CN105554802B - A kind of event driven sensor data transmission method of remote status estimation - Google Patents
A kind of event driven sensor data transmission method of remote status estimation Download PDFInfo
- Publication number
- CN105554802B CN105554802B CN201510902539.0A CN201510902539A CN105554802B CN 105554802 B CN105554802 B CN 105554802B CN 201510902539 A CN201510902539 A CN 201510902539A CN 105554802 B CN105554802 B CN 105554802B
- Authority
- CN
- China
- Prior art keywords
- remote status
- event
- driven
- moment
- sensor
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/0215—Traffic management, e.g. flow control or congestion control based on user or device properties, e.g. MTC-capable devices
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/10—Scheduling measurement reports ; Arrangements for measurement reports
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/0252—Traffic management, e.g. flow control or congestion control per individual bearer or channel
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/12—Wireless traffic scheduling
- H04W72/1221—Wireless traffic scheduling based on age of data to be sent
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W72/00—Local resource management
- H04W72/50—Allocation or scheduling criteria for wireless resources
- H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
Abstract
The invention discloses a kind of event driven sensor data transmission strategies of remote status estimation, determine whether the metrical information of sensor is sent to remote status estimator by event-driven detector.The frequency that the present invention can send metrical information by reducing sensor to reduce the utilization rate of wireless channel and significantly reduces the estimation performance of Kalman filtering, while extending the service life of sensor battery.
Description
Technical field
The present invention relates to a kind of event driven sensor data transmission strategies of remote status estimation, belong to the communication information
Process field.
Background technique
In the past ten years, wireless sensor network technology develops rapidly.In industrial process, intelligent building, medical treatment is protected
The fields such as strong and battlefield monitoring are all widely used.Sensing in these application processes, in wireless sensor network
Device and estimator are communicated by wireless network.It is compared with traditional wired sensor, the battery capacity of wireless sensor is logical
Often it is limited, and it is usually of a high price to replace the battery that electricity exhausts, or even can not be achieved.At the same time, wireless channel
Capacity may change with the variation of external environment, and the channel capacity of time-varying may then will affect a dynamical system
Overall performance.Of course, it is possible to select that sensor is allowed not transmit data, however in this case those rely on raw sensor measurement
The evaluated error of the bottom parameter of data, may increase to the degree for being difficult to receive.Therefore event driven transmission plan is
It balances the transmission rate of wireless channel well and evaluated error provides feasible solution.
Summary of the invention
Technical problem to be solved by the invention is to provide a kind of event driven sensing datas of remote status estimation
Transmission strategy balances evaluated error and transmission rate with this well.
The present invention uses following technical scheme to solve above-mentioned technical problem：
The present invention provides a kind of event driven sensor data transmission strategy of remote status estimation, passes through event-driven
Detector determines whether the metrical information of sensor is sent to remote status estimator, specially：
Discrete stochastic systems are established, i.e.,
xk+1=Axk+wk
yk=Cxk+vk
In formula, k is discrete time index, xk+1It is the state variable of k+1 moment Discrete stochastic systems, xkIt is that the k moment is discrete
The state variable of stochastic system, ykThe metrical information of k moment sensor, A, C be respectively Discrete stochastic systems transfer matrix,
Output matrix, wkProcess noise, v for k moment Discrete stochastic systemskFor the measurement noise of k moment sensor；
Set event driven trigger mechanism as：
In formula, γkFor the decision variable of k moment event-driven detector,It is the state of k moment remote status estimator
Predictor, M be event-driven trigger mechanism threshold value, and M according toOff-line calculation obtains, Q, λ difference
Error weight, transmission weight for setting；R is vkCovariance, R > 0；
If γk=0, then event-driven detector forbids the metrical information of sensor to be sent to remote status estimator, together
When event-driven detector run recursive calculationRemote status estimator runs recursive calculationWherein,It is the state estimation variable of k+1 moment remote status estimator；
If γk=1, then event-driven detector allows the metrical information of sensor to be sent to remote status estimator, far
Journey state estimator runs recurrenceAnd remote status estimator passes through closed loop feedbackExtremely
Event-driven detector；Wherein, Kk=APk -CT(CPk -CT+R)-1, Pk -=APk-1AT+Qw, Pk=(I-KkC)Pk -, I is unit square
Battle array, QwIt is wkCovariance, Qw≥0。
As a further optimization solution of the present invention, remote status estimator carries out long-range shape using standard Kalman filtering
State estimation.
As a further optimization solution of the present invention, event-driven detector passes through wireless channel and remote status estimator
It is connected.
As a further optimization solution of the present invention, event-driven detector, wireless channel, remote status estimator be two-by-two
It is connected and constitutes closed circuit.
As a further optimization solution of the present invention, error weight Q, transmission weight λ are set according to actual needs.
The invention adopts the above technical scheme compared with prior art, has the following technical effects：By reducing sensor
Send the frequency of metrical information to reduce the utilization rate of wireless channel and significantly reduce the estimation performance of Kalman filtering, together
The service life of time delay tall sensor battery.
Detailed description of the invention
Fig. 1 is the system construction drawing that modeling obtains.
Specific embodiment
Technical solution of the present invention is described in further detail with reference to the accompanying drawing：
Disclosed in this invention is a kind of event driven sensor data transmission strategy of remote status estimation, very with this
Good balance evaluated error and transmission rate.
The threshold value of event-driven trigger mechanism carries out off-line calculation：
Following Discrete stochastic systems are obtained by mathematical modeling：
xk+1=Axk+wk (1)
yk=Cxk+vk (2)
In formula (1) and (2), k is discrete time index, xk+1It is the state variable of k+1 moment Discrete stochastic systems,
xkIt is the state variable of k moment Discrete stochastic systems, ykIt is the metrical information of k moment sensor；A, C it is known that respectively when it is discrete with
Transfer matrix, the output matrix of machine system；wkProcess noise, v for k moment Discrete stochastic systemskFor the survey of k moment sensor
Measure noise, x0It is the original state of Discrete stochastic systems, wk、vk、x0It is independent from each other zero-mean white noise, Qw、R、P0Respectively
For wk、vk、x0Covariance, and Qw>=0, R > 0 and P0> 0.
In addition, it is understood that (A, C) is considerable,Controllably.As metrical information ykWhen being acquired by the sensor, event-driven
Detector determines metrical information ykWhether remote status estimator is sent to by wireless channel.Enable γkFor a decision variable,
Work as γkY is indicated when=1kIt is sent to long-range estimator, otherwise will not be sent.
Enabling Q and λ is respectively the weight of given error and transmission, passes through the event driven threshold value of formula (3) off-line calculation
M：
Event driven trigger mechanism
The Kalman filtering of consideration standard, works as γkWhen=1, event-driven detector allows metrical information ykIt is sent to long-range
State estimator, the state estimation variable of k moment remote status estimatorMeet following recursive calculation：
Kk=APk -CT(CPk -CT+R)-1
Pk -=APk-1AT+gw
Pk=(I-KkC)Pk - (4)
Meanwhile remote status estimator needs to pass through closed loop feedbackIt is transferred to event-driven detector.Wherein,It is the state estimation variable of k+1 moment remote status estimator.
Work as γkWhen=0, event-driven detector runs simple recurrenceForbid metrical information y simultaneouslykIt sends
Give remote status estimator, and remote status estimatorMeet following recursive calculation：
In this way, providing the mechanism of judgement event triggering：
The present invention will be further described with reference to the accompanying drawing:
Fig. 1 is the structure chart of whole system, and sensor obtains metrical information y from controlled devicek, work as γk=0 isWhen, event-driven detector allows metrical information ykRemote status is transmitted to estimate
Gauge, otherwise remote status estimator voluntarily carries out following recurrenceFrom figure 1 it appears that algorithm of the invention is not
It needs to install local estimator, reduces additional facility.Only work as γkWhen=1, event-driven detector just needs to pass through closed loop
Circuit feedbackTo event-driven detector.Work as γkWhen=0, it is only necessary to which event-driven detector runs simple recurrence?.
The above, the only specific embodiment in the present invention, but scope of protection of the present invention is not limited thereto, appoints
What is familiar with the people of the technology within the technical scope disclosed by the invention, it will be appreciated that expects transforms or replaces, and should all cover
Within scope of the invention, therefore, the scope of protection of the invention shall be subject to the scope of protection specified in the patent claim.
Claims (5)
1. a kind of event driven sensor data transmission method of remote status estimation, which is characterized in that pass through event-driven
Detector determines whether the metrical information of sensor is sent to remote status estimator, specially：
Discrete stochastic systems are established, i.e.,
xk+1=Axk+wk
yk=Cxk+vk
In formula, k is discrete time index, xk+1It is the state variable of k+1 moment Discrete stochastic systems, xkIt is k moment Discrete Stochastic
The state variable of system, ykIt is the metrical information of k moment sensor, A, C are the transfer matrix of Discrete stochastic systems, output respectively
Matrix, wkProcess noise, v for k moment Discrete stochastic systemskFor the measurement noise of k moment sensor；
Set event driven trigger mechanism as：
In formula, γkFor the decision variable of k moment event-driven detector,It is the state estimation of k moment remote status estimator
Variable, M be event-driven trigger mechanism threshold value, and M according toOff-line calculation obtains, and Q, λ are respectively to set
Fixed error weight, transmission weight；R is vkCovariance, R > 0；
If γk=0, then event-driven detector forbids the metrical information of sensor to be sent to remote status estimator, simultaneous events
Detector is driven to run recursive calculationRemote status estimator runs recursive calculationWherein,It is k+
The state estimation variable of 1 moment remote status estimator；
If γk=1, then event-driven detector allows the metrical information of sensor to be sent to remote status estimator, remote status
Estimator runs recurrence and remote status estimator is driven by closed loop feedback to event
Dynamic detector；Wherein,I is unit square
Battle array, QwIt is wkCovariance, Qw≥0。
2. a kind of event driven sensor data transmission method of remote status estimation according to claim 1, special
Sign is that remote status estimator carries out remote status estimation using standard Kalman filtering.
3. a kind of event driven sensor data transmission method of remote status estimation according to claim 1, special
Sign is that event-driven detector is connected by wireless channel with remote status estimator.
4. a kind of event driven sensor data transmission method of remote status estimation according to claim 3, special
Sign is that event-driven detector, wireless channel, remote status estimator are connected two-by-two constitutes closed circuit.
5. a kind of event driven sensor data transmission method of remote status estimation according to claim 1, special
Sign is, sets error weight Q, transmission weight λ according to actual needs.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510902539.0A CN105554802B (en) | 2015-12-09 | 2015-12-09 | A kind of event driven sensor data transmission method of remote status estimation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510902539.0A CN105554802B (en) | 2015-12-09 | 2015-12-09 | A kind of event driven sensor data transmission method of remote status estimation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105554802A CN105554802A (en) | 2016-05-04 |
| CN105554802B true CN105554802B (en) | 2018-11-27 |
Family
ID=55833684
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510902539.0A Active CN105554802B (en) | 2015-12-09 | 2015-12-09 | A kind of event driven sensor data transmission method of remote status estimation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105554802B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110366232B (en) * | 2019-06-19 | 2022-02-11 | 东南大学 | Sensor transmission energy control method for remote state estimation |
| CN112269318B (en) * | 2020-11-09 | 2022-06-10 | 南京工程学院 | Finite time remote safety state estimation method for time delay uncertain system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7719461B1 (en) * | 2008-08-05 | 2010-05-18 | Lockheed Martin Corporation | Track fusion by optimal reduced state estimation in multi-sensor environment with limited-bandwidth communication path |
| CN104331630A (en) * | 2014-11-19 | 2015-02-04 | 北京理工大学 | State estimation and data fusion method for multi-rate observation data |
| CN104750086A (en) * | 2013-12-26 | 2015-07-01 | 清华大学 | Fault and state estimation method and fault and state estimation device |
-
2015
- 2015-12-09 CN CN201510902539.0A patent/CN105554802B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7719461B1 (en) * | 2008-08-05 | 2010-05-18 | Lockheed Martin Corporation | Track fusion by optimal reduced state estimation in multi-sensor environment with limited-bandwidth communication path |
| CN104750086A (en) * | 2013-12-26 | 2015-07-01 | 清华大学 | Fault and state estimation method and fault and state estimation device |
| CN104331630A (en) * | 2014-11-19 | 2015-02-04 | 北京理工大学 | State estimation and data fusion method for multi-rate observation data |
Non-Patent Citations (4)
| Title |
|---|
| An Event-Based Online Scheduling Approach for Networked Embedded Control Systems;Sven Reimann，Sanad Al-Areqi,Steven Liu;《2013 American Control Conference (ACC)》;20130619;全文 * |
| Event- Triggered Dynamic Output Feedback Control of LTI Systems over Sensor-Controller-Actuator Networks;Pavankumar Tallapragada，Nikhil Chopra;《52nd IEEE Conference on Decision and Control》;20131213;全文 * |
| On Set-Valued Kalman Filtering and Its Application to Event-Based State Estimation;Dawei Shi et al.;《IEEE TRANSACTIONS ON AUTOMATIC CONTROL》;20150531;全文 * |
| 基于事件触发的复杂网络系统的状态估计;谭玉顺;《系统科学与数学》;20150831;全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105554802A (en) | 2016-05-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xu et al. | Hierarchical recursive signal modeling for multifrequency signals based on discrete measured data | |
| Yu et al. | A novel fog computing enabled temporal data reduction scheme in iot systems | |
| CN109163720A (en) | Kalman filter tracking method based on fading memory exponent | |
| Zhang et al. | Information fusion based smart home control system and its application | |
| CN113037349A (en) | Physical layer security design method based on alternate iteration in IRS-assisted MISO system | |
| TWI592679B (en) | Method and processor circuitry for determining location of a mobile device in an enclosed area | |
| CN105553442B (en) | The H of network-based Lipschitz nonlinear system∞Filter information processing method | |
| CN105554802B (en) | A kind of event driven sensor data transmission method of remote status estimation | |
| EP3563266B1 (en) | Normalization of sensors | |
| CN104331630B (en) | A kind of multi tate observes the state estimation and data fusion method of data | |
| CN106454892B (en) | Integrated value Kalman filtering method based on quantization and event | |
| CN115065992A (en) | Edge calculation-based cooperative sensing and resource allocation method | |
| Ray | An Internet of Things based approach to thermal comfort measurement and monitoring | |
| Li et al. | Predictive control for vehicular sensor networks based on round-trip time-delay prediction | |
| CN106372649B (en) | Integrated value Kalman filtering method based on quantization | |
| Nguyen | Study on realtime control system in IoT based smart factory: Interference awareness, architectural elements, and its application | |
| CN105224381A (en) | A kind of method, Apparatus and system moving virtual machine | |
| Islam et al. | Information prediction in sensor networks using Milne-Simpson's scheme | |
| Sivaranjani et al. | Energy efficient deployment of mobile node in wireless Sensor networks | |
| Che et al. | Research on Abnormal Monitoring of Sensor Data of Stadiums Based on Internet of Things Technology | |
| Cheikh et al. | Energy efficient enhancement of TDEEC wireless sensors network protocol based on passive RFID implementation | |
| Huang et al. | Distributed-Solver for Networked Neural Network | |
| KR20110066501A (en) | System and method for transmitting and receiving real-time sensor data using models | |
| KR102554902B1 (en) | The Method that Estimate Energy Consumption of Group Households based on Federated Learning, and The System and Computer-readable Medium for Performing The Same | |
| WO2017208363A1 (en) | Mobile communication terminal and control method therefor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20220712 Address after: No. 198, Liqiang Road, Zhili Town, Wuxing District, Huzhou City, Zhejiang Province 313000 Patentee after: Huzhou Huayi Environmental Protection Technology Co.,Ltd. Address before: No. 1800 road 214122 Jiangsu Lihu Binhu District City of Wuxi Province Patentee before: Jiangnan University |
|
| TR01 | Transfer of patent right |