Volume 14 , Issue 1 , PP: 160-178, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Gowrishankar J. 1 * , Bhargavi Gaurav Deshpande 2 , Dhiraj Singh 3 , Awakash Mishra 4 , Zeeshan Ahmad Lone 5 , Bharat Bhushan 6
Doi: https://doi.org/10.54216/JCIM.140111
Since heterogeneous wireless sensor networks consist of sensor nodes of varying capacity and energy-constrained, effective routing techniques are essential to ensure the proper functioning of the systems. Most traditional routing techniques fail to dynamically adjust to varying network conditions, leading to ineffective use of energy and poor performance. Therefore, deep Q-Networks implementation of reinforcement learning provides a beneficial approach to the problem due to adaptive routing decisions depending on the environmental signals and systems’ performance. Therefore, the suggested approach integrates Deep Q-Network into the data routing framework for different Wireless Sensor Networks to improve energy-efficiency and ensure data delivery. The DQN agent is designed to pick routing functions that maximize total rewards which depend on energy consumption, packet delivery, and network stability. Hence, the decentralized learning allows each sensor node to develop its routing policy based on the local environment under the interactions with their neighbors. Therefore, the approach promotes the ability to adapt and learn, crucial for changing network conditions. Thus, extensive simulation was conducted to assess the applicability of the DQN-based routing for different WSNs, proving the significant reducing of energy consumption compared to traditional routing approaches with an average of 25% regardless of the network formation and traffic conditions . This approach also demonstrates lower packet loss of 15%, revealing enhanced data transfer reliability . In particular, the existing on demand routing protocols, only forward the request that arrives first from each route discovery process. The attacker exploits this property of the operation of route discovery. The network lifetime was extended by 30% showing growing energy efficiency for a long-term run. In general, the integration of Deep Q-Networks into data routing provides an excellent opportunity to improve energy-efficiency in different types of wireless sensor networks. Hence, the proposed approach effectively optimizes the routing solutions in real-time, using adaptive lenience, also showing enhancing data delivery, and improving the systems’ lifetime. Hence, the presented results prove the capability of reinforcement learning methods to address the growing challenges of WSNs and leave space for further research in autonomous WSN improvement.
Wireless Sensor Networks (WSNs) , Energy Efficiency , Deep Q-Networks (DQN) , Heterogeneous Networks , Data Routing , Performance Metrics , Cyber Security
[1] Guo, H., Wu, R., Qi, B., & Xu, C. (2022). Deep-Q-networks-based adaptive dual-mode energy-efficient routing in rechargeable wireless sensor networks. IEEE Sensors Journal, 22(10), 9956-9966.
[2] Kaur, G., Chanak, P., & Bhattacharya, M. (2021). Energy-efficient intelligent routing scheme for IoT-enabled WSNs. IEEE Internet of Things Journal, 8(14), 11440-11449.
[3] Hsieh, C. K., Chan, K. L., & Chien, F. T. (2021). Energy-efficient power allocation and user association in heterogeneous networks with deep reinforcement learning. Applied Sciences, 11(9), 4135.
[4] Zhu, X., Wang, L., Li, Y., Song, S., Ma, S., Yang, F., & Zhai, L. (2022). Path planning of multi-UAVs based on deep Q-network for energy-efficient data collection in UAVs-assisted IoT. Vehicular Communications, 36, 100491.
[5] V. Roy. "An Effective FOG Computing Based Distributed Forecasting of Cyber-Attacks in Internet of Things" Journal of Cybersecurity and Information Management, Vol. 12, No. 2, 2023 ,PP. 8-17.
[6] Kim, T., Vecchietti, L. F., Choi, K., Lee, S., & Har, D. (2020). Machine learning for advanced wireless sensor networks: A review. IEEE Sensors Journal, 21(11), 12379-12397.
[7] Suresh, S. S., Prabhu, V., Parthasarathy, V., Senthilkumar, G., & Gundu, V. (2024). Intelligent data routing strategy based on federated deep reinforcement learning for IOT-enabled wireless sensor networks. Measurement: Sensors, 31, 101012.
[8] Muniandi, B., Raut, K. J., Gawande, P. G., Maurya, P. K., & Howard, E. (2024). AI-Driven Energy Efficient Routing Protocols for Wireless Sensor Networks. NATURALISTA CAMPANO, 28(1), 1906-1915.
[9] Han, D., & So, J. (2023). Energy-efficient resource allocation based on deep Q-network in V2V communications. Sensors, 23(3), 1295.
[10] Liang, F., Yu, W., Liu, X., Griffith, D., & Golmie, N. (2021). Toward deep Q-network-based resource allocation in industrial internet of things. IEEE Internet of Things Journal, 9(12), 9138-9150.
[11] Yuan, J., Peng, J., Yan, Q., He, G., Xiang, H., & Liu, Z. (2024). Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks. Sensors, 24(5), 1632.
[12] V. Roy. "Breast cancer Classification with Multi-Fusion Technique and Correlation Analysis" Fusion: Practice & Applications, Vol. 9, No. 2, 2023 ,PP. 48-61.
[13] S Hariharan , Monika Gupta, Improving Cloud-based ECG Monitoring, Detection and Classification using GAN, Fusion: Practice and Applications, Vol. 2 , No. 2 , (2020) : 42-49 (Doi : https://doi.org/10.54216/FPA.020201).
[14] P. Kumar, A. Baliyan, K. R. Prasad, N. Sreekanth, P. Jawarkar, V. Roy, E. T. Amoatey, "Machine Learning Enabled Techniques for Protecting Wireless Sensor Networks by Estimating Attack Prevalence and Device Deployment Strategy for 5G Networks", Wireless Communications and Mobile Computing, vol. 2022, Article ID 5713092, 15 pages, 2022. https://doi.org/10.1155/2022/5713092
[15] Sarthak Gupta , Monil Pahwa , Prayant Gupta , Surinder Kaur, ARZARA: Augmented reality app to try watch on your wrist, Fusion: Practice and Applications, Vol. 2 , No. 2 , (2020) : 50-56 (Doi : https://doi.org/10.54216/FPA.020202)
[16] Sudhakar, M., & Anne, K. R. (2024). Optimizing data processing for edge-enabled IoT devices using deep learning based heterogeneous data clustering approach. Measurement: Sensors, 31, 101013.
[17] Vishwanathrao, B. A., & Vikhar, P. A. (2024). Reinforcement Machine Learning-based Improved Protocol for Energy Efficiency on Mobile Ad-Hoc Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 654-670.
[18] Gherairi, S. (2022). Healthcare: A priority-based energy harvesting scheme for managing sensor nodes in WBANs. Ad Hoc Networks, 133, 102876.
[19] Muhammad Edmerdash, Waleed khedr, Ehab Rushdy, An Overview of Cloud-Based Secure Services for Enterprise Drug–Drug Interaction Systems, International Journal of Wireless and Ad Hoc Communication, Vol. 2 , No. 2 , (2021) : 49-58 (Doi : https://doi.org/10.54216/IJWAC.020201)
[20] Noushini Nikeetha P. , Pavithra D. , Sivakarthiga K. , Karthika S. , Yashitha R. , Kirubasri G.V., A Survey on IoT based Wearable Sensor for Covid-19 Pandemic, International Journal of Wireless and Ad Hoc Communication, Vol. 2 , No. 2 , (2021) : 77-87 (Doi : https://doi.org/10.54216/IJWAC.020203)
[21] Preeth, S. S. L., Dhanalakshmi, R., & Shakeel, P. M. (2020). An intelligent approach for energy efficient trajectory design for mobile sink based IoT supported wireless sensor networks. Peer-to-Peer networking and applications, 13, 2011-2022.
[22] Banoth, S. P. R., Donta, P. K., & Amgoth, T. (2021). Dynamic mobile charger scheduling with partial charging strategy for WSNs using deep-Q-networks. Neural Computing and Applications, 33(22), 15267-15279.
[23] Joshi, U., & Kumar, R. (2020). A novel deep neural networks based path prediction. Cluster Computing, 23(4), 2915-2924.
[24] Esraa Mohamed, The Relationship between Artificial Intelligence and Internet of Things: A quick review, Journal of Cybersecurity and Information Management, Vol. 1 , No. 1 , (2020) : 30-34 (Doi : https://doi.org/10.54216/JCIM.010101)
[25] Dr. Ajay B. Gadicha , Dr. Vijay B. Gadicha, Implicit Authentication Approach by Generating Strong Password through Visual Key Cryptography, Journal of Cybersecurity and Information Management, Vol. 1 , No. 1 , (2020) : 5-16 (Doi : https://doi.org/10.54216/JCIM.010102)
[26] Xu, Y., Yu, J., & Buehrer, R. M. (2020). The application of deep reinforcement learning to distributed spectrum access in dynamic heterogeneous environments with partial observations. IEEE Transactions on Wireless Communications, 19(7), 4494-4506.
[27] Reem Atassi, Aditi Sharma, Intelligent Traffic Management using IoT and Machine Learning, Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 2 , (2023) : 08-19 (Doi : https://doi.org/10.54216/JISIoT.080201)
[28] Khder Alakkari, Alhumaima Ali Subhi, Hussein Alkattan, Ammar Kadi, Artem Malinin, Irina Potoroko, Mostafa Abotaleb, El-Sayed M El-kenawy, Forecasting COVID-19 Infection Using Encoder-Decoder LSTM and Attention LSTM Algorithms, Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 2 , (2023) : 20-33 (Doi : https://doi.org/10.54216/JISIoT.080202)
[29] V. Roy. "An Improved Image Encryption Consuming Fusion Transmutation and Edge Operator." Journal of Cybersecurity and Information Management, Vol. 8, No. 1, 2021 ,PP. 42-52.
