Scalable Multi-objects meta-level coordinated learning in Internet of Things
Wang JP(王军平); JUNPING WANG
2015-10
发表期刊Personal and Ubiquitous Computing
卷号19期号:7页码:1133–1144
摘要The coordinated learning is importance of technique
for cooperative multi-objects system in large-scale
Internet of Things. The coordinated learning has attracted a
lot of attention for its applications in Internet of Things.
However, the self-adaptive makes the coordinated learning
difficult to be used in IoT. This paper proposes multi-objects
scalable coordinated learning algorithm based on the maximumpotential
loss of coordination. The algorithm defines an
interaction measure that allows objects to dynamically estimate
the potential utility loss of coordination with any cluster
of objects. The interaction mechanism makes each object
compute their beneficial coordination set in different situations
and makes the best use of their limited communication
resource in Internet of Things. As a result of experiments, our
algorithm adapts policy learning of object and their coordination
network for different context. Finally, the experiments
with the smart agriculture data set demonstrate that the
proposed scheme is effective and robust.
关键词Coordinated Multi-objects System Meta-level Control Coordinated Learning
收录类别SCI
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12231
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者JUNPING WANG
作者单位Laboratory of Precision Sensing and Control Center, Institute of Automation, Chinese Academy
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Wang JP,JUNPING WANG. Scalable Multi-objects meta-level coordinated learning in Internet of Things[J]. Personal and Ubiquitous Computing,2015,19(7):1133–1144.
APA Wang JP,&JUNPING WANG.(2015).Scalable Multi-objects meta-level coordinated learning in Internet of Things.Personal and Ubiquitous Computing,19(7),1133–1144.
MLA Wang JP,et al."Scalable Multi-objects meta-level coordinated learning in Internet of Things".Personal and Ubiquitous Computing 19.7(2015):1133–1144.
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