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Cited by 90 publications
(45 citation statements)
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“…Also, we put Y ∞ = −∞ as naturally the cost of an infinite number of observation is infinite. Consider now the V * the expected quality reward for the System based on an optimal stopping rule in (3). Suppose that the mechanism induces cost c and observe the M 1 .…”
Section: Time-optimized Quality-driven Mechanismmentioning
confidence: 99%
See 1 more Smart Citation
“…Also, we put Y ∞ = −∞ as naturally the cost of an infinite number of observation is infinite. Consider now the V * the expected quality reward for the System based on an optimal stopping rule in (3). Suppose that the mechanism induces cost c and observe the M 1 .…”
Section: Time-optimized Quality-driven Mechanismmentioning
confidence: 99%
“…In the state of the art, it is possible to find quite a few IoT monitoring and predictive analytics solutions such as forest monitoring [2], fire-event prediction and classification [3], agriculture monitoring [4], marine environment states prediction [5], watershed prediction systems [20], health states prediction in rivers [21], or energy management solutions to reduce both the amount of resources needed and the atmospheric emissions [22]. The reader could also refer to the survey [23] and the references therein.…”
Section: Introductionmentioning
confidence: 99%
“…However, several fusion models (as in [4] [5]) treat the alert issue based on the acquisition of data provided from the nodes deployed in the surveillance field, in the case of a fire event and at the very beginning, minorities of the nodes immediately detect the fire, while, the other nodes that are still far from the event, have not detected the fire yet. The system, then, starts the calculation based on all the collected data.…”
Section: Introductionmentioning
confidence: 99%
“…In addition, many of these proposed approaches of researchers (as in [4] and [5]), have not discussed the information filtering processing, which is an interesting operation that allows to avoid the impact of erroneous data on the processing of information fusion.…”
Section: Introductionmentioning
confidence: 99%
“…Information fusion is the merging of information from disparate sources with differing conceptual, contextual, and typographical representations. It has been successfully applied in data mining and the consolidation of data from unstructured or semi-structured resources, and it has also led to many achievements in various fields [1,4,8]. Fusion methods include product fusion (such as the Bayes posterior probability model), linear fusion (SVM classifiers), and nonlinear fusion (super-kernel integration) [23].…”
Section: Introductionmentioning
confidence: 99%