networks. Wireless Pers Commun 97(1):1305–1321. https://doi.org/10.1007/s11277-017-4567-7

    Article  Google Scholar 

  • Frank P, Regattieri M, Weingaertner D (2021) Treasure hunt framework: distributing metaheuristics on high performance computing systems. Swarm Evol Comput 65:100906

    Article  Google Scholar 

  • Gunjan (2022) A review on multi-objective optimization in wireless sensor networks using nature inspired meta-heuristic algorithms. Neural Process Lett. https://doi.org/10.1007/s11063-022-10851-4

    Article  Google Scholar 

  • Gunjan, Sharma AK, Verma K (2020) Layered energy balanced unequal clustering and routing (LEBUCR) protocol for wireless sensor networks. Ad Hoc & Sensor Wireless Networks 46

  • Heinzelman WB, Chandrakasan AP, Balakrishnan H (2002) An application-specific protocol architecture for wireless microsensor networks. IEEE Trans Wireless Commun 1(4):660–670. https://doi.org/10.1109/TWC.2002.804190

    Article  Google Scholar 

  • Hejazi H, Rajab H, Cinkler T, Lengyel L (2018) “Survey of platforms for massive IoT,” in Proc. IEEE Int. Conf. Future IoT Technol. (Future IoT), Eger, Hungary. 1–8

  • Hu K-C, Tsai C-W, Chiang M-C (2020) A multiple-search multi-start framework for metaheuristics for clustering problems. IEEE Access 8:96173–96183. https://doi.org/10.1109/ACCESS.2020.2994813

    Article  Google Scholar 

  • IMT Traffic Estimates for the Years 2020 to 2030 (2015) International Telecommunication Union, ITU-Recommendation, document M.2370-0

  • Junping H, Yuhui J, Liang D (2008) “A time-based cluster-head selection algorithm for LEACH,” In 2008 IEEE symposium on computers and communications, pp. 1172–1176. doi: https://doi.org/10.1109/ISCC.2008.4625714

  • Kamal ARM, Hamid M (2017) Supervisory routing control for dynamic load balancing in low data rate wireless sensor networks. Wireless Netw 23(4):1085–1099. https://doi.org/10.1007/s11276-016-1209-z

    Article  Google Scholar 

  • Kang SH, Nguyen T (2012) Distance based thresholds for cluster head selection in wireless sensor networks. IEEE Commun Lett 16(9):1396–1399. https://doi.org/10.1109/LCOMM.2012.073112.120450

    Article  Google Scholar 

  • Kaushik A, Goswami M, Manuja M, Indu S, Gupta D (2020) A binary PSO approach for improving the performance of wireless sensor networks. Wireless Pers Commun 113(1):263–297

    Article  Google Scholar 

  • Kuila P, Jana PK (2014) Energy efficient clustering and routing algorithms for wireless sensor networks: particle swarm optimization approach. Eng Appl Artif Intell 33:127–140. https://doi.org/10.1016/j.engappai.2014.04.009

    Article  Google Scholar 

  • Liang JJ, Qu BY, Suganthan PN (2013) Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 635(2)

  • Maheshwari P, Sharma AK, Verma K (2021) Energy efficient cluster based routing protocol for WSN using butterfly optimization algorithm and ant colony optimization. Ad Hoc Netw 110:102317

    Article  Google Scholar 

  • Mansour RF (2022) Blockchain assisted clustering with intrusion detection system for industrial internet of things environment. Expert Syst Appl 207:117995

    Article  Google Scholar 

  • Oyelade ON, Ezugwu AES, Mohamed TI, Abualigah L (2022) Ebola optimization search algorithm: a new nature-inspired metaheuristic optimization algorithm. IEEE Access 10:16150–16177

    Article  Google Scholar 

  • Peiravi A, Mashhadi HR, Javadi SH (2013) An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithms. Int J Commun Syst 26(1):114–126. https://doi.org/10.1002/dac.1336

    Article  Google Scholar 

  • Rambabu B, Reddy AV, Janakiraman S (2022) Hybrid artificial bee colony and monarchy butterfly optimization algorithm (HABC-MBOA)-based cluster head selection for WSNs. J King Saud Univ Comput Inform Sci 34(5):1895–1905

    Google Scholar 

  • Riaz MN (2018) Clustering algorithms of wireless sensor networks: a survey. Int J Wireless Microwave Technol (IJWMT) 8(4):40–53

    Article  Google Scholar 

  • Senthilkumar C, Manickam J (2017) PCM: a path-aware clustering mechanism for energy-efficient routing protocol in wireless sensor networks. J Comput Theor Nanosci 14(11):5478–5483. https://doi.org/10.1166/jctn.2017.6974

    Article  Google Scholar 

  • Stankovic JA (2014) Research directions for the internet of things. IEEE Internet Things J 1(1):3–9. https://doi.org/10.1109/JIOT.2014.2312291

    Article  Google Scholar 

  • Tariq F, Khandaker MRA, Wong K-K, Imran MA, Bennis M, Debbah M (2020) A speculative study on 6G. IEEE Wireless Commun 27(4):118–125

    Article  Google Scholar 

  • Tsai C-W, Liu S-J, Wang Y-C (2018) A parallel Metaheuristic data clustering framework for cloud. J Parallel Distrib Comput 116:39–49. https://doi.org/10.1016/j.jpdc.2017.10.020

    Article  Google Scholar 

  • Xia H, Zhang R-H, Yu J, Pan Z-K (2016) Energy-efficient routing algorithm based on unequal clustering and connected graph in wireless sensor networks. Int J Wireless Inf Netw 23(2):141–150. https://doi.org/10.1007/s10776-016-0304-5

    Article  Google Scholar 

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Authors and Affiliations

  1. SRM University, Sonipat, Haryana, India

    Anju Arya & Kanika Pahwa

  2. NIT Delhi, Delhi, India

    Gunjan

Authors
  1. Anju Arya
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  2. Kanika Pahwa
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  3. Gunjan
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Correspondence to Gunjan.

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Arya, A., Pahwa, K. & Gunjan A butterfly optimization approach for improving the performance of futuristic internet-of-things. Evolving Systems (2023). https://doi.org/10.1007/s12530-023-09539-4

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