基于蜻蜓政治优化算法的物联网植物健康监测土壤湿度和热量预测

S. Muppidi, K. Bhamidipati, Sajeev Ram Arumugam
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引用次数: 1

摘要

本文设计了一个有效的基于学生心理学的蜻蜓政治优化器(SPDPOA),用于预测热水平和土壤湿度,以监测物联网(IoT)中的植物健康。建立了基于学生心理的优化算法(SPBO)、蜻蜓算法(DA)和政治优化算法(PO)的SPDPOA模型。预测过程在基站(BS)中完成,基站使用深度递归神经网络(Deep RNN)通过最优簇头(CH)收集物联网节点的信息。此外,利用SPDPOA方案建立了CH选择和路由过程。数据转换和特征选择过程分别基于Box-Cox变换和包装器模型进行,有助于选择最佳特征。此外,所开发的SPDPOA方案在包投递率(PDR)、能量和测试精度方面分别达到0.7232、0.6342 J和0.9372 J,具有较好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SPDPOA: Student Psychology Dragonfly Political Optimizer Algorithm-Based Soil Moisture and Heat-Level Prediction for Plant Health Monitoring in Internet of Things
This article devised an effective Student Psychology-based Dragonfly Political Optimizer (SPDPOA) for predicting heat level and soil moisture to monitor plant health in the Internet of Things (IoT). The developed SPDPOA is modeled by integrating the Student Psychology-based Optimization (SPBO) algorithm, Dragonfly Algorithm (DA) and Political optimizer (PO), respectively. The prediction process is done in the base station (BS), which gathers the IoT nodes’ information through optimal Cluster Head (CH) using Deep Recurrent Neural Network (Deep RNN). Moreover, the CH selection and routing process are established using a developed SPDPOA scheme. The data transformation and feature selection processes are done based on Box-Cox transformation and wrapper model, correspondingly, which helps in the selection of best features. Moreover, the developed SPDPOA scheme attained better performance in Packet Delivery Ratio (PDR), energy and testing accuracy of 0.7232, 0.6342 J and 0.9372, respectively.
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