CN104507180B - Wireless sensor network data fusion method based on the packing density degree of correlation - Google Patents
Wireless sensor network data fusion method based on the packing density degree of correlation Download PDFInfo
- Publication number
- CN104507180B CN104507180B CN201410834538.2A CN201410834538A CN104507180B CN 104507180 B CN104507180 B CN 104507180B CN 201410834538 A CN201410834538 A CN 201410834538A CN 104507180 B CN104507180 B CN 104507180B
- Authority
- CN
- China
- Prior art keywords
- node
- data
- wireless sensor
- sensor network
- nodes
- 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.)
- Expired - Fee Related
Links
- 238000007500 overflow downdraw method Methods 0.000 title claims abstract description 21
- 238000012856 packing Methods 0.000 title abstract 2
- 230000008447 perception Effects 0.000 claims abstract description 20
- 238000000034 method Methods 0.000 claims description 22
- 238000012847 principal component analysis method Methods 0.000 claims description 3
- 230000004927 fusion Effects 0.000 abstract description 8
- 238000005265 energy consumption Methods 0.000 abstract description 5
- 238000012544 monitoring process Methods 0.000 abstract description 2
- 238000004891 communication Methods 0.000 description 5
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000000691 measurement method Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007499 fusion processing Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
- H04W84/20—Master-slave selection or change arrangements
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
- Arrangements For Transmission Of Measured Signals (AREA)
Abstract
The invention discloses a kind of wireless sensor network data fusion methods based on the packing density degree of correlation, by the way that whole nodes of wireless sensor network are separately arranged as three types: representing node, member node and isolated node, the collected perception data of node and isolated node institute will be represented and upload to external network, and the collected perception data of member node institute is then represented the collected perception data of node institute and is represented, and without uploading, the data fusion of wireless sensor network is realized.Data fusion method of the invention has the advantages that accuracy is high, energy consumption is small, practical and application mode is flexible, frequently uploads especially suitable for node dense deployment, node data, and monitoring object changes slow occasion.
Description
Technical Field
The invention relates to a wireless sensor network data fusion method based on data density correlation.
Background
The data fusion is a process of removing redundant data, or obtaining certain characteristics of each data, or obtaining certain event conclusion in a wireless sensor network according to data correlation or actual application requirements of data from a plurality of data sources. The implementation of the data fusion technology aims to reduce the transmission quantity of data in the wireless sensor network, thereby reducing the network energy consumption and prolonging the network service life.
In the wireless sensor network in the dense deployment mode, data between adjacent nodes has strong correlation. In order to accurately perform fusion of node-related data, an accuracy measurement method with inter-node correlation is required, and therefore, the research on the inter-node correlation measurement method is directly related to the accuracy of a data fusion algorithm.
At present, researches on a method for describing data relevancy among nodes by scholars mainly comprise a description method based on deployment characteristics (node positions and node coverage) and data characteristics. The relevance description method based on the deployment characteristics comprises a relevance description method based on the node position and a relevance description method based on the node perception range overlapping degree. The data feature-based correlation description method comprises correlation description based on node data correlation coefficients and a data distribution-based correlation description method.
The correlation description method based on the node deployment characteristics reflects the correlation degree between the nodes through the linear distance of the positions between the nodes or the overlapping degree of the perception ranges of the adjacent nodes, and establishes a correlation formula between the nodes. The correlation formula has the advantages of simple modeling and convenient calculation. However, the correlation description method has the defects that: noise among all nodes needs to be Gaussian noise which is independently and identically distributed, and the actual environment of the WSN is difficult to meet; in a complex environment, the problem of inconsistent deployment positions among nodes and the correlation among the nodes exists. The drawback of this correlation description method is mainly due to the fact that the actual environment when the wireless sensor network is deployed is not considered.
The correlation description method based on the node data correlation coefficient adopts a Pearson correlation coefficient to measure the correlation degree between two nodes. The correlation description method can accurately describe the linear correlation between two node data, and is suitable for describing application occasions with small change of event areas and simple deployment environments. However, in practical application, a large amount of original data needs to be uploaded to the Sink node when the Pearson correlation coefficient between node data is calculated, and the data uploading process consumes more energy, which is not beneficial to energy saving. Especially, the problem of energy consumption is more serious when original data far away from a Sink node is transmitted. In addition, when the environment changes, the correlation between the nodes changes, and the correlation between the nodes needs to be recalculated, which consumes a large amount of node energy.
The method describes the correlation degree of the node relative to the neighbor nodes thereof through the relative position of the node data relative to the neighbor node data thereof. The correlation description method has the defects of single description method, limited applicable occasions and incapability of comprehensively and accurately describing the correlation between node data. Resulting in poor practicality of the described method.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a wireless sensor network data fusion method based on data density correlation is provided.
The technical scheme adopted by the invention is as follows:
a wireless sensor network data fusion method based on data density correlation comprises the following steps:
step one, each node i of the wireless sensor network sends all neighbor nodes v to the wireless sensor network at the same time1,v2,…,vnSending the sensing data D at the moment and receiving all the neighbor nodes v1,v2,…,vnPerception data D 'of the moment transmitted to the same'1,D′2,...D′nWherein n is the total number of neighbor nodes of the node i;
step two, each node i of the wireless sensor network respectively judges whether the node i is a core node;
step three, each wireless sensor network is used as a node i of a core node, and all neighbor nodes v of the wireless sensor network are respectively used1,v2,…,vnDivision into epsilon neighborhood interior node set NodeSet (i)innerAnd epsilon neighborhood outer node set NodeSet (i)outer；
Step four, each node i of the wireless sensor network respectively calculates and stores the data density correlation degree sim (i) of the node i relative to the neighbor node;
step five, each node i of the wireless sensor network, which is taken as a core node, is respectively assembled with NodeSet (i) in the epsilon neighborhoodinnerEach node in the set transmits a packet (k)i1, sim (i), set NodeSet (i) to nodes outside of its epsilon neighborhoodouterEach node in the set transmits a packet (k)i-1, sim (i)), each node i of the wireless sensor network being a non-core node, towards all its neighbour nodes v, respectively1,v2,…,vnBroadcast information packet (k)i0, sim (i)), wherein k in the packetiFor the number of the node i, sim (i) the data density correlation degree of the node i relative to the neighbor nodes, which is stored for the node i, and "1", "-1", and "0" are mark information;
and each node i of the wireless sensor network receives all the neighbor nodes v thereof respectively1,v2,…,vnA packet sent to it;
step six, each node i of the wireless sensor network is judged according to the received information packet so as to be set as a corresponding node type:
for a node i serving as a non-core node, if the flag information of all the received information packets is '0' or '-1', setting the node i as an independent node, otherwise, setting the node i as a member node;
for a node i as a core node, if it does not receive a packet with flag information of "1", it is set as a representative node;
for a node i as a core node, if it receives one or more packets with flag information of "1", it numbers its node kiThe stored data density correlation sim (i) and the node number k in the packet with the received flag information "1iSending the information to a sink node of a wireless sensor network, dividing a core node which sends the information to the sink node into a plurality of local relevant areas by the sink node, marking the core node with the maximum data density correlation degree sim (i) in each local relevant area as a representative node, marking the rest core nodes in the local relevant area as member nodes, sending the marking information back to the corresponding core node, and setting the core node which receives the marking information as the corresponding node type, wherein any core node in one local relevant areaIt sends the information packet with the mark information of '1' to at least one core node in the local relevant area, or it receives the information packet with the mark information of '1' sent by at least one core node in the local relevant area;
and seventhly, uploading the perception data collected by the representative node and the independent node to an external network.
In a preferred embodiment of the present invention, in the second step, the core node satisfies a determination condition of minPts ≦ N, and the non-core node satisfies a determination condition of N < minPts, where N is all neighbor node sensing data D 'of the node i'1,D′2,...D′nIn the formula I satisfies | D'jNeighbor node perception data D 'of D | < epsilon'jE is a preset data threshold, and minPts is a preset number threshold.
In the third step, the node set NodeSet (i) belonging to the epsilon neighborhoodinnerNode of which perception data satisfies | D'jD < epsilon and belongs to the set of nodes NodeSet (i) outside the epsilon neighborhoodouterNode of which perception data satisfies | D'j-D|≥ε。
In the fourth step, the data density correlation degree of the node i as the core node with respect to the neighboring nodes is:
wherein,D′j1,D′j2,...,D′jNto belong to the epsilon neighborhood inner node set NodeSet (i)innerAll nodes of D'j1,D′j2,...,D′jNSequentially N satisfy | D'jNeighbor node perception data D 'of D | < epsilon'jThe 1 st to the nth ones of (a),a1、a2and a3Is a weight, a1+a2+a3＝1，
For a node i as a non-core node, the data density correlation degree relative to the neighbor nodes is as follows: sim (i) ═ 0.
In the second step of the invention, the number threshold value minPts represents the minimum number of neighbor nodes required by the node i to represent the neighbor nodes, and the data threshold value epsilon represents the maximum allowable error between the sensing data of the core node and the sensing data of the represented node.
In the fourth step, the weight a is a preferred embodiment of the present invention1、a2And a3And calculating the wireless sensor network by a principal component analysis method.
In the seventh step, a data uploading path of the wireless sensor network is calculated by using an energy efficient routing algorithm, and the sensing data collected by the representative node and the independent node is uploaded to a sink node of the wireless sensor network according to the data uploading path and is uploaded to an external network.
Compared with the prior art, the invention has the following beneficial effects:
first, the data fusion method of the present invention sets all nodes of a wireless sensor network to three types, respectively: the representative node, the member node and the independent node upload the sensing data collected by the representative node and the independent node to an external network, and the sensing data collected by the member node is represented by the sensing data collected by the representative node and is not uploaded, so that the data fusion of the wireless sensor network is realized;
secondly, the invention accurately describes the correlation among the nodes by using the data density correlation degree, improves the accuracy of selecting the representative node and the member nodes represented by the representative node from the nodes of the wireless sensor network and the description accuracy of the representative node on events in the monitoring area, thereby improving the accuracy of data fusion;
thirdly, the data fusion method of the invention adopts a partial distributed implementation method, namely, the non-core node i judges whether the flag information of all the received information packets is '0' or '1', and partial core nodes judge whether the information packets with the flag information of '1' are not received, so that the node type can be set without uploading data to a sink node for judging the node type, thereby reducing the energy consumption in the data fusion process and improving the practicability for the wireless sensor network with limited energy;
fourthly, according to the actual application scene of the wireless sensor network, the data fusion method can meet different application requirements of users on the number of the representative nodes, the number of the independent nodes and the global relative error of the sensing data of the wireless sensor network by adjusting three parameters of the communication radius of the node i, the number threshold minPts and the data threshold epsilon;
in conclusion, the data fusion method has the advantages of high accuracy, low energy consumption, strong practicability and flexible application mode, and is particularly suitable for occasions with densely deployed nodes, frequent uploading of node data and slow change of monitored objects.
Drawings
The invention is described in further detail below with reference to the following figures and specific examples:
fig. 1 is a flow chart of a data fusion method of a wireless sensor network according to the present invention.
Detailed Description
As shown in fig. 1, the data fusion method for the wireless sensor network based on the data density correlation is suitable for the wireless sensor network in which the sensing data is single-feature and the communication radii of the nodes are equal, and the single-feature means that the sensing data of all the nodes are the same, such as temperature or pressure. The data fusion method comprises the following steps:
step one, each node i of the wireless sensor network sends all neighbor nodes v to the wireless sensor network at the same time1,v2,…,vnSending the sensing data D at the moment and receiving all the neighbor nodes v1,v2,…,vnPerception data D 'of the moment transmitted to the same'1,D′2,...D′nAnd the neighbor nodes of the node i refer to nodes within the communication radius of the node i, and n is the total number of the neighbor nodes of the node i.
Step two, each node i of the wireless sensor network respectively judges whether the node i is a core node, the judgment condition met by the core node is that minPts is not less than N and not more than N, the judgment condition met by the non-core node is that N is less than minPts, wherein N is sensing data D 'of all neighbor nodes of the node i'1,D′2,...D′nIn the formula I satisfies | D'jNeighbor node perception data D 'of D | < epsilon'jThe number of the core nodes is represented by the minimum neighbor node number required by the node i for representing the neighbor nodes, wherein minPts is a preset number threshold value, epsilon is a preset data threshold value and represents the maximum allowable error between the core node sensing data and the represented node sensing data;
step three, each wireless sensor network is used as a node i of a core node, and all neighbor nodes v of the wireless sensor network are respectively used1,v2,…,vnDivision into epsilon neighborhood interior node set NodeSet (i)innerAnd epsilon neighborhood outer node set NodeSet (i)outerWherein, the nodes belong to an epsilon neighborhood inner node set NodeSet (i)innerNode of which perception data satisfies | D'jD < epsilon and belongs to the set of nodes NodeSet (i) outside the epsilon neighborhoodouterNode of which perception data satisfies | D'j-D|≥ε。
Step four, each node i of the wireless sensor network respectively calculates and stores the data density correlation degree sim (i) of the node i relative to the neighbor node;
for a node i as a core node, the data density correlation degree of the node i relative to a neighbor node is as follows:
wherein,D′j1,D′j2,...,D′jNto belong to the epsilon neighborhood inner node set NodeSet (i)innerAll nodes of D'j1,D′j2,...,D′jNN satisfy | D'jNeighbor node perception data D 'of D | < epsilon'j1 st to nth of (a)1、a2And a3Is a weight, a1+a2+a31, weight a1、a2And a3The method is obtained by calculating a specific wireless sensor network by a principal component analysis method, and for the wireless sensor network, a2＞a3＞a1；
For a node i as a non-core node, the data density correlation degree relative to the neighbor nodes is as follows: sim (i) ═ 0.
Step five, each node i of the wireless sensor network, which is taken as a core node, is respectively assembled with NodeSet (i) in the epsilon neighborhoodinnerEach node in the set transmits a packet (k)i1, sim (i), set NodeSet (i) to nodes outside of its epsilon neighborhoodouterEach node in the set transmits a packet (k)i-1, sim (i)), each node i of the wireless sensor network being a non-core node, towards all its neighbour nodes v, respectively1,v2,…,vnBroadcast information packet (k)i0, sim (i)), wherein k in the packetiIs the number of node i, sim (i) is node iThe stored data density correlation degree relative to the neighbor node is marked information, namely '1', '1' and '0';
and each node i of the wireless sensor network receives all the neighbor nodes v thereof respectively1,v2,…,vnThe packet sent to it.
Step six, each node i of the wireless sensor network is judged according to the received information packet so as to be set as a corresponding node type:
for the node i as a non-core node, if the flag information of all the received information packets is '0' or '-1', the node i is set as an independent node, otherwise, the node i is set as a member node.
For a node i as a core node, if it does not receive a packet with flag information of "1", it is set as a representative node;
for a node i as a core node, if it receives one or more packets with flag information of "1", it numbers its node kiThe stored data density correlation sim (i) and the node number k in the packet with the received flag information "1iThe method comprises the steps that Sink nodes are sent to a Sink node of a wireless sensor network, the Sink node divides core nodes sending information to the Sink node into a plurality of local relevant areas, the core node with the largest data density correlation degree sim (i) in each local relevant area is marked as a representative node, other core nodes in the local relevant area are marked as member nodes, marking information is sent back to the corresponding core nodes, and the core nodes receiving the marking information are set to be of corresponding node types, wherein for any core node in one local relevant area, the Sink node at least sends an information packet with the marking information of '1' to one core node in the local relevant area or at least receives an information packet with the marking information of '1' sent by one core node in the local relevant area;
thus, all nodes of the wireless sensor network are set to three types, respectively: a representative node, a member node, and an independent node; the three types of nodes represent the following meanings: each representative node is taken as a core to establish a global relevant area, a plurality of neighbor nodes of the representative node are member nodes of the global relevant area, the perception data collected by the member nodes can be represented by the perception data collected by the representative node, and the independent node does not belong to any global relevant area.
Step seven, calculating a data uploading path of the wireless sensor network by using an energy efficient routing algorithm, and uploading sensing data acquired by the representative node and the independent node to a sink node of the wireless sensor network according to the data uploading path so as to transmit the sensing data to an external network; the sensing data collected by the member nodes are represented by the sensing data collected by the representative nodes and are not uploaded, so that data fusion of the wireless sensor network is realized.
The three parameters of the communication radius, the number threshold minPts and the data threshold epsilon of the node i are set according to the practical application scene of the wireless sensor network, and the three parameters can be adjusted according to the three requirements of the number of the representative nodes, the number of the independent nodes and the global relative error of the sensing data of the wireless sensor network so as to meet different practical application requirements, specifically: if the communication radius of the node i is larger, the number of the representative nodes is smaller, the number of the independent nodes is smaller, the global relative error is larger, and vice versa; if the number threshold minPts is larger, the larger the number of representative nodes is, the larger the number of independent nodes is, the smaller the global relative error is, and vice versa; if the data threshold ε is larger, the number of representative nodes is smaller, the number of independent nodes is smaller, the global relative error is larger, and vice versa.
The present invention is not limited to the above embodiments, and various other equivalent modifications, substitutions or alterations can be made without departing from the basic technical concept of the invention according to the common technical knowledge and conventional means in the field.
Claims (7)
1. A wireless sensor network data fusion method based on data density correlation comprises the following steps:
step one, each node i of the wireless sensor network sends all neighbor nodes v to the wireless sensor network at the same time1,v2,…,vnSending the sensing data D at the moment and receiving all the neighbor nodes v1,v2,…,vnPerception data D 'of the moment transmitted to the same'1,D′2,...D′nWherein n is the total number of neighbor nodes of the node i;
step two, each node i of the wireless sensor network respectively judges whether the node i is a core node;
step three, each wireless sensor network is used as a node i of a core node, and all neighbor nodes v of the wireless sensor network are respectively used1,v2,…,vnDivision into epsilon neighborhood interior node set NodeSet (i)innerAnd epsilon neighborhood outer node set NodeSet (i)outer；
Step four, each node i of the wireless sensor network respectively calculates and stores the data density correlation degree sim (i) of the node i relative to the neighbor node;
step five, each node i of the wireless sensor network, which is taken as a core node, is respectively assembled with NodeSet (i) in the epsilon neighborhoodinnerEach node in the set transmits a packet (k)i1, sim (i), set NodeSet (i) to nodes outside of its epsilon neighborhoodouterEach node in the set transmits a packet (k)i-1, sim (i)), each node i of the wireless sensor network being a non-core node, towards all its neighbour nodes v, respectively1,v2,…,vnBroadcast information packet (k)i0, sim (i)), wherein k in the packetiFor the number of the node i, sim (i) the data density correlation degree of the node i relative to the neighbor nodes, which is stored for the node i, and "1", "-1", and "0" are mark information;
and each node i of the wireless sensor network receives all the neighbor nodes v thereof respectively1,v2,…,vnA packet sent to it;
step six, each node i of the wireless sensor network is judged according to the received information packet so as to be set as a corresponding node type:
for a node i serving as a non-core node, if the flag information of all the received information packets is '0' or '-1', setting the node i as an independent node, otherwise, setting the node i as a member node;
for a node i as a core node, if it does not receive a packet with flag information of "1", it is set as a representative node;
for a node i as a core node, if it receives one or more packets with flag information of "1", it numbers its node kiThe stored data density correlation sim (i) and the node number k in the packet with the received flag information "1iThe method comprises the steps that a sink node is sent to a wireless sensor network, the sink node divides a core node sending information to the sink node into a plurality of local relevant areas, the core node with the maximum data density correlation degree sim (i) in each local relevant area is marked as a representative node, other core nodes in the local relevant area are marked as member nodes, marking information is sent back to the corresponding core node, and the core node receiving the marking information is set to be a corresponding node type, wherein for any core node in one local relevant area, the sink node at least sends an information packet with the mark information of '1' to one core node in the local relevant area or at least receives an information packet with the mark information of '1' sent by one core node in the local relevant area;
and seventhly, uploading the perception data collected by the representative node and the independent node to an external network.
2. The data fusion method of claim 1, wherein: in the second step, the judgment condition met by the core node is that minPts is less than or equal to N and less than or equal to N, and the judgment condition met by the non-core node is that N is less than minPts, wherein N is all neighbor node sensing data D 'of the node i'1,D′2,...D′nIn the formula I satisfies | D'jNeighbor node perception data D 'of D | < epsilon'jE is a preset data threshold, and minPts is a preset number threshold.
3. The data fusion method of claim 2, wherein: in the third step, the nodes belonging to the epsilon neighborhood inner node set NodeSet (i)innerNode of which perception data satisfies | D'jD < epsilon and belongs to the set of nodes NodeSet (i) outside the epsilon neighborhoodouterA node of, sensing dataSatisfy | D'j-D|≥ε。
4. The data fusion method of claim 3, wherein: in the fourth step, for the node i as the core node, the data density correlation degree relative to the neighbor node is as follows:
wherein,D′j1,D′j2,...,D′jNto belong to the epsilon neighborhood inner node set NodeSet (i)innerAll nodes of D'j1,D′j2,...,D′jNSequentially N satisfy | D'jNeighbor node perception data D 'of D | < epsilon'j1 st to nth of (a)1、a2And a3Is a weight, a1+a2+a3＝1，
For a node i as a non-core node, the data density correlation degree relative to the neighbor nodes is as follows: sim (i) ═ 0.
5. The data fusion method of claim 4, wherein: in the second step, the number threshold minPts represents the minimum number of neighbor nodes required by the node i to represent the neighbor nodes, and the data threshold epsilon represents the maximum allowable error between the core node sensing data and the represented node sensing data.
6. The data fusion method of claim 4, wherein: in the fourth step, the weight a1、a2And a3And calculating the wireless sensor network by a principal component analysis method.
7. The data fusion method of claim 4, wherein: and seventhly, calculating a data uploading path of the wireless sensor network by using an energy efficient routing algorithm, uploading the sensing data acquired by the representative node and the independent node to a sink node of the wireless sensor network according to the data uploading path, and uploading the sensing data to an external network.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410834538.2A CN104507180B (en) | 2014-12-29 | 2014-12-29 | Wireless sensor network data fusion method based on the packing density degree of correlation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410834538.2A CN104507180B (en) | 2014-12-29 | 2014-12-29 | Wireless sensor network data fusion method based on the packing density degree of correlation |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104507180A CN104507180A (en) | 2015-04-08 |
| CN104507180B true CN104507180B (en) | 2019-04-02 |
Family
ID=52948877
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410834538.2A Expired - Fee Related CN104507180B (en) | 2014-12-29 | 2014-12-29 | Wireless sensor network data fusion method based on the packing density degree of correlation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104507180B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108401006A (en) * | 2017-11-02 | 2018-08-14 | 圆通速递有限公司 | A kind of data transmission method and system based on correlation models |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101199370A (en) * | 2007-12-13 | 2008-06-18 | 重庆大学 | Data processing method for robot tactile sensing information syncretizing |
| CN104123569A (en) * | 2014-07-21 | 2014-10-29 | 中国人民解放军63908部队 | Video person number information statistics method based on supervised learning |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9237024B2 (en) * | 2013-03-15 | 2016-01-12 | Cooper Technologies Company | Informational broadcast messages and its uses in wireless multihop networks |
-
2014
- 2014-12-29 CN CN201410834538.2A patent/CN104507180B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101199370A (en) * | 2007-12-13 | 2008-06-18 | 重庆大学 | Data processing method for robot tactile sensing information syncretizing |
| CN104123569A (en) * | 2014-07-21 | 2014-10-29 | 中国人民解放军63908部队 | Video person number information statistics method based on supervised learning |
Non-Patent Citations (2)
| Title |
|---|
| Data Density Correlation Degree Clustering Method for Data Aggregation in WSN;Fei Yuan,Yiju Zhan, and Yonghua Wang;《IEEE Sensors Journal》;20140430;第14卷(第4期);第III节至第IV节 |
| 无线传感器网络中数据密度相关度融合算法;袁飞等;《控制理论与应用》;20141130;第31卷(第11期);第2节至第4节 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104507180A (en) | 2015-04-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hribar et al. | Using correlated information to extend device lifetime | |
| Li et al. | Automatic ARIMA modeling-based data aggregation scheme in wireless sensor networks | |
| EP1665658B1 (en) | Hierarchical routing in ad-hoc networks | |
| CN102752784B (en) | Detection method of distribution type event domain based on graph theory in wireless sensor network | |
| CN104507180B (en) | Wireless sensor network data fusion method based on the packing density degree of correlation | |
| Shafiabadi et al. | New method to improve energy savings in wireless sensor networks by using SOM neural network | |
| CN105072582B (en) | The adaptive wireless sensor network passive type localization method of distance based on RSS distributions | |
| Reis et al. | Data-aware clustering for geosensor networks data collection | |
| Vu et al. | An energy-aware routing protocol for wireless sensor networks based on k-means clustering | |
| CN112488151A (en) | Semi-supervised manifold learning-based position fingerprint database construction method | |
| CN105979564B (en) | Method and device for sending data | |
| CN108880708A (en) | Compressed sensing link quality estimation method based on intertidal zone wireless sensor network | |
| Bisht et al. | Performance analysis of hierarchical and nonhierarchical routing techniques in wireless sensor networks | |
| Gayan et al. | Sensor network based adaptable system architecture for emergency situations | |
| CN103945488A (en) | Network community establishment method based on geographic position and network nodes | |
| Bhavana et al. | Spatial correlation based clustering algorithm for random and uniform topology in WSNs | |
| KR101355228B1 (en) | Method and system for adapting traffic reporting frequency | |
| Dhivya et al. | Cluster based approximate data collection for wireless sensor networks | |
| Chen et al. | A data fusion algorithm in LEACH protocol using gauss membership function | |
| Moad et al. | CC_SCR: A compression cluster-based scheme in a spatial correlated region for wireless sensor networks | |
| Bisht et al. | Energy Efficient Hierarchical Routing Protocol (EEHRP) for Wireless Sensor Network | |
| Wang et al. | Data fusion algorithm of wireless sensor based on combination between cluster head election improvement and neural network | |
| Surendheran et al. | A survey of energy-efficient communication protocol for wireless sensor networks | |
| Natsume et al. | Data fusion method exploiting data correlation for physical wireless parameter conversion sensor networks | |
| Ollos et al. | Sleep scheduling protocol for mobile WSNs |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20190402 Termination date: 20211229 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |