CN111541587A - Multi-point network connectivity test method - Google Patents

Multi-point network connectivity test method Download PDF

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CN111541587A
CN111541587A CN202010342441.5A CN202010342441A CN111541587A CN 111541587 A CN111541587 A CN 111541587A CN 202010342441 A CN202010342441 A CN 202010342441A CN 111541587 A CN111541587 A CN 111541587A
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CN111541587B (en
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何杏宇
杨桂松
张兆
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University of Shanghai for Science and Technology
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L43/00—Arrangements for monitoring or testing data switching networks
    • H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
    • H04L43/0805—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability
    • H04L43/0811—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters by checking availability by checking connectivity

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Abstract

The invention relates to a multipoint network connectivity test method, which is used in the application of the Internet of things, wherein no matter the connectivity of a node to a node or the connectivity of a group is unknown before data conversion or group formation is completed, and data conversion or task cooperation failure can be caused. A node connection random graph is constructed and is decomposed into components according to the direct connection probability of the nodes, so that the test cost can be reduced on the premise of ensuring the test accuracy; a direct communication test method and an indirect communication test method are provided, a test edge between target components and a test edge between the target components and a relay component are respectively tested, a test priority step related to direct communication probability is given, and test priorities of the components and the test edge are sequenced, so that the test method can obtain a test result more quickly and with less cost. And the reliability of node cooperation in the Internet of things is guaranteed.

Description

Multi-point network connectivity test method
Technical Field
The invention relates to the technology of Internet of things, in particular to a multipoint network connectivity testing method.
Background
In an internet of things system, nodes may have different operating modes, energy levels, duty cycles, environmental conditions, social attributes, and mobility patterns. In the face of the heterogeneity and complexity of internet of things systems, the determination of network connectivity is very important and challenging. In some opportunistic transmission schemes, network connectivity is estimated by analyzing past connectivity records or social network properties of nodes and data transmission processes. In addition, as new technologies such as crowd sensing and edge computing are used for encouraging node cooperation in the application of the internet of things, network connectivity also becomes a precondition for reliable task allocation among cooperative nodes.
Disclosure of Invention
The invention provides a multipoint network connectivity testing method aiming at the problem of how to test the connectivity between any multipoint in a network with low cost and high accuracy. A general model is established based on the stochastic graph theory, and the reliability of node cooperation in the Internet of things is guaranteed.
The technical scheme of the invention is as follows: a multipoint network connectivity test method, in a network model, nodes are randomly distributed, and connectivity among a plurality of nodes is determined, and the method comprises the following specific steps:
1) defining a node needing to determine connectivity as a target node, and other nodes as relay nodes; a component containing at least one target node is called a target component; components that do not contain any target nodes are called relay components; the connectivity of the cooperative group formed by the target nodes is called group connectivity;
to determine connectivity of multiple target nodes in a cooperative group, a direct connectivity probability between nodes is defined: the probability of direct connection between nodes is the probability of direct connection between nodes, N for any two nodesiAnd NjWith a direct probability of connectivity P during a preset time interval TijThe calculation is as follows:
Figure BDA0002468974510000011
wherein T isijIs node NiDuring T with node NjThe time of communication;
2) direct connectivity testing was performed on each pair of target assemblies as follows:
2.1) calculating the direct connection capacity measurement of each pair of target components, wherein the larger the direct connection capacity measurement of each pair of target components is, the higher the test priority of each pair of target components is;
the direct connection capability measurement of the target component pair is in direct proportion to the direct connection probability between two target components of the target component pair and in inverse proportion to the number of the test edges to be tested which are estimated between the two target components of the target component pair;
2.2) sequencing and testing each pair of target assemblies according to the step 2.1), sequencing the testing priorities of the testing sides between the same pair of target assemblies from high to low according to the direct connection probability of each testing side, and finishing the test as long as one testing side testing result is regular in the testing process according to the priority without testing the rest testing sides;
3) after the direct connection test among the target components according to the step 2), if no less than 1 target component exists in the node connectivity random graph, executing an indirect connection test to test a test edge between the target component and the relay component in the node connectivity random graph, and performing sequencing test according to the priority of the following steps:
3.1) calculating indirect communication capacity measurement for each relay assembly, and testing the relay assemblies according to the priority sequence of the indirect communication capacity measurement from high to low;
the indirect communication capacity measurement of the relay assembly is in direct proportion to the probability that the relay assembly can be communicated with at least two target assemblies and in inverse proportion to the number of the estimated testing edges needing to be tested;
and 3.2) the target relay component pair comprises a target component and a relay component, the target relay component pair corresponding to the same relay component can be tested in a random sequence, but the test sides between the target component and the relay component of the same target relay component pair are tested according to the priority sequence from high to low of the direct connection probability until the test result of one test side is positive or the test of the target relay component pair is finished by all the test sides.
Said step 2) of each pair of target assemblies SiAnd SjMeasure of direct connectivity between ESi-SjThe calculation is as follows:
Figure BDA0002468974510000021
wherein N isSi-SjIs the target component SiAnd a target component SjTotal number of test edges in between α is representing PSi-SjAnd CSi-SjCoefficient of the weight ratio of (a); pSi-SjIs the target component SiAnd a target component SjThe probability of direct connectivity between them; cSi-SjIs the target component SiAnd a target component SjThe number of the testing edges to be tested is estimated; pSi-SjAnd CSi-SjIt is calculated as follows:
Figure BDA0002468974510000031
Figure BDA0002468974510000032
wherein P isSi-Sj(h) Is the target component SiAnd a target component SjH test edge of between, direct connection probability, PSi-Sj(b) Is SiAnd SjThe direct connection probability of the b-th testing edge between the two edges is pi, and the product is obtained.
The step 3) of the relay component RiMeasure of indirect connectivity ERiThe calculation is as follows:
Figure BDA0002468974510000033
in the formula, NSj-RiIs the target component SjAnd a relay component RiTotal number of test edges in between; m is a test relay component Riβ is a representation of PRiAnd CRiCoefficient of the weight ratio of (a); cRiIs a test relay module RiEstimating the number of the testing edges to be tested in the indirect connection probability; pRiIs RiAs a relay component, it can connect two target groupsThe probability of a piece can be calculated by the following formula:
Figure BDA0002468974510000034
Figure BDA0002468974510000035
PSh-Riis the target component ShAnd a relay component RiThe direct connection probability between the two is calculated by the same method as PSi-Sj；CSj-RiIs the target component SjAnd a relay component RiThe number of the testing edges to be tested is estimated, and the calculation method is the same as that of the testing edges CSi-Sj。
The invention has the beneficial effects that: the multipoint network connectivity test method constructs a node connection random graph, decomposes the node connection random graph into components according to the direct connection probability of the nodes, and can reduce the test cost on the premise of ensuring the test accuracy; a direct communication test method and an indirect communication test method are provided, a test edge between target components and a test edge between the target components and a relay component are respectively tested, a test priority step related to direct communication probability is given, and test priorities of the components and the test edge are sequenced, so that the test method can obtain a test result more quickly and with less cost.
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FIG. 1 is a diagram of the component detachment process of the present invention;
FIG. 2 is a schematic view of a test edge between two assemblies of the present invention;
FIG. 3 is a schematic diagram of a direct connectivity test process of the present invention;
FIG. 4 is a schematic diagram of an indirect connectivity test process according to the present invention.
Detailed Description
In the network model, nodes are randomly distributed, and may be static or mobile, in order to determine connectivity among multiple nodes, the node needing to determine connectivity is referred to as a target node, and other nodes are referred to as relay nodes. Connectivity of the cooperative group formed by the target nodes is referred to as group connectivity.
To determine connectivity of multiple target nodes in a cooperative group, a direct connectivity probability between nodes is defined: the probability of direct connectivity between nodes is the probability of direct connectivity between nodes. For any two nodes NiAnd NjProbability of direct connection P between them during a preset time interval TijThe following can be calculated:
Figure BDA0002468974510000041
wherein T isijIs node NiDuring T with node NjThe communication time may be a preset time interval T, for example, any time interval of each day, or a repeating time period of the operation mode.
As shown in fig. 1, the left diagram of fig. 1 is a schematic node connectivity diagram, and in order to determine group connectivity among a plurality of target nodes, a node connectivity random diagram G (V, E, P) is first constructed according to direct connectivity probabilities of all nodes in a network. In the node connectivity random graph, V represents a set of nodes, E represents a set of edges between the nodes, and P represents a set of direct connectivity probabilities of the edges.
The nodes in the node connectivity random graph also define a direct connectivity probability threshold value Pthreshold. If the probability of direct connectivity between two nodes is not greater than PthresholdThen the edge between them does not exist (dashed line shown in the left diagram of fig. 1); otherwise, the edges between them are considered to exist (solid lines shown in the left diagram of fig. 1).
In the node connectivity random graph, a plurality of components form a graph as shown in the right graph of fig. 1, and nodes in each component are fully connected by edges. The component containing at least one target node is called a target component and uses SiAnd ( i 1,2, 3). Components that do not contain any target nodes are called relay components and use RiAnd ( i 1,2, 3).
If two target nodes are in the same component, they are connected with each other, otherwise, the connectivity between two target nodes depends on the connectivity between two target components respectively containing them, and needs to be tested.
For two nodes located in different components, there exists a test edge between them, and the test edge needs to be tested to determine whether there is connectivity between them. If the test result of the test edge between two nodes is positive, it means that the two nodes are directly connected and the two components in which they are located are connected, and therefore, all the nodes in the two components are determined to be connected. For example, as shown in FIG. 2, the schematic diagram of the test edge between two components, S1And S2There are 6 test edges in between, and if the test result of one of them is positive, it can be determined that S1And S2Is connected and S1And S2Is also connected.
In the present invention, an efficient method is presented that includes two phases to determine group connectivity between a plurality of target nodes in a network. The first stage is a direct connectivity test, testing the test edges between the target components. The second phase is an indirect connectivity test, testing the test edge between the target component and the relay component. In both phases, the components that are determined to be connected will fuse together to form a new component. After the first phase is finished, if a plurality of target components still exist in the node connectivity random graph, the second phase is executed.
In the direct connectivity test, in order to increase the test efficiency, the following method steps are used to order the test priorities of the target components and the test edges between the target components.
Step 1, calculating the direct connection capacity measurement of each pair of target components, wherein the larger the direct connection capacity measurement of each pair of target components is, the higher the test priority of each pair of target components is.
And 2, for the test edges between the same target component pair, the test priorities of the test edges are sorted from high to low according to the direct connection probability of each test edge, and the test is finished as long as one test edge test result is regular in the test process according to the priorities without testing the rest test edges.
The direct connectivity metric of a target component pair is proportional to the probability of direct connectivity between its two target components and inversely proportional to the number of test edges between its two target components that are predicted to need testing, e.g., target component SiAnd SjMeasure of direct connectivity between ESi-SjCan be calculated as:
Figure BDA0002468974510000061
wherein N isSi-SjIs the target component SiAnd a target component SjTotal number of test edges in between α is representing PSi-SjAnd CSi-SjCoefficient of the weight ratio of (a); pSi-SjIs the target component SiAnd SjThe probability of direct connectivity between them; cSi-SjIs SiAnd SjThe number of the testing edges to be tested is estimated; pSi-SjAnd CSi-SjIt is calculated as follows:
Figure BDA0002468974510000062
Figure BDA0002468974510000063
wherein P isSi-Sj(h) Is the target component SiAnd a target component SjH test edge of between, direct connection probability, PSi-Sj(b) Is SiAnd SjThe direct connection probability of the b-th testing edge between the two edges is pi, and the product is obtained.
In (4), the test assembly S is considerediAnd SjAll that may occur in the process of (1). Case 1 is the target component pair Si-SjThe test result of the first test edge is positive, and the probability of case 1 is PSi-Sj(1) The number of test edges is 1. Case 2 is the target component pair Si-SjThe test result of the second test edge is positive, and the probability of case 2 is (1-P)Si-Sj(1))PSi-Sj(2) The number of test edges is 2. Case 3 is the target component pair Si-SjThe test result of the third test edge is positive, and the probability of case 3 is (1-P)Si-Sj(1))(1-PSi-Sj(2))PSi-Sj(3) The number of test edges is 3. Likewise, case h is target component pair Si-SjThe test result of the h-th test edge is positive, and the probability of the case h is (1-P)Si-Sj(1))(1-PSi-Sj(2))(1-PSi-Sj(3))......PSi-Sj(h) The number of test edges is h, and the case h +1 is the target component pair Si-SjThe test results of all the test edges in the case h +1 are negative, and the probability of the case h +1 is (1-P)Si-Sj(1))(1-PSi-Sj(2))(1-PSi-Sj(3))......(1-PSi-Sj(h) H) the number of test edges.
Fig. 3 is a use example for explaining steps 1, 2. In FIG. 3 (a), target component S1And S3Three testing sides are arranged between the two testing sides, and the direct connection probability of the three testing sides is PS1-S3(1)、PS1-S3(2) And PS1-S3(3) If P isS1-S3(1)>PS1-S3(2)>PS1-S3(3) According to step 2, the test sequence of the three test edges is S1-S3(1)→S1-S3(2)→S1-S3(3)。
In FIG. 3 (b), the direct connectivity metric for 6 target component pairs is denoted as ES1-S2、ES1-S3、ES1-S2、ES1-S4、ES2-S3And ES2-S4. If E isS1-S2>ES2-S3>ES1-S3>ES1-S4>ES2-S4>ES3-S4According to step 1, the test sequence of the target component pair is: s1-S2→S2-S3→S1-S3→S1-S4→S2-S4→S3-S4. If P isS1-S3(1)>PS1-S3(2)>PS1-S3(3) And PS3-S4(2)>PS3-S4(1) Considering step 1 and step 2 simultaneously, all the testing edges between the target componentsThe test sequence of (1) is: s1-S2(1)→S2-S3(1)→S1-S3(1)→S1-S3(2)→S1-S3(3)→S1-S4(1)→S2-S4(1)→S2-S4(2)→S3-S4(1)。
After the direct connection test between the target components, if there are not less than 1 target component (i.e. there are also 2 or more unconnected target components) in the node connectivity random graph, an indirect connection test needs to be performed to test the test edge between the target component and the relay component in the node connectivity random graph. In indirect connectivity testing, to improve testing efficiency, the steps use the following method steps to order the testing priority of the testing edge and the relay component.
And step 3: an indirect connectivity metric is calculated for each relay component and the relay components are tested in order of priority from high to low in the connectivity metric therebetween.
And 4, step 4: the target relay component pairs corresponding to the same relay component can be tested in a random sequence, but the test sides between the target component and the relay component included in the same target relay component pair are tested according to the priority sequence from high to low of the direct connection probability of the target component and the relay component until the test result of one test side is positive or all the test sides finish testing the target relay component pair. (target Relay component Pair comprising one target component and one Relay component)
The indirect connectivity metric of a relay component is proportional to the probability that it can connect at least two target components and inversely proportional to the number of test edges predicted to be tested. For example, the relay component RiMeasure of indirect connectivity ERiThe calculation is as follows:
Figure BDA0002468974510000081
in the formula, NSj-RiIs the target component SjAnd a relay component RiTotal number of test edges in between(ii) a M is a test relay component Riβ is a representation of PRiAnd CRiCoefficient of the weight ratio of (a); cRiIs a test relay module RiEstimating the number of the testing edges to be tested in the indirect connection probability; pRiIs RiAs a relay component, the probability that it can connect two target components can be calculated by the following formula:
Figure BDA0002468974510000082
Figure BDA0002468974510000083
PSh-Riis the target component ShAnd a relay component RiThe probability of direct connection between them can be calculated according to (3), CSj-RiIs the target component SjAnd a relay component RiThe number of the test edges to be tested is estimated, and the calculation can be carried out according to the step (4).
Fig. 4 is an example for explaining 4 steps 3, 4. In FIG. 4, there are 3 target assemblies (S)1To S3) And 2 relay modules (R)1And R2). If E isR1>ER2According to the definition of step 3, then R1Prior to R2And (6) carrying out testing. For the target relay component pair S defined in step 41-R1、S2-R1And S3-R1They can be tested in a random order. For the relay component R1And a target component S1The direct connection probability of 3 testing edges between the two is respectively PS1-R1(1),PS1-R1(2) And PS1-R1(3) Indicates if P isS1-R1(1)>PS1-R1(2)>PS1-R1(3) According to the definition of step 4, the test order of these test edges is: s1-R1(1)→S1-R1(2)→S1-R1(3)。

Claims (3)

1. A multipoint network connectivity test method is disclosed, in a network model, nodes are randomly distributed, and the connectivity among a plurality of nodes is determined, and the method is characterized by comprising the following specific steps:
1) defining a node needing to determine connectivity as a target node, and other nodes as relay nodes; a component containing at least one target node is called a target component; components that do not contain any target nodes are called relay components; the connectivity of the cooperative group formed by the target nodes is called group connectivity;
to determine connectivity of multiple target nodes in a cooperative group, a direct connectivity probability between nodes is defined: the probability of direct connection between nodes is the probability of direct connection between nodes, N for any two nodesiAnd NjWith a direct probability of connectivity P during a preset time interval TijThe calculation is as follows:
Figure FDA0002468974500000011
wherein T isijIs node NiDuring T with node NjThe time of communication;
2) direct connectivity testing was performed on each pair of target assemblies as follows:
2.1) calculating the direct connection capacity measurement of each pair of target components, wherein the larger the direct connection capacity measurement of each pair of target components is, the higher the test priority of each pair of target components is;
the direct connection capability measurement of the target component pair is in direct proportion to the direct connection probability between two target components of the target component pair and in inverse proportion to the number of the test edges to be tested which are estimated between the two target components of the target component pair;
2.2) sequencing and testing each pair of target assemblies according to the step 2.1), sequencing the testing priorities of the testing sides between the same pair of target assemblies from high to low according to the direct connection probability of each testing side, and finishing the test as long as one testing side testing result is regular in the testing process according to the priority without testing the rest testing sides;
3) after the direct connection test among the target components according to the step 2), if no less than 1 target component exists in the node connectivity random graph, executing an indirect connection test to test a test edge between the target component and the relay component in the node connectivity random graph, and performing sequencing test according to the priority of the following steps:
3.1) calculating indirect communication capacity measurement for each relay assembly, and testing the relay assemblies according to the priority sequence of the indirect communication capacity measurement from high to low;
the indirect communication capacity measurement of the relay assembly is in direct proportion to the probability that the relay assembly can be communicated with at least two target assemblies and in inverse proportion to the number of the estimated testing edges needing to be tested;
and 3.2) the target relay component pair comprises a target component and a relay component, the target relay component pair corresponding to the same relay component can be tested in a random sequence, but the test sides between the target component and the relay component of the same target relay component pair are tested according to the priority sequence from high to low of the direct connection probability until the test result of one test side is positive or the test of the target relay component pair is finished by all the test sides.
2. The multipoint network connectivity test method of claim 1, wherein the step 2) each pair of target components SiAnd SjMeasure of direct connectivity between ESi-SjThe calculation is as follows:
Figure FDA0002468974500000021
wherein N isSi-SjIs the target component SiAnd a target component SjTotal number of test edges in between α is representing PSi-SjAnd CSi-SjCoefficient of the weight ratio of (a); pSi-SjIs the target component SiAnd a target component SjThe probability of direct connectivity between them; cSi-SjIs the target component SiAnd a target component SjThe number of the testing edges to be tested is estimated; pSi-SjAnd CSi-SjIt is calculated as follows:
Figure FDA0002468974500000022
Figure FDA0002468974500000023
wherein P isSi-Sj(h) Is the target component SiAnd a target component SjH test edge of between, direct connection probability, PSi-Sj(b) Is SiAnd SjThe direct connection probability of the b-th testing edge between the two edges is pi, and the product is obtained.
3. The multipoint network connectivity test method of claim 2, wherein the step 3) relay component RiMeasure of indirect connectivity ERiThe calculation is as follows:
Figure FDA0002468974500000031
in the formula, NSj-RiIs the target component SjAnd a relay component RiTotal number of test edges in between; m is a test relay component Riβ is a representation of PRiAnd CRiCoefficient of the weight ratio of (a); cRiIs a test relay module RiEstimating the number of the testing edges to be tested in the indirect connection probability; pRiIs RiAs a relay component, the probability that it can connect two target components can be calculated by the following formula:
Figure FDA0002468974500000032
Figure FDA0002468974500000033
PSh-Riis the target component ShAnd a relay component RiThe direct connection probability between the two is calculated by the same method as PSi-Sj；CSj-RiIs the target component SjAnd a relay component RiThe number of the testing edges to be tested is estimated, and the calculation method is the same as that of the testing edges CSi-Sj。
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7016313B1 (en) * 2001-06-28 2006-03-21 Cisco Technology, Inc. Methods and apparatus for generating network topology information
CN101179499A (en) * 2007-12-06 2008-05-14 上海大学 Method for wireless self-organizing of monitoring system to form tree-shaped routing
US20100177772A1 (en) * 2009-01-12 2010-07-15 Swapnesh Banerjee Method and system for deriving tunnel path information in mpls networks
US20140258454A1 (en) * 2013-03-09 2014-09-11 Sas Institute Inc. Parallel community detection
CN106102115A (en) * 2016-07-29 2016-11-09 广州市香港科大霍英东研究院 A kind of low power consumption network topology control method being applicable to wireless sense network
KR101755978B1 (en) * 2016-01-11 2017-07-10 이화여자대학교 산학협력단 Location determination method of relay nodes in wireless sensor network with bi-connectivity
CN110247826A (en) * 2019-07-10 2019-09-17 上海理工大学 A kind of point to point network continuity testing method

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7016313B1 (en) * 2001-06-28 2006-03-21 Cisco Technology, Inc. Methods and apparatus for generating network topology information
CN101179499A (en) * 2007-12-06 2008-05-14 上海大学 Method for wireless self-organizing of monitoring system to form tree-shaped routing
US20100177772A1 (en) * 2009-01-12 2010-07-15 Swapnesh Banerjee Method and system for deriving tunnel path information in mpls networks
US20140258454A1 (en) * 2013-03-09 2014-09-11 Sas Institute Inc. Parallel community detection
KR101755978B1 (en) * 2016-01-11 2017-07-10 이화여자대학교 산학협력단 Location determination method of relay nodes in wireless sensor network with bi-connectivity
CN106102115A (en) * 2016-07-29 2016-11-09 广州市香港科大霍英东研究院 A kind of low power consumption network topology control method being applicable to wireless sense network
CN110247826A (en) * 2019-07-10 2019-09-17 上海理工大学 A kind of point to point network continuity testing method

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
WANG ZHENGYU: "《Analysis_of_the_Downlink_Connectivity_Probability_within_the_Two-Hop_Coverage_of_an_RSU_in_VANET》", 《2017 IEEE 85TH VEHICULAR TECHNOLOGY CONFERENCE (VTC SPRING)》 *
李燕君等: "衰落信道下无线多跳网络的连通性研究", 《计算机科学》 *
杨桂松: "《无线传感器网络中基于节点连通性的机会路由》", 《小型微型计算机系统》 *
毛莺池等: "节点位置无关的无线传感器网络连通性部分覆盖协议", 《微电子学与计算机》 *

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