Proceedings of International Conference on Applied Innovation in IT  ·  2026/03/31  ·  Vol. 14  ·  Issue 1  ·  pp. 503–511
Improve Data Transmission Efficiency for Wireless Sensor Networks Through Routing Protocols
Fatima Idrees Ibrahim and Dheyab Salman Ibrahim
📄 Download PDF DOI: 10.25673/123595
Abstract
Wireless Sensor Networks (WSNs) are fundamental components of Internet of Things (IoT) applications, including environmental monitoring, healthcare, industrial automation, and smart cities. However, limited battery capacity, routing overhead, transmission delay, and dynamic topology changes remain major challenges that reduce network lifetime and communication reliability. Therefore, the development of energy-efficient and scalable routing protocols has become a critical research issue in modern WSN environments. This study presents a systematic review and comparative analysis of recent routing protocols for WSNs published between 2020 and 2025. A PRISMA-based review methodology was adopted to identify and evaluate relevant studies from major scientific databases, including IEEE Xplore, ScienceDirect, Springer, MDPI, and Google Scholar. Initially, 120 studies were collected, and after applying inclusion and exclusion criteria, 34 high-quality studies were selected for detailed analysis. The reviewed protocols are classified according to network structure, routing strategy, optimization mechanism, and intelligent decision-making approaches. In addition, this review provides comparative benchmarking using key performance metrics, including energy consumption, scalability, routing overhead, latency, throughput, packet delivery ratio, reliability, and network lifetime. Particular attention is given to intelligent routing approaches based on machine learning, deep reinforcement learning, fuzzy systems, and metaheuristic optimization algorithms. The analysis shows that hybrid intelligent routing approaches significantly improve energy efficiency and routing adaptability in dynamic WSN environments. However, challenges related to scalability, computational complexity, real-time adaptability, and lightweight AI integration remain unresolved. Finally, this study highlights current research gaps and proposes future directions for developing intelligent and energy-efficient routing frameworks for next-generation WSN applications.
Keywords
Wireless Sensor Networks Internet of Things Routing Protocols Energy Efficiency Network Scalability.
References
  1. S. Thakur, N. I. Sarkar, and S. Yongchareon, “AI-driven energy-efficient routing in IoT-based wireless sensor networks: A comprehensive review,” Sensors, 2025.
  2. P. Bekal, S. Kumar, and K. R. Babu, “A comprehensive review of energy efficient routing protocols for query driven wireless sensor networks,” Journal of Ambient Intelligence and Humanized Computing, 2024.
  3. S. El Khediri et al., “Energy-efficient cluster routing protocol for wireless sensor networks,” Ad Hoc Networks, 2024.
  4. M. A. Tawfeek et al., “Improving energy efficiency and routing reliability in wireless sensor networks,” EURASIP Journal on Wireless Communications and Networking, 2025.
  5. N. Benaouda, “Efficient routing for delay-energy tradeoff in wireless sensor networks,” Journal of Telecommunications and Information Technology, 2024.
  6. A. Ojha and B. Gupta, “Evolving landscape of wireless sensor networks: A survey of trends and future perspectives,” Discover Applied Sciences, 2025.
  7. L. Kaur and S. Kaur, “A survey on energy-efficient routing techniques in wireless sensor networks,” Materials Today: Proceedings, 2021.
  8. Y. Alsarhan, N. Turab, H. Abualese, and H. A. Owida, “A review of energy-efficient clustering and routing techniques in wireless sensor networks: Key metrics and future trend,” International Journal of Innovative Research and Scientific Studies, vol. 8, no. 4, pp. 1250-1259, 2025.
  9. B. Saemi and F. Goodarzian, “Energy-efficient routing protocol for underwater wireless sensor networks using a hybrid metaheuristic algorithm,” Engineering Applications of Artificial Intelligence, vol. 133, p. 108132, 2024.
  10. L. K. Ketshabetswe, A. M. Zungeru, C. K. Lebekwe, and B. Mtengi, “A compression-based routing strategy for energy saving in wireless sensor networks,” Results in Engineering, vol. 23, p. 102616, 2024.
  11. S. H. Gopalan, D. G. Takale, B. Jayaprakash, and V. P. Raj, “An energy efficient routing protocol with fuzzy neural networks in wireless sensor network,” Ain Shams Engineering Journal, vol. 15, no. 10, p. 102979, 2024.
  12. A. Ali et al., “Enhanced fuzzy logic zone stable election protocol for cluster head election (E-FLZSEPFCH) and multipath routing in wireless sensor networks,” Ain Shams Engineering Journal, vol. 15, no. 2, p. 102356, 2024.
  13. A. Janarthanan and V. Srinivasan, “Multi-objective cluster head-based energy aware routing using optimized auto-metric graph neural network for secured data aggregation in wireless sensor network,” International Journal of Communication Systems, vol. 37, no. 3, p. e5664, 2024.
  14. M. Tolani, S. A. A. Biabani, and P. Kumar, “Energy-efficient deviation aware adaptive bit-mapping medium access control protocol for wireless sensor network,” IEEE Access, 2024.
  15. D. S. Ibrahim, S. T. Hasson, and P. A. Johnson, “Optimizing LEACH routing protocols for WSN: An analysis study,” AIP Conference Proceedings, vol. 2591, no. 1, p. 020011, 2023, doi: 10.1063/5.0123619.
  16. S. S. Suresh, V. Prabhu, V. Parthasarathy, G. Senthilkumar, and V. Gundu, “Intelligent data routing strategy based on federated deep reinforcement learning for IoT-enabled wireless sensor networks,” Measurement: Sensors, vol. 31, p. 101012, 2024.
  17. M. M. Al-Heeti, J. A. Hammad, and A. S. Mustafa, “Design and implementation of energy-efficient hybrid data aggregation in heterogeneous wireless sensor network,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 2, pp. 1392-1399, 2024.
  18. J. B. Fernandes et al., “Reliable and efficient routing for water quality monitoring in underwater WSN,” in Proc. 2024 2nd Int. Conf. Computer, Communication and Control (IC4), 2024, pp. 1-5.
  19. G. Kaur, “Enhancing energy use efficiency of wireless sensor networks using newly proposed fault tolerance multipath routing protocol (MRP-FT),” Operations Research and Decisions, vol. 34, no. 3, pp. 143-164, 2024.
  20. D. Liu et al., “LEACH-D: A low-energy, low-delay data transmission method for industrial Internet of Things wireless sensors,” Internet of Things and Cyber-Physical Systems, vol. 4, pp. 129-137, 2024.
  21. D. S. Ibrahim, F. K. Zaidan, J. Kadum, H. H. Saleh, L. T. Rasheed, and W. S. Nsaif, “Routing protocols-based clustering in WSNs,” in Proc. 2021 4th Int. Iraqi Conf. Engineering Technology and Their Applications (IICETA), Najaf, Iraq, 2021, pp. 201-205, doi: 10.1109/IICETA51758.2021.9717419.
  22. P. P. Bairagi, M. Dutta, and K. S. Babulal, “An energy-efficient protocol based on recursive geographic forwarding mechanisms for improving routing performance in WSN,” IETE Journal of Research, vol. 70, no. 3, pp. 2212-2224, 2024.
  23. S. Vhatkar, Z. Aalam, and M. Atique, “Advancing wireless sensor networks through performance evaluation of M-PDCH routing protocol for enhanced quality of service,” Indian Journal of Science and Technology, vol. 17, no. 9, pp. 794-803, 2024.
  24. A. N. Pathak and A. R. Yadav, “Scheduling based on residual energy of sensors to extend the lifetime of network in wireless sensor network,” Journal of Engineering and Applied Science, vol. 71, no. 1, p. 100, 2024.
  25. Ö. M. Gül, “A novel energy-aware path planning by autonomous underwater vehicle in underwater wireless sensor networks,” Turkish Journal of Maritime and Marine Sciences, vol. 10, Special Issue 1, pp. 81-94, 2024.
  26. M. A. N. Suman, V. Geethasri, and A. S. A. I. Pavan, “A routing protocol for efficiently managing energy in underwater wireless sensor networks with depth control,” Journal of Science & Technology, vol. 9, no. 4, pp. 59-73, 2024.
  27. D. S. Ibrahim, S. T. Hasson, and P. A. Johnson, “Selecting an optimal cluster head using PSO algorithm in WSNs,” in Proc. 2022 Int. Conf. Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, 2022, pp. 1-4, doi: 10.23919/SoftCOM55329.2022.9911416.
  28. D. U. S. Rajkumar, K. P. Karani, R. Sathiyaraj, and P. Vidyullatha, “Optimal shortest path selection using an evolutionary algorithm in wireless sensor networks,” International Journal of Electrical and Computer Engineering, vol. 14, no. 6, 2024.
  29. M. Elhoseny, K. Shankar, and J. Uthayakumar, “Energy-efficient routing protocol for wireless sensor networks based on hybrid optimization,” Computer Communications, 2024.
  30. H. Hu, X. Fan, and C. Wang, “Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks,” Scientific Reports, vol. 14, no. 1, p. 18595, 2024.
  31. I. Surenther, K. Sridhar, and M. K. Roberts, “Enhancing data transmission efficiency in wireless sensor networks through machine learning-enabled energy optimization: A grouping model approach,” Ain Shams Engineering Journal, vol. 15, no. 4, p. 102644, 2024.

Proceedings of the International Conference on Applied Innovations in IT by Anhalt University of Applied Sciences is licensed under CC BY-SA 4.0  ·  This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License

ICAIIT 2026
International Conference on Applied Innovation in IT
Navigation
Publisher
ISSN2199-8876
Publisher Edition Hochschule Anhalt
Location Anhalt University of Applied Sciences
Email leiterin.hsb@hs-anhalt.de
Phone +49 (0) 3496 67 5611
Address Building 01, Room 425
Bernburger Str. 55
D-06366 Köthen, Germany
Open Access License
CC BY-SA 4.0

All works are licensed under the Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0), unless otherwise noted.

Published by ICAIIT in cooperation with Anhalt University of Applied Sciences.

© 2026 ICAIIT — International Conference on Applied Innovations in IT. Anhalt University of Applied Sciences, Köthen, Germany.
Visitors: site traffic counter