Minimization of latency and energy consumption in cloud-fog hybrid environments based on the schedule of requests in the Internet of Things using the plant defense optimization algorithm

Articles in Press

Document Type : Research Paper

Authors

1 -Ph.D. Student of Electrical Engineering, Islamic Azad University, Kerman Branch, Iran

2 Assistant Professor, Electrical Department, Kerman Branch, Islamic Azad University, Kerman, Iran

Abstract

Providing services for real-time, latency-sensitive IoT applications is a major challenge. . Fog environments can significantly reduce the latency of services because they move resources to the nearest edge of the network. On the other hand, fog nodes are not able to provide all the required resources at a large scale due to energy and processing power limitations. For this reason, the problem of optimizing service requests and energy consumption is raised as a major challenge. The nature of this problem is NP-hard, and therefore, exact optimization solutions are insufficient and impractical for large-scale problems. In this regard, a new approach based on the plant defense algorithm (Plant Defense Optimization Algorithm) is proposed as a novel solution to this challenge. This algorithm is inspired by the genetic defense behavior of plants. In the proposed algorithm, genes are first classified using the K-means clustering method. Then, the plant defense optimization algorithm operators are applied specifically to each solution. These operators are designed to avoid local optimality. In this method, the cost function is calculated as a combination of two factors: time delay and energy consumption. The plant defense optimization algorithm is tested in a simulated environment in which environmental dynamics and changes are carefully considered. Finally, the performance of the plant defense optimization algorithm is evaluated and compared with other methods. The experimental results show that the overall delay in the proposed approach is improved between 23.84% and 48.51% compared to other algorithms.

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Journal of Modeling in Engineering

Articles in Press, Accepted Manuscript
Available Online from 17 November 2025

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History

  • Receive Date: 19 June 2024
  • Revise Date: 16 September 2025
  • Accept Date: 12 November 2025

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