Abstract
Wireless sensor networks (WSN) are an assortment of sensor nodes that are used in multiple fields. Wireless sensor networks, often known as WSNs, have garnered much interest recently owing to their limitless potential. Because the WSN field is barely ten years old and WSN has typical characteristics and constraints, there are many problems associated with WSN that need to be studied, analyzed, and solved as well as many challenges that need to be met for its widespread use and easy acceptance by users. These problems and challenges can be attributed to WSNs having typical characteristics and constraints. The growth of WSN technology is limited by lifetime issues. A major portion of power is wasted by forwarding redundant data from the sensor nodes (SN) to the base station (BS). So, a specific and accurate data aggregation technique is needed for successful WSN use. In this work, two major contributions are proposed. Initially, Sail Fish Optimization (SFO) based on cluster head selection algorithm was introduced for clustering. Then, an improved SVM classification algorithm was proposed for data aggregation. The hyperparameters of SVM are adjusted by using Sailfish Optimization. Sailfish Optimization is one of the many nature-inspired optimization techniques. It is based on the hunting nature of sailfish in oceans. In comparison to existing algorithms, the proposed algorithm’s performance is measured in terms of delay, energy, packet delivery ratio, and data classification accuracy compared to other algorithms. The proposed work achieves the overhead with minimal value of 5.56% compared to existing methods.
Similar content being viewed by others
Data Availability
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
References
Ahmad, S, Mehfuz, S, Mebarek-Oudina, F, Beg, J (2022) RSM analysis based cloud access security broker: a systematic literature review. Clust Comput, 1–31
Ammari HM (2012) CSI: An Energy-Aware Cover-Sense-Inform Framework for k-Covered Wireless Sensor Networks. IEEE Trans Parallel Distrib Syst 23(4):651–658
Abba Ari AA, Gueroui A, Yenke BO, Labraoui N (2016) Energy efficient clustering algorithm for wireless sensor networks using the ABC metaheuristic. 2016 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, India, pp 1–6. https://doi.org/10.1109/ICCCI.2016.7480010
Borkar G, Patil L (2019) A novel clustering approach and adaptive SVM classifier for intrusion detection in WSN: a data mining concept. Sustain Comput: Inform Syst 23. https://doi.org/10.1016/j.suscom.2019.06.002
Cheng L, Guo S, Wang Y, Yang Y (2016) Lifting wavelet compression based data aggregation in big data wireless sensor networks. 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), Wuhan, China, pp 561–568. https://doi.org/10.1109/ICPADS.2016.0080
Dao K, Trong-The N, Jeng-Shyang P, Yu Q, Quoc-Anh L (2020) Identification failure data for cluster heads aggregation in WSN Based on improving classification of SVM. IEEE Access 1–1. https://doi.org/10.1109/ACCESS.2020.2983219
Fakhet W, Khediri SE, Dallali A, Kachouri A (2017) New K-means algorithm for clustering in wireless sensor networks. 2017 International Conference on Internet of Things, Embedded Systems and Communications (IINTEC), Gafsa, Tunisia, pp 67–71. https://doi.org/10.1109/IINTEC.2017.8325915
Fattoum M, Jellali Z, Atallah LN (2020) Fuzzy logic-based two-level clustering for data aggregation in WSN. 2020 17th International Multi-Conference on Systems, Signals & Devices (SSD), Monastir, Tunisia, pp 360–365. https://doi.org/10.1109/SSD49366.2020.9364181
Gielow F, Nogueira M, Santos A (2014) Data similarity aware dynamic nodes clustering for supporting management operations. 2014 IEEE Network Operations and Management Symposium (NOMS), Krakow, Poland, pp 1–8. https://doi.org/10.1109/NOMS.2014.6838264
Jain S, Bharot N (2019) K medoids based clustering algorithm with minimum spanning tree in wireless sensor network. 2019 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, pp 1771–1776. https://doi.org/10.1109/ICCES45898.2019.9002548
Zheng J, Wang P, Li C (2010) Distributed data aggregation using slepian-wolf coding in cluster-based wireless sensor networks. IEEE Trans Veh Technol 59(5):2564–2574. https://doi.org/10.1109/TVT.2010.2042186
Kamalesh S, Ganesh Kumar P (2017) Data aggregation in wireless sensor network using SVM-based failure detection and loss recovery. J Exp Theor Artif Intell 29(1):133–147
Maivizhi R, Yogesh P (2020) Spatial correlation based data redundancy elimination for data aggregation in wireless sensor networks. 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India, pp 1–5. https://doi.org/10.1109/ICITIIT49094.2020.9071535
Miranda K, Ramos V (2016) Improving data aggregation in wireless sensor networks with time series estimation. IEEE Lat Am Trans 14(5):2425–2432. https://doi.org/10.1109/TLA.2016.7530441
Mohanaradhya, Sumithra Devi KA (2019) Novel Approaches to Enhance Wireless Sensor Network Life time by Even Distribution of Cluster Heads and Avoiding Redundant Data, 2019 1st International Conference on Advanced Technologies in Intelligent Control, Environ Comput Commun Eng (ICATIECE)
Morell A, Correa M. Barcelo, Vicario JL (2016) Data Aggregation and Principal Component Analysis in WSNs. IEEE Trans Wirel Commun 15(6):3908–3919
Nyo, MT, Mebarek-Oudina, F, Hlaing, SS, Khan, NA (2022) Otsu’s thresholding technique for MRI image brain tumor segmentation. Multimed Tools Appl, 1–13
Ram MS, Rao KN, Basha SJ (2020) Cluster Head and Optimal Path Slection Using K-GA and T-FA Algorithms for Wireless Sensor Networks, 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA)
Ren F, Zhang J, Wu Y, He T, Chen C, Lin C (2013) Attribute-Aware Data Aggregation Using Potential-Based Dynamic Routing in Wireless Sensor Networks. IEEE Trans Parallel Distrib Syst 24(5):881–892
Roy NR, Chandra P (2019) EEDAC-WSN: Energy Efficient Data Aggregation in Clustered WSN, 2019 International Conference on Automation, Computational and Technology Management (ICACTM)
Sert SA, Alchihabi A, Yazici A (2018) A two-tier distributed fuzzy logic based protocol for efficient data aggregation in multihop wireless sensor networks. IEEE Trans Fuzzy Syst 26(6):3615–3629. https://doi.org/10.1109/TFUZZ.2018.2841369
Shu Q, Hu Q, Zheng J, Mitton N (2013) A dependable slepian-wolf coding based clustering algorithm for data aggregation in wireless sensor networks. 2013 International Conference on Wireless Communications and Signal Processing, WCSP 2013, pp 1–6. https://doi.org/10.1109/WCSP.2013.6677109
Villas LA, Boukerche A, Ramos HS, de Oliveira HABF, de Araujo RB, Loureiro AAF (2013) DRINA: a lightweight and reliable routing approach for in-network aggregation in wireless sensor networks. IEEE Trans Comput 62(4):676–689. https://doi.org/10.1109/TC.2012.31
Wu D, Wong MH (2011) Fast and simultaneous data aggregation over multiple regions in wireless sensor networks. IEEE Trans Syst Man Cybern Part C Appl Rev 41(3):333–343. https://doi.org/10.1109/TSMCC.2010.2056919
Yuan F, Zhan Y, Wang Y (2014) Data density correlation degree clustering method for data aggregation in WSN. IEEE Sensors J 14(4):1089–1098. https://doi.org/10.1109/JSEN.2013.2293093
Yun W-K, Yoo S-J (2021) Q-learning-based data-aggregation-aware energy-efficient routing protocol for wireless sensor networks. IEEE Access 9:10737–10750. https://doi.org/10.1109/ACCESS.2021.3051360
Zhu T, Li J, Gao H, Li Y (2022) Data aggregation scheduling in battery-free wireless sensor networks. IEEE Trans Mob Comput 21(6):1972–1984. https://doi.org/10.1109/TMC.2020.3035671
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that we have no conflict of interest.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Amutha, R., Sivasankari, G.G. & Venugopal, K.R. Node clustering and data aggregation in wireless sensor network using sailfish optimization. Multimed Tools Appl 82, 44107–44122 (2023). https://doi.org/10.1007/s11042-023-15225-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-15225-z