CN114125759A - Wireless sensor network clustering method based on improved particle swarm - Google Patents
Wireless sensor network clustering method based on improved particle swarm Download PDFInfo
- Publication number
- CN114125759A CN114125759A CN202111432864.7A CN202111432864A CN114125759A CN 114125759 A CN114125759 A CN 114125759A CN 202111432864 A CN202111432864 A CN 202111432864A CN 114125759 A CN114125759 A CN 114125759A
- Authority
- CN
- China
- Prior art keywords
- nodes
- particle
- cluster head
- wireless sensor
- sensor network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 239000002245 particle Substances 0.000 title claims abstract description 95
- 238000000034 method Methods 0.000 title claims abstract description 54
- 241000854291 Dianthus carthusianorum Species 0.000 claims abstract description 42
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 7
- 238000004891 communication Methods 0.000 claims abstract description 7
- 230000007246 mechanism Effects 0.000 claims abstract description 5
- 239000013598 vector Substances 0.000 claims description 35
- 230000005540 biological transmission Effects 0.000 claims description 7
- 230000006870 function Effects 0.000 claims description 7
- 238000013459 approach Methods 0.000 claims description 4
- 238000013480 data collection Methods 0.000 claims description 4
- 238000012544 monitoring process Methods 0.000 claims description 4
- 238000005457 optimization Methods 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000013178 mathematical model Methods 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 abstract description 4
- 238000004088 simulation Methods 0.000 abstract description 4
- 230000005059 dormancy Effects 0.000 abstract description 2
- 230000008859 change Effects 0.000 description 3
- 238000005265 energy consumption Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
-
- 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)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention relates to a wireless sensor network clustering method based on improved particle swarm, which comprises the following steps: s1: initializing and setting a Wireless Sensor Network (WSN), constructing a node group, and selecting energy; s2: selecting cluster head nodes by using an improved PSO algorithm; s3: and adding the common nodes into the nearest cluster structure through calculating the distance, entering a communication stage, and starting to transmit data. Firstly, a particle swarm method is improved, then a dormancy mechanism of a neighboring node group is introduced, so that part of nodes do not participate in work when a network runs, then the optimal cluster head number of the network is analyzed again under the condition of adding the neighboring node group, whether the cluster head nodes are properly selected or not is judged by utilizing a fitness function, and the cluster head nodes are selected. The method effectively solves the problems of excessively random cluster head nodes and data redundancy, and has obvious advantages in indexes such as network average energy, stored movable node number and the like compared with a classical clustering method in simulation analysis.
Description
Technical Field
The invention relates to the field of Internet of things, in particular to a wireless sensor network clustering method based on improved particle swarm.
Background
In recent years, internet of things (IoT) technology has developed rapidly, and has also driven the development of other industries. As an underlying technical support of IoT, a Wireless Sensor Network (WSN) has become a popular research field by virtue of its characteristics of low cost, easy deployment, and wide application scenarios. And the WSN has limited node energy and cannot supplement energy to the battery, so that the life cycle of the WSN is limited. Therefore, how to prolong the service life of the nodes becomes a technical problem in the field of WSN. In the traditional clustering method, the situation that cluster head nodes are distributed unevenly can occur due to the random cluster head node selection mode, so that the node loads are uneven, part of the nodes are excessive in energy consumption, the problem of energy holes occurs, and the life cycle of the WSN is too short.
In addition, in the clustering method, how to form a reasonable cluster structure is one of the main problems at present, and as for the existing clustering method, a plurality of problems are not solved.
(1) The selection of cluster head nodes is too random:
when a lot of clustering methods are used for selecting cluster head nodes in the past, although some weight factors are added in a selection formula, the cluster head nodes are still random, the cluster head nodes are unevenly distributed, the resource waste condition exists in a dense area, and the node energy consumption condition exists in a sparse area.
(2) Partial data redundancy:
because the nodes are randomly deployed, the distance between partial nodes is inevitably too small, if each node participates in data collection and transmission, the monitoring area is repeated, the repeatability of partial data is high, and the energy of the nodes is wasted to do useless work.
Disclosure of Invention
The invention aims to provide a wireless sensor network clustering method based on an improved particle swarm, namely, the clustering method based on the improved particle swarm is provided, the problems of over-random cluster head nodes and data redundancy are effectively solved, and the method has obvious advantages in indexes such as network average energy, stored node number and the like compared with a classical clustering method in simulation analysis.
In order to solve the problems, the technical scheme adopted by the invention is as follows: the wireless sensor network clustering method based on the improved particle swarm comprises the following steps:
s1: initializing and setting a Wireless Sensor Network (WSN), constructing a node group, and selecting energy;
s2: selecting cluster head nodes by using an improved PSO algorithm;
s3: and adding the common nodes into the nearest cluster structure through calculating the distance, entering a communication stage, and starting to transmit data.
By adopting the technical scheme, the particle swarm optimization method is firstly improved, then a dormancy mechanism of the adjacent node group is introduced, so that part of nodes do not participate in the operation when the network operates, then the optimal cluster head number of the network is re-analyzed under the condition of adding the adjacent node group, whether the cluster head nodes are properly selected or not is judged by utilizing a fitness function, and finally the cluster head nodes are selected. The situation that cluster head nodes are distributed unevenly in a random cluster head node selecting mode in a traditional clustering method can cause uneven node load, excessive energy consumption of partial nodes and energy holes, and further the problem that the life cycle of the WSN is too short is caused. The technical scheme is based on the clustering method of the improved particle swarm, the problems of over-random cluster head nodes and data redundancy are effectively solved, and the method has obvious advantages in indexes such as network average energy, stored node number and the like compared with a classical clustering method in simulation analysis.
As a preferred embodiment of the present invention, the step S1 specifically includes the following steps:
s11: initializing and setting the WSN (wireless sensor network), including the position information and node energy of the nodes;
s12: calculating the distance between the nodes through a distance calculation formula, and comparing the distance with a set distance threshold; constructing a neighboring node group, and in the first round, randomly selecting nodes in the neighboring node group, then performing cluster head election and executing data collection and forwarding tasks;
s13: and comparing the node energy values and screening.
As a preferred embodiment of the present invention, the step S2 specifically includes the following steps:
s21: an initialization particle swarm is provided with N particles, the size of N is the number of cluster head nodes in the network, and each particle has a corresponding position vector xiAnd velocity vector vi；
S22: calculating an initial fitness value of each particle, an individual extreme value p of the particleidExpressed as the fitness value f of the particleiGroup extremum pgdExpressed as the maximum of the individual extrema of all particles;
s23: updating own position vector and speed vector of each particle according to the following formula;
s24: after each particle updates the position vector and the velocity vector thereof, recalculating the fitness value thereof;
s25: updating the individual extreme value and the whole extreme value through the obtained fitness value, and replacing the individual extreme value with the fitness value if the fitness value is larger than the individual extreme value;
s26: and repeating the steps S21-25 until the iteration times are reached or the termination condition is met, wherein the obtained nodes corresponding to all the extreme values are the required cluster head nodes.
As a preferred embodiment of the present invention, the step S3 specifically includes the following steps: after the cluster head node is selected in step S2, the common node is added to the nearest cluster structure by calculating the distance, and the communication phase is entered, and the same data transmission mechanism as that in the LEACH algorithm is used to perform data transmission.
As a preferred technical solution of the present invention, the mathematical model of the clustering method that preferentially selects cluster head nodes in step S2 is as follows:
assuming that there are N particles in a D-dimensional space, there is a corresponding fitness function f (x) to determine whether the current position of the particle is optimal; the following parameter vectors are included in the method:
the position vector of the ith particle is: x is the number ofi＝(xi1，xi2，…，xiD)；
The velocity vector for the ith particle is: v. ofi＝(vi1，vi2，…，viD)；
The fitness function value of particle i is: fitnessi＝f(xi)，i＝1，2，3，…N；
The optimal position vector that particle i passes through is: pbesti＝(pi1，pi2，…，piD)；
The position of the particle i during the whole iteration
And speed
The update formula is:
wherein i is 1,2,3, …, N; d is 1,2,3, …, D; c. C1And c2Is a learning factor, w is an inertial weight coefficient, r1And r2Is a random number in the range (0, 1);
is the position and velocity variation range of the particle in d-dimensional space, if
And
if the variable variation range is exceeded, the variable variation range is limited to be outside the boundary value;
when the optimal solution is found by using the particle swarm method, the inertia weight coefficient w influences the direction of particle swarm search, and the particles can be controlled to approach to the global optimal solution or the local optimal solution by adjusting the size of the inertia weight coefficient w, as shown in formula (3), when the inertia weight coefficient w is large, the proportion of the first term in the formula is large, namely the velocity vector of the particles is greatly influenced, the limitation on the movement of the particles is small, and the easiness of finding the global optimal solution can be improved: when the inertia weight coefficient w is small, the proportion of the last two terms in the formula is large, the particle speed is small, and a local optimal solution is easy to find;
therefore, the inertia weight coefficient of the particle swarm optimization method is improved nonlinearly, as shown in the following formula (7):
wmin、wmaxfor the range of variation of the inertia weight coefficient w, TmaxIs the maximum number of iterations of the method.
As a preferred technical solution of the present invention, in the step S12, a clustering method of preferentially selecting cluster head nodes is adopted, and a neighboring node group is introduced, and the specific steps are as follows:
s121: after the WSN is initialized, the distance d between the nodes is obtained by using a distance formulai-1Presetting a distance threshold Rg3; if the distance between the nodes is less than this value, i.e. di-1≤RgIf so, the monitoring areas of the two nodes are considered to be repeated, and the data information collected by the two nodes is the same; if both nodes work at the moment, data redundancy is generated;
s122: will satisfy the condition di-1≤RgIs grouped into a set P, P ═ n1，n2… }, n in the set P1,n2… denotes a node in the WSN; in the operation process of the WSN, only part of nodes in each set P participate in the tasks of collecting and forwarding data, and when the number of the nodes in the set P is small, even only one node participates in the work; that is, in each round, there will be nodes in the dormant state;
s123: judging whether the nodes in each set P participate in the current round of work according to the energy, and if the nodes with the energy larger than a set energy threshold value are in an active state, executing the operation of information collection and data forwarding; and if the energy of the node is less than the set energy threshold value, the node is in a dormant state, and when one round is finished, the energy is compared again for selection, and then the next round of work is started.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that: the wireless sensor network clustering method based on the improved particle swarm effectively solves the problems of excessively random cluster head nodes and data redundancy, and has obvious advantages in indexes such as network average energy, stored node number and the like compared with a classical clustering method in simulation analysis.
Drawings
The technical scheme of the invention is further described by combining the accompanying drawings as follows:
FIG. 1 is a flow chart of a wireless sensor network clustering method based on improved particle swarm in accordance with the present invention;
fig. 2 is a network structure of a wireless sensor network WSN in the improved particle swarm-based wireless sensor network clustering method of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b): as shown in fig. 1, the method for clustering a wireless sensor network based on improved particle swarm comprises the following steps:
s1: initializing and setting a Wireless Sensor Network (WSN), constructing a node group, and selecting energy;
the step S1 specifically includes the following steps:
s11: initializing and setting the WSN (wireless sensor network), including the position information and node energy of the nodes;
s12: calculating the distance between the nodes through a distance calculation formula, and comparing the distance with a set distance threshold; constructing a neighboring node group, and in the first round, randomly selecting nodes in the neighboring node group, then performing cluster head election and executing data collection and forwarding tasks;
in step S12, a clustering method of preferentially selecting cluster head nodes is adopted, and a neighboring node group is introduced, and the specific steps are as follows:
s121: after the WSN is initialized, the distance d between the nodes is obtained by using a distance formulai-1Presetting a distance threshold Rg3; if the distance between the nodes is less than this value, i.e. di-1≤RgIf so, the monitoring areas of the two nodes are considered to be repeated, and the data information collected by the two nodes is the same; if both nodes work at the moment, data redundancy is generated;
s122: will satisfy the condition di-1≤RgIs grouped into a set P, P ═ n1，n2... }, n in set P1,n2… denotes a node in the WSN; in the operation process of the WSN, only part of nodes in each set P participate in the tasks of collecting and forwarding data, and when the number of the nodes in the set P is small, even only one node participates in the work; that is, in each round, there will be nodes in the dormant state;
s123: judging whether the nodes in each set P participate in the current round of work according to the energy, and if the nodes with the energy larger than a set energy threshold value are in an active state, executing the operation of information collection and data forwarding; if the energy of the node is smaller than the set energy threshold value, the node is in a dormant state, and when one round is finished, the energy is compared again for selection, and then the next round of work is started; after the adjacent node groups are introduced, the network structure of the wireless sensor network WSN may also change, as shown in fig. 2, it can be seen that, in one adjacent node group, only part of nodes participate in the operation of the whole network, and other nodes are in a dormant state;
s13: comparing the values of the node energies, and screening;
s2: selecting cluster head nodes by using an improved PSO algorithm;
the step S2 specifically includes the following steps:
s21: an initialization particle swarm is provided with N particles, the size of N is the number of cluster head nodes in the network, and each particle has a corresponding position vector xiAnd velocity vector vi；
S22: calculating an initial fitness value of each particle, an individual extreme value p of the particleidExpressed as the fitness value f of the particleiGroup extremum pgdExpressed as the maximum of the individual extrema of all particles;
s23: updating own position vector and speed vector of each particle according to the following formula;
s24: after each particle updates the position vector and the velocity vector thereof, recalculating the fitness value thereof;
s25: updating the individual extreme value and the whole extreme value through the obtained fitness value, and replacing the individual extreme value with the fitness value if the fitness value is larger than the individual extreme value;
s26: repeating the step S21-25 until the iteration times are reached or the termination condition is met, wherein the obtained nodes corresponding to all the extreme values are the required cluster head nodes; the nodes corresponding to all the extreme values obtained in the above steps are the required cluster head nodes, but the position vector of the particle does not exactly correspond to the position of the node, so that correction is needed, and the node closest to the position vector of the particle becomes a cluster head;
the mathematical model of the clustering method adopting the preferred selection of the cluster head nodes in the step S2 is as follows:
assuming that there are N particles in a D-dimensional space, there is a corresponding fitness function f (x) to determine whether the current position of the particle is optimal; the following parameter vectors are included in the method:
the position vector of the ith particle is: x is the number ofi＝(xi1，xi2，…，xiD)；
The velocity vector for the ith particle is: v. ofi＝(vi1，vi2，…，viD)；
The fitness function value of particle i is: fitnessi＝f(xi)，i＝1，2，3，…N；
The optimal position vector that particle i passes through is: pbesti＝(pi1，pi2，…，piD)；
The position of the particle i during the whole iteration
And speed
The update formula is:
wherein i is 1,2,3, …, N; d is 1,2,3, …, D; c. C1And c2Is a learning factor, w is an inertial weight coefficient, r1And r2Is a random number in the range (0, 1);
is the position and velocity variation range of the particle in d-dimensional space, if
And
if the variable variation range is exceeded, the variable variation range is limited to be outside the boundary value;
when the optimal solution is found by using the particle swarm method, the inertia weight coefficient w influences the direction of particle swarm search, and the particles can be controlled to approach to the global optimal solution or the local optimal solution by adjusting the size of the inertia weight coefficient w, as shown in formula (3), when the inertia weight coefficient w is large, the proportion of the first term in the formula is large, namely the velocity vector of the particles is greatly influenced, the limitation on the movement of the particles is small, and the easiness of finding the global optimal solution can be improved: when the inertia weight coefficient w is small, the proportion of the last two terms in the formula is large, the particle speed is small, and a local optimal solution is easy to find;
therefore, the inertia weight coefficient of the particle swarm optimization method is improved nonlinearly, as shown in the following formula (7):
wmin、wmaxfor the range of variation of the inertia weight coefficient w, TmaxIs the maximum number of iterations of the method; according to the improvement on the inertia weight coefficient, the change of w' at the early stage is slow, the global optimal solution is convenient to find, and at the later stage, the change of w is fast, so that the particles can approach the local optimal solution more quickly;
s3: adding a common node into a nearest cluster structure through calculating distance, entering a communication stage, and starting to transmit data; the step S3 specifically includes the following steps: after the cluster head node is selected in step S2, the common node is added to the nearest cluster structure by calculating the distance, and the communication phase is entered, and the same data transmission mechanism as that in the LEACH algorithm is used to perform data transmission.
It is obvious to those skilled in the art that the present invention is not limited to the above embodiments, and it is within the scope of the present invention to adopt various insubstantial modifications of the method concept and technical scheme of the present invention, or to directly apply the concept and technical scheme of the present invention to other occasions without modification.
Claims (6)
1. A wireless sensor network clustering method based on improved particle swarm is characterized by comprising the following steps:
s1: initializing and setting a Wireless Sensor Network (WSN), constructing a node group, and selecting energy;
s2: selecting cluster head nodes by using an improved PSO algorithm;
s3: and adding the common nodes into the nearest cluster structure through calculating the distance, entering a communication stage, and starting to transmit data.
2. The improved particle swarm based wireless sensor network clustering method according to claim 1, wherein the step S1 specifically comprises the following steps:
s11: initializing and setting the WSN (wireless sensor network), including the position information and node energy of the nodes;
s12: calculating the distance between the nodes through a distance calculation formula, and comparing the distance with a set distance threshold; constructing a neighboring node group, and in the first round, randomly selecting nodes in the neighboring node group, then performing cluster head election and executing data collection and forwarding tasks;
s13: and comparing the node energy values and screening.
3. The improved particle swarm based wireless sensor network clustering method according to claim 1, wherein the step S2 specifically comprises the following steps:
s21: an initialization particle swarm is provided with N particles, the size of N is the number of cluster head nodes in the network, and each particle has a corresponding position vector xiAnd velocity vector vi；
S22: calculating an initial fitness value of each particle, an individual extreme value p of the particleidExpressed as the fitness value f of the particleiGroup extremum pgdExpressed as the maximum of the individual extrema of all particles;
s23: updating own position vector and speed vector of each particle according to the following formula;
s24: after each particle updates the position vector and the velocity vector thereof, recalculating the fitness value thereof;
s25: updating the individual extreme value and the whole extreme value through the obtained fitness value, and replacing the individual extreme value with the fitness value if the fitness value is larger than the individual extreme value;
s26: and repeating the steps S21-25 until the iteration times are reached or the termination condition is met, wherein the obtained nodes corresponding to all the extreme values are the required cluster head nodes.
4. The improved particle swarm based wireless sensor network clustering method according to claim 1, wherein the step S3 specifically comprises the following steps: after the cluster head node is selected in step S2, the common node is added to the nearest cluster structure by calculating the distance, and the communication phase is entered, and the same data transmission mechanism as that in the LEACH algorithm is used to perform data transmission.
5. The improved particle swarm based wireless sensor network clustering method according to claim 3, wherein the mathematical model of the clustering method using preferentially selecting cluster head nodes in step S2 is as follows:
assuming that there are N particles in a D-dimensional space, there is a corresponding fitness function f (x) to determine whether the current position of the particle is optimal; the following parameter vectors are included in the method:
the position vector of the ith particle is: x is the number ofi＝(xi1，xi2，…，xiD)；
The velocity vector for the ith particle is: v. ofi＝(vi1，vi2，…，viD)；
The fitness function value of particle i is: fitnessi＝f(xi)，i＝1，2，3，…N；
The optimal position vector that particle i passes through is: pbesti＝(pi1，pi2，…，piD)；
The position of the particle i during the whole iteration
And speed
The update formula is:
wherein, i ═ 1,2, 3.., N; d ═ 1,2,3, ·, D; c. C1And c2Is a learning factor, w is an inertial weight coefficient, r1And r2Is a random number in the range (0, 1);
is the position and velocity variation range of the particle in d-dimensional space, if
And
if the variable variation range is exceeded, the variable variation range is limited to be outside the boundary value;
when the optimal solution is found by using the particle swarm method, the inertia weight coefficient w influences the direction of particle swarm search, and the particles can be controlled to approach to the global optimal solution or the local optimal solution by adjusting the size of the inertia weight coefficient w, in the formula (3), when the inertia weight coefficient w is large, the proportion of the first term in the formula is large, namely the velocity vector of the particles is greatly influenced, the limitation on the movement of the particles is small, and the easiness of finding the global optimal solution can be improved: when the inertia weight coefficient w is small, the proportion of the last two terms in the formula (3) is large, the particle speed is small, and a local optimal solution is easy to find;
therefore, the inertial weight coefficient w of the particle swarm optimization method is improved nonlinearly as shown in the following formula (7):
wmin、wmaxfor the range of variation of the inertial weight coefficient, TmaxIs the maximum number of iterations of the method.
6. The improved particle swarm based wireless sensor network clustering method according to claim 3, wherein a clustering method of preferentially selecting cluster head nodes is adopted in the step S12, and a neighboring node group is introduced, and the specific steps are as follows:
s121: after the WSN is initialized, the distance d between the nodes is obtained by using a distance formulai-1Presetting a distance threshold Rg3; if the distance between the nodes is less than this value, i.e. di-1≤RgIf so, the monitoring areas of the two nodes are considered to be repeated, and the data information collected by the two nodes is the same; if both nodes work at the moment, data redundancy is generated;
s122: will satisfy the condition di-1≤RgIs grouped into a set P, P ═ n1，n2... }, n in set P1，n2,.. represent nodes in a WSN; in the operation process of the WSN, only part of nodes in each set P participate in the tasks of collecting and forwarding data, and when the number of the nodes in the set P is small, even only one node participates in the work; that is, in each round, there will be nodes in the dormant state;
s123: judging whether the nodes in each set P participate in the current round of work according to the energy, and if the nodes with the energy larger than a set energy threshold value are in an active state, executing the operation of information collection and data forwarding; and if the energy of the node is less than the set energy threshold value, the node is in a dormant state, and when one round is finished, the energy is compared again for selection, and then the next round of work is started.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111432864.7A CN114125759B (en) | 2021-11-29 | 2021-11-29 | Wireless sensor network clustering method based on improved particle swarm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111432864.7A CN114125759B (en) | 2021-11-29 | 2021-11-29 | Wireless sensor network clustering method based on improved particle swarm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114125759A true CN114125759A (en) | 2022-03-01 |
| CN114125759B CN114125759B (en) | 2024-09-13 |
Family
ID=80371440
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111432864.7A Active CN114125759B (en) | 2021-11-29 | 2021-11-29 | Wireless sensor network clustering method based on improved particle swarm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114125759B (en) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103269507A (en) * | 2013-03-05 | 2013-08-28 | 江苏科技大学 | Routing method of double-cluster head wireless sensor network |
| US20170339572A1 (en) * | 2014-11-27 | 2017-11-23 | Shenyang Institute Of Automation, Chinese Academy Of Sciences | Robust coverage method for relay nodes in double-layer structure wireless sensor network |
| CN108566663A (en) * | 2018-01-10 | 2018-09-21 | 重庆邮电大学 | SDWSN energy consumption balance routing algorithms based on disturbance particle group optimizing |
| CN110225569A (en) * | 2019-06-10 | 2019-09-10 | 桂林电子科技大学 | A method of based on the WSNs clustering and multi-hop Routing Protocol for improving particle swarm algorithm |
-
2021
- 2021-11-29 CN CN202111432864.7A patent/CN114125759B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103269507A (en) * | 2013-03-05 | 2013-08-28 | 江苏科技大学 | Routing method of double-cluster head wireless sensor network |
| US20170339572A1 (en) * | 2014-11-27 | 2017-11-23 | Shenyang Institute Of Automation, Chinese Academy Of Sciences | Robust coverage method for relay nodes in double-layer structure wireless sensor network |
| CN108566663A (en) * | 2018-01-10 | 2018-09-21 | 重庆邮电大学 | SDWSN energy consumption balance routing algorithms based on disturbance particle group optimizing |
| CN110225569A (en) * | 2019-06-10 | 2019-09-10 | 桂林电子科技大学 | A method of based on the WSNs clustering and multi-hop Routing Protocol for improving particle swarm algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114125759B (en) | 2024-09-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Hong et al. | A clustering-tree topology control based on the energy forecast for heterogeneous wireless sensor networks | |
| CN110401958B (en) | Node dynamic coverage enhancement method based on virtual force | |
| CN112995289A (en) | Internet of vehicles multi-target computing task unloading scheduling method based on non-dominated sorting genetic strategy | |
| CN108282822B (en) | Collaborative optimization algorithm for user association and power control in heterogeneous cellular network | |
| CN102014344A (en) | Clustering control method of intelligent wireless sensor network based on DPSO (Discrete Particle Swarm Optimization) | |
| CN104411000A (en) | Method for selecting cluster head of hierarchical routing protocol in wireless sensor network | |
| CN117528649A (en) | Method for establishing end-edge cloud system architecture, task unloading and resource allocation optimization method and end-edge cloud system architecture | |
| Prasath et al. | RMCHS: Ridge method based cluster head selection for energy efficient clustering hierarchy protocol in WSN | |
| CN117042083A (en) | Distributed reliable transmission guarantee method for unmanned cluster networking | |
| Raval et al. | Optimization of clustering process for WSN with hybrid harmony search and K-means algorithm | |
| CN115915327A (en) | Double-cluster-head WSNs self-adaptive relay routing method based on optimized clustering | |
| CN115118728A (en) | Ant colony algorithm-based edge load balancing task scheduling method | |
| CN111339616A (en) | Topology optimization method for maximizing fundamental frequency of mechanical structure | |
| CN114125759A (en) | Wireless sensor network clustering method based on improved particle swarm | |
| CN112987789A (en) | Unmanned aerial vehicle cluster network topology design method for improving Leach protocol | |
| CN115550837B (en) | DV-Hop positioning method based on chaotic mapping and gray wolf algorithm optimization | |
| CN105392176B (en) | A kind of calculation method of actuator node executive capability | |
| CN114783215B (en) | Unmanned aerial vehicle clustering method and device and electronic equipment | |
| CN110113798B (en) | Isomorphic routing protocol method in multi-source wireless sensor network environment | |
| CN115987886B (en) | Underwater acoustic network Q learning routing method based on meta learning parameter optimization | |
| CN110048945B (en) | Node mobility clustering method and system | |
| CN114531665B (en) | Wireless sensor network node clustering method and system based on Lewy flight | |
| Li et al. | Energy efficient multi-target clustering algorithm for WSN-based distributed consensus filter | |
| Rathee et al. | Developed distributed energy-efficient clustering (DDEEC) algorithm based on fuzzy logic approach for optimizing energy management in heterogeneous WSNs | |
| CN115515144B (en) | Heterogeneous AIoT Ad hoc network signal full-coverage method and device |
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 |