Volunteer Sensor Networks Node Availability Prediction via Stronger Intelligent Selection Optimization Algorithm

We proposed an approach to predict the availability of volunteer sensor networks (VSN) node. It is based on the Stronger Intelligent selection (SIS). First, the availability of VSN node is analyzed and predicted based on its location. The stronger model is defined and Studied on the Optimization Rules and Solution Tactics of availability. A simple and efficient stronger searching mechanism is presented in the constructed of candidates. The mechanism can estimate the VSN´s capacity of attributes. The optimized matching node is selected. And these nodes will enable us to make quicker and better decisions of resource scheduling and control. In addition, we use the modified Minimum Mean Squared Error algorithm for the located ranges of the estimate we are making in this paper. The ranges are Numerical simulations show that the algorithm has higher accuracy and rapid convergence. The selected range is as the initial Iteration value. Research on the Prediction Problem is abstracted into an Unconstrained Optimization Problems. The Stronger Intelligent Selection method is an efficient method for solving unconstrained optimization problems. The simulations show that the SIS has higher Positioning accuracy and computational complexity than Particle Swarm Optimization (PSO) or Genetic algorithm (GA).