CN113038569A - PFCM-based wireless sensor network node charging method and system - Google Patents
PFCM-based wireless sensor network node charging method and system Download PDFInfo
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Abstract
The invention discloses the technical field of wireless charging, and particularly relates to a PFCM-based wireless sensor network node charging method and system, which can improve the energy utilization rate of a charging trolley, ensure the balance of network energy consumption, improve the stability of a network and ensure the lasting operation of the network. The 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; performing clustering effect analysis on each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result; determining the optimal cluster number and the corresponding charging trolley number based on the analysis result; and the charging trolley sequentially charges the nodes according to the charging priority of the nodes.
Description
Technical Field
The invention belongs to the technical field of wireless charging, and particularly relates to a PFCM-based wireless sensor network node charging method and system.
Background
As a key technology in the internet of things, Wireless Sensor Networks (WSNs) technology is developed by more and more students and research teams. The wireless sensor network is a network formed by self-organizing a plurality of sensor nodes, and the nodes have wireless communication capability and can be deployed in various environments for monitoring, sensing, collecting and transmitting environmental data. However, with the diversification of the application scenarios of the wireless sensor network, the network data traffic volume is increased sharply, and the nodes need to work continuously, which puts higher requirements on the energy supply capacity of the nodes. The traditional wireless sensor network is an energy-limited network, and the energy of the battery carried by the node is very limited. Thanks to the development of Wireless Charging technology in recent years, the Wireless Charging Vehicle (WCV) is adopted to supplement energy for the nodes, which gradually becomes a research hotspot, not only can solve the energy problem, but also enables the Wireless sensor network to adapt to more complex monitoring environments. The problems that the charging trolley is low in energy utilization rate, the balance of network energy consumption cannot be guaranteed, the stability of a network is poor and the like exist in the prior art.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides the PFCM-based wireless sensor network node charging method and the PFCM-based wireless sensor network node charging system, which can improve the energy utilization rate of a charging trolley, ensure the balance of network energy consumption, improve the stability of a network and ensure the lasting operation of the network.
In order to achieve the 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; performing clustering effect analysis on each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result; determining the optimal cluster number and the corresponding charging trolley number based on the analysis result; and the charging trolley sequentially charges the nodes according to the charging priority of the nodes.
Further, initializing the wireless sensor network refers to determining the scale and the node position of the wireless sensor network; the wireless sensing network is divided into three parts: the base station is fixed in position and used for scheduling the charging trolleys in a lump and converging 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 and communicating with the base station, the nodes are randomly distributed in the wireless sensor network, and the positions of the nodes are fixed 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 iiRate of consumption e of datatAnd data-aware energy consumption rate esThe method comprises the following two parts:
pi＝et+es＝(e0+e1dα)bt+e2bs (1)
wherein e is0Representing the energy expended to encode and modulate 1-bit data, e1Representing the energy expended in transmitting 1bit data to 1 meter away, e2Represents the energy consumed by perceiving 1-bit data, d represents the transmission distance, a represents the path loss exponent, btRepresenting the data transmission rate, bsIndicating the perception rate of the data.
Further, based on the energy consumption rate of each node, the nodes in the wireless sensor network are divided into a plurality of clusters by using a fuzzy C-means clustering algorithm based on energy constraint, specifically:
setting the node coordinate set X as X1,x2,…,xnDivide it into c clusters, their cost functionThe following were used:
wherein n represents the number of nodes of each cluster, m represents a weighting index for controlling the partition fuzziness, and beta represents an energy consumption influence factor; u. ofikAnd representing the membership value of the ith fuzzy subset of the node k, which is subordinate to the X, and meeting the constraint condition:
dikrepresenting the distance difference value, pik representing the energy consumption difference value, and calculating the following modes respectively:
dik＝||xk-vi|| (4)
pik＝||pk-pi|| (5)
wherein x iskCoordinates, v, representing node kiRepresenting coordinates of cluster center nodes, also called "cluster center nodes", pkRepresenting the energy consumption rate of the node k, wherein the node i is a cluster core node, and pi represents the energy consumption rate of the cluster core node;
calculating and updating a membership matrix U ═ U of each node relative to all clusters based on an energy constraint fuzzy C-means clustering algorithmik}c×nAnd cluster center vector V ═ V1,v2,…,vcAnd continuously iterating until the cost function is converged, and calculating U and V according to the c value corresponding to the cost function in convergence.
Further, the clustering effect analysis is performed on each cluster by using the effectiveness function considering the energy constraint to obtain an analysis result, specifically:
calculating the compactness Ins between nodes in the cluster:
the compactness among the nodes in the clusters represents the distance between each node in the clusters and the cluster center node, and the smaller the value of the compactness is, the better the clustering result is;
calculating separability between clusters Sep:
the separation among clusters represents the distance between the centers of all clusters, and the larger the value is, the better the clustering result is;
calculating the validity function value Val:
further, the determining the optimal cluster number and the corresponding charging trolley number based on the analysis result specifically comprises: c which enables the Val value to be minimum is taken as the optimal clustering number, namely the number of clusters in the network; subsequently, a charging trolley is assigned to each cluster.
Further, the charging trolley sequentially charges the nodes according to the charging priority of the nodes, and specifically comprises the following steps:
determining the charging priority CP of each node:
b represents a first factor of the importance of the membership degree of the regulation node i and the urgency degree of the charging task, q represents a second factor of the importance of the membership degree of the regulation node i and the urgency degree of the charging task, and TiRepresents the time it takes for the car to charge node i, tcurIndicates the current time, tendRepresents the latest charging time allowed by the node;
after the network is initialized, all nodes and charging trolleys start to run, 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 time, after the charging request is received, the priority of the charging task is calculated, the charging request is inserted into an existing charging request queue according to the sequence from high to low, and the node with the high charging priority is charged preferentially;
the charging trolley sequentially moves to the position of each corresponding node in the charging request queue to charge the charging trolley; after the charging task of one node is completed, the charging request is deleted from the charging request queue, then the priority of the charging task corresponding to each charging request in the queue is updated, and the charging requests are sorted 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 starts to charge the nodes in the network again 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; the fourth module is used for analyzing the clustering effect of each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result; a fifth module for determining the optimal cluster number and the corresponding number of charging trolleys based on the analysis result; and the charging trolley is used for sequentially charging the nodes according to the charging priority of each node.
Compared with the prior art, the invention has the following beneficial effects:
(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 on the spatial position and relatively average on the energy consumption rate, the energy utilization rate of the charging trolley can be improved, the network energy consumption balance 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 sensor network clustering algorithm is analyzed by fuzzy clustering effect analysis to obtain the optimal clustering scheme and the optimal number of charging trolleys of the sensor network in a certain scale, and at the moment, the clustering effect of the nodes based on membership degree is the best, and the average energy consumption rate is the lowest;
(3) according to the invention, a charging priority calculation method of the wireless sensor network is designed by combining the membership degree of a fuzzy C-means clustering algorithm based on energy constraint and the urgency degree of a charging task, so that a charging trolley can timely charge nodes required in the network, the stability of the network is improved, and the network can be ensured to run persistently.
Drawings
Fig. 1 is a schematic flowchart of a PFCM-based wireless sensor network node charging method 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 diagram of sensor network clustering results in an embodiment of the present invention;
fig. 4 is a schematic diagram of a charging cart charging nodes in a network according to charging priorities in the 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 illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
as shown in fig. 1, a method for charging a wireless sensor network node 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 Power based Fuzzy C-means (PFCM) algorithm based on energy constraint; performing clustering effect analysis on each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result; determining the optimal cluster number and the corresponding charging trolley number based on the analysis result; and the charging trolley sequentially charges the nodes according to the charging priority of the nodes.
The method comprises the following steps: 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 is MxN in the embodiment; the wireless sensing network is divided into three parts: the base station is fixed in position and used for scheduling the charging trolleys in a lump and converging 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 and communicating with the base station, and the nodes are randomly distributed in the wireless sensor network, are fixed in position 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 the fifth step;
step two: determining the energy consumption rate of each node in the wireless sensor network, comprising:
energy consumption rate p of node iiRate of consumption e of datatAnd data-aware energy consumption rate esThe method comprises the following two parts:
pi＝et+es＝(e0+e1dα)bt+e2bs (1)
wherein e is0Representing the energy expended to encode and modulate 1-bit data, e1Representing the energy expended in transmitting 1bit data to 1 meter away, e2Represents the energy consumed by perceiving 1bit data, d represents the transmission distance, alpha represents the path loss index, and the value range is [2,4 ]]，btRepresenting the data transmission rate, bsRepresenting a data perception rate; in this example, take e0Is 45 x 10- 9J/bit，e1Is 10 x 10-12J/bit/m，e2Is 60X 10-9J/bit; alpha is 2.
Step three: 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 Power based Fuzzy C-means (PFCM) algorithm based on energy constraint, which specifically comprises the following steps:
setting the node coordinate set X as X1,x2,…,xnDivide it into c clusters, its cost function is as follows:
wherein n represents the number of nodes of each cluster, m represents a weighting index for controlling the partition fuzziness, and beta represents an energy consumption influence factor, and the value is 0.9; u. ofikAnd representing the membership value of the ith fuzzy subset of the node k, which is subordinate to the X, and meeting the constraint condition:
dikrepresenting a value of distance difference, pikRepresenting the energy consumption difference value, and calculating the following modes respectively:
dik＝||xk-vi|| (4)
pik＝||pk-pi|| (5)
wherein x iskCoordinates, v, representing node kiRepresenting coordinates of cluster center nodes, also called "cluster center nodes", pkRepresenting the energy consumption rate of the node k, when the node i is a cluster core node, then piRepresenting the energy consumption rate of the cluster core nodes;
calculating and updating a membership matrix U ═ U of each node relative to all clusters based on an energy constraint fuzzy C-means clustering algorithmik}c×nAnd cluster center vector V ═ V1,v2,…,vcThe iterative formula is:
and continuously iterating until the cost function J converges, and calculating U and V according to a c value corresponding to the cost function J in the converging process.
The steps of the PFCM algorithm are as follows:
(3-1) initializing cluster number c (c is more than or equal to 2 and less than or equal to n), setting iteration convergence threshold theta, and randomly initializing cluster center matrix U(0)T is the number of iterations;
(3-2) updating the membership matrix V(t)；
(3-3) updating 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 is t +1, and the step is shifted to the step (3-2).
Step four: the method comprises the following steps of carrying out clustering effect analysis on each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result, evaluating according to a clustering result construction function obtained by a PFCM (pulse frequency modulation cm) algorithm, and adding the energy consumption rate of nodes into the evaluation of the compactness among the nodes in the cluster and the separability among the clusters, wherein the method specifically comprises the following steps:
calculating the compactness Ins between nodes in the cluster:
introducing an energy consumption influence factor beta in the compactness among the nodes in the cluster to represent the influence degree of the node energy consumption on the clustering result, wherein the compactness among the nodes in the cluster represents the distance between each node in the cluster and a cluster center node, and is a key factor for evaluating the quality of a clustering result, and the smaller the value of the compactness among the nodes in the cluster is, the better the clustering result is;
calculating separability between clusters Sep:
the separation among clusters represents the distance between the centers of all clusters, and the larger the value is, the better the clustering result is;
calculate the significance function value Val (after setting the value of c to 2,3, 4.. n, respectively, the value of the significance function Val is calculated):
step five: determining the optimal cluster number and the corresponding charging trolley number based on the analysis result; the method specifically comprises the following steps: c which enables the Val value to be minimum is taken as the optimal clustering number, namely the number of clusters in the network; subsequently, a charging trolley is assigned to each cluster.
Step six: the charging trolley sequentially charges the nodes according to the charging priority of the nodes, 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 charging task density is defined according to the ratio of the time required by charging the node i to the remaining life time of the node under the condition of no charging
The larger the value is, the larger the proportion of the time required for the charging task in the time for charging the node by the trolley is, and the calculation method is as follows:
wherein, TiRepresents the time it takes for the car to charge node i, tcurIs the current time, tendThe latest charging moment allowed for the node.
δ is defined as the charging task urgency, and its value increases with the time t, and is calculated as follows:
wherein q (q is more than or equal to 1) represents a second factor for regulating the membership degree of the node i and the importance of the charging task urgency, and the charging task density
Has a value interval of (0, 1)]Therefore, the value interval of the charging task urgency degree δ is (0, q)]；
Charging priority CP of each node:
wherein b represents a first factor of importance of membership degree and charging task urgency degree of the regulation node i, TiRepresents the time it takes for the car to charge node i, tcurIndicates the current time, tendRepresents the latest charging time allowed by the node;
(6-2) after the network is initialized, all the nodes and the charging trolleys start to run, 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;
(6-3) the charging trolley can receive the charging request of the node at any time, after the charging request is received, the priority of the charging task is calculated, the charging request is inserted into an existing charging request queue according to the sequence from high to low, and the node with the high charging priority is charged preferentially;
(6-4) sequentially moving the charging trolley to the positions of the nodes corresponding to the charging request queue to charge the charging trolley; after the charging task of one node is completed, the charging request is deleted from the charging request queue, then the priority of the charging task corresponding to each charging request in the queue is updated, and the charging requests are sorted 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 starts to charge the nodes in the network again after the energy supplement is completed.
In the embodiment, 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 spatial position and relatively average in energy consumption rate, the energy utilization rate of the charging trolley can be improved, the network energy consumption balance can be ensured, the stability of the network is improved, and the network can be ensured to run durably; the sensor network clustering algorithm is analyzed through fuzzy clustering effect analysis, so that an optimal clustering scheme and an optimal number of charging trolleys of a sensor network in a certain scale are obtained, and at the moment, the clustering effect of the nodes based on the membership degree is the best, and the average energy consumption rate is the lowest; in the embodiment, a charging priority calculation method for the wireless sensor network is designed by combining membership of a fuzzy C-means clustering algorithm based on energy constraint and urgency of a charging task, so that a charging trolley can timely charge nodes required in the network, the stability of the network is improved, and the network can be ensured to run persistently.
Example two:
based on the first embodiment of the charging method for the wireless sensor network node, the present embodiment provides a charging system for the wireless sensor network node, 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; the fourth module is used for analyzing the clustering effect of each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result; a fifth module for determining the optimal cluster number and the corresponding number of charging trolleys based on the analysis result; and the charging trolley is used for sequentially charging the nodes according to the charging priority of each node.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A wireless sensor network node charging method is characterized by 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;
performing clustering effect analysis on each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result;
determining the optimal cluster number and the corresponding charging trolley number based on the analysis result;
and the charging trolley sequentially charges the nodes according to the charging priority of the nodes.
2. The wireless sensor network node charging method according to claim 1, wherein the initializing of the wireless sensor network means determining the scale and the node position of the wireless sensor network; the wireless sensing network is divided into three parts: the base station is fixed in position and used for scheduling the charging trolleys in a lump and converging 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 and communicating with the base station, the nodes are randomly distributed in the wireless sensor network, and the positions of the nodes are fixed and known by the base station.
3. The method of claim 1, wherein the determining the energy consumption rate of each node in the wireless sensor network comprises:
energy consumption rate p of node iiRate of consumption e of datatAnd data-aware energy consumption rate esThe method comprises the following two parts:
pi＝et+es＝(e0+e1dα)bt+e2bs (1)
wherein e is0Representing the energy expended to encode and modulate 1-bit data, e1Representing the energy expended in transmitting 1bit data to 1 meter away, e2Represents the energy consumed by perceiving 1-bit data, d represents the transmission distance, a represents the path loss exponent, btRepresenting the data transmission rate, bsIndicating the perception rate of the data.
4. The method for charging the nodes of the wireless sensor network according to claim 1, wherein the nodes in the wireless sensor network are divided into a plurality of clusters by using a fuzzy C-means clustering algorithm based on energy constraint based on the energy consumption rate of each node, and specifically:
setting the node coordinate set X as X1,x2,…,xnDivide it into c clusters, its cost function is as follows:
wherein n represents the number of nodes of each cluster, m represents a weighting index for controlling the partition fuzziness, and beta represents an energy consumption influence factor; u. ofikAnd representing the membership value of the ith fuzzy subset of the node k, which is subordinate to the X, and meeting the constraint condition:
dikrepresenting a value of distance difference, pikRepresenting the energy consumption difference value, and calculating the following modes respectively:
dik＝||xk-vi|| (4)
pik＝||pk-pi|| (5)
wherein x iskCoordinates, v, representing node kiRepresenting coordinates of cluster center nodes, also called "cluster center nodes", pkRepresenting the energy consumption rate of the node k, when the node i is a cluster core node, then piRepresenting the energy consumption rate of the cluster core nodes;
calculating and updating a membership matrix U ═ U of each node relative to all clusters based on an energy constraint fuzzy C-means clustering algorithmik}c×nAnd cluster center vector V ═ V1,v2,…,vcAnd continuously iterating until the cost function is converged, and calculating U and V according to the c value corresponding to the cost function in convergence.
5. The wireless sensor network node charging method according to claim 1, wherein the clustering effect analysis is performed on each cluster by using an effectiveness function considering energy constraint to obtain an analysis result, specifically:
calculating the compactness Ins between nodes in the cluster:
the compactness among the nodes in the clusters represents the distance between each node in the clusters and the cluster center node, and the smaller the value of the compactness is, the better the clustering result is;
calculating separability between clusters Sep:
the separation among clusters represents the distance between the centers of all clusters, and the larger the value is, the better the clustering result is;
calculating the validity function value Val:
6. the wireless sensor network node charging method according to claim 5, wherein the determining the optimal cluster number and the corresponding charging trolley number based on the analysis result specifically comprises: c which enables the Val value to be minimum is taken as the optimal clustering number, namely the number of clusters in the network; subsequently, a charging trolley is assigned to each cluster.
7. The charging method for the nodes of the wireless sensor network according to claim 1, wherein the charging trolley sequentially charges the nodes according to the charging priority of the nodes, and specifically comprises the following steps:
determining the charging priority CP of each node:
b represents a first factor of the importance of the membership degree of the regulation node i and the urgency degree of the charging task, q represents a second factor of the importance of the membership degree of the regulation node i and the urgency degree of the charging task, and TiRepresents the time it takes for the car to charge node i, tcurIndicates the current time, tendRepresents the latest charging time allowed by the node;
after the network is initialized, all nodes and charging trolleys start to run, 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 time, after the charging request is received, the priority of the charging task is calculated, the charging request is inserted into an existing charging request queue according to the sequence from high to low, and the node with the high charging priority is charged preferentially;
the charging trolley sequentially moves to the position of each corresponding node in the charging request queue to charge the charging trolley; after the charging task of one node is completed, the charging request is deleted from the charging request queue, then the priority of the charging task corresponding to each charging request in the queue is updated, and the charging requests are sorted 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 starts to charge the nodes in the network again after the energy supplement is completed.
8. 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;
the fourth module is used for analyzing the clustering effect of each cluster by adopting an effectiveness function considering energy constraint to obtain an analysis result;
a fifth module for determining the optimal cluster number and the corresponding number of charging trolleys based on the analysis result;
and the charging trolley is used for sequentially charging the nodes according to the charging priority of each node.
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