Adaptive Thresholding in TDEEC to Achieve Energy Saving and Long Life of WSNs
DOI:
https://doi.org/10.24237/04.02.875Keywords:
Threshold Distributed Energy Efficient Clustering (TDEEC), Wireless Sensor Networks (WSN), Energy Efficiency, Cluster Head (CH).Abstract
Wireless sensor networks (WSNs) have become a technology foundation in the new era of technological ecosystems and can be used in multiple fields. Nevertheless, sensor nodes are energy-constrained, and network stability and lifetime are directly dependent on the energy capacity of the sensor nodes. Routing protocols based on clustering have been popular to resolve energy efficiency issues, but they still face limitations. To solve these issues, the current study suggests an improved Threshold Distributed Energy Efficient Clustering (TDEEC) protocol, which will add a dynamic and adaptive threshold-based CH election mechanism. This adaptive technique will guarantee a fairer allocation of Cluster Head (CH) roles, minimize the premature mortality of nodes as well as provide equalized energy consumption in the network. The results showed that the improved TDEEC protocol has high stability, long network lifetime, and high throughput. The findings reflect sluggish first-node fatality, enhanced energy efficiency, and augmented data packet volume that has been efficiently conveyed to Base Station (BS) by 25% more than the original protocol. improved TDEEC overcomes the weaknesses of current models.
Downloads
References
[1] M. U. Mushtaq, H. Venter, A. Singh, and M. Owais, “Advances in Energy Harvesting for Sustainable Wireless Sensor Networks: Challenges and Opportunities,” Hardware, vol. 3, no. 1, p. 1, Feb. 2025, doi: https://doi.org/10.3390/hardware3010001
[2] A. Oztoprak, R. Hassanpour, A. Ozkan, and K. Oztoprak, “Security Challenges, Mitigation Strategies, and Future Trends in Wireless Sensor Networks: A Review,” ACM Computing Surveys, vol. 57, no. 4, pp. 1–29, Dec. 2024, doi: https://doi.org/10.1145/3706583
[3] Z. K. Maseer, R. Yusof, S. A. Mostafa, N. Bahaman, O. Musa, and B. Ali Saleh Al-rimy, “DeepIoT.IDS: Hybrid Deep Learning for Enhancing IoT Network Intrusion Detection,” Computers, Materials & Continua, vol. 69, no. 3, pp. 3945–3966, 2021, doi: https://doi.org/10.32604/cmc.2021.016074
[4] Hassan, A. A. H., Shah, W. M., Iskandar, M. F., Al-Mhiqani, M. N., & Naseer, Z. K. (2018). Unequal clustering routing algorithms in wireless sensor networks: A comparative study. Journal of Advanced Research in Dynamical and Control Systems, 10(2 Special Issue), 2142-2156.
[5] S. Zhang, X. Liu, and M. Trik, “Energy efficient multi hop clustering using Artificial Bee Colony metaheuristic in WSN,” Scientific Reports, vol. 15, no. 1, Jul. 2025, doi: https://doi.org/10.1038/s41598-025-12321-y
[6] O. Amenchar, M. Baghouri, S. Chakkor, and A. Dkiouak, “A new enhanced TDEEC protocol for 3D HWSN,” e-Prime – Advances in Electrical Engineering, Electronics and Energy, vol. 10, p. 100863, Dec. 2024, doi: 10.1016/j.prime.2024.100863.
[7] H. H. El-Sayed and Z. M. Hashem, “Comparison of the new version of DEEC protocol to extend WSN lifetime,” EURASIP Journal on Wireless Communications and Networking, vol. 2023, no. 1, Jul. 2023, doi: 10.1186/s13638-023-02265-0.
[8] C. Sureshkumar and S. Sabena, “Fuzzy-based secure authentication and clustering algorithm for improving the energy efficiency in wireless sensor networks,” Wireless Personal Communications, vol. 112, no. 3, pp. 1517–1536, Jan. 2020, doi: 10.1007/s11277-020-07113-8.
[9] K. Maheshwar, S. Veenadhari, and M. Almelu, “Energy efficient heterogeneous WSN clustering using machine learning,” SMART MOVES Journal IJOSCIENCE, vol. 7, no. 6, pp. 24–29, Jun. 2021, doi: 10.24113/ijoscience.v7i6.391.
[10] P. Mehrotra and D. Bhardwaj, “Evaluating energy efficiency and performance of WSN routing protocols: A comparative study of LEACH, DEEC, DDEEC, and EESAA,” in Algorithms for Intelligent Systems. Singapore: Springer, 2025, pp. 91–100, doi: 10.1007/978-981-96-3333-3_8.
[11] S. Singh and A. Malik, “Heterogeneous DEEC protocol for prolonging lifetime in wireless sensor networks,” Journal of Information and Optimization Sciences, vol. 38, no. 5, pp. 699–720, Jul. 2017, doi: 10.1080/02522667.2016.1220083.
[12] S. Neelakandan, A. Mardani, P. Mani, A. R. Mishra, and P. Ezhumalai, “A fuzzy logic and DEEC protocol-based clustering routing method for wireless sensor networks,” AIMS Mathematics, vol. 8, no. 4, pp. 8310–8331, Jan. 2023, doi: 10.3934/math.2023419.
[13] V. Nehra, A. K. Sharma, and R. K. Tripathi, “I-DEEC: Improved DEEC for blanket coverage in heterogeneous wireless sensor networks,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 9, pp. 3687–3698, Oct. 2019, doi: 10.1007/s12652-019-01552-3.
[14] Krishna and C. Senthilkumar, “An effective delay reduction routing protocol for WSN using optimized distributed energy efficient clustering (O-DEEC) protocol compared with DEEC protocol,” AIP Conference Proceedings, vol. 3082, p. 060008, Jan. 2024, doi: 10.1063/5.0186148.
[15] N. F. Omran, N. M. M. Abdelnapi, E. M. Mohamed, and N. M. Labib, “Design of an enhanced threshold sensitive distributed energy efficient clustering routing protocol for WSN-based IoT,” International Journal of Electronics, vol. 110, no. 8, pp. 1373–1392, Jun. 2022, doi: 10.1080/00207217.2022.2087919.
[16] M. Bilal, E. U. Munir, and F. K. Alarfaj, “Hybrid clustering and routing algorithm with threshold-based data collection for heterogeneous wireless sensor networks,” Sensors, vol. 22, no. 15, p. 5471, Jul. 2022, doi: 10.3390/s22155471.
[17] N. Tripathi, “Performance analysis of Mod-LEACH, LEACH, DEEC, EDEEC, TDEEC in wireless camera sensor network,” IMRJR, vol. 2, no. 2, Mar. 2025, doi: 10.17148/imrjr.2025.020205.
[18] S. Bhushan, R. Pal, and S. Antoshchuk, “Energy efficient clustering protocol for heterogeneous wireless sensor network: A hybrid approach using GA and K-means,” in Proc. IEEE DSMP, Aug. 2018, doi: 10.1109/DSMP.2018.8478538.
[19] D. W. Sambo, B. Yenke, A. Förster, and P. Dayang, “Optimized clustering algorithms for large wireless sensor networks: A review,” Sensors, vol. 19, no. 2, p. 322, Jan. 2019, doi: 10.3390/s19020322.
[20] G. M. Tamilselvan, “TEDDEEC: Threshold enhanced developed distributed energy-efficient clustering for heterogeneous wireless sensor networks,” Journal of Applied Research and Technology, vol. 22, no. 3, pp. 336–350, Jun. 2024, doi: 10.22201/icat.24486736e.2024.22.3.2301.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 CC BY 4.0
This work is licensed under a Creative Commons Attribution 4.0 International License.
