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 :
Internet of Things is a popular and interactive technology of sensors, gateways, servers, and information access platforms that collect data and monitoring information based on sensors. Internet of Things has been rapidly developed and welcomed with various advantages; but it also comes with numerous challenges. Data security, energy consumption, scheduling, resource management, routing, etc. are among these challenges. The present research aims to provide a model to improve resource management in the Internet of Things based on fog computing by combining machine learning and meta-heuristic algorithms. This research focuses on the management of Internet of Things resources and presents a new solution for it. In the proposed model, if the user request is not sensitive to delay; it is referred to the cloud environment, but if it requires real-time response, it is sent to fog computing. When servicing requests, the situation is first predicted based on time series and then the resource allocation process is performed with the Golden Eagle optimization algorithm. The proposed method was implemented in the MATLAB environment and its efficiency was evaluated in four indicators: response time, cost, load balance and resource efficiency. The results showed that the proposed thesis design was superior to the basic design (Router) in all indicators and allocated resources in an appropriate way. In a way, there was an improvement of 18% in the load balance 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. 8-17, 2018.
[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