The abstract should summarize the contents of the paper in short terms, i.e. 150–250. This paper explores the applications of exponential smoothing techniques in evaluating time collection records generated using Wireless Sensor Networks (WSNs). Empirical results demonstrate that exponential smoothing outperforms conventional time series models, supplying users with extra accurate analysis and forecasting of WSNs. The essential blessings of this method encompass decreased errors in predicting destiny values, progressed reliability, and stepped forward performance in developing forecasts. This paper affords a comprehensive review of exponential smoothing and its packages in WSNs, including an assessment of the outcomes of applying those techniques and the capability of future research avenues. In addition, it gives hints for practitioners trying to leverage exponential smoothing for time series analysis of WSNs. Exponential smoothing is a critical time series forecasting method that may be applied to evaluate Wi-Fi sensor Networks (WSN). It is miles primarily based on the belief that the fashion in a time collection is a clean continuation of past values. The rules work by calculating an exponentially weighted shifting average, wherein facts points are exponentially weighted and given more importance the closer they are to the present-day time point words.