CN108900996A - A kind of wireless sensor network data transmission method based on the double-deck fuzzy algorithmic approach - Google Patents
A kind of wireless sensor network data transmission method based on the double-deck fuzzy algorithmic approach Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/12—Shortest path evaluation
- H04L45/124—Shortest path evaluation using a combination of metrics
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/46—Cluster building
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- 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
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- 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
- H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
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- 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
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Abstract
The invention discloses the wireless sensor network data transmission methods based on the double-deck fuzzy algorithmic approach, include the following steps：Netinit determines node location；Determine energy consumption model；Determine cluster head number；Sub-clustering is carried out by innovatory algorithm；With fuzzy knowledge processing, determine cluster head；Cluster interior nodes send information to cluster head；The data of fused cluster interior nodes are sent to base station using the mode that fuzzy rule is handled between cluster head；Finally judge whether to meet the condition for terminating network life cycle, after all nodes are all dead in cluster, carry out sub-clustering next time, effectively extend Network morals, the balanced energy consumption of network optimizes the performance of network.
Description
Technical field
The invention belongs to wireless sensor network technology field, in particular to a kind of wireless biography based on the double-deck fuzzy algorithmic approach
Sensor network data transmission method.
Background technique
With single-chip microcontroller, wireless communication, sensor, embedded and energy resource system continuous development, wireless sensor network
Network (WSN) is born under development in science and technology and the demand of life application and scientific research.Since WSN has anti-ruin
Property it is strong, adaptivity is strong and can quickly carry out the advantages that, therefore its regardless of daily life or professional scientific research field all
Have a very wide range of applications scene.
As WSN is using more and more extensive, wherein there is some bottlenecks for technically being difficult to break through.Wherein close the most
Key is exactly energy problem, due to being dispersed with a large amount of sensor nodes in WSN, these nodes be all be distributed according to demand it is specific
Region, some are likely distributed in remote mountain area, and some is likely distributed in the desert of bad environments, and some is likely distributed in people
Work is difficult to the abyssal floor reached, just because of the limitation of such particular surroundings, leads to the sensor section for being distributed in this
The maintenance of point is extremely difficult, can not even be safeguarded in special rugged environment, furthermore, these sensor nodes are due to being suitable
Itself volume very little of the demand of various applications is answered, in addition the limitation of current energy technology, each sensor node institute
Band is all minicell, and the electricity of such minicell institute energy band is limited, once so sensor node itself institute band
The electric energy of minicell exhaust, be difficult to carry out it energy continuation of the journey maintenance substantially in some special applications.
Currently, having much for the Routing Protocol of wireless sensor network data transmission, mainly there is LEACH agreement,
LEACH_C agreement, LEA2C agreement, PEGASIS agreement etc..One duty cycle is known as one " wheel ", every wheel point by LEACH agreement
For cluster formation stages and data acquisition phase two parts.Remembered in each node of cluster formation stages according to the history for serving as cluster head recently
Record decides whether epicycle becomes cluster head in its sole discretion, is each node but generates the random number for being located at [0,1] by random function, and
A threshold T (n) is obtained according to formula (1), if the random number generated is less than T (n), this node just becomes cluster
It is first.After cluster head generation, cluster head broadcasts its message for being elected as cluster head to the whole network, other nodes receive after the message according to reception
To signal the power cluster that selects it to be added, it is closer at a distance from the cluster for signal strong representation, ordinary node and nearest
Cluster head sends the application that cluster is added, and after the completion of cluster addition, according to the quantity of cluster member, cluster head generates a TDMA time scheduling
Table, and it is sent to all members in cluster.In data acquisition phase, cluster member continues to monitor environment by the TDMA slot being arranged
Data, and it is sent to cluster head, cluster head is transferred directly to converge after carrying out fusion treatment from the data that each member node receives
Node.After number as defined in reaching in data acquisition, network starts the duty cycle of a new round, re-elects cluster head.
LEACH_C agreement is improved LEACH, deposits that cluster head number in node is unstable, position distribution is non-uniform asks
Topic.When every wheel sub-clustering starts, the position of oneself and dump energy are all sent to aggregation node, aggregation node by all-network node
The biggish node of portion of energy is selected as candidate cluster head, average distance and the smallest principle further according to node to cluster head are adopted
Final cluster head is selected with simulated annealing, last aggregation node is sent to sub-clustering result all network nodes.LEA2C association
View is the new Routing Protocol of one kind that proposes based on unsupervised study mechanism and LEACH-C agreement.In the sub-clustering stage, it is based on
The coordinate of sensor node, with self organizing neural network algorithm sub-clustering, then in the result of self organizing neural network sub-clustering
The sub-clustering of K-means algorithm is transported, according to：1) select in cluster the maximum node of dump energy as cluster head；2) it selects nearest from emphasis
Node as cluster head；3) node that select cluster interior nearest from base station is as cluster head.
The various agreements of the prior art can realize the transmission of data to a certain extent, but due to energy problem, with
Machine chooses the communication mode between strategy, cluster head and the base station of cluster head, and generally existing network energy consumption is too fast, when network is integrally survived
Between too short problem, keep the performance of network very low.In terms of specific existing defect usually shows following two：
(1) due to its leader cluster node be it is randomly selected, will appear the problem of leader cluster node is unevenly distributed in this way, saving
The less region of point can select multiple leader cluster nodes, and in the intensive region of Node distribution, a possible cluster head does not have yet.This
In the case of, sub-clustering result be not it is optimal, energy-efficient effect is also not achieved for entire wireless sensor network, can disappear instead
Consume more energy.
(2) Transmission system between the leader cluster node and base station of wireless sensor generally uses single-hop mode, if network
Larger, it is far from base station to also result in some leader cluster nodes, and it is excessive equally to will cause leader cluster node consumption, reduces network
Service life.
Therefore, it is necessary to be directed to the shortcomings and deficiencies of WSN Routing Protocol, chooses from sub-clustering, cluster head, done in terms of data transmission three
It improves out, so that sub-clustering result is optimal, reduces the consumption of energy.
Summary of the invention
In view of the deficienciess of the prior art, the present invention provides a kind of wireless sensor network based on the double-deck fuzzy algorithmic approach
Data transmission method, effectively extends Network morals, and the balanced energy consumption of network optimizes the property of network
Energy.
In order to solve the above-mentioned technical problem, the technical solution adopted by the present invention is that：A kind of nothing based on the double-deck fuzzy algorithmic approach
Line sensor network data transmission method, includes the following steps：
Step 1: netinit, determines node location：All the sensors node is randomly distributed in a square region
In domain, and make following setting：1) base station location is fixed, the center in square area；2) all the sensors node location is solid
Fixed, location information is it is known that have unique identification, primary power E0 having the same；3) all nodes of the complete awareness network in base station
Energy level and position；
Step 2: determining energy consumption model；
Step 3: determining cluster head number kopt；
Step 4: carrying out sub-clustering by innovatory algorithm；
Step 5: initial cluster center is as initial cluster head；
Step 6: with fuzzy knowledge processing, determining spare cluster head；
Step 7: cluster interior nodes send information to cluster head：Cluster interior nodes route the message to cluster head by the way of single-hop；
Step 8: the data of fused cluster interior nodes are sent to base using the mode that fuzzy rule is handled between cluster head
It stands；
Step 9: judge whether meet terminate network life cycle condition, if judging result be it is no, continue to walk
Rapid four to eight；In cluster after all death of all nodes, sub-clustering next time is carried out.
Preferably, when carrying out energy consumption model selection in step 2, sensor node transmission k bit data to distance is d
Position, the energy of consumption is lost two parts and formed by transmit circuit loss and power amplification, and specific formula for calculation is as follows
Wherein, EelecFor transmit circuit loss of energy；εfsAnd εampFor power amplification coefficient of energy dissipation；d0Indicate distance threshold,
Value isIf transmission range d < d0, then power amplification loss is using free space model；If transmission range d
≥d0, then power amplification loss is using multipath attenuation model；
In step 3, optimal sub-clustering number election formula is：
Wherein, dtosinkNode is represented to the distance of base station, N represents the number of node, and the region that M represents network distribution is big
It is small.
FCM algorithm is in classification, random initializtion cluster centre, when the cluster centre randomly selected is excessively concentrated, holds
It is excessive to easily lead to its calculation amount, in step 4, by improved FCM algorithm carry out sub-clustering, improved FCM algorithm base station into
Row：
(1) it is first center of birdsing of the same feather flock together that the maximum node of density is first selected in region；Choose second cluster centre
When, consider that the distance of its first cluster centre of distance and the density of itself are weighted processing, most suitable node is selected to make
For second cluster centre, due to the inconsistent advanced normalized of the unit of distance and density, it then follows distance first poly-
Class center is remote as far as possible, and density weighting principle big as far as possible selects optimal second cluster centre；It selects in this approach
It selects out and k is obtained by step 3optA cluster centre vj(=1,2....., kopt), set the condition of convergence φ of iteration；
(2) coordinate set of postulated point is X={ x1,x2...xn, utilize formula 4) it calculates during each nodal distance respectively clusters
The degree of membership u of the heartij；
(3) if choose cluster centre be not it is optimal, i.e., error be greater than φ, recycle formula (5) recalculate cluster
Center；
Wherein m is FUZZY WEIGHTED index, value 2；
(4) it iterates to calculate, terminates until error is less than φ calculating, obtain degree of membership of each node about each cluster centre
With cluster centre coordinate.
Further, the specific method of step 6 is the k obtained for step 4optCluster of a cluster centre as first time
Head, apart from this koptNode in the certain range R of a cluster centre is as secondary alternative cluster head, apart from this koptIt is a poly-
Alternative cluster head of node of class center in the range of 2R as third time, and so on, it is selected using the widened mode of radius of circle
Spare cluster head is selected, until Network morals terminate；Wherein, R=d/3.
When the cluster head that first time is chosen is less than energy threshold, the optimal cluster in spare cluster head is selected using fuzzy rule
Head, using the dump energy of node, the distance apart from cluster center, the density of neighbouring node as the input variable of fuzzy rule.
Indicate that dump energy and the node linguistic variable with a distance from the cluster heart of node are respectively divided into three levels：It is low, in,
It is high；There are three levels for the density linguistic variable of expression near nodal node:Closely, sufficiently, it is sparse；Represent node cluster head voting machine
The result of meeting is divided into seven levels:Very small, very little is fairly small, medium, quite greatly, very greatly, very greatly；Fuzzy rule
Library includes following rule:If energy is high, density is high, and close from center, then the cluster head choice of node is big；It is subordinate to using triangle
Function indicates in fuzzy set, sufficiently and trapezoidal membership function indicates low, high, close, sparse fuzzy set.
Further, in step 8, after the data sent out of leader cluster node fusion cluster interior nodes, if cluster head is to the distance of base station
< d0, then directly communicated with base station；If cluster head is to distance >=d of base station0, then handled using fuzzy rule；It selects optimal
Cluster head is as next-hop node, the dump energy next-hop node, the distance apart from base station, itself distance apart from next-hop
Input variable of the factor as fuzzy rule of three aspects carries out data transmission in such a way that fuzzy rule is handled between cluster head.
Compared with prior art, the invention has the advantages that：
(1) sub-clustering is carried out to sensor node using improved FCM, no longer changed after sub-clustering, until owning in entire cluster
Node is all dead, just carries out sub-clustering next time, reduces expense when sub-clustering, ensure that uniform point of cluster in whole network
Cloth, the balanced energy consumption of network.
(2) with fuzzy knowledge processing, selection cluster head, select cluster head when consider node dump energy, apart from cluster mass center
Distance, the density of neighbouring node, select optimal cluster head, can preferably extend Network morals.
(3) when data are transmitted, in the data transmission procedure of cluster head and base station, according to the remote of leader cluster node and base station distance
Closely, carried out data transmission by the way of fuzzy rule processing between cluster head, send information to base station, reduce energy consumption,
Network morals are effectively extended, the performance of network is optimized.
Detailed description of the invention
Fig. 1 is flow chart of the invention.
Specific embodiment
With reference to the accompanying drawing and specific embodiment the present invention is further illustrated.
As shown in Figure 1, the wireless sensor network data transmission method based on the double-deck fuzzy algorithmic approach, includes the following steps：
Step 1: netinit, determines node location：All the sensors node is randomly distributed in a square region
In domain, and make following setting：1) base station location is fixed, the center in square area, Infinite Energy；2) all the sensors section
Point position is fixed, and location information is it is known that have unique identification, primary power E0 having the same；3) the complete awareness network in base station
The energy level of all nodes and position.
Step 2: determining energy consumption model：
Sensor node sends the position that k bit data is d to distance, and the energy of consumption is lost by transmit circuit and power
Amplification loss two parts composition, specific formula for calculation are as follows
Wherein, EelecFor transmit circuit loss of energy；εfsAnd εampFor power amplification coefficient of energy dissipation；d0Indicate distance threshold,
Value isIf transmission range d < d0, then power amplification loss is using free space model；If transmission range d
≥d0, then power amplification loss is using multipath attenuation model.
Step 3: determining cluster head number kopt：Optimal sub-clustering number election formula is：
Wherein, dtosinkNode is represented to the distance of base station, N represents the number of node, and the region that M represents network distribution is big
It is small.
Step 4: carrying out sub-clustering by innovatory algorithm：FCM algorithm is in classification, random initializtion cluster centre, when random
It when the cluster centre of selection is excessively concentrated, is easy to cause its calculation amount excessive, in step 4, is carried out by improved FCM algorithm
Sub-clustering, improved FCM algorithm are carried out in base station：
(1) it is first center of birdsing of the same feather flock together that the maximum node of density is first selected in region；Choose second cluster centre
When, consider that the distance of its first cluster centre of distance and the density of itself are weighted processing, most suitable node is selected to make
For second cluster centre, due to the inconsistent advanced normalized of the unit of distance and density, it then follows distance first poly-
Class center is remote as far as possible, and density weighting principle big as far as possible selects optimal second cluster centre；It selects in this approach
It selects out and k is obtained by step 3optA cluster centre vj(=1,2....., kopt), set the condition of convergence φ of iteration；
(2) n node, it is assumed that the coordinate set of point is X={ x1,x2...xn, utilize formula 4) calculate each nodal distance
The degree of membership u of each cluster centreij；
xiFor i-th of node coordinate, i=1,2....n, j=1,2....kopt。
(3) if choose cluster centre be not it is optimal, i.e., error be greater than φ, recycle formula (5) recalculate cluster
Center；
Wherein, m is FUZZY WEIGHTED index, value 2；
(4) it iterates to calculate, terminates until error is less than φ calculating, obtain degree of membership of each node about each cluster centre
With cluster centre coordinate, reduce the number of iterations, improves the accuracy rate of division.
Step 5: initial cluster center is as initial cluster head；
Step 6: with fuzzy knowledge processing, determining cluster head：The k obtained for step 4optA cluster centre is as first time
Cluster head, apart from this koptNode in the certain range R of a cluster centre is as secondary alternative cluster head, apart from this koptIt is a
Alternative cluster head of node of cluster centre in the range of 2R as third time, and so on, use the widened mode of radius of circle
Spare cluster head is selected, until Network morals terminate；Wherein, R=d/3.
When the cluster head that first time is chosen is less than energy threshold, the optimal cluster in spare cluster head is selected using fuzzy rule
Head, using the dump energy of node, the distance apart from cluster center, the density of neighbouring node as the input variable of fuzzy rule.
Indicate that dump energy and the node linguistic variable with a distance from the cluster heart of node are respectively divided into three levels:It is low, in,
It is high；There are three levels for the density linguistic variable of expression near nodal node:Closely, sufficiently, it is sparse；Represent node cluster head voting machine
The result of meeting is divided into seven levels:Very small, very little is fairly small, medium, quite greatly, very greatly, very greatly；Fuzzy rule
Library includes following rule：If energy is high, density is high, and close from center, then the cluster head choice of node is big；It is subordinate to using triangle
Function indicates in fuzzy set, sufficiently and trapezoidal membership function indicates low, high, close, sparse fuzzy set.
Step 7: cluster interior nodes send information to cluster head：Cluster interior nodes are according to receiving cluster head information, by the way of single-hop
Route the message to cluster head.
Step 8: the transmission mode between cluster head is handled using fuzzy rule：The number that leader cluster node fusion cluster interior nodes are sent out
According to rear, if cluster head is to the distance < d of base station0, then directly communicated with base station；If cluster head is to distance >=d of base station0, then use
Fuzzy rule processing；Select optimal cluster head as next-hop node, the dump energy of next-hop node, apart from base station away from
From, with a distance from itself the aspect of distance three of next-hop input variable of the factor as fuzzy rule, using fuzzy rule between cluster head
The mode of processing carries out data transmission.
Step 9: judge whether meet terminate network life cycle condition, if judging result be it is no, continue to walk
Rapid four to eight；In cluster after all death of all nodes, sub-clustering next time is carried out.
In conclusion the present invention proposes a kind of wireless sensor network data transmission method based on the double-deck fuzzy algorithmic approach,
It chosen from sub-clustering, cluster head, make improvement in terms of data transmission three.It is using the double-deck fuzzy algorithmic approach because fuzzy rule can be used
Library select cluster head, the dump energy including node, the distance apart from cluster mass center, the density of neighbouring node, to sensor node into
Row sub-clustering selects optimal cluster head, then organizes cluster.In steady-state process, cluster head collects the data of aggregation and executes signal processing
Function compresses data into a signal, and in such a way that fuzzy rule handles its transmission, this composite signal is sent to base
It stands.By the election and optimal transmission mode of optimal cluster head, the consumption of energy is reduced, the Life Cycle of network is extended
Phase optimizes the performance of network.
Sub-clustering is carried out to sensor node using improved FCM first, is no longer changed after sub-clustering, until owning in entire cluster
Node is all dead, just carries out sub-clustering next time, reduces expense when sub-clustering, ensure that uniform point of cluster in whole network
Cloth.Then with fuzzy knowledge processing, selection cluster head, select to consider when cluster head the dump energy of node, the distance apart from cluster mass center,
The density of neighbouring node, selects optimal cluster head, can preferably extend Network morals.When final data transmits, cluster head
Between using fuzzy rule processing by the way of carry out data transmission, send information to base station, reduce energy consumption.
Certainly, the above description is not a limitation of the present invention, and the present invention is also not limited to the example above, the art
Those of ordinary skill, within the essential scope of the present invention, the variations, modifications, additions or substitutions made all should belong to the present invention
Protection scope.
Claims (6)
1. a kind of wireless sensor network data transmission method based on the double-deck fuzzy algorithmic approach, which is characterized in that including following step
Suddenly：
Step 1: netinit, determines node location：All the sensors node is randomly distributed in a square area
It is interior, and make following setting：1) base station location is fixed, the center in square area；2) all the sensors node location is fixed,
Location information is it is known that have unique identification, primary power E0 having the same；3) energy of all nodes of the complete awareness network in base station
Grade and position；
Step 2: determining energy consumption model；
Step 3: determining cluster head number kopt；
Step 4: carrying out sub-clustering by innovatory algorithm；
Step 5: initial cluster center is as initial cluster head；
Step 6: with fuzzy knowledge processing, determining spare cluster head；
Step 7: cluster interior nodes send information to cluster head：Cluster interior nodes route the message to cluster head by the way of single-hop；
Step 8: the data of fused cluster interior nodes are sent to base station using the mode that fuzzy rule is handled between cluster head；
Step 9: judge whether meet terminate network life cycle condition, if judging result be it is no, continue step 4
To eight；In cluster after all death of all nodes, sub-clustering next time is carried out.
2. the wireless sensor network data transmission method according to claim 1 based on the double-deck fuzzy algorithmic approach, feature
It is：When carrying out energy consumption model selection in step 2, sensor node sends the position that k bit data is d to distance, consumption
Energy is lost two parts and is formed by transmit circuit loss and power amplification, and specific formula for calculation is as follows
Wherein, EelecFor transmit circuit loss of energy；εfsAnd εampFor power amplification coefficient of energy dissipation；d0Indicate distance threshold, value
ForIf transmission range d < d0, then power amplification loss is using free space model；If transmission range d >=
d0, then power amplification loss is using multipath attenuation model；
In step 3, optimal sub-clustering number election formula is：
Wherein, dtosinkNode is represented to the distance of base station, N represents the number of node, and M represents the area size of network distribution.
3. the wireless sensor network data transmission method according to claim 2 based on the double-deck fuzzy algorithmic approach, feature
It is, in step 4, sub-clustering is carried out by improved FCM algorithm：
(1) it is first center of birdsing of the same feather flock together that the maximum node of density is first selected in region；When choosing second cluster centre, examine
The distance and the density of itself for considering its first cluster centre of distance are weighted processing, select most suitable node as second
A cluster centre, due to the inconsistent advanced normalized of the unit of distance and density, it then follows first cluster centre of distance
Remote as far as possible, density weighting principle big as far as possible selects optimal second cluster centre；Select in this approach by
Step 3 obtains koptA cluster centre vj(=1,2....., kopt), set the condition of convergence φ of iteration；
(2) coordinate set of postulated point is X={ x1,x2...xn, utilize formula 4) calculate each each cluster centre of nodal distance
Degree of membership uij；
(3) if choose cluster centre be not it is optimal, i.e., error be greater than φ, recycle formula (5) recalculate cluster centre；
Wherein m is FUZZY WEIGHTED index, value 2；
(4) it iterates to calculate, terminates until error is less than φ calculating, obtain each node about the degree of membership of each cluster centre and gather
Class centre coordinate.
4. the wireless sensor network data transmission method according to claim 3 based on the double-deck fuzzy algorithmic approach, feature
It is：The specific method of step 6 is the k obtained for step 4optCluster head of a cluster centre as first time, apart from this
koptNode in the certain range R of a cluster centre is as secondary alternative cluster head, apart from this koptA cluster centre is in 2R
In the range of alternative cluster head of the node as third time, and so on, select spare cluster head using the widened mode of radius of circle,
Until Network morals terminate；Wherein, R=d/3.
5. the wireless sensor network data transmission method according to claim 4 based on the double-deck fuzzy algorithmic approach, feature
It is：When the cluster head that first time is chosen is less than energy threshold, the optimal cluster head in spare cluster head is selected using fuzzy rule,
The input variable of the dump energy of node, the distance apart from cluster center, the density of neighbouring node as fuzzy rule.
6. the wireless sensor network data transmission method according to claim 5 based on the double-deck fuzzy algorithmic approach, feature
It is：In step 8, after the data sent out of leader cluster node fusion cluster interior nodes, if cluster head is to the distance < d of base station0, then directly
It is communicated with base station；If cluster head is to distance >=d of base station0, then handled using fuzzy rule；Select optimal cluster head as under
One hop node, the dump energy next-hop node, the distance apart from base station, itself aspect of distance three apart from next-hop because
Input variable of the element as fuzzy rule carries out data transmission in such a way that fuzzy rule is handled between cluster head.
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| CN110087273A (en) * | 2019-03-13 | 2019-08-02 | 西安电子科技大学 | The method of Wireless sensor network clustering routing, wireless sensor network protocols platform |
| CN110191497A (en) * | 2019-05-05 | 2019-08-30 | 广东工业大学 | A kind of LEACH algorithm improvement method based on DBSCAN |
| CN110536372A (en) * | 2019-07-17 | 2019-12-03 | 长春工业大学 | A kind of annular wireless sensor network Uneven Cluster algorithm based on fuzzy control |
| CN111200856A (en) * | 2020-02-19 | 2020-05-26 | 重庆邮电大学 | Multi-hop optimal path selection method of wireless sensor |
| CN111818553A (en) * | 2020-05-21 | 2020-10-23 | 长春工业大学 | Fuzzy logic-based wireless sensor network improved multi-hop LEACH protocol |
| CN113115249A (en) * | 2021-04-09 | 2021-07-13 | 中国工商银行股份有限公司 | Method, device and system for determining cluster head nodes |
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