ENERGY EFFICIENCY OPTIMIZATION IN IOT-ENABLED MANUFACTURING USING FUZZY ALGORITHMS
DOI:
https://doi.org/10.5281/zenodo.19383540Keywords:
IoT, fuzzy logic, energy efficiency, manufacturing, real-time optimization, predictive maintenance, sustainability, machine learningAbstract
This review examines the integration of the Internet of Things (IoT) and fuzzy logic algorithms for improving energy efficiency in manufacturing systems. Rising energy costs, environmental concerns, and the need for sustainable industrial operations have increased interest in intelligent energy management approaches. IoT technologies enable real-time monitoring of machine performance, process conditions, and energy consumption through interconnected sensors and devices. However, the large volume of data generated in such environments is often uncertain, incomplete, or imprecise. Fuzzy logic algorithms provide a suitable solution by supporting flexible decision-making under uncertain conditions. The article discusses how the combination of IoT and fuzzy logic can optimize energy use, reduce waste, improve operational efficiency, and support predictive maintenance in manufacturing environments. It also highlights key implementation challenges, including data processing complexity, system integration, and the need for adaptive control strategies. In addition, the review outlines the major benefits of this approach, such as cost reduction, environmental sustainability, and improved production performance. Overall, the review shows that IoT-enabled manufacturing systems supported by fuzzy logic offer strong potential for intelligent and sustainable energy optimization in modern industrial settings.
Downloads
References
Alshammri, G. H. (2025). Enhancing wireless sensor network lifespan and efficiency through improved cluster head selection using improved squirrel search algorithm. Artificial Intelligence Review, 58(3), 79. https://doi.org/10.1007/s10462-025-06434-9
Das, R., & Dwivedi, M. (2024). Cluster head selection and malicious SN detection using large-scale energy-aware trust optimization algorithm for HWSN. Journal of Reliability and Intelligent Environment, 10(1), 55–71. https://doi.org/10.1007/s40473-024-00109-x
Gharaei, N., & Alabdali, A. M. (2025). Optimizing smart health monitoring systems: Enhancing energy efficiency and reducing latency with multi-level clustering and grey wolf optimizer. Cluster Computing, 28(2), 87. https://doi.org/10.1007/s10586-025-03789-7
Hu, H., Fan, X., & Wang, C. (2024). Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks. Scientific Reports, 14(1), 18595. https://doi.org/10.1038/s41598-024-75781-5
Jabeen, T., et al. (2023). An intelligent healthcare system using IoT in wireless sensor network. Sensors, 23(11), 5055. https://doi.org/10.3390/s23115055
Kareem, D. A., & Rajesh, D. (2025). Enhancing WBAN performance with cluster-based routing protocol using black widow optimization for healthcare application. Journal of Intelligent Systems and Internet of Things, 14(1). https://doi.org/10.1007/s40723-025-00010-x
Khan, M. N. U., Cao, W., Tang, Z., Ullah, A., & Pan, W. (2024). Energy-efficient de-duplication mechanism for healthcare data aggregation in IoT. Future Internet, 16(2), 66. https://doi.org/10.3390/fi16020066
Krishnamoorthy, S., Dua, A., & Gupta, S. (2023). Role of emerging technologies in future IoT-driven Healthcare 4.0 technologies: A survey, current challenges, and future directions. Journal of Ambient Intelligence and Humanized Computing, 14(1), 361–407. https://doi.org/10.1007/s12652-023-03752-0
Kumar, P., et al. (2023). A blockchain-orchestrated deep learning approach for secure data transmission in IoT-enabled healthcare system. Journal of Parallel and Distributed Computing, 172, 69–83. https://doi.org/10.1016/j.jpdc.2023.07.010
Lekhraj Singh, R. K., Upadhyay, S., Tiwari, V., & Singh, S. K. (2024). Cluster-head selection in WSNs using modified MADM approach by considering conflicting parameters for IoT applications. International Journal of Information Technology, 16(7), 4667–4675. https://doi.org/10.1007/s41870-024-00849-3
Li, C., Wang, J., Wang, S., & Zhang, Y. (2024). A review of IoT applications in healthcare. Neurocomputing, 565, 127017. https://doi.org/10.1016/j.neucom.2024.127017
Prakash, V., Singh, D., Pandey, S., Singh, S., & Singh, P. K. (2024). Energy-optimization route and cluster head selection using m-PSO and GA in wireless sensor networks. Wireless Personal Communications, 1–26. https://doi.org/10.1007/s11277-024-11096-1
Rajalingam, R., Kavitha, K., & Anu, M. (2024). A smart healthcare system for IOMT using an ant-colony optimized centroid-based hybrid protocol with cluster-centric energy-efficient routing in WSN-assisted IoT. International Journal of Communication Networks and Information Security, 16(3), 223–236.
Rejeb, A., et al. (2023). The Internet of Things (IoT) in healthcare: Taking stock and moving forward. Internet of Things, 22, 100721. https://doi.org/10.1016/j.iot.2023.100721
Roberts, M. K., & Ramasamy, P. (2023). An improved high-performance clustering-based routing protocol for wireless sensor networks in IoT. Telecommunication Systems, 82(1), 45–59. https://doi.org/10.1007/s11235-023-01123-9
Safa, M., Pandian, A., Gururaj, H. L., Ravi, V., & Krichen, M. (2023). Real-time healthcare big data analytics model for improved QoS in cardiac disease prediction with IoT devices. Health Technology, 13(3), 473–483. https://doi.org/10.1007/s12553-023-00345-1
Sahoo, L., Sen, S. S., Tiwary, K., Moslem, S., & Senapati, T. (2024). Improvement of wireless sensor network lifetime via intelligent clustering under uncertainty. IEEE Access, 12, 25018–25033. https://doi.org/10.1109/ACCESS.2024.3264260
Saini, P., Ahuja, R., & Sai, V. (2024). Wireless sensor networks and IoT revolutionizing healthcare: Advancements, applications, and future directions. In Emerging Technologies and the Application of WSN and IoT (pp. 228–250). CRC Press.
Sennan, S., Ramasubbareddy, S., Dhanaraj, R. K., Nayyar, A., & Balusamy, B. (2024). Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization. Telecommunication Systems, 87(1), 119–135. https://doi.org/10.1007/s11235-024-01323-1
Shabu, S. J., et al. (2024). An improved adaptive neuro-fuzzy inference framework for lung cancer detection and prediction on Internet of Medical Things platform. International Journal of Computational Intelligence Systems, 17(1), 228. https://doi.org/10.1007/s44177-024-00087-y
Siddiq, A., & Ghazwani, Y. J. (2024). Hybrid optimized deep neural network-based intrusion SN detection and modified energy-efficient centralized clustering routing protocol for wireless sensor network. IEEE Transactions on Consumer Electronics, 70(3), 6303–6313. https://doi.org/10.1109/TCE.2024.2993031
Sikarwar, N., & Tomar, R. S. (2023). A hybrid MFCM-PSO approach for tree-based multi-hop routing using modified fuzzy C-means in wireless sensor network. IEEE Access, 11, 128745–128761. https://doi.org/10.1109/ACCESS.2023.3245912
Somula, R., Cho, Y., & Mohanta, B. K. (2024). SWARAM: Osprey optimization algorithm-based energy-efficient cluster head selection for wireless sensor network-based internet of things. Sensors, 24(2), 521. https://doi.org/10.3390/s24020521
Tumula, S., et al. (2024). An opportunistic energy-efficient dynamic self-configuration clustering algorithm in WSN-based IoT networks. International Journal of Communication Systems, 37(1), e5633. https://doi.org/10.1002/dac.5633
Vijayakumar, M. (2023). Network statistics-based routing and path orient data encryption scheme for efficient healthcare monitoring with IoT in WSN. International Journal of Communication Systems, 36(1), e5361. https://doi.org/10.1002/dac.5361
Xu, H., Liu, W. D., Li, L., Yao, D. J., & Ma, L. (2024). FSRW: Fuzzy logic-based whale optimization algorithm for trust-aware routing in IoT-based healthcare. Scientific Reports, 14(1), 16640. https://doi.org/10.1038/s41598-024-77797-w
Downloads
Published
Issue
Section
License
Copyright (c) 2026 International Journal of Renewable Energy and Environment
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accepted paper becomes the permanent property of the International Journal of Renewable Energy and Environment (IJREE) and may not be reproduced by any means without the written permission of the Editorial board.
