摘要
针对大规模无线传感器网络(WSN)中的事件检测问题(EDP),传统的方法通常依赖先验信息,阻碍了实际应用。该文为EDP提出了一种基于深度学习的算法,称为交替方向乘子法网络(ADMM-Net)。首先,采用低秩稀疏矩阵分解来建模事件的时空相关性。之后,EDP被表述为一个带约束的优化问题并用交替方向乘子法(ADMM)求解。然而,优化算法收敛慢且算法的性能依赖于对先验参数的仔细选择。该文基于深度学习中“展开”的概念,提出了一种用于EDP的深度神经网络ADMM-Net。通过“展开”ADMM算法的方式得到。ADMM-Net具有固定层数,其参数可以通过监督学习训练获得。无需先验信息。相比于传统算法,提出的ADMM-Net收敛快且不需先验信息。人造数据集和真实数据集的仿真结果验证了ADMM-Net的有效性。
Considering the Event Detection Problem(EDP)in the large-scale Wireless Sensor Network(WSN),the conventional methods rely generally on some prior information,which obstacles the actual application.In this paper,a deep learning-based algorithm,named as Alternating Direction Multiplier Method Network(ADMM-Net),is proposed for the EDP.Firstly,the low rank and sparse matrix decomposition is adopted to capture the spatial-temporal correlation of events.After that,the EDP is formulated as a constrained optimization problem and solved by the Alternating Direction Multiplier Method(ADMM).However,the optimization algorithm suffers from low convergence.Besides,the algorithm’s performance relies heavily on the careful selection of prior parameters.By adopting the conception of“unfolding”in deep learning field,a deep learning network which is named ADMM-Net,is proposed for the EDP in this paper.The ADMM-Net is obtained by unfolding the ADMM algorithm.The ADMM-Net is with fixed layers,whose parameters can be trained via supervised learning.No prior information is required.Compared to the conventional methods,the proposed ADMM-Net does not require any prior information while enjoying fast convergence.Simulation results on both synthesis and realistic datasets verify the effectiveness of the proposed ADMM-Net.
作者
胡世成
杨柳
康凯
钱骅
HU Shicheng;YANG Liu;KANG Kai;QIAN Hua(Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China;School of Information Science and Technology,ShanghaiTech University,Shanghai 201210,China;University of Chinese Academy of Sciences,Beijing 100049,China;Shanghai Institute of Microsystem and Information Technology,Chinese Academy of Sciences,Shanghai 200050,China)
出处
《电子与信息学报》
EI
CSCD
北大核心
2023年第7期2634-2641,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金(61971286)
国家重点研究发展计划(2020YFB2205603)
上海市科学技术委员会科技创新行动计划(19DZ1204300)。
关键词
事件检测
无线传感器网络
时空相关性
低秩稀疏分解
深度学习
交替方向乘子法网络
Event detection
Wireless Sensor Networks(WSN)
Spatial-temporal correlation
Low rank and sparse matrix decomposition
Deep learning
Alternating Direction Multiplier Method Network(ADMM-Net)
