CN110061538B - WSN node intelligent clustering and mobile charging equipment path planning method - Google Patents
WSN node intelligent clustering and mobile charging equipment path planning method Download PDFInfo
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- CN110061538B CN110061538B CN201910224184.2A CN201910224184A CN110061538B CN 110061538 B CN110061538 B CN 110061538B CN 201910224184 A CN201910224184 A CN 201910224184A CN 110061538 B CN110061538 B CN 110061538B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- 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
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B40/00—Technologies aiming at improving the efficiency of home appliances, e.g. induction cooking or efficient technologies for refrigerators, freezers or dish washers
Abstract
The invention relates to a WSN node intelligent clustering and mobile charging equipment path planning method. The invention combines the characteristics of the wireless sensor network and the mobile charging equipment to plan the path of the mobile charging equipment in two stages. In the first stage, the chargeable sensor nodes are clustered into a plurality of node clusters by adopting an unsupervised learning method, and a plurality of charging modes are selected according to the energy surplus of the chargeable sensor nodes in each node cluster to finish charging of the nodes in the clusters. And in the second stage, the path of the mobile charging equipment is planned by using a heuristic evolutionary algorithm, so that the total energy consumption required for completing one charging task is minimized. Therefore, the invention can provide various charging strategies, improve the charging efficiency and effectively prolong the life cycle of the wireless sensor network.
Description
Technical Field
The invention relates to the field of wireless sensor networks, in particular to a WSN (wireless sensor) node intelligent clustering and mobile charging equipment path planning method.
Background
Wireless Sensor Networks (WSNs) are Networks composed of dozens to thousands of Sensor nodes with Wireless signal transceiving capability, and can collect data in a monitoring range and send the data to a processing center for processing through Wireless signals. The method is widely applied to the fields of industrial monitoring, environmental monitoring, medical health and the like. Sensor nodes in a wireless sensor network are typically powered by batteries and power cannot be supplemented. The limited power causes that the wireless sensor network cannot operate for a long time, and the application and the development of the wireless sensor network are restricted.
Wireless Rechargeable Sensor Networks (WRSNs) were developed from traditional Wireless Sensor Networks. In recent years, research on wireless charging technology has made breakthrough, a new solution is provided for realizing long-term operation of a wireless sensor network, and a wireless rechargeable sensor network is generated. In the Wireless chargeable sensor network, a Wireless power receiving device is arranged on a node, and when the power of the node is about to be exhausted, the node is charged by a mobile Wireless Charging Equipment (WCE), so that the long-term operation of the network is realized. However, in practical applications, the charging energy carried by the mobile charging device is often limited, the energy of all sensor nodes cannot be fully charged in a single charging process, and only the operating time of the wireless sensor network can be prolonged, so that the charging mode of the mobile charging device needs to be selected according to the energy distribution condition of the wireless sensor network and a moving path needs to be planned. In addition, when a charging mode with a large number of pairs is selected, the charging effect is influenced by the selection of the charging anchor point because the charging efficiency of the charging equipment is greatly influenced by the distance.
Disclosure of Invention
In order to solve the defects that the mobile charging equipment in the prior art cannot fully charge the energy of all sensor nodes in a single charging process, and the charging efficiency of the charging equipment is greatly influenced by the distance and the charging anchor point, the invention provides an intelligent WSN node clustering and mobile charging equipment path planning method.
In order to realize the purpose of the invention, the technical scheme is as follows:
a WSN node intelligent clustering and mobile charging equipment path planning method comprises the following steps:
step S1: clustering the nodes of the chargeable sensor into a plurality of node clusters by adopting an unsupervised learning method;
step S2: selecting a charging mode according to the energy surplus of the chargeable sensor node in each node cluster, and determining the residence time and the charging anchor point position of the charging equipment in each cluster node according to the charging mode;
and step S3: and charging the charging equipment, planning a path of the mobile charging equipment by using a heuristic evolutionary algorithm, and calculating the minimum total energy consumption required for completing one-time charging task.
Preferably, the specific steps in step S1 are as follows:
clustering the chargeable sensor nodes into a plurality of node clusters by using a k-means algorithm, randomly selecting a plurality of sensor nodes from the sensor nodes as initial centroids, assigning each sensor node to the nearest centroid to form a cluster, updating the centroid of each cluster, and repeating the steps until the cluster centroid is not changed any more.
Preferably, the specific method for selecting the charging mode in step S2 is as follows:
in each node cluster, calculating the residual working time of the chargeable sensor nodes in the cluster, taking the minimum residual working time as the residual working time of the node cluster, and calculating the average residual working time T of all the node clusters;
and judging whether the residual working time of each node cluster is greater than T, if so, charging the node cluster by the mobile charging equipment in a node residual energy maximization charging mode, otherwise, adopting a node residual energy equalization charging mode.
Preferably, the specific method for determining the residence time and the charging anchor point position of the charging device in each cluster node by the node residual energy maximizing charging mode is as follows:
determining the residence time of the charging equipment in each cluster node:
wherein, the first and the second end of the pipe are connected with each other,
average remaining operating time, t, for all sensor nodes i Setting the time of arrival at anchor point i, tau, for mobile charging i The time for the mobile charging equipment to stay at the anchor point i for charging;
wherein, T j (t) is the remaining working time of the sensor node j, and N is the number of the sensor nodes;
wherein e is j (t) is the residual energy of sensor node j, P j Is the consumed power of sensor node j;
wherein e is j (t) is a piecewise function of,
based on the charging efficiency function>
Distance, P, from sensor node j to charging anchor point ch Charging power for mobile charging equipment, E max Is the energy upper limit of the sensor node;
charging anchor position of the charging device in each cluster node:
screening all chargeable sensor nodes with energy smaller than a preset threshold value a in the cluster nodes, placing the chargeable sensor nodes into a node set Q, calculating the central points of all the nodes in the set Q, and setting the central points as charging anchor points; charging after the charging equipment moves to the anchor point until the residual energy of one chargeable sensor node in the set Q is not less than a preset threshold value a, deleting the node from the set Q, and recalculating the charging anchor point;
and repeating the steps until the residence time of the charging equipment reaches the required residence time.
Preferably, the specific method for determining the residence time and the charging anchor point position of the charging device in each cluster node by the node residual energy equalization charging mode is as follows:
determining the residence time of the charging equipment in each cluster node:
wherein the content of the first and second substances,
variance of remaining operating time, t, for all sensor nodes i Reach for mobile chargingTime of anchor point i, τ i The time for the mobile charging equipment to stay at the anchor point i for charging; />
Wherein, T j (t) is the remaining operating time of sensor node j,
the average remaining working time of all the sensor nodes is defined, and N is the number of the sensor nodes;
wherein e is j (t) is the residual energy of sensor node j, P j Is the power consumed by sensor node j;
wherein e is j (t) is a function of the segmentation,
based on the charging efficiency function>
Distance, P, from sensor node j to charging anchor point ch Charging power for mobile charging equipment, E max Is the energy upper limit of the sensor node;
determining the position of a charging anchor point of the charging equipment in each cluster node:
setting the chargeable sensor node with the least residual energy in the cluster nodes as a charging anchor point, moving the charging equipment to the anchor point, charging until the residual energy of the chargeable sensor node is not less than a preset threshold value a, and recalculating the charging anchor point;
and repeating the steps until the residence time of the charging equipment reaches the required residence time.
Preferably, the heuristic evolutionary algorithm of step S3 includes the following specific steps:
firstly, constructing individuals and populations, and forming a group of random sequences as the individuals according to anchor point labels; generating a plurality of individuals as a population, determining the fitness of each individual, and performing crossing, variation and selection operations on the population; and repeating the iteration of the steps until the required iteration times are reached, and outputting the individual with the minimum fitness in the population.
Preferably, the specific steps for determining the fitness value of each individual are as follows:
fit＝E tra +E ch (10)
wherein fit is the total energy consumption, E tra For moving the charging device, consuming energy, E ch Charging energy consumed by all sensor nodes in the WSN for the mobile charging equipment;
wherein, P tra The running power of the mobile charging equipment is V, the running speed of the mobile wireless charging equipment is V, and the running distance of the mobile charging equipment for completing a charging task is D;
wherein m is the number of anchor points. d i,i+1 Distance, d, from anchor point i to anchor point i +1 m,0 The distance from the anchor point m to the charging station;
wherein, P ch Charging power, τ, for mobile charging devices i The residence time of the mobile charging device at anchor point i.
Compared with the prior art, the invention has the beneficial effects that:
the method includes clustering sensor network nodes into a plurality of clusters, and setting cluster centers as charging anchor points; determining a charging mode of the mobile charging equipment according to the energy surplus condition of the sensor nodes in each cluster; and planning a path with minimum total energy consumption required by the charging equipment to complete the charging task through a biological heuristic algorithm. The invention can effectively prolong the service life of the network and improve the charging efficiency.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of the K-means algorithm;
FIG. 3 is a flow chart diagram of a charging mode determination method;
FIG. 4 is a flow chart of a method for determining a charging anchor point for a node residual energy maximizing charging mode;
fig. 5 is a flowchart of a method for determining a charging anchor point in a node residual energy equalization charging mode;
FIG. 6 is a flow chart of a differential evolution algorithm.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
the invention is further illustrated by the following figures and examples.
Example 1
As shown in fig. 1 to 6, a method for intelligent clustering of WSN nodes and path planning of mobile charging devices includes the following steps:
step S1: clustering the nodes of the chargeable sensor into a plurality of node clusters by adopting an unsupervised learning method;
step S2: selecting a charging mode according to the energy surplus of the chargeable sensor node in each node cluster, and determining the residence time and the charging anchor point position of the charging equipment in each cluster node according to the charging mode;
and step S3: and charging the charging equipment, planning a path of the mobile charging equipment by using a heuristic evolutionary algorithm, and calculating the minimum total energy consumption required for completing one-time charging task.
As a preferred embodiment, the specific steps in step S1 are as follows:
clustering the chargeable sensor nodes into a plurality of node clusters by using a k-means algorithm, randomly selecting a plurality of sensor nodes from the sensor nodes as initial centroids, assigning each sensor node to the nearest centroid to form a cluster, updating the centroid of each cluster, and repeating the steps until the cluster centroid is not changed any more.
Preferably, the specific method for selecting the charging mode in step S2 is as follows:
in each node cluster, calculating the residual working time of the chargeable sensor nodes in the cluster, taking the minimum residual working time as the residual working time of the node cluster, and calculating the average residual working time T of all the node clusters;
and judging whether the residual working time of each node cluster is greater than T, if so, charging the node cluster by the mobile charging equipment in a node residual energy maximization charging mode, otherwise, adopting a node residual energy equalization charging mode.
As a preferred embodiment, the specific method for determining the residence time and the charging anchor point position of the charging device in each cluster node by the node residual energy maximizing charging mode is as follows:
determining the residence time of the charging equipment in each cluster node:
wherein, the first and the second end of the pipe are connected with each other,
average remaining operating time, t, for all sensor nodes i For mobile chargingTime of arrival at anchor point i, τ i The time for the mobile charging equipment to stay at the anchor point i for charging;
wherein, T j (t) is the remaining working time of the sensor node j, and N is the number of the sensor nodes;
wherein e is j (t) is the residual energy of sensor node j, P j Is the consumed power of sensor node j;
wherein e is j (t) is a piecewise function of,
based on the charging efficiency function>
Distance, P, from sensor node j to charging anchor point ch Charging power for mobile charging equipment, E max Is the energy upper limit of the sensor node;
charging anchor point position of the charging device in each cluster node:
screening all chargeable sensor nodes with energy smaller than a preset threshold value a in the cluster nodes, placing the chargeable sensor nodes into a node set Q, calculating the central points of all the nodes in the node set Q, and setting the central points as charging anchor points; charging after the charging equipment moves to the anchor point until the residual energy of one chargeable sensor node in the set Q is not less than a preset threshold value a, deleting the node from the set Q, and recalculating the charging anchor point;
and repeating the steps until the residence time of the charging equipment reaches the required residence time.
As a preferred embodiment, the specific method for determining the residence time and the charge anchor point position of the charging device in each cluster node by the point residual energy equalization charge mode is as follows:
determining the residence time of the charging equipment in each cluster node:
wherein the content of the first and second substances,
variance of remaining operating time, t, for all sensor nodes i Setting the time of arrival at anchor point i, tau, for mobile charging i The time for the mobile charging equipment to stay at the anchor point i for charging;
wherein, T j (t) is the remaining operating time of sensor node j,
the average residual working time of all the sensor nodes is obtained, and N is the number of the sensor nodes;
wherein e is j (t) is the residual energy of sensor node j, P j Is the power consumed by sensor node j;
wherein e is j (t) is a piecewise function of,
as a function of charging efficiency>
Distance, P, from sensor node j to charging anchor point ch Charging power for mobile charging equipment, E max Is the energy upper limit of the sensor node;
determining the position of a charging anchor point of the charging equipment in each cluster node:
setting the chargeable sensor node with the least residual energy in the cluster nodes as a charging anchor point, moving the charging equipment to the anchor point, charging until the residual energy of the chargeable sensor node is not less than a preset threshold value a, and recalculating the charging anchor point;
and repeating the steps until the residence time of the charging equipment reaches the required residence time.
As a preferred embodiment, the step S3 is characterized in that the heuristic evolutionary algorithm comprises the following specific steps:
firstly, constructing individuals and populations, and forming a group of random sequences as the individuals according to anchor point labels; generating a plurality of individuals as a population, determining the fitness of each individual, and performing crossing, variation and selection operations on the population; and repeating the iteration of the steps until the required iteration times are reached, and outputting the individual with the minimum fitness in the population.
As a preferred embodiment, the specific steps for determining the fitness value of each individual are as follows:
fit＝E tra +E ch (10)
wherein fit is the total energy consumption, E tra For moving the charging device, consuming energy, E ch Charging energy consumed by all sensor nodes in the WSN for the mobile charging equipment;
wherein, P tra The running power of the mobile charging equipment is V, the running speed of the mobile wireless charging equipment is V, and the distance traveled by the mobile charging equipment to complete a charging task is D;
wherein m is the number of anchor points, d i,i+1 Distance, d, from anchor point i to anchor point i +1 m,0 The distance from the anchor point m to the charging station;
wherein, P ch For charging power of mobile charging equipment, tau i The dwell time of the mobile charging device at anchor point i.
It should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (4)
1. A WSN node intelligent clustering and mobile charging equipment path planning method is characterized by comprising the following steps:
step S1: clustering the chargeable sensor nodes into a plurality of cluster nodes by adopting an unsupervised learning method;
step S2: selecting a charging mode according to the energy surplus of the chargeable sensor node in each cluster node, and determining the residence time and the charging anchor point position of the charging equipment in each cluster node according to the charging mode;
and step S3: charging the charging equipment according to the selected charging mode, and planning a path of the mobile charging equipment by using a heuristic evolutionary algorithm according to the residence time of the charging equipment in each cluster node and the position of a charging anchor point;
the specific method for selecting the charging mode in step S2 is as follows:
in each cluster node, calculating the residual working time of the chargeable sensor node in the cluster, taking the minimum residual working time as the residual working time of the cluster node, and calculating the average residual working time T of all the cluster nodes;
judging whether the residual working time of each cluster node is greater than T, if so, charging the cluster node by the mobile charging equipment in a node residual energy maximization charging mode, otherwise, charging the cluster node in a node residual energy equalization charging mode;
the specific method for determining the residence time and the charging anchor point position of the charging equipment in each cluster node by the node residual energy maximized charging mode is as follows:
determining the residence time of the charging equipment in each cluster node:
wherein the content of the first and second substances,
average remaining operating time, t, for all sensor nodes i For the moment when the mobile charging device reaches anchor point i, τ i The time for the mobile charging equipment to stay at the anchor point i for charging;
wherein, T j (t) is sensingThe remaining working time of the node j is N, and the number of the sensor nodes is N;
wherein e is j (t) is the residual energy of sensor node j, P j Is the consumed power of sensor node j;
wherein e is j (t) is a piecewise function of,
based on the charging efficiency function>
Distance, P, from sensor node j to charging anchor point ch Charging power for mobile charging equipment, E max Is the energy upper limit of the sensor node;
charging anchor point position of the charging device in each cluster node:
screening all chargeable sensor nodes with energy smaller than a preset threshold value a in the cluster nodes, placing the chargeable sensor nodes into a node set Q, calculating the central points of all the nodes in the node set Q, and setting the central points as charging anchor points; the charging equipment carries out charging after moving to the anchor point until the residual energy of one chargeable sensor node in the set Q is not less than a preset threshold value a, the chargeable sensor node of which the residual energy is not less than the preset threshold value a in the set Q is deleted from the set Q, and the charging anchor point is recalculated;
repeating until the residence time of the charging device reaches the required residence time;
the specific method for determining the residence time and the charging anchor point position of the charging equipment in each cluster node by the node residual energy equalization charging mode is as follows:
determining the residence time of the charging equipment in each cluster node:
wherein the content of the first and second substances,
variance of remaining operating time, t, for all sensor nodes i For the moment when the mobile charging device reaches anchor point i, tau i The time for the mobile charging equipment to stay at the anchor point i for charging;
wherein, T j (t) is the remaining operating time of sensor node j,
the average remaining working time of all the sensor nodes is defined, and N is the number of the sensor nodes;
wherein e is j (t) is the residual energy of sensor node j, P j Is the power consumed by sensor node j;
wherein e is j (t) is a piecewise function of,
as a function of charging efficiency>
Distance, P, from sensor node j to the charging anchor ch Charging power for mobile charging equipment, E max Is the energy upper limit of the sensor node;
determining the position of a charging anchor point of the charging equipment in each cluster node:
setting the chargeable sensor node with the least residual energy in the cluster nodes as a charging anchor point, moving the charging equipment to the anchor point, charging until the residual energy of the chargeable sensor node with the least residual energy in the cluster nodes is not less than a preset threshold value a, and recalculating the charging anchor point;
and repeating until the residence time of the charging equipment reaches the required residence time.
2. The method for intelligent clustering of WSN nodes and path planning of mobile charging equipment according to claim 1, wherein the specific steps in step S1 are as follows:
clustering the chargeable sensor nodes into a plurality of cluster nodes by using a k-means algorithm, randomly selecting a plurality of sensor nodes from the sensor nodes as initial centroids, assigning each sensor node to the nearest centroid to form a cluster, updating the centroid of each cluster, and repeating the step S1 until the cluster centroid is not changed any more.
3. The WSN node intelligent clustering and mobile charging equipment path planning method according to claim 1, wherein the heuristic evolutionary algorithm of the step S3 specifically comprises the following steps:
firstly, constructing individuals and populations, and forming a group of random sequences as the individuals according to anchor point labels; generating a plurality of individuals as a population, determining the fitness of each individual, and performing crossover, variation and selection operations on the population; and repeating the iteration until the required iteration times are reached, and outputting the individual with the minimum fitness in the population.
4. The WSN node intelligent clustering and mobile charging equipment path planning method according to claim 3, wherein the specific steps of determining the fitness value of each individual are as follows:
fit＝E tra +E ch (10)
wherein fit is the total energy consumption, E tra For moving the charging device, consuming energy, E ch Charging energy consumed by all sensor nodes in the WSN for the mobile charging equipment;
wherein, P tra The running power of the mobile charging equipment is V, the running speed of the mobile wireless charging equipment is V, and the distance traveled by the mobile charging equipment to complete a charging task is D;
wherein m is the number of anchor points, d i,i+1 Distance, d, from anchor point i to anchor point i +1 m,0 The distance from the anchor point m to the charging station;
wherein, P ch For charging power of mobile charging equipment, tau i The dwell time of the mobile charging device at anchor point i.
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| CN111787500B (en) * | 2020-05-11 | 2023-07-25 | 浙江工业大学 | Multi-target charging scheduling method for mobile charging vehicle based on energy priority |
| CN112738752B (en) * | 2020-12-24 | 2023-04-28 | 昆明理工大学 | WRSN multi-mobile charger optimal scheduling method based on reinforcement learning |
| CN113570022B (en) * | 2021-03-05 | 2023-05-23 | 福建工程学院 | Chargeable mobile trolley path planning method and device in sensor network |
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