CN111163479A - Node optimization deployment method suitable for wireless locatable sensor network - Google Patents
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Abstract
The invention belongs to the field of wireless sensor networks, and relates to a node optimal deployment method suitable for a wireless locatable sensor network. The method aims to process the optimized deployment of the wireless locatable sensing network node, and comprises the following steps: acquiring a sensing node antenna gain estimation model in a discrete state; constructing a double-dipole antenna radiation gain model and a field intensity estimation model in a simultaneous state; obtaining an optimization objective function through communication among the mobile charger, the sensing node and the common node; and optimizing the target function by adopting a multi-task evolutionary algorithm based on an information forward migration mechanism to obtain an optimal deployment mode of the sensing nodes. The invention has the characteristics of effectively improving the energy utilization rate of the charger, reducing the charging inactivation time and simultaneously realizing the requirements of high-precision positioning and large-range coverage of the whole system.
Description
Technical Field
The invention belongs to the field of wireless sensor networks, and relates to a node optimal deployment method suitable for a wireless locatable sensor network.
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 ispathIs the path running time, τiIs the dwell time, S represents the number of dwell positions.
And 4, step 4: with the charging time of the sensing node under the charging scene optimized as the target, at each moment T of the charging period TinAcquiring 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 Tlost_timeThe 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
tP_in＜P_thAnd 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 PTThe sensitivity threshold of the sensing node in the receiving link is PRThen 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 nodenDefining the coverage factor of the nth common node as GnOnly when
When, Gn1, otherwise Gn0, wherein Dn，mIs 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 factor2And a coverage evaluation function f3And 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，yR，zR) In an attitude of
The coordinate of the sensing node antenna is (x)T，yT，zT) 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,
representing the rotation angle of the sensing node antenna, wherein the 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 respectively obtained by derivation
Wherein the gain angles of the two antennas are respectively thetaR＝arccos(Y1/d)，
d is the distance from the sensing node antenna to the charger antenna, xR，T＝xR-xT，yR，T＝yR-yT，zR，T＝zR-zT。
Description of the drawings:
FIG. 1 is a block flow diagram of the present invention;
FIG. 2 is a schematic diagram of a dipole tag antenna gain model;
FIG. 3 is a schematic diagram of a model of radiation gain of a dual dipole antenna suitable for a WRSNs system;
FIG. 4 is a schematic diagram of a system model of a wireless locatable sensing network;
FIG. 5 is a schematic diagram of a charging path and a rest position of the charger;
fig. 6 is a functional fitness value curve diagram under the MFEA algorithm and the single-task optimization algorithm under the condition that N is 48.
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, OeForms a ray vector with a point A in space
θeIs the Z-axis to ray vector
Angle of arrival ofeAs 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, xR，T＝xR-xT，yR，T＝yR-yT，zR，T＝zR-zT，(xR，yR，zR) Is the coordinate of the charger antenna, (x)T，yT，zT) 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 periodinCalculating whether the charging power P _ in received by the sensing node from the charger is higher than the minimum power threshold P _ th, as shown in FIG. 5, let the charger count 1mThe speed of/s is constant and the sensor nodes are charged in the fixed position for 2s, and the sensor nodes within the charging radius of the charger can be charged in the whole charging period, wherein the charging period is defined as the sum of the running time and the staying time of the path and is expressed as the sum
Wherein tau ispathIs the path running time, τiIs the dwell time, S represents the number of dwell positions. Charging deactivation time Tlost_timeDefined as the sum of time nodes at which the charging power is below a power threshold, thereby obtaining a charging deactivation time function of
tP_in＜P_thAnd 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 isRAnd GTRespectively 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, ρLIs a polarization loss factor, PtxFor transmitting power, Γ is the Fresnel reflection coefficient, μT∈[0，1]For transmission efficiency. Suppose N is commonThe number of the common nodes is that the radiation power value of the nth common node receiving the 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 PTThe sensitivity threshold of the sensing node in the receiving link is PRThen 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 nodenDefining the coverage factor of the nth common node as GnOnly when
When, Gn1, otherwise Gn0, wherein Dn，mIs 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 factor2And a coverage evaluation function f3And is provided with
The method comprises the steps of designing a multi-task evolution algorithm (MFEA) optimization WRSNs objective function based on an information forward migration mechanism, wherein two randomly selected tasks with population adequacy in the MFEA algorithm have to have relevance to be crossed, and in order to improve the relevance between the multi-tasks and enable the multi-tasks to provide effective genetic factors in the final optimization process, introducing the information forward migration mechanism.
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 at the same time, it is called a compound multitasking problem and is denoted as (30G, 30T, 30C), and if only one problem, for example, 30G, is processed, this task is being solved in the form of single-object Optimization (SOO) and is denoted as (30G, none).
The result of the node optimization deployment method suitable for the wireless locatable sensor network is shown in fig. 6, a blue line is a single-task optimization algorithm simulation result, a red line is an algorithm simulation result of the invention, a graph (a) represents an adaptability value curve when a task 1 is a main function and a task 2 and a task 3 are auxiliary functions, (30T, 30G, 30C) represents a processing result of an information forward migration multi-task optimization algorithm, and (30T, none) represents a processing result of a single-task optimization algorithm, and a graph (b) and a graph (C) are the same representation methodsAnd the fitness value reaches the optimum, and when the iteration times are 50 generations, the performance of the wireless locatable sensor network constructed around the minimization of charging inactivation time, the maximization of location precision and the maximization of coverage degree reaches the optimum respectively as f1＝60，f2＝10，f3＝0.08。
Claims (2)
1. A node optimization deployment method suitable for a wireless locatable sensor network comprises the following specific steps:
step 1: 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 precision 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: taking a charging scene and positioning requirements of a wireless chargeable sensor network as a basis, bringing two dipole antenna gain radiation models in a discrete state into the same Cartesian coordinate system, and obtaining a double dipole antenna pose gain expression and a field intensity estimation model in a simultaneous state by combining a coordinate axis rotation formula of a three-dimensional space;
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 ispathIs the path running time, τiIs the dwell time, S represents the number of dwell positions;
and 4, step 4: the charging time of the sensing node is optimized in the charging scene, and each charging period T isA time tinAcquiring 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 Tlost_timeThe 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
tP_in＜P_thThe method comprises the steps that at the moment when the energy transmission value of a charger is smaller than a charging threshold value, M is the number of sensing nodes;
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 PTThe sensitivity threshold of the sensing node in the receiving link is PRThen two conditions for the normal node to be successfully identified are
And
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 nodenDefining the coverage factor of the nth common node as GnOnly when
When, Gn1, otherwise Gn0, wherein Dn，mIs 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 factor2And a coverage evaluation function f3And 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.
2. The charging scenario suitable for the optimized deployment method of the wireless locatable sensing network node as claimed in claim 2, wherein two dipole antenna gain models are connected in parallel to obtain a dual dipole antenna radiation gain model in the same cartesian coordinate system, and the coordinate of the charger antenna is defined as (x)R，yR，zR) In an attitude of
The coordinate of the sensing node antenna is (x)T，yT，zT) 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,
representing the rotation angle of the sensing node antenna, wherein the 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 respectively obtained by derivation
Wherein the gain angles of the two antennas are respectively thetaR＝arccos(Y1/d)，
d is the distance from the sensing node antenna to the charger antenna, XR，T＝xR-xT，yR，T＝yR-yT，zR，T＝zR-zT。
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