For the hierarchical organization of sensors, the wireless sensor network aggregates function into categories. The sensor nodes directly relay aggregate data to the remote base station and prolonging life time of WSNs is most crucial. The current Fuzzy formulation is used for the development of a FUZZY KNN and a FUZZY KNN PCA basis theory analytical part; however it is ineffectual to create a hybrid fuzzy machine learning algorithm.  In this article, developing Probabilistic KNN and fuzzy base-logic theory parameter analyses for KNN is carried through MAC routing. KNN process executes the aggregated data classification process. The classification based on the supervised approach of machine learning is based on mathematical procedure to obtain the feasible response on several sinks. Initially, the FUZZY KNN and the fuzzy logic fundamental analytical part approach classify the weighted accuracy record. In combination with two fine and weight Fuzzy KNNs, the accuracy of KNN is 100%. Compared with two others, Fuzzy's PCA difference of 96 percent in less time was at 10.34 seconds in 100 percent. RMS is correlated with Fuzzy KNN PCA with a time of 2.76e, but still RMS is less than 1.4e per 10 second regression. The proposed scheme provides prolonged network lifetime in all cases, and lead to increased accuracy with linear regression.  

Keywords : KNN,KNN-PCA,FUZZY- KNN, Energy, Base station.