Enhancing Routing, and Data Aggregation in WSNs Using Artificial Intelligence: A Survey

المؤلفون

  • Adel Elgaber Computer Department, Faculty of Engineering, Sabratha University, Regdalin, Libya Author
  • Basma Ali Slisal Computer Department, Institute of Science and Technology, Regdalin, Libya Author

DOI:

https://doi.org/10.65405/tqvjbc06

الكلمات المفتاحية:

شبكات الاستشعار اللاسلكية (WSNs)، التوجيه، الذكاء الاصطناعي (AI)، تجميع البيانات، قابلية التوسع

الملخص

تُظهر هذه الدراسة أن بناء شبكة استشعار لاسلكية فعّالة يتطلب اختيار أفضل سيناريو للنشر والتركيز على عدة عوامل مهمة. أهم هذه العوامل هو إطالة عمر الشبكة من خلال تمكين عقد الاستشعار من العمل لأطول فترة ممكنة. يكتسب هذا الأمر أهمية خاصة نظرًا لأن شبكات الاستشعار اللاسلكية غالبًا ما تُنشر في مناطق نائية ويصعب الوصول إليها.

لتحقيق ذلك، يجب تحديد معايير تصميم واضحة، والحد من نقاط ضعف النظام باستخدام تقنيات الذكاء الاصطناعي المناسبة. يمكن لهذه الخوارزميات تحسين أداء الشبكة، لا سيما في التوجيه وتجميع البيانات، مع توفير الطاقة.

تُستخدم شبكات الاستشعار اللاسلكية في العديد من التطبيقات اليومية، ولكل تطبيق متطلباته الخاصة. على سبيل المثال، في الزراعة، قد لا يكون أمن البيانات مصدر قلق رئيسي، ولكن دقة البيانات وكفاءة استهلاك الطاقة أمران بالغا الأهمية. في مثل هذه الحالات، تُستخدم خوارزميات الذكاء الاصطناعي لتقليل استهلاك الطاقة أثناء نقل البيانات مع الحفاظ على موثوقية البيانات ودقتها.

التنزيلات

تنزيل البيانات ليس متاحًا بعد.

المراجع

1. Thakur, S., Sarkar, N. I., & Yongchareon, S. (2025). AI-Driven Energy-Efficient Routing in IoT-Based Wireless Sensor Networks: A Comprehensive Review. Sensors, 25(24), 7408. https://doi.org/10.3390/s25247408

2. Shokouhifar, M., Fanian, F., Kuchaki Rafsanjani, M., Hosseinzadeh, M., & Mirjalili, S. (2024). AI-driven cluster-based routing protocols in WSNs: A survey of fuzzy heuristics, metaheuristics, and machine learning models. Computer Science Review, 54, Article 100684. https://doi.org/10.1016/j.cosrev.2024.100684

3. Gaidhani, A. R., & Potgantwar, A. D. (2023). A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime. Engineering Proceedings, 59(1), 231. https://doi.org/10.3390/engproc2023059231

4. Ojha, A., Gupta, B. Evolving landscape of wireless sensor networks: a survey of trends, timelines, and future perspectives. Discov Appl Sci 7, 825 (2025). https://doi.org/10.1007/s42452-025-07070-6

5. Gururaj S. Kori, Mahabaleshwar S. Kakkasageri, Poornima M. Chanal, Rajani S. Pujar, Vinayak A. Telsang,Wireless sensor networks and machine learning centric resource management schemes: A survey, Ad Hoc Networks,Volume 167,2025,103698,ISSN 1570-8705,https://doi.org/10.1016/j.adhoc.2024.103698.

6. Priyadarshi, R., Kumar, R.R., Ranjan, R. et al. AI-based routing algorithms improve energy efficiency, .latency, and data reliability in wireless sensor networks. Sci Rep 15, 22292 (2025). https://doi.org/10.1038/s41598-025-08677-w

7. Chakravarthy, R. A., Sureshkumar, C., Arun, M., & Bhuvaneswari, M. (2025). AI-Driven Energy-Efficient Data Aggregation and Routing Protocol Modeling to Maximize Network Lifetime in Wireless Sensor Networks. NDT, 3(4), 22. https://doi.org/10.3390/ndt3040022

8. Hu, L., Han, C., Wang, X., Zhu, H., & Ouyang, J. (2024). Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks. Sensors, 24(6), 1993. https://doi.org/10.3390/s24061993

9. N, T.; N, S. Deep learning based Enhanced Data Aggregation with Multi-Objective Optimization method in Wireless Sensor Networks. Peer-to-Peer Netw. Appl. 2025, 18, 1–17, https://doi.org/10.1007/s12083-025-01943-8.

10. Saranraj, G. & K, Selvamani & Malathi, P.. (2021). A novel data aggregation using multi objective based male lion optimization algorithm (DA-MOMLOA) in wireless sensor network. Journal of Ambient Intelligence and Humanized Computing. 13. 10.1007/s12652-021-03230-9.

11. S. Sheeja Rani, K. Siva Sankar,Improved buffalo optimized deep feed forward neural learning based multipath routing for energy efficient data aggregation in WSN,Measurement: Sensors,Volume 27,2023,100662,ISSN26659174,https://doi.org/10.1016/j.measen.2022.100662.(https://www.sciencedirect.com/science/article/pii/S2665917422002963).

12. Yun, Wan-Kyu & Yoo, Sang-Jo. (2021). Q-Learning-Based Data-Aggregation-Aware Energy-Efficient Routing Protocol for Wireless Sensor Networks. IEEE Access. 9. 10737-10750. 10.1109/ACCESS.2021.3051360.

13. Kamel, S., Al Qahtani, A., & Al-Shahrani, A. S. M. (2024). Particle Swarm Optimization for Wireless Sensor Network Lifespan Maximization. Engineering, Technology & Applied Science Research, 14(2), 13665–13670. https://doi.org/10.48084/etasr.6752

14. Sajid, Y., & Al-Asfoor, M. (2021). Enhanced Ant Colony Optimization for Routing in WSNs An Energy Aware Approach. International Journal of Intelligent Engineering and Systems.

15. Paulraj, S.S.S., Deepa, T. Energy-efficient data routing using neuro-fuzzy based data routing mechanism for IoT-enabled WSNs. Sci Rep 14, 30081 (2024). https://doi.org/10.1038/s41598-024-79590-x

16. Jain, K., Mehra, P.S., Dwivedi, A.K., & Agarwal, A. (2022). SCADA: scalable cluster-based data aggregation technique for improving network lifetime of wireless sensor networks. The Journal of Supercomputing, 78, 13624 - 13652.

17. Dash, L., Pattanayak, B. K., Mishra, S. K., Sahoo, K. S., Jhanjhi, N. Z., Baz, M., & Masud, M. (2022). A Data Aggregation Approach Exploiting Spatial and Temporal Correlation among Sensor Data in Wireless Sensor Networks. Electronics, 11(7), 989. https://doi.org/10.3390/electronics11070989

18. All Sciences Academy. (2025). WSN Routing Protocols: A Clear and Comprehensive Review. International Journal of Advanced Natural Sciences and Engineering Researches, 9(3), 1–14. https://doi.org/10.5281/zenodo.14957401

19. Tawfeek, M.A., Alrashdi, I., Alruwaili, M. et al. Improving energy efficiency and routing reliability in wireless sensor networks using modified ant colony optimization. J Wireless Com Network 2025, 22 (2025). https://doi.org/10.1186/s13638-025-02449-w

20. Abbas, S. S. A., Dag, T., & Gucluoglu, T. (2025). Optimizing Mobile Base Station Placement for Prolonging Wireless Sensor Network Lifetime in IoT Applications. Applied Sciences, 15(3), 1421. https://doi.org/10.3390/app15031421

21. Pushpa, G., Babu, R.A., Subashree, S. et al. Optimizing coverage in wireless sensor networks using deep reinforcement learning with graph neural networks. Sci Rep 15, 16681 (2025). https://doi.org/10.1038/s41598-025-01841-2

22. Hosseinzadeh, M., Ionescu-Feleaga, L., Ionescu, B.-Ș., Sadrishojaei, M., Kazemian, F., Rahmani, A. M., & Khan, F. (2022). A Hybrid Delay Aware Clustered Routing Approach Using Aquila Optimizer and Firefly Algorithm in Internet of Things. Mathematics, 10(22), 4331. https://doi.org/10.3390/math10224331

23. Sudha, S.P., Loret, J.B.S. A review on machine learning-based precision agriculture techniques for crop farming monitoring with IOT. Discov Environ 4, 10 (2026). https://doi.org/10.1007/s44274-025-00305-8

24. A New Method Based on Machine Learning to Increase Efficiency in Wireless SensorNetworks.(2023). Informatica, 46(9). https://doi.org/10.31449/inf.v46i9.4396

25. Dao, T.C., Tam, N.T., Quy, N.Q. et al. An energy-efficient scheme for maximizing data aggregation tree lifetime in wireless sensor network. J Ambient Intell Human Comput 14, 12329–12344 (2023). https://doi.org/10.1007/s12652-023-04621-w

26. Osamy, W., Khedr, A. M., Salim, A., AlAli, A. I., & El-Sawy, A. A. (2022). Recent Studies Utilizing Artificial Intelligence Techniques for Solving Data Collection, Aggregation and Dissemination Challenges in Wireless Sensor Networks: A Review. Electronics, 11(3), 313. https://doi.org/10.3390/electronics11030313

27. Weijia Jin,Monitoring and management of green information in water ecological environment based on sensors and big data,Measurement: Sensors,Volume 34,2024,101255,ISSN 2665-9174,https://doi.org/10.1016/j.measen.2024.101255.

28. Walid Miloud Dahmane, Samir Ouchani, Hafida Bouarfa,Smart cities services and solutions: A systematic review,Data and Information Management,Volume 9, Issue 2,2025,100087,ISSN 2543-9251, https://doi.org/10.1016/j.dim.2024.100087.

29. Boursalie, O., Samavi, R. & Doyle, T.E. Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges. J Healthc Inform Res 2, 179–203 (2018). https://doi.org/10.1007/s41666-018-0021-1

30. Seo, J. K., Lee, J., Kim, B., Shim, W., & Seo, J. T. (2026). AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review. Electronics, 15(1), 20. https://doi.org/10.3390/electronics15010020

31. Pandith, A.M., Kumar, M., Kumar, N., Goyal, N., Ahuja, S. et al. (2025). Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs. Computers, Materials & Continua, 83(1), 259–279. https://doi.org/10.32604/cmc.2025.062608

32. Sharma, S., Chmaj, G., & Selvaraj, H. (2026). Data Fusion Method for Multi-Sensor Internet of Things Systems Including Data Imputation. IoT, 7(1), 11. https://doi.org/10.3390/iot7010011

33. Salim El khediri, Awatef Benfradj, Adel Thaljaoui, Tarek Moulahi, Rehan Ullah Khan, Abdullatif Alabdulatif, Pascal Lorenz,Integration of artificial intelligence (AI) with sensor networks: Trends, challenges, and future directions,Journal of King Saud University - Computer and Information Sciences,Volume 36, Issue 1,2024,101892,ISSN 1319-1578, https://doi.org/10.1016/j.jksuci.2023.101892.

34. Farhana, Umme & Rakin, Md Masrur Hossain & Vasquez, Deslynn & Quinn, Liam & Aslan, Semih. (2025). Enhancements in WSN Energy Efficiency Using Machine Learning: A Comparative Analysis and Real-Time Challenges. Journal of Computer and Communications. 13. 1-16. 10.4236/jcc.2025.138001.

35. Tran Van Quang, Dat Tien Doan, Jack Ngarambe, Ali Ghaffarianhoseini, Amirhosein Ghaffarianhoseini, Tongrui Zhang,

36. AI management platform for privacy-preserving indoor air quality control: Review and future directions,Journal of Building Engineering,Volume 100,2025,111712,ISSN 2352-7102,https://doi.org/10.1016/j.jobe.2024.111712.

التنزيلات

منشور

2026-09-22

كيفية الاقتباس

Enhancing Routing, and Data Aggregation in WSNs Using Artificial Intelligence: A Survey . (2026). مجلة الفاروق للعلوم, 2(4), 509-520. https://doi.org/10.65405/tqvjbc06