CN110062345A - A kind of unmanned plane-internet of things data acquisition method and system - Google Patents
A kind of unmanned plane-internet of things data acquisition method and system Download PDFInfo
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Abstract
The present invention relates to a kind of unmanned plane-internet of things data acquisition methods, it include: S1, building unmanned plane-internet of things data acquisition system, the unmanned plane-internet of things data acquisition system includes the sensor acquired for data that ground is arranged in and the unmanned plane communicated with the sensor, adhere to RFID label tag on the sensor wherein to store acquisition data, loads RFID reader on the unmanned plane；When S2, the unmanned plane leap deployment region, the data stored in the RFID label tag on the sensor are received by the acquisition of ATG channel based on frame slot algorithm.Implement unmanned plane of the invention-internet of things data acquisition method and system, it is not only able to the sensing data in the region for the traditional multihop trunking method being located in remote districts or danger zone or other not applicable mobile ad-hoc networks and is acquired, additionally it is possible to obtain optimal energy efficiency.
Description
Technical field
The present invention relates to unmanned plane-internet of things field, more specifically to a kind of unmanned plane-internet of things data acquisition
Method and system.
Background technique
In the past twenty years, the huge advance of wireless technology, microsensor, RFID and embedded system, in addition work
The great demand of industry automation and intelligent home network, has pushed the surge of Internet of Things.In Internet of things system, sensor or
RFID label tag is attached in data collection target, and a large amount of physics and virtual " things " are integrating seamlessly into internet, is realized long-range
Monitoring and these related objectives of intelligent control.
Although Internet of Things application seem very attractive, must first be in network edge side collect data, so as to
Cloud center is further analyzed and handles.In the area for possessing abundant infrastructure support, data collection is easy to.But
Telecommunications and the very high remote districts of power infrastructures lower deployment cost or these area working environments it is hostile and
In the case where cannot be introduced into, data collection is extremely difficult or is difficult to realize.For example, a large amount of internet of things sensors is put
It sets and is tracked in severe landform for environmental monitoring or wild animal, the data that internet of things data is collected can not be transmitted to outside
The world, because these remote regions are not in the coverage area of cellular network；In another example RFID label tag is attached in pasture
On the ear of every ox, its physiology and position data are constantly collected, due to the height random mobility of drove, is adopted manually
It is infeasible for collecting data；In another example staff does not allow access into danger in nobody the automatic harbour for deploying Internet of Things
Cargo processing region, sensor/label for installing from the container that harbour operator is badly in need of need to be used for using automatic device
Collect information.
In above-mentioned application scenarios, the traditional multihop trunking method in mobile ad-hoc network is infeasible, because
Sensor is powered by minicell and has weak communication and computing capability, therefore a large amount of IoT data will make network quickly
It can't bear the heavy load.Therefore, in physical limitation and harsh environmental restrictions greatly constrain the application deployment of Internet of Things.
Therefore, it is necessary to one kind can be to positioned at remote districts or danger zone or other not applicable mobile ad hoc networks
The method and system that sensing data in the region of traditional multihop trunking method in network is acquired.
Summary of the invention
The technical problem to be solved in the present invention is that in view of the above drawbacks of the prior art, providing a kind of unmanned plane-Internet of Things
Network data acquisition method and system are not only able to for being located at remote districts or danger zone or other not applicable movements
Sensing data in the region of traditional multihop trunking method in self-organizing network is acquired, additionally it is possible to obtain optimal energy
Amount efficiency.
The technical solution adopted by the present invention to solve the technical problems is: constructing a kind of unmanned plane-internet of things data acquisition
Method, comprising:
S1, building unmanned plane-internet of things data acquisition system, the unmanned plane-internet of things data acquisition system include setting
The sensor acquired for data on ground and the unmanned plane communicated with the sensor are set, wherein on the sensor
Attachment RFID label tag loads RFID reader on the unmanned plane to store acquisition data；
When S2, the unmanned plane leap deployment region, received on the sensor based on frame slot algorithm by ATG channel
RFID label tag on the acquisition data that store.
In unmanned plane of the present invention-internet of things data acquisition method, step S2 further comprises:
S21, the optimum length of frame coefficient that the unmanned plane receives the acquisition data is calculated；
S22, the unmanned plane reception acquisition data are controlled based on the optimum length of frame coefficient.
In unmanned plane of the present invention-internet of things data acquisition method, the step S21 further comprises:
S211, based on optimum capacity described in the unmanned plane-internet of things data acquisition system optimum capacity efficiency calculation
Efficiency frame length coefficient；And/or
S212, based on optimum system described in the unmanned plane-internet of things data acquisition system optimizer system throughput calculation
System efficiency frame length coefficient.
In unmanned plane of the present invention-internet of things data acquisition method, the step S211 includes:
S2111, system effectiveness and energy efficiency are calculated separately according to following formula (7) and (10)；
Wherein ηEIndicate energy efficiency；ηsIndicate system effectiveness, EOIndicate that sensor starts in each time slot because monitoring
The energy consumption of QueryRep, EsIndicate energy consumption of the sensor in success time slot, EcIndicate sensor in conflict time slot
Energy consumption, L indicate frame length, N indicate number of probes, αtIndicate normalization empty slot period, βtWhen indicating normalization conflict
The gap period；
S2112, equation (7) and (10) are rewritten much larger than 1 as equation (13) based on frame length and number of probes
S2113, optimum capacity efficiency is obtained to obtain optimum capacity efficiency frame length L according to formula (13)E_opt；
S2114, optimum capacity efficiency frame length coefficient is solved according to formula (13) maximum system efficiencyWherein αeIndicate the synchronous normalized energy consumption monitored in sensor time slot timing；
βeIndicate normalized energy consumption of the sensor in conflict time slot.
In unmanned plane of the present invention-internet of things data acquisition method, the step S212 includes:
S2121, system effectiveness and energy efficiency are calculated separately according to following formula (7) and (10)；
Wherein ηEIndicate energy efficiency；ηsIndicate system effectiveness, EOIndicate that sensor starts in each time slot because monitoring
The energy consumption of QueryRep, EsIndicate energy consumption of the sensor in success time slot, EcIndicate sensor in conflict time slot
Energy consumption, L indicate frame length, N indicate number of probes, αtIndicate normalization empty slot period, βtWhen indicating normalization conflict
The gap period；
S2122, equation (7) and (10) are rewritten much larger than 1 as equation (13) based on frame length and number of probes
S2123, it is based on parameter preset αt、βt、αe、βeCalculate optimizer system efficiency frame length factor betaS_optFor definite value.
In unmanned plane of the present invention-internet of things data acquisition method, the step S2 further comprises:
S2a, flying speed, flying height and the frame length coefficient for calculating the unmanned plane；
S2b, the deployment region is leapt based on unmanned plane described in the flying speed, flight altitude control, and be based on institute
It states frame length coefficient and controls the unmanned plane reception acquisition data.
In unmanned plane of the present invention-internet of things data acquisition method, the step S2a further comprises:
S2a1, equation (18.a)-(18.c) is constrained according to optimum capacity efficiency and performance constraints building:
Tf=dl α [T0βe-1/β+TSe-1/β+TC(β-e-1/β-βe-1/β)](18.c)；
Wherein l indicates unmanned plane-sensor efficient communication region on ground, and v indicates the flying speed of unmanned plane, and d is indicated
The density of the sensor of ground configuration, TfIndicate the time span of every wheel read cycle, ∈ indicates the rate of data collection limitation, β table
Show frame length coefficient, T0, TcAnd TsRespectively indicate the time span of empty, conflict, success time slot；
S2a2, according to constraint equation (18.a)-(18.c) using frame length coefficient described in PSO regioselective, it is described to fly
Scanning frequency degree and flying height.
In unmanned plane of the present invention-internet of things data acquisition method, the step S2a2 further comprises:
S2a21, the flying height is limited to minimum flight altitude；
S2a22, the flying speed is selected according to actual needs；
S2a23, the optimum system for meeting optimizer system efficiency and optimum capacity efficiency is solved respectively according to the flying speed
System efficiency frame length coefficient and optimum capacity efficiency frame length coefficient.
In unmanned plane of the present invention-internet of things data acquisition method, the step S2a2 further comprises:
S2a24, according to the optimizer system efficiency frame length coefficient and optimum capacity efficiency frame length coefficient switch it is described nobody
Machine-Internet of Things data collection task state.
The technical solution used to solve the technical problems of the present invention is that constructing a kind of unmanned plane-internet of things data acquisition system
System, including the sensor for data acquisition on ground and the unmanned plane that is communicated with the sensor is arranged in, wherein institute
It states and adheres to RFID label tag on sensor to store acquisition data, load RFID reader on the unmanned plane；The unmanned plane flies
More deployment region when, the acquisition stored in the RFID label tag on the sensor is received by ATG channel based on frame slot algorithm
Data；Wherein the unmanned plane includes the computer program of processor and storage on the processor, the computer program
It is performed the steps of when being executed by processor
S21, the optimum length of frame coefficient that the unmanned plane receives the acquisition data is calculated；
S22, the unmanned plane reception acquisition data are controlled based on the optimum length of frame coefficient；And/or
S2a, flying speed, flying height and the frame length coefficient for calculating the unmanned plane；
S2b, the deployment region is leapt based on unmanned plane described in the flying speed, flight altitude control, and be based on institute
It states frame length coefficient and controls the unmanned plane reception acquisition data.
Implement unmanned plane of the invention-internet of things data acquisition method and system, is not only able to for being located at remotely
Sensing in area or danger zone or the region of the traditional multihop trunking method in other not applicable mobile ad-hoc networks
Device data are acquired, additionally it is possible to obtain optimal energy efficiency.Further, data acquisition is carried out by control unmanned plane
Frame length can obtain optimum capacity efficiency or optimizer system handling capacity.Further, fast by adjusting the flight of unmanned plane
Degree, height and frame length, can obtain optimum capacity efficiency.
Detailed description of the invention
Present invention will be further explained below with reference to the attached drawings and examples, in attached drawing:
Fig. 1 is the flow chart of unmanned plane of the invention-internet of things data acquisition method first embodiment；
Fig. 2 shows the variation ranges of unmanned plane height h and covering radius R；
Fig. 3 shows the optimizer system efficiency and energy efficiency changed with frame length；
Fig. 4 shows the optimum length of frame system of optimizer system efficiency and optimum capacity efficiency with the variation of number of sensors
Number；
Fig. 5 shows the tradeoff between system optimizer system efficiency and optimum capacity efficiency；,
Fig. 6 A-D respectively illustrates the PHY-MAC parameter of calculating；
Fig. 7 shows the linear search algorithm for optimum capacity efficiency frame length coefficient；
Fig. 8 shows the scounting line comparison schematic diagram of two kinds of searching algorithms；
Fig. 9 A-B respectively illustrates preferably PHY-MAC parameter；
Figure 10 respectively illustrates the efficiency under " system effectiveness mode " and " energy efficiency mode "；
Figure 11 shows the quantity for competing sensor under " system effectiveness mode " and " energy efficiency mode " in every wheel inquiry；
Figure 12 shows the number of sensor related with flying speed under " system effectiveness mode " and " energy efficiency mode "
Amount；
Figure 13 shows the linear search algorithm of optimum length of frame；
Figure 14 and 15 is shown respectively for the optimum length of frame variation of different sensor densities and efficiency change；
Figure 16 shows the energy efficiency gains of acquisition.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Fig. 1 is the flow chart of unmanned plane of the invention-internet of things data acquisition method first embodiment.Such as Fig. 1 institute
Show, in step sl, constructs unmanned plane-internet of things data acquisition system.In preferred implementation of the invention, the unmanned plane-
Internet of things data acquisition system includes that the sensor acquired for data on ground is arranged in and is communicated with the sensor
Unmanned plane, read wherein adhering to RFID label tag on the sensor and loading RFID to store acquisition data, on the unmanned plane
Device.In step s 2, when the unmanned plane leaps deployment region, the sensing is received by ATG channel based on frame slot algorithm
The acquisition data stored in RFID label tag on device.It in a preferred embodiment of the invention, can be in the step S2
The optimum length of frame coefficient that the unmanned plane receives the acquisition data is calculated first；It is then based on the optimum length of frame coefficient control
The unmanned plane receives the acquisition data.In a further preferred embodiment of the invention, the optimum length of frame coefficient
It can be optimum capacity efficiency frame length coefficient, be also possible to optimizer system efficiency frame length coefficient.For example, can based on it is described nobody
Optimum capacity efficiency frame length coefficient described in machine-internet of things data acquisition system optimum capacity efficiency calculation；It can also be based on institute
State optimizer system efficiency frame length coefficient described in unmanned plane-internet of things data acquisition system optimizer system throughput calculation.At this
In another preferred embodiment of invention, in step s 2, the flying speed, flying height and frame of the unmanned plane can be calculated
Long coefficient；It is then based on unmanned plane described in the flying speed, flight altitude control and leaps the deployment region, and based on described
Frame length coefficient controls the unmanned plane and receives the acquisition data.
Implement unmanned plane of the invention-internet of things data acquisition method, be not only able to for be located at remote districts or
Sensing data in danger zone or the region of the traditional multihop trunking method in other not applicable mobile ad-hoc networks
It is acquired, additionally it is possible to obtain optimal energy efficiency.Further, the frame length of data acquisition is carried out by control unmanned plane,
Optimum capacity efficiency or optimizer system handling capacity can be obtained.Further, pass through the flying speed of adjusting unmanned plane, height
Degree and frame length, can obtain optimum capacity efficiency.
Below in conjunction with specific embodiment to unmanned plane of the invention-internet of things data acquisition method be described further as
Under.
In unmanned plane of the invention-internet of things data acquisition method, unmanned plane is stored in ground by the downloading of ATG channel
Internet of things equipment, i.e. data in sensor, compared with terrestrial channel, due to the higher flying height of unmanned plane, ATG channel holds
Easily has the condition for meeting LoS line-of-sight transmission.The path loss of ATG channel depends on the distance between transmitter and receiver D,
The statistical parameter of wave frequency f and ground environment.Herein, we use known LoS probabilistic model, it is based on survey
The curve matching for measuring data, can be by simplified Sigmoid function come approximate
Wherein PrbLosIt is the LoS probability of ATG channel, h is the height of unmanned plane, and r is the radius of the channel coverage ATG,
A and b is the fitting parameter closely related with ground environment.Therefore, it is Prb that ATG channel, which is unsatisfactory for the probability of LoS condition,NLos=
1-PrbLos。
The path loss of ATG channel for LoS and NLoS may be expressed as:
Wherein ξLoSAnd ξNLoSBe respectively with LoS and NLoS extra path loss associated with channel,For the distance between unmanned plane and sensor, c is the light velocity；ATG channel average path loss is writeable are as follows:
In order to effectively be communicated between unmanned plane and sensor, it will be assumed that the maximum that ATG channel can be born
Path loss is PLmax, it corresponds to maximum r=R, i.e., is only located at the ground installation in the unmanned plane covering circle that radius is R
Path loss is less than PLmax, therefore the node only in circle can send unmanned plane for their data.It allowsAnd r=R, then by the way that equation (1) and (2) are substituted into (3), we are obtained:
It may be seen that covering radius is the implicit function of unmanned plane height h in (4), therefore we seek h relative to R
It leads, so that derivative is 0, obtainsThen we obtain optimum height, to maximize the coverage area of unmanned plane.Fig. 2
Variation range of the R relative to height h is depicted, wherein parameter is a=1, b=0.65, ξLoS=0.1, ξNLoS=21, f=
915MHz.It can be seen from the figure that the raising of height increases LoS probability, cause radio coverage bigger.However, this
Kind influences to be offset by further increasing for unmanned plane height, because the rising of height also results in the increase of transmission range, from
And cause the decaying of radio signal higher.
We assume that on each sensor of RFID label tag attachment on the ground.The data of sensor collection are stored in label
On, and it is sent to the unmanned plane for being mounted with RFID reader.Label is powered by the battery on sensor, is active label.Due to
ATG channel is conducive to LoS transmission, and label is active, therefore efficient communication range is suitable for suburb and field ring up to hundreds of meters
Long distance transmission in border.We discuss that RFID sensor air-data acquires under EPC global standard card cage, EPC
Global is the widely applied most popular standard of world's every profession and trade.The standard is based on frame CDMA slotted ALOHA (FSA), wherein logical
The letter time is divided into the successive frame being made of multiple time slots, to make the channel access between label be randomized and reduce transmission punching
Prominent probability.Its mechanism is summarized as follows:
In the beginning of each frame, reader broadcasts " Query " order, including frame length (timeslot number) to start poll
Period (inventory round).When receiving order, randomly and the time slot in frame is selected independently in neighbouring label
To send the data of its storage.The position of time slot is used as transmission counter in frame.If counter is zero, label can be sent immediately
Data packet；Reader uses " QueryRep " order starting time slot.After hearing this order, its counter is successively decreased 1 by each label.
When it reaches zero, label is by sending 16 bit lengths " RN16 " data packet comprising the interim ID of label to reader come contention
The time slot；If multiple labels send " RN16 " data packet to reader simultaneously, it can clash and waste this time slot.If
There is no label to send " RN16 " data packet, then the time slot is sky.In either case, reader will pass through broadcast
" QueryRep " order starts next time slot, and its counter is subtracted 1 again by each waiting label；If only one is marked
It signs to reader and sends " RN16 ", then reader confirms reception by sending back to " Ack " data packet to label, confirms the time slot
Successful reservation.Then, the data (including PC, EPC, the data and CRC-16 of storage) that label is stored are sent to reader,
Complete data collection.
It will be apparent from the above that a frame is made of the time slot of multiple and different types, T is arranged with according to timing length ascending order0<Tc<
Ts, wherein T0, TcAnd TsIt is the time span of sky, conflict, success time slot respectively.The par of different time-gap depends in frame
The number N and frame length L of sensor.Mathematically, they can be indicated are as follows:
Wherein α0, αsAnd αcRespectively indicate the sky in frame, the par of success and conflict time slot.Channel transport efficiency is fixed
Justice is the percentage of success time slot total length in frame, can be write as:
We pass through setting αt=T0/TsAnd βt=Tc/TsBy T0And TcRelative to TsIt is normalized.(5) are substituted into
(6), Wo Menyou(7)；
For giving number of probes N, optimizer system efficiency is the function of frame length L.Therefore to ηsOpposite L carries out derivation,
And allow derivativeIt obtains
1-N/L=(1- αt/βt)(1-1/L)N (8)
By solving equation (8), we obtain so that the maximum optimum length of frame L of handling capacityS_opt, i.e. reader can be with highest
Handling capacity collects data from ground transaucer.Following table lists the expression of related symbol used in this section.
We analyze the energy consumption of ground transaucer now.In every wheel data collection, sensor randomly chooses one in frame
A time slot is transmitted.Transmission all successful (single time slot) or unsuccessful (conflict time slot) may lead to respectively E every timesAnd Ec's
Energy consumption.In addition, sensor, which needs to monitor " QueryRep " order, for slot synchronization, to be caused in the beginning of each time slot
Energy consumption EO.Therefore, on average, the energy of each sensor consumed due to monitoring is in frameSimilarly, Wo Mentong
Cross setting αe=EO/EsAnd βe=Ec/EsBy EOAnd EcRelative to EsIt is normalized.We assume that there is N number of sensor competition frame
In L time slot.Therefore, energy efficiency (percentage for being defined as the used energy of all the sensors Successful transmissions data in every frame)
It is writeable are as follows:
(5) are substituted into (9), are obtainedAllow β=L/N, it is false
If L > > 1 and N > > 1, then (1-1/L)N-1≈e-1/βTherefore energy efficiency is reduced to
As N > > L, the number of sensors vied each other obtains β → 0, η considerably beyond frame lengthE→ 0, this is meaned in frame
Whole time slot collisions are used for successful data transmission without energy.As L > > N, obtain 1/ β → 0 andThis
Mean that no time slot collision, energy are used for data packet transmission monitoring synchronous with time slot.If we it is further assumed that
Es> > EO, it means that the energy consumption monitored for slot synchronization can be ignored, and η is obtainedE→1。
Given number of sensors N, maximum power efficiency, which is equal to, in (10) minimizes function The single order and second dervative of f (L) is respectively as follows:
Obviously, f " (L) > 0, therefore f (L) is the convex function of L, indicates there is unique L when minimizing f (L)E_opt.Allow f '
(L)=0 equation E, is solvedOL2-EONL-2EcN=0 obtains two roots:Because of LE_opt
> 0, the frame length of optimum capacity efficiency can be expressed as
We are using physical parameter (the 40Kbps channel data rates based on BPSK and 96 ID) and N=1000 is arranged.
Using equation (7) and (9), our available system effectivenesies and energy efficiency changed with frame length, i.e. Fig. 3.It can from Fig. 3
To find out, LE_optAnd LS_optIt is different, this also means that system effectiveness and energy efficiency cannot be optimised simultaneously.And
There is mutual restricting relation between the two.In unmanned plane-internet of things data acquisition system, it will be assumed that N > > 1 and L > >
1, therefore equation (7) and (10) can be rewritten as:
By ηsWritingIn this g (β)=(αt-βt)β+βtβe1/β.The single order and second dervative of g (β)
G ' (β)=(α is write respectivelyt-βt)+βte1/β-βtβ-1e1/β" (β)=β with gtβ-3e1/β.It can see g " (β) > 0, therefore g (β)
For the convex function of β, this indicates system effectiveness ηsIt is recessed.Allow g ' (β)=0 and solve equation, obtaining can be by ηsIt is maximized unique
βS_opt.In equation (11) and (12), we are obtained ηEMaximized unique solution
Therefore optimum capacity efficiency frame length coefficient is arranged to by we
In described above, it is seen that βS_optIt is the root of equation g ' (β)=0, therefore βS_optDefinite value howeverIt is closely related with number of probes N.According to the parameter of setting: αt=0.01, βt=
0.65, αe=0.025, βe=0.12, it solves equation g ' (β)=0, obtains root βS_opt=5.998, be and number of probes N
Unrelated definite value.
We also have found in solving equation (7), unless number of sensors N, less than 10, otherwise root is very close to 5.998
(see Fig. 4).When the optimum length coefficient under N > > 1, " system effectiveness mode " be the constant value, can be approximated to be βS_opt=
5.998。
In figures 3 and 4, observe that unmanned plane-internet of things data acquisition system cannot be simultaneously in best " system effectiveness mould
It works under formula " and best " energy efficiency mode ".We can only realize higher system as cost using more energy consumptions
Handling capacity, vice versa.Therefore, there is tradeoff between two indices.In order to more clearly explain this phenomenon, we allow frame length
Factor beta is in section [βE_opt, βS_opt] between change, and using equation (13) calculate ηsAnd ηE.As a result as shown in Figure 5.In the figure
We can observe that the mutual restricting relation between two kinds of efficiency, thus we can not optimize two kinds of efficiency simultaneously.In addition, I
Observe can use the adjustment of frame length to adjust the energy consumption and throughput of system of sensor simultaneously.Therefore, according to applied field
Scape, by appropriate adjustment frame length, we, which can choose, allows system in best " system effectiveness mode " or best " energy efficiency mould
It works under formula ".
In unmanned plane-Internet of things system, it is observed that the frame length setting of MAC layer can influence throughput of system and energy
Consumption.On the other hand, the parameter-drone flying height and speed of PHY layer, can also actually influence the two indexs.Flight
The increase of speed can make to collect more data in the unit time, and still, this also can make channel competition more fierce, from
And increase energy consumption；Lower flying speed can alleviate channel competition, but it is low to will lead to channel resource utilization efficiency.Together
Sample, the variation of flying height can lead to radio coverage, throughput of system, compete the quantity of sensor and relevant
The fluctuation of ground node energy and throughput of system.In this section, how we adjust PHY-MAC layers of three parameters by discussing
It is wasted to avoid unnecessary ground node energy and channel resource.Specifically, in unmanned plane-internet of things data acquisition system
In, we will find optimal PHY-MAC parameter, it is intended to the saving energy as much as possible, while meeting required system performance.
When unmanned plane leaps deployment region with speed v, sensor is by data transmission of internet of things to unmanned plane.L indicates machine
Carry effective read range of reader.Sensor i.e. only within the scope of this reliably could send nobody for the data of storage
Machine.The radius of overlay area is R, this is determined by the drone flying height h of Such analysis, wherein l=2R.Notice biography
The time restriction that sensor transmits its data is l/v, has crossed the time restriction, and unmanned plane will fly out effective communication range.Deployment
Sensor density be d, i.e., on average, every meter of flight path on landing ground has d sensor.Following table gives
The meaning of symbol used in this section
In the reading of every wheel, sensor sends data packet by the time slot in random selection frame come competitive channel access.
Therefore, sensor can undergo multiple read cycles, until the data of its storage are ultimately routed to unmanned plane.We assume that every
When wheel starts, the percentage for the sensor to be read is α, therefore, the number of the sensor of awaiting transmission in efficient communication region
Amount is dl α, therefore frame length should be set as dl α.According to equation (5), after the reading of every wheel, in the dl α sensor of waiting,
The percentage of sensor for being successfully transmitted its data to unmanned plane isIf TfIt is
The average duration of frame length, can be written as Tf=α0T0+αsTs+αcTc.Assuming that L=dl α β > > 1 and N=dl α > > 1, by using
Equation (5), we can simplify T by following approximate formulaf:
It is assumed that effectively read range is l and unmanned plane during flying speed is v, the maximum wheel number that reads that sensor may be undergone isEvery wheel is read, the number of sensors being newly added is dl/k.Therefore in the biography of communication zone awaiting transmission
The quantity of sensor is
Therefore, we obtain
For the offer quality assurance in data acquire, it is specified that after k wheel is read, the percentage of unread sensor
No more than ∈, i.e., we have performance constraints (1-PI)k≤ ∈ can be further written as
Data are acquired, it would be desirable to reduce the energy consumption of ground transaucer as much as possible, while meet the property in (17)
It can constraint.Therefore, according to the definition of energy efficiency in (10), optimization problem can be stated are as follows:
It should be noted that effective unmanned plane-sensor communication zone l can be by unmanned plane on frame length L=dl α β and ground
The influence of flying height h.In the optimization problem of formula (18), it can be observed that the minimum of objective function needs correct setting
PHY-MAC variable v, h and β.In addition, by the constraint of (18.a)-(18.c).The variation of each variable will lead to other two variables
Adjustment, which increase the complex natures of the problem.
It may be seen that the objective function and constraint in problem (18) are non-convex, therefore we use heuristic side
Method-particle group optimizing (PSO) finds optimum solution.PSO solving optimization problem by the following method: for giving quality standard,
It is iteratively improving candidate solutions (also referred to as particle).Specifically, it by based on particle group individual position and speed
Degree improved group in search space.The movement of particle is by its in the local optimum known location of its own and entire group
He determines the best known location of particle discovery.Whenever finding better position, two positions can all update, therefore work as PSO
When final convergence, group will be moved into optimum solution.
Before solving the problem using PSO, it would be desirable to determine the search space of parameter group { v, h, β }.To unmanned plane
Flying height, in order to avoid because colliding caused by ground obstacle, there is one the smallest safe altitude to limit on unmanned plane.?
Herein, h is arranged in weminIt is 10 meters.Meanwhile highest flying height is set as hmax, under the height, unmanned plane possesses maximum
Ground coverage.By will be in equation (4)Highest flying height can be calculated.Therefore, we obtain nothing
Man-machine height [hmin, hmax] search space.For frame length factor beta, it is β that optimizer system efficiency mode is obtained in figureS_opt=
5.998, therefore it can be set to β upper limit value, there is βmax=βS_optIn order to ensure search space includes optimum value, we are selected
Relatively low lower limit value βmin=0.2.Therefore the search space range of β is [βmin, βmax]=[0.2,5.998].Then, I
Determine unmanned plane during flying speed search space [vmin, vmax] by equation (18.b) and (18.c), obtains
In order to simplify equation (19), a is allowed1=T0βe-1/β+TSe-1/β+TC(β-e-1/β- βe-1/β), a2=dve1/β, a3=1-
e-1/β, such equation (19) writing Therefore, the maximum wheel number that reads is:
Work as k>0 and 0<a3< 1, obtain a1a2> 1, it means that v > (da1e1/β)-1.Therefore, v is allowedmin=(da1e1/β)-1.It is existing
The upper limit of maximum flying speed is analyzed at us.Constraint condition (18.a), which applies flying speed, to be limited, and constraint condition can weigh
It is written as:
Meanwhile the boundary condition writing of constraint condition (18.a)
We can observe that (18.b), (18.c) and (22) is that there are three the independent equations of variable { v, l, β } (should infuse for tool
It anticipates and arrives, there are one-to-one mappings between h and l).By the way that β=β is arrangedS_opt, we can be by using Numerical Methods Solve equation
(18.b), (18.c) and (22) obtain boundary speed v2.Boundary speed v2Corresponding to nobody under optimizer system efficiency mode
Machine flying speed.Therefore v is arranged in wemax=min (v1, v2), we obtain the search space [v of v in this waymin, vmax]。
In PSO algorithm, in order to be balanced between the time solving quality and calculate, we are by group size and iteration
Number is respectively set to 200 and 300.Sensor parameters are listed in Table 3 below.Calculated PHY-MAC parameter { v, h, β } such as Fig. 6 A-
Shown in 6D.In the figure it is observed that flight optimization height is located at the minimum boundary point of search space.This also means that in order to
The energy of ground transaucer is saved, we should be by setting minimum for radio coverage, so as to limit competition biography
The quantity of sensor.The mechanism of the parameter setting will be in rear portion detailed analysis.Meanwhile we have also observed that flight optimization speed
It is reduced with the increase of sensor density.This is because the increase of sensor density leads to the increase of data acquisition load, and
And in order to meet performance constraints, flying speed must slow down, to avoid causing system full because of ever-increasing number of sensors
With.We are, it is also observed that flight optimization speed is located at the minimum boundary point v of search space from figuremin=(da1e1/β)-1Place.
In order to illustrate more clearly of this point, we are by β calculated in Fig. 6 BE_optSubstitute into vmin=(da1e1/β)-1.It adopts
Use vminBy calculating vopt/vminNormalize vopt.As it can be seen that normalized value small wave near 1 in the result shown in figure 6 c
It is dynamic, it means that numerical convergence is in boundary point vmin.The sensor parameters of use are shown in following table
We rewrite the energy efficiency in equation (10)It can be seen that for given
Frame length, the increase of number of sensors N can will lead to ηEIt reduces.Particularly, when system under best " energy efficiency mode " work
When making, obtainHereinMeanwhileWork as increasing
When adding number of sensors N, LE_optWithIt will increase, so that ηEIt reduces.It means that being passed to save ground as much as possible
The energy of sensor, we should set minimum for flying height h, to reduce radio coverage area, thus limit competition
The quantity of sensor.
Using restrictive condition (18.c), obtainIt is substituted into (18.b), is obtained
When fixed frame length factor beta, a1Value is also fixed (referring to a1Definition) the such α of is dependent only on flying speed v.Because
v>vmin=(da1e1/β)-1, therefore 0 < (dva1e1/β)-1< 1 and ln [1- (dva1e1/β)-1]<0.And because β > 0, ln [1- e1 /β] < 0, the percentage for competing sensor areAllow f (v)=- vln [1- (dva1e1 /β)-1], therefore f ' (v)=- ln [1- (dva1e1/β)-1]- (dva1e1/β-1)-1.In order to be further simplified f ' (v), x=is allowed
(dva1e1/β)-1, thereforeBecause of 0 < x < 1, by using Taylor expansion, obtainWithF ' (v) in this way can be expressed as Because of f (v)=- vln [1-
(dva1e1/β)-1]>0 and f ' (v)<0, thereforeIt will increase with flying speed v.It changes
Sentence is talked about, and when we increase v, competes the quantity N of sensorw=dl α will also increase, therefore according to the analysis of the preceding paragraph, energy
Amount efficiency ηEIt will reduce.Therefore, in order to save the energy of ground transaucer, we should allow unmanned plane as far as possible with minimum speed
vminFlight.
From the above analysis, it is known that we can fly unmanned plane in order to maximize the energy efficiency of ground transaucer
Row height and speed are set as Minimum Acceptable Value.Therefore, the optimization of PHY-MAC parameter { v, h, β } can simplify to find βE_opt
One-dimensional search, i.e., minimum energy consumption.This will greatly reduce the calculating time of heuristic search in PSO.One-dimensional optimization
It is given by:
Here, passing through setting h=hminL can be calculated using equation (1-4).Meanwhile by the way that v=v is arrangedmin, and use
Equation (18.b) and (18.c) can calculate α.Equation (23) function that is reduced to only one variable β in this way.Note that function
It (23) is not convex or recessed, and in order to find Function Extreme Value point, we can set its derivative value to zero, then select
Select the best β for minimizing function (23)E_optPoint.Meanwhile to ensure a1a2> 1 and function (23) in vminNeighbouring differentiable, we
V=v is setmin+ σ, wherein σ is a sufficiently small positive value (such as σ=10-5).Linear search algorithm is as shown in Figure 7.In order to
Verify the feasibility of linear search algorithm shown in fig. 7, we by the calculated result obtained from the algorithm with it is heuristic from PSO
The calculated result that search obtains is compared.As a result as shown in Figure 8, wherein it may be seen that calculated result and PSO scounting line
It coincide very much.This indicates that simplified linear search can be used to substitute time-consuming PSO search plan in we.
Using the simplification algorithm provided in Fig. 7, we can calculate joint PHY-MAC optimal parameter, as a result such as Fig. 9 A-B
It is shown.Note that showing that flight optimization height is 10 meters in above-mentioned analysis.By using these parameters, unmanned plane-Internet of Things system
System can work under " energy efficiency mode ", and in such a mode, we, which can try one's best, reduces the energy of ground transaucer
Consumption, while meeting performance constraints.In figure 9 a, it can be seen that optimum length of frame factor betaE_optSubtract as sensor density increases
It is few, this is becauseSensor density increase constitutes competition the increase of number of sensors N,
Lead to several βE_optIt reduces.It observes in figures 9 b and 9, when sensor density rises, in order to meet performance limitation, unmanned plane is necessary
Slow down flying speed, so that system accommodates more sensors.
By the way that frame length factor beta=β is arrangedS_opt, we can allow unmanned plane-Internet of things system work in " system effectiveness mould
Formula ", so that system can acquire data from ground transaucer with maximum throughput.In other words, under same performance constraint, with
" energy efficiency mode " is compared, and unmanned plane can permit using higher flying speed, cost is under " system effectiveness mode "
The energy of ground transaucer consumption is more.
In order to calculate maximum unmanned plane during flying speed under " system effectiveness mode ", we rewrite the constraint of function (18)
Following.We assume that system should meet boundary condition, therefore the sign of inequality constrained in (18.a) is replaced by (18.a1)
Equal sign.
Tf=dl α [T0βe-1/β+TSe-1/β+TC(β-e-1/β-βe-1/β)]=dl α a1(18.c)
In order to calculate maximum flying speed, in equation (18.a1), β=β is allowedS_opt=5.998, then we can count
It calculatesThe calculated value is substituted into (18.b), we can also calculate α.Then using (18.a1) and
(18.c),It can use β=βS_optIt is calculated with α.Maximum in this way under " system effectiveness mode "
Speed can be only by β=βS_optIt determines, and it is unrelated with flying height h (or l).
Under " system effectiveness mode ", there is β=βS_opt=5.998, the system effectiveness under the modeIt is steady state value, therefore it is unrelated with flying height h and flying speed v.
In order to save the energy of ground transaucer, we should be competing to reduce by setting Minimum Acceptable Value for flying height h (or l)
Strive the quantity of sensor.
Our computing system efficiency and energy efficiencies under " system effectiveness mode " and " energy efficiency mode ", as a result as schemed
Shown in 10.Subscript " E_opt " and " S_opt " in figure respectively indicate system and work under " energy efficiency mode " and in " system
It works under efficiency mode ".In order to decline low sensor energy consumption in " system effectiveness mode ".Drone flying height is arranged for we
For minimum allowable value, i.e. h=hmin=10m.As shown, it may be seen that " system effectiveness mode " can provide it is highest
System effectiveness (about 90%), this is 1.2-1.3 times under " energy efficiency mode ".This also means that in " system effectiveness mould
Under formula ", the ground data handling capacity of unmanned plane reaches maximum.Accordingly, it is considered to sensor density d and performance constraints on ground
∈, the flying speed that unmanned plane allows are higher than the flying speed in " energy efficiency mode ".However, its cost is energy efficiency
Very low (10% or lower).Because more multisensor will be added in channel competition (see Figure 12) when flying speed increases.Just
As was expected, and the energy efficiency under " energy efficiency mode " is much higher than the energy dose-effect under " system effectiveness mode " in the figure
Rate (about 4-6 times).Again, we can be seen in the figure that, energy efficiency is under two kinds of operating modes with sensor density
Increase and decline, because the increase of competition number of sensors can reduce energy efficiency.
Figure 11 shows the quantity for competing sensor under two kinds of operating modes in every wheel inquiry.As shown, competition sensing
The quantity of device rises with ground transaucer density linear.In addition, the higher flying speed under " system effectiveness mode " is also meaned
The load of heavier data collection, therefore the quantity for competing sensor is greater than " energy efficiency mode " (about 2.5 times), so that " being
The energy efficiency of system efficiency mode " is more much lower than the energy efficiency of " energy efficiency mode ".
For giving performance constraints ∈, maximum allowable flying speed is as shown in figure 12, wherein vE_opt、vS_optIt respectively represents
Maximum speed under " energy efficiency mode " and " system effectiveness mode ".We can observe in figure, work as sensor density
When increase, in order to meet constraint condition, it is necessary to slow down flying speed.As expected, vS_optHigher than vE_opt(about 1.3-1.4 times),
This corresponds to the more high data throughput of " system effectiveness mode ".But as shown in Figure 10, it is about " energy that cost, which is energy consumption,
4-6 times of efficiency mode ".
From analysis above, it is observed that the higher system effectiveness of mutual restricting relation-between two indices, but energy
Source efficiency is lower；Or higher energy efficiency, but system effectiveness is lower.In other words, we can not allow simultaneously system effectiveness and
Energy efficiency reaches maximum.The key of problem is how to obtain balance therebetween.Obviously, allow system always in high energy consumption
It is unreasonable for working under " system effectiveness mode ".Design concept should consume just enough energy to meet and required be
System performance requirement.Next, we will discuss how according to the flying speed of variation to adjust system parameter to balance the two
The index of conflict.
In certain application scenarios, acquisition remote environment information is extremely important in time, especially in changes in environmental conditions urgency
In the case where hurriedly.For example, when shine upon one day moment at noon be it is strongest, the temperature and humidity in vineyard is at noon
It can be varied widely in short time.Therefore, unmanned plane needs quickly to fly to collect and send back to the environment sensed in time
Data.Therefore as the case may be, unmanned plane has minimum speed constraint v relevant to concrete applicationapp.Therefore, unmanned plane-Internet of Things
Net system includes two performance constraints-minimum flying speed vappWith maximum allowable data Loss Rate ∈.Two kinds are listed in following table
PHY-MAC parameter (h=10 meters) under operating mode
| D=5sen/m | D=8sen/m | D=12sen/m | D=18sen/m | |
| (βE_opt, vE_opt) | (1.041,46.17m/s) | (0.972,28.01m/s) | (0.930,18.30m/s) | (0.901,12.02m/s) |
| (βS_opt, vS_opt) | (5.998,59.94m/s) | (5.998,37.46m/s) | (5.998,24.97m/s) | (5.998,16.65m/s) |
In subscript, the flying speed v of " system effectiveness mode " and " energy efficiency mode " is listedS_optAnd vE_opt,
And related best βS_optAnd βE_opt.Particularly.vapp>vS_optIt is infeasible region, in the zone, is unable to satisfy performance constraints
ε；If vapp<vE_opt, v=v is setE_optWith β=βE_opt, to reduce the energy consumption of ground transaucer；If vE_opt<vapp<
vS_opt, v=v is setappAnd adjust β suitably to meet performance constraints ∈, while reducing energy consumption to the maximum extent.We beg for now
By how in vE_opt<vapp<vS_optWhen adjust β.Allow v=vappAnd constraint condition (18.b) and (18.c) are substituted into, we
Optimization problem is rewritten are as follows:
Tf=dl α a1 (18.c)
Using (21), by rearranging (18.a2), (18.b2) and (18.c) is obtained We then calculate the area of feasible solutions B for meeting the β of the inequality1.Meanwhile before us also
Prove v > (da1e1/β)-1, we then calculate area of feasible solutions B2, make v in this sectionapp>(da1e1/β)-1.Then will
(18.b2) and (18.c) substitutes into (23), only remains next variable β in the present function.In this way, the minimum of the E (β) in (23)
Change becomes linear search solution again, can be by area of feasible solutions B=B1∩B2In find the β for meeting E ' (β)=0appCome
It solves.Calculating best β is shown in Figure 13appSearching algorithm.
In upper table, the flying speed v under " system effectiveness mode " and " energy efficiency mode " is listedS_optWith
vE_opt, and related best βS_optAnd βE_opt.We are according to the algorithm provided in Figure 13 in [vE_opt, vS_opt] change in range
Become and applies relevant flying speed vapp, and calculate best βapp.As a result as shown in figure 14, wherein it is observed that working as vapp
From vE_optChange to vS_opt, to meet performance constraints, algorithm can be adaptively adjusted βappAllow system automatically from " energy efficiency
Mode " (βapp=βE_opt) it is switched to " system effectiveness mode " (βapp=βS_opt=5.998, ginseng sees the above table middle both of which
βoptValue).Corresponding to the variation in Figure 14, the associated change of system effectiveness and energy efficiency is as shown in figure 15, wherein " ηE" and
“ηS" respectively represent energy efficiency and system effectiveness.Equally, under the constraint of performance requirement ∈, when the flight speed with association
Spend vappWhen increase, according to the calculated result in Figure 14, system can be increased automatically by sacrificing the energy efficiency of ground transaucer
Throughput of system.Therefore, according to performance constraints vappAnd ∈, MAC parameter beta can be used in weappTo be adaptively adjusted energy consumption
And throughput of system, to keep the balance between energy efficiency and system effectiveness.
According to the best β provided in Figure 14app, can be in " system effectiveness the mode " (η in Figure 10E,S_opt) under energy
Amount efficiency is normalized, and result is shown in FIG. 16.It is observed that in best βappAdjusting under, we may be implemented
The significant raising of energy efficiency.After the optimization, the energy efficiency under " energy efficiency mode " is under " system effectiveness mode "
4-5 times.When further increasing vappWhen, in order to meet performance constraints, as shown in figure 14, system self-adaption by βappFrom βE_optIncrease
It is added to βS_opt, to βappAnd be adjusted, so that unmanned plane-platform of internet of things is gradually switched to " system effectiveness mode " (βapp
=βS_optAnd gain=1).
As seen from the above analysis, best PHY-MAC parameter --- the calculating of frame length, unmanned plane during flying speed, height,
Need to comprehensively consider the relevant information of mac layer and physical layer.Specifically, pass through the comprehensive sense come self-application and physical layer
Know that load, performance constraints, channel information, parameter optimiser execute calculating, then by result parameter be output to MAC layer (frame length) and
Physical layer (unmanned plane speed and height).Under the parametric environmental after these are optimized, unmanned plane-Internet of things system can be with high energy
Ground data is acquired to amount efficiency, while meeting system performance constraint.Therefore, the interlayer co-design of system is necessary, because
The balance of system performance and energy efficiency can be better achieved for us.
Herein, we discuss in unmanned plane-Internet of things system based on energy-efficient data collection problems.It is specific and
Speech, we disclose the restricting relation between throughput of system and energy efficiency --- and we can be with the more of ground transaucer
Energy consumption can save sensor energy by reducing throughput of system to improve system effectiveness or we for cost.
Based on this observation, in order to obtain balance between system effectiveness and energy efficiency, we are by unmanned plane-internet of things data acquisition
It is described as a non-convex problem.Pass through PSO heuristic search, it has been found that optimum value can be found on boundary point, thus significantly simple
The complex nature of the problem is changed, primal problem can simplify as one-dimensional optimization.By the solution of problem, we can obtain most Canon
PHY-MAC parameter --- the frame length of amount efficiency, unmanned plane during flying speed, height.Under the system environments of these optimal parameters, lead to
Cross layer design is crossed, we can realize about 4-5 times of gain in terms of energy efficiency, and meet performance constraints simultaneously.In addition,
It was also found that the frame length of MAC layer may be used as one " control handle ", energy consumption can be adaptively adjusted according to performance requirement
And throughput of system.The discovery has certain reference to anticipate network engineers in the algorithm design and product of related fields are realized
Justice.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention
Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.
Claims (10)
1. a kind of unmanned plane-internet of things data acquisition method characterized by comprising
S1, building unmanned plane-internet of things data acquisition system, the unmanned plane-internet of things data acquisition system include that setting exists
The sensor acquired for data on ground and the unmanned plane communicated with the sensor, wherein adhering on the sensor
RFID label tag loads RFID reader on the unmanned plane to store acquisition data；
When S2, the unmanned plane leap deployment region, received on the sensor based on frame slot algorithm by ATG channel
The acquisition data stored in RFID label tag.
2. unmanned plane according to claim 1-internet of things data acquisition method, which is characterized in that step S2 is further wrapped
It includes:
S21, the optimum length of frame coefficient that the unmanned plane receives the acquisition data is calculated；
S22, the unmanned plane reception acquisition data are controlled based on the optimum length of frame coefficient.
3. unmanned plane according to claim 2-internet of things data acquisition method, which is characterized in that the step S21 is into one
Step includes:
S211, based on optimum capacity efficiency described in the unmanned plane-internet of things data acquisition system optimum capacity efficiency calculation
Frame length coefficient；And/or
S212, it is imitated based on optimizer system described in the unmanned plane-internet of things data acquisition system optimizer system throughput calculation
Rate frame length coefficient.
4. unmanned plane according to claim 3-internet of things data acquisition method, which is characterized in that the step S211 packet
It includes:
S2111, system effectiveness and energy efficiency are calculated separately according to following formula (7) and (10)；
Wherein ηEIndicate energy efficiency；ηsIndicate system effectiveness, EOIndicate that sensor starts in each time slot because monitoring QueryRep
Energy consumption, EsIndicate energy consumption of the sensor in success time slot, EcIndicate that energy of the sensor in conflict time slot disappears
Consumption, L indicate that frame length, N indicate number of probes, αtIndicate normalization empty slot period, βtIndicate normalization conflict slot cycle；
S2112, equation (7) and (10) are rewritten much larger than 1 as equation (13) based on frame length and number of probes
S2113, optimum capacity efficiency is obtained to obtain optimum capacity efficiency frame length L according to formula (13)E_opt；
S2114, optimum capacity efficiency frame length coefficient is solved according to formula (13) maximum energy efficiency
Wherein αeIndicate the synchronous normalized energy consumption monitored in sensor time slot timing；βeIndicate sensor in conflict time slot
Normalized energy consumption；N indicates number of sensors.
5. unmanned plane according to claim 3-internet of things data acquisition method, which is characterized in that the step S212 packet
It includes:
S2121, system effectiveness and energy efficiency are calculated separately according to following formula (7) and (10)；
Wherein ηEIndicate energy efficiency；ηsIndicate system effectiveness, EOIndicate that sensor starts in each time slot because monitoring QueryRep
Energy consumption, EsIndicate energy consumption of the sensor in success time slot, EcIndicate that energy of the sensor in conflict time slot disappears
Consumption, L indicate that frame length, N indicate number of probes, αtIndicate normalization empty slot period, βtIndicate normalization conflict slot cycle；
S2122, equation (7) and (10) are rewritten much larger than 1 as equation (13) based on frame length and number of probes
S2123, it is based on parameter preset αt、βt、αe、βeCalculate optimizer system efficiency frame length factor betaS_optFor definite value.
6. unmanned plane according to claim 1-internet of things data acquisition method, which is characterized in that the step S2 is into one
Step includes:
S2a, flying speed, flying height and the frame length coefficient for calculating the unmanned plane；
S2b, the deployment region is leapt based on unmanned plane described in the flying speed, flight altitude control, and be based on the frame
Long coefficient controls the unmanned plane and receives the acquisition data.
7. unmanned plane according to claim 6-internet of things data acquisition method, which is characterized in that the step S2a is into one
Step includes:
S2a1, equation (18.a)-(18.c) is constrained according to optimum capacity efficiency and performance constraints building:
Tf=dl α [T0βe-1/β+TSe-1/β+TC(β-e-1/β-βe-1/β)] (18.c)；
Wherein l indicates unmanned plane-sensor efficient communication region on ground, and v indicates the flying speed of unmanned plane, and d indicates ground
The density of the sensor of arrangement, TfIndicate the time span of every wheel read cycle, ∈ indicates the rate of data collection limitation, and β indicates frame
Long coefficient, T0, TcAnd TsRespectively indicate empty, conflict, the time span of success time slot indicates；
S2a2, according to constraint equation (18.a)-(18.c) using frame length coefficient described in PSO regioselective, the flight is fast
Degree and flying height.
8. unmanned plane according to claim 7-internet of things data acquisition method, which is characterized in that the step S2a2 into
One step includes:
S2a21, the flying height is limited to minimum flight altitude；
S2a22, the flying speed is selected according to actual needs；
S2a23, the optimizer system effect for meeting optimizer system efficiency and optimum capacity efficiency is solved respectively according to the flying speed
Rate frame length coefficient and optimum capacity efficiency frame length coefficient.
9. unmanned plane according to claim 8-internet of things data acquisition method, which is characterized in that the step S2a2 into
One step includes:
S2a24, the unmanned plane-object is switched according to the optimizer system efficiency frame length coefficient and optimum capacity efficiency frame length coefficient
The data collection task state of networking.
10. a kind of unmanned plane-internet of things data acquisition system, including be arranged in ground for data acquisition sensor and with
The unmanned plane that the sensor is communicated, wherein adhering to RFID label tag on the sensor to store acquisition data, the nothing
Man-machine upper loading RFID reader；When the unmanned plane leaps deployment region, institute is received by ATG channel based on frame slot algorithm
State the acquisition data stored in the RFID label tag on sensor；Wherein the unmanned plane includes processor and is stored in the processing
Computer program on device, the computer program perform the steps of when being executed by processor
S21, the optimum length of frame coefficient that the unmanned plane receives the acquisition data is calculated；
S22, the unmanned plane reception acquisition data are controlled based on the optimum length of frame coefficient；And/or
S2a, flying speed, flying height and the frame length coefficient for calculating the unmanned plane；
S2b, the deployment region is leapt based on unmanned plane described in the flying speed, flight altitude control, and be based on the frame
Long coefficient controls the unmanned plane and receives the acquisition data.
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Cited By (7)
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| CN113259946A (en) * | 2021-01-14 | 2021-08-13 | 西安交通大学 | Ground-to-air full coverage power control and protocol design method based on centralized array antenna |
| CN113267213A (en) * | 2021-05-08 | 2021-08-17 | Tcl通讯（宁波）有限公司 | Unmanned aerial vehicle environment data acquisition system and unmanned aerial vehicle environment data acquisition method |
| CN113985917A (en) * | 2021-10-26 | 2022-01-28 | 深圳大学 | Fairness-based energy consumption and service life balancing method for sensor and unmanned aerial vehicle Internet of things system |
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| CN111132009A (en) * | 2019-12-23 | 2020-05-08 | 北京邮电大学 | Mobile edge calculation method, device and system of Internet of things |
| CN111060079A (en) * | 2019-12-31 | 2020-04-24 | 华东理工大学 | River foreign matter identification method and river foreign matter monitoring platform system |
| CN111061299A (en) * | 2020-01-21 | 2020-04-24 | 南京智能信通科技发展有限公司 | Ground sensor data acquisition method based on flight trajectory of unmanned aerial vehicle |
| CN111061299B (en) * | 2020-01-21 | 2020-12-29 | 南京智能信通科技发展有限公司 | Ground sensor data acquisition method based on flight trajectory of unmanned aerial vehicle |
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| CN111711974B (en) * | 2020-07-10 | 2023-03-17 | 河南科技大学 | Flight self-organizing network energy efficient opportunity routing method based on trajectory prediction |
| CN113259946A (en) * | 2021-01-14 | 2021-08-13 | 西安交通大学 | Ground-to-air full coverage power control and protocol design method based on centralized array antenna |
| CN113267213A (en) * | 2021-05-08 | 2021-08-17 | Tcl通讯（宁波）有限公司 | Unmanned aerial vehicle environment data acquisition system and unmanned aerial vehicle environment data acquisition method |
| CN113267213B (en) * | 2021-05-08 | 2023-04-07 | Tcl通讯（宁波）有限公司 | Unmanned aerial vehicle environment data acquisition system and unmanned aerial vehicle environment data acquisition method |
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