CN105282267B - A kind of network entity City-level localization method based on landmark clustering - Google Patents
A kind of network entity City-level localization method based on landmark clustering Download PDFInfo
- Publication number
- CN105282267B CN105282267B CN201510585592.2A CN201510585592A CN105282267B CN 105282267 B CN105282267 B CN 105282267B CN 201510585592 A CN201510585592 A CN 201510585592A CN 105282267 B CN105282267 B CN 105282267B
- Authority
- CN
- China
- Prior art keywords
- delay
- network entity
- vector
- region
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 16
- 230000004807 localization Effects 0.000 title claims abstract description 13
- 238000001514 detection method Methods 0.000 claims abstract description 44
- 239000000523 sample Substances 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000000205 computational method Methods 0.000 claims description 4
- 238000009412 basement excavation Methods 0.000 abstract description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000035800 maturation Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L2101/00—Indexing scheme associated with group H04L61/00
- H04L2101/60—Types of network addresses
- H04L2101/69—Types of network addresses using geographic information, e.g. room number
Abstract
The invention discloses a kind of network entity City-level localization method based on landmark clustering, includes the following steps：A：Detection source is disposed respectively in multiple and different geographical locations；B：Measure the round-trip delay between each detection source and all entity terrestrial references；C：By all entity terrestrial references according to region division be different groups, obtain each region average delay vector；D：Measure the round-trip delay between each detection source and network entity to be positioned；E：The relative time delay for calculating network entity to be positioned to each region is vectorial；F：Minimum relative time delay vector is chosen, the region corresponding to minimum relative time delay vector is network entity position to be positioned.The present invention is compared with the network entity location technology based on network measure, network congestion, load balancing, heterogeneous network and the factors such as performance of network equipments is poor can be effectively avoided to impact network delay expansion and delay variation, significantly improve the quantity of entity terrestrial reference excavation, the positioning accuracy of network entity City-level is improved simultaneously, can provide reliable terrestrial reference for cyber city's grade positioning.
Description
Technical field
The present invention relates to field of information security technology more particularly to a kind of network entity City-level based on landmark clustering are fixed
Position method.
Background technology
Network entity positions, and also referred to as IP positioning refers to institute of the corresponding network entity of determining IP address in geographical space
In position, its essence is the mapping relations obtained between network entity IP address and this network entity geographical location.Due to grasping
The geographical location of IP address can provide to the user it is various with specific aim and personalized High-effective Services, therefore, IP in recent years
Location technology is gradually paid close attention to by commercial company, government bodies even individual, such as：Commercial company can be according to IP address
Geographical location targetedly to target group's advertisement and provides location based service (Location Based
Service, LBS) etc. services；Government bodies can push local weather forecast by the geographical location of IP address to different zones
With the information such as natural calamity early warning；Individual can also judge credit card fraud behavior, processing rubbish according to the geographical location of IP address
Mail and improvement P2P (Peer to Peer) network performance etc..
Network entity location technology based on network measure is to obtain one of the major way of high accuracy positioning result.So
And network congestion, load balancing, heterogeneous network and the factors such as performance of network equipments is poor often cause network delay expand and
The problems such as delay variation, these factors can all influence the positioning method accuracy based on latency measurement.Therefore, above-mentioned feelings how to be reduced
Influence of the condition to positioning result, and how accurately to describe some region of average delay is to improve the net based on measurement
One of the key of network entity positioning accuracy.
Invention content
The object of the present invention is to provide a kind of network entity City-level localization method based on landmark clustering can utilize poly-
Average delay in the region that class algorithm is found out, to realize that high-precision network entity positions.
The present invention uses following technical proposals：
A kind of network entity City-level localization method based on landmark clustering, it is characterised in that：Include the following steps successively：
A：According to location requirement, detection source is disposed respectively in multiple and different geographical locations；Then step B and step are respectively enterd
Rapid D；
B：Measure the round-trip delay between each detection source and all entity terrestrial references；Then according to it is obtained round-trip when
Prolong as each entity terrestrial reference setup delay vector；Subsequently into step C；
C：According to region division it is different groups by all entity terrestrial references, and the same area will be in using clustering algorithm
Interior physically target time delay vector clusters are multiple clusters, then choose the cluster that number is most in cluster result and calculate the cluster
Barycenter will be calculated barycenter as the average delay in region vector, finally obtain the average delay vector of each region；So
After enter step E；
D：Measure the round-trip delay between each detection source and network entity to be positioned；Then according to it is obtained round-trip when
Prolong as network entity setup delay to be positioned vector；Subsequently into step E；
E：It is real according to the network to be positioned obtained in the average delay vector sum step D of each region obtained in step C
The time delay vector of body, the relative time delay for calculating network entity to be positioned to each region are vectorial；Subsequently into step F；
F：In the relative time delay vector of the network entity to be positioned being calculated to each region, when choosing minimum opposite
Prolong vector, the region corresponding to minimum relative time delay vector is network entity position to be positioned.
In the step B, the detection source entity terrestrial reference all into network respectively of diverse geographic location deployment is utilized
Icmp probe data packet is sent, the round-trip delay between each detection source and all entity terrestrial references is measured, by all detections
Round-trip delay between source and some network entity terrestrial reference is denoted as di1,di2,di3,…,dim, i=1,2 ..., m, whereiniIt indicates
TheiA detection source, and respectively each terrestrial reference setup delay vector DV, DV=(di1,di2,di3,L,dim), i=1,2 ..., m.
It is that unit is divided into different groups by all cities that is physically marked with, using K-means in the step C
Algorithm by the physically target time delay vector clusters in the same area be multiple clusters, then choose cluster result in number most
More cluster and the barycenter for calculating the cluster, will be calculated average delay vector ADV=(delay of the barycenter as the region1,
delay2,…,delaym), wherein the K in K-means algorithms is empirical thresholds value, finally obtains the average delay of each region
Vector.
In the step D, sent respectively to network entity to be positioned using the detection source of diverse geographic location deployment
Icmp probe data packet measures the round-trip delay between each detection source and network entity to be positioned；Then according to obtained
Round-trip delay is network entity setup delay vector DV_target to be positioned,
DV_target=(d'i1,d'i2,…,d'im), i=1,2 ... m；WhereiniIndicate theiA detection source.
In the step E, according to what is obtained in the average delay vector sum step D of each region obtained in step C
The time delay vector of network entity to be positioned, network entity to be positioned is calculated separately to each region using relative time delay computational methods
Relative time delay vector RDV,
Relative time delay calculation formula is as follows：
RDV=(| delay1-d'i1|,|delay2-d'i2|,……|delaym-d'im|), i=1,2 ... ..., m, i≤m,
WhereiniIndicate theiA detection source.
It is selected in the step F, in from the network entity to be positioned being calculated to the relative time delay vector in each region
Minimum relative time delay vector is taken, the city corresponding to minimum relative time delay vector is city where network entity to be positioned, i.e.,
The final City-level positioning result of network entity to be positioned.
The invention firstly uses the detection sources of diverse geographic location to obtain between each detection source and all entity terrestrial references
Round-trip delay RTT, be then each entity terrestrial reference setup delay vector DV according to obtained round-trip delay RTT, then will be real
Body terrestrial reference utilizes clustering algorithm to obtain the average delay vector ADV of each region by after region division, greatly improves each
The precision of region average delay.Then, the present invention using the detection source of diverse geographic location obtain each detection source with it is to be positioned
Round-trip delay RTT between network entity, and network entity time delay vector DV_target to be positioned is established, further according to each area
The average delay vector ADV in domain and the time delay vector DV_target of network entity to be positioned are calculated separately and are obtained network to be positioned
Entity is to the relative time delay vector RDV in each region, and finally, the present invention is from the network entity to be positioned being calculated to each area
Minimum relative time delay vector RDV, the region corresponding to minimum relative time delay vector RDV are chosen in the relative time delay vector RDV in domain
Network entity position as to be positioned.The present invention is compared with the network entity location technology based on network measure, Neng Gouyou
Effect avoids network congestion, load balancing, heterogeneous network and the factors such as performance of network equipments is poor to network delay expansion and time delay
Shake impacts, and significantly improves the quantity of entity terrestrial reference excavation, while improving the positioning accuracy of network entity City-level, can be
Cyber city's grade positioning provides reliable terrestrial reference.
Description of the drawings
Fig. 1 is the flow diagram of the present invention；
Fig. 2 is the principle schematic of relative time delay computational methods.
Specific implementation mode
The present invention is made with detailed description below in conjunction with drawings and examples：
As shown in Figure 1, the network entity City-level localization method of the present invention based on landmark clustering, main includes poly-
Class part and position portion.Cluster part includes step A to step C；Position portion includes step D to step F.The present invention chooses
City-level datum node, the multiple regions City-level network chosen under same ISP (Internet Service Provider) are real
Datum node of the body terrestrial reference as location algorithm.
Network entity City-level localization method of the present invention based on landmark clustering, includes the following steps successively：
A：According to location requirement, detection source is disposed respectively in multiple and different geographical locations；It, can be according to fixed in the present embodiment
Position accuracy requirement disposes m detection source in diverse geographic location.
B：Using the detection source of diverse geographic location deployment, into network, all entity terrestrial reference sends probe messages respectively,
Measure the round-trip delay RTT (Round-Trip Time) between each detection source and all entity terrestrial references；Then according to gained
The round-trip delay RTT arrived is each entity terrestrial reference setup delay vector DV (Delay Vector).
In step B, using the detection source of diverse geographic location deployment, into network, all entity terrestrial reference is sent respectively
Icmp probe data packet measures the round-trip delay RTT between each detection source and all entity terrestrial references, by all detection sources
Round-trip delay note RTT between some entity terrestrial reference is di1,di2,di3,…,dim, i=1,2 ..., m, wherein i indicate i-th
A detection source, and respectively each entity terrestrial reference setup delay vector DV, DV=(di1,di2,di3,…,dim), i=1,2 ... m；
C：According to region division it is different groups by all entity terrestrial references, and the same area will be in using clustering algorithm
Interior physically target time delay vector DV clusters are multiple clusters, then choose the cluster that number is most in cluster result and calculate the cluster
Barycenter, average delay vector ADV (Average Delay Vector) of the barycenter as the region will be calculated, obtain each
The average delay vector ADV in a region.
It is that unit is divided into different groups by all cities that is physically marked in the present invention, it will using K-means algorithms
In same city incity, physically target time delay vector DV clusters are multiple clusters, and it is most then to choose number in cluster result
Average delay vector ADV=(delay of the barycenter as the region will be calculated in cluster and the barycenter for calculating the cluster1,
delay2,…,delaym)；
ADV=Centroid (MaxCluster (cluster1,…,clusterk))
=(delay1,delay2,…,delaym)；
Wherein, Centroid () is centroid calculation function, and MaxCluster () is that maximum cluster obtains function,
cluster1、……、clusterkRespectively cluster K cluster of generation, delay1、delay2、……、delaymIt is detected for m
The time delay vector that source measures respectively.
K-means algorithms are the existing algorithm of maturation, and details are not described herein.K in K-means algorithms is an experience door
Limit value can determine the demand of positioning accuracy by many experiments and in conjunction with user.The calculating of clustering algorithm and barycenter is same
Sample belongs to the prior art.
D：Icmp probe data packet is sent to network entity to be positioned respectively using the detection source of diverse geographic location deployment,
Measure the round-trip delay RTT between each detection source and network entity to be positioned；Then it is according to obtained round-trip delay RTT
Network entity setup delay vector DV_target to be positioned；
DV_target=GeTargetRTT (probes, target)=(d'i1,d'i2,…,d'im), i=1,2 ... m；
Wherein, GeTargetRTT () is to obtain the round-trip delay function of target, and probes is detection source, and target is to wait for
Position network entity, d'i1,d'i2,…,d'imRespectively m detection source measures round-trip between network entity to be positioned
Time delay,iIndicate theiA detection source.
E：According to the network to be positioned obtained in the average delay vector ADV of each region obtained in step C and step D
The time delay vector DV_target of entity, calculate network entity to be positioned to each region relative time delay vector RDV
(relative Delay Vector)；
In the present invention, using relative time delay computational methods calculate separately network entity to be positioned to each region it is opposite when
Prolong vectorial RDV,
Relative time delay calculation formula is as follows：
RDV=(| delay1-d'i1|,|delay2-d'i2|,……|delaym-d'im|), i=1,2 ... ..., m, i≤m,
WhereiniIndicate theiA detection source；
F：In the relative time delay vector of the network entity to be positioned being calculated to each region, when choosing minimum opposite
Prolong vector, the region corresponding to minimum relative time delay vector is place city, i.e., net to be positioned where network entity to be positioned
The final City-level positioning result of network entity.
The choosing method of minimum relative time delay vector belongs to the prior art, can be by calculating network entity to be positioned to each
The average of the relative time delay vector in region, and chosen according to average size.
Claims (6)
1. a kind of network entity City-level localization method based on landmark clustering, it is characterised in that：Include the following steps：
A：According to location requirement, detection source is disposed respectively in multiple and different geographical locations；Then step B and step D are respectively enterd；
B：Measure the round-trip delay between each detection source and all entity terrestrial references；Then it is according to obtained round-trip delay
Each entity terrestrial reference setup delay vector；Subsequently into step C；
C：By all entity terrestrial references according to region division be different groups, and using clustering algorithm will be in the same area
Physically target time delay vector clusters are multiple clusters, then choose the cluster that number is most in cluster result and the matter for calculating the cluster
The heart will be calculated barycenter as the average delay in region vector, finally obtain the average delay vector of each region；Then
Enter step E；
D：Measure the round-trip delay between each detection source and network entity to be positioned；Then it is according to obtained round-trip delay
Network entity setup delay vector to be positioned；Subsequently into step E；
E：According to the network entity to be positioned obtained in the average delay vector sum step D of each region obtained in step C
Time delay vector, the relative time delay for calculating network entity to be positioned to each region are vectorial；Subsequently into step F；
F：In the relative time delay vector of the network entity to be positioned being calculated to each region, choose minimum relative time delay to
It measures, the region corresponding to minimum relative time delay vector is network entity position to be positioned.
2. the network entity City-level localization method according to claim 1 based on landmark clustering, it is characterised in that：It is described
Step B in, using the detection source of diverse geographic location deployment, into network, all entity terrestrial reference sends icmp probe respectively
Data packet measures the round-trip delay between each detection source and all entity terrestrial references, by all detection sources and each net
Round-trip delay between network entity terrestrial reference is denoted as di1,di2,di3,…,dim, i=1,2 ..., m, wherein i i-th of detection source of expression,
And respectively each terrestrial reference setup delay vector DV, DV=(di1,di2,di3,L,dim), i=1,2 ..., m.
3. the network entity City-level localization method according to claim 2 based on landmark clustering, it is characterised in that：It is described
Step C in, be that unit is divided into different groups by all cities that are physically marked with, will be in same using K-means algorithms
Physically target time delay vector clusters in one region are multiple clusters, then choose number is most in cluster result cluster and calculating
Average delay vector ADV=(delay of the barycenter as the region will be calculated in the barycenter of the cluster1,delay2,…,
delaym), wherein the K in K-means algorithms is empirical thresholds value, delay1、delay2、……、delaymFor m detection source
The time delay vector measured respectively finally obtains the average delay vector of each region.
4. the network entity City-level localization method according to claim 3 based on landmark clustering, it is characterised in that：It is described
Step D in, using diverse geographic location deployment detection source respectively to network entity to be positioned send icmp probe data packet,
Measure the round-trip delay between each detection source and network entity to be positioned；Then it is to be positioned according to obtained round-trip delay
Network entity setup delay vector DV_target,
DV_target=(d'i1,d'i2,…,d'im), i=1,2 ... m；Wherein i indicates i-th of detection source.
5. the network entity City-level localization method according to claim 4 based on landmark clustering, it is characterised in that：It is described
Step E in, it is real according to the network to be positioned that is obtained in the average delay vector sum step D of each region obtained in step C
Body time delay vector, using relative time delay computational methods calculate separately network entity to be positioned to each region relative time delay to
RDV is measured,
Relative time delay calculation formula is as follows：
RDV=(| delay1-d'i1|,|delay2-d'i2|,……|delaym-d'im|), i=1,2 ... ..., m, i≤m, wherein
I indicates i-th of detection source.
6. the network entity City-level localization method according to claim 5 based on landmark clustering, it is characterised in that：It is described
Step F in, when choosing minimum opposite in from the network entity to be positioned being calculated to the relative time delay vector in each region
Prolong vector, the city corresponding to minimum relative time delay vector is city where network entity to be positioned, i.e., network to be positioned is real
The final City-level positioning result of body.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510585592.2A CN105282267B (en) | 2015-08-31 | 2015-09-15 | A kind of network entity City-level localization method based on landmark clustering |
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510545711 | 2015-08-31 | ||
| CN2015105457111 | 2015-08-31 | ||
| CN201510585592.2A CN105282267B (en) | 2015-08-31 | 2015-09-15 | A kind of network entity City-level localization method based on landmark clustering |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105282267A CN105282267A (en) | 2016-01-27 |
| CN105282267B true CN105282267B (en) | 2018-08-14 |
Family
ID=55150570
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510585592.2A Active CN105282267B (en) | 2015-08-31 | 2015-09-15 | A kind of network entity City-level localization method based on landmark clustering |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105282267B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5745682A (en) * | 1995-12-04 | 1998-04-28 | Ncr Corporation | Method and apparatus for utilizing location codes to identify a physical location of a computer station on a NetBIOS computer network |
| EP1062825A1 (en) * | 1998-03-09 | 2000-12-27 | Ericsson Inc. | System and method for informing network of terminal-based positioning method capabilities |
| CN1985186A (en) * | 2004-05-12 | 2007-06-20 | 诺基亚公司 | Locating mobile terminals |
| CN101031144A (en) * | 2007-02-02 | 2007-09-05 | 华为技术有限公司 | Method and system for positioning mobile terminal |
| CN101548197A (en) * | 2006-12-05 | 2009-09-30 | 高通股份有限公司 | Methods and apparatus for location determination in a wireless communication device |
| CN102405420A (en) * | 2009-04-21 | 2012-04-04 | 高通股份有限公司 | Method and apparatus for supporting positioning for terminals in a wireless network |
| CN104715012A (en) * | 2015-01-15 | 2015-06-17 | 罗向阳 | Network entity city-level landmark mining algorithm based on Internet forum |
-
2015
- 2015-09-15 CN CN201510585592.2A patent/CN105282267B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5745682A (en) * | 1995-12-04 | 1998-04-28 | Ncr Corporation | Method and apparatus for utilizing location codes to identify a physical location of a computer station on a NetBIOS computer network |
| EP1062825A1 (en) * | 1998-03-09 | 2000-12-27 | Ericsson Inc. | System and method for informing network of terminal-based positioning method capabilities |
| CN1985186A (en) * | 2004-05-12 | 2007-06-20 | 诺基亚公司 | Locating mobile terminals |
| CN101548197A (en) * | 2006-12-05 | 2009-09-30 | 高通股份有限公司 | Methods and apparatus for location determination in a wireless communication device |
| CN101031144A (en) * | 2007-02-02 | 2007-09-05 | 华为技术有限公司 | Method and system for positioning mobile terminal |
| CN102405420A (en) * | 2009-04-21 | 2012-04-04 | 高通股份有限公司 | Method and apparatus for supporting positioning for terminals in a wireless network |
| CN104715012A (en) * | 2015-01-15 | 2015-06-17 | 罗向阳 | Network entity city-level landmark mining algorithm based on Internet forum |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105282267A (en) | 2016-01-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102282367B1 (en) | System and Method for Location Determination, Mapping, and Data Management through Crowdsourcing | |
| Eriksson et al. | A learning-based approach for IP geolocation | |
| EP3176981B1 (en) | Method and device for detecting the type of a network data flow | |
| CN105227689B (en) | Target IP location algorithm based on local time delay distribution similarity measurement | |
| Dong et al. | Network measurement based modeling and optimization for IP geolocation | |
| CN104506591A (en) | Target IP (Internet protocol) geographic position locating method based on nearest common router | |
| Arif et al. | Internet host geolocation using maximum likelihood estimation technique | |
| WO2014166284A1 (en) | Method and apparatus for determining area of ip address | |
| CN105245627B (en) | A kind of IP localization method based on network coordinate system | |
| CN105262849B (en) | IP localization methods based on tolerable error | |
| CN111064817B (en) | City-level IP positioning method based on node sorting | |
| Zu et al. | IP-geolocater: a more reliable IP geolocation algorithm based on router error training | |
| CN105282267B (en) | A kind of network entity City-level localization method based on landmark clustering | |
| Chen et al. | A landmark calibration-based IP geolocation approach | |
| CN105812204B (en) | A kind of recurrence name server online recognition method based on Connected degree estimation | |
| CN106528559A (en) | Location information provision method and device | |
| CN109005501A (en) | Vehicle positioning method, device, server and system | |
| CN105116373B (en) | Target IP region city-class positioning algorithm based on indirect time delay | |
| Eriksson et al. | Posit: An adaptive framework for lightweight ip geolocation | |
| Lu et al. | Vehicle tracking using particle filter in Wi-Fi network | |
| Ding et al. | A street-level IP geolocation method based on delay-distance correlation and multilayered common routers | |
| Zhang et al. | Street-Level IP Geolocation Algorithm Based on Landmarks Clustering. | |
| Hillmann et al. | Dragoon: advanced modelling of IP geolocation by use of latency measurements | |
| Komosny et al. | Estimation of internet node location by latency measurements: the underestimation problem | |
| Zhu et al. | City-level geolocation algorithm of network entities based on landmark clustering |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20200721 Address after: 450001 No. 62 science Avenue, hi tech Zone, Henan, Zhengzhou Patentee after: Information Engineering University of the Chinese People's Liberation Army Strategic Support Force Address before: 450001 Information Engineering University, 62 science Avenue, hi tech Zone, Henan, Zhengzhou Co-patentee before: Luo Xiangyang Patentee before: Liu Fenlin |