Research On Dynamic Spectrum Allocation Algorithm In Wireless Sensor Network

ISM Bands(Industrial Scientific and Medical)where wireless sensor networks work are faced with the shortage of spectrum resources problem.Aimed at this case,dynamic spectrum allocation of cognitive radio is applied to wireless sensor networks to reuse the spectrum.In this way,the spectrum efficiency is improved,besides the data monitored by the wireless sensor networks can also be transmitted smoothly.This paper analyzes the research status of dynamic spectrum allocation in cognitive radio.At present,there are four mainstream spectrum allocation models,including game theory model,interference temperature model,auction bidding model and graph coloring model.After analyzing the traditional spectrum allocation algorithms,the genetic algorithm is used to solve the problem of spectrum allocation.In this paper,genetic algorithm is modified and the improved genetic algorithm is more targeted to solve the dynamic spectrum allocation problem in wireless sensor networks.The improved genetic algorithm takes the minimum spectrum switching frequency and the maximum network revenue as the objective function to reduce node energy consumption.In coding,the improved genetic algorithm maps mechanism between chromosomes and graph coloring model which makes the search space of the algorithm greatly reduced.At the same time,in order to avoid premature phenomenon and reduce the computation,adaptive probabilities of crossover and mutation are used in improved genetic algorithm.Through the simulation in MATLAB,this paper compares the average network revenue and spectrum switching frequency among the improved genetic algorithm,traditional genetic algorithm and color sensitive graph coloring algorithm.In the simulation part,this paper explores the influence of network environment parameters and genetic algorithm parameters on the results of genetic algorithm.Experimental results prove that the proposed algorithm can not only improve the spectral efficiency but also reduce energy consumption and it can be well applied in wireless sensor networks.