Edge computing is a paradigm that can distribute the complexity of predictive analysis into smaller pieces, physically placed at the source of contextual information. This allows in processing large amounts of data where it is intricate to use a centralized cloud. Edge Computing makes this possible by taking control of data and services from central hubs, which reduces computational latency on servers. Humidity is one of the main factors that maintain the life of the surface. This article explains how to perform computational analysis at the "edge" by using humidity data sets, and also shows that the most modern data is sufficient for data analysis. Linear Regression and Random Forest Regression algorithms are utilized for data analysis. In addition, this article illustrates the importance of data series for predicting humidity by comparing the analysis of unshuffled data and shuffled data. Metrics have been used to assess the accuracy and point out the importance of sequential data feeds for analysis.