间歇连接传感器网络中的本地和云传感器融合
Exploiting Local and Cloud Sensor Fusion in Intermittently Connected Sensor Networks
摘要：
我们考虑一个检测问题，传感器经历嘈杂的测量和间歇性的通信机会，一个集中的融合中心（或云）。该问题的目的是要达到正确的估计事件检测的环境。传感器可以在本地与其他传感器（本地集群）通信，其中它们融合它们的噪声传感器数据以在本地估计事件的检测。此外，每个传感器集群可以间歇地与云通信，其中集中式融合中心融合来自所有传感器集群的估计，以做出关于跨部署区域的事件发生的最终确定。我们将这种混合通信方案称为云集群架构。在我们的混合通信方案中，噪声传感器间歇性地连接到云的网络的预期损失函数最小化，据我们所知还没有研究过。我们利用最近改进的浓度不等式，以达到每个集群的优化决策规则，我们分析了预期的检测性能，从我们的混合方案。我们的分析表明，在与云通信概率较低的情况下，对传感器进行聚类可以提供对噪声的弹性。对于较大的集群，通过使用我们的云集群架构，即使对于低通信概率，检测性能也有可能大幅提高。
英文摘要：
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.
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