CN101511099A - Collection method for wireless sensor network data based on time series prediction model - Google Patents
Collection method for wireless sensor network data based on time series prediction model Download PDFInfo
- Publication number
- CN101511099A CN101511099A CNA2009100261015A CN200910026101A CN101511099A CN 101511099 A CN101511099 A CN 101511099A CN A2009100261015 A CNA2009100261015 A CN A2009100261015A CN 200910026101 A CN200910026101 A CN 200910026101A CN 101511099 A CN101511099 A CN 101511099A
- Authority
- CN
- China
- Prior art keywords
- node
- bunch
- sensor network
- data
- sampling
- 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.)
- Granted
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- 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
Landscapes
- Arrangements For Transmission Of Measured Signals (AREA)
Abstract
The invention provides a method for collecting a wireless sensor network data based on a time sequence predicting model which uses the time sequence predicting model for completing a dynamic transfer to the sensor node and optimizing sampling frequency, solves a requirement problem of the wireless sensor network energy consumption minimum. The method can increase data processing performance of the whole sensor network aiming at the sensor self characteristic a certain extent. The method is a data sampling method of the sensor network based on a data probability model. Each sensor node realizes P phase autoregression model by using a layering blocking structure. Each sensor node confirms self sampling and communication occasions by communication alternative among the cluster head and reaches aims of minimum sampling frequency and communication amount.
Description
Technical field
The present invention is a kind of wireless sensor network data management domain that is used for, and based on the data processing technique of cluster structured network communication models and probabilistic forecasting mechanism, realizes the dynamic dispatching to sensor node, optimizes sample frequency, reduces the network energy consumption.Present technique belongs to network information Distributed Calculation application.
Background technology
Along with the develop rapidly of the communication technology, embedded computing technique and sensor technology and increasingly mature, the microsensor with perception, computing capability and communication capacity begins worldwide to occur.The wireless sensor network that is made of these microsensors has caused people's very big concern.It is the main application of wireless sensor network that some specific region or target are monitored.Sensor network combines sensor technology, embedded computing technique, distributed information processing and the communication technology, the various environment in monitoring in real time collaboratively, perception and the collection network distributed areas or the information of monitoring target, and these information are handled, obtain detailed and information accurately, be sent to the user who needs these information.Save the energy in the network work process, the life cycle of maximization network is the major issue that wireless sensor network need solve.In order to save the energy, need to study the traffic that how as far as possible to reduce between sensor node, how to reduce the listening period of sensor node, and how under the prerequisite that satisfies the system monitoring precision, to minimize sampling number.
In many practical applications, the sensor node of a plurality of same types will be arranged in the area to be monitored.They finish same monitoring task by the mode of cooperation.The sensor node measured value is pressed the regular time interval sampling, and by the multi-hop mode data is sent to the user.Sensor network is because the dense distribution of node, has certain correlation when empty between the data value of each node collection, in normal operation, for each sensor node, time series of the data of a series of collections of Pai Lieing formation in chronological order.
Time series predicting model is a kind of advanced person's a statistical method, and this model is to utilize the relevant information in the time series to set up, and is the description of sequence dynamic and development and change rule.The sequence prediction model can be realized the following value of institute's sampling time sequence data is predicted by settling time on wireless sensor network node, and then optimizes the sample frequency of sensor node, reduces the energy consumption of sensor node.Under the prerequisite of the required precision of satisfying the user, dynamically adjust the cycle of node sample, and reduce the transmission quantity of data, thereby reduce the energy consumption of sensor node.
Summary of the invention
Technical problem: the purpose of this invention is to provide a kind of collection method for wireless sensor network data based on time series predicting model, utilize time series predicting model to finish dynamic dispatching to sensor node, optimize sample frequency, solve the energy consumption minimized needs of problems of wireless sensor network.Different with the data acquisition related algorithm that has found out at present, this method mainly adopts distributed Time series analysis method.The method that the application of the invention proposes can improve the data processing performance of sensor network integral body to a certain extent at the characteristic of perception data itself.
Technical scheme: method of the present invention is a kind of data sampling method of the sensor network based on the perception data probabilistic model.The employing stratification is cluster structured, realizes P rank autoregression model on each sensor node, by with bunch head between communication interaction determine oneself sampling and the opportunity of communicating by letter to reach the purpose that minimizes the sample frequency and the traffic.
Collection method for wireless sensor network data based on time series predicting model of the present invention utilizes time series predicting model to finish dynamic dispatching to sensor node, optimize sample frequency, solve the energy consumption minimized needs of problems of wireless sensor network, specific as follows:
Step 1). each node is selected the random number between [0,1] at random in the sensor network, if this is counted less than estimating threshold value, then this node becomes a bunch head,
Step 2). the node that becomes bunch head is broadcast message towards periphery, other nodes according to the intensity of the broadcast message that receives select that it will add bunch, and inform corresponding bunch of head,
Step 3). the node continuous collecting detects data, and is sent to a bunch head together with the relevant information of residue energy of node,
Step 4). calculate the average of the sampled value that obtains in bunch first calculating sampling cycle, and according to bunch in the dump energy of member node determine its corresponding distribution number of samples and the time that begins to sample,
Step 5). bunch first node is above-mentioned steps result notification bunch inner member node, and each member node is according to the operation plan of bunch head, an initial P rank sampled data is carried out after zero-mean handles, with sampled value with return to a bunch first node,
Step 6). bunch first node according to bunch in the result that returns of member node, adopt P rank autoregression model AR (P) to realize estimation, and estimated value be notified to member node following sampled data,
Step 7). in the initial sampling period of member node in bunch, every the time in sampling period, node is waken up,
Step 8). if the current time still is in during the sampling time, and the precision between actual sample value and predicted value then increases the sampling period less than error range; If the precision between actual sample value and predicted value then shortens the sampling period, and returns sampled data less than error range; Otherwise sensor node is in resting state.
Beneficial effect: the inventive method has proposed to adopt cluster structured, utilizes probability forecasting method to realize the FREQUENCY CONTROL that wireless sensor network data is gathered.Finish dynamic dispatching by the time series analysis of transducer institute perception data, optimize sample frequency, solve the energy consumption minimized needs of problems of wireless sensor network sensor node.
Different with the existing sampling dispatching algorithm of present industry, this method mainly adopts distributed Time series analysis method.The method that the application of the invention proposes can improve the data processing performance of sensor network integral body to a certain extent at the characteristic of perception data itself.
Description of drawings
Fig. 1 is the cluster structured schematic diagram of wireless sensor network stratification.Comprise among the figure: aggregation node Sink, bunch first node and bunch in member node.
Fig. 2 is the schematic flow sheet of algorithm 2.1.Represent the flow process signal of sensor node AR of the present invention (p) modelling algorithm.
Fig. 3 is the schematic flow sheet of algorithm 2.2.Represent the flow process signal of sensor node sampling dispatching algorithm of the present invention.
Embodiment
One, architecture
The distributed nature of combining with wireless sensor network data, method of data capture in the net of analyzing based on time series forecasting that the present invention proposes, its network communication models adopts stratification cluster structured, as shown in Figure 1.In hierarchy, wireless sensor network is divided into a plurality of bunches, and each bunch is made of a bunch of head and a plurality of member node.Bunch first node not only is responsible for the collection and the fusion treatment of the internal information of administering, forwarding of data between also responsible bunch.
Because therefore the resource-constrained of sensor node adopts P rank autoregression model AR (p) to represent the sampled data of sensor node.The following formal definition that provides AR (p) model:
x
t＝φ
1x
t-1+φ
2x
t-2+…+φ
px
t-p+ε
t(t≥P)，ε
t～NID(0，δ
t 2) (1)
Wherein, x
tBe the data that collect at moment t, φ is a regression coefficient, ε
tBe white noise sequence and and previous moment sequence x
k(k＜t) uncorrelated obeys standardized normal distribution.P rank model adds that with a linear combination of a preceding p sampled data random white noise predicts the data of t constantly.
Two, method flow
The data collection of wireless sensor network is unit with the wheel, and each sensor node obtains Perception Area numeric field data information, finishes the cluster process that bunch head is selected.Each sensor node by and affiliated bunch bunch head between communication interaction, determine oneself sampling and the opportunity of communicating by letter to minimize the sample frequency and the traffic according to the probabilistic model of perception data.
The groundwork flow process is as shown in Figure 2:
(1) bunch first formation: selecting node at random is a bunch head, and the node that becomes bunch head is broadcast message towards periphery, other nodes according to the intensity that receives broadcast message select that it will add bunch, and inform corresponding bunch of head.In this stage, do not send the data of actual sensing.
Select a random number to determine whether becoming a bunch head the sensor node from 0 to 1 arbitrarily, if this numerical value is less than the threshold values T (n) that sets in front-wheel, then this node becomes leader cluster node, and T (n) is calculated as follows:
Wherein, P is for the percentage of leader cluster node in all sensing nodes of expectation, and for different networks, the best value of P is also different.R is when the front-wheel number, and G is the set of node that does not become bunch first node in nearest 1/P wheel.
(2) data modeling in the sensor network: each bunch set up P rank autoregression model AR (p) to the sampled data of institute's monitoring objective, sensor node AR (p) modelling algorithm 2.1, and flow process specifies as follows as shown in Figure 3:
Algorithm 2.1 sensor node AR (p) modelling algorithms
Step 1: a bunch first node calculates at time period T
mThe average of interior sampled value
Step 2: bunch first node is according to the dump energy E of node i
iDetermine to distribute to the number of node i sampling
Wherein M represents a bunch of inner interstitial content of participating in calculating, and N represents the sampling sum, and P represents the exponent number of AR (P) model.
Step 3: a bunch first node calculates the time that each node begins to sample
Step 4: bunch first node is above-mentioned steps result notification bunch inner member node, and each member node is according to the operation plan of bunch first node, an initial P sampled data is carried out after zero-mean handles, with sampled value with return to a bunch first node.
Step 5: bunch first node according to bunch in the result that returns of member node, carry out column count down:
Based on the data collection structure of sub-clustering, the node sample sequential value X=(x in setting bunch
1, x
2... x
m)
T, Y=(x
P-1, x
P-2... x
m)
TBy least-squares estimation, obtain φ
1, φ
2... φ
p
If
Formula (1) is expressed as Y=A φ+ε, coefficient matrix
Obtain by least square method calculating: θ=(AA
T)
-1AY
Step 6: a bunch first node is notified to member node with parameter θ, receives the error amount s that returns from member node
2With dump energy E
i, calculate white noise sequence in AR (P) model
(3) sensor node sampling scheduling: utilize the forecast function of above-mentioned AR (P) model, predict data in the future according to historical data.Simultaneously,, adopt wireless sensor network sampling dispatching algorithm 2.2, dynamically adjust sampling time interval according to user's required precision.If the value of prediction satisfies required precision, then sensor node is in dormancy, does not carry out actual samples and data and transmits.
Algorithm 2.2 sensor nodes sampling dispatching algorithm
Step 1: the initial sampling period T of each node of sensor network
1=T, every the time in sampling period, node is waken up, and carries out the following step;
Step 2: if the current time still is in during the sampling time, and the precision between actual sample value and predicted value is less than error range | X
t-x
t|＜error_bound then increases sampling period T
1=T
1+ T/2, if | X
t-x
t| error_bound, sampling period T then shortened
1=max (T
1, T
1-T/2), and return sampled data; Otherwise sensor node is in resting state.
For convenience of description, our supposition has following application example:
One, sensor network nodes polymerization cluster
Sensor network nodes polymerization cluster planning process is as follows:
1. lay M=100 sensor node and be in certain monitored area,,, calculate Initial Hurdle T (n) when front-wheel is counted r=1 according to the percentage p=10% of bunch first node in all nodes of regional scale initialization expectation.
2. each sensor node is selected the random number between [0,1] at random, if this is counted less than T (n), then this node becomes a bunch head, and T (n) is set to 0.
3. after node was elected as bunch head, the message that notifies was informed other nodes, and which bunch non-bunch of first node selects to add according to the distance between own and bunch head, and informs this bunch head.
4. after bunch head receives all adding information, just produce a TDMA timed message, and notify all nodes in this bunch.
5. the used CDMA of all nodes encodes in bunch first this bunch of decision, and this coding regularly sends together with TDMA.When bunch in after member node receives this message, will in time slot separately, send data (comprising sampled data and residual energy value).
Two, sensor node sequence analysis settling time model
1. bunch first Time Calculation is at time period T
mThe average of the sampled value that calculate to obtain in=1 second, and according to bunch in the dump energy of member node determine its corresponding distribution number of samples and the time that begins to sample.
2. bunch first node is above-mentioned steps result notification bunch inner member node, and each member node is according to the operation plan of bunch head, an initial P=5 sampled data is carried out after zero-mean handles, with sampled value with return to a bunch first node.
3. bunch first node according to bunch in the result that returns of member node, adopt AR (P) series model to realize estimation, and estimated value be notified to member node following sampled data.
Three, sensor node sampling scheduling
1. the initial sampling period T of member node bunch
1=T=1 second, every the time in sampling period, node is waken up.
2. still be in during the sampling time as if the current time, and the precision between actual sample value and predicted value then increases sampling period T less than error range (error_bound=0.001)
1=T
1+ T/2; If the precision between actual sample value and predicted value then shortens sampling period T less than error range
1=max (T
1, T
1-T/2), and return sampled data; Otherwise sensor node is in resting state.
Claims (1)
1. collection method for wireless sensor network data based on time series predicting model, it is characterized in that utilizing time series predicting model to finish dynamic dispatching to sensor node, optimize sample frequency, solve the energy consumption minimized needs of problems of wireless sensor network, specific as follows:
Step 1). each node is selected the random number between [0,1] at random in the sensor network, if this is counted less than estimating threshold values, then this node becomes a bunch head,
Step 2). the node that becomes bunch head is broadcast message towards periphery, other nodes according to the intensity of the broadcast message that receives select that it will add bunch, and inform corresponding bunch of head,
Step 3). the node continuous collecting detects data, and is sent to a bunch head together with the relevant information of residue energy of node,
Step 4). bunch first average of in the sampling period, calculating the sampled value that obtains of calculating, and according to bunch in the dump energy of member node determine its corresponding distribution number of samples and the time that begins to sample,
Step 5). bunch first node is above-mentioned steps result notification bunch inner member node, and each member node is according to the operation plan of bunch head, an initial P rank sampled data is carried out after zero-mean handles, with sampled value with return to a bunch first node,
Step 6). bunch first node according to bunch in the result that returns of member node, adopt P rank autoregression model AR (P) to realize estimation, and estimated value be notified to member node following sampled data,
Step 7). in the initial sampling period of member node in bunch, every the time in sampling period, node is waken up,
Step 8). if the current time still is in during the sampling time, and the precision between actual sample value and predicted value then increases the sampling period less than error range; If the precision between actual sample value and predicted value then shortens the sampling period, and returns sampled data less than error range; Otherwise sensor node is in resting state.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2009100261015A CN101511099B (en) | 2009-04-01 | 2009-04-01 | Collection method for wireless sensor network data based on time series prediction model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2009100261015A CN101511099B (en) | 2009-04-01 | 2009-04-01 | Collection method for wireless sensor network data based on time series prediction model |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101511099A true CN101511099A (en) | 2009-08-19 |
| CN101511099B CN101511099B (en) | 2011-06-15 |
Family
ID=41003325
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2009100261015A Active CN101511099B (en) | 2009-04-01 | 2009-04-01 | Collection method for wireless sensor network data based on time series prediction model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101511099B (en) |
Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101977433A (en) * | 2010-11-09 | 2011-02-16 | 西安电子科技大学 | Method for average clock synchronization of wireless sensor network |
| CN101662777B (en) * | 2009-09-11 | 2012-01-11 | 北京交通大学 | Wireless sensor network distributed hibernation method based on probability statistics |
| CN102625425A (en) * | 2012-02-29 | 2012-08-01 | 电子科技大学 | Event adaptive sensor node |
| CN102625486A (en) * | 2012-04-16 | 2012-08-01 | 中国科学院声学研究所 | Agent node and sensor network |
| CN102869079A (en) * | 2012-09-20 | 2013-01-09 | 电子科技大学 | Method for adjusting regular sleep period of terminal node in self-adaptive manner |
| CN103024797A (en) * | 2012-11-30 | 2013-04-03 | 无锡清华信息科学与技术国家实验室物联网技术中心 | Statistic-based wireless sensor network flow evaluation method |
| CN103096442A (en) * | 2013-01-04 | 2013-05-08 | 南京信息工程大学 | Node battery recovery and energy searching method in wireless sensor network |
| CN103249158A (en) * | 2013-05-20 | 2013-08-14 | 无锡赛睿科技有限公司 | Optimal sampling frequency allocation method and device of wireless sensor network |
| US8750412B2 (en) | 2011-04-14 | 2014-06-10 | Fujitsu Limited | Data processing system and data processing method |
| CN103974268A (en) * | 2013-01-29 | 2014-08-06 | 上海携昌电子科技有限公司 | Low-delay sensor network data transmission method capable of adjusting fine granularity |
| CN104090262A (en) * | 2014-05-23 | 2014-10-08 | 浙江工业大学 | Moving target tracking method based on multiple-sampling-rate multiple-model fusion estimation |
| CN104378812A (en) * | 2014-11-28 | 2015-02-25 | 北京农业信息技术研究中心 | Differentiated topology control method of renewable energy source nodes of farmland self-organizing network |
| CN104394580A (en) * | 2014-07-29 | 2015-03-04 | 湖南大学 | Pattern-statistic-based time redundant data elimination method |
| CN104930660A (en) * | 2015-06-24 | 2015-09-23 | 北京奇虎科技有限公司 | Air state monitoring method and device |
| CN105188117A (en) * | 2015-09-21 | 2015-12-23 | 北京农业信息技术研究中心 | Time-space domain multiplexing farmland wireless sensor network frequency spectrum interleaved access method |
| CN105320835A (en) * | 2014-07-15 | 2016-02-10 | 通用电气智能平台有限公司 | Apparatus and method for time series data analysis method market |
| CN105491615A (en) * | 2015-11-19 | 2016-04-13 | 杨明霞 | Sensor self-adaptive sampling method based on time sequence |
| CN107864071A (en) * | 2017-11-02 | 2018-03-30 | 江苏物联网研究发展中心 | A kind of dynamic measuring method, apparatus and system towards active safety |
| CN108682140A (en) * | 2018-04-23 | 2018-10-19 | 湘潭大学 | A kind of enhanced method for detecting abnormality based on compressed sensing and autoregression model |
| CN109618384A (en) * | 2019-01-28 | 2019-04-12 | 南京邮电大学 | Wireless sensor network system |
| CN109982357A (en) * | 2019-04-09 | 2019-07-05 | 合肥工业大学 | A kind of sampling period optimization method based on multi-hop wireless sensor network |
| CN111314473A (en) * | 2020-02-22 | 2020-06-19 | 洋浦美诺安电子科技有限责任公司 | Environmental monitoring system based on artificial intelligence |
| CN111404222A (en) * | 2020-03-05 | 2020-07-10 | 深圳渊联技术有限公司 | Equipment sampling device, working method, management platform and management system |
| CN113762399A (en) * | 2021-09-10 | 2021-12-07 | 华能澜沧江水电股份有限公司 | Method for collecting and visually presenting time-space correlation monitoring data of gravity dam |
| CN114217017A (en) * | 2021-12-07 | 2022-03-22 | 杭州中科先进技术研究院有限公司 | Deep learning-based energy-saving interactive negative oxygen ion detection method and system |
| CN117817675A (en) * | 2024-03-06 | 2024-04-05 | 泓浒(苏州)半导体科技有限公司 | Prediction method of motion trail of wafer handling mechanical arm based on time sequence |
-
2009
- 2009-04-01 CN CN2009100261015A patent/CN101511099B/en active Active
Cited By (43)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101662777B (en) * | 2009-09-11 | 2012-01-11 | 北京交通大学 | Wireless sensor network distributed hibernation method based on probability statistics |
| CN101977433B (en) * | 2010-11-09 | 2013-03-20 | 西安电子科技大学 | Method for average clock synchronization of wireless sensor network |
| CN101977433A (en) * | 2010-11-09 | 2011-02-16 | 西安电子科技大学 | Method for average clock synchronization of wireless sensor network |
| US8750412B2 (en) | 2011-04-14 | 2014-06-10 | Fujitsu Limited | Data processing system and data processing method |
| CN102625425A (en) * | 2012-02-29 | 2012-08-01 | 电子科技大学 | Event adaptive sensor node |
| CN102625425B (en) * | 2012-02-29 | 2015-06-17 | 电子科技大学 | Event adaptive sensor node |
| CN102625486A (en) * | 2012-04-16 | 2012-08-01 | 中国科学院声学研究所 | Agent node and sensor network |
| CN102625486B (en) * | 2012-04-16 | 2014-12-10 | 中国科学院声学研究所 | Agent node, sensor network, and method for acquiring sensor network data |
| CN102869079B (en) * | 2012-09-20 | 2015-04-15 | 电子科技大学 | Method for adjusting regular sleep period of terminal node in self-adaptive manner |
| CN102869079A (en) * | 2012-09-20 | 2013-01-09 | 电子科技大学 | Method for adjusting regular sleep period of terminal node in self-adaptive manner |
| CN103024797A (en) * | 2012-11-30 | 2013-04-03 | 无锡清华信息科学与技术国家实验室物联网技术中心 | Statistic-based wireless sensor network flow evaluation method |
| CN103024797B (en) * | 2012-11-30 | 2015-06-24 | 无锡清华信息科学与技术国家实验室物联网技术中心 | Statistic-based wireless sensor network flow evaluation method |
| CN103096442A (en) * | 2013-01-04 | 2013-05-08 | 南京信息工程大学 | Node battery recovery and energy searching method in wireless sensor network |
| CN103096442B (en) * | 2013-01-04 | 2015-05-20 | 南京信息工程大学 | Node battery recovery and energy searching method in wireless sensor network |
| CN103974268A (en) * | 2013-01-29 | 2014-08-06 | 上海携昌电子科技有限公司 | Low-delay sensor network data transmission method capable of adjusting fine granularity |
| CN103974268B (en) * | 2013-01-29 | 2017-09-29 | 上海携昌电子科技有限公司 | The adjustable low delay sensor network data transmission method of fine granulation |
| CN103249158A (en) * | 2013-05-20 | 2013-08-14 | 无锡赛睿科技有限公司 | Optimal sampling frequency allocation method and device of wireless sensor network |
| CN104090262A (en) * | 2014-05-23 | 2014-10-08 | 浙江工业大学 | Moving target tracking method based on multiple-sampling-rate multiple-model fusion estimation |
| CN104090262B (en) * | 2014-05-23 | 2016-06-29 | 浙江工业大学 | A kind of method for tracking moving target merging estimation based on multi-sampling rate multi-model |
| CN105320835A (en) * | 2014-07-15 | 2016-02-10 | 通用电气智能平台有限公司 | Apparatus and method for time series data analysis method market |
| CN104394580B (en) * | 2014-07-29 | 2019-01-04 | 湖南大学 | Time redundancy data erasing method based on mode statistical |
| CN104394580A (en) * | 2014-07-29 | 2015-03-04 | 湖南大学 | Pattern-statistic-based time redundant data elimination method |
| CN104378812A (en) * | 2014-11-28 | 2015-02-25 | 北京农业信息技术研究中心 | Differentiated topology control method of renewable energy source nodes of farmland self-organizing network |
| CN104378812B (en) * | 2014-11-28 | 2017-09-29 | 北京农业信息技术研究中心 | Farmland self-organizing network regenerative resource node difference topology control method |
| CN104930660A (en) * | 2015-06-24 | 2015-09-23 | 北京奇虎科技有限公司 | Air state monitoring method and device |
| CN104930660B (en) * | 2015-06-24 | 2017-09-22 | 北京奇虎科技有限公司 | Air condition monitoring method and monitoring device |
| CN105188117A (en) * | 2015-09-21 | 2015-12-23 | 北京农业信息技术研究中心 | Time-space domain multiplexing farmland wireless sensor network frequency spectrum interleaved access method |
| CN105188117B (en) * | 2015-09-21 | 2018-07-06 | 北京农业信息技术研究中心 | The farmland wireless sensor network frequency interlace cut-in method of time-space domain multiplexing |
| CN105491615A (en) * | 2015-11-19 | 2016-04-13 | 杨明霞 | Sensor self-adaptive sampling method based on time sequence |
| CN107864071B (en) * | 2017-11-02 | 2021-06-22 | 江苏物联网研究发展中心 | Active safety-oriented dynamic data acquisition method, device and system |
| CN107864071A (en) * | 2017-11-02 | 2018-03-30 | 江苏物联网研究发展中心 | A kind of dynamic measuring method, apparatus and system towards active safety |
| CN108682140A (en) * | 2018-04-23 | 2018-10-19 | 湘潭大学 | A kind of enhanced method for detecting abnormality based on compressed sensing and autoregression model |
| CN108682140B (en) * | 2018-04-23 | 2020-07-28 | 湘潭大学 | Enhanced anomaly detection method based on compressed sensing and autoregressive model |
| CN109618384A (en) * | 2019-01-28 | 2019-04-12 | 南京邮电大学 | Wireless sensor network system |
| CN109982357A (en) * | 2019-04-09 | 2019-07-05 | 合肥工业大学 | A kind of sampling period optimization method based on multi-hop wireless sensor network |
| CN109982357B (en) * | 2019-04-09 | 2021-08-20 | 合肥工业大学 | Sampling period optimization method based on multi-hop wireless sensor network |
| CN111314473A (en) * | 2020-02-22 | 2020-06-19 | 洋浦美诺安电子科技有限责任公司 | Environmental monitoring system based on artificial intelligence |
| CN111404222A (en) * | 2020-03-05 | 2020-07-10 | 深圳渊联技术有限公司 | Equipment sampling device, working method, management platform and management system |
| CN111404222B (en) * | 2020-03-05 | 2023-10-31 | 深圳渊联技术有限公司 | Equipment sampling device, working and management methods, management platform and management system |
| CN113762399A (en) * | 2021-09-10 | 2021-12-07 | 华能澜沧江水电股份有限公司 | Method for collecting and visually presenting time-space correlation monitoring data of gravity dam |
| CN114217017A (en) * | 2021-12-07 | 2022-03-22 | 杭州中科先进技术研究院有限公司 | Deep learning-based energy-saving interactive negative oxygen ion detection method and system |
| CN117817675A (en) * | 2024-03-06 | 2024-04-05 | 泓浒(苏州)半导体科技有限公司 | Prediction method of motion trail of wafer handling mechanical arm based on time sequence |
| CN117817675B (en) * | 2024-03-06 | 2024-04-30 | 泓浒(苏州)半导体科技有限公司 | Prediction method of motion trail of wafer handling mechanical arm based on time sequence |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101511099B (en) | 2011-06-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101511099B (en) | Collection method for wireless sensor network data based on time series prediction model | |
| Tayeh et al. | A distributed real-time data prediction and adaptive sensing approach for wireless sensor networks | |
| CN105608513B (en) | A kind of reservoir optimizing and dispatching method coupling long, medium and short phase Runoff Forecast information | |
| CN102402726B (en) | Method for predicting electric quantity of large-scale distribution network based on regional load analysis | |
| CN102123473A (en) | Dynamic clustering mechanism-based target tracking method for wireless sensor network | |
| CN108430088B (en) | Wireless sensor network system and node awakening method thereof | |
| CN111198808B (en) | Method and device for predicting performance index, storage medium and electronic equipment | |
| CN108024207A (en) | Flow of the people monitoring method based on three layers of prevention and control circle | |
| CN101267446A (en) | Time domain data amalgamation method for wireless sensor network | |
| CN102244882A (en) | Mobility-agent-based intelligent data acquisition method for wireless sensor network | |
| CN102594904A (en) | Method for detecting abnormal events of wireless sensor network in distributed way | |
| CN107274008A (en) | Electric automobile charging and conversion electric Demand Forecast method, storage device and system | |
| CN106991504A (en) | Building energy consumption Forecasting Methodology, system and building based on metering separate time series | |
| CN103139863A (en) | Method of target tracking and energy consumption optimization of dynamic cluster mechanism of wireless sensor network | |
| CN102186241A (en) | Parallel distributed particle filter based wireless sensor network target tracking method | |
| CN108632963B (en) | A kind of change grain size characteristic method of sampling based on multi-media sensor | |
| Ou et al. | An interval chance-constrained fuzzy modeling approach for supporting land-use planning and eco-environment planning at a watershed level | |
| CN102118884B (en) | Data transmission method for opportunistic mobile sensor network based on closeness centrality | |
| CN102256381A (en) | Distributed adaptive particle filter-based wireless sensor network target tracking method | |
| CN116506239B (en) | Energy-saving data processing method and storage server | |
| Esnaashari et al. | Dynamic Point Coverage Problem in Wireless Sensor Networks: A Cellular Learning Automata Approach. | |
| CN105636081A (en) | Improved EAST (Efficient Data Collection Aware of Spatio-Temporal Correlation) algorithm based on grey model prediction | |
| Liu et al. | An energy-efficient data collection scheme for wireless sensor networks | |
| CN111586615B (en) | Sampling scheduling method and system for agricultural wireless sensor network | |
| CN101827388A (en) | Event driving type wireless sensor network energy monitoring method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20090819 Assignee: Jiangsu Nanyou IOT Technology Park Ltd. Assignor: Nanjing Post & Telecommunication Univ. Contract record no.: 2016320000217 Denomination of invention: Collection method for wireless sensor network data based on time series prediction model Granted publication date: 20110615 License type: Common License Record date: 20161118 |
|
| LICC | Enforcement, change and cancellation of record of contracts on the licence for exploitation of a patent or utility model | ||
| EC01 | Cancellation of recordation of patent licensing contract | ||
| EC01 | Cancellation of recordation of patent licensing contract |
Assignee: Jiangsu Nanyou IOT Technology Park Ltd. Assignor: Nanjing Post & Telecommunication Univ. Contract record no.: 2016320000217 Date of cancellation: 20180116 |