Purpose

Based on the positive features of the shark smell optimization (SSO) algorithm, the purpose of this paper is to propose a method based on this algorithm, dynamic voltage and frequency scaling (DVFS) model and fuzzy logic to minimize the energy consumption of integrated circuits of internet of things (IoT) nodes and maximize the load-balancing among them.

Design/methodology/approach

Load balancing is a key problem in any distributed environment such as cloud and IoT. It is useful when a few nodes are overloaded, a few are under-loaded and the remainders are idle without interrupting the functioning. As this problem is known as an NP-hard one and SSO is a powerful meta-hybrid method that inspires shark hunting behavior and their skill to detect and feel the smell of the bait even from far away, in this research, this study have provided a new method to solve this problem using the SSO algorithm. Also, the study have synthesized the fuzzy logic to counterbalance the load distribution. Furthermore, DVFS, as a powerful energy management method, is used to reduce the energy consumption of integrated circuits of IoT nodes such as processor and circuit bus by reducing the frequency.

Findings

The outcomes of the simulation have indicated that the proposed method has outperformed the hybrid ant colony optimization – particle swarm optimization and PSO regarding energy consumption. Similarly, it has enhanced the load balance better than the moth flame optimization approach and task execution node assignment algorithm.

Research limitations/implications

There are many aspects and features of IoT load-balancing that are beyond the scope of this paper. Also, given that the environment was considered static, future research can be in a dynamic environment.

Practical implications

The introduced method is useful for improving the performance of IoT-based applications. We can use these systems to jointly and collaboratively check, handle and control the networks in real-time. Also, the platform can be applied to monitor and control various IoT applications in manufacturing environments such as transportation systems, automated work cells, storage systems and logistics.

Originality/value

This study have proposed a novel load balancing technique for decreasing energy consumption using the SSO algorithm and fuzzy logic.

中文翻译：

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基于模糊逻辑和鲨鱼气味优化算法的物联网负载均衡

目的

基于鲨鱼气味优化（SSO）算法的积极特点，本文的目的是提出一种基于该算法、动态电压和频率缩放（DVFS）模型和模糊逻辑的方法来最小化集成电路的能耗物联网 (IoT) 节点，并最大化它们之间的负载平衡。

设计/方法/方法

负载平衡是任何分布式环境（例如云​​和物联网）中的关键问题。当一些节点过载，一些节点负载不足，其余节点空闲而不中断运行时，它很有用。由于这个问题被称为 NP-hard 问题，而 SSO 是一种强大的元混合方法，可以激发鲨鱼狩猎行为以及他们即使从远处检测和感受诱饵气味的技能，在这项研究中，这项研究提供了一种使用 SSO 算法解决此问题的新方法。此外，该研究还综合了模糊逻辑来平衡负载分配。此外，DVFS作为一种强大的能源管理方法，通过降低频率来降低​​处理器和电路总线等物联网节点集成电路的能耗。

发现

仿真结果表明，所提出的方法在能耗方面优于混合蚁群优化-粒子群优化和粒子群算法。同样，它比飞蛾火焰优化方法和任务执行节点分配算法更好地增强了负载平衡。

研究限制/影响

物联网负载平衡的许多方面和特性超出了本文的范围。此外，鉴于环境被认为是静态的，未来的研究可以在动态环境中进行。

实际影响

引入的方法对于提高基于物联网的应用程序的性能很有用。我们可以使用这些系统来联合协作实时检查、处理和控制网络。此外，该平台还可用于监控和控制制造环境中的各种物联网应用，例如运输系统、自动化工作单元、存储系统和物流。

原创性/价值

本研究提出了一种使用 SSO 算法和模糊逻辑来降低能耗的新型负载平衡技术。