Energy-Optimized Edge-Fog Computing With Fuzzy Based Iot Routing
DOI:
https://doi.org/10.29284/0r8fgk52Keywords:
Fog computing, multi-level decision making, fuzzy logic and optimized routing.Abstract
The rapid expansion of the Internet of Things (IOT) has led to an unprecedented increase in data traffic, demanding real-time processing, low latency, and energy efficiency. Traditional cloud-centric architectures face significant challenges in managing massive IOT networks due to high power consumption, network congestion, and increased transmission delays. To address these issues, this research proposes an Energy-Optimized Edge-Fog Computing framework with Fuzzy-Based IOT Routing, designed to enhance energy efficiency, minimize traffic delays, and optimize data transmission across large-scale IOT networks. This novel approach integrates edge and fog computing to decentralize computational tasks, reducing reliance on distant cloud servers and enabling faster decision-making closer to IOT devices. The core innovation of this research lies in utilizing fuzzy logic-based intelligent routing that dynamically adjusts energy consumption, bandwidth allocation, and data priority across edge and fog layers. Unlike conventional deterministic routing methods, fuzzy logic provides a human-like reasoning mechanism to handle uncertainty, imprecise traffic conditions, and dynamic network variations. This enables Io T devices to make adaptive routing decisions, selecting the most energy-efficient paths while ensuring low end-to-end latency. Furthermore, the system incorporates machine learning (ML)-assisted predictive analytics, allowing IoT nodes to anticipate congestion patterns and preemptively reroute data traffic to optimize performance.
In this framework, fog computing acts as an intermediary between edge devices and cloud infrastructure, reducing unnecessary long-distance data transmissions and significantly lowering energy consumption. The proposed model ensures that critical real-time applications, such as autonomous vehicles, healthcare monitoring, and industrial automation, benefit from low-latency data processing, while non-time-sensitive data can be scheduled for cloud offloading. The energy-aware task allocation mechanism efficiently distributes computational loads among edge and fog nodes, preventing resource exhaustion and extending network lifespan. A key aspect of this research is the integration of reinforcement learning with fuzzy logic-based decision-making, enabling IoT networks to self-optimize over time. The system continuously learns from historical data, adapting to network topology changes, varying traffic loads, and unpredictable IoT device behaviors. By leveraging fuzzy multi-criteria decision- making (FMCDM), the routing process balances energy efficiency, latency constraints, and quality of service (QoS) requirements dynamically, ensuring an optimized network operation.
References
1. Zhang, Y., Wang, X. and Li, J., 2018. Energy-efficient data offloading in fog computing for IoT applications. IEEE Access, 6, pp.61253-61265.
2. Rahmani, A.M., Liljeberg, P., Dandekar, K. and Djouani, K., 2018. Low-latency fog computing: A multimodal approach for IoT applications. IEEE Communications Magazine, 56(10), pp.54-61.
3. Hou, J., Chen, Y. and Chen, H., 2020. Fuzzy logic-based routing in IoT: A survey and future directions. IEEE Internet of Things Journal, 7(5), pp.4127-4140.
4. Yousefi, M., Shrestha, R. and Martinez, A., 2019. Adaptive edge-fog computing architecture for energy-efficient IoT data processing. IEEE Transactions on Sustainable Computing, 4(2), pp.136-148.
5. Khan, L.U., Nazir, S. and Rehman, S., 2020. Energy-optimized task scheduling in fog computing: A fuzzy logic approach. IEEE Transactions on Cloud Computing, 8(3), pp.660-672.
6. Chen, M., Zhang, Y. and Li, F., 2021. Fuzzy-based routing and resource allocation in edge-fog computing for IoT networks. IEEE Access, 9, pp.14502-14515.
7. Al-Araji, Zaid Jasim, et al. "Fuzzy theory in fog computing: review, taxonomy, and open issues." IEEE Access 10 (2022): 126931-126956.
8. Abdulazeez, Dezheen H., and Shavan K. Askar. "A novel offloading mechanism leveraging fuzzy logic and deep reinforcement learning to improve IoT application performance in a three-layer architecture within the fog-cloud environment." IEEE Access 12 (2024): 39936-39952.
9. Essalhi, Salah Eddine, et al. "Optimized Energy Management with Fuzzy Clustering for Heterogeneous Fog-Mist-IoT Networks." 2023 6th International Conference on Advanced Communication Technologies and Networking (CommNet). IEEE, 2023.
10. Khan, Zahoor Ali, et al. "Energy management in smart sectors using fog based environment and meta-heuristic algorithms." IEEE Access 7 (2019): 157254-157267.
11. Memon, Imran, et al. "Energy-efficient fuzzy management system for internet of things connected vehicular ad hoc networks." Electronics 10.9 (2021): 1068.
12. Yadav, Pratibha, and Deo Prakash Vidyarthi. "An efficient fuzzy‐based task offloading in edge‐fog‐cloud architecture." Concurrency and Computation: Practice and Experience 35.26 (2023): e7843.
13. Udayaprasad, P. K., et al. "Energy efficient optimized routing technique with distributed SDN-AI to large scale I-IoT networks." IEEE Access 12 (2024): 2742-2759.
14. Kopras, Bartosz, et al. "Task allocation for energy optimization in fog computing networks with latency constraints." IEEE Transactions on Communications 70.12 (2022): 8229-8243.
15. Konecny, Jaromir, Michal Prauzek, and Monika Borova. "Fuzzy Controlled Wavelet-Based Edge Computing Method for Energy-Harvesting IoT Sensors." IEEE Internet of Things Journal 10.21 (2023): 18909-18918.
16. Ahmed, Adeel, et al. "An energy-efficient data aggregation mechanism for IoT secured by blockchain." IEEE Access 10 (2022): 11404-11419.
17. Katal, Avita, and Vitesh Sethi. "Energy-efficient cloud and Fog computing in internet of things: Techniques and challenges." Cloud and Fog Computing Platforms for Internet of Things. Chapman and Hall/CRC, 2022. 67-83.
18. Ahmad, Mahmoud Al, Sudhansu Shekhar Patra, and Rabindra K. Barik. "Energy-efficient resource scheduling in fog computing using SDN framework." Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2019. Singapore: Springer Singapore, 2020. 567-578.
19. Arunraja, Muruganantham, Veluchamy Malathi, and Erulappan Sakthivel. "Energy conservation in WSN through multilevel data reduction scheme." Microprocessors and Microsystems 39.6 (2015): 348-357.
20. Sakthivel, Erulappan, Veluchamy Malathi, and Muruganantham Arunraja. "MATHA: Multiple sense amplifiers with transceiver for high performance improvement in NoC Architecture." Microprocessors and Microsystems 38.7 (2014): 692-706.
21. Sakthivel, Erulappan, et al. "NABI: low power, high speed FPGA based novel approach for bilateral filter." Intl. J. Innovation and Scientific Res 25.2 (2016): 646-653.
22. Stanley, R. S., and S. Pavalarajan. "Improvement of Power Quality for Smart Microgrid Using Fuzzy Based ANN with Microcontroller." (2022).
23. Sasikala, N., and S. Pavalarajan. "Energy-aware redundancy-aware clustering in wireless sensor networks using Spined Loach Searching Optimization." Int. J. Commun Syst 36.3 (2022): e5385.
24. Madavan, R., and E. Sakthivel. "Optimized deep residual networks for early detection of myocardial infarction from ECG signals." BMC Cardiovascular Disorders 25 (2025): 371.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
