International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 16, No. 3, 8 Jun. 2026
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An Adaptive Multi-objective approach for energy-aware routing in WSNs using Deep Q Learning and Coral Reef Optimization
PDF (585KB), PP.336-349
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Author(s)
Padma priya S.
1,*
Pavalarajan S.
1
Index Terms
Dynamic routing, energy aware, balanced energy, reliable routing, energy efficiency
Abstract
Wireless Sensor Networks play a vital role in the Internet of Things, smart cities, and industrial automation, yet there are open ended challenges in terms of efficient energy management and reliable data transmission. This paper presents a novel, two-phase routing framework comprising Dynamic Channel Selection and Energy-Efficient Routing Optimization to address these issues. In the first phase, Deep Q-Learning is utilized to identify stable communication channels, thereby enabling congestion-free data transfer across the network. The second phase implements Coral Reef Optimization to derive energy-efficient routing paths, significantly minimizing power consumption. Additionally, Adaptive Modulation and coding dynamically adjusts transmission parameters in real time to improve data throughput and reduce network delays. Existing solutions have been limited by network instability, poor scalability, and inefficient spectrum usage; In contrast, the integrated approach leverages Deep Q-Learning for intelligent channel allocation and Coral Reef Optimization for optimized route selection, while Adaptive Modulation and Coding fine-tunes the communication process to achieve optimal performance. Compared to existing models which shows high packet drop ratio and scalability constraints, our model achieves a 68% reduction in energy consumption, increases network lifetime by 82%, lowers error rate by 77%, enhances routing stability by 85%, and boosts overall throughput by 79%. These results highlight the proposed model’s potential as a highly adaptive, low-latency, and scalable solution for next-generation wireless sensor network applications.
Cite This Paper
Padma priya S., Pavalarajan S., "An Adaptive Multi-objective approach for energy-aware routing in WSNs using Deep Q Learning and Coral Reef Optimization", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.3, pp. 336-349, 2026. DOI:10.5815/ijwmt.2026.03.22
Reference
[1]F. Kiani,(2017) "Reinforcement Learning Based Routing Protocol for Wireless Body Sensor Networks" IEEE 7th International Symposium on Cloud and Service Computing (SC2), Kanazawa, Japan, pp. 71-78, doi: 10.1109/SC2.2017.18.
[2]F. Kiani, E. Amiri, M. Zamani, T. Khodadadi, and A. Abdul Manaf,(2015) "Efficient Intelligent Energy Routing Protocol in Wireless Sensor Networks," International Journal of Distributed Sensor Networks, vol. 11, no. 3, doi: 10.1155/2015/618072.
[3]W. Rehan, S. Fischer, and M. Rehan,(2016) "Machine-Learning Based Channel Quality and Stability Estimation for Stream-Based Multichannel Wireless Sensor Networks," Sensors, vol. 16, no. 9, p. 1476, doi: 10.3390/s16091476.
[4]V. K. Krishnamoorthy, I. Izonin, S. Subramanian, S. K. Shandilya, S. Velayutham, T. R. Munichamy, and M. Havryliuk,(2023), "Energy Saving Optimization Technique-Based Routing Protocol in Mobile Ad-Hoc Network with IoT Environment," Energies, vol. 16, no. 3, p. 1385, doi: 10.3390/en16031385.
[5]Y. Xu, O. Ding, R. Qu, and K. Li,(2018). "Hybrid Multi-Objective Evolutionary Algorithms Based on Decomposition for Wireless Sensor Network Coverage Optimization," Applied Soft Computing, doi: 10.1016/j.asoc.2018.03.053.
[6]N. A. Khalid, Q. Bai, and A. Al-Anbuky,(2019), "Adaptive Trust-Based Routing Protocol for Large Scale WSNs," IEEE Access, vol. 7, pp. 143539-143549,doi:10.1109/ACCESS.2019.2944648.
[7]J. Kang, J. Kim, M. Kim, and M. Sohn, (2020),"Machine Learning-Based Energy-Saving Framework for Environmental States-Adaptive Wireless Sensor Network," IEEE Access, vol. 8, pp.69359-69367,doi:10.1109/ACCESS.2020.2986507.
[8]S. G., K. A. Bakar, S. Rahim, N. A. K. Khani, and T. Bibi,(2021) "Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey," Technologies, vol. 9, no. 4, p. 76, doi: 10.3390/technologies9040076.
[9]A. F. E. Abadi, S. A. Asghari, M. B. Marvasti, G. Abaei, M. Nabavi, and Y. Savaria,(2022), "RLBEEP: Reinforcement-Learning-Based Energy Efficient Control and Routing Protocol for Wireless Sensor Networks," IEEE Access, vol.10,pp.44123-44135,doi:10.1109/ACCESS.2022.3167058.
[10]W. -K. Yun and S. -J. Yoo,(2021), "Q-Learning-Based Data-Aggregation-Aware Energy-Efficient Routing Protocol for Wireless Sensor Networks," in IEEE Access, vol. 9, pp. 10737-10750,doi:10.1109/ACCESS.2021.3051360.
[11]H. Mostafaei,(2019),"Energy-Efficient Algorithm for Reliable Routing of Wireless Sensor Networks," in IEEE Transactions on Industrial Electronics, vol. 66, no. 7, pp. 5567-5575, doi: 10.1109/TIE.2018.2869345.
[12]P. Kumar and C. Asbe, (2023),"An Energy Efficient Routing Algorithm for WSN Using Q-Learning Based Data Aggregation Method," 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON), Mysore, India, pp. 1-8, doi: 10.1109/INDISCON58499.2023.10270449.
[13]J. Gao, J. Wang, J. Gu and W. Shi,(2024), "Q-Learning-Based Routing Optimization Algorithm for Underwater Sensor Networks," in IEEE Internet of Things Journal, vol. 11, no. 22, pp. 36350-36357, 15, doi: 10.1109/JIOT.2024.3398797.
[14]C. Wang, X. Shen, H. Wang, W. Xie, H. Mei and H. Zhang,(2024), "Q Learning-Based Routing Protocol With Accelerating Convergence for Underwater Wireless Sensor Networks," in IEEE Sensors Journal, vol. 24, no. 7, pp. 11562-11573, doi: 10.1109/JSEN.2024.3364683.
[15]H. Guo, R. Wu, B. Qi and C. Xu,(2022), "Deep-q-Networks-Based Adaptive Dual-Mode Energy-Efficient Routing in Rechargeable Wireless Sensor Networks," in IEEE Sensors Journal, vol. 22, no. 10, pp. 9956-9966, doi: 10.1109/JSEN.2022.3163368.
[16]X. Su, Y. Ren, Z. Cai, Y. Liang and L. Guo,(2023), "A Q-Learning-Based Routing Approach for Energy Efficient Information Transmission in Wireless Sensor Network," in IEEE Transactions on Network and Service Management, vol. 20, no. 2, pp. 1949-1961, doi: 10.1109/TNSM.2022.3218017.
[17]X. Liu and F. Zhou,(2024) "Q-Learning-Based Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks," 2024 OES China Ocean Acoustics (COA), Harbin, China,pp.1-6,doi:10.1109/COA58979.2024.10723718.
[18]V. K. Sharma, S. S. P. Shukla and V. Singh,(2012), "A tailored Q- Learning for routing in wireless sensor networks," 2nd IEEE International Conference on Parallel, Distributed and Grid Computing, Solan, India, 2012, pp. 663-668, doi: 10.1109/PDGC.2012.6449899.
[19]T. Amgoth and P. K. Jana, (2015),"Energy-Aware Routing Algorithm for Wireless Sensor Networks," Computers & Electrical Engineering, vol. 41, pp. 357–367, doi:10.1016/j.compeleceng.2014.07.010.
[20]W. Xiang, N. Wang and Y. Zhou, (2016),"An Energy-Efficient Routing Algorithm for Software-Defined Wireless Sensor Networks," in IEEE Sensors Journal, vol. 16, no. 20, pp. 7393-7400, doi: 10.1109/JSEN.2016.2585019.
[21]N. Chen and R. Wen,(2025) "Energy Efficient Tree-Based Routing Algorithm for Wireless Sensor Networks," in IEEE Access, vol. 13, pp. 20149-20159,doi:10.1109/ACCESS.2025.3535229.
[22]W. Qiu, E. Skafidas, and P. Hao,(2009), "Enhanced Tree Routing for Wireless Sensor Networks," Ad Hoc Networks, vol. 7, no. 3, pp. 638–650, doi: 10.1016/j.adhoc.2008.07.006.
[23]M. Liu, S. Xu, and S. Sun,(2012), "An Agent-Assisted QoS-Based Routing Algorithm for Wireless Sensor Networks," Journal of Network and Computer Applications, vol. 35, no.1, pp. 29–36, doi: 10.1016/j.jnca.2011.03.031.
[24]Goel, S., Sharma, K.P., Mittal, N. et al(2026),” Energy efficient clustering protocol in wireless sensor networks using an adaptive hybrid optimization algorithm.” Sci Rep 16, 6300 . https://doi.org/10.1038/s41598-026-36957-6
[25]Z. Zhang, G. Ren, S. Liang and D. Guan,(2026) "A Novel Two-Phase NOMA-ALOHA Protocol Enhanced by User Coordination for Wireless Sensor Networks," in IEEE Sensors Journal, vol. 26, no. 4, pp. 6372-6387, doi: 10.1109/JSEN.2025.3644038.
[26]Nadanam, P., Kumaratharan, N., & Anousouya Devi, M. (2026),” Innovative Hybrid Framework for Routing and Clustering in Wireless Sensor Networks with Quantum Optimization”,Cybernetics and Systems, 57(3), 449–484. https://doi.org/10.1080/01969722.2025.2521710