重庆邮电大学移通学院 机构 通信与物联网工程学院,重庆,401520
纸质出版:2019
移动端阅览
黄颖, 费莉, 赖小龙. 带有隐私保护的无线传感网能量有效数据融合机制[J]. 微电子学与计算机, 2019, 36(7): 65-69, 75.
HUANG Ying, FEI Li, LAI Xiao-long. Energy-efficient wireless sensor network data fusion mechanism with privacy protection method[J]. 2019, 36(7): 65-69.
传感数据的节能传输与隐私保护对深化无线传感网的应用具有重要的意义.提出带有隐私保护的无线传感网能量有效数据融合机制
以解决当前无线传感网数据融合机制效率较低且缺乏隐私安全保护的问题.设计了传感数据隐私保护方法
通过在传感节点对传感数据进行加密
保障传感数据的隐私安全.研究了能量有效的数据融合机制
基于深度学习理论
设计了有效的数据融合模型
提高传感数据融合的效率.仿真结果表明
所提机制可有效保障传感数据的隐私安全
提高数据融合的效率
并降低节点能量消耗.
The energy-saving transmission and privacy protection of sensor data is of great significance for deepening the applications of wireless sensor network. An energy-efficient wireless sensor network data fusion mechanism with privacy protection method is proposed to solve the problem that the current data fusion mechanism is less efficient and lacks privacy protection method. The sensor data privacy protection method is designed to protect the privacy of the sensor data by encrypting the sensor data at the sensor node. The energy-efficient data fusion mechanism is studied. Based on the deep learning theory
an efficient data fusion model is presented to improve the efficiency of sensor data fusion process. Simulation results show that the proposed mechanism can effectively guarantee the privacy of sensor data
improve the efficiency of data fusion
and reduce the energy consumptions of nodes.
YU Y, FENG X, HU J. Multi-sensor data fusion algorithm of triangle module operator in WSN[C]//2014 10th International Conference on Mobile Ad-hoc and Sensor Networks. Maui, HI, 2014: 105-111.
SHI L, LI M Y, LI X. WSN data fusion approach based on improved BP algorithm and clustering protocol[C]//The 27th Chinese Control and Decision Conference (2015 CCDC), Qingdao, China, 2015: 1450-1454.
HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7): 1527-1554. DOI: 1527
LAJARA R, PÉREZ-SOLANO J J, PELEGRI-SEBASTIÁ J. Predicting the batteries' state of health in wireless sensor networks applications[J]. IEEE Transactions on Industrial Electronics, 2018, 65(11): 8936-8945. DOI: 8936
高洁, 吴宪海, 文琼瑶.基于节点轮换的无线传感器网络节能策略研究[J].传感技术学报, 2018, 31(3):477-482. DOI: 477
任志玲, 张广全, 林冬, 等.无线传感器网络应用综述[J].传感器与微系统, 2018, 37(3):1-10. DOI: 1
俞姝颖, 吴小兵, 陈贵海, 等.无线传感器网络在桥梁健康监测中的应用[J].软件学报, 2015, 26(6):1486-1498. DOI: 1486
窦桂琴, 万润泽, 张兴艳.基于模糊理论的无线传感器网络数据融合算法[J].微电子学与计算机, 2012, 29(9):133-136. DOI: 133
杨浩.无线传感器网络环境下能量有效的虚拟坐标构建[J].小型微型计算机系统, 2018, 39(2):245-248. DOI: 245
LIU J, SHEN H, YU L, et al. Characterizing data deliverability of greedy routing in wireless sensor networks[J]. IEEE Transactions on Mobile Computing, 2018, 17(3): 543-559. DOI: 543
叶继华, 王文, 江爱文.一种基于LEACH的异构WSN能量均衡成簇协议[J].传感技术学报, 2015, 28(12):1853-1860. DOI: 1853
XIE K, NING X P, WANG X, et al. An efficient privacy-preserving compressive data gathering scheme in WSNs[J]. Information Sciences, 2017(390): 82-94. DOI: 82
CHENG L, NIU J, LUO C, et al. Towards minimum-delay and energy-efficient flooding in low-duty-cycle wireless sensor networks[J]. Computer Networks, 2018, 134(4): 66-77. DOI: 66
徐艮凤, 张曦煌.低延迟和低能耗的无线传感器网络数据融合算法研究[J].微电子学与计算机, 2017, 34(9):11-14. DOI: 11
薛亮, 王燕龙, 李志华, 等.无线传感器网络中基于数据融合的时变速率路由算法[J].微电子学与计算机, 2015, 32(12):130-135. DOI: 130
余修武, 范飞生, 周利兴, 等.无线传感器网络自适应预测加权数据融合算法[J].传感技术学报, 2017, 30(5):772-776. DOI: 772
0
浏览量
19
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010602201714号