CN112929888A - Static charging base station deployment method based on core node selection - Google Patents
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Abstract
The invention discloses a deployment method of static charging base stations in a wireless sensor network based on a core node selection rule. Compared with the traditional static charging base station deployment method, the method can determine the minimum number of static charging base stations in a rechargeable wireless sensor network through the core node selection rule, and can optimize the mutually overlapped charging range of each charging base station, thereby improving the charging effect.
Description
Technical Field
The invention relates to the technical field of wireless sensing, in particular to a static charging base station deployment method based on core node selection.
Background
A Wireless Sensor Network (WSN) is a multi-hop Wireless network formed by Sensor nodes deployed in a monitoring area in a Wireless self-organizing manner, and sensing, collecting and processing information of sensing objects in a network coverage area cooperatively among the nodes. The method is widely applied to the fields of environmental monitoring, urban monitoring, human body monitoring and the like. In a traditional wireless sensor network, because sensor nodes are small in size and limited in carrying capacity, and it is difficult to replace batteries for the nodes in some application scenarios, the energy problem becomes a big challenge of the WSN. In order to solve the above problems, there are two solutions today: energy conservation and energy capture. Energy saving technology aims at reducing energy consumption of sensors, and currently, technologies such as a sensor dormancy/activation mechanism, power management, network load balancing, routing and the like are researched, so that energy consumption in unit time is reduced, and the life cycle of a network is prolonged. However, this does not fundamentally address the problem of sensor power, and the approach requires a tradeoff between power savings and transmission performance, which may be a loss of transmission performance. The basic idea of energy harvesting technology is to harvest energy from the environment, such as solar energy, radio frequency energy, wind energy, vibration energy, etc., which has the advantage of extracting energy from the surrounding environment. However, because the energy in the environment is unstable and the density is low, in order to achieve a certain energy acquisition rate, each sensor node needs to be provided with a large-sized energy converter, and the energy conversion efficiency is low. Furthermore, since the external environment is unpredictable, the energy harvesting process is not controllable and difficult to predict accurately.
In order to solve the above problems, people have started to aim at wireless energy transfer technology (WPT), and thus a Wirelessly Rechargeable Sensor Network (WRSN) has been proposed. The WRSN continuously and stably supplies energy to the nodes in the sensor network through the wireless charger, so that the energy constraint problem is really solved. The charging method is divided according to whether the charger is movable in the sensor network, and can be divided into two forms of static charging and movable charging.
The mobile charging is realized by utilizing a magnetic coupling resonance wireless charging transmission technology of a near field, and the charging is high in efficiency and short in time. The realization mode is flexible, and the research content can integrate the functions of data acquisition, data transmission and the like besides path planning. However, under certain real-world conditions, the sensor nodes are not easily accessible, and the energy transmitting coil and the energy receiving coil are not easily aligned, so that the energy transmission efficiency is low, and even the charging cannot be performed.
The static charging is realized by utilizing a far-field radiation type wireless energy transmission technology, has a larger coverage area, and is suitable for the deployment conditions of dense sensor node deployment and low sensor energy consumption. One of the problems to be solved is how to minimize the number of static charging base stations and minimize power consumption, i.e., maximize charging utility, on the premise of ensuring that all nodes work normally. For this reason, many researchers have conducted related studies. Two optimization algorithms have been proposed by scholars: one is a central clustering algorithm, which optimizes a coverage area after clustering sensor nodes, but due to the defects of the clustering algorithm, a charging base station is deployed with more charging overlapping areas; the other is based on the idea of surface segmentation, which divides the sensor network into a plurality of small areas to cover the whole sensor network, but the randomness of the algorithm is higher, which may result in more sensor nodes in some areas and less sensor nodes in other areas.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects of the prior art, the invention provides a static charging base station deployment method based on core node selection, which can solve the problems of unreasonable charging base station position deployment, more overlapping areas of charging ranges and low charging effectiveness caused by excessive charging base stations in the traditional static charging base station deployment method.
The technical scheme is as follows: the invention discloses a static charging base station deployment method based on core node selection, which comprises the following steps:
(1) abstracting a wireless chargeable sensor network into an area on a two-dimensional plane, and deploying a plurality of sensor nodes in the wireless chargeable sensor network;
(2) selecting a first core node, wherein the first core node is a sensor node closest to the origin position of the two-dimensional coordinate, and initializing j to 1;
(3) calculating the distances between the jth sensor and other n-j sensors, and sequencing the distances from small to large;
(4) adding a current core node to a set SSjAnd adding the sensor nodes with the distance not greater than 2D into the set SS according to the sequencing resultjAnd mixing SSjThe set is put into an area set U, wherein D is the effective charging radius of the charging base station;
(5) judging whether all nodes in the sensor network are in the area set U, if all the nodes are in the area set U, executing a step 6, otherwise, selecting a next core node, namely, making j equal to j +1, and jumping to a step 4, wherein the core node is the node which has the minimum distance with the previous core node and the distance is more than 2D;
(6) acquiring set SS in area set UjNumber of each set SSjThe sensor node in (1) is used as a small area, a charging base station is placed in each small area, and each subset SS in an area set U is searchedjThe best charging base station location.
Further, the method comprises the following steps:
in the step (6), each subset SS in the region set U is calculatedjThe optimal charging base station location specifically includes:
(61) defining an objective function T:
wherein (x)j,yj) Charging base station coordinate of jth cell, (xx)ji,yyji) Is the coordinates of the sensor nodes in a certain cell, k is the number of sensor nodes in each cell, d ((x)j,yj),(xxji,yyji) Is the distance from the ith sensor node to the jth charging base station, PwiFor the energy consumption power of the sensor node, alpha is the sensing coefficient, PrCharging power received by the sensor node at the distance;
(62) iteratively solving the minimum of the objective function T, i.e. calculating d ((x)j,yj),(xxji,yyji) ) is increased, since,
and is
Namely:
wherein, beta is a compensation parameter of Fris free space equation under the condition of near field transmission of the antenna, P0Transmitting power for the charging base station;
(63) calculating the physical distance between the charging base station and each sensor multiplied by the corresponding charging power, and calculating the point with the maximum sum, namely the corresponding point Pr(d((xj,yj),(xxji,yyji) B) maximum value by applying d ((x) in an iterative process of the generalized Fermat algorithmj,yj),(xxji,yyji) The minimum value of the objective function obtained by iterative computation is brought into the objective function T, and the horizontal and vertical coordinates x ', y' of the optimal charging base station are represented by:
in the above formula, x and y are the horizontal and vertical coordinates of the last iteration respectively.
Further, the method comprises the following steps:
in the step (1), the two-dimensional plane includes an X axis and a Y axis which are perpendicular to each other, the origin is O, the area is a circle arranged in the first quadrant of the XOY axis, and the circle is tangent to the X axis and the Y axis.
Further, the method comprises the following steps:
the sensing coefficient is expressed as:
wherein G issFor charging base station antenna gain, GrFor the gain of the receiving antenna, eta is the rectifier efficiency, LpFor polarization loss, λ is the wavelength.
Further, the method comprises the following steps:
charging power P obtained by sensor at point (x, y)rGreater than or equal to the power consumption P of the sensorw。
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: the invention applies the core node selection rule to determine the least number of the static charging base stations and the optimal position of each static charging base station, thereby improving the charging utility.
Drawings
Fig. 1 is a schematic diagram of a core node selection rule according to the present invention, wherein fig. 1a is a schematic diagram of a first core node selection, fig. 1b is a schematic diagram of a second core node selection, and fig. 1c is a schematic diagram of a third core node selection;
FIG. 2 is a schematic diagram illustrating a process for deploying static charge point base stations in a sensor area according to an embodiment of the present invention;
fig. 3 is a flow chart of the steps of a static charge deployment method of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
The architecture based on the invention is a wireless sensor network deployed in a two-dimensional space, and comprises sensor nodes, a static charging base station and a base station. Each part will be specifically described below.
(1) A sensor node: the sensor nodes are randomly deployed on some nodes on the two-dimensional space position, the sensor nodes have the functions of monitoring the surrounding environment, and data can be transmitted among the nodes through a route, so that different nodes have different energy consumption rates. The total energy of the batteries of all the sensor nodes is the same.
(2) Static charging base station: the static charging base station is a charging device with a charging radius D, and can charge the sensor nodes within the charging radius D by transmitting radio frequency energy to the periphery.
(3) A base station: the base station is a fixed point in a network center, and can collect data of the whole network sensor by a multi-hop routing transmission method, including the collected data and the information of the electric quantity of the base station.
The invention provides a static charging base station deployment method based on core node selection. The traditional static charging base station deployment method has the problems that the position deployment of the charging base station is not reasonable enough, the charging range has more overlapped areas, and the charging effectiveness is not high due to the excessive number of the charging base stations. Compared with the traditional static charging base station deployment, the method and the device can determine the minimum number of charging base stations in a wireless sensor network through the core node selection rule, and optimize the mutually overlapped charging range of each charging base station, thereby improving the charging utility.
Taking the schematic diagram of the core node selection rule in fig. 1 as an example, the core node selection rule is that on a coordinate axis, a first core node is determined as a node closest to an origin position, point a in fig. 1a, a next core node is a point which is more than 2D away from the first core node and is closest to the first core node, point B in fig. 1B, and D represents an effective charging radius of the charging base station. By analogy, point C in fig. 1C is the closest point to point B and the distance is greater than 2D, because the 2D range is just the charging coverage radius of the charging base station, and exceeds 2D, that is, exceeds the charging radius of the charging base station. And determining all core nodes until all sensor nodes in the area are covered. Through the core node selection rule, the minimum number of the charging base stations is determined, the charging range of each charging base station which is overlapped with each other is reduced, and charging coverage of the whole area can be carried out by using the minimum number of the charging base stations.
After the whole area is divided into a plurality of small areas by the core node, the optimal charging base station position is determined in each divided small area. In the invention, the position of the charging base station in each cell is obtained by adopting a convergence algorithm of the generalized Fermat problem.
As can be seen from the figure, by adopting the core node selection rule, the purposes of optimizing the deployment position of the static charging base station and reducing the number of the base stations to improve the charging utility can be achieved in one chargeable sensor network.
The specific deployment method of the static charging base station comprises the following steps:
step 1, abstracting a wireless chargeable sensor network into an area on a two-dimensional plane, and deploying a plurality of sensor nodes in the wireless chargeable sensor network; wherein n chargeable sensor nodes are deployed, and S is ═ S1，s2，s3，...，snRepresents a set comprising n chargeable sensors.
In this embodiment, the two-dimensional plane includes an X axis and a Y axis perpendicular to each other, the origin is O, the area is a circle disposed in the first quadrant of the XOY axis, and the circle is tangent to the X axis and the Y axis. Each sensor has a maximum battery capacity of SmaxDuring operation, calculating the power consumption of each sensor
Calculating the power consumption of each sensor
The specific method comprises the following steps:
let time t, j2Sensor direction j3The sensor sends data with power Ps(T), also at time T, node j2Receiving node j1Transmitted power is Pr(t), then at time t, j2Energy consumption power P of sensorw(t) is:
Pw(t)＝Ps(t)+Pr(t)
step 2, selecting a first core node, wherein the first core node is a sensor node closest to the origin position of the two-dimensional coordinate, and initializing j to 1;
step 3, calculating the distance between the jth sensor and other n-j sensors, and sequencing the distances from small to large;
charging power P obtained by sensor at point (x, y)rGreater than or equal to the power consumption P of the sensorw，PrCan be expressed as:
where α is a sensing coefficient and is a fixed value
GSFor charging base station antenna gain, GrFor the gain of the receiving antenna, eta is the rectifier efficiency, LpFor polarization loss, λ is the wavelength, β is the compensation parameter for the Friis free space equation for near field transmission of the antenna, P0And D is the distance between the sensor node and the charging base station, and D represents the effective charging radius of the charging base station.
Step 4, adding the current core node into the set SSjMiddle, i.e. SSjThe number of the initial elements is k is 1;
and adding the sensor nodes with the distance not greater than 2D into the set SS according to the sequencing result of the step 3jAnd mixing SSjThe set is put into an area set U, wherein D is the effective charging radius of the charging base station;
step 5, judging whether all nodes in the sensor network are in the area set U, if all nodes are in the area set U, executing step 6, otherwise, selecting a next core node, namely, making j equal to j +1, and jumping to step 4, wherein the core node is the node which has the minimum distance with the previous core node and the distance of which is more than 2D;
step 6, acquiring set SS in area set UjNumber of each set SSjThe sensor node in (1) is used as a small area, a charging base station is placed in each small area, and each subset SS in an area set U is searchedjThe best charging base station location.
In step (6), each subset SS in the region set U is calculatedjThe optimal charging base station location specifically includes:
(61) defining an objective function T:
wherein (x)j,yj) Charging base station coordinate of jth cell, (xx)ji,yyji) Is the coordinates of the sensor nodes in a certain cell, k is the number of sensor nodes in each cell, d ((x)j,yj),(xxji,yyji) Is the distance from the ith sensor node to the jth charging base station, PwiFor the energy consumption power of the sensor node, alpha is the sensing coefficient, PrCharging power received by the sensor node at the distance;
(62) the minimum value of the objective function T is solved in an iterative mode, and when the position of each wireless charging base station is solved, the energy consumption power P of each sensor is obtainedwiAnd the charging power P obtained therebyriIs the minimum of the sum of the ratios of (a), (b), (c), and (d) ((x)j,yj),(xxji,yyji) ) is increased, since,
and is
Namely:
wherein, beta is a compensation parameter of Fris free space equation under the condition of near field transmission of the antenna, P0Transmitting power for the charging base station;
(63) calculating the physical distance between the charging base station and each sensor multiplied by the corresponding charging power by adopting a convergence algorithm of the generalized Fermat problem, and calculating the point with the maximum sum, namely the corresponding Pr(d((xj,yj),(xxji,yyji) ) is calculated) to obtain a minimum value of the objective function, and the optimal charge base is calculatedThe station abscissa x ', y' is represented as:
in the above formula, x and y are the horizontal and vertical coordinates of the last iteration respectively.
Namely: the physical distance between the charging base station and each sensor is multiplied by the corresponding charging efficiency (of each sensor), then the sum of the values is calculated, and finally a point is found, wherein the sum of the physical distance between the point and each sensor multiplied by the corresponding charging efficiency is the maximum value. This is the optimal charging base station deployment location within the small area. This is the point to be iteratively found by the generalized Fermat problem.
Since the best charging base station location within each cell is initially unknown, it is necessary to compute this best location iteratively through the generalized Fermat problem.
For convenience of description, the scenario in fig. 2 is taken as an example:
when a rechargeable wireless sensor network is put into operation, as shown in fig. 3, the following steps are performed:
step 1: abstracting a wireless chargeable sensor network into an area L multiplied by W on a two-dimensional plane, wherein the drawing is abstracted by circles, wherein n is 22 chargeable sensor nodes are deployed, and the 22 nodes are added into a set S;
step 2: each sensor has a maximum battery capacity of SmaxWhen it is in operation, the electricity consumption of the sensor is power
It is assumed here that the energy consumption value of each sensor during operation is Pw；
And step 3: determining a first core node as a node closest to the origin position, and initializing j to 1;
and 4, step 4: calculating the distance between the 1 st sensor and the other 22-1 sensors, and sequencing the distances from small to large;
and 5: adding the sensors with the distance not greater than 2D into the set SS according to the sorting result in the step 4jIn the figure, 4 nodes around the first core node are added to a set SS as shown1In (1), SS1The set is placed in an area set U, and D represents the charging radius of the static charging base station;
step 6: and judging whether all the nodes in the set s are in the area set U or not. If all nodes are in the region set U, step 7 is executed, otherwise, let j ═ j +1 (the location of the next core node), jump to step 4
And 7: traversing and calculating each subset SS in the region set U through a heuristic convergence algorithm of the generalized Fermat problemjCharging base station location (per small area set)
Through the steps, 22 sensor nodes are deployed in one wireless chargeable area, 5 core nodes are determined through a core node selection rule, and 5 static charging base station nodes are needed.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (5)
1. A static charging base station deployment method based on core node selection is characterized by comprising the following steps:
(1) abstracting a wireless chargeable sensor network into an area on a two-dimensional plane, and deploying a plurality of sensor nodes in the wireless chargeable sensor network;
(2) selecting a first core node, wherein the first core node is a sensor node closest to the origin position of the two-dimensional coordinate, and initializing j to 1;
(3) calculating the distances between the jth sensor and other n-j sensors, and sequencing the distances from small to large;
(4) adding a current core node to a set SSjAnd adding the sensor nodes with the distance not greater than 2D into the set SS according to the sequencing resultjAnd mixing SSjThe set is put into an area set U, wherein D is the effective charging radius of the charging base station;
(5) judging whether all nodes in the sensor network are in the area set U, if all the nodes are in the area set U, executing a step 6, otherwise, selecting a next core node, namely, making j equal to j +1, and jumping to a step 4, wherein the core node is the node which has the minimum distance with the previous core node and the distance is more than 2D;
(6) acquiring set SS in area set UjNumber of each set SSjThe sensor node in (1) is used as a small area, a charging base station is placed in each small area, and each subset SS in an area set U is searchedjThe best charging base station location.
2. The core node selection-based static charging base station deployment method as claimed in claim 1, wherein in the step (6), each subset SS in a region set U is calculatedjThe optimal charging base station location specifically includes:
(61) defining an objective function T:
wherein (x)j,yj) Charging base station coordinate of jth cell, (xx)ji,yyji) Is the coordinates of the sensor nodes in a certain cell, k is the number of sensor nodes in each cell, d ((x)j,yj),(xxji,yyji) Is the distance from the ith sensor node to the jth charging base station, PwiFor the energy consumption power of the sensor node, alpha is the sensing coefficient, PrCharging power received by the sensor node at the distance;
(62) iteratively solving the minimum of the objective function T, i.e. calculating d ((x)j,yj),(xxji,yyji) ) is increased, since,
and is
Namely:
wherein, beta is a compensation parameter of Fris free space equation under the condition of near field transmission of the antenna, P0Transmitting power for the charging base station;
(63) calculating the physical distance between the charging base station and each sensor multiplied by the corresponding charging power, and calculating the point with the maximum sum, namely the corresponding point Pr(d((xj,yj),(xxji,yyji) B) maximum value by applying d ((x) in an iterative process of the generalized Fermat algorithmj,yj),(xxji,yyji) The minimum value of the objective function obtained by iterative computation is brought into the objective function T, and the horizontal and vertical coordinates x ', y' of the optimal charging base station are represented by:
in the above formula, x and y are the horizontal and vertical coordinates of the last iteration respectively.
3. The core node selection-based static charging base station deployment method of claim 2, wherein in the step (1), the two-dimensional plane includes an X axis and a Y axis perpendicular to each other, the origin is O, the area is a circle disposed in the first quadrant of the XOY axis, and the circle is tangent to the X axis and the Y axis.
4. The core node selection-based static charging base station deployment method of claim 2, wherein the sensing coefficient is expressed as:
wherein G issFor charging base station antenna gain, GrFor the gain of the receiving antenna, eta is the rectifier efficiency, LpFor polarization loss, λ is the wavelength.
5. The core node selection-based static charging base station deployment method of claim 2, wherein the charging power P obtained by the sensor at point (x, y)rGreater than or equal to the power consumption P of the sensorw。
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