{"title":"Simulation of optimal selection algorithm for wireless sensor cluster head node Bayesian statistical network","authors":"Yingqi Xu","doi":"10.3233/JIFS-219093","DOIUrl":null,"url":null,"abstract":"This paper proposes a routing algorithm of cluster tree network and further combines the hierarchical structure of clustering with that of neural network, and designs a data fusion algorithm based on clustering routing protocol. Then, aiming at the difficulty in selecting the weights of neural network, a weight optimization neural network based on particle swarm optimization algorithm is proposed and applied to multi-sensor fusion. The simulation results show that the number of cluster heads of ACEC protocol is more concentrated on the expected value and has good stability. The algorithm selects cluster head nodes by non-uniform clustering and dynamic threshold, which ensures the balanced distribution of cluster head nodes in the network, reduces the network energy consumption and prolongs the service life of the network. The success rate of ancec protocol is similar to debug protocol, but with the increase of transmission time, LEACH protocol and debug protocol do not consider the link quality factor when forwarding data, so the communication link quality is uneven when selecting the next hop relay point in each round, so the data transmission success rate has a relatively obvious downward trend. The fusion result is clearly better than the poor two sensors, but inferior to the best sensor. This is due to the low SNR of sensors SNL and SN2, so their recognition effect is relatively poor, which also conforms to the rule of multi-sensor fusion. The results show that the method based on qdpso-bp network fusion is feasible.","PeriodicalId":44705,"journal":{"name":"International Journal of Fuzzy Logic and Intelligent Systems","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2021-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fuzzy Logic and Intelligent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/JIFS-219093","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 2
Abstract
This paper proposes a routing algorithm of cluster tree network and further combines the hierarchical structure of clustering with that of neural network, and designs a data fusion algorithm based on clustering routing protocol. Then, aiming at the difficulty in selecting the weights of neural network, a weight optimization neural network based on particle swarm optimization algorithm is proposed and applied to multi-sensor fusion. The simulation results show that the number of cluster heads of ACEC protocol is more concentrated on the expected value and has good stability. The algorithm selects cluster head nodes by non-uniform clustering and dynamic threshold, which ensures the balanced distribution of cluster head nodes in the network, reduces the network energy consumption and prolongs the service life of the network. The success rate of ancec protocol is similar to debug protocol, but with the increase of transmission time, LEACH protocol and debug protocol do not consider the link quality factor when forwarding data, so the communication link quality is uneven when selecting the next hop relay point in each round, so the data transmission success rate has a relatively obvious downward trend. The fusion result is clearly better than the poor two sensors, but inferior to the best sensor. This is due to the low SNR of sensors SNL and SN2, so their recognition effect is relatively poor, which also conforms to the rule of multi-sensor fusion. The results show that the method based on qdpso-bp network fusion is feasible.
期刊介绍:
The International Journal of Fuzzy Logic and Intelligent Systems (pISSN 1598-2645, eISSN 2093-744X) is published quarterly by the Korean Institute of Intelligent Systems. The official title of the journal is International Journal of Fuzzy Logic and Intelligent Systems and the abbreviated title is Int. J. Fuzzy Log. Intell. Syst. Some, or all, of the articles in the journal are indexed in SCOPUS, Korea Citation Index (KCI), DOI/CrossrRef, DBLP, and Google Scholar. The journal was launched in 2001 and dedicated to the dissemination of well-defined theoretical and empirical studies results that have a potential impact on the realization of intelligent systems based on fuzzy logic and intelligent systems theory. Specific topics include, but are not limited to: a) computational intelligence techniques including fuzzy logic systems, neural networks and evolutionary computation; b) intelligent control, instrumentation and robotics; c) adaptive signal and multimedia processing; d) intelligent information processing including pattern recognition and information processing; e) machine learning and smart systems including data mining and intelligent service practices; f) fuzzy theory and its applications.