Wireless Sensor Network to Improve Security Performance and Packet Delivery Ratio Using FCL-Boost Based Classification Method

Wireless Sensor Networks are constantly monitoring such changes over a rapidly changing environment. This dynamic behavior is triggered by external factors or initiated by the system designer. In such situations, adapting sensor networks generally use machine learning techniques to reduce unnecessary redesign. Learn machines also trigger multiple practical solutions to maximize the utilization of resources and extend network life. This FCL-boost based classification algorithm is used to create a detailed authorization. The first classification level (FCL- Boost) according to the method, and different classifications to choose the name of the classification level or with the same type of FCL. The next step is to create a combined classification. FCL-Boost.L1 (Level 1) is to apply the user request level1 and FCL-Boost.L2 (Level 2) are being support under the route energy same or different classifications to support side. The According to the obtained results, the data is safe and reliable and written statements. K-Nearest Neighbor (KNN) has a lower accuracy rate, and Random Forest and Support Vector Machine (SVM) compared the previous methods slightly compared to the literature's number of protocols. Because of the many steps that the Foremost Classification Level FCL-boost algorithm process will be faster. Because of these advantages is the means of success.