论文标题
在间歇连接的传感器网络中利用本地和云传感器融合
Exploiting Local and Cloud Sensor Fusion in Intermittently Connected Sensor Networks
论文作者
论文摘要
我们考虑了一个检测问题，传感器经历了集中式融合中心（或云）的嘈杂测量和间歇性的沟通机会。问题的目的是得出环境中事件检测的正确估计。传感器可以与其他传感器（本地簇）进行局部通信，在那里它们融合了嘈杂的传感器数据以估计当地事件的检测。此外，每个传感器群集都可以间歇地通信到云，在该云中，集中式融合中心融合了所有传感器簇的估计，以最终确定整个部署区域的事件发生。我们将此混合通信方案称为云集群体系结构。如我们所知，在我们所知的混合通信方案中，在嘈杂的传感器间歇性地连接到云的网络的预期损耗函数中，尚未对云进行连接。我们利用最近提高的浓度不平等，为每个集群提供了优化的决策规则，并分析了由混合方案产生的预期检测性能。我们的分析表明，在与云的沟通概率较低的情况下，传感器的聚类为噪声提供了弹性。对于较大的集群，即使使用我们的云群集体系结构，即使对于低通信概率，检测性能也可以显着改善。
We consider a detection problem where sensors experience noisy measurements and intermittent communication opportunities to a centralized fusion center (or cloud). The objective of the problem is to arrive at the correct estimate of event detection in the environment. The sensors may communicate locally with other sensors (local clusters) where they fuse their noisy sensor data to estimate the detection of an event locally. In addition, each sensor cluster can intermittently communicate to the cloud, where a centralized fusion center fuses estimates from all sensor clusters to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a cloud-cluster architecture. Minimizing the expected loss function of networks where noisy sensors are intermittently connected to the cloud, as in our hybrid communication scheme, has not been investigated to our knowledge. We leverage recently improved concentration inequalities to arrive at an optimized decision rule for each cluster and we analyze the expected detection performance resulting from our hybrid scheme. Our analysis shows that clustering the sensors provides resilience to noise in the case of low communication probability with the cloud. For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture.
