Multi-instance multi-label learning based area monitoring method used in wireless sensor network

A wireless sensor network, multi-label learning technology, applied in network topology, wireless communication, data exchange network and other directions, can solve problems such as mass sensor data compression

Active Publication Date: 2016-06-08
NANJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0013] In order to solve the technical problems raised by the above-mentioned background technology, the present invention aims to provide an area monitoring method based on multi-instance and multi-label learning in wireless sensor network

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  • Multi-instance multi-label learning based area monitoring method used in wireless sensor network
  • Multi-instance multi-label learning based area monitoring method used in wireless sensor network
  • Multi-instance multi-label learning based area monitoring method used in wireless sensor network

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Embodiment Construction

[0064] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0065] Such as figure 1 The overall flowchart of the present invention is shown, and the present invention includes three processes: data collection, model training / updating, and network state prediction.

[0066] First, it is necessary to train a prediction model through the collected data, such as figure 2 As shown, the steps are as follows:

[0067] Step 1: Collect sensor network data at T moments to form a data set D o ={(N t ,Y t ), t=1,2,...,T}, N t ={s t,1 ,s t,2 ,...,S t,i ,...,S t,M} represents the data collected by the sensor network at time t, s t,i Indicates the data collected by the i-th sensor at time t, M is the total number of sensors in the entire sensor network, Y t ={y t,1 ,y t,2 ,...,y t,l ,...,y t,L} represents the state vector of the sensor network at time t, Y t There are L states y that need to be mo...

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Abstract

The invention discloses a multi-instance multi-label learning based area monitoring method used in a wireless sensor network. Fisher Kernel is utilized to compress multiple sensor node data at the same moment; after compression, the whole wireless sensor network corresponds to a single sample data rather than multiple sample data at a certain moment; an MMD is utilized to compress historical packet data, and most representative packet data is picked out to form a data subset. Training of the compressed data and assessment on a monitoring region state are completed by utilizing a rapid multi-instance multi-label algorithm. Rapid network state prediction can be achieved by establishing a sensor network state prediction model on sensing datasets subjected to two-layer compression.

Description

technical field [0001] The invention belongs to the field of wireless sensor network technology and machine learning technology, and in particular relates to an area monitoring method based on multi-instance multi-label learning. Background technique [0002] As an emerging technology that can have a profound impact on the future life of human beings, Wireless Sensor Networks (WSNs) technology has received widespread attention in recent years. The application of WSNs technology involves many fields, including national defense technology, Internet of Things technology, environmental monitoring, remote control, interstellar exploration, etc. The research on WSNs is of great significance. [0003] An important source of data in the era of big data is the sensor network. The task of the sensor network is to help people monitor the situation of the monitoring object in a certain area, rather than the situation of a specific node. The nodes in the sensor network are meaningless ...

Claims

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Application Information

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IPC IPC(8): H04L12/24H04W84/18G06K9/62G06K9/66G06F17/30
CPCH04L41/142H04L41/145H04L41/147H04W84/18G06F16/285G06V30/194G06F18/24G06F18/214
Inventor 胡海峰唐川吴建盛
Owner NANJING UNIV OF POSTS & TELECOMM
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