Research on Real-time Feedback Method for Multimedia Sensing Network Learning Monitoring

YANG Liu-qing; WANG Chong

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Research on Real-time Feedback Method for Multimedia Sensing Network Learning Monitoring

更新时间：2026-09-04

- Research on Real-time Feedback Method for Multimedia Sensing Network Learning Monitoring
- Vol. 35, Issue 4, Pages: 130-134(2018)
- 作者机构：
  
  1. 桂林电子科技大学机构计算机与信息安全学院,广西,桂林,541004
  2. 玉林师范学院机构教育技术中心,广西,玉林,537000
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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杨柳青, 王冲. 多媒体传感网络学习监控实时反馈方法研究[J]. 微电子学与计算机, 2018, 35(4): 130-134.

YANG Liu-qing, WANG Chong. Research on Real-time Feedback Method for Multimedia Sensing Network Learning Monitoring[J]. 2018, 35(4): 130-134.
杨柳青, 王冲. 多媒体传感网络学习监控实时反馈方法研究[J]. 微电子学与计算机, 2018, 35(4): 130-134. DOI：

YANG Liu-qing, WANG Chong. Research on Real-time Feedback Method for Multimedia Sensing Network Learning Monitoring[J]. 2018, 35(4): 130-134. DOI：

摘要

为了降低多媒体传感器网络的数据丢包和时间延迟

提高网络学习的监控实时反馈能力

提出了基于线性移位自适应寻优的多媒体传感网络学习监控实时反馈方法.构建多媒体传感网络的节点优化部署模型

采用簇内传感节点融合跟踪方法进行多媒体信息融合和二维熵特征提取

根据传感节点的阈值进行模糊自适应加权控制

结合线性移位信道分配方法进行多媒体传感网络学习监控反馈链路均衡处理

采用智能寻优算法进行多媒体传感网络传输延迟控制

提高数据的实时反馈能力.仿真结果表明

采用该方法进行多媒体传感网络学习监控实时反馈控制

数据输出的丢包率较低

多媒体信息反馈的时延较小

整个网络的链路均衡性较好.

Abstract

In order to reduce the multimedia sensor network data packet loss and time delay

improve the real-time feedback ability of monitoring network learning

proposes a multimedia sensor network optimization based on linear shift adaptive learning monitor real-time feedback method. The optimal deployment model of multimedia sensor network

the sensor nodes within the cluster fusion tracking method for multimedia information fusion and the two-dimensional entropy feature extraction

fuzzy adaptive control based on sensor node weighted threshold

combined with linear shift channel allocation method for multimedia sensor network learning monitoring feedback link equalization

using intelligent optimization algorithm for multimedia sensor network transmission delay control

improve the ability of real-time feedback data. Simulation results show that using the method of multimedia the sensor network monitoring real-time feedback control

output data lost packet rate is low

the time delay of the multimedia information feedback is small

and the link balance of the whole network is better.

关键词

Keywords

references

张莉, 张春红. 多跳水下传感器网络中的功率分配算法[J]. 舰船科学技术, 2012, 34(8): 73-75. DOI: 73

KHALILI A, SAMI A. SysDetect: a systematic approach to critical state determination for industrial intrusion detection systems using Apriori algorithm[J]. Journal of Process Control, 2015, 2776: 154-160. DOI: 154

MORADI M, KEYVANPOUR M R. An analytical review of XML association rules mining[J]. Artificial Intelligence Review, 2015, 43(2): 277-300. DOI: 277

李梓杨, 于炯, 卞琛, 等. 基于负载感知的数据流动态负载均衡策略[J]. 计算机应用, 2017, 37(10): 2760-2766. DOI: 2760

MARIMON M C, TANGONAN G, LIBATIQUE N J, et al. Development and evaluation of wave sensor nodes for ocean wave monitoring[J]. IEEE Systems Journal, 2015, 9(1): 292-302. DOI: 292

DONG G L, RYU K S, BASHIR M, et al. Discovering medical knowledge using association rule mining in young adults with acute myocardial infarction[J]. Journal of Medical Systems, 2013, 37(2): 1-10. DOI: 1

LEONARDO E J, YACOUB M D. Exact formulations for the throughput of IEEE 802.11 DCF in Hoyt, Rice, and Nakagami-m fading channels[J]. IEEE Transactions on Wireless Communications, 2013, 12(5): 2261-2271. DOI: 2261

KESHAVAMURTHY B N, KHAN A M, TOSHNIWAL D. Privacy preserving association rule mining over distributed databases using genetic algorithm[J]. Neural Computing % Applications, 2013, 22(Supp 1): 351-364. DOI: 351

朱丹, 姜宇. 基于光纤传感器和ZigBee技术的网络可靠性研究[J]. 激光杂志, 2016, 37(8): 63-66. DOI: 63

龙昭华, 龚俊, 王波, 等. 无线传感器网络中分簇安全路由协议保密通信方法的能效研究[J]. 电子与信息学报, 2015, 37(8): 2000-2006. DOI: 2000

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