The detection of insulator umbrella disc shedding is very important to the stable operation of a transmission line. In order to accomplish the accurate detection of the insulator umbrella disc shedding in foggy weather, a two-stage detection model combined with a defogging algorithm is proposed. In the dehazing stage of insulator images, solving the problem of real hazy image data is difficult; the foggy images are dehazed by the method of synthetic foggy images training and real foggy images fine-tuning. In the detection stage of umbrella disc shedding, a small object detection algorithm named FA-SSD is proposed to solve the problem of the umbrella disc shedding occupying only a small proportion of an aerial image. On the one hand, the shallow feature information and deep feature information are fused to improve the feature extraction ability of small targets; on the other hand, the attention mechanism is introduced to strengthen the feature extraction network’s attention to the details of small targets and improve the model’s ability to detect the umbrella disc shedding. The experimental results show that our model can accurately detect the insulator umbrella disc shedding defect in the foggy image; the accuracy of the defect detection is 0.925, and the recall is 0.841. Compared with the original model, it improved by 5.9% and 8.6%, respectively.
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.
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