CN107360093A - Unmanned plane and satellite hybrid network communication routing method and system - Google Patents
Unmanned plane and satellite hybrid network communication routing method and system Download PDFInfo
- Publication number
- CN107360093A CN107360093A CN201710591295.8A CN201710591295A CN107360093A CN 107360093 A CN107360093 A CN 107360093A CN 201710591295 A CN201710591295 A CN 201710591295A CN 107360093 A CN107360093 A CN 107360093A
- Authority
- CN
- China
- Prior art keywords
- connection
- probability
- unmanned plane
- node
- hybrid network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/02—Topology update or discovery
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/14—Routing performance; Theoretical aspects
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/30—Routing of multiclass traffic
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Radio Relay Systems (AREA)
Abstract
The invention discloses a kind of unmanned plane and satellite hybrid network communication routing method and system, its method includes：Based on random connection forecast model, the probability connection figure containing random node and determination node is constructed；Design routing algorithm based on probability connection figure carry out unmanned plane and satellite hybrid network communication lines by.Hybrid network of the invention based on unmanned plane and moonlet, it is proposed that a kind of routing algorithm, which solves traditional route, can not tackle the Network route Problem of a variety of nodes, and proposes the forecast model connected at random and obtain the link information needed for this routing algorithm；In addition, this routing algorithm and traditional routing algorithm have been carried out into comparative analysis, can obtain algorithm proposed by the invention can obtain less propagation delay time and transmission loss in hybrid network Route Selection.
Description
Technical field
The invention belongs to empty day to synthesize DTN network technique fields, more particularly to a kind of unmanned plane leads to satellite hybrid network
Believe method for routing and system.
Background technology
At present, routing policy includes the routing policy and random node DTN network router strategy based thereons for determining node DTN networks,
Wherein：
Determine that node DTN networks are primarily referred to as satellite network.Its orbit equation of satellite node is to determine, passes through its track
Equation can calculate the movement locus of satellite.Meanwhile it can be obtained and other satellites and ground according to the communication radius of satellite
The Connection Time at face station, and relative distance.The routing policy for determining node DTN networks is the connection letter based on precognition and determination
It is connection figure routing algorithm (CGR) that breath, which carries out routing decision,.CGR algorithms are family's algorithms：First according to link information structure
Connection figure is made, then starting connection checking process (CRP) according to connection figure finds the mainly inspection of neighbor node chaining check process
Look into whether current connection meets the required time requirement and link capacity requirement of bundle forwardings, turn finally by retransmission process
Next-hop is dealt into, its retransmission process considers bundle priority, is forwarded for bundle points of multi-form of different priorities.
So as to form end-to-end path.CGR routes ensure that the reliability of data transfer using storage-carrying-forward mode.
Random node DTN routing policy main representatives are to infect route (Epidemic) algorithm, are applied to solution and contain
The DTN network data transmission problems of random node, in the case where resource is unrestricted, it has most under any DTN scenes
Good routing performance.EPI main thought is " storage-carrying-forwarding ".Each node is the data acquisition system in caching in network
Entitled SV (Summary Vector) index is established, respective SV is exchanged when two nodes enter mutual communication range,
Node judges whether to forward data message to other side and forwards which information by contrasting the SV of both sides.With joint movements,
Data message is finally copied to any node in network.Epedemic routing algorithms can be in the network containing random node
In ensure that information is finally reached destination node, and obtain higher delivery rate.
But CGR routing policies can only solve the determination Network route Problem that can be predicted in advance, it is impossible to which reply is containing random
The Network route Problem of node.
Though Epidemic routing policies can ensure the end-to-end transmission of information in containing random node network, and have higher
Delivery rate, but in the hybrid network of unmanned plane and satellite, number of nodes is more, but this similar mechanism to flood can be made
Into bandwidth and the huge waste of spatial cache
Not only containing determine node again include random node network in, its node species is not single, and number of nodes compared with
It is more, can not solve the routing issue of satellite and unmanned plane hybrid network with traditional routing policy.
The content of the invention
It is a primary object of the present invention to provide a kind of unmanned plane and satellite hybrid network communication routing method and system, with
Solves the routing issue of satellite and unmanned plane hybrid network.
To achieve the above object, a kind of unmanned plane provided by the invention and satellite hybrid network communication routing method, including
Following steps：
Based on random connection forecast model, the probability connection figure containing random node and determination node is constructed；
Design routing algorithm based on probability connection figure carry out unmanned plane and satellite hybrid network communication lines by.
Wherein, the step of routing algorithm of the design based on probability connection figure includes：
The connection probability of uncertain connection is added in the probability connection figure, and probability is added in checking process is connected
The screening of threshold value；
The connection probability is considered in retransmission process, generates the routing algorithm based on the probability connection figure.
Wherein, the link information in the probability connection figure between each node includes transmission node, receiving node, link company
Logical time started, link connection end time, link transmission rate, connection probability.
Wherein, the connection checking process includes：
To the neighbour that the link information in probability connection figure is the node selection energy transmitting for currently holding forwarding bundle
Occupy node listing.
Wherein, the unmanned plane also includes with satellite hybrid network communication routing method：
Performance Evaluation is carried out to the routing algorithm based on probability connection figure.
Wherein, the unmanned plane also includes with satellite hybrid network communication routing method：
Create the random connection forecast model.
The present invention also proposes that a kind of unmanned plane communicates route system with satellite hybrid network, including memory, processor with
And the computer program on the memory is stored in, the computer program is realized as described above during the computing device
Method the step of.
The beneficial effects of the invention are as follows：
Hybrid network based on unmanned plane and moonlet, it is proposed that a kind of routing algorithm, which solves traditional route, to tackle
The Network route Problem of a variety of nodes.And propose the forecast model connected at random and obtain the connection letter needed for this routing algorithm
Breath；In addition, this routing algorithm and traditional routing algorithm have been carried out into comparative analysis, calculation proposed by the invention can be obtained
Method can obtain less propagation delay time and transmission loss in hybrid network Route Selection.
Brief description of the drawings
Fig. 1 is satellite of the present invention and unmanned plane mixing DTN network protocol stacks；
Fig. 2 is PCGR algorithm structures block diagram of the present invention；
Fig. 3 is probability connection figure of the present invention；
Fig. 4 is two random node relative movement orbit schematic diagrames；
Fig. 5 is state migration procedure schematic diagram；
Fig. 6 is the crucial bundle propagation delay times schematic diagram of the present invention；
Fig. 7 is the non-key bundle propagation delay times schematic diagram of the present invention；
Fig. 8 is the crucial bundle transmission loss schematic diagram of the present invention；
Fig. 9 is the non-key bundle transmission loss schematic diagram of the present invention.
The realization, functional characteristics and advantage of the object of the invention will be described further referring to the drawings in conjunction with the embodiments.
Embodiment
It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not intended to limit the present invention.
Present invention is mainly applied to the communication route technology that empty day synthesizes DTN networks, unmanned plane and satellite, concrete application side
Formula is：Network is synthesized with moonlet based on unmanned plane, using proposed by the invention based on random connection forecast model, constructed
Probability connection figure containing random node and determination node, and the routing algorithm (PCGR) based on probability connection figure is devised to solve
Certainly unmanned plane communicates routing issue with satellite hybrid network.
Specifically, the present invention proposes a kind of unmanned plane and satellite hybrid network communication routing method, including：
Step S1, based on random connection forecast model, construct the probability connection figure containing random node and determination node；
Step S2, design routing algorithm based on probability connection figure carry out unmanned plane and satellite hybrid network communication lines by.
Wherein, the step of routing algorithm of the design based on probability connection figure includes：
The connection probability of uncertain connection is added in the probability connection figure, and probability is added in checking process is connected
The screening of threshold value；
The connection probability is considered in retransmission process, generates the routing algorithm based on the probability connection figure.
Link information in the probability connection figure between each node includes transmission node, receiving node, link and connects and open
Begin time, link connection end time, link transmission rate, connection probability.
The connection checking process includes：
To the neighbour that the link information in probability connection figure is the node selection energy transmitting for currently holding forwarding bundle
Occupy node listing.
Further, the present invention program also includes carrying out Performance Evaluation to the routing algorithm based on probability connection figure.And
Create the random connection forecast model.
This embodiment scheme is described in detail below：
First, the mixing DTN networks of unmanned plane and satellite and random connection figure routing algorithm are introduced
1. the mixing DTN networks of unmanned plane and satellite
Both have in the hybrid network of unmanned plane and satellite comprising the satellite node determined comprising random unmanned plane node.Its
In, unmanned plane node is divided into two kinds：Be loaded with satellite antenna gateway unmanned plane node can with satellite and other unmanned planes and
Earth station is communicated, and another unmanned plane is not loaded with satellite antenna, it is impossible to which carrying out communication with satellite can only be carried out with unmanned plane
Communication.In unmanned plane and the network of satellite cooperation, remote sensing information is passed to earth station by unmanned plane two ways：1) unmanned plane
Be passed directly to earth station by gateway node, 2) making relaying by gateway unmanned plane node and satellite passes to earth station.This hair
Bright to be directed to second of information transmission mode and be designed routing algorithm, the transmission of its information uses storage-carrying-forward mode,
The protocol stack structure of this mixing DTN network is as shown in figure 1, Fig. 1 is satellite of the present invention and unmanned plane mixing DTN network protocol stacks.
A variety of connections be present in the hybrid network Satellite of unmanned plane and satellite：
1) the connection C (Sa, Sa) between satellite；
2) connection C (Sa, the UA of satellite and gateway unmanned planeG)；
3) the connection C (UA of common unmanned plane and gateway unmanned planeO,UAG)。
It is to determine for first two connection, there is following inference：
Inference 1：It is to determine for the connection between satellite due to track, so what connection was to determine, it connects probability
For
PC(sa,sa)=1 (1)
Inference 2：Because the flying speed of satellite is far longer than the flying speed of unmanned plane, and the communication overlay of satellite half
Footpath is far longer than the covering radius of unmanned plane, connection C (Sa, UA between satellite and unmanned plane gatewayG) satellite can be converted into
What the covering with the covering problem C (Sa, TA) of unmanned plane monitored area, and satellite and unmanned plane monitored area was to determine, therefore
Connection probability between satellite and unmanned plane gateway is
For the third connection, prediction will will be modeled according to Semi-Markov Process and Markov Chain in back
It connects probability.
2. random connection figure routing algorithm
Random connection nomography (PCGR) is the improvement to traditional CGR algorithms, and random section is introduced on the basis of primal algorithm
Point, the connection probability of uncertain connection is added in connection figure, and the screening of probability threshold value is added in checking process is connected, most
Connection probability is considered in retransmission process afterwards.The structured flowchart of its algorithm is as shown in Figure 2.
(1) probability connection figure
Link information between each node includes transmission node, receiving node, link connection time started, link connection knot
Between beam, link transmission rate, connection information, these link informations such as probability can be stored by all DTN network nodes,
Then it is as shown in Figure 3 to generate a probability connection figure for each node.Both include the link information for determining node in connection figure, wrap again
Include the link information of random node.It is determined that connection probability is set to 1, it is determined that the time and distance of connection can be according to the tracks of satellite
Equation obtains, and the present invention no longer repeats.Link rate is set in advance.And it is by pre- to connect Connection Time and probability at random
What the model of survey connection probability obtained.Its forecast model will discuss in lower section.
(2) checking process is connected
Connection checking process (PCRP) is currently to hold forwarding bundle node to the link information in probability connection figure
Selection can transmitting its specific algorithm step of neighboring node list for：
1) the connection m currently analyzed destination node mENDNODE is added and excludes node listing EN.
2) current bundle (B) out-of-service time F is assigned to the earliest opening time T of this connection
3) probability threshold value α screenings are attached, the current probability P c that connects disclosure satisfy that threshold condition then carries out sentencing in next step
It is disconnected otherwise to jump out.
4) it is less than T if current time and to connect opening time mSTARTTIME no less than carrying out if T judging in next step
Then jump out.
5) if the destination node mENDNODE currently connected is bundle final purpose node, we will currently connect
End time mENDTIME be assigned to bundle earliest arrival times EPT
If 6) the source node mTRANSMITNODE of sentenced connection is the local node L for currently holding bundle, and is worked as
The link residual capacity mRESCAP of preceding connection is more than the capacity ECC (bases in traditional CGR algorithms needed for bundle forwarding estimations
Link rate rlWith connection distance rcThe ECC present invention is tried to achieve no longer to tell about), and the destination node mENDNODE of this connection belongs to
The neighbor node of local node then carries out next step judgement, otherwise then jumps out
7) if bundle it is expected that earliest transmission time EPT is less than or equal to bundle and reaches the destination node currently connected
Time AEPT, EPT is assigned to AEPT by us, and it is APNH and phase that B can be reached into mENDNODE minimum hop count APNH
Hop count NH minimum value is hoped to be assigned to APNH.
If 8) be unsatisfactory for the condition in (7), the minimum hop count APNH that B can be reached to mENDNODE is assigned to it is expected
Hop count NH, the time AEPT that bundle reaches the destination node currently connected are assigned to the bundle earliest transmission time EPT of expectation, and
MENDNODE is added in the neighboring node list PN of local node.
If 9) the source node mTRANSMITNODE of sentenced connection is not in node listing EN is discharged, we by EPT and
A mENDTIME minimum value is assigned to EPT, and it is APNH and expectation that B can be reached into mENDNODE minimum hop count APNH
Hop count NH adds 1 and updates the Connection Time respectively, mENDNODE is deleted from discharge node listing, restarting connection checks journey
Sequence, check other unchecked connections.
Its pseudo-code of the algorithm is as follows：
Table 1.PCRP algorithms
(3) retransmission process
By repeating to call connection checking process (PCRP), all neighbours' sections can be obtained from source node to destination node
Point.An effective retransmission process (FBP) is formulated to choose the correct path of destination be vital.
In traditional CGR algorithms, FBP transmits bundle in two different situations：
1) each node that crucial bundle will be forwarded in reliable neighboring node list；
2) non-key bundle will select the optimal next-hop to be sent (beeline or earliest opening time etc.).So
And in mixed satellite/unmanned plane DTN, some random nodes be present, it should improve traditional FBP in the case of considering two kinds.
Crucial bundle is forwarded in a conventional manner is transmitted to all neighbor nodes, but non-key is to need to distinguish random connection and really
Qualitative connection, if connection probability is not 1, the neighbor node that the present invention should select to connect maximum probability is forwarded.
3. random node connection prediction
Generally, it is contemplated that the continually changing characteristic in instantaneous velocity and direction in different UAV motion processes, that is, accelerate,
Slow down and Uniform Movement, the present invention describe the relation of unmanned plane instantaneous velocity and direction using half Markov motion model
It is as follows：
Wherein, Vj-1 and ΦjIt is two Gaussian random variables with zero-mean and unit variance, For
Adjust the temporal correlation of node speed, vαIt is the target stabilized speed of semi-Markov model initialization.vjAnd φjIt is respectively
The instantaneous velocity of random node and direction in jth step.The relative velocity scope of two nodes is [0,2 in Semi-Markov Process
(v α+δ v)], and the relative velocity Rayleigh distributed of two nodes.
The two unmanned plane nodes with half Markov kinetic characteristic are carried out mathematical modeling its movement locus such as by the present invention
Shown in Fig. 4.It is as follows that its geometrical relationship can be obtained：
Wherein,It is the relative distance vector of m steps,Be m step relative velocity vector, θmIt isWithIt is two
The angle of vector, θm(0, π] section meets and is uniformly distributed.Two node relative velocity scopes in half Markov motion model
ForAnd meet rayleigh distributed.
As shown in figure 5, R is the scope radius that both can communicate, it is λ to be divided into n parts length, so R=n λ.In [(i-
1) λ, i λ] in represent that current state is Ri, represent the distance of current two random node as Ri.P is expressed as apart from transition probability
Matrix such as formula (8), wherein Pij are the probability of the State Transferring after single time step from Ri to Rj.Pay attention to, work as correlation
When distance is more than R, two counterpart nodes are not connected to.Therefore, using discrete state transition method, by calculating two sections in R
The residence time of the relative distance of point and transition probability, can obtain required link information.Its run duration and state are all
Discrete, so with reference to the semi-Markov characteristic of joint movements parameter, the present invention proposes a kind of relative for describing two
The Markov chain of the state conversion process of motion.In chain, it can be described as apart from transition probability matrix P：
To represent to facilitate us by its matrix table vector matrix in column：
P=[P1 … Pi … Pn] (9)
The status probability matrix of initial time node is
Γ(0)=[Γ (1) ... Γ (i) ... Γ (n)] (10)
The probability matrix that two unmanned plane node relative position status are walked by m is then converted to by a step：
Connection probability by the m node of time slot two is：
Wherein, a step state transition probability is：
Its state transition probability density function can be expressed as：
Rayleigh distributed is met due to the speed of related movement of any two node of half Markov motion model so：
So being analyzed more than, connection distance Rj of two random nodes in period m can be obtained, and connection is generally
Rate Cuw. is so as to solving the link information forecasting problem of the random connection of algorithm proposed by the invention.
4. Performance Evaluation
This experiment porch is divided into network environment simulating and Propagation Simulation two parts.Network parameter is write by matlab
In STK, including the orbit parameter of satellite and unmanned plane motion model parameters such as table 2 are combined with STK by matlab and defended
Star and unmanned plane Mixed Weibull distribution, the connection of node is obtained by STK.Using ION emulation platforms by the connection of node
Situation writes configuration file, carries out data transmission.By forwarding the data to the Linux computers of analogue transmission node, data pass
More machines of defeated experiment are equipped with ION software kit, simulate DTN network nodes, and the routing policy according to formulation carries out data
Transmission simulation, the channel simulation in experiment are passed through to network interface card receiving terminal packet loss and delay using parameter set in advance such as table 3
Simulation of the realization to true environment.
The satellite of table 2 and unmanned plane hybrid network parameter
| Bundle size | 10kbytes |
| Bundle life cycle | 300s |
| Propagation delay | 1280ms |
| BERs | 10-5 |
| Segment size | 5kbytes |
| Block size | 100kbytes |
Table 3.ION parameter configurations
Suitable path is found by PCGR algorithms, the last nodal information in path is subjected to node by ION emulation platforms
Information configuration, and by order and the test of communication environment constraint progress link performance of giving out a contract for a project, it is of the invention that PCGR is route calculation
The path that method is found is carried out with traditional certainty route CGR and traditional stochastic route flooding forwarding Epidemic algorithms
Contrast.
As shown in Figure 6 and Figure 7, Fig. 6 is the crucial bundle propagation delay times schematic diagram of the present invention, wherein, N=20, α=
0.5)；Fig. 7 is the non-key bundle propagation delay times schematic diagram of the present invention, wherein, (N=20, α=0.5).
It can be seen that by Fig. 6, Fig. 7：
1.Epidemic forwardings are also screened without CRP programs regardless of bundle ranks, chain can be caused when bundle is excessive
Road congestion, produce higher delay；
2.CGR forwards next-hop in the network containing random node, because crucial bundle screens without probability threshold value,
With the increase of bundle quantity, a large amount of useless forwardings can be caused, delay also increases therewith.Non-key bundle only selects a neighbours
Node forwards, and due to not accounting for probability factor retransmission failure probability can be caused to increase, so the performance meeting when bundle is less
It is worse than Epidemic；
3.PCGR has probability threshold value screening, will not cause excessive useless forwarding when crucial bundle is forwarded, and because
The maximum next-hop forwarding of non-key bundle select probabilities is so forward successful likelihood ratio CGR big, so delay is less than
CGR。
Here, define a kind of performance indications；Its model of transmission loss TL is as follows：
It can be seen that by Fig. 8, Fig. 9：
1.Epidemic is crucial or non-key bundle is using flooding pass-through mode, can cause the wasting of resources,
Transmission loss can be higher than other two kinds of routing policies；
2.CGR is a kind of routing policy for deterministic network, and crucial bundle does not have probability screen choosing so producing
And invalid loss can increase gradual increase with random node；Non-key bundle forwardings do not account for maximum probability institute
With meeting increasing with random node, the increase of bust this probability, invalid loss increase；
3.PCGR routing policies consider the problem of connecting probability and are broadly divided into three kinds of situations:(1) when probability threshold value is smaller
When, crucial bundle transmission performances are more or less the same with CGR, and non-key bundle is due to considering Maximum Contact probability so performance is excellent
In traditional CGR, especially when random node is more, performance difference becomes apparent from；
(2) when probability threshold value is larger, the trusted path that crucial bundle is selected at least can be with the increase of random node
The probability of retransmission failure can be caused to increase, then transmission consumption increase, the size of probability threshold value turns to PCGR non-key bundle
The influence unobvious of the transmission loss of hair；(3) PCGR is when threshold value is moderate, and random node increases crucial bundle and non-key
The transmission loss of bundle forwarding influences little.
As shown in Fig. 7, Fig. 8, Fig. 9, in the case where there is great deal of nodes (including determining node and random node), PCGR is calculated
Method route shortest path CGR algorithms, and the biography of traditional stochastic route flooding forwarding Epidemic algorithms than traditional determination
Defeated combination property will get well.
Compared with prior art, a kind of hybrid network of the invention based on unmanned plane and moonlet, it is proposed that routing algorithm solution
Certainly traditional route can not tackle the Network route Problem of a variety of nodes, and propose the forecast model connected at random and obtain this road
As the link information needed for algorithm.In addition, this routing algorithm and traditional routing algorithm are contrasted, this can be obtained
The itd is proposed algorithm of invention can obtain less propagation delay time and transmission loss in hybrid network Route Selection.
In addition, the present invention also proposes that a kind of unmanned plane communicates route system with satellite hybrid network, including memory, handle
Device and the computer program being stored on the memory, the computer program are realized as above during the computing device
The step of described method, it will not be repeated here.
The preferred embodiments of the present invention are the foregoing is only, are not intended to limit the scope of the invention, every utilization
Equivalent structure or the flow conversion that description of the invention and accompanying drawing content are made, or directly or indirectly it is used in other related skills
Art field, is included within the scope of the present invention.
Claims (7)
1. a kind of unmanned plane and satellite hybrid network communication routing method, it is characterised in that comprise the following steps：
Based on random connection forecast model, the probability connection figure containing random node and determination node is constructed；
Design routing algorithm based on probability connection figure carry out unmanned plane and satellite hybrid network communication lines by.
2. unmanned plane according to claim 1 and satellite hybrid network communication routing method, it is characterised in that the design
The step of routing algorithm based on probability connection figure, includes：
The connection probability of uncertain connection is added in the probability connection figure, and probability threshold value is added in checking process is connected
Screening；
The connection probability is considered in retransmission process, generates the routing algorithm based on the probability connection figure.
3. unmanned plane according to claim 2 and satellite hybrid network communication routing method, it is characterised in that the probability
Link information in connection figure between each node includes transmission node, receiving node, link connection time started, link connection knot
Beam time, link transmission rate, connection probability.
4. unmanned plane according to claim 1 and satellite hybrid network communication routing method, it is characterised in that the connection
Checking process includes：
Link information in probability connection figure is saved currently to hold the neighbours of forwarding bundle node selection energy transmitting
Point list.
5. unmanned plane according to claim 1 and satellite hybrid network communication routing method, it is characterised in that it is described nobody
Machine also includes with satellite hybrid network communication routing method：
Performance Evaluation is carried out to the routing algorithm based on probability connection figure.
6. the unmanned plane and satellite hybrid network communication routing method, its feature according to any one of claim 1-5 exist
In the unmanned plane also includes with satellite hybrid network communication routing method：
Create the random connection forecast model.
The route system 7. a kind of unmanned plane communicates with satellite hybrid network, it is characterised in that including memory, processor and deposit
The computer program on the memory is stored up, the computer program is realized such as claim 1- during the computing device
The step of method any one of 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710591295.8A CN107360093B (en) | 2017-07-19 | 2017-07-19 | Unmanned aerial vehicle and satellite hybrid network communication routing method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710591295.8A CN107360093B (en) | 2017-07-19 | 2017-07-19 | Unmanned aerial vehicle and satellite hybrid network communication routing method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107360093A true CN107360093A (en) | 2017-11-17 |
| CN107360093B CN107360093B (en) | 2020-06-19 |
Family
ID=60284756
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710591295.8A Active CN107360093B (en) | 2017-07-19 | 2017-07-19 | Unmanned aerial vehicle and satellite hybrid network communication routing method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107360093B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108234316A (en) * | 2017-12-07 | 2018-06-29 | 北京邮电大学 | A kind of unmanned plane network route selecting method based on cubic space region division |
| CN110505153A (en) * | 2019-08-12 | 2019-11-26 | 武汉大学 | A kind of mixed logic dynamic method of Incorporate |
| CN110943927A (en) * | 2019-12-06 | 2020-03-31 | 湖南大学 | Named data network transmission control method, device and equipment based on time delay management |
| CN110958661A (en) * | 2019-11-25 | 2020-04-03 | 北京邮电大学 | Unmanned aerial vehicle network route selection method and device and unmanned aerial vehicle node |
| CN113015105A (en) * | 2021-03-08 | 2021-06-22 | 哈尔滨工业大学（深圳） | Method for jointly optimizing code cache content placement and resources of air-space-ground integrated network |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101964958A (en) * | 2010-09-27 | 2011-02-02 | 北京航空航天大学 | On-board network system and backbone node message processing method thereof |
| US20120158280A1 (en) * | 2008-01-14 | 2012-06-21 | Ravenscroft Donald L | Computing route plans for routing around obstacles having spatial and temporal dimensions |
| CN103354479A (en) * | 2013-03-22 | 2013-10-16 | 哈尔滨工业大学深圳研究生院 | LT code based spatial delay/disruption tolerant network (DTN) transmission method |
| CN103679740A (en) * | 2013-12-30 | 2014-03-26 | 中国科学院自动化研究所 | ROI (Region of Interest) extraction method of ground target of unmanned aerial vehicle |
| CN103697889A (en) * | 2013-12-29 | 2014-04-02 | 北京航空航天大学 | Unmanned aerial vehicle self-navigation and positioning method based on multi-model distributed filtration |
| CN105119650A (en) * | 2015-08-24 | 2015-12-02 | 杨珊珊 | Signal relay system based on unmanned aircraft, and signal relay method thereof |
| CN204945802U (en) * | 2015-08-24 | 2016-01-06 | 深圳市诺亚星辰科技开发有限公司 | A kind of unmanned plane task chain hardware system |
-
2017
- 2017-07-19 CN CN201710591295.8A patent/CN107360093B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120158280A1 (en) * | 2008-01-14 | 2012-06-21 | Ravenscroft Donald L | Computing route plans for routing around obstacles having spatial and temporal dimensions |
| CN101964958A (en) * | 2010-09-27 | 2011-02-02 | 北京航空航天大学 | On-board network system and backbone node message processing method thereof |
| CN103354479A (en) * | 2013-03-22 | 2013-10-16 | 哈尔滨工业大学深圳研究生院 | LT code based spatial delay/disruption tolerant network (DTN) transmission method |
| CN103697889A (en) * | 2013-12-29 | 2014-04-02 | 北京航空航天大学 | Unmanned aerial vehicle self-navigation and positioning method based on multi-model distributed filtration |
| CN103679740A (en) * | 2013-12-30 | 2014-03-26 | 中国科学院自动化研究所 | ROI (Region of Interest) extraction method of ground target of unmanned aerial vehicle |
| CN105119650A (en) * | 2015-08-24 | 2015-12-02 | 杨珊珊 | Signal relay system based on unmanned aircraft, and signal relay method thereof |
| CN204945802U (en) * | 2015-08-24 | 2016-01-06 | 深圳市诺亚星辰科技开发有限公司 | A kind of unmanned plane task chain hardware system |
Non-Patent Citations (3)
| Title |
|---|
| FU JIANG ET AL.: "A space-time graph based multipath routing in disruption-tolerant earth-observing satellite networks", 《IEEE TOAES》 * |
| 万鹏: "基于网络编码的空间DTN中CGR改进算法", 《飞行器测控学报》 * |
| 王乐: "基于卫星中继的无人机信息传输系统", 《卫星通信》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108234316A (en) * | 2017-12-07 | 2018-06-29 | 北京邮电大学 | A kind of unmanned plane network route selecting method based on cubic space region division |
| CN108234316B (en) * | 2017-12-07 | 2019-05-31 | 北京邮电大学 | A kind of unmanned plane network route selecting method based on cubic space region division |
| CN110505153A (en) * | 2019-08-12 | 2019-11-26 | 武汉大学 | A kind of mixed logic dynamic method of Incorporate |
| CN110505153B (en) * | 2019-08-12 | 2021-09-14 | 武汉大学 | Heaven and earth integrated hybrid routing method |
| CN110958661A (en) * | 2019-11-25 | 2020-04-03 | 北京邮电大学 | Unmanned aerial vehicle network route selection method and device and unmanned aerial vehicle node |
| CN110958661B (en) * | 2019-11-25 | 2021-11-05 | 北京邮电大学 | Unmanned aerial vehicle network route selection method and device and unmanned aerial vehicle node |
| CN110943927A (en) * | 2019-12-06 | 2020-03-31 | 湖南大学 | Named data network transmission control method, device and equipment based on time delay management |
| CN113015105A (en) * | 2021-03-08 | 2021-06-22 | 哈尔滨工业大学（深圳） | Method for jointly optimizing code cache content placement and resources of air-space-ground integrated network |
| CN113015105B (en) * | 2021-03-08 | 2022-06-03 | 哈尔滨工业大学（深圳） | Method for jointly optimizing code cache content placement and resources of air-space-ground integrated network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107360093B (en) | 2020-06-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107360093A (en) | Unmanned plane and satellite hybrid network communication routing method and system | |
| US10142909B2 (en) | Artificial intelligence-augmented, ripple-diamond-chain shaped rateless routing in wireless mesh networks with multi-beam directional antennas | |
| Ramanathan et al. | Prioritized epidemic routing for opportunistic networks | |
| Rezende et al. | A reactive and scalable unicast solution for video streaming over VANETs | |
| Li et al. | An intersection-based QoS routing in vehicular ad hoc networks | |
| CN108322925A (en) | The transmission path computational methods of differentiated service type in ultra dense degree isomery converged network | |
| Nayyar et al. | Opportunistic networks: present scenario-a mirror review | |
| CN112803988B (en) | Hybrid contact graph routing method based on link error rate prediction | |
| Han et al. | Time-varying topology model for dynamic routing in LEO satellite constellation networks | |
| Tang et al. | Blockchain-based trusted traffic offloading in space-air-ground integrated networks (sagin): A federated reinforcement learning approach | |
| RU2636665C1 (en) | Method of multipath routing using data traffic flow splitting | |
| Madoery et al. | Routing heterogeneous traffic in delay-tolerant satellite networks | |
| Lent | Enabling cognitive bundle routing in NASA's high rate DTN | |
| CN117041132B (en) | Distributed load balancing satellite routing method based on deep reinforcement learning | |
| Tang et al. | Constructing a DRL decision making scheme for multi-path routing in All-IP access network | |
| Yu et al. | Provisioning QoS-aware and robust applications in Internet of Things: A network perspective | |
| Celes et al. | MOP: A novel mobility-aware opportunistic routing protocol for connected vehicles | |
| Lent | Evaluation of cognitive routing for the interplanetary internet | |
| Bhering et al. | Multipath Selection Mechanism for Wireless Video-Surveillance Systems. | |
| Liu et al. | A grid and vehicle density prediction-based communication scheme in large-scale urban environments | |
| Murugan et al. | Performance of Packet Loss Differentiation Model by using Cuckoo Search Back-Propagation Neural Network (CSBPNN) for Mobile Ad-hoc Networks | |
| Visca et al. | Path sampling, a robust alternative to gossiping for opportunistic network routing | |
| Bhavsar et al. | Productive Enhancement In Routing Protocol Based On Network Congestion Dominance Occurs in MANET | |
| Ding et al. | Data Transmission Evaluation and Allocation Mechanism of the Optimal Routing Path: An Asynchronous Advantage Actor‐Critic (A3C) Approach | |
| Ghorbani et al. | Modern blockchain-based routing protocol to increase reliability in delay-tolerant networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |