CN109560972B - Non-cooperative inference method for Ad Hoc network physical topology - Google Patents

Non-cooperative inference method for Ad Hoc network physical topology Download PDF

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CN109560972B
CN109560972B CN201811614989.XA CN201811614989A CN109560972B CN 109560972 B CN109560972 B CN 109560972B CN 201811614989 A CN201811614989 A CN 201811614989A CN 109560972 B CN109560972 B CN 109560972B
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physical topology
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牛钊
马涛
马春来
束妮娜
黄郡
单洪
王怀习
王晨
常超
刘春生
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National University of Defense Technology
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    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L45/02—Topology update or discovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention relates to an Ad Hoc network physical topology non-collaborative inference method, and belongs to the technical field of wireless networks. The method comprises the following steps: s1: the nodes in the Ad Hoc network are distinguished and positioned by adopting radio positioning; s2: acquiring the size of a network deployment area according to the node distinguishing and positioning information; s3: calculating the important communication distance of the node according to the size of the network deployment area and the node distinguishing and positioning information; s4: and deducing the network physical topology according to the node distinguishing positioning information and the important communication distance of the node. The invention realizes the automatic inference of the network physical topology under the condition of unknown relevant parameters such as the communication power of the nodes, and the like, and compared with the prior method, the invention improves the structural effectiveness of the network physical topology and reduces the computational complexity of the realization process.

Description

Non-cooperative inference method for Ad Hoc network physical topology
Technical Field
The invention relates to the technical field of wireless networks, in particular to a construction method for non-collaborative inference and optimization of Ad Hoc network physical topology.
Background
The Ad Hoc network can realize rapid deployment and does not need to erect network facilities, so that the Ad Hoc network has wide application in civil and military.
The existing research aiming at network topology mainly focuses on logical topology, and is mainly based on the condition that the maximum communication distance of nodes is known or the longest distance of a minimum spanning tree constructed by using network nodes is used as the maximum communication distance in the process of analyzing physical topology. Since the node is usually deployed in an unsafe area, it is difficult to obtain the maximum communication distance of the node without knowing the relevant parameters such as the communication power of the node. The use of the longest distance of the minimum spanning tree as the maximum communication distance does not consider the situation that a single node is separated from the network and cannot effectively deal with the change of the node position, and the minimum spanning tree is reconstructed by the node to calculate the longest distance every time the position of the node is changed, so that the complexity of calculation is increased.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide a non-collaborative inference method for physical topology of Ad Hoc network, so as to solve the physical topology inference problem of network nodes without acquiring communication parameters of node devices, or the other problems mentioned above.
The purpose of the invention is mainly realized by the following technical scheme:
the embodiment of the invention provides a non-collaborative inference method for Ad Hoc network physical topology, which comprises the following steps: s1: the nodes of the Ad Hoc network are distinguished and positioned by adopting radio positioning; s2: acquiring the size of a network deployment area according to the node distinguishing and positioning information; s3: calculating the important communication distance of the node according to the size of the network deployment area and the node distinguishing and positioning information; s4: and deducing the network physical topology according to the node distinguishing positioning information and the important communication distance of the node.
And further, geometrically controlling the inferred network physical topology based on the Delaunay triangulation rule, eliminating links which do not meet the conditions, and finishing the optimization of the inferred result.
Further, the step S1 specifically includes: receiving signals in a working frequency band by adopting a wireless broadband receiver; carrying out signal detection and preprocessing on the received signals; and positioning and distinguishing the nodes by adopting composite angle positioning or time difference positioning.
Furthermore, the composite angle positioning is based on radio direction finding, the same signal is subjected to direction finding through a plurality of radio monitoring stations, and the intersection of the direction-finding ray angles is utilized for positioning; and the time difference positioning is based on the time when the signal reaches the monitoring station, and intersection positioning is carried out through the time distance.
Further, in step S2, the size of the network deployment area is estimated according to the image information and the communication signal obtained by the node distinguishing and positioning information.
Further, in step S3, the relationship between the network connectivity rate and the important communication distance is fitted according to the communication distance formula, and the important communication distance is calculated according to the network connectivity rate after a fitting function is obtained.
Further, the calculation formula of the important communication distance is as follows:
Figure BDA0001925626440000021
obtaining a minimum communication distance value for ensuring network communication; wherein, n nodes are respectively deployed in C cells, and all nodes are randomly distributed in R ═ 0, l]dWherein l is the side length of the deployment area, the communication distance of each node is r, and the important communication distance rcAnd the number of nodes in the network and the size of a deployment area exist rc dn＝αldInl, d is 2,
Figure BDA0001925626440000022
further, the network connectivity rate is a ratio of a node logarithm of direct connectivity or multi-hop relay in the network to a total node logarithm, and a calculation formula is as follows:
Figure BDA0001925626440000031
where n is the total number of nodes in the networkM is the number of sub-networks in the state of non-full connection, i is the number of sub-networks, niIs the number of nodes contained in subnet i.
Further, in step S4, the euclidean distance between nodes is calculated from the node-specific positioning information, and it is determined whether or not a link between nodes exists by comparing the euclidean distance with the important communication distance, thereby completing the estimation of the physical topology.
Further, when the Euclidean distance between the nodes is smaller than a set threshold value, judging that a connecting edge between the network physical topology nodes exists, otherwise, judging that the connecting edge does not exist; the set threshold is the important communication distance of the node.
The beneficial effects of the above technical scheme are as follows: the embodiment of the invention discloses a non-cooperative inference method for Ad Hoc network physical topology, which comprises the following steps: s1: the nodes of the Ad Hoc network are distinguished and positioned by adopting radio positioning; s2: acquiring the size of a network deployment area according to the node distinguishing and positioning information; s3: calculating the important communication distance of the node according to the size of the network deployment area and the node distinguishing and positioning information; s4: and deducing the network physical topology according to the node distinguishing positioning information and the important communication distance of the nodes. The invention solves the problems of physical topology inference of network nodes under the condition of not obtaining the communication parameters of the node equipment and the problem that the calculation complexity is increased by reconstructing the minimum spanning tree to calculate the longest distance when the position of the node changes every time. The judgment of the connecting edges between the nodes can be realized by solving the important communication distance, the inference of the network topology can be realized by combining the position information of the nodes, the connecting edges which do not accord with the conditions in the network are deleted according to the related rules of topology control on the basis of the inference, and the optimization of the physical topology of the network is realized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
Fig. 1 is a schematic plan structure diagram of an Ad Hoc network structure according to an embodiment of the present invention;
fig. 2 is a flowchart of a non-cooperative inference method for Ad Hoc network physical topology according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a process of inferring and optimizing a physical topology of a network according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a triangle mesh constructed in a graph according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of two adjacent triangles forming a convex quadrilateral according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of two adjacent triangles forming a transformed diagonal of a convex quadrilateral according to an embodiment of the present invention;
FIG. 7 is a graph of network connectivity rate as a function of k value in accordance with an embodiment of the present invention;
FIG. 8 is a graph of a fit of network connectivity versus k-value according to an embodiment of the present invention;
fig. 9 is a graph of a network connectivity rate varying with a k value under different node numbers according to the embodiment of the present invention;
FIG. 10 is a graph of network connectivity rate variation for 100 experimental results in accordance with an embodiment of the present invention;
FIG. 11 is a graph showing the variation of network connectivity rate from 1000 experimental results according to an embodiment of the present invention;
FIG. 12 is a schematic diagram of an inferred network physical topology according to an embodiment of the present invention;
FIG. 13 is a diagram illustrating an optimized network physical topology according to an embodiment of the present invention;
FIG. 14 is a schematic diagram of an embodiment of an MST-based inference result structure;
fig. 15 is a schematic structural diagram of an inference result based on DTG according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form a part hereof, and which together with the embodiments of the invention serve to explain the principles of the invention and not to limit its scope.
Abbreviations and Key term definitions
Ad Hoc network: a multi-hop, centerless, ad hoc wireless network consisting of a set of wireless communication nodes, also known as a multi-hop network, an infrastructure-less network, or an ad hoc network.
Physical topology: and the network components such as the host, the switch, the router and the like are in a wired or wireless physical connection mode.
Ad Hoc network physical topology: the topology graph is formed by physical communication links among wireless communication nodes in the Ad Hoc network.
Physical communication link: wireless communication nodes in a network are within communication range of each other, i.e., a physical communication link exists between two nodes.
The Ad Hoc network structure is mainly divided into a layered structure and a planar structure, the technical concept of the invention is mainly directed to the planar structure, namely a homogeneous network, nodes in the network use omnidirectional antennas, and the nodes are homogeneous nodes. A schematic plane structure diagram of the Ad Hoc network structure shown in fig. 1. The physical topology of the network is a set of connecting edges between nodes, and under the condition that the position information of the nodes is known, the inference of the physical topology is realized, and the key is to complete the calculation of the connecting edge set. The key of the acquisition of the connection edge set lies in the judgment of a communication distance threshold, and the existence of the connection edge between the nodes can be judged under the condition that the Euclidean distance between the nodes is smaller than the threshold, wherein the threshold is set as the important communication distance of the nodes, and the important communication distance is closely related to the network communication rate. The judgment of the connecting edges between the nodes can be realized by solving the important communication distance, the network topology can be deduced by combining the position information of the nodes, the edges which do not accord with the conditions in the network are deleted according to the related rules of topology control on the basis of deduction, and the optimization of the network physical topology is realized.
Fig. 2 is a flowchart of a non-cooperative inference method for physical topology of an Ad Hoc network according to the present invention.
A specific embodiment of the present invention, as shown in fig. 2, discloses an Ad Hoc network physical topology non-collaborative inference method, which includes the following steps:
s1, distinguishing and positioning nodes of the Ad Hoc network by adopting radio positioning;
s2, acquiring the size of a network deployment area according to the node distinguishing and positioning information;
s3, calculating the important communication distance of the node according to the size of the network deployment area and the node distinguishing and positioning information;
and S4, deducing the network physical topology according to the node distinguishing and positioning information and the important communication distance of the nodes.
The method aims to solve the problems that in the process of analyzing the physical topology of the existing network topology, the maximum communication distance of the nodes is difficult to obtain under the condition that the communication power of the nodes and other related parameters are unknown, and the calculation complexity is increased because the minimum spanning tree is reconstructed to calculate the longest distance every time the nodes change positions. Compared with the prior art, the invention realizes the interruption of the network physical topology under the condition that the maximum communication distance of the nodes is unknown, or effectively solves the problem of high calculation complexity that the longest distance of the minimum spanning tree constructed by using the network nodes is used as the maximum communication distance.
It should be noted that, in conjunction with the schematic flow chart of network physical topology inference and optimization shown in fig. 3, the method is further detailed as follows:
the step S1 further includes: receiving signals in the working frequency band by using a broadband receiver; carrying out signal detection and preprocessing on the received signals, and rejecting useless signals, such as interference signals of noise and the like; and positioning and distinguishing the nodes by adopting a composite angle positioning method or a time difference positioning method.
The step S2 further includes: and estimating the size of the deployment area according to the acquired image information and the acquired signal.
The step S3 further includes: calculating the important transmission distance on the basis of the acquired size of the deployment area and the acquired number of the network nodes; specifically, fitting a relation curve between the network communication rate and the important communication distance by using an important communication distance formula calculation, and solving a fitting function; and calculating the important communication distance based on the analysis of the network communication rate.
The step S4 further includes: reading the position information of the nodes, and calculating the Euclidean distance between the nodes; comparing the Euclidean distance with the important communication distance, if the Euclidean distance is smaller than the important communication distance, judging that a physical link exists between the nodes, otherwise, judging that the physical link does not exist; constructing an adjacency matrix and storing the communication relation between nodes; and deducing the physical topology according to the position information of the nodes and the adjacency matrix.
The step S5 further includes: and (4) optimizing the acquired physical topology according to a topology control rule, namely constructing a Delaunay (Delaunay) triangle according to the inference result in the step (4) according to the principle of minimum angle maximization.
In a specific embodiment of the invention, the inferred network physical topology is geometrically controlled based on the delaunay triangulation rule, links which do not meet the conditions are removed, and the optimization of the inferred result is completed.
Specifically, the intersection of the topology inference result and the constructed Delaunay triangulation is used as the optimized final topology inference result; the construction of the Delaunay triangular mesh is based on the characteristics of an empty circle and the principle of maximizing a minimum angle, and comprises the following steps:
1, constructing a super triangle aiming at all nodes in the network, so that all the nodes are contained in the super triangle, and putting all the nodes into a triangle linked list;
2, aiming at all nodes in the node set, sequentially inserting, searching a triangle of which the circumscribed circle contains the inserted node in a linked list of the triangle, calling the triangle containing the node as an influencing triangle, deleting a common edge in the influencing triangle, and connecting the inserted point with all the nodes influencing the triangle to realize the insertion of one node in the Delaunay triangle linked list;
optimizing a locally formed triangle according to a minimum angle maximization principle, analyzing a convex quadrangle formed by two adjacent triangles, exchanging diagonals in the quadrangle, selecting the condition that the minimum angle in six internal angles is not increased any more as an optimization result, and adding the formed triangle into a Delaunay triangle linked list;
and 4, circularly executing the step two until all the nodes are inserted.
It should be noted here that to satisfy delaunay triangulation, two important rules need to be met: (1) empty circle characteristic: the triangular net constructed in the graph is unique, any four points in the net cannot be in a common circle, and other nodes do not exist in the range of the circumscribed circle of any triangle in the net constructed by Delaunay triangles; taking the nodes a, b, and c as an example, the schematic diagram is shown in fig. 4, and there are no other nodes in the triangle constructed by the nodes a, b, and c. (2) Minimum angle maximization principle: two adjacent triangles form a convex quadrangle, and after the diagonal lines are exchanged, the minimum angle in the six interior angles is not enlarged any more. As shown in fig. 5 and 6, when the diagonal bc is changed to ad, the minimum angle becomes large. In the process of constructing the delaunay triangle network, the Bowyer-Watson algorithm is used for reference, and it can be understood by those skilled in the art that the detailed description is omitted here, and the specific steps are as described above. In this embodiment, a connection edge set is constructed by obtaining connection edges existing in the delaunay triangulation, and then an intersection with the connection edge set in the topology inference result is obtained to generate a new connection edge set, and finally a final topology map is generated according to the node position information and the new connection edge set.
In an embodiment of the present invention, the step S1 specifically includes: receiving signals in a working frequency band by adopting a wireless broadband receiver; carrying out signal detection and preprocessing on the received signals to eliminate interference signals; and positioning and distinguishing the nodes by adopting composite angle positioning or time difference positioning.
Preferably, in step S1, the received signal is subjected to signal detection and preprocessing to remove interference signals, which may be further detailed as the following steps:
s11, preprocessing the received signals in the working frequency band, deleting the signals which are not in the required frequency range, and obtaining preprocessed signals;
and S12, carrying out signal detection on the preprocessed signal to obtain a signal detection result, wherein the signal detection result is the arrival time of the TH-PPM-UWB pulse.
Preferably, in step S11, the signal preprocessing includes wavelet denoising to remove high frequency noise in the received signal and improve the signal-to-noise ratio. The signal preprocessing adopts a wavelet denoising method for removing high-frequency noise in the signal in the working frequency band. The signal detection adopts a segment correlation average method for detecting the arrival time of the TH-PPM-UWB pulse. Specifically, the segmented correlation average method is referred to as "a new method for detecting ultra-wideband signal under negative signal-to-noise ratio" published by seikaga, warrior and young calf, and is not described herein again. It is noted that the TH-PPM-UWB pulse has a direction.
In a specific embodiment of the present invention, the composite angle positioning is based on radio direction finding, the same signal is direction-found through a plurality of radio monitoring stations, and the positioning is performed by intersection of direction-finding ray angles; and the time difference positioning is based on the time when the signal reaches the monitoring station, and intersection positioning is carried out through the time distance. That is, each node in the node information detection result is located by a composite angle location method or a time difference location method.
In an embodiment of the invention, in the step S2, the size of the network deployment area is estimated according to the image information and the communication signal obtained by the node distinguishing and positioning information. That is, the network deployment area size is obtained from the radio positioning scout information.
In a specific embodiment of the present invention, in the step S3, the relationship between the network connectivity rate and the important communication distance is fitted according to a communication distance formula, and the important communication distance is calculated according to the network connectivity rate after a fitting function is obtained.
It should be noted that, from the perspective of network coverage, important communication distances are analyzed, a deployed area is divided into C cells with equal size, n communication nodes are deployed in the C cells, and the deployed area is divided into the C cellsAfter the communication nodes are deployed, the number of the empty cells is analyzed and is expressed by mu (n, C). Suppose in any case that the probability that a node is deployed in the ith cell is
Figure BDA0001925626440000091
Wherein i 1. The following theorem exists for important communication distances.
Theorem 1: for n nodes, assuming that the communication distance of each node is R, all nodes are randomly distributed in the condition that R is [0, l]dWhere d is 2, assuming rdn＝αldInl, alpha is 0, r (l) is less than l, n (l) is 1. If α > d.alphadOr α ═ d · αdAnd r (l) > 1, the entire communication graph is fully connected, where α isd＝2ddd/2。
Theorem 2: assume that n nodes are deployed at R ═ 0, l]dWherein d is 2, r (r) (l) is less than l, and n (n) (l) is 1. If r isdn∈O(ld) Then the communication graph is not connected.
In an embodiment of the present invention, the formula for calculating the important communication distance is:
Figure BDA0001925626440000101
obtaining a minimum communication distance value for ensuring network communication; wherein, n nodes are respectively deployed in C cells, and all nodes are randomly distributed in R ═ 0, l]dIn the area (2), the communication distance of each node is r, and the important communication distance and the number of nodes in the network and the size of a deployment area exist rc dn＝αldInl, d is 2,
Figure BDA0001925626440000102
it should be noted that r exists according to the above theorem, the important communication distance, the number of nodes in the network and the deployment area sizec dn＝αldInl are provided. Whether the network is in a connected state or not is ensured to be closely related to the value of alpha, and the setting is carried out
Figure BDA0001925626440000103
The calculation formula of the important communication distance obtained by transforming the formula is as follows:
Figure BDA0001925626440000104
the important communication distance can pass
Figure BDA0001925626440000105
The specific value is closely associated with the k value, the k value is too small to ensure that the randomly deployed network is in a connected state every time, and the k value is too large to cause too many redundant links in the network.
In a specific embodiment of the present invention, the network connectivity rate is a ratio of a node pair number of direct connectivity or multi-hop connectivity in a network to a total node pair number, and a calculation formula is as follows:
Figure BDA0001925626440000106
wherein n is the total number of nodes in the network, m is the number of sub-networks when the network is in a non-fully connected state, i is the number of the sub-networks, n is the number of the sub-networksiIs the number of nodes contained in subnet i.
Specifically, the construction and related calculation of the network are completed by using the network X in the python environment, and the size of a deployment area is (1 x 1) km2And (4) carrying out an experiment aiming at the condition that the number of the nodes is 40, and analyzing the influence of different k values on the network communication rate. The step size of the k value is set to 0.02, 10 experiments are performed each time, the network connectivity is calculated, and the obtained result is shown in fig. 7.
It can be seen from fig. 7 that as the k value increases, the change of the network connectivity rate occurs in three stages:
(1) in the initial stage, y increases slowly with the increase of x, and the rising ratio of the curve is gentle;
(2) in the middle stage, the increasing speed of y is gradually increased along with the increase of x, and the curve is rapidly increased;
(3) after the inflection point is reached, the increase of y is slower along with the increase of x, the increase speed approaches to 0, and the curve develops in a horizontal shape.
The network connectivity rate and k value form a curve similar to the growth curve. The growth curve function model is also called Logistic function model. In the embodiment, a more widely applied Pearls curve model is selected from a Logistic function model in the analysis process to fit scattered points formed in the network.
The general model of the pierce growth curve is:
Figure BDA0001925626440000111
wherein a is a constant, and f (x) is b0+b1x+b2x2+.... A-0.995, f (x) -16.365x +7.033 were obtained by fitting scattered points in the network, and the resulting fitted curve is shown in fig. 8. And calculating according to the function obtained by fitting, and obtaining that when the communication rate is 95%, k is approximately equal to 0.63, when the communication rate is 99%, k is approximately equal to 0.75, and when k is equal to 0.8, the communication rate of the network is 99.8%, namely when k is equal to or more than 0.8, the communication rate of the network is basically kept at about 100%.
Experiments are carried out on the conditions that the number of the nodes is respectively 20,40,60,80 and 100, and the influence of different k values on the network communication rate is analyzed. The k value step is set to 0.04, and the average value of the network connectivity is obtained by performing 100 experiments each time, and the obtained result is shown in fig. 9. And fitting calculation is carried out on each curve, so that the network connectivity is basically kept about 100% when k is more than or equal to 0.8.
Actually, 100 times and 1000 times of experiments are respectively performed in a simulation environment, and the average value of the connectivity of the network is counted, and the obtained statistical results are shown in fig. 10 and 11, where fig. 10 is an experimental result of 100 times of operation, and fig. 11 is an experimental result of 1000 times of operation.
In general, the maximum transmission distance for realizing the network connection state is analyzed from the perspective of the network connection rate, and the network connection rate is continuously increased and maximized with the increase of the maximum transmission distanceThe final stability is 100%. By the formula rc 2n＝αl2Inl, order
Figure BDA0001925626440000121
The important communication distance of the nodes can be obtained according to a functional relation between the network communication rate and k obtained by fitting under the condition that the size of a deployment area and the number of the nodes are known, experiments show that when k is larger than or equal to 0.8, the communication rate of the network is kept at about 100%, the important communication distance under the condition that k is 0.8 is calculated, and the inference of the network physical topology is further realized by combining the position information of the nodes.
In an embodiment of the present invention, in step S4, the euclidean distance between nodes is calculated according to the node distinguishing and positioning information, and it is determined whether a link between nodes exists by comparing with the important communication distance, so as to complete the inference of the physical topology.
Specifically, with node a (x)a,ya) And node b (x)b,yb) For example, the euclidean distance between two nodes may be expressed as:
Figure BDA0001925626440000122
calculation formula of important communication distance:
Figure BDA0001925626440000123
in a specific embodiment of the present invention, when the euclidean distance between the nodes is smaller than a set threshold, it is determined that a connection edge exists between the network physical topology nodes, otherwise, the connection edge does not exist; the set threshold is an important communication distance of the node.
Preferably, in step S4, the following steps can be further detailed:
s41, reading the number n of nodes, position information and the size of a deployment area;
s42, constructing an adjacency matrix A of the nodes, initializing the adjacency matrix A to be a zero matrix, wherein the adjacency matrix can be expressed as the following form:
Figure BDA0001925626440000124
in the formula, aijRepresenting the communication relation between the ith node and the jth node, wherein i and j are any values from 1 to n, n represents the total number of the nodes, and if a link exists between the ith node and the jth node, aij1, otherwise, aij＝0；
S43, finding the important communication distance when k is 0.8
Figure BDA0001925626440000131
S44, traversing the nodes in the network to obtain the European distances between the nodes and other nodes in the network, namely calculating the European distances between any two nodes in all the nodes according to the node position information;
s45, comparing Euclidean distances E and r between nodescIf E is less than or equal to rcDetermining that a link exists between the two points, and modifying the value corresponding to the adjacent matrix into 1;
and S46, constructing a topological graph according to the node position coordinates and the adjacency matrix.
The technical effect of the Ad Hoc network physical topology non-collaborative inference and optimization method is verified by a method for establishing a simulation environment shown in figure 1 by using the Exata software for experiment:
step 1, setting up a scene: constructing a plane Ad Hoc network by using a random deployment model in the Exata software;
step 2, basic parameters are configured: and (3) deploying 40 nodes in the deployment area under the simulation environment, wherein relevant parameters are shown in a table 1.
Parameter(s) Numerical value
Number of nodes 40
Deployment area size (1×1)km2
Routing protocol AODV
Fading value 4dB
Communication frequency 2.4GHz
Type of service CBR
TABLE 1
Step 3, calculating the important communication distance: aiming at the size of a deployment area and the number of nodes, when the k value is set to be 0.8, the network connectivity rate is close to 100%, the r can be obtained by calculating through an important communication distance formulac≈332m。
Step 4, physical topology inference: and calculating the European distance between the nodes according to the position information between the nodes, and judging whether the link between the nodes exists or not by comparing the European distance with the important transmission distance so as to finish the inference of the physical topology. Therefore, the physical topology is inferred by combining the node position information and the important communication distance, and the obtained physical topology graph is shown in fig. 12.
Step 5, physical topology optimization: and constructing the Delaunay triangular net according to the inference result in the step 4 according to the principle of the maximization of the minimum angle. The optimization results from this based on the delaunay triangulation rule are shown in fig. 13.
It is noted that the topology obtained based on the geometry is compared with the estimation result obtained by the maximum communication distance, and it is known thatSet the maximum transmission distance for the node
Figure BDA0001925626440000141
Construct a physical topological graph (when the maximum transmission distance is set to be
Figure BDA0001925626440000142
In the case of (2), although the network is ensured to be in a connected state at any time, a high requirement is imposed on the power of the node), geometric control is performed by using the minimum spanning tree mst (minimum spanning tree) and the delaunay triangulation dtg (delay triangulation graph), respectively, and the topology graph 14 generated based on the minimum spanning tree is a proper subset of the topology estimation result of fig. 12. Fig. 12 includes 93.6% of the connecting edges of the topology generated based on delaunay triangulation, and therefore, all the connecting edges cannot be included, because the influence of the increase of the communication distance on the node power is not considered in the topology generated based on DTG, and the path loss increases by 6dB when the communication distance increases by one time through the calculation formula in the free space loss model, and the working power of the node needs to increase by 4 times of the original power, so that the connecting edge between the node 17 and the node 37 in fig. 15 does not exist in combination with the node shown in fig. 1, for example, from the viewpoint of energy consumption. The connecting edge which is not in accordance with the actual situation in the network is removed, the formed topological structure is shown in figure 13, and the obtained topological structure is a proper subset of the topological graph obtained through the important transmission distance, so that the reliability of the inferred result of the method is verified.
In summary, the present invention discloses a non-collaborative inference method for physical topology of Ad Hoc network, comprising the following steps: s1: carrying out distinguishing and positioning on nodes of the Ad Hoc network by adopting radio positioning; s2: acquiring the size of a network deployment area according to the node distinguishing and positioning information; s3: calculating the important communication distance of the node according to the size of the network deployment area and the node distinguishing and positioning information; s4: and deducing the network physical topology according to the node distinguishing positioning information and the important communication distance of the node. The invention aims at the problems that the maximum communication distance of the nodes is difficult to obtain under the condition that the communication power of the nodes and other related parameters are unknown in the physical topology analysis process of the existing network topology, and the calculation complexity is increased because the minimum spanning tree is reconstructed to calculate the longest distance when the nodes are subjected to position change every time. Compared with the prior art, the method realizes the disconnection of the network physical topology under the condition that the maximum communication distance of the node is unknown, or effectively solves the problem of high calculation complexity by using the longest distance of the minimum spanning tree constructed by the network node as the maximum communication distance.
It will be understood by those skilled in the art that all or part of the flow of the method in the above embodiments may be implemented by a computer program instructing associated hardware, and the program may be stored in a computer readable storage medium. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention.

Claims (9)

1. A non-cooperative inference method for physical topology of an Ad Hoc network is characterized by comprising the following steps:
s1: the nodes of the Ad Hoc network are distinguished and positioned by adopting radio positioning;
s2: acquiring the size of a network deployment area according to the node distinguishing and positioning information;
s3: calculating the important communication distance of the node according to the size of the network deployment area and the node distinguishing and positioning information;
s4: deducing network physical topology according to the node distinguishing positioning information and the important communication distance of the node;
geometrically controlling the inferred network physical topology based on the Delaunay triangulation rule, eliminating links which do not meet the conditions, and completing the optimization of the inferred result; wherein, the intersection of the inference result and the constructed Delaunay triangle network is used as the final topology inference result of the optimization; the construction of the Delaunay triangular mesh is based on the characteristics of a hollow circle and the principle of maximizing a minimum angle.
2. The method according to claim 1, wherein the step S1 specifically includes:
receiving signals in a working frequency band by adopting a wireless broadband receiver;
carrying out signal detection and preprocessing on the received signals;
and positioning and distinguishing the nodes by adopting composite angle positioning or time difference positioning.
3. The method of claim 2, wherein the composite angular position is based on radio direction finding, wherein the same signal is direction-found by multiple radio monitoring stations, and wherein the position is found by intersection of direction-finding ray angles;
and the time difference positioning is based on the time when the signal reaches the monitoring station, and intersection positioning is carried out through the time distance.
4. The method according to claim 1, wherein in step S2, the network deployment area size is estimated according to the image information and the communication signals obtained by the node-based locator information.
5. The method according to claim 1, wherein in step S3, the relationship between the network connectivity rate and the important communication distance is fitted according to a communication distance formula, and the important communication distance is calculated according to the network connectivity rate after obtaining a fitting function.
6. The method of claim 5, wherein the important communication distance is calculated by the formula:
Figure FDA0003096506210000021
obtaining a minimum communication distance value for ensuring network communication;
wherein, n nodes are respectively deployed in C cells, and all nodes are randomly distributed in R ═ 0, l]dIn the area (2), the communication distance of each node is r, and the important communication distance and the number of nodes in the network and the size of a deployment area exist rc dn＝αldInl, d is 2,
Figure FDA0003096506210000022
7. the method of claim 5, wherein the network connectivity rate is a ratio of a node logarithm of direct connectivity or multi-hop relay to a total node logarithm in the network, and is calculated by the following formula:
Figure FDA0003096506210000023
wherein n is the total number of nodes in the network, m is the number of sub-networks in the state that the network is not in full communication, i is the number of the sub-networks, niIs the number of nodes contained in subnet i.
8. The method according to claim 1, wherein in step S4, the euclidean distance between nodes is calculated according to the node distinguishing and positioning information, and the comparison with the important communication distance is used to determine whether a link exists between nodes to complete the inference of the physical topology.
9. The method according to claim 8, wherein when the Euclidean distance between the nodes is smaller than a set threshold, the existence of the connection edge between the network physical topology nodes is determined, otherwise, the connection edge does not exist; the set threshold is an important communication distance of the node.
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