2017
DOI: 10.1007/s10489-017-1032-y
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Predictive intelligence to the edge through approximate collaborative context reasoning

Abstract: We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich … Show more

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Cited by 21 publications
(15 citation statements)
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“…Then, we use the approximatef to address Problem 2 to approximate the data function g by a data-PLR functionĝ. Concerning Problem 1 and Theorem 2, we approximate f (x, θ) = E[y|x, θ] that minimizes (5). However, the answer y in (4) involves the average of the outputs g(…”
Section: Methodology Overviewmentioning
confidence: 99%
See 3 more Smart Citations
“…Then, we use the approximatef to address Problem 2 to approximate the data function g by a data-PLR functionĝ. Concerning Problem 1 and Theorem 2, we approximate f (x, θ) = E[y|x, θ] that minimizes (5). However, the answer y in (4) involves the average of the outputs g(…”
Section: Methodology Overviewmentioning
confidence: 99%
“…Out-with DMS environments, statistical packages like MAT-LAB and R 5 support fitting regression functions. However, 5 https://www.r-project.org/. their algorithms for doing so are inefficient and hardly scalable.…”
Section: Related Workmentioning
confidence: 99%
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“…The vehicles can generate different types of data to be processed by the server. For example, these data can be for real time services such as image recognition, image processing or analytical task for data generated by embedded sensors within the car [16] or any other tasks as inferential and predictive analytics [17], statistical learning models building and/or models selection, [18]. Furthermore, the task can be generated by the passengers in vehicles using smart phones.…”
Section: A System Model and Problem Formulationmentioning
confidence: 99%