2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 18 , Issue 2 , PP: 157-168, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Maha A. Hutaihit 1 * , Samir I. Badrawi 2 , Haider Makki Alzaki 3 , Riyadh Khlf Ahmed 4 , Marwa Falah Hasan 5
Doi: https://doi.org/10.54216/JISIoT.180212
To enhance the efficiency of edge-integrated Industrial IoT (IIoT) networks, this paper proposes a deep learning-based resource-scheduling framework for optimized asset booking in Wireless Sensor Networks (WSNs). The novelty of this work lies in the integration of a hybrid Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) model, which enables intelligent allocation of computational resources based on real-time asset demand characteristics. The proposed model is evaluated using the Intel Berkeley WSN dataset and demonstrates superior performance in terms of latency reduction, execution time, and resource utilization compared to conventional approaches such as Genetic Algorithm (GA), Improved Particle Swarm Optimization (IPSO), Long Short-Term Memory (LSTM), and Bidirectional Recurrent Neural Network (BRNN). With a maximum efficiency of 99.48% and the lowest observed average delay, the model proves effective for real-time industrial automation scenarios. This research contributes to the development of scalable, energy-efficient, and responsive WSN architectures by leveraging deep learning for asset booking in edge-IoT environments.
Cognitive industrial internet of things , Electric-field measurement system , Radio-Access Network-As-A-Service , Multi-InputMulti-Output
[1] Chehri, R. Saadane, N. Hakem, and H. Chaibi, "Enhancing energy efficiency of wireless sensor network for mining industry applications," Procedia Comput. Sci., vol. 176, pp. 261–270, 2020.
[2] S. Ahmed, M. Z. Chowdhury, and Y. M. Jang, "Energy-efficient UAV-to-user scheduling to maximize throughput in wireless networks," IEEE Access, vol. 8, pp. 21215–21225, 2020.
[3] O. Alamu, A. Gbenga-Ilori, M. Adelabu, A. Imoize, and O. Ladipo, "Energy efficiency techniques in ultra-dense wireless heterogeneous networks: An overview and outlook," Eng. Sci. Technol. Int. J., vol. 23, no. 6, pp. 1308–1326, 2020.
[4] T. A. Alghamdi, "A hybrid algorithm for energy-efficient cluster head selection in wireless sensor networks," IEEE Access, vol. 8, pp. 189211–189222, 2020.
[5] V. K. Arora, V. Sharma, and M. Sachdeva, "A survey on LEACH and other routing protocols in wireless sensor networks," Optik, vol. 127, no. 16, pp. 6590–6600, 2016.
[6] Bagwari, J. Logeshwaran, K. Usha, R. Kannadasan, M. H. Alsharif, P. Uthansakul, and M. Uthansakul, "An Enhanced Energy Optimization Model for Industrial Wireless Sensor Networks Using Machine Learning," IEEE Access, vol. 11, pp. 103377–103391, 2023.
[7] J. Bhola, S. Soni, and G. K. Cheema, "Genetic algorithm based optimized LEACH protocol for energy efficient wireless sensor networks," J. Ambient Intell. Humaniz. Comput., vol. 11, pp. 1281–1288, 2020.
[8] S. Abella et al., "Autonomous energy-efficient wireless sensor network platform for home/office automation," IEEE Sens. J., vol. 19, no. 9, pp. 3501–3512, 2019.
[9] S. M. Chowdhury and A. Hossain, "Different energy saving schemes in wireless sensor networks: A survey," Wirel. Pers. Commun., vol. 114, no. 3, pp. 2043–2062, 2020.
[10] H. Kathiriya, A. Pandya, V. Dubay, and A. Bavarva, "State of art: energy efficient protocols for self-powered wireless sensor network in IIoT to support industry 4.0," in Proc. 8th Int. Conf. Rel., Infocom Technol. Optim. (Trends Future Directions) (ICRITO), 2020, pp. 1311–1314.
[11] J. Blanco, A. García, and J. D. L. Morenas, "Design and implementation of a wireless sensor and actuator network to support the intelligent control of efficient energy usage," Sensors, vol. 18, no. 6, p. 1892, 2018.
[12] J. Li, J. Lv, P. Zhao, Y. Sun, H. Yuan, and H. Xu, "Research and application of energy-efficient management approach for wireless sensor networks," Sensors, vol. 23, no. 3, p. 1567, 2023.
[13] R. Jin, X. He, and H. Dai, "Communication efficient federated learning with energy awareness over wireless networks," IEEE Trans. Wireless Commun., vol. 21, no. 7, pp. 5204–5219, 2022.
[14] K. Karunanithy and B. Velusamy, "Cluster-tree based energy efficient data gathering protocol for industrial automation using WSNs and IoT," J. Ind. Inf. Integr., vol. 19, p. 100156, 2020.
[15] U. K. Lilhore, O. I. Khalaf, S. Simaiya, C. A. Tavera Romero, G. M. Abdulsahib, and D. Kumar, "A depth controlled and energy-efficient routing protocol for underwater wireless sensor networks," Int. J. Distrib. Sensor Netw, vol. 18, no. 9, 2022.
[16] S. N. Mohanty and M. R. Patra, "Deep Learning-Based Distributed Data Mining (DDM) model for energy-efficient wireless sensor networks," IEEE Access, vol. 8, pp. 67890–67901, 2020.
[17] S. N. Mohanty, E. L. Lydia, M. Elhoseny, M. M. G. Al Otaibi, and K. Shankar, "Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks," Phys. Commun., vol. 40, p. 101097, 2020.
[18] Nakas, D. Kandris, and G. Visvardis, "Energy efficient routing in wireless sensor networks: A comprehensive survey," Algorithms, vol. 13, no. 3, p. 72, 2020.
[19] J. O. Ogbebor, A. L. Imoize, and A. A. A. Atayero, "Energy efficient design techniques in next-generation wireless communication networks: emerging trends and future directions," Wirel. Commun. Mobile Comput., vol. 2020, p. 1–19, 2020.
[20] P. Sreedevi and S. Venkateswarlu, "Comparative analysis of energy efficient routing protocols with optimization in WSN," Int. J. Interact. Des. Manuf., pp. 1–16, 2022.
[21] R. Tanash, M. AlQudah, and S. Al-Agtash, "Enhancing energy efficiency of IEEE 802.15.4-based industrial wireless sensor networks," J. Ind. Inf. Integr., vol. 33, p. 100460, 2023.
[22] S. Sivakumar, J. Logeshwaran, R. Kannadasan, M. Faheem, and D. Ravikumar, "A novel energy optimization framework to enhance the performance of sensor nodes in Industry 4.0," Energy Sci. Eng., vol. 12, no. 3, pp. 835–859, 2024.
[23] M. Sahoo, T. Amgoth, and H. M. Pandey, "Particle swarm optimization based energy efficient clustering and sink mobility in heterogeneous wireless sensor networks," Ad Hoc Netw., vol. 106, p. 102237, 2020.
[24] T. M. Behera et al., "Energy-efficient routing protocols for wireless sensor networks: Architectures, strategies, and performance," Electronics, vol. 11, no. 15, p. 2282, 2022.
[25] M. Tinatin, Q. Guibin, and Z. Jinghui, "Evolution of wireless communication network and its impact on human life," J. Comput. Sci. Technol., vol. 27, no. 5, pp. 857–868, 2012.
[26] W. Mao, Z. Zhao, Z. Chang, G. Min, and W. Gao, "Energy-efficient industrial Internet of Things: Overview and open issues," IEEE Trans. Ind. Informat., vol. 17, no. 11, pp. 7225–7237, 2021.
[27] T. Wang, X. Li, Y. Liu, and Y. Zhang, "Fog-based data collection for wireless sensor networks with mobile sinks," IEEE Trans. Ind. Informat., vol. 13, no. 2, pp. 850–859, 2017.
[28] Z. Yang, M. Chen, W. Saad, C. S. Hong, and M. Shikh-Bahaei, "Energy efficient federated learning over wireless communication networks," IEEE Trans. Wireless Commun., vol. 20, no. 3, pp. 1935–1949, 2020.
[29] Z. Yang, M. Chen, W. Saad, W. Xu, M. Shikh-Bahaei, H. V. Poor, and S. Cui, "Energy-efficient wireless communications with distributed reconfigurable intelligent surfaces," IEEE Trans. Wireless Commun., vol. 21, no. 1, pp. 665–679, 2021.
[30] Yousaf, F. Ahmad, S. Hamid, and F. Khan, "Performance comparison of various LEACH protocols in Wireless Sensor Networks," in Proc. IEEE 15th Int. Colloq. Signal Process. It is Appl. (CSPA), 2019, pp. 108–113.
[31] X. Zhuo, M. Liu, Y. Wei, G. Yu, F. Qu, and R. Sun, "AUV-aided energy-efficient data collection in underwater acoustic sensor networks," IEEE Internet Things J., vol. 7, no. 10, pp. 10010–10022, 2020.
