An energy‐aware approach for resource managing in the fog‐based Internet of Things using a hybrid algorithm,International Journal of Communication Systems

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An energy‐aware approach for resource managing in the fog‐based Internet of Things using a hybrid algorithm
International Journal of Communication Systems ( IF 1.7 ) Pub Date : 2020-10-21 , DOI: 10.1002/dac.4652
Xiaojun Ren ₁ , Zhijun Zhang ₁ , Seyedeh Maryam Arefzadeh ₂

Affiliation

Regarding the recent information technology improvement, the fog computing (FC) emergence increases the ability of computational equipment and supplies modern solutions for traditional industrial applications. In the fog environment, Internet of Things (IoT) applications are completed by computing nodes that are intermediate in the fog, and the physical servers in data centers of the cloud. From the other side, because of resource constraints, dynamic nature, resource heterogeneity, and volatility of fog environment, resource management problems must be considered as one of the challenging issues of fog. The resource managing problem is an NP‐hard issue, so, in the current article, a powerful hybrid algorithm for managing resources in FC‐based IoT is proposed using an ant colony optimization (ACO) and a genetic algorithm (GA). GAs are computationally costly because of some problems such as the lack of guarantee for obtaining optimal solutions. Then, the precision and speed of convergence can be optimized by the ACO algorithm. Therefore, the powerful affirmative feedback pros of ACO on the convergence rate is considered. The algorithm uses GA's universal investigation power, and then it is transformed into ACO primary pheromone. This algorithm outperforms ACO and GA under equal conditions, as the simulation experiments showed.

中文翻译：

基于能源的能源方法，使用混合算法在基于雾的物联网中进行资源管理

关于最近的信息技术改进，雾计算（FC）的出现增加了计算设备的能力，并为传统工业应用提供了现代解决方案。在雾环境中，物联网（IoT）应用程序由雾中的中间计算节点以及云数据中心中的物理服务器完成。另一方面，由于资源约束，动态性质，资源异质性以及雾环境的波动性，必须将资源管理问题视为雾的挑战性问题之一。资源管理问题是一个NP难题，因此，在当前文章中，提出了一种功能强大的混合算法，用于使用蚁群优化（ACO）和遗传算法（GA）来管理基于FC的IoT中的资源。由于某些问题（例如，缺乏获得最佳解决方案的保证），遗传算法的计算成本很高。然后，可以通过ACO算法优化收敛的精度和速度。因此，考虑了ACO对收敛速度的强大肯定反馈。该算法利用GA的通用调查能力，然后将其转换为ACO主信息素。仿真实验表明，在相同条件下，该算法的性能优于ACO和GA。然后将其转化为ACO主信息素。仿真实验表明，在相同条件下，该算法的性能优于ACO和GA。然后将其转化为ACO主信息素。仿真实验表明，在相同条件下，该算法的性能优于ACO和GA。

更新日期：2020-12-03

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