CN107148026B - Radio frequency energy source optimized deployment method for supplying energy to body area network nodes - Google Patents

Radio frequency energy source optimized deployment method for supplying energy to body area network nodes Download PDF

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CN107148026B
CN107148026B CN201710238970.9A CN201710238970A CN107148026B CN 107148026 B CN107148026 B CN 107148026B CN 201710238970 A CN201710238970 A CN 201710238970A CN 107148026 B CN107148026 B CN 107148026B
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李燕君
陈雨哲
池凯凯
朱艺华
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Zhejiang University of Technology ZJUT
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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
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J50/00—Circuit arrangements or systems for wireless supply or distribution of electric power
    • H02J50/20—Circuit arrangements or systems for wireless supply or distribution of electric power using microwaves or radio frequency waves
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B13/005—Transmission systems in which the medium consists of the human body
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Abstract

A radio frequency energy source optimized deployment method for supplying energy to body area network nodes comprises the following steps: modeling a moving mode of a user into a graph formed by a stop point and a track edge; iteratively calculating the positions of newly added energy sources in stages according to different objective functions, wherein in the first stage, the newly added energy sources enable the charging power of the body area network nodes at all the stop points to be larger than the energy consumption power; in the second stage, an energy source is newly added, so that the maximum energy accumulated net consumption value of all track edges does not exceed the capacity of the energy storage element of the node; and in the third stage, an energy source is newly added, so that the actual energy uninterrupted probability of the body area network nodes meets the system requirement. The method is suitable for a scene that the body area network nodes can capture radio frequency energy for charging, can reasonably deploy the energy source according to the moving mode of the user, meets the requirement of the system on energy uninterrupted, and reduces the deployment cost.

Description

Radio frequency energy source optimized deployment method for supplying energy to body area network nodes
Technical Field
The invention relates to a radio frequency energy source optimized deployment method for supplying energy to body area network nodes, which is suitable for a body area network for capturing radio frequency energy to work.
Background
Along with the development of wearable technology and wireless communication technology, intelligent sensing equipment is used for human monitoring more and more, and various user data are caught to these equipment, upload to internet high in the clouds anytime and anywhere, become new thing networking entry. The wireless network composed of the human body wearable sensor or the biological sensor implanted in the human body is called a body area network, and the all-weather online characteristic of the wireless network enables convenient and continuous physiological monitoring, such as continuous physiological monitoring and early warning of patients with heart diseases, epilepsy, diabetes and the like. With the increasing popularity of implantable smart devices, body area networks will integrate with people and become an indispensable part of daily life.
Traditional body area network nodes are powered by batteries or charged regularly, continuous uninterrupted work of the network cannot be achieved, and particularly for application of being implanted into a human body, the cost of replacing batteries or taking out implanted equipment for charging is huge. Thanks to the breakthrough of wireless energy transmission technology, body area network nodes can capture energy from radio waves emitted by devices such as RFID readers, Wi-Fi hotspots, cellular base stations, etc. to support sensing, computing and communication.
The reasonable planning of the radio frequency energy source position can effectively improve the energy capture power of the body area network nodes. Because the body area network nodes are deployed on the human body, the energy source deployment problem needs to consider the moving mode of the user. The problem to be solved by the invention is how to deploy the least radio frequency energy source to supply energy to the body area network nodes, so that the energy of the nodes carried by a user is not easy to interrupt in the moving process. Patent documents CN105550480A and CN105722104A respectively provide greedy and particle swarm optimization-based energy source deployment methods in a radio frequency charging wireless sensor network, aiming at using the least energy sources to make the charging power of a sensing node at a given position always greater than or equal to the energy consumption power thereof. There is a document that considers a scene that a sensing node is movable, and an Energy source deployment method is proposed when the sensing node appears in a plane area with equal probability, aiming at using the least Energy source to make the average value of the charging power of any point of the plane area greater than or equal to the Energy consumption power of the sensing node (see Energy Provisioning in Wireless Rechargeable Sensor Networks, published in IEEE Transactions on Mobile Computing, 2013). However, the above method is not suitable for a scenario in which the user has a specific movement pattern.
Disclosure of Invention
In order to overcome the defects that the existing radio frequency energy source position planning method cannot adapt to a user moving mode and cannot meet the requirement of energy uninterrupted probability, the invention provides the radio frequency energy source optimal deployment method which is suitable for the user moving mode and can effectively meet the requirement of the energy uninterrupted probability and supply energy to the body area network nodes.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a radio frequency energy source optimized deployment method for supplying energy to body area network nodes comprises the following steps:
step 1, a body area network node is positioned on the body surface or in the body of a user, and data acquisition and communication are carried out by capturing wireless signal energy emitted by a peripheral radio frequency energy source; according to the position information of a plurality of mobile users in a certain area in a period of time, a clustering algorithm is used for collecting V (V) as a frequent stop point for a group user movement model1,v2,...,vNDescribing a directed graph G (V, E) formed by the cluster track set E, wherein N is the number of stop points, and directed edges Ei,jE E denotes the presence of a slave stay point v in the regioniTo a stop point vjThe movement trajectory of (2); user at any dwell point viThe residence time t at the position of the E.V is subject to truncation normal distribution, and the probability density function of the residence time t is
Figure BDA0001268798580000021
0 ≦ t ≦ infinity, where μ and σ are2Mean and variance, respectively, α is a normalization constant, let
Figure BDA0001268798580000022
Figure BDA0001268798580000023
To ensure
Figure BDA0001268798580000024
Step 2, uniformly dividing the deployment area into X multiplied by Y grids, wherein the size of the grids is determined by precision requirements and computing power, the candidate deployment position of the energy source is set as the center of each grid, and a plurality of energy sources can be deployed in one grid at the same time;
step 3, traversing all grids, calculating a first objective function value of an energy source deployed in each grid, deploying a newly increased energy source in the grid which enables the first objective function value to be minimum, and randomly deploying the newly increased energy source in one grid if the first objective function values deployed in the grids are equal;
step 4, judging whether the first objective function value under the current deployment scheme is 0, if so, ensuring that the charging power of the body area network nodes at all the stay points is greater than the energy consumption power, and entering step 5; otherwise, repeating the step 3;
step 5 for track edge ei,jE, assuming it is of length li,jDivide it into
Figure BDA0001268798580000031
The value of delta l is determined by precision requirements and computing power, and when the body area network node moves on a line segment with the length equal to or less than delta l, the charging power of the body area network node carried by a user is kept unchanged and is the charging power at the center point of the line segment;
step 6, traversing all grids, calculating a second objective function value of the energy source deployed in each grid, deploying the newly increased energy source in the grid which enables the second objective function value to be minimum, and randomly deploying the newly increased energy source in one grid if the second objective function values deployed in the grids are equal;
step 7, judging whether a second objective function value under the current deployment scheme is 0, if so, ensuring that the maximum energy accumulated net consumption value of all track edges does not exceed the capacity of the node energy storage element, and entering step 8; otherwise, repeating the step 6;
step 8, traversing all grids, calculating a third objective function value of the energy source deployed in each grid, deploying the newly increased energy source in the grid which enables the third objective function value to be minimum, and randomly deploying the newly increased energy source in one grid if the third objective function values deployed in the grids are equal;
step 9, judging whether a third objective function value under the current deployment scheme is 0, if so, ensuring that the actual energy uninterrupted probability of the body area network node meets the system requirement; finishing the operation; otherwise, repeat step 8.
Further, in step 3, the first objective function expression is:
Figure BDA0001268798580000032
wherein, PcRepresenting the energy consumption power of the body area network nodes,
Figure BDA0001268798580000033
indicates the dwell point viThe charging power, if there are K energy sources in the current deployment scenario,
Figure BDA0001268798580000041
calculated from equation (2):
Figure BDA0001268798580000042
wherein η is the rectification efficiency, GsIs the transmit antenna gain, GrIs the receive antenna gain, LpIs the polarization loss, λ is the wavelength, and ε is the tuning parameter to ensure
Figure BDA0001268798580000043
Limited value, dk,iIs the kth energy source and point viA distance between PsIs the transmitted power of the energy source,
Figure BDA0001268798580000044
is the phase offset of the radio frequency signal and represents the modulo of the complex number therein.
Still further, in step 6, the second objective function expression is:
Figure BDA0001268798580000045
wherein E iscRepresenting the energy storage element capacity of the body area network node,
Figure BDA0001268798580000046
represents the point v from restiTo the stop point vjThe maximum energy accumulation net consumption value in the moving process is calculated by the formula (4):
Figure BDA0001268798580000047
wherein
Figure BDA0001268798580000048
Indicating the track edge ei,jMiddle mth section line central point ui,j,mThe charging power can be calculated by formula (2) < i >i,j,mIndicating the track edge ei,jThe length of the line segment of the m-th segment,
Figure BDA00012687985800000411
representing the average rate of movement of the user.
Further, in step 8, the third objective function expression is:
Figure BDA0001268798580000049
wherein p is0Probability of uninterrupted energy of body area network nodes, p, representing system requirementsi,jIndicating that the user is from the stop point viTo the stop point vjThe energy uninterrupted probability in the moving process is calculated by the formula (6):
Figure BDA00012687985800000410
wherein t isi,jIndicating the user along the edge e of the tracki,jDuring the moving process, at least a stopping point v is needed for ensuring the energy of the node is not interruptediThe residence time is calculated by the formula (7):
Figure BDA0001268798580000051
the invention has the following beneficial effects: the method is suitable for a scene that the body area network nodes can capture radio frequency energy for charging, can reasonably deploy energy sources according to the mobile mode of a user, meets the requirement of uninterrupted energy of a system, and reduces deployment cost.
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FIG. 1 is a flow chart of an embodiment of the present invention;
fig. 2 is a schematic diagram of a directed graph G describing a user movement pattern in the present embodiment;
FIG. 3 is a diagram illustrating a probability density function of the dwell time of the user at the dwell point in the present embodiment.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 3, a method for optimizing and deploying a radio frequency energy source for supplying energy to a body area network node includes the following steps:
step 1, the body area network nodes are positioned on the body surface or in the body of a user, and data acquisition and communication are carried out by capturing wireless signal energy emitted by a peripheral radio frequency energy source. During the movement of a user in a certain area, there are several staying points with long staying time and some relatively fixed movement tracks. Therefore, first, according to the above feature, the movement pattern of the user is modeled. According to the position information of a plurality of mobile users in a certain area in a period of time, a clustering algorithm is used for collecting V (V) as a frequent stop point for a group user movement model1,v2,...,vNDescribing a directed graph G (V, E) formed by the cluster track set E, wherein N is the number of stop points, and directed edges Ei,jE E denotes the presence of a slave stay point v in the regioniTo a stop point vjAs shown in fig. 2; user at any dwell point viThe residence time t at e V follows a truncated normal distribution, as shown in FIG. 3, with a probability density function of
Figure BDA0001268798580000061
T 0 ≦ t < ∞, mu and sigma2Mean and variance, respectively, α is a normalization constant, let
Figure BDA0001268798580000062
Figure BDA0001268798580000063
To ensure
Figure BDA0001268798580000064
The mean and variance can be obtained by statistical calculation based on actual dwell time data for a large number of users.
Step 2, uniformly dividing a deployment area into X multiplied by Y grids, setting the candidate arrangement position of the energy source as the center of each grid, and simultaneously deploying a plurality of energy sources in one grid, wherein the size of the grid is determined by deployment precision requirements and computing power, and the smaller the grid is, the higher the deployment precision is, but the higher the computing complexity is;
and 3, firstly, ensuring that the body area network nodes can be effectively charged at each stopping point, namely the charging power of each stopping point is greater than the energy consumption power. Traversing all grids, calculating a first objective function value of an energy source deployed under each grid, deploying a newly increased energy source in the grid which enables the first objective function value to be minimum, and randomly deploying the newly increased energy source in one of the grids if the first objective function values deployed in the grids are equal;
further, in step 3, the first objective function expression is:
Figure BDA0001268798580000065
wherein, PcRepresenting the energy consumption power of the body area network nodes,
Figure BDA0001268798580000066
indicates the dwell point viThe charging power, if there are K energy sources in the current deployment scenario,
Figure BDA0001268798580000067
calculated from equation (2):
Figure BDA0001268798580000068
wherein η is the rectification efficiency, GsIs the transmit antenna gain, GrIs the receive antenna gain, LpIs the polarization loss, λ is the wavelength, εIs to adjust the parameters to ensure
Figure BDA0001268798580000069
Limited value, dk,iIs the kth energy source and point viA distance between PsIs the transmitted power of the energy source,
Figure BDA00012687985800000610
is the phase shift of the rf signal, where i represents the modulus of the complex number therein, in this embodiment, η is 0.3, Gs＝8dBi，Gr＝2dBi，Lp＝3dB，λ＝0.33m，ε＝0.2316m，Ps＝1～4W；
Step 4, judging whether the first objective function value under the current deployment scheme is 0, if so, ensuring that the charging power of the body area network nodes at all the stay points is greater than the energy consumption power, and entering step 5; otherwise, repeating the step 3;
step 5, because the capacity of the energy storage element is limited, the maximum accumulated net consumption of energy during the movement of the user on each track side cannot exceed the capacity of the energy storage element. To calculate the maximum energy-accumulated net consumption value, each track edge is segmented. For track edge ei,jE, assuming it is of length li,jDivide it into
Figure BDA0001268798580000071
When the line segment moves on the line segment with the length equal to or less than delta l, the charging power of the body area network node carried by a user is kept unchanged and is the charging power at the center point of the line segment, the value of the delta l is determined by the accuracy requirement and the calculation capability, and the smaller the delta l is, the higher the calculation accuracy is, but the higher the calculation complexity is;
step 6, traversing all grids, calculating a second objective function value of the energy source deployed in each grid, deploying the newly increased energy source in the grid which enables the second objective function value to be minimum, and randomly deploying the newly increased energy source in one grid if the second objective function values deployed in the grids are equal;
further, in step 6, the second objective function expression is:
Figure BDA0001268798580000072
wherein E iscRepresenting the energy storage element capacity of the body area network node,
Figure BDA0001268798580000073
represents the point v from restiTo the stop point vjThe maximum energy accumulation net consumption value in the moving process is obtained by calculating the energy accumulation net consumption values of all line segments of a track of a user in sequence by formula (4) and taking the maximum value:
Figure BDA0001268798580000074
wherein
Figure BDA0001268798580000075
Indicating the track edge ei,jThe charging power at the central point of the m-th line segment can be calculated by formula (2), li,j,mIndicating the track edge ei,jThe length of the line segment of the m-th segment,
Figure BDA0001268798580000076
the average moving speed of the user is represented and can be obtained through statistical calculation according to the moving speed data of a large number of users.
Step 7, judging whether a second objective function value under the current deployment scheme is 0, if so, ensuring that the maximum energy accumulated net consumption value of all track edges does not exceed the capacity of the node energy storage element, and entering step 8; otherwise, repeating the step 6;
and 8, the actual energy uninterrupted probability of the body area network nodes needs to meet the system requirement. Traversing all grids, calculating a third objective function value of the energy source deployed in each grid, deploying the newly increased energy source in the grid which enables the third objective function value to be minimum, and randomly deploying the newly increased energy source in one of the grids if the third objective function values deployed in the grids are equal;
further, in step 8, the third objective function expression is:
Figure BDA0001268798580000081
wherein p is0Probability of uninterrupted energy of body area network nodes, p, representing system requirementsi,jIndicating that the user is from the stop point viTo the stop point vjThe energy uninterrupted probability in the moving process is calculated by the formula (6):
Figure BDA0001268798580000082
wherein t isi,jIndicating the user along the edge e of the tracki,jDuring the moving process, at least a stopping point v is needed for ensuring the energy of the node is not interruptediThe residence time is calculated by the formula (7):
Figure BDA0001268798580000083
step 9, judging whether a third objective function value under the current deployment scheme is 0, if so, ensuring that the actual energy uninterrupted probability of the body area network node meets the system requirement, and ending the operation; otherwise, repeat step 8.

Claims (1)

1. A radio frequency energy source optimization deployment method for supplying energy to body area network nodes is characterized in that: the method comprises the following steps:
step 1, a body area network node is positioned on the body surface or in the body of a user, and data acquisition and communication are carried out by capturing wireless signal energy emitted by a peripheral radio frequency energy source; according to the position information of a plurality of mobile users in a certain area in a period of time, a clustering algorithm is used for collecting V (V) as a frequent stop point for a group user movement model1,v2,...,vNDescribing a directed graph G (V, E) formed by the cluster track set E, wherein N is the number of stop points, and directed edges Ei,jE E denotes the presence of a slave stay point v in the regioniTo stopLeft point vjThe movement trajectory of (2); user at any dwell point viThe residence time t at the position of the E.V is subject to truncation normal distribution, and the probability density function of the residence time t is
Figure FDA0002242443460000011
Wherein, mu and sigma2Mean and variance, respectively, α is a normalization constant, let
Figure FDA0002242443460000012
Figure FDA0002242443460000013
To ensure
Figure FDA0002242443460000014
Step 2, uniformly dividing the deployment area into X multiplied by Y grids, wherein the size of the grids is determined by precision requirements and computing power, the candidate deployment position of the energy source is set as the center of each grid, and a plurality of energy sources can be deployed in one grid at the same time;
step 3, traversing all grids, calculating a first objective function value of an energy source deployed in each grid, deploying a newly increased energy source in the grid which enables the first objective function value to be minimum, and randomly deploying the newly increased energy source in one grid if the first objective function values deployed in the grids are equal;
step 4, judging whether the first objective function value under the current deployment scheme is 0, if so, ensuring that the charging power of the body area network nodes at all the stay points is greater than or equal to the energy consumption power, and entering step 5; otherwise, repeating the step 3;
step 5 for directed edge ei,jE, assuming it is of length li,jDivide it into
Figure FDA0002242443460000015
The value of delta l is determined by precision requirement and computing power, and when the user moves on a line segment with the length equal to or less than delta l, the body area network carried by the userThe node charging power is kept unchanged and is the charging power at the central point of the line segment;
step 6, traversing all grids, calculating a second objective function value of the energy source deployed in each grid, deploying the newly increased energy source in the grid which enables the second objective function value to be minimum, and randomly deploying the newly increased energy source in one grid if the second objective function values deployed in the grids are equal;
step 7, judging whether a second objective function value under the current deployment scheme is 0, if so, ensuring that the maximum energy accumulated net consumption value of all track edges does not exceed the capacity of the node energy storage element, and entering step 8; otherwise, repeating the step 6;
step 8, traversing all grids, calculating a third objective function value of the energy source deployed in each grid, deploying the newly increased energy source in the grid which enables the third objective function value to be minimum, and randomly deploying the newly increased energy source in one grid if the third objective function values deployed in the grids are equal;
step 9, judging whether a third objective function value under the current deployment scheme is 0, if so, ensuring that the actual energy uninterrupted probability of the body area network node meets the system requirement, and ending the operation; otherwise, repeating the step 8;
in step 3, the first objective function expression is:
Figure FDA0002242443460000021
wherein, PcRepresenting the energy consumption power of the body area network nodes,
Figure FDA0002242443460000022
indicates the dwell point viThe charging power, if there are K energy sources in the current deployment scenario,
Figure FDA0002242443460000023
calculated from equation (2):
Figure FDA0002242443460000024
wherein η is the rectification efficiency, GsIs the transmit antenna gain, GrIs the receive antenna gain, LpIs the polarization loss, λ is the wavelength, and ε is the tuning parameter to ensure
Figure FDA0002242443460000025
Limited value, dk,iIs the kth energy source and point viA distance between PsIs the transmitted power of the energy source,
Figure FDA0002242443460000031
is the phase offset of the radio frequency signal, | | | | represents taking the module to the complex number therein;
in step 6, the second objective function expression is:
Figure FDA0002242443460000032
wherein E iscRepresenting the energy storage element capacity of the body area network node,
Figure FDA0002242443460000033
represents the point v from restiTo the stop point vjThe maximum energy accumulation net consumption value in the moving process is calculated by the formula (4):
Figure FDA0002242443460000034
wherein
Figure FDA0002242443460000035
Indicating the track edge ei,jMiddle mth section line central point ui,j,mThe charging power can be calculated by formula (2) < i >i,j,mIndicating the track edge ei,jThe length of the line segment of the m-th segment,
Figure FDA0002242443460000039
representing the average moving rate of the user;
in step 8, the third objective function expression is:
Figure FDA0002242443460000036
wherein p is0Probability of uninterrupted energy of body area network nodes, p, representing system requirementsi,jIndicating that the user is from the stop point viTo the stop point vjThe energy uninterrupted probability in the moving process is calculated by the formula (6):
Figure FDA0002242443460000037
wherein t isi,jIndicating the user along the edge e of the tracki,jDuring the moving process, at least a stopping point v is needed for ensuring the energy of the node is not interruptediThe residence time is calculated by the formula (7):
Figure FDA0002242443460000038
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