Research On Energy And Spectrum Resource Optimization Algorithm In Wireless Sensor Network

Wireless Sensor Network(WSN),as a key technology for the Internet of Things to obtain data from the physical world,has the characteristics of intelligence and miniaturization,and is widely used in agriculture,environment and other fields.It has become one of the hot spots of research in the intelligent applications today.However,WSN nodes are limited in energy and hard to replenish due to cost and size requirements,so it has always been the focus and hotpots of WSN research to optimize energy,reduce network energy consumption and improve the network energy resource utilization.With the popularization of intelligent applications,the spectrum resources in wireless communication are becoming increasingly scarce,resulting in greater interference between nodes and thus affecting information collection and transmission,which not only causes data packet loss(affects the network performance),but also causes additional retransmission energy consumption,further increasing the energy supply burden of nodes.Thus,it is clear that spectrum optimization plays a crucial role in data transmission,and energy and spectrum interact,so it is of great theoretical and practical significance to study energy and spectrum resource optimization of WSN comprehensively.This topic starts from reducing energy consumption and interference to carry out the optimization of energy and spectrum resources,and the specific research work is as follows.For the limited energy characteristic,in order to effectively reduce network energy consumption to improve node energy efficiency,this paper firstly explores the impact of multipath fading phenomenon on network energy consumption during information transmission,and constructs an energy consumption metric model by combining the role of node load and interference on energy consumption.Based on this model and with the optimization objectives of minimizing network energy consumption and maximizing network signal interference noise ratio(SINR),this paper uses particle swarm optimization algorithm to design an efficient power control algorithm to optimize the WSN topology in order to guarantee the successful transmission of network data,improve the quality of data service,and achieve the purpose of reducing energy consumption and improving energy resource utilization.Based on the existing topology and considering the of spectrum resources scarcity,in order to reasonably allocate channels to reduce interference,this paper uses a physical interference model to measure the interference and combines node energy consumption and residual energy to construct a node interference efficiency model to reflect the urgency of the node to reduce interference in response to its energy consumption and residual energy.Based on this model,a channel allocation game algorithm is designed to reduce interference and energy consumption and ensure communication quality,and allocate channels with less interference to nodes with greater urgency to balance node lifetime,which extends network lifetime and improves spectrum resource utilization.In order to realize the cooperative optimization of energy and spectrum resources,a the characteristics of the interactive influence of power and channel,this paper constructs the interactive influence model of energy consumption and interference based on power and channel,and designs a joint optimization algorithm of power control and channel allocation with reducing energy consumption,interference and improving SINR.The algorithm uses a hybrid particle swarm multi-objective optimization algorithm to improve the convergence of the algorithm and achieve the optimization of the performance of network energy consumption,interference,and communication quality.In order to reduce the complexity of the algorithm while achieving the cooperative optimization of energy and spectrum resources,this paper proposes a low-complexity multi-objective optimization algorithm based on the Differential evolution-Game with the objectives of minimizing interference efficiency and minimizing communication quality balance.The algorithm uses the differential evolution algorithm to solve the dominant solution that meets the better response strategy to achieve the joint allocation of power and channels that reduces interference and energy consumption and improves network communication quality.The theoretical analysis proves its convergence and complexity,and the effectiveness of the algorithm is verified by simulation.