Background
Thanks to the rapid development of Wireless communication technology, remote sensing technology, computer technology and micro-electronics manufacturing technology, Wireless Rechargeable Sensor Networks (WRSNs) have been developed and widely used. Different from the traditional nodes powered by batteries, the sensing nodes in the wireless rechargeable sensor network collect energy through energy sources such as radio frequency signals, the charging process is less affected by changes of the surrounding environment, and the normal service life of the network can be effectively prolonged. In WRSNs, the deployment mode of the sensing node is an important factor affecting the charging time, the charging efficiency and the positioning accuracy of the common node, and optimal deployment of the node based on WRSNs has become a research hotspot in the industry.
In a WRSNs-based node optimization deployment system, node resource planning aims at improving the energy transmission rate of a charger and reducing energy loss, and the node resource planning problem can be divided into two situations: charger-based resource planning problems and sensor node-based resource planning problems. When the charger charges the sensing nodes, the microwave energy transmitted in the space is attenuated along with the increase of the distance, and when no sensing node receives the energy, the energy is wasted, so that the position arrangement of the charger and the sensing nodes has a large influence on the node resource planning problem. However, in the existing wireless charging research, the position of the sensing node is usually fixed, then a movable charging device is deployed in the network, and the optimal travelling route of the charging device is planned by using a path optimization algorithm without considering the influence of the dynamic change of the position deployment of the sensing node on the charging problem. Meanwhile, in the existing research aiming at the energy transmission model, the sensing node is generally communicated with the surrounding common nodes by adjusting the self optimal data transmission rate, the link flow and the routing path based on the self adaptive distributed algorithm, and the energy transmission model is not constructed based on the antenna radiation characteristics.
Based on the background, the invention aims to realize higher energy transmission rate, higher positioning accuracy and larger coverage range, adopts a double-dipole antenna radiation gain model as an energy transmission model among a mobile charger, a sensing node and a common node, obtains minimum charging inactivation time, maximum positioning accuracy and coverage range by optimizing the position and the posture of the sensing node, and provides a node optimization deployment method suitable for a wireless locatable sensing network.
Disclosure of Invention
The invention aims to provide a node optimized deployment method suitable for a wireless locatable sensing network. The method comprises the steps of firstly constructing a gain estimation model based on the dipole antenna in a separated state, further obtaining a double-dipole antenna radiation gain model and a field intensity estimation model in a simultaneous state, then obtaining charging inactivation time, positioning accuracy and a coverage range objective function based on the field intensity estimation model among a mobile charger, a sensing node and a common node, finally providing a multi-task optimization algorithm based on an information forward migration mechanism, and using the multi-task optimization algorithm in the system to obtain an optimal sensing node deployment mode.
The method comprises the following specific steps:
step 1: the method comprises the steps of constructing a wireless chargeable sensor network system, wherein the system consists of a mobile charger, a sensing node, a common node and a service station, aiming at improving the positioning accuracy and the charging efficiency of the system and expanding the coverage area, selecting a dipole antenna as the antennas of the mobile charger, the sensing node and the common node based on the market popularization degree, and acquiring a dipole antenna gain estimation model in a discrete state according to a classical electromagnetic field theory.
Step 2: on the basis of a charging scene and positioning requirements of a wireless chargeable sensor network, bringing two dipole antenna gain radiation models in a discrete state into the same Cartesian coordinate system, and combining a coordinate axis rotation formula of a three-dimensional space to obtain a double dipole antenna pose gain expression and a field intensity estimation model in a simultaneous state.
And step 3: in the charging scene in the step 2, a mobile charger and a plurality of sensing nodes are deployed, the initial positions of the sensing nodes are known, the traveling path of the mobile charger is determined according to the initial positions of the sensing nodes, all the sensing nodes within the charging radius are charged by periodically operating the mobile charger, and the time taken for the charger to travel for one circle is defined as T and expressed as T
Wherein tau is
path Is the path running time, τ
i Is the dwell time, S represents the number of dwell positions.
And 4, step 4: aiming at optimizing the charging time of the sensing node in the charging scene, and at each moment T of the charging period T
in Acquiring the transmission energy value P _ in of the charger and the sensing node, and recording the sum of time points lower than a charging threshold value P _ th as charging inactivation time T
lost_time The smaller the charging deactivation time is, the higher the charging efficiency of the charger to the sensing node is, and the charging deactivation time function is defined as
t
P_in<P_th And M is the number of the sensing nodes at the moment when the transmission energy value of the charger is smaller than the charging threshold value.
And 5: aiming at improving the positioning accuracy and the coverage range of a positioning system, establishing a WRSNs system communication link model meeting the conditions of a transmitting link and a receiving link according to the Fraiss classical theory, and assuming that N is the number of common nodes and the radiation power value of an nth common node receiving an mth sensing node antenna is
The value of the backscattering power received by the nth common node by the mth sensing node is
Common node sensitivity threshold in the transmit chain is P
T The sensitivity threshold of the sensing node in the receiving link is P
R Then two conditions for the normal node to be successfully identified are
Wherein M is [1, M ]],n∈[1,N]。
Step 6: combining the communication link model provided by the
step 5, and defining the geometric precision factor of the nth common node as GDOP (generalized vector operational phase) through the communication between the sensing node and the common node
n Defining the coverage factor of the nth common node as G
n Only when
When, G
n 1, otherwise G
n 0, wherein D
n,m Is a link factor representing whether the sending link and the receiving link can normally communicate, and obtains a positioning degree evaluation function f according to the geometric precision factor and the coverage factor
2 And a coverage evaluation function f
3 And is provided with
And 7: designing a multi-task evolution algorithm (MFEA) based on an information forward migration mechanism to optimize WRSNs objective functions provided by the steps 4 and 6, wherein two randomly selected tasks with population adequacy in the MFEA algorithm have to have correlation to be crossed, and in order to improve the correlation between the multi-tasks and provide effective genetic factors in the final optimization process, the information forward migration mechanism is introduced, and in a multi-task unified search space, when one task is taken as a main task, corresponding weights are configured for other tasks to enable the tasks to be consistent with the search space of the main task objective function, so that the main task is provided with forward genetic factors to assist the main task to be optimized in the optimization process, and the whole algorithm searches for the optimal solution by mining potential genetic complementation between the multiple tasks based on the implicit parallelism of population search.
In step 2, two dipole antenna gain models which are simultaneously connected in the charging scene are in the same Cartesian coordinate system to obtain a double dipole antenna radiation gain model, and the coordinate of the charger antenna is defined as (x)
R ,y
R ,z
R ) In an attitude of
The coordinate of the sensing node antenna is (x)
T ,y
T ,z
T ) In an attitude of
The pitch angle of the charger antenna is shown,
indicating the angle of rotation of the charger antenna, and, similarly,
the pitch angle of the sensing node antenna is shown,
indicating the rotation angle of the sensing node antenna, whereinThe gain angle and the rotation angle can ensure the charging coverage rate of the sensing node and the identification rate of the common node, and the gains of the charger antenna and the sensing node antenna are obtained through derivation and are respectively
Wherein the gain angles of the two antennas are respectively theta
R =arccos(Y
1 /d),
d is the distance from the sensing node antenna to the charger antenna, x
R,T =x
R -x
T ,y
R,T =y
R -y
T ,z
R,T =z
R -z
T 。
The specific implementation mode is as follows:
firstly, a wireless locatable sensing network system is constructed on the basis of a charger, a sensing node, a common node and a service station, the charging inactivation time of the system is reduced, the system location accuracy and the coverage range are improved, dipole antennas are selected as the charger, the sensing node and the common node antennas based on the marketization popularization degree, and a dipole antenna gain estimation model in a discrete state is obtained according to the classical electromagnetic field theory.
As shown in fig. 2, which is a schematic diagram of a coordinate system of a dipole tag antenna, assuming that the antenna size satisfies the "half-wavelength" condition, the gain estimation model thereof can be described as
Wherein, O
e Forms a ray vector with a point A in space
θ
e Is the Z-axis to ray vector
Angle of arrival of
e As a ray vector
After projection on the XOY plane, the X-axis is at the angle of the projection.
Then, constructing an application scene of the wireless locatable sensing network system as shown in fig. 4, deploying initial positions of sensing nodes randomly, determining a travelling path and a stopping position of a charger according to the positions of the sensing nodes, deploying common nodes randomly in a certain rectangular area, placing a charger antenna and a sensing node antenna radiation model in a discrete state in the same cartesian coordinate system, constructing a double dipole antenna gain estimation model and a field intensity estimation model in a simultaneous state as shown in fig. 3, and updating the gain estimation model of the charger antenna and the gain estimation model of the sensing node antenna to be the same as the gain estimation model of the charger antenna and the gain estimation model of the sensing node antenna
In the above formula, x
R,T =x
R -x
T ,y
R,T =y
R -y
T ,z
R,T =z
R -z
T ,(x
R ,y
R ,z
R ) Is the coordinate of the charger antenna, (x)
T ,y
T ,z
T ) Is the coordinate of the sensing node antenna, d is the distance from the sensing node antenna to the charger antenna,
indicating the angle of rotation of the charger antenna,
indicating the angle of rotation of the charger antenna,
is the pitch angle of the sensing node antenna.
In order to obtain the charging deactivation time of each sensing node, it is required to obtain the charging deactivation time of each sensing node at each time point t of the charging period
in Calculating whether the charging power P _ in received by the sensing node by the charger is higher than the minimum power threshold P _ th, as shown in FIG. 5, enabling the charger to travel on a fixed path at a constant speed of 1m/s, and stay at the fixed position for 2s to charge the surrounding sensing nodes, wherein the sensing nodes within the charging radius of the charger can be charged in the whole charging period, which is defined as the sum of the path running time and the stay time and is expressed as the sum of the path running time and the stay time
Wherein tau is
path Is the path running time, τ
i Is the dwell time, S represents the number of dwell positions. Loss of chargeLive time T
lost_time Defined as the sum of time nodes at which the charging power is below a power threshold, thereby obtaining a charging deactivation time function of
t
P_in<P_th And M is the number of the sensing nodes at the moment when the transmission energy value of the charger is smaller than the charging threshold value.
In order to improve the positioning accuracy and the coverage range of a positioning system, a WRSNs system communication link model meeting the conditions of a transmitting link and a receiving link is established based on a Fries power loss model, and the charging power obtained by a sensing node in the transmitting link and the reflected signal power obtained in the receiving link can be respectively expressed as
Wherein G is
R And G
T Respectively adopting the expressions in the formula (2) and the formula (3),
is the channel path loss, λ is the electromagnetic wavelength, τ is the modulation efficiency, ρ
L Is a polarization loss factor, P
tx For transmitting power, Γ is the Fresnel reflection coefficient, μ
T ∈[0,1]For transmission efficiency. Assuming that N is the number of the common nodes, the radiation power value of the mth sensing node antenna received by the nth common node is
The value of the backscattering power received by the nth common node by the mth sensing node is
Common node sensitivity threshold in the transmit chain is P
T The sensitivity threshold of the sensing node in the receiving link is P
R Then two conditions for the normal node to be successfully identified are
And
wherein M is [1, M ]],n∈[1,N]。
Combining the communication link model, and defining the geometric precision factor of the nth common node as GDOP through the communication between the sensing node and the common node
n Defining the coverage factor of the nth common node as G
n Only when
When, G
n 1, otherwise G
n 0, wherein D
n,m Is a link factor representing whether the sending link and the receiving link can normally communicate, and obtains a positioning degree evaluation function f according to the geometric precision factor and the coverage factor
2 And a coverage evaluation function f
3 And is provided with
Designing a multi-task evolution algorithm (MFEA) based on an information forward migration mechanism to optimize a proposed WRSNs objective function, wherein two randomly selected tasks with population adequacy in the MFEA algorithm have to have correlation to be crossed, and in order to improve the correlation between the multi-tasks and provide effective genetic factors in the final optimization process, the information forward migration mechanism is introduced, and in a multi-task unified search space, when one task is taken as a main task, corresponding weights are configured for other tasks to enable the tasks to be consistent with the search space of the main task objective function, so that the main task is provided with the forward genetic factors to assist the main task to optimize in the optimization process, and the whole algorithm is based on the implicit parallelism of population search and seeks an optimal solution by mining potential genetic complementation between a plurality of tasks.
The following is a specific example: as shown in fig. 5, 16 sensing nodes, a charger, and a fixed charger are placed in a scene of 20m × 20m × 5m, a traveling path of the fixed charger is as shown in the figure, with the ground as a reference plane, the charger antenna is deployed at a height of 2m, the sensing nodes and the common nodes are deployed at heights of 1.5m and 1m, and 36, 48, and 84 common sensing nodes are deployed for the environment and are simulated at intervals of 3m, 2m, and 1m, respectively.
A simulation dimension is set to be 30 in a multitask evolution algorithm based on an information forward migration mechanism, a charging inactivation time function is defined as a task 1 and is represented as 30T, a positioning degree evaluation function is defined as a task 2 and is represented as 30G, and a node coverage degree evaluation function is defined as a task 3 and is represented as 30C. In multitasking, if 30G, 30T and 30C are solved simultaneously, it is called a compound multitasking problem, denoted as (30G, 30T, 30C), and if only one problem, such as 30G, is processed, this task is being solved in the form of single-object Optimization (SOO), denoted as (30G, none).
The results of the node optimization deployment method suitable for the wireless locatable sensor network are shown in fig. 6, the blue line is the simulation result of the single task optimization algorithm, the red line is the simulation result of the algorithm of the invention, FIG. (a) shows a fitness value curve when task 1 is a primary function and tasks 2 and 3 are secondary functions, (30T, 30G, and 30C) show the processing result of the multitask optimization algorithm in which information is being migrated, (30T, none) shows the processing result of the single-task optimization algorithm, and FIGS. (b) and (C) are the same representation methods, compared with the single task optimization algorithm, the multi-task optimization algorithm based on the information forward migration mechanism has higher function convergence speed and optimal fitness value, when the iteration times are 50 generations, f is respectively the optimal performance of the wireless locatable sensor network constructed around the charge inactivation time minimization, the location precision maximization and the coverage degree maximization. 1 =60,f 2 =10,f 3 =0.08。