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System and methods to share machine learning functionality between cloud and an IoT network

A network and intelligent lighting technology, applied in the system field, can solve problems such as sending, not knowing how to apply deep learning, and being unable to afford raw data, etc., to achieve the effect of reducing delay

Inactive Publication Date: 2020-08-21
SIGNIFY HLDG BV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The first problem with the prior art is that it is not known how deep learning can be applied in practice for smart lighting applications where each light emitter includes small sensors that generate triggers about specific features in the environment
The second problem lies in the fact that existing (deep learning) methods require all data from sensors to be sent to the cloud, where all data is processed
However, cloud computing alone is not sufficient to address the above-mentioned shortcomings: smart networks such as lighting networks are often bandwidth-constrained and cannot afford to send all raw data to remote clouds
Also, running the entire deep learning algorithm in the cloud is not efficient

Method used

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  • System and methods to share machine learning functionality between cloud and an IoT network
  • System and methods to share machine learning functionality between cloud and an IoT network
  • System and methods to share machine learning functionality between cloud and an IoT network

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Embodiment Construction

[0048] Although the present invention allows many different forms of embodiments, one or more specific embodiments are shown in the drawings and will be described in detail herein, in which it should be understood that the present disclosure should be regarded as the principle of the present invention Examples are not intended to limit the invention to the specific embodiments shown and described.

[0049] Hereinafter, for ease of understanding, the elements of the embodiment are described in operation. However, it will be apparent that the corresponding elements are configured to perform the functions described as being performed by them.

[0050] In addition, the present invention is not limited to the embodiments, and the present invention resides in each novel feature or combination of features described herein or in mutually different dependent claims.

[0051] figure 1 A representation form of system elements according to an embodiment of the present invention is shown. Such ...

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Abstract

A system and methods are provided for using deep learning based on convolutional neural networks (CNN) as applied to Internet of Things (IoT) networks that includes a plurality of sensing nodes and aggregating nodes. Events of interest are detected based on collected data with higher reliability, and the IoT network improves bandwidth usage by dividing processing functionality between the IoT network and a cloud computing network.

Description

Technical field [0001] The present invention relates to a system and method that uses deep learning based on a convolutional neural network applied to an IoT network, more specifically, to detect events with higher reliability based on collected data, and through the IoT network and The processing functions are divided among the clouds to save bandwidth. Background technique [0002] Smart lighting systems with multiple light emitters and sensors are experiencing steady growth in the market. Intelligent lighting system is a lighting technology designed for energy efficiency. This can include high-efficiency devices and automated controls that adjust based on conditions such as occupancy or daylight availability. Lighting is a deliberate application of light to achieve some aesthetic or practical effects. It includes task lighting, accent lighting and general lighting. [0003] Such smart lighting systems can use multi-mode sensor inputs in the form of occupancy and light measur...

Claims

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Application Information

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IPC IPC(8): H05B47/10H04W4/38G06N3/08H04L29/08
CPCH04L67/125H05B47/19G06N3/084G06N3/045G06N20/00G16Y40/30G06F18/23G06N3/048
Inventor O·加尔西亚-马尔雄A·莫蒂
Owner SIGNIFY HLDG BV