Provide a Model for Improving IoT Resource Management Based on Fog Computing By Combining Machine Learning and Meta-Innovation Algorithms
Subject Areas : Technology Transfer and Commercialization of Researchesjavad mahmoodian 1 , Fatemeh Nasiri 2
1 - Tehran Science and Research Branch, Islamic Azad University, Tehran
2 -
Keywords: Improvement Managemant Resource, IOT, Fog Computation, Algorithms Machine Learning, Algorithms Meta- Heuristic.,
Abstract :
The Internet of Things is a popular and interactive technology consisting of sensors, gateways, servers, and information access platforms that collect data and monitoring information through sensors. The Internet of Things has been rapidly developed and widely adopted due to its various advantages; however, it also comes with numerous challenges. Data security, energy consumption, scheduling, resource management, and routing are among these challenges. The present research aims to provide a model for improving resource management in the Internet of Things based on fog computing by combining machine learning and metaheuristic algorithms. This research focuses on the management of Internet of Things resources and presents a new solution for this purpose. In the proposed model, if a user request is not sensitive to delay, it is referred to the cloud environment; however, if it requires a real-time response, it is sent to the fog computing environment. When servicing requests, the situation is first predicted based on time series, and then the resource allocation process is performed using the Golden Eagle Optimization Algorithm. The proposed method was implemented in the MATLAB environment, and its efficiency was evaluated using four indicators: response time, cost, load balancing, and resource efficiency. The results showed that the proposed design outperformed the basic design (Router) across all indicators and allocated resources appropriately. Specifically, there was an improvement of 18% in the load balancing index, 23% in the response time index, 31% in the cost index, and finally, 28% in the resource efficiency index.
1- U. Z. A. Hamid, H. Zamzuri, and D. K. Limbu, "Internet of vehicle (IoV) applications in expediting the implementation of smart highway of autonomous vehicle: A survey," in Performability in Internet of Things: Springer, 2019, pp. 137-157.
2- P. Podder, M. Mondal, S. Bharati, and P. K. Paul, "Review on the security threats of internet of things," arXiv preprint arXiv:2101.05614, 2021.
3- S. Enshaeifar et al., "The internet of things for dementia care," IEEE Internet Computing, vol. 22, no. 1, pp. 8-1, 7, 2017.
4- K. B. Kiadehi, A. M. Rahmani, and A. S. Molahosseini, "A fault-tolerant architecture for internet-of-things based on software-defined networks," Telecommunication Systems, vol. 77, no. 1, pp. 155-169, 2021.
5- J. Zhang, S. Rajendran, Z. Sun, R. Woods, and L. Hanzo, "Physical layer security for the Internet of Things: Authentication and key generation," IEEE Wireless Communications, vol. 26, no. 5, pp. 92-98, 2019.
6- Z. Sang, R. Fang, H. Lei, J. Yan, D. Yang, and Y. Wang, "The Internet of Things Based Fault Tolerant Redundancy for Energy Router in the Interacted and Interconnected Micro Grid," International Journal on Artificial Intelligence Tools, vol. 29, no. 07n08, p. 2040019, 2020.
7- S. Wang, Y. Ruan, Y. Tu, S. Wagle, C. G. Brinton, and C. Joe-Wong, "Network-aware optimization of distributed learning for fog computing," IEEE/ACM Transactions on Networking, 2021.
8- D. Tychalas and H. Karatza, "A scheduling algorithm for a fog computing system with bag-of-tasks jobs: Simulation and performance evaluation," Simulation Modelling Practice and Theory, vol. 98, p. 101982, 2020.
9- Z. Aghapour, S. Sharifian, and H. Taheri, "Task offloading and resource allocation algorithm based on deep reinforcement learning for distributed AI execution tasks in IoT edge computing environments," Computer Networks, p. 109577, 2023.
10- S. Latif et al., "An efficient pareto optimal resource allocation scheme in cognitive radio-based internet of things networks," Sensors, vol. 22, no. 2, p. 451, 2022.
11- M. Ouyang, J. Xi, W. Bai, and K. Li, "Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud," IEEE Access, vol. 10, pp. 86844-86863, 2022.
12- P. Qin, Y. Fu, X. Zhao, K. Wu, J. Liu, and M. Wang, "Optimal task offloading and resource allocation for C-NOMA heterogeneous air-ground integrated power Internet of Things networks," IEEE Transactions on Wireless Communications, vol. 21, no. 11, pp. 9276-9292, 2022.
13- H. Kharrufa, H. A. Al-Kashoash, and A. H. Kemp, "RPL-based routing protocols in IoT applications: A Review," IEEE Sensors Journal, vol. 19, no. 15, pp. 5952-5967, 2019.
14- L. Dong, W. He, and H. Yao, "Task Offloading and Resource Allocation for Tasks with Varied Requirements in Mobile Edge Computing Networks," Electronics, vol. 12, no. 2, p. 366, 2023.
15- W. Fan, Z. Chen, Z. Hao, F. Wu, and Y. a. Liu, "Joint Task Offloading and Resource Allocation for Quality-Aware Edge-Assisted Machine Learning Task Inference," IEEE Transactions on Vehicular Technology, 2023.
16- F. Chai, Q. Zhang, H. Yao, X. Xin, R. Gao, and M. Guizani, "Joint Multi-task Offloading and Resource Allocation for Mobile Edge Computing Systems in Satellite IoT," IEEE Transactions on Vehicular Technology, 2023.