Simulation of optimal selection algorithm for wireless sensor cluster head node Bayesian statistical network

IF 1.5 Q2 COMPUTER SCIENCE, THEORY & METHODS
Yingqi Xu
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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.
无线传感器簇头节点贝叶斯统计网络最优选择算法仿真
本文提出了一种聚类树网络的路由算法,并将聚类的层次结构与神经网络的层次结构进一步结合,设计了一种基于聚类路由协议的数据融合算法。然后,针对神经网络权重选择困难的问题,提出了一种基于粒子群优化算法的权重优化神经网络,并将其应用于多传感器融合。仿真结果表明,ACEC协议簇首数更集中于期望值,具有较好的稳定性。该算法采用非均匀聚类和动态阈值选择簇头节点,保证了簇头节点在网络中的均衡分布,降低了网络能耗,延长了网络的使用寿命。ancec协议的成功率与调试协议相似,但随着传输时间的增加,LEACH协议和调试协议在转发数据时不考虑链路质量因素,因此在每轮选择下一跳中继点时,通信链路质量参差不齐,数据传输成功率有比较明显的下降趋势。融合效果明显优于差的两个传感器,但不如最好的传感器。这是由于传感器SNL和SN2的信噪比较低,因此其识别效果相对较差,这也符合多传感器融合的规律。结果表明,基于qdpso-bp网络融合的方法是可行的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.80
自引率
23.10%
发文量
31
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.
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