Data Quality Techniques in the Internet of Things: Random Forest Regression

Internet of Things (IoTs) is one of the most promising fields in computer science. It consists of physical devices, automobiles, home appliances, embedded hardware, sensors and actuators which empowers these objects to interface and share information with other devices over the network. The data gathered from these devices is used to make intelligent decisions. If the data quality is poor, decisions are likely to be flawed. A little work has been carried out regarding data quality in the Internet of Things, but there is no scheme which is experimentally proved. In this paper we will identify data quality challenges in the Internet of Things domain and propose a model which ensure data quality standards provided by ISO 8000. We evaluated our model on the weather dataset and used the random forest prediction method to calculate the accuracy of our data. Results show that when compared with the baseline model the proposed system improves accuracy by 38.88%.