CN109922511A - Cluster-head node selection method, node clustering method and cluster-head node selection device - Google Patents
Cluster-head node selection method, node clustering method and cluster-head node selection device Download PDFInfo
- Publication number
- CN109922511A CN109922511A CN201910356556.7A CN201910356556A CN109922511A CN 109922511 A CN109922511 A CN 109922511A CN 201910356556 A CN201910356556 A CN 201910356556A CN 109922511 A CN109922511 A CN 109922511A
- Authority
- CN
- China
- Prior art keywords
- node
- cluster head
- energy
- cluster
- residual energy
- 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.)
- Pending
Links
- 241000854291 Dianthus carthusianorum Species 0.000 title claims abstract description 154
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000010187 selection method Methods 0.000 title claims abstract description 18
- 230000008901 benefit Effects 0.000 claims description 20
- 238000005265 energy consumption Methods 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
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
- Mobile Radio Communication Systems (AREA)
Abstract
The present invention provides a kind of cluster-head node selection method, node clustering method and cluster-head node selection device, belongs to wireless sensor network technology field.Cluster-head node selection method of the invention, comprising: receive the dump energy that each node is sent；Judge whether each node meets preset cluster head race condition according to the dump energy；If the node meets the cluster head race condition, the related data of the node is obtained；The related data includes: centrad, the history duration for serving as cluster head；Optimal node is selected as leader cluster node according to the related data and the dump energy.
Description
Technical Field
The invention belongs to the technical field of wireless sensor networks, and particularly relates to a cluster head node selection method, a node clustering method and a cluster head node selection device.
Background
Sensor nodes in a wireless sensor network are generally deployed under severe environmental conditions, and the nodes transmit data to a base station in a wireless communication mode.
In order to efficiently utilize the energy resources in the network and minimize the transmission of invalid (redundant) data within the network, researchers have proposed clustering methods. As can be seen from the working mechanism of the cluster, the energy consumption rate of the cluster head node is far higher than that of other nodes in the cluster due to the fact that the cluster head node bears more preprocessing work such as data fusion and forwarding work.
The currently common node clustering method mainly comprises the following steps: a hierarchical clustering method and a non-uniform clustering method.
The basic idea of the hierarchical clustering method is that a node searches for a neighbor node closest to the node according to signal strength, and the signal strength is continuously changed, so that signals sent by the node can only be monitored by the neighbor node closest to the node. The formed communication link is utilized to enable the message to be finally delivered to the destination node. Although the method prolongs the network lifetime, the network needs to adjust the topology all the time, which results in a large consumption of energy.
The basic idea of the non-uniform clustering method is to use the distance between the cluster head and the base station to realize clustering. The clusters closer to the base station are of smaller size and the clusters further from the base station are of larger size, thus dividing the network into non-uniform clusters. However, in the method, due to uneven distribution of the nodes, the cluster heads close to the base station can seriously consume energy, so that the nodes die in advance, and the survival time of the network is reduced.
In summary, in the current node clustering method, either the cluster head near the base station may consume energy seriously to cause node death in advance, or the network needs to adjust the topology structure all the time to cause energy consumption to be large.
Disclosure of Invention
The invention aims to at least solve one of the technical problems in the prior art and provides a cluster head node selection method which can realize energy consumption of nodes in a homoenergetic network and prolong the network lifetime.
The technical scheme adopted for solving the technical problem of the invention is a cluster head node selection method, which comprises the following steps:
receiving residual energy sent by each node;
judging whether each node meets a preset cluster head competition condition or not according to the residual energy;
if the node meets the cluster head competition condition, acquiring related data of the node; the relevant data includes: centrality, historical duration of acting as a cluster head;
and selecting the best node as a cluster head node according to the related data and the residual energy.
Preferably, the step of determining whether each node satisfies a preset cluster head competition condition according to the remaining energy includes:
calculating an energy benefit value P of each node according to a first formula and the residual energy of each node;
and judging whether the energy benefit value of each node is larger than a preset value, if so, the node meets a cluster head competition condition.
Further preferably, the first formula includes:
wherein, P (V)i) Represents a node ViEnergy efficiency value of E (V)i) Represents a node ViResidual energy of, EaveRepresenting the average of the remaining energy of all nodes in the network.
Further preferably, the preset value is 1.
Further preferably, the step of selecting an optimal node as a cluster head node according to the relevant data and the remaining energy of each node includes:
calculating the weight W of each node according to a second formula;
selecting the node with the minimum weight as a cluster head node;
wherein the second formula comprises:
wherein, W (V)i) Represents a node ViCentral degree of (d), dg (V)i) Represents a node ViCentrality of, T (V)i) Represents a node ViA historical length of time to assume a cluster head; t isthrRepresenting the total time of operation of the network, P (V)i) Represents a node Viα + β + γ being 1.
The technical scheme adopted for solving the technical problem of the invention is a node clustering method, which comprises any one of the cluster head node selection methods.
Preferably, the node clustering method includes:
and (3) node self-checking: each node automatically judges whether the node has the cluster head function, and if so, the node sends the residual energy of the node to a cluster head node selection device;
a cluster head node selection stage: the cluster head node selection apparatus selects a cluster head node according to the cluster head node selection method of any one of claims 1 to 5.
The technical scheme adopted for solving the technical problem of the invention is a cluster head node selection device, which comprises:
the receiving unit is used for receiving the residual energy sent by each node;
the judging unit is used for judging whether each node meets a preset cluster head competition condition or not according to the residual energy;
a data obtaining unit, configured to obtain relevant data of the node when the node satisfies a cluster head competition condition; the relevant data includes: centrality, historical duration of acting as a cluster head;
a selecting unit, configured to select an optimal node as a cluster head node according to the relevant data and the remaining energy of each node;
preferably, the judging unit includes:
the first calculation module is used for calculating an energy benefit value P of each node according to a first formula and the residual energy of each node;
and the judging module is used for judging whether the energy benefit value of each node is greater than a preset value or not, and if so, the nodes meet cluster head competition conditions.
Preferably, the selection unit includes:
the second calculation module is used for calculating the weight W of each node according to a second formula;
a cluster head determining module, configured to select the node with the smallest weight as a cluster head node;
wherein the second formula comprises:
wherein, W (V)i) Represents a node ViCentral degree of (d), dg (V)i) Represents a node ViCentrality of, T (V)i) Represents a node ViA historical length of time to assume a cluster head; t isthrRepresenting the total time of operation of the network, P (V)i) Represents a node Viα + β + γ being 1.
Drawings
Fig. 1 is a flowchart of a cluster head node selection method according to embodiment 1 of the present invention;
fig. 2 is a flowchart of a node clustering method according to embodiment 2 of the present invention;
fig. 3 is a block diagram of a cluster head node selection apparatus according to embodiment 3 of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Example 1:
as shown in fig. 1, the present embodiment provides a method for selecting a cluster head node, which may be used for selecting a cluster head node in a wireless sensor network.
In the prior art, nodes in a wireless sensor network include reduced function nodes and full function nodes. The reduced function node has only a partial function of the full function node (for example, only has a function of receiving data, but does not have a function of forwarding data), and cannot operate as a cluster head node, that is, does not have a cluster head function.
The cluster head node selection method provided in this embodiment selects a node as a cluster head node from full-function nodes. That is, the nodes in the present embodiment all refer to nodes having a cluster head function.
The cluster head node selection method provided by this embodiment may include the following steps:
and S11, receiving the residual energy transmitted by each node.
The residual energy sent by the node refers to the residual energy value of the node at the current moment.
It can be understood that the remaining energy of a node has a direct relationship with the vitality of the node, and the more the remaining energy of the node, the longer it can survive and the more work can be borne.
And S12, judging whether each node meets the preset cluster head competition condition according to the residual energy.
In this step, some nodes with relatively more residual energy are selected according to a preset cluster head competition condition, so that cluster head nodes are selected from the nodes meeting the cluster head competition condition in the subsequent step, and the processing workload in the subsequent step is reduced.
Preferably, the method specifically comprises the following steps:
and S121, calculating an energy benefit value P of each node according to a first formula and the residual energy of each node.
Preferably, the first formula includes:
wherein, P (V)i) Represents a node ViEnergy efficiency value of E (V)i) Represents a node ViResidual energy of, EaveRepresenting the average of the remaining energy of all fully functional nodes in the network.
And S122, judging whether the energy benefit value P of each node is greater than a preset value, if so, enabling the node to meet the cluster head competition condition.
As can be seen from the first formula in step S121, the energy benefit value of a node in this embodiment is a ratio of the remaining energy of the node to an average value of the remaining energy values of all nodes. The larger the energy benefit value of a node is, the more the remaining energy of the node is relative to other nodes. Specifically, the energy benefit value is 1 for example, and when the energy benefit value of a node is less than 1, it indicates that the residual energy of the node is smaller than that of most nodes; when the energy benefit value of the node is larger than 1, the residual energy of the node is larger than that of most nodes.
It can be understood that if a node wants to become a cluster head node, the remaining energy of the node must be greater than that of other nodes. Therefore, in this step, by setting a preset value and taking the residual energy of the nodes as a parameter, the nodes with the residual energy greater than the preset value are selected from the nodes sending the residual energy as the nodes meeting the cluster head competition condition, and then the cluster head nodes can be directly selected from the nodes meeting the cluster head competition condition.
And S13, if the node meets the cluster head competition condition, acquiring the related data of the node. The relevant data includes: centrality, historical length of time to act as a cluster head.
In this step, the relevant data of each node may be obtained from the information list of each node stored in advance. It can be understood that, in this step, only the relevant data of the full-function node satisfying the cluster head competition condition needs to be acquired.
And S14, selecting the best node as a cluster head node according to the related data and the residual energy.
In the step, the weight of each node is calculated based on the relevant data and the residual energy of the node, and the cluster head node is determined according to the weight.
The specific step S14 includes the following steps:
and S141, calculating the weight W of each node according to a second formula.
Preferably, the second formula includes:
wherein, W (V)i) Represents a node ViCentral degree of (d), dg (V)i) Represents a node ViCentrality of, T (V)i) Represents a node ViA historical length of time to assume a cluster head; t isthrRepresenting the total time of operation of the network, P (V)i) Represents a node Viα + β + γ being 1.
In this embodiment, in the subsequent step S142, a cluster head node is selected based on the weight of the node. It can be understood that, when selecting a cluster head node, a node with a higher centrality, more residual energy (i.e. a higher energy benefit value) and a shorter history duration serving as a cluster head should be selected (generally, a node with a shorter history duration serving as a cluster head has more storage space and stronger computing power). According to the second formula, the weight of the node is in negative correlation with the centrality and the energy benefit value of the node, and is in positive correlation with the historical time of the node serving as the cluster head, so that the node with the smaller weight can be directly selected when the cluster head node is selected.
It should be understood that α, γ is a coefficient, and specific values of the coefficient, γ, and γ can be set or adjusted according to actual situations, which is not limited in this embodiment.
And S142, selecting the node with the minimum weight value as the cluster head node.
As can be known from the second formula in step S141, the node with the smallest weight is a node calculated by combining the centrality of the node, the history duration serving as the cluster head, and the energy benefit value, and the node is the optimal selection of the cluster head node.
In the cluster head node selection method provided in this embodiment, the nodes are selected by combining the remaining energy and the centrality of each node and the historical duration of serving as a cluster head, so as to balance network energy consumption to the maximum extent and prolong network lifetime while ensuring the connectivity of the wireless sensor network.
Example 2:
as shown in fig. 2, the present embodiment provides a node clustering method, which includes the cluster head node selection method provided in embodiment 1, and can be used for node clustering in a wireless sensor. The node clustering method comprises the following steps: a node self-checking stage and a cluster head node selection stage.
It can be understood that before node clustering is performed, an initialization stage is included: and sending a cluster head competition message to each node in the wireless sensor network, and starting the cluster head competition.
Specifically, the node clustering method of this embodiment includes the following steps:
and (3) node self-checking:
and S01, each node judges whether the node has the cluster head function.
The nodes in the wireless sensor network comprise reduced function nodes and full function nodes. The reduced function node has only a partial function of the full function node (for example, only has a function of receiving data, but does not have a function of forwarding data), and cannot operate as a cluster head node, that is, does not have a cluster head function.
In the process of node clustering, only the full-function node can be used as a cluster head node, so in this embodiment, after receiving the cluster head competition message, the node performs self-detection to determine whether the node has a cluster head function (i.e., whether the node is a full-function node), and if the node has the cluster head function, the node applies for a competition cluster head.
And S02, if yes, the node sends the self residual energy to the cluster head node selection device.
In this step, after determining that the node has the cluster head function, the node sends its remaining energy to the cluster head node selection device to indicate the willingness of the node to compete for the cluster head and the current condition of the node.
A cluster head node selection stage:
the cluster head node selecting means selects a cluster head node. Specifically, the cluster head node selection apparatus may select a cluster head node from among nodes having a cluster head function according to the cluster head node selection method provided in embodiment 1. Specifically, the cluster head node may include the following steps:
and S11, the cluster head node selection device receives the residual energy transmitted by each node.
It can be understood that the node in this step is a node after self-detection by the node self-detection stage, that is, a full-function node. The residual energy sent by the node refers to the residual energy value of the node at the current moment.
And S12, judging whether each node meets the preset cluster head competition condition according to the residual energy.
And S13, if the node meets the cluster head competition condition, acquiring the related data of the node. The relevant data includes: centrality, historical length of time to act as a cluster head.
And S14, selecting the best node as a cluster head node according to the related data and the residual energy.
In the step, the weight of each node is calculated based on the relevant data and the residual energy of the node, and the optimal cluster head node is determined according to the weight.
The specific steps in the cluster head node selection stage may refer to embodiment 1, which is not described in detail in this embodiment.
Preferably, in the node clustering method provided in this embodiment, after the cluster head is selected, the method further includes the following steps:
and S21, sending the cluster head node information to other nodes.
And S22, the other nodes send joining requests to the cluster head nodes.
And S23, the cluster head node brings the nodes into the cluster structure according to the request sent by each node.
The specific scheme that the other nodes join the cluster head node to form the cluster structure and then perform communication refers to the prior art, which is not described in detail in this embodiment.
It should be noted that the node clustering method provided in this embodiment is a single clustering step, and in an actual operation process of the wireless sensor network, re-clustering may be performed according to the node clustering method provided in this embodiment according to a certain period, and a cluster head may be reselected, so as to ensure balanced consumption of network energy and prolong network lifetime on the premise of ensuring network connectivity.
Example 3:
as shown in fig. 3, the present embodiment provides a cluster head node selection apparatus, including: the device comprises a receiving unit, a judging unit, a data acquiring unit and a selecting unit. Wherein,
the receiving unit is used for receiving the residual energy sent by each node.
The judging unit is used for judging whether each node meets the preset cluster head competition conditions or not according to the residual energy.
The data acquisition unit is used for acquiring the related data of the nodes when the nodes meet the cluster head competition conditions; the relevant data includes: centrality, historical length of time to act as a cluster head.
And the selection unit is used for selecting the optimal node as the cluster head node according to the relevant data and the residual energy of each node.
Preferably, the judging unit includes: the device comprises a first calculating module and a judging module. Wherein,
the first calculation module is used for calculating the energy benefit value P of each node according to a first formula and the residual energy of each node.
And the judging module is used for judging whether the energy benefit value of each node is greater than a preset value or not, and if so, the nodes meet the cluster head competition condition.
Preferably, the selection unit includes: a second calculation module and a cluster head determination module. Wherein,
the second calculation module is used for calculating the weight W of each node according to a second formula.
And the cluster head determining module is used for selecting the node with the minimum weight as the cluster head node.
Wherein the second formula comprises:
wherein, W (V)i) Represents a node ViCentral degree of (d), dg (V)i) Represents a node ViCentrality of, T (V)i) Represents a node ViA historical length of time to assume a cluster head; t isthrRepresenting the total time of operation of the network, P (V)i) Represents a node Viα + β + γ being 1.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (10)
1. A cluster head node selection method is characterized by comprising the following steps:
receiving residual energy sent by each node;
judging whether each node meets a preset cluster head competition condition or not according to the residual energy;
if the node meets the cluster head competition condition, acquiring related data of the node; the relevant data includes: centrality, historical duration of acting as a cluster head;
and selecting the best node as a cluster head node according to the related data and the residual energy.
2. The method for selecting cluster head nodes according to claim 1, wherein the step of determining whether each node satisfies a preset cluster head competition condition according to the remaining energy comprises:
calculating an energy benefit value P of each node according to a first formula and the residual energy of each node;
and judging whether the energy benefit value of each node is larger than a preset value, if so, the node meets a cluster head competition condition.
3. The node clustering method according to claim 2, wherein the first formula comprises:
wherein, P (V)i) Represents a node ViEnergy efficiency value of E (V)i) Represents a node ViResidual energy of, EaveRepresenting the average of the remaining energy of all nodes in the network.
4. The node clustering method according to claim 2, wherein the preset value is 1.
5. The node clustering method according to claim 2, wherein the step of selecting an optimal node as a cluster head node according to the relevant data and the remaining energy of each node comprises:
calculating the weight W of each node according to a second formula;
selecting the node with the minimum weight as a cluster head node;
wherein the second formula comprises:
wherein, W (V)i) Represents a node ViCentral degree of (d), dg (V)i) Represents a node ViCentrality of, T (V)i) Represents a node ViA historical length of time to assume a cluster head; t isthrRepresenting the total time of operation of the network, P (V)i) Represents a node Viα + β + γ being 1.
6. A node clustering method comprising the cluster head node selection method according to any one of claims 1 to 5.
7. The node clustering method according to claim 6, comprising:
and (3) node self-checking: each node automatically judges whether the node has the cluster head function, and if so, the node sends the residual energy of the node to a cluster head node selection device;
a cluster head node selection stage: the cluster head node selection apparatus selects a cluster head node according to the cluster head node selection method of any one of claims 1 to 5.
8. A cluster head node selection apparatus, comprising:
the receiving unit is used for receiving the residual energy sent by each node;
the judging unit is used for judging whether each node meets a preset cluster head competition condition or not according to the residual energy;
a data obtaining unit, configured to obtain relevant data of the node when the node satisfies a cluster head competition condition; the relevant data includes: centrality, historical duration of acting as a cluster head;
and the selecting unit is used for selecting an optimal node as a cluster head node according to the relevant data and the residual energy of each node.
9. The node clustering device according to claim 8, wherein the judging unit comprises:
the first calculation module is used for calculating an energy benefit value P of each node according to a first formula and the residual energy of each node;
and the judging module is used for judging whether the energy benefit value of each node is greater than a preset value or not, and if so, the nodes meet cluster head competition conditions.
10. The node clustering apparatus according to claim 8, wherein the selecting unit comprises:
the second calculation module is used for calculating the weight W of each node according to a second formula;
a cluster head determining module, configured to select the node with the smallest weight as a cluster head node;
wherein the second formula comprises:
wherein, W (V)i) Represents a node ViCentral degree of (d), dg (V)i) Represents a node ViCentrality of, T (V)i) Represents a node ViA historical length of time to assume a cluster head; t isthrRepresenting the total time of operation of the network, P (V)i) Represents a node Viα + β + γ being 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910356556.7A CN109922511A (en) | 2019-04-29 | 2019-04-29 | Cluster-head node selection method, node clustering method and cluster-head node selection device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910356556.7A CN109922511A (en) | 2019-04-29 | 2019-04-29 | Cluster-head node selection method, node clustering method and cluster-head node selection device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109922511A true CN109922511A (en) | 2019-06-21 |
Family
ID=66978827
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910356556.7A Pending CN109922511A (en) | 2019-04-29 | 2019-04-29 | Cluster-head node selection method, node clustering method and cluster-head node selection device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109922511A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111629414A (en) * | 2020-05-26 | 2020-09-04 | 中国联合网络通信集团有限公司 | Routing method, device, terminal equipment and computer readable storage medium |
| CN112218259A (en) * | 2020-10-10 | 2021-01-12 | 北京瑞拓电子技术发展有限公司 | Novel rail transit integrated monitoring system |
| CN114223183A (en) * | 2019-08-20 | 2022-03-22 | 三菱电机株式会社 | A method for providing network collaboration for industrial communication systems |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101267404A (en) * | 2008-05-13 | 2008-09-17 | 北京科技大学 | An Assistant-Based Clustering Method in Ad Hoc Networks |
| CN102036308A (en) * | 2010-12-09 | 2011-04-27 | 江南大学 | Energy balancing wireless sensor network clustering method |
| CN101835277B (en) * | 2010-02-09 | 2013-03-20 | 重庆理工大学 | Wireless sensor network topology control method based on LEACH-ANT algorithm |
| CN103533595A (en) * | 2013-10-28 | 2014-01-22 | 天津工业大学 | Multi-hop clustering routing algorithm (GEEMHCR) for wireless sensor networks |
| CN103973789A (en) * | 2014-05-07 | 2014-08-06 | 重庆邮电大学 | VANET clustering method combining historical credit of vehicle with current state of vehicle |
| CN104469879A (en) * | 2014-12-18 | 2015-03-25 | 武汉大学 | A dynamic k-value clustering routing method |
| CN107071811A (en) * | 2017-04-18 | 2017-08-18 | 长春师范大学 | A kind of fault-tolerant Uneven Cluster algorithms of WSN based on fuzzy control |
| CN109510763A (en) * | 2019-01-03 | 2019-03-22 | 中国联合网络通信集团有限公司 | A kind of node cluster head electoral machinery and system |
| US20190098578A1 (en) * | 2017-09-26 | 2019-03-28 | King Fahd University Of Petroleum And Minerals | Node placement for pipeline monitoring |
| CN109688557A (en) * | 2019-02-21 | 2019-04-26 | 中国联合网络通信集团有限公司 | A kind of cooperative node selection method, device |
-
2019
- 2019-04-29 CN CN201910356556.7A patent/CN109922511A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101267404A (en) * | 2008-05-13 | 2008-09-17 | 北京科技大学 | An Assistant-Based Clustering Method in Ad Hoc Networks |
| CN101835277B (en) * | 2010-02-09 | 2013-03-20 | 重庆理工大学 | Wireless sensor network topology control method based on LEACH-ANT algorithm |
| CN102036308A (en) * | 2010-12-09 | 2011-04-27 | 江南大学 | Energy balancing wireless sensor network clustering method |
| CN103533595A (en) * | 2013-10-28 | 2014-01-22 | 天津工业大学 | Multi-hop clustering routing algorithm (GEEMHCR) for wireless sensor networks |
| CN103973789A (en) * | 2014-05-07 | 2014-08-06 | 重庆邮电大学 | VANET clustering method combining historical credit of vehicle with current state of vehicle |
| CN104469879A (en) * | 2014-12-18 | 2015-03-25 | 武汉大学 | A dynamic k-value clustering routing method |
| CN107071811A (en) * | 2017-04-18 | 2017-08-18 | 长春师范大学 | A kind of fault-tolerant Uneven Cluster algorithms of WSN based on fuzzy control |
| US20190098578A1 (en) * | 2017-09-26 | 2019-03-28 | King Fahd University Of Petroleum And Minerals | Node placement for pipeline monitoring |
| CN109510763A (en) * | 2019-01-03 | 2019-03-22 | 中国联合网络通信集团有限公司 | A kind of node cluster head electoral machinery and system |
| CN109688557A (en) * | 2019-02-21 | 2019-04-26 | 中国联合网络通信集团有限公司 | A kind of cooperative node selection method, device |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114223183A (en) * | 2019-08-20 | 2022-03-22 | 三菱电机株式会社 | A method for providing network collaboration for industrial communication systems |
| CN111629414A (en) * | 2020-05-26 | 2020-09-04 | 中国联合网络通信集团有限公司 | Routing method, device, terminal equipment and computer readable storage medium |
| CN112218259A (en) * | 2020-10-10 | 2021-01-12 | 北京瑞拓电子技术发展有限公司 | Novel rail transit integrated monitoring system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP5909159B2 (en) | Control apparatus and control method | |
| Prasanth et al. | Implementation of efficient intra-and inter-zone routing for extending network consistency in wireless sensor networks | |
| US8055740B2 (en) | Wireless sensor network system and cluster management method thereof | |
| CN109922511A (en) | Cluster-head node selection method, node clustering method and cluster-head node selection device | |
| CN105323818B (en) | Energy saving cluster routing method based on network area division and distance | |
| CN109547966A (en) | The route planning and fault diagnosis backup method of transmission line of electricity wireless sensor network | |
| CN111698728B (en) | Topology control system for dynamic network and control method thereof | |
| CN108990129A (en) | A kind of wireless sensor network cluster-dividing method and system | |
| CN106454905B (en) | An Improved Hierarchical Multilink Method for Wireless Sensor Networks | |
| CN109309620B (en) | Lightweight heterogeneous network clustering method facing edge calculation | |
| CN108696903A (en) | A kind of LEACH algorithm optimization methods based on energy consumption balance | |
| CN108770036A (en) | Communication means and wireless sensor network Routing Protocol between cluster head | |
| CN108900996A (en) | A kind of wireless sensor network data transmission method based on the double-deck fuzzy algorithmic approach | |
| CN110121200B (en) | An Energy Efficient Networking Method in Heterogeneous Sensor Networks | |
| KR20190134907A (en) | Marine data collection apparatus, maritime iot device and method for transferring marine data | |
| CN112073939A (en) | Communication method and system based on ocean floating platform | |
| CN110830945A (en) | Intelligent substation monitoring system | |
| CN108449733A (en) | It is a kind of that system is monitored based on the medical room environment of big data and WSN technology | |
| CN114222344A (en) | A wireless sensor network data transmission method and device | |
| CN116456418A (en) | Cluster head node routing method based on wireless sensor network | |
| CN113595903B (en) | A Sleep Scheduling Method for Wireless Sensor Network Nodes Based on FCM Clustering Topology | |
| CN106507425B (en) | Improved distributed energy efficient clustering method suitable for heterogeneous wireless sensor network | |
| CN201426183Y (en) | A Cluster-Based Routing System for Wireless Sensor Networks | |
| CN110691404A (en) | Multi-level link data uploading and issuing method | |
| CN116567107A (en) | Smart city-oriented wireless sensor network energy-saving data transmission method |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190621 |
|
| RJ01 | Rejection of invention patent application after publication |