Deep Learning-Based Reduction of Computational Overhead and Energy Consumption in IoT-Assisted WSNs

  • Authors

    • Mohan Akole Department of Electronics & Communication Engineering, Sathyabama Institute of Science & Technology, Chennai-119, India
    • P. Kavipriya Department of Electronics & Communication Engineering, Sathyabama Institute of Science & Technology, Chennai-119, India
    https://doi.org/10.14419/21dkwk15

    Received date: June 29, 2025

    Accepted date: August 1, 2025

    Published date: August 15, 2025

  • Wireless Sensor Networks (WSN); Internet of Things (IoT); Localization, Deep learning; Gated Recurrent Unit (GRU); Clustering, Upgraded Butterfly Backtracking Optimization algorithm

  • Abstract

    Localization of Internet of Things-assisted Wireless Sensor Networks has gained significant interest from the research community. The key issues of energy consumption and computational overhead in existing studies are addressed by proposing a deep learning-based method to reduce both in the Internet of Things-assisted Wireless Sensor Networks presented here. Network topology is built using the topographic network framework to optimize energy use and computational efficiency. Anchor node deployment is performed using the Upgraded Butterfly Backtracking Optimization (UBBO) algorithm. Pilot agents are responsible for clustering sensor nodes with the Enhanced Density Peak Clustering Algorithm (E-DenPeC), which solves device heterogeneity and orientation issues through bias regression. For clustered sensor nodes, localization is carried out using the Squeeze-Excitation Network embedded Skip Connection Gated Recurrent Unit and Accelerated Iterative Algorithm (SEN-GRU-AI), based on five different measurement techniques (FLMT). Finally, collaborative localization is achieved through deep learning technology by reducing energy consumption and computational overhead. The results show that the proposed method surpasses existing approaches.

  • References

    1. . F. Alawad and F. A. Kraemer, “Value of Information in Wireless Sensor Network Applications and the IoT: A Review,” IEEE Sens. J., vol. 22, no. 10, pp. 9228–9245, May 2022, doi: 10.1109/JSEN.2022.3165946.
    2. . K. Panwar and P. Babu, “Majorization-Minimization based Hybrid Localization Method for High Precision Localization in Wireless Sensor Networks,” Nov. 28, 2023, arXiv: arXiv:2205.03881. doi: 10.48550/arXiv.2205.03881.
    3. . M. A. Alanezi, H. R. E. H. Bouchekara, and Mohammed. S. Javaid, “Range-Based Localization of a Wireless Sensor Network for Internet of Things Using Received Signal Strength Indicator and the Most Valuable Player Algorithm,” Technologies, vol. 9, no. 2, p. 42, June 2021, doi: 10.3390/technologies9020042.
    4. . V. Kotiyal, A. Singh, S. Sharma, J. Nagar, and C.-C. Lee, “ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks,” Sensors, vol. 21, no. 11, p. 3576, May 2021, doi: 10.3390/s21113576.
    5. . S. Phoemphon, C. So-In, and N. Leelathakul, “A hybrid localization model using node segmentation and improved particle swarm optimization with obstacle-awareness for wireless sensor networks,” Expert Syst. Appl., vol. 143, p. 113044, Apr. 2020, doi: 10.1016/j.eswa.2019.113044.
    6. . V. R. Sabbella, D. R. Edla, A. Lipare, and S. R. Parne, “An Efficient Localization Approach in Wireless Sensor Networks Using Krill Herd Optimization Algorithm,” IEEE Syst. J., vol. 15, no. 2, pp. 2432–2442, June 2021, doi: 10.1109/JSYST.2020.3004527.
    7. . A. Guidara, G. Fersi, M. B. Jemaa, and F. Derbel, “A new deep learning-based distance and position estimation model for range-based indoor localization systems,” Ad Hoc Netw., vol. 114, p. 102445, Apr. 2021, doi: 10.1016/j.adhoc.2021.102445.
    8. . U. Gustavsson et al., “Implementation Challenges and Opportunities in Beyond-5G and 6G Communication,” IEEE J. Microw., vol. 1, no. 1, pp. 86–100, Jan. 2021, doi: 10.1109/JMW.2020.3034648.
    9. . D. V. D. Hemanand, P. Shobha Rani, Rajalakshmi S, “Recognition of Energy Harvesting Techniques for Enhanced Operation in WSN Using IoT with Deep Learning,” J. Electr. Syst., vol. 20, no. 3s, pp. 142–149, Apr. 2024, doi: 10.52783/jes.1260.
    10. . P. Sruthi and K. Sahadevaiah, “A Novel Efficient Heuristic Based Localization Paradigm in Wireless Sensor Network,” Wirel. Pers. Commun., vol. 127, no. 1, pp. 63–83, Nov. 2022, doi: 10.1007/s11277-021-08091-1.
    11. . P. Sekhar, E. L. Lydia, M. Elhoseny, M. Al-Akaidi, M. M. Selim, and K. Shankar, “An effective metaheuristic based node localization technique for wireless sensor networks enabled indoor communication,” Phys. Commun., vol. 48, p. 101411, Oct. 2021, doi: 10.1016/j.phycom.2021.101411.
    12. . A. Mahapatro and V. C. S. R. Rayavarapu, “Anchor-Free Localization Using a Deep Neural Network in Wireless Sensor Networks with Multiple Sinks,” Aug. 09, 2021. doi: 10.36227/techrxiv.15104223.v1.
    13. . Y. Zhu, F. Yan, S. Zhao, S. Xing, and L. Shen, “On improving the cooperative localization performance for IoT WSNs,” Ad Hoc Netw., vol. 118, p. 102504, July 2021, doi: 10.1016/j.adhoc.2021.102504.
    14. . F. Watanabe, “Wireless Sensor Network Localization Using AoA Measurements With Two-Step Error Variance-Weighted Least Squares,” IEEE Access, vol. 9, pp. 10820–10828, 2021, doi: 10.1109/ACCESS.2021.3050309.
    15. . D. Arbula and S. Ljubic, “Indoor Localization Based on Infrared Angle of Arrival Sensor Network,” Sensors, vol. 20, no. 21, p. 6278, Nov. 2020, doi: 10.3390/s20216278.
    16. . K. Lakshmanna, N. Subramani, Y. Alotaibi, S. Alghamdi, O. I. Khalafand, and A. K. Nanda, “Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks,” Sustainability, vol. 14, no. 13, p. 7712, June 2022, doi: 10.3390/su14137712.
    17. . A. Sharma and P. K. Singh, “Localization in Wireless Sensor Networks for Accurate Event Detection:,” Int. J. Healthc. Inf. Syst. Inform., vol. 16, no. 3, pp. 74–88, July 2021, doi: 10.4018/IJHISI.20210701.oa5.
    18. . I. B. F. De Almeida, M. Chafii, A. Nimr, and G. Fettweis, “Blind Transmitter Localization in Wireless Sensor Networks: A Deep Learning Approach,” in 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland: IEEE, Sept. 2021, pp. 1241–1247. doi: 10.1109/PIMRC50174.2021.9569361.
    19. . M. Alkhayyal and A. M. Mostafa, “Enhancing LoRaWAN Sensor Networks: A Deep Learning Approach for Performance Optimizing and Energy Efficiency,” Comput. Mater. Contin., vol. 83, no. 1, pp. 1079–1100, 2025, doi: 10.32604/cmc.2025.061836.
    20. . C. S. Mishra, J. Sampson, M. T. Kandmeir, V. Narayanan, and C. R. Das, “Synergistic and Efficient Edge-Host Communication for Energy Harvesting Wireless Sensor Networks,” Aug. 26, 2024, arXiv: arXiv:2408.14379. doi: 10.48550/arXiv.2408.14379.

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  • How to Cite

    Akole , M. ., & Kavipriya, P. . (2025). Deep Learning-Based Reduction of Computational Overhead and Energy Consumption in IoT-Assisted WSNs. International Journal of Basic and Applied Sciences, 14(SI-2), 233-242. https://doi.org/10.14419/21dkwk15