Energy Saving Research Of Wireless Sensor Network Based On Data Sensing And Similarity

The wireless sensor network is a distributed sensing network in the form of free-form organization and combining a large number of sensor nodes,which in turn forms a network.Sensors in the network for wireless communication,and flexible network settings,but also through the wired or wireless way to connect with the Internet to form a multi-hop self-organizing network.Sensor types are diverse,not only with the ability to detect temperature and humidity,light intensity,earthquakes,and other environmental factors,but also have the ability to collect,calculate and transmit information,so its application areas are very wide.However,since sensor nodes have limited energy,how to use energy efficiently to maximize the network life cycle is a key challenge for sensor networks.Energy saving is an important research direction for wireless sensor networks,and designing clustered routing algorithms or reducing the amount of data transmitted becomes an important means to reduce the energy consumption of the network.The clustering algorithm can directly affect the energy consumption and scalability of wireless sensor networks.The applications of wireless sensor networks need to collect environmental information and complete based on data collection.There will be a large amount of data to be transmitted and processed in the network,and a lot of data are similar,and data sensing and processing will have a great impact on the energy consumption of the network.Therefore,this paper focuses on data-aware clustering algorithms and collection strategies suitable for similarity clustering.By designing data-aware clustering algorithms based on data similarity and applying reasonable data collection strategies,we can effectively eliminate data redundancy,promote data fusion in the network,reduce the communication volume,and thus reduce the energy consumption of the network.This paper firstly describes the background and significance of research on clustering and data collection strategies for wireless sensor networks,introduces the basic overview of wireless sensor networks,several typical clustering algorithms,and data collection frameworks,then analyzes their advantages and shortcomings,and summarizes the main problems to be solved in this paper.The current clustering algorithm using data awareness often has the problems of too strict conditions for judging similar nodes and the too-small number of redundant nodes identification.Therefore,this paper proposes a CDSW clustering algorithm based on the CRPAW clustering algorithm combined with similarity.This clustering algorithm adopts the structure of the CRPAW algorithm in the part of selecting the initial cluster head,fully considers various factors affecting network energy consumption such as node similarity and residual energy in the clustering process,and defines two weighting functions for entering and competing for cluster heads.In the node similarity measurement,this paper defines two improved similarity judgment models to complement each other according to the distribution and change trend of environmental information and other characteristics and proposes the corresponding data estimation methods.Existing data-aware clustering algorithms achieve the goal of maintaining a high degree of consistency of nodes within a cluster by sacrificing the rationality and energy efficiency of the cluster structure.Therefore,in this paper,a similarity-based clustering data collection strategy(DSCDC strategy)is proposed based on the cluster structure formed by the CDSW clustering algorithm.In the DSCDC strategy,to give full play to node similarity to reduce the communication volume,this paper proposes a data collection method based on representative(R)nodes.To eliminate temporal redundant data,length compression coding is used in the DSCDC strategy.This strategy also improves the sleep scheduling algorithm to achieve the effect of adaptively changing the spatial sampling rate.Finally,to verify the effectiveness and superiority of the proposed algorithm and strategy,simulation experiments are conducted using Matlab software with similarity judgment as part of the CDSW clustering and CDSW as part of the DSCDC strategy composition.Meanwhile,each part of the DSCDC strategy in the simulation experiment results is analyzed in detail,and some key parameters are discussed.The DSCDC strategy is compared with the EEDC,DSCCF,and DCFA data collection strategies using data reduction percentage,data accuracy,network lifetime,and network energy consumption as performance evaluation metrics.The simulation results show that the DSCDC strategy has a better data reduction effect and its network lifetime can be improved to1.78 times on average compared with the other three strategies.This shows that the DSCDC strategy can form a more reasonable cluster structure,effectively eliminate data redundancy,reduce network communication energy consumption,and significantly extend the network lifetime.