CN109286913A - The mobile edge calculations system energy consumption optimization method of unmanned plane based on Cellular Networks connection - Google Patents
The mobile edge calculations system energy consumption optimization method of unmanned plane based on Cellular Networks connection Download PDFInfo
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- H—ELECTRICITY
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Abstract
The mobile edge calculations system energy consumption optimization method of unmanned plane based on Cellular Networks connection, using the data processing energy consumption and flight energy consumption for minimizing unmanned plane node as target, and consider the constraint of itself flying condition of unmanned plane node and the constraint of terrestrial cellular Network Communication base station energy consumption, it establishes model and combined optimization is carried out to the parameters such as the data distribution amount of unmanned plane node and flight path, the velocity and acceleration of unmanned plane node.The invention has the advantages that: solving the mobile edge calculations system energy consumption optimization problem of existing unmanned plane, the energy consumption of unmanned plane node is reduced.
Description
Technical field
The present invention relates to wireless communication and field of cloud computer technology, and the unmanned plane specifically based on Cellular Networks connection moves
Dynamic edge calculations system energy consumption optimization method.
Background technique
It is base with the fast-developing and continuous maturation of unmanned plane (UnmannedAerial Vehicle, UAV) technology
Wide opportunity to develop is brought in the relevant industries of unmanned plane aerial platform, such as, aerial road supervision, agricultural region prison
Depending on unmanned plane logistics distribution relays emergency communication, the shunting of hot spot region unmanned plane load of base station etc. in the air.It is same with this
When, with the rapid development of the information technologies such as wireless communication, image signal process, by carrying advanced number on unmanned plane
According to processing module, radio-frequency communication module, audio-video sensor etc., so that mobile Internet applies the application in unmanned aerial vehicle platform
More and more extensive, such as real-time high definition image returns, target identification, virtual reality etc..However, with these mobile applications
Universal, for the computing resource of unmanned plane, energy resource, storage resource etc., higher requirements are also raised.Especially to calculating
For the mobile application of responsive type, a large amount of data information needs processing and operation in real time, and it is flat thus substantially to consume unmanned plane
The energy resource of platform, and occupy excessive hardware computing resource.For unmanned aerial vehicle platform limited for physical size, electricity
Pond energy and calculation resources be all it is very limited, especially most unmanned planes rely primarily on self-powered module at present
Or fuel tank is powered.Energy consumption when unmanned plane is in addition to data processing, another part is then derived mainly to fly in unmanned plane
Capable or hovering phase energy consumption.How under conditions of unmanned plane energy constraint, make full use of effective energy resource for nothing
Man-machine data processing and flight is a critical issue of the following unmanned plane application.
In order to cope with energy and resource consumption problem of the energy constraint type unmanned plane node in information processing, Ge great Yan
Study carefully mechanism and scholar proposes mobile cloud computing system (Mobile Cloud Computing System), i.e. unmanned plane node
The cloud resource pond that partial data processing task is transmitted to distal end is subjected to data payload shunting by wireless transmission method, thus
Unmanned plane node is dropped in local data processing energy consumption.In order to further decrease unmanned plane node to distal end cloud resource pond
Propagation delay time and path loss, save the energy consumption of unmanned plane node, and guarantee system service quality, researcher is again
Mobile edge calculations system (Mobile Edge Computing System) is proposed, i.e., in the short distance area of unmanned plane node
Data processing node is disposed in domain, so that the data processing task to unmanned plane node carries out load bridging.However, for nobody
For machine node, due to its flight path maneuverability, there is random distribution nature on geographical location.Mobile edge meter
Calculation system is in order to realize preferable edge calculations coverage, it is necessary to by disposing a large amount of edge calculations node, to further
At a distance from unmanned plane node, the load bridging in nearby region is completed, however will cause mobile edge calculations system in this way
Unite lower deployment cost it is unprecedented soaring.
In view of existing cellular communications networks are highly developed, the base station in cellular cell is supplied in computing resource, energy
It gives, coverage area and website quantity etc. all have biggish advantage.Using cellular network communication base station as edge calculations
Unmanned plane node in node, with its coverage area carries out data transmission, and carries out data processing in base station, constitutes and is based on bee
The mobile edge calculations system of the unmanned plane of nest net connection, is a kind of efficient data payload shunt mode.It is worth noting that, nothing
Man-machine node needs to send data-signal to base station during data payload shunts, and this process will consume itself
Energy.It therefore, is that more data or are stayed in unmanned plane node and be originally located in by more data distributions to base station
Reason, is a complicated compromise optimization problem.On the other hand, unmanned plane node is in flight course, to the nothing between base station
Line channel condition can also change with flight path, under good channel condition more suitable for transmit data, and channel condition compared with
When poor, then few transmission data are more likely to.Therefore, from the angle of unmanned plane node energy consumption, how in unmanned plane node
Each flight moment, between unmanned plane node and base station data information bits distribution, unmanned plane flight path, fly
The parameters such as row velocity and acceleration optimize, and will be of great practical significance.
Summary of the invention
Technical problem to be solved by the invention is to provide the mobile edge calculations system energy of the unmanned plane joined based on Cellular Networks
Optimization method is consumed, the mobile edge calculations system energy consumption optimization problem of existing unmanned plane is solved, the energy for reducing unmanned plane node disappears
Consumption.
Used technical solution is the present invention to solve above-mentioned technical problem: the unmanned plane based on Cellular Networks connection is mobile
Edge calculations system energy consumption optimization method, the mobile edge calculations system of the unmanned plane includes a unmanned plane node and one
Terrestrial cellular Network Communication base station, unmanned plane node have a certain amount of pending data bit, and unmanned plane node is according to specified circuit
Diameter, speed and acceleration flight, at each flight moment, part pending data is sent to ground by unmanned plane node
Cellular network communication base station, terrestrial cellular Network Communication base station carry out calculation process to these data, establish three-dimensional space rectangular coordinate system
(x, y, z), the height position information in z-axis coordinate representation space, the coordinate w=(x of terrestrial cellular Network Communication base stationw,yw,H1)T,
Wherein, ()TRepresenting matrix/vector transposition, unmanned plane node have L pending data information bit, and ρ L information bit exists
Unmanned plane node carries out local computing, and (1- ρ) L information bit was flown by load bridging mode in unmanned plane node
Terrestrial cellular Network Communication base station is successively transferred in journey, terrestrial cellular Network Communication base station handles these streamed datas,
In, 0≤ρ≤1 indicates information bit distribution factor, for weighing the local computing of unmanned plane node and the data of load bridging
Amount ratio；Unmanned plane node is in three dimensions with fixed height H2Flight, single flight time are T, which is divided
For N+1 time slot, each time slot width is δ, i.e. T=δ (N+1)；The flight parameter of n-th of time slot unmanned plane node includes: nothing
Man-machine position coordinates q [n]=(x [n], y [n], H2)T, unmanned plane during flying velocity vector v [n]=(vx[n],vy[n],0)T, nothing
Man-machine vector acceleration a [n]=(ax[n],ay[n],0)T, the energy consumption optimization method the following steps are included:
(1) it is defined according to data information bits calculation process energy consumption, establishes unmanned plane node and handle data in time span T
Energy consumption model E when bitcomp；
(2) flight energy consumption model E of the unmanned plane node in single flight time T is establishedfly；
(3) data processing energy consumption and flight energy consumption model based on unmanned plane node in step (1) and step (2), to minimize
Unmanned plane node total energy consumption is optimization aim, and considers the constraint of unmanned plane node flying condition and base station energy consumption constraint, establishes and closes
In the mathematical model of unmanned plane node flight parameter and information bit distribution parameter and solution.
Energy consumption model E when unmanned plane node handles data bit in time span T in step (1) of the present inventioncomp
Are as follows:
Wherein, G indicates the hardware computing capability constant of unmanned plane node.
Flight energy consumption model of the unmanned plane node established in step (2) of the present invention in single flight time T
EflyAre as follows:
Wherein, c1And c2It is related positive number constant factor, g table with unmanned plane node weight, wing area, atmospheric density etc.
Show acceleration of gravity,The kinetic energy change amount for indicating unmanned plane node, if unmanned plane node
Start-stop speed parameter fix, then Δ p is fixed amount, and m indicates unmanned plane node total weight, it is assumed that unmanned plane node start-stop speed
Spend identical, then Δ p=0.
The number about unmanned plane node flight parameter and information bit distribution parameter that step (3) of the present invention is established
Learn model are as follows:
C7:0≤ρ≤1
Wherein,qIIndicate unmanned plane node initial position, qFIndicate unmanned plane
Node final position, VmaxIndicate unmanned plane node maximum flying speed, amaxIndicate unmanned plane node peak acceleration；Lu[n]
Indicate nth slot in unmanned plane node to base station send bit data number, data processing delay be 1 time slot, (n+1)th
Time slot base station handles Lb[n+1] a bit data,Indicate flight parameter,Indicate that data bit distributes parameter, B indicates channel width, and P indicates the data of unmanned plane node
Transimission power, and be fixed value, σ2Indicate additivity white complex gaussian noise power；D [n] indicates nth slot unmanned plane node to base
The distance stood, β0Indicate channel gain reference value when distance is 1m, signal transmission power is 1W, | | | |-indicate that Europe is several
In norm, C1 indicate terrestrial cellular Network Communication base station be used for handle data information ceiling capacity binding occurrence be Etotal, C2
It indicates in nth slot, under the conditions of channel width is B, the contributing beam of the data transmission ratio of unmanned plane node, C3 indicates bit-level
Connection constraint, i.e., the information bit of each flight moment terrestrial cellular Network Communication base station calculation process be no more than unmanned plane node to
Its information bit transmitted, C4 indicate the unmanned plane node total information bit number that cellular network communication base station is transmitted to the ground, C5
Indicate that the total information bit number of shunting handled by terrestrial cellular Network Communication base station, C6 indicate the pass between unmanned plane during flying parameter
System's constraint, C7 and C8 indicate the feasible zone edge-restraint condition of optimization parameter.
The beneficial effects of the present invention are: the method for the present invention is to minimize the data processing energy consumption and flight of unmanned plane node
Energy consumption is target, and considers the constraint of itself flying condition of unmanned plane node and base station energy consumption constraint, to the data of unmanned plane node
The parameters such as the flight path of shunt volume and unmanned plane node, velocity and acceleration carry out combined optimization, to obtain so that nobody
The energy consumption minimized flight parameter of machine and data load bridging amount.
Detailed description of the invention
Fig. 1 is the system model figure of the method for the present invention；
Fig. 2 is the unmanned plane that solution required by the method for the present invention obtains in emulation experiment under the conditions of the single flight time T=50 seconds
Node flight path；
Fig. 3 is the unmanned plane that solution required by the method for the present invention obtains in emulation experiment under the conditions of the single flight time T=50 seconds
Node flying speed and acceleration change curve；
Fig. 4 is the unmanned plane that solution required by the method for the present invention obtains in emulation experiment under the conditions of the single flight time T=50 seconds
The data information bits that node is handled in the data information bits of distributed transmission of each moment and terrestrial cellular Network Communication base station
Change curve；
Fig. 5 is the unmanned plane that solution required by the method for the present invention obtains in emulation experiment under the conditions of different base station energy constraint values
Data information bits distribution factor (1- ρ) change curve of node.
Specific embodiment
The embodiment of the present invention is explained in detail with reference to the accompanying drawings of the specification.
The mobile edge calculations system energy consumption optimization method of unmanned plane based on Cellular Networks connection, the mobile edge of the unmanned plane
Computing system includes a unmanned plane node and a terrestrial cellular Network Communication base station, and unmanned plane node has a certain amount of wait locate
Data bit is managed, unmanned plane node flies according to specified path, speed and acceleration, at each flight moment, unmanned plane
Part pending data is sent to terrestrial cellular Network Communication base station by node, terrestrial cellular Network Communication base station to these data into
Row calculation process, specifically includes the following steps:
(1) it establishes three-dimensional space rectangular coordinate system (x, y, z), the height position information in z-axis coordinate representation space, ground
Coordinate w=(the x of cellular network communication base stationw,yw,H1)T, wherein ()TRepresenting matrix/vector transposition, unmanned plane node have L
Pending data information bit, ρ L information bit carry out local computing in unmanned plane node, and (1- ρ) L information bit passes through
Load bridging mode, is successively transferred to base station in unmanned plane node flight course, base station to these streamed datas at
Reason, wherein 0≤ρ≤1 indicates information bit distribution factor, for weighing the local computing and load bridging of unmanned plane node
Data volume ratio；Unmanned plane node is in three dimensions with fixed height H2Flight, the single flight time is T, by the time
Section is divided into N+1 time slot, and each time slot width is δ, i.e. T=δ (N+1)；The flight parameter of n-th of time slot unmanned plane node
It include: unmanned plane position coordinates q [n]=(x [n], y [n], H2)T, unmanned plane during flying velocity vector v [n]=(vx[n],vy
[n],0)T, unmanned plane vector acceleration a [n]=(ax[n],ay[n],0)T, three meets following relationship:
Wherein,qIIndicate unmanned plane node initial position, qFIndicate unmanned plane node final position, Vmax
Indicate unmanned plane node maximum flying speed, amaxIndicate unmanned plane node peak acceleration；Assuming that time slot width is sufficiently small, nothing
Three man-machine flight parameters can be gathered as composed by the parameter of each time slot to be described, i.e. the position point set of flight path
It closesFlying speed setAcceleration setAssuming that in nth slot unmanned plane node to
Base station sends Lu[n] a bit data, data processing delay are 1 time slot, and the (n+1)th time slot base station handles Lb[n+1] a bit
Data, and the two meets bit cascade connection, as follows
Wherein,Assuming that wireless channel between unmanned plane node and base station is direct-view diameter, then nth slot
The wireless channel of interior unmanned plane node to base station obeys free space path loss model, i.e.,
Wherein, d [n] indicates distance of the nth slot unmanned plane node to base station, β0Indicate that distance is 1m, signal transmission power is
Channel gain reference value when 1W, | | | |-indicate Euclid norm.
(2) according to information theory Shannon channel capacity formula, in nth slot, under the conditions of channel width is B, unmanned plane section
The data transmission ratio spy L of pointu[n] meets following relational expression:
Wherein, B indicates channel width, and P indicates the data transmission utilization measure of unmanned plane node, and is fixed value, σ2Indicate that additivity is multiple
White Gaussian noise power；
(3) it is defined according to data information bits calculation process energy consumption it is found that for given M-bit pending data amount
With processing time Δ, when calculation processing information bit consumed ENERGY E are as follows:
Wherein, K is constant, is determined by the hardware computing capability of node itself；Therefore, unmanned plane node is in time span T
Handle energy consumption E when data bitcompFor
Wherein, G indicates the hardware computing capability constant of unmanned plane node in this system.
(4) flight energy consumption E of the unmanned plane node within the single flight time is definedfly, then:
Wherein, c1And c2It is related positive number constant factor, g table with unmanned plane node weight, wing area, atmospheric density etc.
Show acceleration of gravity,The kinetic energy change amount for indicating unmanned plane node, if unmanned plane node
Start-stop speed parameter fix, then Δ p is fixed amount, and m indicates unmanned plane node total weight, it is assumed that unmanned plane node start-stop speed
Identical, then Δ p=0 is spent, can be ignored.
(5) data processing energy consumption and flight energy consumption model based on unmanned plane node in step (3) and step (4), with most
Smallization unmanned plane node total energy consumption is optimization aim, and considers the constraint of unmanned plane node flying condition and base station energy consumption constraint, is built
It is vertical to distribute the mathematical model of parameter about unmanned plane node flight parameter and information bit and the model solution is obtained optimal
Energy optimization scheme, mathematical model are as follows:
C7:0≤ρ≤1
Wherein,Indicate flight parameter,Indicate data bit point
With parameter, the ceiling capacity binding occurrence that C1 indicates that base station is used to handle data information is Etotal, C2 indicate step (2) in number
It is constrained according to transmitted bit, C3 indicates bit cascade constraint, i.e., the information bit of each flight moment base station calculation process is no more than
The information bit that unmanned plane node is transmitted to it, C4 indicate the total information bit number that unmanned plane node is transmitted to base station, C5 table
Show that the total information bit number of shunting handled by base station, C6 indicate that the relation constraint between unmanned plane during flying parameter, C7 and C8 indicate
Optimize the feasible zone edge-restraint condition of parameter.
But the factor and variable being related to due to the model are more, form is also complex, and Optimized Iterative process also will
It is very difficult, therefore using first order Taylor series expansion and convex row approximation method, solution obtains one group of the optimization problem
Suboptimal solution.As follows.
(6) due in step (5) objective function of optimization problem and constraint condition C 2 be it is non-convex, convex row can be used
Approximation method converts it；Introduce slack variable collectionThe flight energy consumption of unmanned plane node in n-th of time slot is indicated
For following form
And increase new constraint condition C9, as follows
Wherein,
(7) will be in step (6) in constraint condition C9 | | v [n] | |2In partial points { vl[n] } carry out first order Taylor series
Expansion, obtains | | v [n] | |2Lower bound, as follows
Wherein, l indicates the l times iteration；Utilize lower bound flbConstraint condition C9 is converted to following form by (v [n])
(8) for constraint condition C2 in step (5), slack variable collection is introducedC2 is expressed as again
Form
Wherein,It indicates to refer to signal-to-noise ratio；And new constraint condition C11 is introduced, as follows
It (9) will be in step (8)In partial points { yl[n] } first order Taylor series expansion is carried out, it obtains
Its lower bound, as follows
Wherein, S [n]=log2(y[n]+Pγ0),It as a result, will about
Beam condition C 2 is converted to new constraint condition C12, as follows
(10) based on flight energy consumption in step (6)With the new constraint in step (7), step (8) and step (9)
Optimization problem in step (5) is converted into following convex optimization problem by condition C 10, C11 and C12
s.t.C1,C3-C8,C10,C11,C12
And it can solve to obtain the suboptimal solution of former problem using the convex optimization method (such as interior point method) of standard.
Emulation experiment
Simulation parameter setting: signal-to-noise ratio γ is referred to0=5 × 103, system bandwidth B=1MHz, base station height H1=20m, nothing
Man-machine node flying height H2=100m, unmanned plane node start-stop position coordinates are respectively qI=(- 500, -500,100)TAnd qF
=(500, -500,100)T, unmanned plane node maximum flying speed Vmax=50m/s, peak acceleration amax=5m/s2, base station
Energy maximum constrained value be Etotal=6 × 103J, pending data bit is 1Mbits, unmanned plane to unmanned plane node in total
Node flight coefficient of energy dissipation c1=0.002 and c2=70.698, unmanned plane node computing capability constant G=10-11, slot length δ
=0.5s, unmanned plane node signal transimission power are fixed as P=2W.
Fig. 2-Fig. 5 gives the unmanned plane node flight parameter that solution required by the present invention obtains and data information bits distribution
Situation.Fig. 2 is the flight path figure for the unmanned plane node that the present invention obtains as unmanned plane node flight time T=50s；Figure
3 give as unmanned plane node flight time T=50s, the flying speed and acceleration of the unmanned plane node that the present invention obtains
Variation tendency；Fig. 4 gives as unmanned plane node flight time T=50s, each flight moment that the present invention obtains nobody
The volume of transmitted data of machine node and the data calculation amount situation of change of base station；Fig. 5 gives as unmanned plane node flight time T
When=50s, the data information bits distribution factor (1- ρ) for the unmanned plane node that the present invention obtains is with base station energy constraint value
Variation tendency.
Claims (4)
1. the mobile edge calculations system energy consumption optimization method of unmanned plane based on Cellular Networks connection, it is characterised in that: the unmanned plane
Mobile edge calculations system includes a unmanned plane node and a terrestrial cellular Network Communication base station, and unmanned plane node has a certain amount of
Pending data bit, unmanned plane node flies according to specified path, speed and acceleration, at each flight moment,
Part pending data is sent to terrestrial cellular Network Communication base station by unmanned plane node, and terrestrial cellular Network Communication base station is to these numbers
According to calculation process is carried out, establish three-dimensional space rectangular coordinate system (x, y, z), the height position information in z-axis coordinate representation space, ground
Coordinate w=(the x of face cellular network communication base stationw,yw,H1)T, wherein ()TRepresenting matrix/vector transposition, unmanned plane node have L
A pending data information bit, ρ L information bit carry out local computing in unmanned plane node, and (1- ρ) L information bit is logical
Overload shunting mode is successively transferred to terrestrial cellular Network Communication base station, terrestrial cellular net in unmanned plane node flight course
Communication base station handles these streamed datas, wherein 0≤ρ≤1 indicates information bit distribution factor, for weighing nobody
The local computing of machine node and the data volume ratio of load bridging；Unmanned plane node is in three dimensions with fixed height H2Fly
Row, single flight time are T, which are divided into N+1 time slot, each time slot width is δ, i.e. T=δ (N+1)；N-th
The flight parameter of a time slot unmanned plane node includes: unmanned plane position coordinates q [n]=(x [n], y [n], H2)T, unmanned plane during flying
Velocity vector v [n]=(vx[n],vy[n],0)T, unmanned plane vector acceleration a [n]=(ax[n],ay[n],0)T, the energy consumption
Optimization method the following steps are included:
(1) it is defined according to data information bits calculation process energy consumption, establishes unmanned plane node and handle data ratio in time span T
Energy consumption model E when specialcomp；
(2) flight energy consumption model E of the unmanned plane node in single flight time T is establishedfly；
(3) data processing energy consumption and flight energy consumption model based on unmanned plane node in step (1) and step (2), to minimize
Unmanned plane node total energy consumption is optimization aim, and considers the constraint of unmanned plane node flying condition and base station energy consumption constraint, establishes and closes
In the mathematical model of unmanned plane node flight parameter and information bit distribution parameter and solution.
2. the mobile edge calculations system energy consumption optimization method of the unmanned plane according to claim 1 based on Cellular Networks connection,
It is characterized in that: energy consumption model E when unmanned plane node handles data bit in time span T in the step (1)compAre as follows:
Wherein, G indicates the hardware computing capability constant of unmanned plane node.
3. the mobile edge calculations system energy consumption optimization method of the unmanned plane according to claim 1 based on Cellular Networks connection,
It is characterized in that: flight energy consumption model E of the unmanned plane node established in the step (2) in single flight time TflyAre as follows:
Wherein, c1And c2It is related positive number constant factor with unmanned plane node weight, wing area, atmospheric density etc., g is indicated
Acceleration of gravity,Indicate the kinetic energy change amount of unmanned plane node, if unmanned plane node
Start-stop speed parameter is fixed, then Δ p is fixed amount, and m indicates unmanned plane node total weight, it is assumed that unmanned plane node start-stop speed phase
Together, then Δ p=0.
4. the mobile edge calculations system energy consumption optimization method of the unmanned plane according to claim 1 based on Cellular Networks connection,
It is characterized in that: the mathematical model about unmanned plane node flight parameter and information bit distribution parameter that the step (3) is established
Are as follows:
Wherein,qIIndicate unmanned plane node initial position, qFIndicate unmanned plane section
Point final position, VmaxIndicate unmanned plane node maximum flying speed, amaxIndicate unmanned plane node peak acceleration；Lu[n] table
Show that for unmanned plane node to the number of base station transmission bit data, data processing delay is 1 time slot, the (n+1)th time slot in nth slot
Base station handles Lb[n+1] a bit data,Indicate flight parameter,
Indicating that data bit distributes parameter, B indicates channel width, and P indicates the data transmission utilization measure of unmanned plane node, and is fixed value,
σ2Indicate additivity white complex gaussian noise power；D [n] indicates distance of the nth slot unmanned plane node to base station, β0Indicate that distance is
Channel gain reference value when 1m, signal transmission power are 1W, | | | |-indicating Euclid norm, C1 indicates terrestrial cellular
The ceiling capacity binding occurrence that Network Communication base station is used to handle data information is Etotal, in nth slot, channel width is for C2 expression
Under the conditions of B, the contributing beam of the data transmission ratio of unmanned plane node, C3 indicates bit cascade constraint, i.e., each flight moment terrestrial cellular
The information bit of Network Communication base station calculation process is no more than the information bit that unmanned plane node is transmitted to it, and C4 indicates nobody
The machine node total information bit number that cellular network communication base station is transmitted to the ground, C5 are indicated handled by terrestrial cellular Network Communication base station
The total information bit number of shunting, C6 indicate that the relation constraint between unmanned plane during flying parameter, C7 and C8 indicate the feasible of optimization parameter
Domain edge-restraint condition.
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