Vector Based Genetic Lavrentyev Paraboloid Network Wireless Sensor Network Lifetime Improvement

In dynamic situations, limited processing power in Wireless Sensor Networks (WSN) makes it difficult to handle network lifetime and coverage. This work proposes a Genetic Lavrentyev Paraboloid Lagrange Support Vector Machine-based (GLPL-SVM) multiclass classification method to optimize WSN performance. The approach involves Genetic Lavrentyev Regularized Machine Learning-based Node Deployment for sensor node placement, Quadrant Count Event-based Data Aggregation for efficient data collection, and Paraboloid Lagrange Multiplier SVM-based Multiclass Classification for dynamic network coverage. The GLPL-SVM method is implemented in a Python simulator and compared with existing methods, demonstrating improvements in scheduling time, network lifetime, energy consumption, and classification accuracy.