Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks

Andrea J. Goldsmith; Michal Yemini; Stephanie Gil

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Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks

Michal Yemini, Stephanie Gil, Andrea J. Goldsmith

Signal Processing · Submitted 3 Oct 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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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arXiv ID2110.01119 (version 3)

Pith IDLLMT2FGK, Pith's permanent record of this paper and its review

CitationAndrea J. Goldsmith, Michal Yemini, Stephanie Gil. "Cloud-Cluster Architecture for Detection in Intermittently Connected Sensor Networks." arXiv:2110.01119 (2021). https://pith.science/paper/LLMT2FGK

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What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.28 · human participants 0.02 · controlled comparison 0.09 · randomized assignment 0.02 · states quantitative result 0.07 · compares with prior results 0.09 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; show every role.

We consider a centralized detection problem where sensors experience noisy measurements and intermittent connectivity to a centralized fusion center. The sensors collaborate locally within predefined sensor clusters and fuse their noisy sensor data to reach a common local estimate of the detected event in each cluster. The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center. Upon receiving the estimates from all the connected sensor clusters the fusion center fuses the received estimates to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a \emph{cloud-cluster} architecture. We propose a method for optimizing the decision rule for each cluster and analyzing the expected detection performance resulting from our hybrid scheme. Our method is tractable and addresses the high computational complexity caused by heterogeneous sensors' and clusters' detection quality, heterogeneity in their communication opportunities, and non-convexity of the loss function. Our analysis shows that clustering the sensors provides resilience to noise in the case of low sensor 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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	We consider a centralized detection problem where sensors experience noisy measurements and intermittent connectivity to a centralized fusion center.	background 1.00	0.17	0.03	asserted 0.95
s1	The sensors collaborate locally within predefined sensor clusters and fuse their noisy sensor data to reach a common local estimate of the detected event in each cluster.	method 0.94	0.21	0.04	asserted 0.99
s2	The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center.	background 0.70	0.17	0.03	asserted 1.00
s3	Upon receiving the estimates from all the connected sensor clusters the fusion center fuses the received estimates to make a final determination regarding the occurrence of the event across the deployment area.	method 0.99	0.24	0.05	asserted 0.99
s4	We refer to this hybrid communication scheme as a \emph{cloud-cluster} architecture.	contribution 0.53	0.55	0.04	asserted 0.98
s5	We propose a method for optimizing the decision rule for each cluster and analyzing the expected detection performance resulting from our hybrid scheme.	contribution 0.98	0.93	0.07	asserted 0.96
s6	Our method is tractable and addresses the high computational complexity caused by heterogeneous sensors' and clusters' detection quality, heterogeneity in their communication opportunities, and non-convexity of the loss function.	contribution 0.77	0.90	0.10	asserted 1.00
s7	Our analysis shows that clustering the sensors provides resilience to noise in the case of low sensor communication probability with the cloud.	result 0.91	0.93	0.41	asserted 0.99
s8	For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture.	result 0.91	0.89	0.41	hedged possibility 0.98

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from proof or derivation 0.96.

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arXiv listing abstract v3

not examined

full text
figures and tables
references and citing sentences
relations to other papers

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P abstract

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S sections
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C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

We consider a centralized detection problem where sensors experience noisy measurements and intermittent connectivity to a centralized fusion center. The sensors collaborate locally within predefined sensor clusters and fuse their noisy sensor data to reach a common local estimate of the detected event in each cluster. The connectivity of each sensor cluster is intermittent and depends on the available communication opportunities of the sensors to the fusion center. Upon receiving the estimates from all the connected sensor clusters the fusion center fuses the received estimates to make a final determination regarding the occurrence of the event across the deployment area. We refer to this hybrid communication scheme as a \emph{cloud-cluster} architecture. We propose a method for optimizing the decision rule for each cluster and analyzing the expected detection performance resulting from our hybrid scheme. Our method is tractable and addresses the high computational complexity caused by heterogeneous sensors' and clusters' detection quality, heterogeneity in their communication opportunities, and non-convexity of the loss function. Our analysis shows that clustering the sensors provides resilience to noise in the case of low sensor 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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1 arXiv paper cite this in 1 sentence

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers whose main result relates to this one's

Found among the papers closest to this one by title and main claim. Pith's classifier compared each pair's main results and a second model family confirmed the relation; checked against two further families, shown conflicts agreed 118 times in 120 and shown same results 218 times in 239. The quoted sentence is the other paper's main claim.

States the same result 1 paper

For larger clusters, a steep improvement in detection performance is possible even for a low communication probability by using our cloud-cluster architecture.

Exploiting Local and Cloud Sensor Fusion in Intermittently Connected Sensor Networks · 1.00

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