CN109451556A - The method to be charged based on UAV to wireless sense network - Google Patents
The method to be charged based on UAV to wireless sense network Download PDFInfo
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- CN109451556A CN109451556A CN201811430560.5A CN201811430560A CN109451556A CN 109451556 A CN109451556 A CN 109451556A CN 201811430560 A CN201811430560 A CN 201811430560A CN 109451556 A CN109451556 A CN 109451556A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
- H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
- H04W52/0261—Power saving arrangements in terminal devices managing power supply demand, e.g. depending on battery level
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The present invention relates to wireless sense network charging technique, for the method to be charged based on UAV to wireless sense network, comprising steps of collecting the ID of each sensor network nodes, location information, dump energy and working condition；It determines member node and cluster scheme, seeks the communication energy consumption of member node and leader cluster node；Determine the energy transmission power module of UAV；Ask in the m times random and periodical cluster scheme UAV to the fully charged required time of each node i by optimization algorithm；UAV is searched in any position to the fully charged required time of all nodes with optimization algorithm from the possible cluster scheme of N kind, is chosen maximum time and is regarded as UAV to the fully charged required time of entire wireless sensor network；From the possible cluster scheme of N kind corresponding maximum time, the smallest time cost value and corresponding optimal UAV charge position or path are found out.The present invention can be fully charged by wireless sense network with minimum time cost after mostly wheel energy consumption, makes wireless sense network can long-time steady operation.
Description
Technical field
The present invention relates to the wireless energy transmission technologies of wireless sense network, are specially charged based on UAV to wireless sense network
Method.
Background technique
It with the increasingly developed of embedded pc system, wireless communication technique and sensor technology and graduallys mature, nothing
The ability that line sensor network makes people carry out contactless interaction with the various situations of real world constantly enhances, wireless sensing
Device network is increasingly becoming a kind of emerging working application mode that can be matched in excellence or beauty with internet.
Wireless sensor network is a kind of distributed wireless sensor network being made of multiple wireless sensor nodes.By
It basic composition is the wireless sensor that can perceive, collect external environment data in it, so monitored related to test object
Data can be by wireless sense network tip --- and wireless sensor is collected, and formation one is unique through wireless communication
Multihop self-organizing network.
From wireless sensor network technology be born since, how the life cycle of prolonging wireless sensor network and guarantee nothing
It is always domestic and foreign scholars' major issue urgently to be resolved that line sensor network, which is stablized,.Since wireless sensor network node is general
It is battery powered, the energy carried is extremely limited, then how wireless sense network could be allowed to carry out data under finite energy
It is still able to maintain the longer network operation life cycle when collecting, merge and transmitting, to make wireless sense network stable as much as possible
Operation, becomes one of hot spot of current research.Energy from the point of view of wireless sensing web frame and function mode, in wireless sense network
Consumption is divided into sensor and calculates energy consumption, sensor communication energy consumption, sensor data acquisition energy consumption etc., wherein sensor communication energy consumption
It is mostly important, and influence of this mode of cluster to sensor communication energy consumption is very big in wireless sensor network.Therefore, exist
It is the Critical policies for extending wireless sense network life cycle that optimal cluster principle is selected in wireless sensing web area.
With the development of emerging unmanned air vehicle technique, domestic and foreign scholars' is caused to unmanned plane Path Planning and design
Extensive concern.It is combined using unmanned plane Path Planning with wireless energy transmission technology to extend the life of wireless sense network
Period also becomes a current research hotspot.
Summary of the invention
It is an object of the invention to solve the problems, such as that existing wireless sense network stability difference and service life are too short, propose to be based on
The method that UAV charges to wireless sense network, this method can be sought by optimization algorithms such as exhaustive search algorithm or genetic algorithms
Look for optimal chargeable UAV position and optimal cluster head selection scheme, so as to mostly wheel energy consumptions after with it is least when
Between cost wireless sense network is fully charged so that wireless sense network can long-time steady operation.
The invention is realized by the following technical scheme: the method to be charged based on UAV to wireless sense network, including following step
It is rapid:
S1, the ID of each sensor network nodes in wireless sense network, location information, dump energy and work shape are collected
State；Wherein sensor network nodes are divided into leader cluster node and member node；
S2, member node and cluster scheme in wireless sensing web area are determined, seeks corresponding member node communication energy
Consumption and leader cluster node communication energy consumption；
S3, the energy transmission power module for determining energy transmission module UAV；
S4, ask in the m times random and periodical cluster scheme energy transmission module UAV to wireless sense network by optimization algorithm
In each fully charged required time of sensor network nodes i；
S5, from the possible cluster scheme of N kind with optimization algorithm search energy transmission module UAV in entire wireless sense network
Any position chooses maximum time therein to the fully charged required time of all the sensors network node in region, assert that it is
Energy transmission module UAV gives the entire wireless sensor network fully charged required time；
S6, from the possible cluster scheme of N kind corresponding maximum time, find out the smallest time cost value and it is corresponding most
Excellent UAV charge position or path.
As can be known from the above technical solutions, the present invention is in entire wireless sensing web area, the wireless sense network of each piecemeal
Region carries out cluster head random-selection and member's allocation plan, the wireless sensing web area cluster scheme of each piecemeal are independent of each other,
Cluster scheme in each region is combined with each other the cluster head for foring entire wireless sense network and member's allocation plan (i.e. cluster side later
Case), compared with prior art, the beneficial effect of acquirement includes:
By corresponding to cluster head to entire wireless sense network and member's allocation plan full of needed for wireless sense network most
The excellent time compares, and selects wherein time minimum corresponding cluster scheme and the corresponding optimal charge position of UAV；I.e.
Under the cluster head of wireless sense network and the corresponding energy consumption of member's allocation plan, the position of corresponding UAV time optimal cost is found out
It sets or path.The present invention finds optimal time cost position in every kind of cluster head and member's allocation plan, finally compares every kind most
Excellent time cost chooses optimal time the cost minimum corresponding position UAV and cluster head member's allocation plan, to wireless sense network
It charges, it can be fully charged by wireless sense network with least time cost after mostly wheel energy consumption.The patent of invention is ground
Study carefully the related optimization algorithm for finding out optimal UAV charge position and path under the conditions of minimum time cost, while selecting optimal cluster
Head selection scheme.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention；
Fig. 2 is wireless sense network applied in one embodiment；
Fig. 3 is the network cluster situation of wireless sense network in one embodiment；
Fig. 4 is the result schematic diagram emulated in one embodiment in entire wireless sense network；
Fig. 5 is the relational graph of target function value and algorithmic algebra in one embodiment；
Fig. 6 optimal position UAV analogous diagram when being fixed communication base station location in one embodiment；
Objective function convergence graph when Fig. 7 is fixed communication base station location in one embodiment.
Specific embodiment
Present invention will now be described in further detail with reference to the embodiments and the accompanying drawings, but embodiments of the present invention are simultaneously
It is without being limited thereto.
Embodiment
In the present invention, sensor network nodes choose rechargeable wireless sensing net node；UAV is wireless energy transfer
Energy sending module, the flight of UAV fixed height transmit electric energy to the wireless sensor node within the scope of energy transmission.
The scene of wireless chargeable Sensor Network is divided into four regions, and the sensor network nodes in each region are divided into cluster
Head node and member node, wherein member node includes suspend mode node, working node and un-activation node, and equiprobability, week
The selection of phase property generates leader cluster node.In wireless chargeable Sensor Network, each working node is by respective positions information, id information
It is transferred in the leader cluster node periodically generated at random by single-hop, the various information received is integrated, merged by leader cluster node
At data packet, then communication base station is passed to by way of single-hop.Wherein wireless sensor network node cluster principle is random
Periodical cluster principle, every kind of cluster head member allocation plan (i.e. cluster scheme) type are the multiplication of each Area Node number, it may be assumed that
N=N1*N2*N3*N4……Nn。
As shown in Figure 1, the implementation process of the method for the present invention and steps are as follows:
One, after wireless sense network L takes turns cluster head rotation, the ID of each sensor network nodes, position letter at this time are collected
Breath, dump energy and working condition etc..
Here, it is assumed that the dump energy of each working node is that (leader cluster node receives E_min (i) due to needing to integrate
Data the operation such as be forwarded, dump energy is smaller, but the smallest lower limit value of dump energy is e_min, working node
When dump energy reaches e_min, working node is changed into suspend mode node), communication energy consumption is that (E is each node to ei=E-E_min
Initial energy), the present invention sends 1bit data according to member node and corresponding leader cluster node receives and the energy of forwarding consumption,
Specifically calculated by energy consumption model dump energy come.
The present invention in application scenarios, adopt by the working node communication energy consumption models (i.e. energy consumption model) of wireless sense network
It is exactly as inter-node communication distance d with single order radio modeliiWhen closer, energy consumption model uses free space channel model；When
Inter-node communication distance diiWhen farther out, energy consumption model uses multipath attenuation model.Due to node type difference, the algorithm scene
Energy consumption model can be divided into member node energy consumption model and leader cluster node energy consumption model.Given that it is known that the node being in communication with each other
Between distance be dii, when sending 1bit data, calculate related energy consumption model:
1, when sending node is member node, the energy of sending node consumption are as follows:
2, when sending node is member node, and do not consider that transmission range compares, then sending node energy consumption are as follows:
3, when sending node is leader cluster node, the energy of information data consumption is sent are as follows:
4, the energy of information data (leader cluster node and member node) consumption is received are as follows:
ERX(K)=KEelec
The above various energy sent for node and consumed when receiving 1bit data, EDAIt is for the energy consumption of Data Integration, i.e., whole
Close the energy of 1bit data consumption；d0It is definite value for the distance threshold of node energy consumption；K is the amount for receiving and sending data；Eelec
The energy consumption of 1bit data is received or sent for a node, and the present invention only considered leader cluster node and carry out information reception；εfs
And εmpFor the property parameters of sensor network nodes；Symbol b is a Boulogne variable, when the data transfer mode between two nodes is
B value is zero when single-hop, and when internodal data transmission mode is multi-hop, b value is 1.
In this algorithm model, mobile UAV is linear energy biography to the energy transmission mathematical model of wireless sensing net node
Defeated model, it may be assumed that
Wherein, Qi(x (t), y (t)) indicates i-th of sensor network nodes point (x (t), y in wireless sensing web area
(t)) energy acceptance power, hkIt (t) is the power order of magnitude with distance dependent, β0Indicate the unit distance 1m in transmission channel
The energy that node receives.P indicates a constant in transimission power.(xk,yk) indicate that UAV flies in the coordinate of two-dimensional surface,
H indicates the flying height in three-dimensional coordinate.
As known from the above, since leader cluster node has more reception energy consumption and sends energy consumption, so the energy of leader cluster node
The energy consumption of loss-rate member node is much bigger.
Two, the member node and cluster scheme in wireless sensing web area are determined, corresponding member node communication energy is sought
Consume eiWith leader cluster node communication energy consumption Ei, wherein leader cluster node communication energy consumption is the sum of to receive information energy consumption and send information energy consumption.
Three, the energy transmission power module of energy transmission module UAV is determined.
Four, energy transmission module UAV in the m times random and periodical cluster scheme is asked to pass to wireless by Optimizing Search algorithm
Each fully charged required time of sensor network nodes i in sense net(including it is fully charged to leader cluster node
When the time required to),
Five, UAV any bit in entire wireless sensing web area is searched for optimization algorithm from the possible cluster scheme of N kind
UAV_loc (x, y) is set to the fully charged required time of all the sensors network nodeWhereinChoose maximum time T thereini_max_UAV_loc(x(t),y(t)) m, assert it for energy biography
Defeated module UAV gives the entire wireless sensor network fully charged required time.
Six, from the corresponding maximum time T of the possible cluster scheme of N kindi_max_UAV_loc(x(t),y(t)) mIn, when finding out the smallest
Between cost valueAnd corresponding optimal UAV charge position
Or path.
Degree of convergence, convergence rate and essence of the Different Optimization algorithm to problem objective function in the invention patent application scenarios
Exactness is different.The present embodiment is to optimize search to the position UAV in sensing web area using basic differential evolution algorithm,
The algorithm is a kind of adaptive global optimization algorithm based on group, belongs to one kind of evolution algorithmic.Since it is with structure letter
It is single, easy to accomplish, convergence quickly, strong robustness the features such as, thus be widely used in data mining, pattern-recognition, number filter
The every field such as the design of wave device, artificial neural network, electromagnetism.It is wireless to assisting using differential evolution algorithm in the present embodiment
In unmanned plane (UAV) time cost function of Sensor Network communication base station and UAV position data made a variation, crossover operation, competition
Then data after the operations such as variation, intersection, competition are substituted into object time cost function, ask wherein most short optimal by operation etc.
Time cost functional value.
The algorithm practical application and simulating scenes are wireless sense network as shown in Figure 2, wireless sense network section in this implementation
Point is divided into two sensing net node regions, and wherein dot is optimal case corresponding UAV energy transmission position.
As shown in figure 3, describing a wherein wheel wireless sense network according to periodically random cluster principle and information flow direction
Network cluster situation, respective node ID information, location information etc. pass to cluster by way of single-hop transmission by working node
The information of each sensor node received is integrated and is merged by head node, leader cluster node, then by way of single-hop transmission
The data packet of fusion is sent to communication base station.
As shown in figure 4, to optimization this variable of the position UAV, by each in entire wireless sensing web area
The kind fully charged required time of cluster scheme compares, and finding out time cost, at least (i.e. chargeable mobile flight UAV is to entire nothing
The fully charged required time of line Sensor Network is minimum), the position UAV at this time is recorded, this position is exactly that chargeable UAV carries out energy biography
The defeated least optimal location of time cost, this cluster scheme are optimal cluster scheme.At this point, solid in the position of communication base station
Under fixed condition, using differential evolution algorithm, to the position UAV, this variable is optimized, and is emulated in entire wireless sense network,
As a result as shown in Figure 4.
Fig. 5, which is shown, is carried out unmanned plane (UAV) flight position using differential evolution algorithm in wireless sensing web area
Optimizing Search, to unmanned plane in the time cost objective function of building in wireless sense network sub-clustering situation and fixed communication base station
(UAV) location variable such as is evolved, is competed, being made a variation at the relational graph of target function value and algorithmic algebra after operation, wherein vertical sit
Time cost target function value is shown in mark, and the operation algebra of algorithm is shown in abscissa, with algorithm operation algebra
Increase, time cost target function value constantly reduces with the increase of operation algebra and tends to restrain.
Fig. 6 show optimal UAV charge position emulation in the wireless sensor network of interstitial content expansion, network size in figure
Slightly expand, is divided into the region of three independent clusters.In theory, in wireless sense network data to be treated be with
Network size constantly expand and increase with showing the order of magnitude.Fig. 7 illustrates objective function when fixed communication base station location and receives
Hold back situation.
As known from the above, with wireless sense network scale constantly expand, algorithm is to be treated in wireless sense network
Data are to increase with wireless sense network scale and be increasing, and the time of algorithm process data is also increasing.
Therefore, for the large-scale wireless Sensor Network in practical application, a kind of efficient unmanned plane energy compensating plan
Show slightly completely necessary.According to the large-scale wireless Sensor Network in practical application, since unmanned plane carries out energy transmission and leads to
Letter is limited in scope, and large-scale wireless Sensor Network is divided into no several piece zonule by the present invention, exist in zonule theoretically UAV into
The optimal location (the case where as studied above) of row energy transmission, i.e., the large-scale wireless Sensor Network in practical application
In, there are the optimal UAV energy transmission positions in many different zones.In order to preferably meet the requirement of practical application, this hair
The scene key of bright quasi- emulation is: 1, the scale of wireless sense network is increased in original scene；2, unmanned plane UAV is same
Shi Zuowei mobile communication relaying and wireless energy transfer side；3, using original scene as the zonule divided in large-scale area,
The optimal location point that wherein UAV carries out that energy transmission and information are collected is denoted as UAV anchor point.
In the application of practical large-scale wireless Sensor Network, since unmanned plane carries out being limited in scope for energy transmission and communication,
And cause UAV that can not carry out a wide range of energy within a certain period of time since wireless sense network scale in practical applications is excessive
Supplement.Based on the above reasons with described scene, the present invention also proposes a kind of based on large-scale wireless Sensor Network and unmanned plane
(UAV) the preferential energy supplement algorithm of technology, the algorithm are moving rail of the unmanned plane on the optimal UAV location point in each zonule
Mark strategy, it is therefore intended that improve unmanned plane energy transmission efficiency by the motion track strategy of this optimization and optimization is extensive
Wireless sense network energy consumption mode.In the case where communication base station is fixed, each wireless sensing net node is needed each acquisition
Data information transfer is forwarded to communication base station, the data flow transmitted beside communication base station to leader cluster node, then by leader cluster node
Measure it is bigger, so the wireless sensor on side has huge energy consumption to bear to this；UAV is to being not fixed the wireless of communication base station
The time cost that Sensor Network carries out energy transmission is less, and the energy consumption of wireless sense network is less.Therefore, the present invention is made using unmanned plane
To carry out energy compensating to large-scale wireless Sensor Network to carry out the mobile communication relaying of energy transmission, unmanned plane is in wireless sensing
Flying method in net is to carry out real-time power compensation according to real-time Direction Probability to collect decision with information.Unmanned plane into
When row energy transmission and Data Collection task, unmanned plane is that energy transmission position is collected using optimal information in each region as UAV
The optimal anchor point of theory, collected and energy transmission task to carry out optimal information.Unmanned plane consider UAV each region into
Row energy datum optimal time cost te, fly to the time cost t of the theoretical optimal anchor point of all directions zonule UAVfAnd cell
Suspend mode rate (the i.e. ratio B of depleted of energy number of nodes and total nodal point number of sensing net node in domaini_empty=nempty/Nz) after,
Unmanned plane next step heading and track are made decisions and planned.When unmanned plane completes previous periodical energy transformation task
Later, return service station carry out energy supplement (or waiting next periodic duty on the spot when UAV energy abundance), wait stay in it is next
Energy transmission is carried out in a period and information collects task.
As described above, the present invention can be better realized.
Claims (6)
1. the method to be charged based on UAV to wireless sense network, which comprises the following steps:
S1, ID, location information, dump energy and the working condition for collecting each sensor network nodes in wireless sense network；
Wherein sensor network nodes are divided into leader cluster node and member node；
S2, member node and cluster scheme in wireless sensing web area are determined, seek corresponding member node communication energy consumption and
Leader cluster node communication energy consumption；
S3, the energy transmission power module for determining energy transmission module UAV；
S4, ask in the m times random and periodical cluster scheme energy transmission module UAV to every in wireless sense network by optimization algorithm
A fully charged required time of sensor network nodes i；
S5, from the possible cluster scheme of N kind with optimization algorithm search energy transmission module UAV in entire wireless sensing web area
Middle any position chooses maximum time therein to the fully charged required time of all the sensors network node, assert it for energy
Transmission module UAV gives the entire wireless sensor network fully charged required time；
S6, from the possible cluster scheme of N kind corresponding maximum time, find out the smallest time cost value and correspondence it is optimal
UAV charge position or path.
2. the method according to claim 1 to be charged based on UAV to wireless sense network, which is characterized in that the cluster head section
Point communication energy consumption is the sum of to receive information energy consumption and send information energy consumption.
3. the method according to claim 1 to be charged based on UAV to wireless sense network, which is characterized in that member's section
Point includes suspend mode node, working node and un-activation node, and the leader cluster node equiprobability, periodically selection generate.
4. the method according to claim 1 to be charged based on UAV to wireless sense network, which is characterized in that the optimization is calculated
Method is basic differential evolution algorithm.
5. the method according to claim 3 to be charged based on UAV to wireless sense network, which is characterized in that the work section
The energy consumption model of point uses single order radio model, as inter-node communication distance diiWhen closer, energy consumption model uses free space
Channel model；As inter-node communication distance diiWhen farther out, energy consumption model uses multipath attenuation model.
6. the method according to claim 1 to be charged based on UAV to wireless sense network, it is characterised in that: energy transmission mould
Block UAV is linear energy transfer model to the energy transmission mathematical model of sensor network nodes:
Wherein, Qi(x (t), y (t)) indicates i-th of sensor network nodes point (x (t), y (t)) in wireless sensing web area
Energy acceptance power, hkIt (t) is the power order of magnitude with distance dependent, β0Indicate that unit distance 1m node connects in transmission channel
The energy received, P indicate a constant in transimission power, (xk,yk) indicate energy transmission module UAV flight in two-dimensional surface
Coordinate, H indicate three-dimensional coordinate in flying height.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110362105A (en) * | 2019-06-17 | 2019-10-22 | 广州大学 | Sensor network wireless charging method based on more UAV |
| CN110418434A (en) * | 2019-07-08 | 2019-11-05 | 东南大学 | A kind of the wireless sensor network charging method and device of unmanned plane auxiliary |
| CN110417458A (en) * | 2019-07-30 | 2019-11-05 | 中国矿业大学 | Co-location and time-optimized method towards the double relay systems of UAV energy information |
| CN112261704A (en) * | 2020-10-23 | 2021-01-22 | 广州大学 | Repeated game routing method based on rechargeable wireless sensor network |
| CN112383893A (en) * | 2020-10-19 | 2021-02-19 | 广州大学 | Time-sharing-based wireless power transmission method for rechargeable sensor network |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103219779A (en) * | 2013-05-13 | 2013-07-24 | 东南大学 | Wireless charging method for sensing network node |
| CN104795861A (en) * | 2015-04-09 | 2015-07-22 | 天津大学 | Wireless charging system and method based on unmanned aerial vehicle |
| CN108173302A (en) * | 2017-12-28 | 2018-06-15 | 电子科技大学 | Charge completion time optimization method of the wireless charger in wireless sensor network |
-
2018
- 2018-11-28 CN CN201811430560.5A patent/CN109451556B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103219779A (en) * | 2013-05-13 | 2013-07-24 | 东南大学 | Wireless charging method for sensing network node |
| CN104795861A (en) * | 2015-04-09 | 2015-07-22 | 天津大学 | Wireless charging system and method based on unmanned aerial vehicle |
| CN108173302A (en) * | 2017-12-28 | 2018-06-15 | 电子科技大学 | Charge completion time optimization method of the wireless charger in wireless sensor network |
Non-Patent Citations (2)
| Title |
|---|
| WEIJIAN TU等: "A Study on Wireless Charging for Prolonging the", 《SENSORS》 * |
| 陈雪寒等: "C-MCC:无线可充电传感器网络中一种基于分簇的多MC协同", 《小型微型计算机系统》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110362105A (en) * | 2019-06-17 | 2019-10-22 | 广州大学 | Sensor network wireless charging method based on more UAV |
| CN110362105B (en) * | 2019-06-17 | 2022-07-19 | 广州大学 | Sensor network wireless charging method based on multiple UAVs |
| CN110418434A (en) * | 2019-07-08 | 2019-11-05 | 东南大学 | A kind of the wireless sensor network charging method and device of unmanned plane auxiliary |
| CN110418434B (en) * | 2019-07-08 | 2022-12-06 | 东南大学 | Unmanned aerial vehicle-assisted wireless sensor network charging method and device |
| CN110417458A (en) * | 2019-07-30 | 2019-11-05 | 中国矿业大学 | Co-location and time-optimized method towards the double relay systems of UAV energy information |
| CN112383893A (en) * | 2020-10-19 | 2021-02-19 | 广州大学 | Time-sharing-based wireless power transmission method for rechargeable sensor network |
| CN112383893B (en) * | 2020-10-19 | 2024-04-02 | 广州大学 | Time-sharing-based wireless power transmission method for chargeable sensing network |
| CN112261704A (en) * | 2020-10-23 | 2021-01-22 | 广州大学 | Repeated game routing method based on rechargeable wireless sensor network |
| CN112261704B (en) * | 2020-10-23 | 2022-12-09 | 广州大学 | Repeated game routing method based on rechargeable wireless sensor network |
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