CN108260074B - Energy source position and transmission power optimization method for wireless energy supply sensor network - Google Patents
Energy source position and transmission power optimization method for wireless energy supply sensor network Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/18—TPC being performed according to specific parameters
- H04W52/28—TPC being performed according to specific parameters using user profile, e.g. mobile speed, priority or network state, e.g. standby, idle or non transmission
- H04W52/283—Power depending on the position of the mobile
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/04—TPC
- H04W52/30—TPC using constraints in the total amount of available transmission power
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
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Abstract
A joint optimization method for energy source position deployment and transmission power configuration in a radio frequency energy capture wireless sensor network is characterized in that for K energy sources, the deployment position (x, y) and the transmission power p of each energy source are determined, and the total transmission power of the lower energy sources is achieved while the energy capture requirement of each deployed sensing node is met; based on a genetic algorithm, firstly, corresponding K energy source positions and transmission powers to a 3K-dimensional position vector of each particle, and then iteratively updating the position vectors and the velocity vectors of the particles, wherein when a scheme corresponding to one particle meets the energy capture requirements of all nodes, the fitness value of the particle is defined as the total transmission power of the energy source, otherwise, the fitness value is defined as the maximum transmission power which is K times; when the specified iteration number is reached, finding out the optimal energy source position deployment and sending power configuration. The method can find out better energy source deployment and transmitting power configuration, achieves lower total transmitting power of the energy source, and has better energy-saving property.
Description
Technical Field
The invention relates to a method for optimizing the energy source position and the transmitting power of a wireless energy supply sensor network, which is suitable for a wireless sensor network with sensor nodes capable of capturing radio frequency energy.
Background
Electromagnetic waves are increasingly receiving attention from both academic and industrial circles as a ubiquitous, environmentally friendly and sustainable energy source. The radio frequency energy capturing wireless sensor network is a novel network for capturing radio frequency energy in an environment and converting the radio frequency energy into electric energy so as to support continuous work of nodes.
However, at present, the rate of capturing the radio frequency energy in the environment by the radio frequency energy capturing sensor node is still very low, which is one of the bottlenecks in the wide application of this kind of new networks. To overcome this weakness, it is a viable and efficient way to deploy a dedicated energy source to power the nodes. Because the radio frequency energy can lose certain energy in the transmission process, namely the farther the energy source is away from the node, the less the radio frequency energy captured by the node, each energy source is reasonably arranged, so that the network can utilize the radio frequency energy to the maximum extent, and the problem of important research is solved.
Meanwhile, from the perspective of energy conservation and environmental protection, radio frequency energy exceeding the energy requirement of the node will be wasted uselessly, and the energy source will consume a large amount of energy, so that it is a significant problem to configure the transmission power of each energy source reasonably.
Disclosure of Invention
Aiming at the situations that the radio frequency energy capturing sensor nodes in the network are already deployed and the coordinates are known, the invention provides a method for jointly optimizing the energy source position deployment and the transmission power configuration in the radio frequency energy capturing wireless sensor network with better energy saving performance,
in order to solve the technical problems, the invention provides the following technical scheme:
a method for optimizing the energy source position and the transmission power of a wireless energy supply sensing network comprises the following steps:
(1.1) determining a unique minimum coverage circle of the area according to the coordinates of sensor nodes which can capture radio frequency energy at N given positions in the wireless sensor network; the minimum coverage circle of the N nodes is a circle which covers all the N nodes and has the minimum radius;
(1.2) representing a specific energy source deployment and transmission power configuration scheme by using a position vector of one particle in a particle swarm optimization, initializing initial position vectors of M particles: for i-1, 2, …, M, the position vector of the ith particle isWhere K is the number of energy sources, the transverse axis of the jth energy sourceCoordinates and ordinateAndthe abscissa and ordinate of a randomly selected point in the smallest coverage circle, the transmission power of the jth energy sourceA transmit power randomly selected within a transmit power range supported by a transmit circuit; the value mode of M is the same as the value mode of the number of particles in the traditional particle swarm algorithm;
(1.3) initializing the initial velocity vector v of the ith particlei0, and initializing the optimal position vector p of the ith particleiIs its initial position vector, i.e. pi←xi;
(1.4) for i 1,2, …, M, a position vector p is calculatediThe sum of the corresponding K energy source transmitting powers f (p)i) Then finding out p with minimum sum of energy source transmitting poweriAnd will global optimum position pgIs set as piI.e. pg←pi;
(1.5) making t ← 1;
(1.6) if t is more than or equal to the Iteration _ times, jumping to the step (1.9), otherwise, updating the current velocity vector v of the ith particle according to the formula (1)iAnd a position vector xi;
Wherein, Iteration _ times is Iteration times, the value of the Iteration times depends on the acceptable running time, the larger the value is, the more the cycle times are, the better position vector can be found, and r ispAnd rgIs a random number between two (0,1), w,Andis a constant value and is used to control the velocity vector viThe updating step of (2) has a value-taking mode same as that of the traditional particle swarm algorithm;
(1.7) for i ═ 1,2, …, M, if position vector xiThe sum of the transmission power f (x) of the corresponding K energy sourcesi) Smaller than the position vector piThe sum of the corresponding K energy source transmitting powers f (p)i) Then let pi←xi(ii) a If the position vector xiThe sum of the transmission power f (x) of the corresponding K energy sourcesi) Smaller than the position vector pgThe sum of the corresponding K energy source transmitting powers f (p)g) Then let pg←xi;
(1.8) returning to step (1.6) by making t ← t + 1;
(1.9) ending the operation with the position vectorThe corresponding energy source deployment and transmission power configuration is the final scheme, namely the deployment coordinate position of the jth energy source is determined asIts transmission power is determined as
Further, in the step (1.4) and the step (1.7), the position vector is obtainedCalculating the total power f (p) of the energy sourcesi) The method comprises the following steps:
step1 is set for the abscissa of the jth energy source with j equal to 1,2, …, KThe ordinate is set asTransmission power setting
Step2 for each sensor node nuN, NuTotal power captured from K radio frequency energy transmitting sources
Wherein η is the rectification efficiency, GsIs the source antenna gain, GrIs the receive antenna gain, LpIs the polarization loss, λ is the wavelength, du,jIs node nuDistance from the jth rf energy source;
step3 if the energy capture power of each node is larger than or equal to the energy demand, that is to sayThe sum f (p) of the transmission powers of the energy sources is calculated according to the formula (3)i) Is composed of
Otherwise let f (p)i) Is KpmaxWherein p ismaxThe maximum transmit power supported by the energy source transmit circuitry.
The invention has the beneficial effects that: the invention solves the problems of deployment and transmission power configuration of the radio frequency energy source by utilizing a particle swarm optimization algorithm, and minimizes the total transmission power of the energy source on the basis of meeting the energy capture requirement of each node by continuously updating the position and the transmission power of the energy source, thereby achieving the purpose of energy conservation.
Detailed Description
The present invention is further explained below.
A method for optimizing the energy source position and the transmission power of a wireless energy supply sensing network comprises the following steps:
(1.1) determining a unique minimum coverage circle of the area according to the coordinates of sensor nodes which can capture radio frequency energy at N given positions in the wireless sensor network; the minimum coverage circle of the N nodes is a circle which covers all the N nodes and has the minimum radius;
(1.2) representing a specific energy source deployment and transmission power configuration scheme by using a position vector of one particle in a particle swarm optimization, initializing initial position vectors of M particles: for i-1, 2, …, M, the position vector of the ith particle isWhere K is the number of energy sources, the abscissa and ordinate of the jth energy sourceAndthe abscissa and ordinate of a randomly selected point in the smallest coverage circle, the transmission power of the jth energy sourceA transmit power randomly selected within a transmit power range supported by a transmit circuit; the value mode of M is the same as the value mode of the number of particles in the traditional particle swarm algorithm;
(1.3) initializing the initial velocity vector v of the ith particlei0, and initializing the optimal position vector p of the ith particleiIs its initial position vector, i.e. pi←xi;
(1.4) for i 1,2, …, M, a position vector p is calculatediThe sum of the corresponding K energy source transmitting powers f (p)i) Then finding out the minimum sum of the transmitting power of the energy sourcesP of (a)iAnd will global optimum position pgIs set as piI.e. pg←pi;
(1.5) making t ← 1;
(1.6) if t is more than or equal to the Iteration _ times, jumping to the step (1.9), otherwise, updating the current velocity vector v of the ith particle according to the formula (1)iAnd a position vector xi;
Wherein, Iteration _ times is Iteration times, the value of the Iteration times depends on the acceptable running time, the larger the value is, the more the cycle times are, the better position vector can be found, and r ispAnd rgIs a random number between two (0,1), w,Andis a constant value and is used to control the velocity vector viThe updating step of (2) has a value-taking mode same as that of the traditional particle swarm algorithm;
(1.7) for i ═ 1,2, …, M, if position vector xiThe sum of the transmission power f (x) of the corresponding K energy sourcesi) Smaller than the position vector piThe sum of the corresponding K energy source transmitting powers f (p)i) Then let pi←xi(ii) a If the position vector xiThe sum of the transmission power f (x) of the corresponding K energy sourcesi) Smaller than the position vector pgThe sum of the corresponding K energy source transmitting powers f (p)g) Then let pg←xi;
(1.8) returning to step (1.6) by making t ← t + 1;
(1.9) ending the operation with the position vectorThe corresponding energy source deployment and transmit power configuration is the final scenario, i.e. secondThe deployment coordinate positions of the j energy sources are determined asIts transmission power is determined as
Further, in the step (1.4) and the step (1.7), the position vector is obtainedCalculating the total power f (p) of the energy sourcesi) The method comprises the following steps:
step1 is set for the abscissa of the jth energy source with j equal to 1,2, …, KThe ordinate is set asTransmission power setting
Step2 for each sensor node nuN, NuTotal power captured from K radio frequency energy transmitting sources
Wherein η is the rectification efficiency, GsIs the source antenna gain, GrIs the receive antenna gain, LpIs the polarization loss, λ is the wavelength, du,jIs node nuDistance from the jth rf energy source;
step3 if the energy capture power of each node is larger than or equal to the energy demand, that is to sayThe sum f (p) of the transmission powers of the energy sources is calculated according to the formula (3)i) Is composed of
Otherwise let f (p)i) Is KpmaxWherein p ismaxThe maximum transmit power supported by the energy source transmit circuitry.
Particular embodiments of the present invention are described with respect to a wireless radio frequency energy capture sensor network given the physical location of each sensor node.
The center of a minimum coverage circle of the sensor nodes at N given positions is calculated firstly.
Each energy source needs to determine 3 parameters, respectively the abscissa and ordinate of its deployment position and its transmit power magnitude. K radio frequency energy sources form one particle, so that the position vector corresponding to each particle is a 3K-dimensional vector, and the initial position vector of the ith particle can be set asInitializationAndfor random coordinate points within the smallest coverage circle, initializingA random power value within the transmission power range is adjusted for the energy source and the initial velocity vector of the ith particle is initialized to 0 and the optimal position of the ith particle is initialized to its initial position.
And then, calculating the sum of the energy captured by each node from each energy source by using a Friis free space propagation model, if the energy captured by the node meets the energy required by the node, calculating the sum of the transmitting power of each energy source corresponding to each particle, and if the energy captured by the node does not meet the energy required by the node, setting the sum of the transmitting power of each energy source as the sum of the maximum transmitting power of each energy source. Updating the optimal vectors of the single particle and the global, setting the optimal vector of each particle as the vector when the sum of the searched powers of the particle is minimum, and setting the global optimal vector as the vector when the sum of the searched powers of all the particles is minimum.
And continuously executing particle optimization operation, wherein the operation continuously carries out iterative updating on the deployment position and the transmission power of the energy source by controlling the vector parameter and the velocity vector of each particle until a fixed iteration number is reached and the operation is finished.
Claims (1)
1. A method for optimizing the energy source position and the transmission power of a wireless energy supply sensor network is characterized by comprising the following steps: the method comprises the following steps:
(1.1) determining a unique minimum coverage circle of an area according to the coordinates of sensor nodes which can capture radio frequency energy at N given positions in a wireless sensor network; the minimum coverage circle of the N nodes is a circle which covers all the N nodes and has the minimum radius;
(1.2) representing a specific energy source deployment and transmission power configuration scheme by using a position vector of one particle in a particle swarm optimization, initializing initial position vectors of M particles: for i-1, 2, …, M, the position vector of the ith particle isWhere K is the number of energy sources, the abscissa and ordinate of the jth energy sourceAndthe abscissa and ordinate of a randomly selected point in the smallest coverage circle, the transmission power of the jth energy sourceA transmit power randomly selected within a transmit power range supported by a transmit circuit; the value mode of M is the same as the value mode of the number of particles in the traditional particle swarm algorithm;
(1.3) initializing the initial velocity vector v of the ith particlei0, and initializing the optimal position vector p of the ith particleiIs its initial position vector, i.e. pi←xi;
(1.4) for i 1,2, …, M, a position vector p is calculatediThe sum of the corresponding K energy source transmitting powers f (p)i) Then finding out p with minimum sum of energy source transmitting poweriAnd will global optimum position pgIs set as piI.e. pg←pi;
(1.5) making t ← 1;
(1.6) if t is more than or equal to the Iteration _ times, jumping to the step (1.9), otherwise, updating the current velocity vector v of the ith particle according to the formula (1)iAnd a position vector xi;
Wherein, Iteration _ times is Iteration times, the value of the Iteration times depends on the acceptable running time, the larger the value is, the more the cycle times are, the better position vector can be found, and r ispAnd rgIs two of0,1) random number, w,Andis a constant value and is used to control the velocity vector viThe updating step of (2) has a value-taking mode same as that of the traditional particle swarm algorithm;
(1.7) for i ═ 1,2, …, M, if position vector xiThe sum of the transmission power f (x) of the corresponding K energy sourcesi) Smaller than the position vector piThe sum of the corresponding K energy source transmitting powers f (p)i) Then let pi←xi(ii) a If the position vector xiThe sum of the transmission power f (x) of the corresponding K energy sourcesi) Smaller than the position vector pgThe sum of the corresponding K energy source transmitting powers f (p)g) Then let pg←xi;
(1.8) returning to step (1.6) by making t ← t + 1;
(1.9) ending the operation with the position vectorThe corresponding energy source deployment and transmission power configuration is the final scheme, namely the deployment coordinate position of the jth energy source is determined asIts transmission power is determined as
In the step (1.4) and the step (1.7), the position vector isCalculating the total power f (p) of the energy sourcesi) The method comprises the following steps:
step1 for j equal to 1,2…, K, the abscissa setting of the jth energy sourceThe ordinate is set asTransmission power setting
Step2 for each sensor node nuN, NuTotal power captured from K radio frequency energy transmitting sources
Wherein η is the rectification efficiency, GsIs the source antenna gain, GrIs the receive antenna gain, LpIs the polarization loss, λ is the wavelength, du,jIs node nuDistance from the jth rf energy source;
step3 if the energy capture power of each node is larger than or equal to the energy demand, that is to sayIf u is 1,2, …, N, the sum of the transmission powers f (p) of the energy sources is calculated according to the formula (3)i) Is composed of
Otherwise let f (p)i) Is KpmaxWherein p ismaxThe maximum transmit power supported by the energy source transmit circuitry.
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CN109219080B (en) * | 2018-09-14 | 2021-08-03 | 浙江工业大学 | Radio frequency energy source arrangement method based on genetic algorithm |
CN110336337B (en) * | 2019-04-04 | 2021-05-18 | 浙江工业大学 | Energy source indoor deployment and power regulation method for optimizing profit of radio frequency charging service |
CN110460167B (en) * | 2019-07-01 | 2021-02-26 | 浙江工业大学 | Radio frequency energy source arrangement and transmission power setting method |
CN110996381B (en) * | 2019-10-29 | 2023-04-07 | 浙江工业大学 | Radio frequency energy source arrangement and emission power setting method based on genetic algorithm |
CN111867030B (en) * | 2020-06-17 | 2023-09-29 | 浙江工业大学 | Particle swarm optimization-based radio frequency energy source arrangement and emission power setting method |
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