CN103354652A - Method and apparatus for lightweight data fusion in WBAN (wireless body area network) - Google Patents
Method and apparatus for lightweight data fusion in WBAN (wireless body area network) Download PDFInfo
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- CN103354652A CN103354652A CN2013102373416A CN201310237341A CN103354652A CN 103354652 A CN103354652 A CN 103354652A CN 2013102373416 A CN2013102373416 A CN 2013102373416A CN 201310237341 A CN201310237341 A CN 201310237341A CN 103354652 A CN103354652 A CN 103354652A
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention relates to a method and an apparatus for lightweight data fusion in a WBAN (wireless body area network). The apparatus and the method, according to spatial and temporal correlation of data acquisition in the WBAN, establishes a prediction model and fuses data, thereby reducing data transmission flow in the WBAN and fulfilling an aim of reducing energy consumption.
Description
Technical field
The present invention relates to the wireless sensor network application, relate in particular to a kind of lightweight data fusion method and device that is applied to wireless body area network.
Background technology
Wireless body area network (Wireless Body Area Network, WBAN) as wireless sensor network (Wireless Sensor Network, WSN) branch and the application in the fields such as biologic medical are the physiological parameter collecting sensors on the human body or be transplanted to the common wireless network that forms of biology sensor in the human body.Comprise a series of low-power consumption, microminiaturization, intrusive mood or non-intrusion type wireless sensor node in the wireless body area network, realize and extraneous communicating by letter by short-distance wireless communication technology, can monitor much information, comprise: brain electric information, ecg information, muscle activity situation, breathing situation, body temperature, pulse, blood oxygen, blood pressure, physical activity or actuating signal and human body place environmental information etc.It is important perception and the part of Internet of Things, by for a long time, simply, easily respond to and the institute that is transmitted detection, diagnosis human body health status in steps, in service fields such as telehealth, special population monitoring and rural community medical treatment huge application value and demand are arranged.
The basic function of wireless body area network is the sensor node image data, sends to aggregation node, is sent to Surveillance center by aggregation node.In this process, transducer carries out periodicity and gathers, produced a large amount of for the treatment of, transmit and the data of access.Some application even need a plurality of node collections and detection range overlapping mutually is with the robustness that improves whole network and the accuracy of the information of detection, so that data have to a certain degree redundancy and very large similitude.How valid function is key issues that wireless body area network is used with these data of management.Yet, sensor node is very limited in the resource of the aspects such as the energy content of battery, disposal ability, storage capacity and radio communication, if each node sends to aggregation node with the raw information of collecting, so not only node energy consumption is large, and being easy to cause the conflict collision, communication efficiency also can reduce.In this case, efficiently utilize energy to prolong the operating time of sensor node, become the primary goal in the wireless body area network design, study that effective technical method is processed and dispatched sensing data, keeps the wireless body area network high-quality service, the reduce energy resource consumption becomes one and is rich in challenging work.
The data that acquisition node sends in the wireless body area network have certain continuity, the data that gather have certain correlation, these correlations represent redundant information, these redundant informations have taken a lot of communication bandwidths, needing to consume a large amount of energy transmits, cause information gathering work in time not finish, reduced information gathering efficient.For example the normally used 12 lead electrocardiogram information that needs 12 nodes to gather is just more accurate, and electroencephalogram (EEG) needs 2 to lead and just can finish current potential and relatively wait at least.The information that this detection obtains spatially has redundancy to a certain degree, and the information of these node reports has very large similitude.In the very high situation of this redundancy, only sending a comprehensive data compares than the data in these node reports are all sent to aggregation node, not only saved a large amount of energy consumption, and owing to reduced the data volume that needs transmission, the transmission that can alleviate network is congested, reduce data transfer and postpone, reduced the conflict when these data send, improved the utilance of wireless channel.
Data fusion is that many piece of data or information are carried out multi-level, many-sided, multi-level processing, namely be combined into more effective, more meet the process of the data of user's request.It mainly contains following effect:
1. save network energy.Usually for the accuracy of the robustness that guarantees a network and the information of detection, the WBAN ordinary circumstance is netted interior processing by the data that in a large number a plurality of nodes collected, remove redundant information, satisfying under the prerequisite of application demand, reduce the traffic of data in the network, reduce largely the energy loss of whole network, prolonged the life cycle of network, but can not affect again the correct information of obtaining simultaneously.
2. obtain more accurately information.In WBAN, the data of collecting the individual node collection are difficult to obtain accurately information, need to by the data of same monitoring target or zone collection are carried out integrated treatment, effectively improve accuracy and the confidence level of acquired information.
3. improve data collection efficiency.Data fusion in the net, reduced data traffic, improved the efficient that wireless sensor network data is collected, even the valid data amount does not reduce, but by a plurality of packets being merged the conflict collision phenomenon that can reduce in packet number and the transmission, also can improve the utilance of wireless channel.
Therefore, be necessary with the data volume that sends in the minimizing process of transmitting, thereby to save energy by the redundant information in the Data fusion technique minimizing data.
Summary of the invention
In order to overcome the deficiency of the resource-constrained problem of wireless body area network sensor node and available data fusion method, the invention provides a kind of lightweight data fusion method and device based on forecast model that is applied to wireless body area network, this device can be according to the temporal correlation of wireless body area network image data, set up forecast model, data are merged, thereby the data transfer throughput in the reduction wireless body area network reaches the purpose that reduces energy consumption.
In order to achieve the above object, technical scheme of the present invention proposes a kind of method of wireless body area network lightweight data fusion.The method is divided into three types with image data in the wireless body area network:
(1) historical data: the data that gathered;
(2) measured data: the data that the current sampling period gathers;
(3) prediction data: the data that adopt the forecast model prediction.
The method is sensor node and aggregation node with the node division in the wireless body area network, comprises following three steps:
Step 1: wireless body area network sensor node internal data merges
(1) netinit, sensor node buffer memory image data is set up history data set, and sends data to aggregation node;
(2) sensor node adopts forecast model according to historical data, carries out single-node and it is predicted;
(3) measured data in next sampling period and prediction data compare:
1) if error in threshold range, does not then send data to aggregation node;
2) if error exceeds threshold range, then the measured data value is sent to aggregation node, and upgrades forecast model.
Step 2: wireless body area network aggregation node data fusion
(1) netinit, aggregation node receive the data that a plurality of sensor nodes send, and set up history data set, and send data to server;
(2) aggregation node adopts forecast model according to historical data, carries out the multinode data prediction;
(3) enter next sampling period:
1) if receive the measured data that sensor node sends, then measured data and prediction data are compared:
If 1. error is in threshold range, then do not send data to server;
If 2. error exceeds threshold range, then measured data is sent to server, and upgrades forecast model.
2) if do not receive the measured data that sensor node sends, do not send data to server.
Step 3: the wireless body area network server data merges
(1) netinit, server receives the data that aggregation node sends, and sets up historical data base;
(2) server adopts forecast model according to historical data, carries out data prediction;
(3) enter next sampling period:
1) if receive the measured data that aggregation node sends, then measured data is sent to the user, and upgrades forecast model.
2) if do not receive the measured data that aggregation node sends, then prediction data is sent to the user.
The present invention also provides a kind of device of wireless body area network lightweight data fusion.This device comprises:
(1) sensor node: be used for perception human body physical sign information, comprise:
1) data acquisition module: be used for gathering the human body physical sign data, the data that buffer memory gathers;
2) data prediction module: according to the historical data of acquisition module buffer memory, Future Data is predicted;
3) data fusion module: the contrast that realizes image data and prediction data is merged;
4) data transmission module: realize sensor node data and aggregation node data transfer.
(2) aggregation node: be used for receiving each sensor node data under the wireless body area network environment, and send data to server.
1) data reception module: be used for receiving the data that each sensor node sends, the data that buffer memory receives;
2) data prediction module: according to the historical data of each sensor node of data reception block cache, Future Data is predicted;
3) data fusion module: the contrast that realizes receive data and prediction data is merged;
4) data transmission module: realize data transfer between sensor node and aggregation node and the aggregation node geodata and services device.
(3) server: be used for showing human body physical sign information, comprise:
1) data reception module: be used for receiving the data that aggregation node sends;
2) human body physical sign database: the human body physical sign data that are used for storing received;
3) data prediction module: according to the historical data of human body physical sign database storage, Future Data is predicted;
4) data transmission module: realize data transfer between sensor node and aggregation node and the aggregation node geodata and services device.
5) data display module: the visual presentation of realizing the human body physical sign data.
Description of drawings:
Fig. 1 is wireless body area network sensor node data fusion method implementing procedure figure
Fig. 2 is wireless body area network aggregation node data fusion method implementing procedure figure
Fig. 3 is wireless body area network server data fusion method implementing procedure figure
Fig. 4 is wireless body area network lightweight data fusion device schematic diagram
Embodiment
The method and apparatus of the wireless body area network lightweight data fusion that the present invention proposes is described as follows in conjunction with the accompanying drawings and embodiments.
Fig. 1 is wireless body area network sensor node data fusion method implementing procedure figure, and as shown in the figure, method may further comprise the steps:
(1) netinit, sensor node buffer memory image data is set up history data set, and sends data to aggregation node;
(2) sensor node adopts forecast model according to historical data, carries out single-node and it is predicted;
(3) measured data in next sampling period and prediction data compare:
1) if error in threshold range, does not then send data to aggregation node;
2) if error exceeds threshold range, then the measured data value is sent to aggregation node, and upgrades forecast model.
Fig. 2 is wireless body area network aggregation node data fusion method implementing procedure figure, and as shown in the figure, method may further comprise the steps:
(1) netinit, aggregation node receive the data that a plurality of sensor nodes send, and set up history data set, and send data to server;
(2) aggregation node adopts forecast model according to historical data, carries out the multinode data prediction;
(3) next sampling period:
1) if receive the measured data that sensor node sends, then measured data and prediction data are compared:
If 1. error is in threshold range, then do not send data to server;
If 2. error exceeds threshold range, then measured data is sent to server, and upgrades forecast model.
2) if do not receive the measured data that sensor node sends, do not send data to server.
Fig. 3 is wireless body area network server data fusion method implementing procedure figure, and as shown in the figure, method may further comprise the steps:
(1) netinit, server receives the data that aggregation node sends, and sets up historical data base;
(2) server adopts forecast model according to historical data, carries out data prediction;
(3) next sampling period:
1) if receive the measured data that aggregation node sends, then measured data is sent to the user, and upgrades forecast model.
2) if do not receive the measured data that aggregation node sends, then prediction data is sent to the user.
Fig. 4 is wireless body area network lightweight data fusion device schematic diagram of the present invention, and as shown in the figure, present embodiment comprises:
(1) sensor node: be used for perception human body physical sign information, comprise:
1) data acquisition module: be used for gathering the human body physical sign data, the data that buffer memory gathers;
2) data prediction module: according to the historical data of acquisition module buffer memory, Future Data is predicted;
3) data fusion module: the contrast that realizes image data and prediction data is merged;
4) data transmission module: realize sensor node data and aggregation node data transfer.
(2) aggregation node: be used for receiving each sensor node data under the wireless body area network environment, and send data to server.
1) data reception module: be used for receiving the data that each sensor node sends, the data that buffer memory receives;
2) data prediction module: according to the historical data of each sensor node of data reception block cache, Future Data is predicted;
3) data fusion module: the contrast that realizes receive data and prediction data is merged;
4) data transmission module: realize data transfer between sensor node and aggregation node and the aggregation node geodata and services device.
(3) server: be used for showing human body physical sign information, comprise:
1) data reception module: be used for receiving the data that aggregation node sends;
2) human body physical sign database: the human body physical sign data that are used for storing received;
3) data prediction module: according to the historical data of human body physical sign database storage, Future Data is predicted;
4) data transmission module: realize data transfer between sensor node and aggregation node and the aggregation node geodata and services device.
5) data display module: the visual presentation of realizing the human body physical sign data.
Above execution mode only is used for explanation the present invention; and be not limitation of the present invention; the those of ordinary skill in relevant technologies field; in the situation that does not break away from the spirit and scope of the present invention; can also make a variety of changes and modification; therefore all technical schemes identical, that substitute also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.
Claims (3)
1. the device of a wireless body area network lightweight data fusion is characterized in that, comprising:
Sensor node is used for perception human body physical sign information;
Aggregation node is used for receiving each sensor node data under the wireless body area network environment, and sends data to server;
Server, for the treatment of with show human body physical sign information.
2. the device of wireless body area network lightweight data fusion as claimed in claim 2 is characterized in that:
Described sensor node comprises:
Data acquisition module is used for gathering the human body physical sign data, and the data of buffer memory collection;
The data prediction module, it is predicted Future Data according to the historical data of data acquisition module block cache;
Data fusion module, its contrast that realizes image data and prediction data is merged;
Data transmission module, it realizes sensor node data and aggregation node data transfer;
Described aggregation node comprises:
Data reception module is used for receiving the data that each sensor node sends, and the data of buffer memory reception;
The data prediction module according to the historical data of each sensor node of data reception block cache, is predicted Future Data;
Data fusion module, its contrast that realizes receive data and prediction data is merged;
Data transmission module, it realizes between sensor node and the aggregation node and the transfer of data between aggregation node and the server;
Described server comprises:
Data reception module, it is used for receiving the data that aggregation node sends;
The human body physical sign database, it is used for the human body physical sign data of storing received;
The data prediction module, it is predicted Future Data according to the historical data of human body physical sign database storage;
Data transmission module, it realizes between sensor node and the aggregation node and the transfer of data between aggregation node and the server;
The data display module, it realizes the visual presentation of human body physical sign data.
3. a device that utilizes any described wireless body area network lightweight data fusion of claim 1-2 carries out the method for data fusion, it is characterized in that the data that gather in the described wireless body area network comprise: historical data, its data for having gathered; Measured data, it is the data that gather in the current sampling period; Prediction data, it is for adopting the data of forecast model prediction; Node in the described wireless body area network comprises sensor node and aggregation node;
The method may further comprise the steps:
Step 1: wireless body area network sensor node internal data merges
Step (1): netinit, sensor node buffer memory image data is set up history data set, and sends data to aggregation node;
Step (2): sensor node adopts forecast model according to historical data, carries out single-node and it is predicted;
Step (3): measured data and the prediction data in next sampling period are compared:
1) if error in threshold range, does not then send data to aggregation node;
2) if error exceeds threshold range, then the measured data value is sent to aggregation node, and upgrades forecast model.
Step 2: wireless body area network sensor node internal data merges
Step (1): netinit, aggregation node receive the data that a plurality of sensor nodes send, and set up history data set, and send data to server;
Step (2): aggregation node adopts forecast model according to historical data, carries out the multinode data prediction;
Step (3): enter next sampling period:
1) if receive the measured data that sensor node sends, then measured data and prediction data are compared:
If 1. error is in threshold range, then do not send data to server;
If 2. error exceeds threshold range, then measured data is sent to server, and upgrades forecast model;
2) if do not receive the measured data that sensor node sends, do not send data to server.
Step 3: wireless body area network sensor node internal data merges
Step (1) netinit, server receives the data that aggregation node sends, and sets up historical data base;
Step (2) server adopts forecast model according to historical data, carries out data prediction;
Step (3) enters next sampling period:
1) if receive the measured data that aggregation node sends, then measured data is sent to the user, and upgrades forecast model;
2) if do not receive the measured data that aggregation node sends, then prediction data is sent to the user.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103889065A (en) * | 2014-02-28 | 2014-06-25 | 中国农业大学 | Wireless body area network data transmission scheduling method and device |
| CN103919538A (en) * | 2014-04-30 | 2014-07-16 | 中国科学院微电子研究所 | Information collecting system and method of body area network system |
| WO2014183653A1 (en) * | 2013-12-05 | 2014-11-20 | 中兴通讯股份有限公司 | Method and device for processing wireless body area network data |
| CN105739575A (en) * | 2016-01-29 | 2016-07-06 | 中国农业大学 | Greenhouse vegetable environmental parameter data fusion method, data fusion device, and data fusion system |
| CN106851548A (en) * | 2017-03-31 | 2017-06-13 | 福建师范大学 | Wearable walking monitoring system and its monitoring method based on wireless body area network |
| CN107152748A (en) * | 2016-03-04 | 2017-09-12 | 富士施乐株式会社 | Image processing equipment and method and image processing system |
| CN107277760A (en) * | 2017-08-08 | 2017-10-20 | 吉林大学 | The collection of energy optimization method of time and power is distributed while wireless body area network |
| WO2018082167A1 (en) * | 2016-11-02 | 2018-05-11 | 深圳市元征科技股份有限公司 | Data processing method and apparatus based on body area network |
| CN108347709A (en) * | 2017-01-21 | 2018-07-31 | 中山大学 | A kind of system adaptive recognition method and system of super low-power consumption traceability system |
| CN109219101A (en) * | 2018-09-21 | 2019-01-15 | 南京理工大学 | Method for routing foundation based on Double moving average predicted method in wireless body area network |
| CN110268692A (en) * | 2018-01-19 | 2019-09-20 | 深圳市大疆创新科技有限公司 | A kind of data processing method, device, controller and movable fixture |
| CN110545282A (en) * | 2019-09-10 | 2019-12-06 | 中国计量大学 | Data acquisition and analysis system based on wireless body area network and low-energy-consumption data fusion privacy protection algorithm |
| CN111031511A (en) * | 2019-12-26 | 2020-04-17 | 北京工业大学 | Variable-granularity real-time environment data acquisition method for Internet of things |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080294020A1 (en) * | 2007-01-25 | 2008-11-27 | Demetrios Sapounas | System and method for physlological data readings, transmission and presentation |
| CN102968550A (en) * | 2012-10-18 | 2013-03-13 | 上海交通大学无锡研究院 | Human health unified management system for community based on body area network |
-
2013
- 2013-06-14 CN CN2013102373416A patent/CN103354652A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080294020A1 (en) * | 2007-01-25 | 2008-11-27 | Demetrios Sapounas | System and method for physlological data readings, transmission and presentation |
| CN102968550A (en) * | 2012-10-18 | 2013-03-13 | 上海交通大学无锡研究院 | Human health unified management system for community based on body area network |
Non-Patent Citations (2)
| Title |
|---|
| 刘艳丽: "《上海交通大学硕士学位论文》", 15 September 2009, article "基于人体环境的无线体域网网络结构研究" * |
| 回春立，崔莉: "无线传感器网络中基于预测的时域数据融合技术", 《计算机工程与应用》, 31 December 2007 (2007-12-31) * |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN104702366A (en) * | 2013-12-05 | 2015-06-10 | 中兴通讯股份有限公司 | Method and device for processing wireless body area network data |
| US9763575B2 (en) | 2013-12-05 | 2017-09-19 | Zte Corporation | Method and device for processing wireless body area network data |
| CN104702366B (en) * | 2013-12-05 | 2019-03-15 | 中兴通讯股份有限公司 | A kind of method and device handling wireless body area network data |
| CN103889065A (en) * | 2014-02-28 | 2014-06-25 | 中国农业大学 | Wireless body area network data transmission scheduling method and device |
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| CN106851548A (en) * | 2017-03-31 | 2017-06-13 | 福建师范大学 | Wearable walking monitoring system and its monitoring method based on wireless body area network |
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Application publication date: 20131016 |