论文标题
使用DT和VAR优化IOT支持物联网的物理位置监视
Optimization of IoT-Enabled Physical Location Monitoring Using DT and VAR
论文作者
论文摘要
这项研究表明，物联网的增强，该物联网获取传感器数据并执行实时识别以筛选物理区域以查找奇怪的情况，并向客户发送警报邮件以进行补救措施，以避免在环境中任何潜在的不幸。每当相机检测到一个人通过通过边缘计算将带宽要求和存储成本减少到云中，传感器数据就会将其推向本地系统，并将其Godaddy Cloud推向godaddy Cloud。该研究表明，决策树（DT）和随机森林给出了相似的宏平均F1分数，以使用传感器数据来预测一个人。实验结果表明，DT是三个不同物理位置的云数据集的最可靠的预测模型，可以预测使用时间戳，精度为83.99％，88.92％和80.97％的人。这项研究还解释了使用矢量自动回归的多元时间序列预测，该预测给出了相当好的均方根误差，以预测温度，湿度，光依赖性电阻和气体时间序列。
This study shows an enhancement of IoT that gets sensor data and performs real-time face recognition to screen physical areas to find strange situations and send an alarm mail to the client to make remedial moves to avoid any potential misfortune in the environment. Sensor data is pushed onto the local system and GoDaddy Cloud whenever the camera detects a person to optimize the physical location monitoring system by reducing the bandwidth requirement and storage cost onto the cloud using edge computation. The study reveals that decision tree (DT) and random forest give reasonably similar macro average f1-scores to predict a person using sensor data. Experimental results show that DT is the most reliable predictive model for the cloud datasets of three different physical locations to predict a person using timestamp with an accuracy of 83.99%, 88.92%, and 80.97%. This study also explains multivariate time series prediction using vector auto regression that gives reasonably good root mean squared error to predict temperature, humidity, light-dependent resistor, and gas time series.
