CN113038569B - Wireless sensor network node charging method and system based on PFCM - Google Patents
Wireless sensor network node charging method and system based on PFCM Download PDFInfo
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- H—ELECTRICITY
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- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
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- 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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- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
Abstract
The invention discloses the technical field of wireless charging, in particular to a wireless sensor network node charging method and system based on PFCM, which can improve the energy utilization rate of a charging trolley, ensure the energy consumption balance of a network, improve the stability of the network and ensure the network to run permanently. Comprising the following steps: initializing a wireless sensor network; determining the energy consumption rate of each node in the wireless sensor network; based on the energy consumption rate of each node, dividing the nodes in the wireless sensor network into a plurality of clusters by adopting a fuzzy C-means clustering algorithm based on energy constraint; carrying out clustering effect analysis on each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result; based on the analysis result, determining the optimal cluster number and the corresponding charging trolley number; the charging trolley charges the nodes in turn according to their charging priorities.
Description
Technical Field
The invention belongs to the technical field of wireless charging, and particularly relates to a wireless sensor network node charging method and system based on PFCM.
Background
As a key technology in the internet of things, the wireless sensor networks (Wireless Sensor Networks, WSNs) technology has been paid attention to by more and more scholars and research teams. A wireless sensor network is a network formed by self-organization of a plurality of sensor nodes, which have wireless communication capability and can be deployed in various environments to monitor, sense, collect and transmit environmental data. However, with the diversification of application scenes of the wireless sensor network, network data traffic is increased sharply, and nodes need to work continuously, which puts higher demands on energy supply capacity. The traditional wireless sensor network is a network with limited energy, and the energy of a battery carried by a node is very limited. Thanks to the development of wireless charging technology in recent years, wireless charging trolleys (Wireless Charging Vehicle, WCV) are adopted to supplement energy for nodes, so that the wireless charging trolley gradually becomes a research hot spot, the energy problem can be solved, and the wireless sensor network can adapt to more complex monitoring environments. In the prior art, the charging trolley has the problems that the energy utilization rate is low, the energy consumption balance of a network cannot be guaranteed, the stability of the network is poor, and the like.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides the wireless sensor network node charging method and system based on the PFCM, which can improve the energy utilization rate of a charging trolley, ensure the balance of network energy consumption, improve the stability of the network and ensure the network to run permanently.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: a wireless sensor network node charging method comprises the following steps: initializing a wireless sensor network; determining the energy consumption rate of each node in the wireless sensor network; based on the energy consumption rate of each node, dividing the nodes in the wireless sensor network into a plurality of clusters by adopting a fuzzy C-means clustering algorithm based on energy constraint; carrying out clustering effect analysis on each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result; based on the analysis result, determining the optimal cluster number and the corresponding charging trolley number; the charging trolley charges the nodes in turn according to their charging priorities.
Further, initializing the wireless sensor network, namely determining the scale and the node position of the wireless sensor network; the wireless sensor network is divided into three parts: the system comprises a base station, a charging trolley and nodes, wherein the position of the base station is fixed and is used for comprehensively scheduling the charging trolley and converging the data of the nodes; the charging trolley is responsible for charging each node; the nodes are responsible for monitoring the wireless sensor network, collecting surrounding data, communicating with the base station, and are randomly distributed in the wireless sensor network, fixed in position and known by the base station.
Further, the determining the energy consumption rate of each node in the wireless sensor network includes:
energy consumption rate p of node i i From the data transmission energy consumption rate e t And data aware energy consumption rate e s The method comprises the following steps:
p i ＝e t +e s ＝(e 0 +e 1 d α )b t +e 2 b s (1)
wherein e 0 Representing the energy expended for encoding and modulating 1bit data, e 1 Represents the energy expended to transmit 1bit data to a distance of 1 meter, e 2 Represents the energy expended to perceive 1bit data, d represents the transmission distance, α represents the path loss index, b t Representing data transmission rate, b s Representing the data perception rate.
Further, the energy consumption rate based on each node adopts a fuzzy C-means clustering algorithm based on energy constraint to divide the nodes in the wireless sensor network into a plurality of clusters, and specifically comprises the following steps:
let node coordinate set x= { X 1 ,x 2 ,…,x n Dividing into c clusters, the cost function of which is as follows:
wherein n represents the number of nodes of each cluster, m represents a weighted index for controlling the partition ambiguity, and β represents an energy consumption influence factor; u (u) ik The membership value of the ith fuzzy subset, which represents that the node k belongs to X, meets the constraint condition:
d ik the distance difference value is represented, pik represents the energy consumption difference value, and the calculation modes are respectively as follows:
d ik ＝||x k -v i || (4)
p ik ＝||p k -p i || (5)
wherein x is k Representing the coordinates of node k, v i Representing cluster center node coordinates, also referred to as "cluster center nodes", p k The energy consumption rate of the node k is represented, when the node i is a cluster core node, pi represents the energy consumption rate of the cluster core node;
energy constraint-based fuzzy C-means clustering algorithm, and membership degree matrix U= { U of each node relative to all clusters is calculated and updated ik } c×n And cluster center vector v= { V 1 ,v 2 ,…,v c And continuously iterating until the cost function converges, and calculating U and V according to the corresponding c value of the cost function during convergence.
Further, the clustering effect analysis is performed on each cluster by adopting an effectiveness function considering energy constraint, so as to obtain an analysis result, which is specifically as follows:
calculating compactness Ins among nodes in the cluster:
the closeness between the nodes in the cluster represents the distance degree between each node in the cluster and the cluster center node, and the smaller the value is, the better the clustering result is;
calculating the separation Sep between clusters:
the separation between clusters represents the distance degree of the distance between the centers of all clusters, and the larger the value is, the better the clustering result is;
calculating a validity function value Val:
further, based on the analysis result, determining the optimal cluster number and the corresponding charging trolley number specifically includes: taking c which minimizes the Val value as the optimal cluster number, namely the number of clusters in the network; then, each cluster is assigned a charging cart.
Further, the charging trolley charges the nodes in turn according to the charging priority of each node, specifically:
determining a charging priority CP of each node:
wherein b represents a first factor for adjusting the importance of the membership of the node i and the urgency of the charging task, q represents a second factor for adjusting the importance of the membership of the node i and the urgency of the charging task, T i Representing the time it takes for the trolley to charge node i, t cur Indicating the current time, t end Representing the latest charging moment allowed by the node;
when the network is initialized, all nodes and the charging trolleys start to operate, and when the residual energy of the nodes is lower than a fixed threshold value, a charging request is sent to the charging trolleys in the cluster;
the charging trolley can receive the charging request of the node at any moment, calculates the priority of the charging task after receiving the charging request, inserts the charging request into the existing charging request queue according to the sequence from high to low, and the node with high charging priority is charged with priority;
the charging trolley sequentially moves to the position of each corresponding node in the charging request queue to charge the corresponding node; after completing the charging task of one node, deleting the charging request from the charging request queue, and then updating the priority of the charging task corresponding to each charging request in the queue, and sequencing each charging request again according to the priority; the charging trolley returns to the base station to supplement the electric quantity before the electric quantity is exhausted, and the charging trolley recharges to the nodes in the network after the energy supplement is completed.
A wireless sensor network node charging system, comprising: the first module is used for initializing the wireless sensor network; the second module is used for determining the energy consumption rate of each node in the wireless sensor network; the third module is used for dividing the nodes in the wireless sensor network into a plurality of clusters by adopting a fuzzy C-means clustering algorithm based on energy constraint based on the energy consumption rate of each node; a fourth module, configured to perform clustering effect analysis on each cluster by using an effectiveness function considering energy constraint, so as to obtain an analysis result; a fifth module, configured to determine an optimal cluster number and a corresponding charging trolley number based on the analysis result; and the charging trolley is used for charging the nodes in turn according to the charging priority of each node.
Compared with the prior art, the invention has the beneficial effects that:
(1) According to the invention, the sensor network is clustered by adopting the fuzzy C-means clustering algorithm based on energy constraint, so that the nodes of each cluster are relatively concentrated in space position and relatively averaged in energy consumption rate, the energy utilization rate of the charging trolley can be improved, the energy consumption balance of the network can be ensured, the stability of the network is improved, and the network can be ensured to run for a long time;
(2) According to the invention, the clustering algorithm of the sensor network is analyzed by using the fuzzy clustering effect analysis to obtain an optimal clustering scheme and an optimal charging trolley number of the sensor network in a certain scale, and at the moment, the clustering effect of the nodes based on membership is best and the average energy consumption rate is lowest;
(3) According to the method, the membership of the fuzzy C-means clustering algorithm based on energy constraint and the urgency of a charging task are combined, and the wireless sensor network charging priority calculating method is designed, so that a charging trolley can timely charge nodes required by a network, the stability of the network is improved, and the network can be ensured to run permanently.
Drawings
Fig. 1 is a schematic flow chart of a wireless sensor network node charging method based on PFCM according to an embodiment of the present invention;
FIG. 2 is a flow chart of a fuzzy C-means clustering algorithm based on energy constraints employed in an embodiment of the present invention;
FIG. 3 is a graph of sensor network clustering results in an embodiment of the invention;
fig. 4 is a schematic diagram of a charging cart charging nodes in a network according to a charging priority in an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Embodiment one:
as shown in fig. 1, a wireless sensor network node charging method includes: initializing a wireless sensor network; determining the energy consumption rate of each node in the wireless sensor network; based on the energy consumption rate of each node, dividing the nodes in the wireless sensor network into a plurality of clusters by adopting a fuzzy C-means (PFCM) algorithm based on energy constraint; carrying out clustering effect analysis on each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result; based on the analysis result, determining the optimal cluster number and the corresponding charging trolley number; the charging trolley charges the nodes in turn according to their charging priorities.
Step one: initializing a wireless sensor network
Initializing a wireless sensor network, namely determining the scale and the node position of the wireless sensor network, wherein the scale of the wireless sensor network in the embodiment is M multiplied by N; the wireless sensor network is divided into three parts: the system comprises a base station, a charging trolley and nodes, wherein the position of the base station is fixed and is used for comprehensively scheduling the charging trolley and converging the data of the nodes; the charging trolley is responsible for charging each node; the nodes are responsible for monitoring the wireless sensor network, collecting surrounding data, communicating with the base station, and randomly distributing the nodes in the wireless sensor network, wherein the positions of the nodes are fixed and are known by the base station; a plurality of clusters are arranged in the network, each cluster is provided with a charging trolley, and the number of the clusters is determined by a step five;
step two: determining the energy consumption rate of each node in the wireless sensor network comprises the following steps:
energy consumption rate p of node i i From the data transmission energy consumption rate e t And data aware energy consumption rate e s The method comprises the following steps:
p i ＝e t +e s ＝(e 0 +e 1 d α )b t +e 2 b s (1)
wherein e 0 Representing the energy expended for encoding and modulating 1bit data, e 1 Represents the energy expended to transmit 1bit data to a distance of 1 meter, e 2 Represents the energy consumed by sensing 1bit data, d represents the transmission distance, alpha represents the path loss index, and the value range is [2,4 ]]，b t Representing data transmission rate, b s Representing a data perception rate; in this embodiment, take e 0 45X 10 - 9 J/bit，e 1 Is 10 multiplied by 10 -12 J/bit/m，e 2 60X 10 -9 J/bit; alpha has a value of 2.
Step three: based on the energy consumption rate of each node, the nodes in the wireless sensor network are divided into a plurality of clusters by adopting a fuzzy C-means (PFCM) algorithm based on energy constraint, and the method specifically comprises the following steps:
let node coordinate set x= { X 1 ,x 2 ,…,x n Dividing into c clusters, the cost function of which is as follows:
wherein n represents the number of nodes of each cluster, m represents a weighted index for controlling the partitioning ambiguity, and beta represents an energy consumption influence factor, and the value is 0.9; u (u) ik The membership value of the ith fuzzy subset, which represents that the node k belongs to X, meets the constraint condition:
d ik representing the distance difference value, p ik The energy consumption difference value is represented by the following calculation modes:
d ik ＝||x k -v i || (4)
p ik ＝||p k -p i || (5)
wherein x is k Representing the coordinates of node k, v i Representing cluster center node coordinates, also referred to as "cluster center nodes", p k Representing the energy consumption rate of the node k, wherein the node i is a cluster core node, and p is i Representing the energy consumption rate of cluster core nodes;
energy constraint-based fuzzy C-means clustering algorithm, and membership degree matrix U= { U of each node relative to all clusters is calculated and updated ik } c×n And cluster center vector v= { V 1 ,v 2 ,…,v c The iterative formula is:
and continuously iterating until the cost function J converges, and calculating U and V according to the corresponding c value of the cost function J in convergence.
The PFCM algorithm is as follows:
(3-1) initializing a cluster number c (2 is more than or equal to c is more than or equal to n), setting an iteration convergence threshold value theta, and randomly initializing a cluster center matrix U (0) T is the iteration number;
(3-2) updating the membership matrix V (t) ；
(3-3) updating the clustering center matrix U (t+1) ；
Judging if J (t+1) -J (t) ||<Theta, stopping the algorithm, and returning to U and V; otherwise t=t+1, turning to step(3-2)。
Step four: the method comprises the steps of carrying out clustering effect analysis on each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result, evaluating the cluster result construction function obtained according to a PFCM algorithm, and adding the energy consumption rate of the nodes into the evaluation of the compactness among the nodes in the clusters and the separability among the clusters, wherein the method specifically comprises the following steps:
calculating compactness Ins among nodes in the cluster:
introducing an energy consumption influence factor beta into the closeness between the nodes in the cluster, representing the influence degree of the energy consumption of the nodes on the clustering result, wherein the closeness between the nodes in the cluster characterizes the distance between each node in the cluster and the cluster center node, and is a key factor for evaluating the good or bad clustering result, and in the embodiment, the smaller the value is, the better the clustering result is;
calculating the separation Sep between clusters:
the separation between clusters represents the distance between the centers of each cluster, and in the embodiment, the larger the value is, the better the clustering result is;
calculating the validity function value Val (after setting the value of c to 2,3,4,..n, respectively):
step five: based on the analysis result, determining the optimal cluster number and the corresponding charging trolley number; the method comprises the following steps: taking c which minimizes the Val value as the optimal cluster number, namely the number of clusters in the network; then, each cluster is assigned a charging cart.
Step six: the charging trolley charges the nodes according to the charging priority of each node in turn, and specifically comprises the following steps:
and (6-1) determining the optimal clustering number and the corresponding charging trolley number according to the clustering effect analysis result. The charge task density is defined according to the ratio of the time required for charging the node i to the remaining life time of the node in the uncharged stateThe larger the value, the larger the proportion of time needed for charging tasks in the time for charging the nodes by the trolley is, and the calculation mode is as follows:
wherein T is i Representing the time it takes for the trolley to charge node i, t cur T is the current time, t end The latest charge moment allowed by the node.
Defining delta as the urgency of the charging task, the value of delta is continuously increased along with the change of time t, and the calculation mode is as follows:
wherein q (q.gtoreq.1) represents a second factor regulating the importance of the membership of node i and the urgency of the charging task, the charging task densityThe value interval of (1) is (0, 1)]Therefore, the interval of charge task urgency δ is (0, q)]；
Charging priority CP of each node:
wherein b represents a first factor of importance of membership of the regulating node i and urgency of charging task, T i Representing the trolley as node iTime spent charging, t cur Indicating the current time, t end Representing the latest charging moment allowed by the node;
(6-2) when the network is initialized, all nodes and the charging trolleys start to operate, and when the residual energy of the nodes is lower than a fixed threshold value, charging requests are sent to the charging trolleys in the cluster;
(6-3) the charging trolley can receive the charging request of the node at any time, and after receiving the charging request, calculates the priority of the charging task, inserts the charging request into the existing charging request queue according to the sequence from high to low, and the node with high charging priority is charged with priority;
(6-4) the charging trolley sequentially moves to the position of each node corresponding to the charging request queue to charge the charging trolley; after completing the charging task of one node, deleting the charging request from the charging request queue, and then updating the priority of the charging task corresponding to each charging request in the queue, and sequencing each charging request again according to the priority; the charging trolley returns to the base station to supplement the electric quantity before the electric quantity is exhausted, and the charging trolley recharges to the nodes in the network after the energy supplement is completed.
According to the embodiment, the sensor network is clustered by adopting the fuzzy C-means clustering algorithm based on energy constraint, so that nodes of each cluster are relatively concentrated in space positions and relatively averaged in energy consumption rate, the energy utilization rate of the charging trolley can be improved, the energy consumption balance of the network can be ensured, the stability of the network is improved, and the network can be ensured to run for a long time; the sensor network clustering algorithm is analyzed through fuzzy clustering effect analysis, so that an optimal clustering scheme and an optimal charging trolley number of a sensor network with a certain scale are obtained, and at the moment, the clustering effect of the nodes based on membership degree is best, and the average energy consumption rate is lowest; according to the embodiment, the membership of the fuzzy C-means clustering algorithm based on energy constraint and the urgency of a charging task are combined, and the wireless sensor network charging priority calculating method is designed, so that a charging trolley can timely charge nodes required in a network, the stability of the network is improved, and the network can be ensured to run permanently.
Embodiment two:
based on the wireless sensor network node charging method described in the first embodiment, the present embodiment provides a wireless sensor network node charging system, including: the first module is used for initializing the wireless sensor network; the second module is used for determining the energy consumption rate of each node in the wireless sensor network; the third module is used for dividing the nodes in the wireless sensor network into a plurality of clusters by adopting a fuzzy C-means clustering algorithm based on energy constraint based on the energy consumption rate of each node; a fourth module, configured to perform clustering effect analysis on each cluster by using an effectiveness function considering energy constraint, so as to obtain an analysis result; a fifth module, configured to determine an optimal cluster number and a corresponding charging trolley number based on the analysis result; and the charging trolley is used for charging the nodes in turn according to the charging priority of each node.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (6)
1. The wireless sensor network node charging method is characterized by comprising the following steps of:
initializing a wireless sensor network;
determining the energy consumption rate of each node in the wireless sensor network;
based on the energy consumption rate of each node, dividing the nodes in the wireless sensor network into a plurality of clusters by adopting a fuzzy C-means clustering algorithm based on energy constraint;
carrying out clustering effect analysis on each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result;
based on the analysis result, determining the optimal cluster number and the corresponding charging trolley number;
the charging trolley charges the nodes in turn according to the charging priority of each node;
the energy consumption rate based on each node adopts an energy constraint-based fuzzy C-means clustering algorithm to divide the nodes in the wireless sensor network into a plurality of clusters, and specifically comprises the following steps:
let node coordinate set x= { X 1 ,x 2 ,…,x n Dividing into c clusters, the cost function of which is as follows:
wherein n represents the number of nodes of each cluster, m represents a weighted index for controlling the partition ambiguity, and β represents an energy consumption influence factor; u (u) ik The membership value of the ith fuzzy subset, which represents that the node k belongs to X, meets the constraint condition:
d ik representing the distance difference value, p ik The energy consumption difference value is represented by the following calculation modes:
d ik ＝||x k -v i || (4)
p ik ＝||p k -p i || (5)
wherein x is k Representing the coordinates of node k, v i Representing cluster center node coordinates, also referred to as "cluster center nodes", p k Representing the energy consumption rate of the node k, wherein the node i is a cluster core node, and p is i Representing the energy consumption rate of cluster core nodes;
energy constraint-based fuzzy C-means clustering algorithm, and membership degree matrix U= { U of each node relative to all clusters is calculated and updated ik } c×n And cluster center vector v= { V 1 ,v 2 ,…,v c Continuously iterating until the cost function converges, and calculating U and V according to the value of c corresponding to the convergence of the cost function;
the clustering effect analysis is carried out on each cluster by adopting an effectiveness function considering energy constraint, so that an analysis result is obtained, and the clustering effect analysis method specifically comprises the following steps:
calculating compactness Ins among nodes in the cluster:
the closeness between the nodes in the cluster represents the distance degree between each node in the cluster and the cluster center node, and the smaller the value is, the better the clustering result is;
calculating the separation Sep between clusters:
the separation between clusters represents the distance degree of the distance between the centers of all clusters, and the larger the value is, the better the clustering result is;
calculating a validity function value Val:
2. the method for charging the wireless sensor network node according to claim 1, wherein the initializing the wireless sensor network means determining the scale and the node position of the wireless sensor network; the wireless sensor network is divided into three parts: the system comprises a base station, a charging trolley and nodes, wherein the position of the base station is fixed and is used for comprehensively scheduling the charging trolley and converging the data of the nodes; the charging trolley is responsible for charging each node; the nodes are responsible for monitoring the wireless sensor network, collecting surrounding data, communicating with the base station, and are randomly distributed in the wireless sensor network, fixed in position and known by the base station.
3. The method for charging the wireless sensor network node according to claim 1, wherein the determining the energy consumption rate of each node in the wireless sensor network comprises:
energy consumption rate p of node i i From the data transmission energy consumption rate e t And data aware energy consumption rate e s Two-part compositionThe calculation method is as follows:
p i ＝e t +e s ＝(e 0 +e 1 d α )b t +e 2 b s (1)
wherein e 0 Representing the energy expended for encoding and modulating 1bit data, e 1 Represents the energy expended to transmit 1bit data to a distance of 1 meter, e 2 Represents the energy expended to perceive 1bit data, d represents the transmission distance, α represents the path loss index, b t Representing data transmission rate, b s Representing the data perception rate.
4. The method for charging the wireless sensor network node according to claim 1, wherein the determining the optimal cluster number and the corresponding charging trolley number based on the analysis result is specifically as follows: taking c which minimizes the Val value as the optimal cluster number, namely the number of clusters in the network; then, each cluster is assigned a charging cart.
5. The wireless sensor network node charging method according to claim 1, wherein the charging trolley charges the nodes in turn according to the charging priority of each node, specifically:
determining a charging priority CP of each node:
wherein b represents a first factor for adjusting the importance of the membership of the node i and the urgency of the charging task, q represents a second factor for adjusting the importance of the membership of the node i and the urgency of the charging task, T i Representing the time it takes for the trolley to charge node i, t cur Indicating the current time, t end Representing the latest charging moment allowed by the node;
when the network is initialized, all nodes and the charging trolleys start to operate, and when the residual energy of the nodes is lower than a fixed threshold value, a charging request is sent to the charging trolleys in the cluster;
the charging trolley can receive the charging request of the node at any moment, calculates the priority of the charging task after receiving the charging request, inserts the charging request into the existing charging request queue according to the sequence from high to low, and the node with high charging priority is charged with priority;
the charging trolley sequentially moves to the position of each corresponding node in the charging request queue to charge the corresponding node; after completing the charging task of one node, deleting the charging request from the charging request queue, and then updating the priority of the charging task corresponding to each charging request in the queue, and sequencing each charging request again according to the priority; the charging trolley returns to the base station to supplement the electric quantity before the electric quantity is exhausted, and the charging trolley recharges to the nodes in the network after the energy supplement is completed.
6. A wireless sensor network node charging system is characterized by comprising:
the first module is used for initializing the wireless sensor network;
the second module is used for determining the energy consumption rate of each node in the wireless sensor network;
the third module is used for dividing the nodes in the wireless sensor network into a plurality of clusters by adopting a fuzzy C-means clustering algorithm based on energy constraint based on the energy consumption rate of each node;
a fourth module, configured to perform clustering effect analysis on each cluster by using an effectiveness function considering energy constraint, so as to obtain an analysis result;
a fifth module, configured to determine an optimal cluster number and a corresponding charging trolley number based on the analysis result;
the charging trolley is used for charging the nodes in turn according to the charging priority of each node;
the energy consumption rate based on each node adopts an energy constraint-based fuzzy C-means clustering algorithm to divide the nodes in the wireless sensor network into a plurality of clusters, and specifically comprises the following steps:
let node coordinate set x= { X 1 ,x 2 ,…,x n Dividing into c clusters, the cost function of whichThe following are provided:
wherein n represents the number of nodes of each cluster, m represents a weighted index for controlling the partition ambiguity, and β represents an energy consumption influence factor; u (u) ik The membership value of the ith fuzzy subset, which represents that the node k belongs to X, meets the constraint condition:
d ik representing the distance difference value, p ik The energy consumption difference value is represented by the following calculation modes:
d ik ＝||x k -v i || (4)
p ik ＝||p k -p i || (5)
wherein x is k Representing the coordinates of node k, v i Representing cluster center node coordinates, also referred to as "cluster center nodes", p k Representing the energy consumption rate of the node k, wherein the node i is a cluster core node, and p is i Representing the energy consumption rate of cluster core nodes;
energy constraint-based fuzzy C-means clustering algorithm, and membership degree matrix U= { U of each node relative to all clusters is calculated and updated ik } c×n And cluster center vector v= { V 1 ,v 2 ,…,v c Continuously iterating until the cost function converges, and calculating U and V according to the value of c corresponding to the convergence of the cost function;
the clustering effect analysis is carried out on each cluster by adopting an effectiveness function considering energy constraint, so that an analysis result is obtained, and the clustering effect analysis method specifically comprises the following steps:
calculating compactness Ins among nodes in the cluster:
the closeness between the nodes in the cluster represents the distance degree between each node in the cluster and the cluster center node, and the smaller the value is, the better the clustering result is;
calculating the separation Sep between clusters:
the separation between clusters represents the distance degree of the distance between the centers of all clusters, and the larger the value is, the better the clustering result is;
calculating a validity function value Val:
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105706284A (en) * | 2013-11-08 | 2016-06-22 | 本田技研工业株式会社 | Two-power-supply load driving fuel cell system |
| CN108990129A (en) * | 2018-08-14 | 2018-12-11 | 长春理工大学 | A kind of wireless sensor network cluster-dividing method and system |
| CN110677864A (en) * | 2019-10-12 | 2020-01-10 | 长春工业大学 | Energy constraint fuzzy c-mean clustering method based on wireless sensor network |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105706284A (en) * | 2013-11-08 | 2016-06-22 | 本田技研工业株式会社 | Two-power-supply load driving fuel cell system |
| CN108990129A (en) * | 2018-08-14 | 2018-12-11 | 长春理工大学 | A kind of wireless sensor network cluster-dividing method and system |
| CN110677864A (en) * | 2019-10-12 | 2020-01-10 | 长春工业大学 | Energy constraint fuzzy c-mean clustering method based on wireless sensor network |
Non-Patent Citations (3)
| Title |
|---|
| Wenqian Li ; Dongqing Wang ; Yan Yang.Power Line Carrier Communication Based on Wireless Charging System.《2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)》.2020,全文. * |
| WRSNs中基于动态优先级的多车协作充电调度技术研究;刘权;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;全文 * |
| 郭红霞 ; 白浩 ; 刘磊 ; 王昱力.统一电能交易市场下的虚拟电厂优化调度模型.《电工技术学报》.2015,全文. * |
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