Exploiting Local and Cloud Sensor Fusion in Intermittently Connected Sensor Networks

Michal Yemini, Stephanie Gil, Andrea Goldsmith

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations


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.

Original languageEnglish
Article number9348131
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
StatePublished - Dec 2020
Externally publishedYes
Event2020 IEEE Global Communications Conference, GLOBECOM 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: 7 Dec 202011 Dec 2020

Bibliographical note

Publisher Copyright:
© 2020 IEEE.


The authors gratefully acknowledge support through Intel and through the National Science Foundation (NSF) CAREER award #1845225.

FundersFunder number
National Science Foundation2114733
Directorate for Computer and Information Science and Engineering1845225
Intel Corporation


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