CN111669767B - Sensor network dynamic deployment method - Google Patents
Sensor network dynamic deployment method Download PDFInfo
- Publication number
- CN111669767B CN111669767B CN202010458814.5A CN202010458814A CN111669767B CN 111669767 B CN111669767 B CN 111669767B CN 202010458814 A CN202010458814 A CN 202010458814A CN 111669767 B CN111669767 B CN 111669767B
- Authority
- CN
- China
- Prior art keywords
- sensor
- nodes
- node
- monitoring area
- radius
- 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
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000012544 monitoring process Methods 0.000 claims abstract description 79
- 239000011159 matrix material Substances 0.000 claims description 24
- 238000010586 diagram Methods 0.000 claims description 7
- 238000004891 communication Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 238000003780 insertion Methods 0.000 claims description 3
- 230000037431 insertion Effects 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 abstract description 2
- 238000011161 development Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000036962 time dependent Effects 0.000 description 1
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
- H04W16/20—Network planning tools for indoor coverage or short range network deployment
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
- H04W16/225—Traffic simulation tools or models for indoor or short range network
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
- H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
-
- 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
-
- 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
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Mobile Radio Communication Systems (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention provides a dynamic deployment method of a sensor network. First, the sensors in the non-critical monitoring area are located and selected as mobile nodes. And secondly, finding out a target node in the heavy point monitoring area by using a concentric circle method. And finally, calculating a cascade moving path for moving the mobile node to the target node according to the Floyard algorithm, so that the dynamic deployment of the water quality sensor network based on the regional characteristic model is realized. According to the invention, by means of cascade movement of the sensor nodes, the dynamic deployment of the key monitoring area of the sensor network can be realized by utilizing shorter time on the premise of guaranteeing the life cycle of the network, so that better coverage performance is achieved.
Description
Technical Field
The invention relates to the field of environment monitoring and sensor networks, in particular to a research on a dynamic deployment method of a sensor network.
Background
Along with the development of electronic communication technology, sensor networks are increasingly used in the fields of environmental monitoring, intelligent home, medical treatment, military and the like. From the perspective of reasonable utilization of resources and ensuring accuracy of monitoring data, the characteristic that the sensors can be moved is utilized, and under the condition of limited sensor number, the sensors in non-key monitoring areas are moved to key monitoring areas for real-time monitoring. The complete network coverage of the monitored area is particularly critical for the deployment of wireless sensors, so that more effective coverage of the key monitored area must be ensured.
When a coverage hole occurs in the monitoring area, the node communication capacity, the information calculation and processing capacity, the network life cycle and the like of the current sensor network are correspondingly affected, so that the coverage hole point in the sensor node needs to be appropriately repaired, and the information sensing and monitoring capacity of the network are not correspondingly affected. Further, the occurrence of coverage holes can cause the failure of adjacent nodes, damage the reliability of the whole network, and further cause a large number of nodes in the network to be underutilized. Therefore, in order to ensure the coverage area, the quality of service and the adequate allocation of network resources in the monitored area, it is necessary to repair the holes formed in the network effectively and timely.
Therefore, the effective deployment of the sensor network can be realized through the movement strategy of the sensor nodes, and a full theoretical basis can be provided for accurate environment monitoring.
Disclosure of Invention
The invention aims to provide a dynamic deployment method of a sensor network, which can provide a theoretical basis for the deployment of the sensor network and can be widely applied to the fields of environment monitoring, intelligent home furnishing and the like.
In order to achieve the above purpose, the invention provides a sensor network dynamic deployment method, which specifically comprises four basic steps of establishing a monitoring area model, determining coordinates of a mobile node, determining coordinates of a target node and cascade movement of sensor nodes.
In one embodiment of the present invention, the establishing the monitoring area model further includes:
the monitoring area comprises a key monitoring area and a non-key monitoring areaA point monitoring area, discretizing the monitoring area into M grid points, wherein any grid point p j Is (x) j ，y j ) Uniformly deploying a group of sensor nodes with the same sensing radius r in a monitoring area, and setting s= { s 1 ，s 2 ，s 3 …s n And represents the set of sensor nodes, any one of which is s i Is (x) i ，y i ) The method comprises the steps of carrying out a first treatment on the surface of the Calculation s i To point p j The euclidean distance of (2) is defined as:
then a certain grid point p in the area is monitored j The cases covered by the sensor nodes are:
P(s i ,p j ) =1 indicates that the grid point can be covered by a sensor node.
Step two, in one embodiment of the present invention, the determining the coordinates of the mobile node further includes:
the mobile node coordinates are sensor node coordinates in the non-key monitoring area, and the range of the non-key monitoring area is known, so that the node coordinates of the corresponding sensor are only needed to be searched in the non-key monitoring area.
Step three, in one embodiment of the present invention, the determining the coordinates of the target node further includes:
the target node coordinates are grid point coordinates which are not covered in the key monitoring area, and the specific steps are as follows:
(1) Determining the number N of the mobile nodes according to the previous step;
(2) Finding out areas which are not covered by the sensor in the key monitoring areas, traversing all grid nodes of the areas which are not covered by the sensor, taking all nodes as circle centers, taking the width of the grid as an initial radius, and taking the width of the grid as an incremental radius to make concentric circles outwards, wherein the maximum radius of the concentric circles is the sensing radius of the sensor;
(3) Stopping the increase of the radius of the concentric circle when the maximum ring of the concentric circle is overlapped with the coverage area of the original sensor, and recording the position of the circle center and the radius at the moment;
(4) Let q= { Q 1 ,q 2 ,...q m Finding out a circle with the largest radius from the set, if the largest radius is the same, taking a circle with the smaller sum of the horizontal coordinates and the vertical coordinates of the circle center position, recording the node of the circle and the size of the radius, and merging the area where the circle is located into the sensor coverage area;
and (3) repeating the steps (2), (3) and (4) in sequence, ending the process when the number of circles to be determined is equal to N, and determining the circle center position to be the position of the target node in sequence.
Step four, in one embodiment of the present invention, the cascade movement of the sensor node further includes:
after determining the coordinates of the mobile node and the target node, a decision is made as to how to move the mobile node over the target node; introducing a Floyd algorithm into cascade movement of the sensors, taking sensor nodes of non-key monitoring areas and target nodes in key monitoring areas as starting points and end points, and taking the rest sensors as path algorithm nodes;
setting a weighted directed graph g= (V, E, C), wherein the set of sensor nodes V, the set of node connected lines E, and the distance adjacency matrix C; element V in sensor node V 0 ,v 1 ,…,v l Representing l sensor nodes; the node communication line set E is formed by connecting nodes in V, wherein the element E k ＝[v i v j ]Node v i And node v j Are connected with each other; c is the distance adjacency matrix corresponding to the graph G, and the element C thereof ij The following formula is shown:
simulating a sensor network by constructing a directed network diagram, and planning the determined shortest path between the mobile node and the target node by using a Fluedel algorithm; the basic idea of the Floyd algorithm is to construct v matrices D in turn by inserting entry points directly in weighted adjacency matrices of the graph (1) 、D (2) 、…、D (v) So that the resulting matrix D (v) The distance matrix of the graph is obtained, and the insertion point matrix is also obtained to obtain the shortest path between two points. Wherein D is (0) Elements of (2)
Representing two connected nodes v in a sensor network model i And v j The distance between the two is expressed by the following formula,
D (k) the elements in (a) are calculated by the following formula:
R (k) the elements in (a) are calculated by the following formula:
obtaining a shortest distance matrix D by using a distance adjacent matrix C of the graph G and through a Floride algorithm (n) And a shortest path matrix R (n) ，R (n) Only the shortest path between the mobile node and the target node is saved, and on the basis, the shortest cascade path is selected; finally, on the premise of ensuring the life cycle of the network, the effective deployment of the heavy-point monitoring area is realized, and the monitoring capability of the network is improved.
Fig. 2 shows a monitoring area model, in which sensor nodes are uniformly deployed in a monitoring area, a black box surrounding area represents an important monitoring area, and a light gray box surrounding area represents a non-important monitoring area. The network deployment diagram after cascade movement is shown in fig. 3, it can be seen that the sensor of the non-key monitoring area moves into the key monitoring area, so that the coverage rate of the key monitoring area is greatly increased. Fig. 4 is a diagram illustrating a comparison of total movement distances of sensor nodes using direct movement and cascade movement, wherein the direct movement is to directly move nodes in non-key monitoring areas to key monitoring areas without passing through intermediate nodes. It can be seen from the figure that when the sensor nodes reach a new equilibrium, the total movement distance of the cascade movement is slightly higher than the direct movement, which is related to the movement of the cascade movement involving the intermediate node. However, compared with the time required by the network to reach equilibrium, 23.963s is required for cascade movement and 35.012s is required for direct movement, so that the time required by cascade movement to reach an equilibrium point is far lower than that of direct movement, and the time for network adjustment can be effectively reduced by cascade movement. Fig. 5 is a comparison diagram of residual energy of cascade movement and direct movement nodes, and it can be seen from fig. 5 that although the direct movement can realize the important coverage of the area with fewer nodes, the residual energy of the moved nodes is lower, and the water quality monitoring task cannot be continuously and well executed. Through cascade movement, more nodes can participate in the movement process of the sensor nodes, so that energy consumption is commonly born, and when the network reaches new balance, the rest energy of the nodes is relatively balanced, so that the service life of the network can be effectively prolonged.
The sensor network dynamic deployment method provided by the invention can realize effective monitoring of the monitoring area and provide a full theoretical basis for effective monitoring and comprehensive treatment of the environment.
Drawings
FIG. 1 is a flow chart of a method for dynamically deploying a sensor network according to an embodiment of the present invention;
FIG. 2 is a monitoring area model according to an embodiment of the present invention;
FIG. 3 is a network deployment diagram after cascaded movement according to an embodiment of the present invention;
FIG. 4 is a graph showing the time-dependent movement distance according to an embodiment of the present invention;
fig. 5 is a graph of node residual energy versus an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the like or similar elements throughout. The following described embodiments are illustrative only and are not to be construed as limiting the invention.
The invention provides a dynamic deployment method of a sensor network aiming at a complex monitoring environment in an environment monitoring process.
In order that the invention may be more clearly understood, a brief description is provided herein. The invention comprises four basic steps: step one, a monitoring area model is established; step two, determining coordinates of the mobile node; step three, determining the coordinates of a target node; and step four, cascading movement of the sensor nodes.
Specifically, fig. 1 is a flowchart of a dynamic deployment method of a sensor network according to an embodiment of the present invention, including the following steps:
step S101, a monitoring area model is built.
In one embodiment of the present invention, the monitoring area includes an important monitoring area and a non-important monitoring area, and the monitoring area is discretized into M grid points, wherein any one grid point p j Is (x) j ，y j ) Uniformly deploying a group of sensor nodes with the same sensing radius r in a monitoring area, and setting s= { s 1 ，s 2 ，s 3 …s n And represents the set of sensor nodes, any one of which is s i Is (x) i ，y i ) The method comprises the steps of carrying out a first treatment on the surface of the Calculation s i To point p j The euclidean distance of (2) is defined as:
then a certain grid point p in the area is monitored j The cases covered by the sensor nodes are:
P(s i ,p j ) =1 indicates that the grid point can be covered by a sensor node;
step S102, determining mobile node coordinates.
In one embodiment of the present invention, the coordinates of the mobile node are the coordinates of the sensor nodes in the non-key monitoring area, and since the range of the non-key monitoring area is known, the coordinates of the corresponding sensor nodes only need to be found in the non-key monitoring area.
Step S103, determining the coordinates of the target node.
In one embodiment of the present invention, the target node coordinates are the uncovered grid point coordinates in the critical monitoring area, and the specific steps are as follows:
(1) Determining the number N of the mobile nodes according to the previous step;
(2) Finding out areas which are not covered by the sensor in the key monitoring areas, traversing all grid nodes of the areas which are not covered by the sensor, taking all nodes as circle centers, taking the width of the grid as an initial radius, and taking the width of the grid as an incremental radius to make concentric circles outwards, wherein the maximum radius of the concentric circles is the sensing radius of the sensor;
(3) Stopping the increase of the radius of the concentric circle when the maximum ring of the concentric circle is overlapped with the coverage area of the original sensor, and recording the position of the circle center and the radius at the moment;
(4) Let q= { Q 1 ,q 2 ,...q m The method comprises the steps of finding out a circle with the largest radius from a set of concentric circles of all grid nodes of an area which is not covered by a sensor, if the largest radius is the same, taking a circle with the smaller sum of the horizontal coordinates and the vertical coordinates of the circle center position, recording the node of the circle and the size of the radius, and merging the area where the circle is located into the sensorA coverage area;
and (3) repeating the steps (2), (3) and (4) in sequence, ending the process when the number of circles to be determined is equal to N, and determining the circle center position to be the position of the target node in sequence.
Step S104, cascade movement of the sensor nodes.
In one embodiment of the invention, after the mobile node and target node coordinates are determined, a decision is made as to how to move the mobile node over the target node; introducing a Fluedel algorithm into cascade movement of the sensors, taking sensor nodes of non-key monitoring areas and target nodes in key monitoring areas as starting points and end points, and taking the rest sensors as path algorithm nodes.
Setting a weighted directed graph g= (V, E, C), wherein the set of sensor nodes V, the set of node connected lines E, and the distance adjacency matrix C; element V in sensor node V 0 ,v 1 ,…,v l Representing l sensor nodes; the node communication line set E is formed by connecting nodes in V, wherein the element E k ＝[v i v j ]Node v i And node v j Are connected with each other; c is the distance adjacency matrix corresponding to the graph G, and the element C thereof ij As shown in formula (3):
simulating a sensor network by constructing a directed network diagram, and planning the determined shortest path between the mobile node and the target node by using a Fluedel algorithm; the basic idea of the Floyd algorithm is to construct v matrices D in turn by inserting entry points directly in weighted adjacency matrices of the graph (1) 、D (2) 、…、D (v) So that the resulting matrix D (v) The distance matrix of the graph is obtained, and the insertion point matrix is also obtained to obtain the shortest path between two points. Wherein D is (0) Elements of (2)
Representing two connected nodes v in a sensor network model i And v j The distance between them is expressed by the formula (4), D (k) 、R (k) Is calculated as formula (5), formula (6):
obtaining a shortest distance matrix D by using a distance adjacent matrix C of the graph G and through a Floride algorithm (n) And a shortest path matrix R (n) ，R (n) Only the shortest path between the mobile node and the target node is saved, and on the basis, the shortest cascade path is selected; finally, on the premise of ensuring the life cycle of the network, the effective deployment of the heavy-point monitoring area is realized, and the monitoring capability of the network is improved.
The sensor network dynamic deployment method provided by the invention can realize the optimal deployment of the sensor network, and provides a full theoretical basis for the effective monitoring and comprehensive treatment of the environment.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting. Those of ordinary skill in the art will appreciate that: it is still possible to modify the technical solutions described in the foregoing embodiments or to make equivalent substitutions for some technical features thereof, without departing from the spirit and scope of the technical solutions of the respective embodiments of the present invention, the scope of which is defined by the appended claims and equivalents thereof.
Claims (1)
1. A dynamic deployment method of a sensor network is characterized in that: the method comprises the following steps of establishing a monitoring area model, determining a mobile node coordinate, determining a target node coordinate and cascading movement of a sensor node;
the establishing the monitoring area model comprises the following steps: the monitoring area comprises a key monitoring area and a non-key monitoring area, discretization processing is carried out on the monitoring area, and the monitoring area is discretized into M grid points, wherein any grid point p j Is (x) j ，y j ) Uniformly deploying a group of sensor nodes with the same sensing radius r in a monitoring area, and setting s= { s 1 ，s 2 ，s 3 …s n And represents the set of sensor nodes, any one of which is s i Is (x) i ，y i ) The method comprises the steps of carrying out a first treatment on the surface of the Calculation s i To point p j The euclidean distance of (2) is defined as:
then a certain grid point p in the area is monitored j The cases covered by the sensor nodes are:
P(s i ,p j ) =1 indicates that the grid point can be covered by a sensor node;
the determining mobile node coordinates includes: the mobile node coordinates are sensor node coordinates in the non-key monitoring area, and as the range of the non-key monitoring area is known, the node coordinates of the corresponding sensor are only needed to be searched in the non-key monitoring area;
the determining the target node coordinates includes: the target node coordinates are grid point coordinates which are not covered in the key monitoring area; the method specifically comprises the following steps:
(1) Determining the number N of mobile nodes;
(2) Finding out areas which are not covered by the sensor in the key monitoring areas, traversing all grid nodes of the areas which are not covered by the sensor, taking all nodes as circle centers, taking the width of the grid as an initial radius, and taking the width of the grid as an incremental radius to make concentric circles outwards, wherein the maximum radius of the concentric circles is the sensing radius of the sensor;
(3) Stopping the increase of the radius of the concentric circle when the maximum ring of the concentric circle is overlapped with the coverage area of the original sensor, and recording the position of the circle center and the radius at the moment;
(4) Let q= { Q 1 ,q 2 ,...q m The method comprises finding out the circle with the largest radius from the set of concentric circles of all grid nodes not covered by the sensor, if the largest radius is the same, taking the circle with smaller sum of the horizontal and vertical coordinates of the circle center position,
recording the node and radius of the circle at the moment, and merging the area where the circle is located into a sensor coverage area;
(5) Sequentially repeating the steps (2), (3) and (4), ending the process when the number of circles to be determined is equal to N, and sequentially determining the positions of the centers of circles to be determined to be the positions of the target nodes;
the cascading movement of the sensor nodes includes: after determining the coordinates of the mobile node and the target node, a decision is made as to how to move the mobile node over the target node; introducing a Floyd algorithm into cascade movement of the sensors, taking sensor nodes of non-key monitoring areas and target nodes in key monitoring areas as starting points and end points, and taking the rest sensors as path algorithm nodes;
setting a weighted directed graph g= (V, E, C), wherein the set of sensor nodes V, the set of node communication lines E, and the distance adjacency matrix C; element V in sensor node V 0 ,v 1 ,…,v l Representing l sensor nodes; the node communication line set E is formed by connecting nodes in V, wherein the element E k ＝[v i v j ]Node v i And node v j Are connected with each other; c is the distance adjacency matrix corresponding to the graph G, and the element C thereof ij As shown in formula (3):
simulating a sensor network by constructing a directed network diagram, and planning the determined shortest path between the mobile node and the target node by using a Fluedel algorithm; the basic idea of the Floyd algorithm is to construct v matrices D in turn by inserting entry points directly in weighted adjacency matrices of the graph (1) 、D (2) 、…、D (v) So that the resulting matrix D (v) Forming a distance matrix of the graph, and simultaneously, solving an insertion point matrix to obtain the shortest path between two points; wherein D is (0) Elements of (2)
Representing two connected nodes v in a sensor network model i And v j The distance between them is expressed by the formula (4), D (k) 、R (k) The calculation of the element in the formula (5), the formula (6):
obtaining a shortest distance matrix D by using a distance adjacent matrix C of the graph G and through a Floride algorithm (n) And a shortest path matrix R (n) ，R (n) Only the shortest path between the mobile node and the target node is saved, and on the basis, the shortest cascade path is selected; finally, on the premise of ensuring the life cycle of the network, the effective deployment of the heavy point monitoring area is realized, and the network is improvedMonitoring the capacity.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010458814.5A CN111669767B (en) | 2020-05-27 | 2020-05-27 | Sensor network dynamic deployment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010458814.5A CN111669767B (en) | 2020-05-27 | 2020-05-27 | Sensor network dynamic deployment method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111669767A CN111669767A (en) | 2020-09-15 |
| CN111669767B true CN111669767B (en) | 2023-05-16 |
Family
ID=72384822
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010458814.5A Active CN111669767B (en) | 2020-05-27 | 2020-05-27 | Sensor network dynamic deployment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111669767B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114489147B (en) * | 2021-12-28 | 2023-10-17 | 中国人民解放军国防科技大学 | Unmanned aerial vehicle cluster self-organizing region coverage method, device and equipment |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102280826A (en) * | 2011-07-30 | 2011-12-14 | 山东鲁能智能技术有限公司 | Intelligent robot inspection system and intelligent robot inspection method for transformer station |
| WO2016082254A1 (en) * | 2014-11-27 | 2016-06-02 | 中国科学院沈阳自动化研究所 | Robust coverage method for relay nodes in double-layer structure wireless sensor network |
| CN107613502A (en) * | 2017-09-07 | 2018-01-19 | 广东工业大学 | A kind of sensor network irregular codes node positioning method and its device |
| CN108064047A (en) * | 2018-01-17 | 2018-05-22 | 北京工商大学 | A kind of water quality sensor network optimization dispositions method based on population |
| CN109874160A (en) * | 2019-03-06 | 2019-06-11 | 安徽建筑大学 | Routing method based on wireless sensor network node reputation evaluation |
| CN109922478A (en) * | 2019-01-14 | 2019-06-21 | 北京工商大学 | A kind of water quality sensor network optimization dispositions method based on improvement cuckoo algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR3064857B1 (en) * | 2017-04-04 | 2020-07-03 | Commissariat A L'energie Atomique Et Aux Energies Alternatives | SECURE END-TO-END COMMUNICATION FOR MOBILE SENSOR IN AN IOT NETWORK |
-
2020
- 2020-05-27 CN CN202010458814.5A patent/CN111669767B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102280826A (en) * | 2011-07-30 | 2011-12-14 | 山东鲁能智能技术有限公司 | Intelligent robot inspection system and intelligent robot inspection method for transformer station |
| WO2016082254A1 (en) * | 2014-11-27 | 2016-06-02 | 中国科学院沈阳自动化研究所 | Robust coverage method for relay nodes in double-layer structure wireless sensor network |
| CN107613502A (en) * | 2017-09-07 | 2018-01-19 | 广东工业大学 | A kind of sensor network irregular codes node positioning method and its device |
| CN108064047A (en) * | 2018-01-17 | 2018-05-22 | 北京工商大学 | A kind of water quality sensor network optimization dispositions method based on population |
| CN109922478A (en) * | 2019-01-14 | 2019-06-21 | 北京工商大学 | A kind of water quality sensor network optimization dispositions method based on improvement cuckoo algorithm |
| CN109874160A (en) * | 2019-03-06 | 2019-06-11 | 安徽建筑大学 | Routing method based on wireless sensor network node reputation evaluation |
Non-Patent Citations (3)
| Title |
|---|
| Nessrine Chakchouk.A Survey on Opportunistic Routing in Wireless Communication Networks.《IEEE》.2015,全文. * |
| 宋志强 ; 方武 ; 卢爱红 ; .基于虚拟力的混合传感器节点覆盖问题研究.仪表技术与传感器.2017,(09),全文. * |
| 郭新明 ; 张瑾 ; 陈伟 ; 李康 ; .基于Voronoi图的无线传感器网络栅栏覆盖算法设计.计算技术与自动化.2020,(01),全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111669767A (en) | 2020-09-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Demirkol et al. | Wireless sensor networks for intrusion detection: packet traffic modeling | |
| CN107113764B (en) | Method and device for improving positioning performance of artificial neural network | |
| CN109104464B (en) | Distributed data updating method for collaborative storage in edge computing environment | |
| CN105530651B (en) | Optimal base station layout method in wireless positioning system | |
| Jain | A coherent approach for dynamic cluster-based routing and coverage hole detection and recovery in bi-layered WSN-IoT | |
| CN111669767B (en) | Sensor network dynamic deployment method | |
| Zhen et al. | An improved method for indoor positioning based on ZigBee technique | |
| Mahboubi et al. | Mobile sensors deployment subject to location estimation error | |
| Miozzo et al. | Distributed and multi-task learning at the edge for energy efficient radio access networks | |
| Li et al. | Coverage blind area repair based on perceived multimedia data driven in mobile wireless sensor networks | |
| Simeon | APPLICATION OF KMEANS CLUSTERING ALGORITHM FOR SELECTION OF RELAY NODES IN WIRELESS SENSOR NETWORK | |
| Xia et al. | Digital twin and artificial intelligence for intelligent planning and energy-efficient deployment of 6G networks in smart factories | |
| CN113691993B (en) | 5G connected cluster base station group flow prediction method and system based on graph neural network | |
| Chen et al. | A High Stability Clustering Scheme for the Internet of Vehicles | |
| CN117615436A (en) | Network energy saving method, system, equipment and medium | |
| CN105392176B (en) | A kind of calculation method of actuator node executive capability | |
| Chen et al. | SOFM neural network based hierarchical topology control for wireless sensor networks | |
| CN112488343B (en) | Smart city intelligent perception terminal address selection method based on hyper-heuristic algorithm | |
| Lin et al. | Dynamic energy-efficient surveillance routing in uncertain group-based industrial wireless sensor networks | |
| KR102165862B1 (en) | Methods and apparatuses for aggregating node data in wireless sensor network | |
| Fan et al. | The probabilistic sense model for coverage hole elimination in WSN | |
| Kenyeres et al. | Multi-Sensor data fusion by average consensus algorithm with fully-distributed stopping criterion: comparative study of weight designs | |
| Lung et al. | Applying hierarchical agglomerative clustering to wireless sensor networks | |
| Xing et al. | Node localization based on multiple radio transmission power levels for wireless sensor networks | |
| Dastgheib et al. | A new method for flat routing in wireless sensor networks using fuzzy logic |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |