CN110798841B

CN110798841B - Multi-hop wireless network deployment method, network capacity determination method and device

Info

Publication number: CN110798841B
Application number: CN201810869586.3A
Authority: CN
Inventors: 吴杰; 李红春; 徐怡; 田军
Original assignee: Fujitsu Ltd
Current assignee: Fujitsu Ltd
Priority date: 2018-08-02
Filing date: 2018-08-02
Publication date: 2022-06-24
Anticipated expiration: 2038-08-02
Also published as: CN110798841A

Abstract

The embodiment of the invention provides a method and a device for multi-hop wireless network deployment and network capacity determination, wherein the multi-hop wireless network deployment device comprises the following steps: the first processing unit is used for processing the deployment schemes in the ith generation deployment scheme set obtained in advance by using a genetic algorithm to generate an i +1 generation deployment scheme set; the deployment scheme set comprises a plurality of deployment schemes, and each deployment scheme comprises more than one path from at least one source node to at least one destination node and antenna information configured by nodes on the path; a first determining unit, configured to determine the i +1 th generation deployment scenario set generated by the first processing unit as a final deployment scenario set when a predetermined condition is satisfied; and when the preset condition is not met, processing the deployment schemes in the (i + 1) th generation deployment scheme set until a final deployment scheme is obtained. By the device of the embodiment, a wireless network deployment scheme can be optimized, and the network performance of the deployment scheme is improved.

Description

Multi-hop wireless network deployment method, network capacity determination method and device

Technical Field

The invention relates to the technical field of communication, in particular to a method and a device for multi-hop wireless network deployment and network capacity determination.

Background

The development of wireless communication technology brings great convenience for users to access a network anytime and anywhere, in a multi-hop wireless network, a typical path from a source node to a destination node is formed by multiple hops, and each node in the multi-hop wireless network can generate or receive data packets and can also serve as a forwarding node to forward the data packets from other nodes. In existing wireless networks, a wireless Ad Hoc network, a wireless sensor network, and a wireless Mesh (Mesh) network all belong to a multi-hop wireless network.

In a multi-hop wireless network, the quality of network performance (such as interference, throughput, end-to-end delay, etc.) is closely related to the selection of node positions and/or the configuration of node interfaces, and if the selection of node positions and/or the configuration of node interfaces are not proper, the performance of the multi-hop wireless network is poor, and the construction cost is increased.

It should be noted that the above background description is only for the sake of clarity and complete description of the technical solutions of the present invention and for the understanding of those skilled in the art. Such solutions are not considered to be known to the person skilled in the art merely because they have been set forth in the background section of the invention.

Disclosure of Invention

The omnidirectional antenna can uniformly distribute energy in different directions, and the directional antenna concentrates the energy in a certain specific direction, so that the directional antenna has a longer transmission distance than the omnidirectional antenna, and because the deployment goal of the multi-hop wireless network is to improve the network coverage, reduce interference and improve the space utilization rate, if the directional antenna is configured on the node in the multi-hop wireless network, the coverage of the wireless multi-hop network can be improved, and the hop count of the multi-hop wireless network can be reduced

In the prior art, when network deployment is performed, usually, on the premise that a node configures an omnidirectional antenna, only node position selection and optimization of the number of nodes are considered, but under the condition that a directional antenna is configured at a node, currently, there is no effective method for optimizing the parameter configuration of the directional antenna at the node.

The embodiment of the invention provides a multi-hop wireless network deployment method and device, which consider the positions of nodes, path selection and antenna configuration of the nodes on the paths, thereby optimizing a wireless network deployment scheme and improving the network performance of the deployment scheme.

In addition, the existing network performance evaluation method only considers some single performance indexes to evaluate the network performance, such as network load, time delay, throughput, etc., but in a multi-hop wireless network, multiple links and multiple paths may exist, and interference may exist between different links and paths, so that the network performance evaluation method in the prior art cannot accurately evaluate the network performance.

The embodiment of the invention provides a method and a device for determining network capacity, which consider the mutual influence between adjacent nodes, determine the network capacity according to the relation between the occupied capacity and the residual capacity of data transmission in a network, and take the network capacity as an index for evaluating the network performance, thereby evaluating the network performance of a multi-hop wireless network more accurately.

The above object of the embodiment of the present invention is achieved by the following technical solutions:

according to a first aspect of the embodiments of the present invention, there is provided a multi-hop wireless network deployment apparatus, the apparatus including:

the first processing unit is used for processing the deployment schemes in the ith generation deployment scheme set obtained in advance by using a genetic algorithm to generate an i +1 generation deployment scheme set; the deployment scheme set comprises a plurality of deployment schemes, each deployment scheme comprises more than one path from at least one source node to at least one destination node and antenna information configured by nodes on the paths, and i is an integer greater than or equal to zero;

a first determining unit, configured to determine the i +1 th generation deployment scenario set generated by the first processing unit as a final deployment scenario set when a predetermined condition is satisfied; and when the preset condition is not met, processing the deployment schemes in the (i + 1) th generation deployment scheme set until a final deployment scheme is obtained.

According to a second aspect of an embodiment of the present invention, there is provided a network capability determining apparatus, including:

a third determining unit, configured to determine the network capability according to the amount of data currently transmitted and the amount of data that can be transmitted by the link with the smallest transmission potential in the network; wherein the transmission potential of the link is determined according to the relation between the residual capacity and the occupied capacity of the link.

The embodiment of the invention has the advantages that when the multi-hop wireless network deployment is carried out, the node position, the path selection and the antenna configuration of the nodes on the path are considered when the network deployment is carried out, so that the wireless network deployment scheme can be optimized, and the network performance of the deployment scheme is improved.

The method and the device have the advantages that the mutual influence among the neighbor nodes is considered, the network capacity is determined according to the relation between the occupied capacity and the residual capacity of the data transmission in the network, and the network capacity is used as an index for evaluating the network performance, so that the network performance of the multi-hop wireless network can be evaluated more accurately.

Specific embodiments of the present invention are disclosed in detail with reference to the following description and drawings, indicating the manner in which the principles of the invention may be employed. It should be understood that the embodiments of the invention are not so limited in scope. The embodiments of the invention include many variations, modifications and equivalents within the spirit and scope of the appended claims.

Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments, in combination with or instead of the features of the other embodiments.

It should be emphasized that the term "comprises/comprising" when used herein, is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps or components.

Drawings

Many aspects of the invention can be better understood with reference to the following drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the invention. For purposes of illustrating and describing some portions of the present invention, corresponding portions of the drawings may be enlarged or reduced. Elements and features depicted in one drawing or one embodiment of the invention may be combined with elements and features shown in one or more other drawings or embodiments. Moreover, in the drawings, like reference numerals designate corresponding parts throughout the several views, and may be used to designate corresponding parts for use in more than one embodiment.

In the drawings:

FIG. 1 is a schematic view of a directional antenna coverage area;

FIG. 2 is a schematic diagram of a directional antenna communication scenario;

fig. 3 is a flowchart of a multi-hop wireless network deployment method in this embodiment 1;

fig. 4A is a schematic diagram of discretization of the region to be deployed in this embodiment 1;

FIG. 4B is a schematic diagram showing the degree of deviation in the present embodiment 1;

FIG. 5 is a flowchart of the method of step 301 in this embodiment 1;

fig. 6 is a flowchart of a fitness calculation method in this embodiment 1;

FIG. 7 is a diagram illustrating the fitness calculation in this embodiment 1;

fig. 8 is a flowchart of a multi-hop wireless network deployment method in the embodiment 2;

fig. 9 is a flowchart of a network capability determination method in the present embodiment 3;

fig. 10 is a schematic diagram of a multi-hop wireless network deployment apparatus in the embodiment 4;

FIG. 11 is a schematic diagram of a first processing unit in the embodiment 4;

fig. 12 is a schematic hardware configuration diagram of the multi-hop wireless network deployment apparatus in embodiment 4.

Fig. 13 is a schematic diagram of the network capability determining apparatus in the present embodiment 5;

fig. 14 is a schematic diagram of the hardware configuration of the network capability determining apparatus in this embodiment 5.

Detailed Description

The foregoing and other features of embodiments of the present invention will become apparent from the following description, taken in conjunction with the accompanying drawings. These embodiments are merely exemplary and are not intended to limit the present invention. In order to enable those skilled in the art to easily understand the principle and the implementation manner of the present invention, the embodiment of the present invention is described by taking a wireless sensor network as an example, but it is to be understood that the embodiment of the present invention is not limited to the wireless sensor network, for example, the method and the apparatus provided by the embodiment of the present invention are also applicable to other networks requiring multi-hop wireless network deployment.

The genetic algorithm and the directional antenna are briefly described below for ease of understanding.

The genetic algorithm is a random search algorithm based on biological natural selection and genetic mechanism, optimized search is completed by simulating a biological evolution process, and in recent years, the genetic algorithm is widely applied to the field of wireless network deployment. The method for calculating the wireless network deployment scheme by using the genetic algorithm mainly comprises the following steps: firstly, establishing a model, coding a deployment scheme into a section of chromosome, wherein each chromosome is composed of a plurality of gene positions (candidate positions), and in order to facilitate the processing process of a genetic algorithm, the deployment scheme in each generation of deployment scheme set needs to be coded; secondly, selecting and applying an adaptive function to determine the advantages and disadvantages of chromosomes, then combining the chromosomes with each other to generate a new generation of chromosomes in a crossed manner, finally obtaining a new deployment scheme by adopting a variation mode, and circularly performing the process until a better deployment scheme is calculated.

Fig. 1 is a schematic diagram of a coverage area of a node configured with a directional antenna, as shown in fig. 1, the directional antenna concentrates energy within an angle θ, the angle θ is referred to as a beam width of the antenna, a sector area with a radius R2 is referred to as a signal coverage area of the antenna, the radius R2 is a transmission distance of the antenna, and a direction (indicated by an arrow in fig. 1) indicated by a directional angle of the directional antenna is referred to as a direction of the antenna; when the nodes are configured with directional antennas, if communication between the nodes is required, not only the distance between the nodes is required to be less than R2, but also two nodes are required to be simultaneously within the coverage of each other, fig. 2 is a schematic view of a communication scene of the directional antennas, and as shown in fig. 2, nodes a and B configured with the directional antennas shown in fig. 1 are within a sector area of the other with a radius of R2, that is, within the coverage.

The following describes a specific embodiment of the present invention with reference to the drawings.

Example 1

This embodiment 1 provides a multi-hop wireless network deployment method; fig. 3 is a flowchart of the multi-hop wireless network deployment method, as shown in fig. 3, the method includes:

step 301, processing deployment schemes in an ith generation deployment scheme set obtained in advance by using a genetic algorithm to generate an (i + 1) th generation deployment scheme set;

step 302, when a predetermined condition is satisfied, determining the generated (i + 1) th generation deployment scheme set as a final deployment scheme set; and when the preset condition is not met, processing the deployment schemes in the (i + 1) th generation deployment scheme set until a final deployment scheme is obtained.

In this embodiment, in order to obtain a better deployment scheme, not only the location deployment of the nodes but also the selection of paths in the multi-hop wireless network and the antenna information configured by the nodes on each path need to be considered, so in this embodiment, the deployment scheme collectively includes a plurality of deployment schemes, each deployment scheme includes more than one path from at least one source node to at least one destination node and the antenna information configured by the nodes on the path, and i is an integer greater than or equal to zero.

By the method of the embodiment, when the multi-hop wireless network deployment is carried out, the node position, the path selection and the antenna configuration of the nodes on the path are considered, so that the wireless network deployment scheme can be optimized, and the network performance of the deployment scheme is improved.

In this embodiment, more than one Path between the at least one source node and the at least one destination node may be represented by nodes (for example, node identifiers) through which the Path passes in sequence, or for convenience of processing the genetic algorithm, each deployment scenario (each individual) may be represented (encoded) by using a Path allocation table (Path _ Tab), where each row of the Path allocation table represents a Path from the source node to the destination node, the number of rows represents the number of paths from the source node to the destination node, each column of the Path allocation table represents the number of hops of nodes on the Path, and the number of columns represents the number of all node positions; the path allocation table is divided into a first part and a second part, where the first part represents a node Identification (ID) passed by each path, the second part represents no node, for example, represented by a number "0" (which may be represented by other values, and this embodiment is not limited thereto), that is, a first non-zero value in each row in the table represents a source node ID, a last non-zero value represents a destination node ID, a zero value represents an unperformed node, or no node, an occurrence of a first 0 in each row represents that a path is ended, and the non-zero values in each row are connected in sequence to form a path from the source node to the destination node, where table 1 below is a schematic of the path allocation table:

table 1 Path assignment table (Path _ Tab)

1	2	3	6	9	0	0	0	0
									1	4	7	8	9	0	0	0	0
5	8	9	0	0	0	0	0	0
									5	6	9	0	0	0	0	0	0

As shown in table 1, the number of nodes of the deployment scenario is 9, and the deployment scenario coexists in 4 paths from the source node to the destination node, which are 1-2-3-6-9 (5-hop path), 1-4-7-8-9 (5-hop path), 5-8-9 (3-hop path), and 5-6-9 (3-hop path), respectively, where 0 indicates that one path does not pass through a node at the corresponding hop count position.

In this embodiment, the path allocation table may also be expressed in other forms, and only the corresponding parameters may be embodied.

In this embodiment, the antenna information configured by the node on each path may include a beam width of the antenna and/or direction information of the antenna, where the direction information includes uplink direction information and/or downlink direction information, the uplink direction information and/or downlink direction information is a deviation degree of an uplink direction and/or a downlink direction from a link line, and the uplink direction and the downlink direction may be represented by directions indicated by directional angles. For convenience of processing the genetic algorithm, each deployment scenario (each individual) may be represented (encoded) by using an antenna allocation table, for example, when the antenna information is uplink direction information and downlink direction information, each row of the antenna allocation table represents a path from a source node to a destination node, and corresponds to table 1 above, the row number represents the number of paths from the source node to the destination node, each column represents a link between adjacent nodes on the path, and the column number represents the number of links on the path; the corresponding value of each row in the table is represented by a real number pair (k1, k2), k1 and k2 respectively represent the deviation degrees of the uplink direction angle and the downlink direction angle from the link line, the deviation degrees have corresponding relations with the actual deviation angles, for example, the deviation degree is 1 when the deviation angle is 0-10 °, the deviation degree is 2 when the deviation angle is 10-30 °, the deviation degree is-2 when the deviation angle is-10-30 °, and so on, which is not exemplified here, the positive and negative values represent the deviation degrees of the direction angles from the link line in opposite directions on both sides of the link line, and the following table 2 is a second schematic of the antenna allocation table:

TABLE 2 antenna Allocation Table

(-1,2)

(0,-2)

(1,3)

(2,-1)

Inf

(2,0)

(-3,2)

(0,1)

(3,0)

Inf

(-3,3)

(2,1)

Inf

(2,-3)

(-1,-3)

Inf

As shown in table 2, the real number pair (-1,2) in the first row and the first column, fig. 4B is a schematic diagram of the deviation degree, as shown in fig. 4B, the real number pair (-1,2) indicates that the deviation degree of the uplink direction angle of the link 1-2 from the connection line of the link 1-2 in the path 1-2-3-6-9 in table 1 is-1 (downward deviation in fig. 4B), the deviation degree of the downlink direction angle of the link 1-2 from the connection line of the link 1-2 is 2 (upward deviation in fig. 4B), inf indicates no link, and other values can be used, which is not limited in this embodiment.

It should be noted that, the real number pair (k1, k2) is used as an example, but the embodiment is not limited thereto, and the deviation degree may also be directly expressed by the deviation angle or other manners, and in addition, the antenna allocation table may only include k1 or only include k 2; or each row and each column of the antenna assignment table may correspond to table 1, and the values in each row and each column respectively represent the beam width k3 of the antenna representing each non-0 node of the path in table 1, and table 3 below is a third schematic of the antenna assignment table: i.e. the antenna information comprises at least one of k1, k2, k3, which is not further exemplified herein.

TABLE 3 antenna Allocation Table

20°

30°

10°

30°

20°

Inf

20°

30°

10°

20°

Inf

20°

30°

20°

Inf

20°

30°

20°

Inf

In this embodiment, optionally, each deployment scenario may further include channel information of each link on the path, and in order to facilitate processing of the genetic algorithm, each deployment scenario (each individual) may be represented (encoded) using a channel allocation table (CA _ Tab), where the number of rows and the number of columns of the channel allocation table are both equal to the number of nodes, and the number of rows and the number of columns respectively correspond to a node Identification (ID) (where the number of rows and the number of columns may or may not be included in the channel allocation table), and each entry in the channel allocation table represents a channel number (ID) used by a link between each two nodes; channels are not used between nodes, or there is no link between nodes, and data does not need to be transmitted, and channels are not allocated, which may be represented by 0 or other values, and this embodiment is not limited thereto, and table 4 below is an illustration of a channel allocation table:

table 4 channel allocation table (CA _ Tab)

	1	2	3	4	5	6	7	8	9
										1	0	3	2	3	3	2	1	3	2
2	0	0	1	3	2	3	1	1	1
										3	0	0	0	1	2	3	2	2	3
4	0	0	0	0	2	2	1	2	1
										5	0	0	0	0	0	1	3	2	1
6	0	0	0	0	0	0	3	3	2
										7	0	0	0	0	0	0	0	2	3
8	0	0	0	0	0	0	0	0	1
										9	0	0	0	0	0	0	0	0	0

As shown in table 4, the number of nodes of the deployment scenario is 9, where the number of the channel used by the links 1-2 is 3, the number of the channel used by the links 1-3 is 2, and the number of the channel used by the links 2-3 is 1, which is not illustrated here, and 0 indicates that there is no link between the node 9 and the node 1, or no channel is allocated between the nodes 9 and 1.

In this embodiment, the channel allocation table may also be represented in other forms, and only corresponding parameters may be represented, for example, the number of rows in the channel allocation table is equal to the number of paths from the source node to the destination node, a column number represents a link sequence number on one path, and each entry in the channel allocation table represents a channel number (ID) used by a link between every two nodes on the path; channels are not used between nodes, or there is no link between nodes, and data does not need to be transmitted, and channels are not allocated, which may be represented by 0 or other values, and this embodiment is not limited thereto, and table 5 below is an illustration of a channel allocation table:

table 5 channel allocation table (CA _ Tab)

1	2	3	1	0	0	0	0	0
									1	3	2	2	0	0	0	0	0
3	1	0	0	0	0	0	0	0
									2	3	0	0	0	0	0	0	0

As shown in table 5, the nonzero values in the first row of table 5 are 1,2,3, and 1, respectively, that is, the channel numbers of the links 1 → 2, 2 → 3,3 → 6, and 6 → 9 representing the path 1 → 2 → 3 → 6 → 9 represented by the first row in the path allocation table 1 are 1,2,3, and 1.

Therefore, when the multi-hop wireless network deployment is carried out, channel allocation can be considered besides the node position, the path selection and the antenna configuration of the nodes on the path, so that the wireless network deployment scheme can be optimized, and the network performance of the deployment scheme is further improved.

In this embodiment, before step 301, the method further includes: firstly, discretizing a to-be-deployed area into a plurality of candidate node positions, and numbering each node position in sequence to obtain a node ID; the discretization method in the present embodiment is explained below with reference to fig. 4A.

Fig. 4A is a schematic diagram of discretization of an area to be deployed in the present embodiment; as shown in fig. 4A, the area to be deployed is discretized into N points according to the communication radius of each node, the source node position and the destination node position, each point represents a candidate node position, and each node (candidate node, source node and destination node) is numbered in sequence as 1-8, where the discretization may be uniform or non-uniform, which is not illustrated here, and the candidate node positions include deployed node positions and undeployed node positions.

In this embodiment, in steps 301 to 302, the ith generation deployment scheme set may be an initial deployment scheme set, and when the value of i is zero, the initial deployment scheme set is a 0 th generation deployment scheme set. Using the encoding method of this embodiment, the deployment schemes in the 0 th generation deployment scheme set are generated according to a predetermined routing algorithm, and then using a genetic algorithm to perform corresponding iteration to generate a final deployment scheme set.

In this embodiment, the predetermined routing algorithm may be a Dijkstra algorithm and a Bellman-Ford algorithm, for example, according to a source node, a candidate node position, and a destination node in a multi-hop wireless network, a Dijkstra algorithm is used to calculate a shortest path from the source node to the destination node, at least one obtained shortest path is represented in the form of a path allocation table, and is used as a deployment scheme, and a plurality of deployment schemes are sequentially generated to obtain an initial deployment scheme set.

For example, after obtaining the initial set of deployment scenarios, the 0 th generation set of deployment scenarios may be processed based on genetic algorithms to obtain an i +1 th generation (1 st generation) set of deployment scenarios. The method for processing the 0 th generation deployment scheme set based on the genetic algorithm mainly comprises the following steps: carrying out processing including selection, intersection and variation on the 0 th generation deployment scheme set, and taking the 1 st generation population or the 1 st generation deployment scheme set as a final deployment scheme set when a preset condition is met; otherwise, for the generated 1 st generation deployment scheme set, the 1 st generation deployment scheme is processed by using a genetic algorithm, that is, i is equal to 1, the processes of selection, intersection and variation in step 301 are repeated, and so on, until a final deployment scheme is obtained, and the multi-hop wireless network deployment method is explained below.

In this embodiment, an improved genetic algorithm matching the individual coding method is also provided, which mainly involves selection, intersection, and mutation processes in the genetic algorithm, so as to obtain an optimized deployment scenario set.

Fig. 5 is a flowchart of an implementation of step 301 in this embodiment. As shown in fig. 5, step 501 includes:

step 501, calculating at least two objective functions of each deployment scheme in the ith generation of route node deployment scheme set, calculating a fitness function of each deployment scheme in the ith generation of route node deployment scheme set according to the at least two objective functions of each deployment scheme, and selecting a deployment scheme meeting a first predetermined condition to obtain a first deployment scheme set; wherein the fitness function is inversely proportional to the minimum Euclidean distance of the pareto solution in each deployment scenario and the (i-1) th generation deployment scenario set;

step 502, selecting a second predetermined number (determined as needed, which is not taken as a limitation in this embodiment) of deployment plans from the first deployment plan set, where each deployment plan group includes two deployment plans, and performing cross processing on the two deployment plans in each deployment plan group;

step 503, selecting a third predetermined number of deployment schemes (determined as needed, which is not limited in this embodiment) from the first deployment scheme set after performing interchange processing on the deployment schemes of the second predetermined number of groups to obtain a second deployment scheme set, and performing variation processing on the deployment schemes in the second deployment scheme set to obtain the (i + 1) th generation deployment scheme set.

In this embodiment, step 501 corresponds to a selection process in a genetic algorithm, in step 501, the at least two objective functions include a function representing network capability and a function representing network cost (for example, calculating performance indicators such as throughput, latency, rate, cost, and the like), the throughput, latency, and rate may be calculated by referring to the prior art, the network cost may be determined according to the number of nodes or the number of nodes and the number of interfaces of each node, and this embodiment is not limited thereto. The specific meaning of the i-1 generation deployment scheme for concentrating pareto solutions (non-dominant solutions) can refer to the prior art, and is not described herein again. The fitness function is inversely proportional to the minimum Euclidean distance between each deployment scheme and the centralized pareto solution of the (i-1) th generation deployment scheme, and can be used for evaluating the quality degree of each deployment scheme, so that the convergence speed of the genetic algorithm can be improved and a better deployment scheme can be selected through the fitness function.

Fig. 6 is a schematic diagram of the fitness calculating method, as shown in fig. 6, the method includes:

step 601, normalizing at least two (m) target functions;

step 602, mapping each deployment scheme and the (i-1) th generation deployment scheme centralized pareto solution into an m-dimensional space coordinate system established according to the at least two objective functions;

step 603, calculating Euclidean distance between each deployment scheme and each pareto solution in the (i-1) th generation deployment scheme set in the coordinate system;

step 604, selecting the minimum distance of the distances;

step 605, the reciprocal of the minimum distance is taken as the fitness function.

In this embodiment, for convenience of description, m is 2, but this embodiment is not limited thereto, and fig. 7 is a schematic diagram of the fitness calculation, and as shown in fig. 7, after normalization processing is performed on each objective function in step 601, a two-dimensional coordinate system is established, an abscissa represents an objective function 1, and an ordinate represents an objective function 2, and in step 602, a deployment plan (a) in an ith-generation deployment plan set and pareto solutions (1 to 6) in an i-1 th-generation deployment plan set are mapped into the two-dimensional coordinate system according to the objective function 1 and the objective function 2 calculated by each deployment plan; in step 603, Euclidean distances Dc between a and pareto solutions 1-6 are calculated, in step 604-605, the minimum Euclidean distance Dc is selected to be the distance Dc2 between a and 2, the reciprocal of Dc2 is taken as the fitness function, namely the minimum value of the distance between the deployment plan in the deployment plan set of the ith generation and each pareto solution in the deployment plan set of the ith generation is smaller, the fitness is larger, and the probability that the deployment plan is selected (reserved) to the (i + 1) th generation is larger.

It should be noted that, the above is only described by taking the reciprocal of the minimum distance as the fitness function, but the embodiment is not limited thereto as long as the fitness function is inversely proportional to the minimum euclidean distance of the pareto solution in each deployment scenario and the i-1 th generation deployment scenario set.

In this embodiment, in order to further increase the speed of searching the optimal solution by the genetic algorithm, in step 501, a deployment scheme (elite solution) meeting a first predetermined condition may be selected to obtain a first deployment scheme set; the first predetermined condition may be:

1) equally dividing the difference value between the maximum value and the minimum value of each objective function of each deployment scheme into N intervals, and selecting a first preset number (m1) of deployment schemes in each interval; for example, the optimal (N1) deployment scenarios of the objective function in each of the N intervals may be selected to obtain the m1 deployment scenarios, and if there is no deployment scenario corresponding to the objective function value in a certain interval, the deployment scenarios may not be selected, and/or,

2) selecting a second predetermined number (m2) of deployment scenarios with the largest fitness function; and/or the presence of a gas in the gas,

3) a third predetermined number (m3) of deployment scenarios (e.g., deployment scenarios b and/or c in fig. 7) are selected in which at least one of the objective functions is an optimal value (the optimal value may be a maximum or minimum value, e.g., the lower the cost, the better the transmission rate).

In this embodiment, m1, m2, and m2 are positive integers, and in step 501, at least one of m1, m2, and m3 may be selected to obtain the first deployment scenario set.

In this embodiment, step 502 corresponds to an intersection process in the genetic algorithm, and step 503 corresponds to a mutation process in the genetic algorithm, which will be described in detail below by way of example.

In step 502, paths of the same source node and the destination node in the two deployment schemes are exchanged, and there is no duplicate path in the exchanged deployment schemes, for example, when the deployment schemes are represented by using the path allocation table, rows in which the paths of the same source node and the destination node are located may be exchanged, for example, the path allocation tables in the two deployment schemes are shown in the following tables 6 and 7:

TABLE 6

1	2	3	6	9	0	0	0	0
									1	4	7	8	9	0	0	0	0
5	8	9	0	0	0	0	0	0
									5	6	9	0	0	0	0	0	0

TABLE 7

1	2	3	6	9	0	0	0	0
									1	2	5	8	9	0	0	0	0
5	8	9	0	0	0	0	0	0
									5	6	9	0	0	0	0	0	0

The paths (and not duplicates) of the homologous node and the destination node in tables 6 and 7 are 1-4-7-8-9 and 1-2-5-8-9, respectively, and the two paths are interchanged to obtain tables 8 and 9 below, where duplicate paths are not present in tables 8 and 9 after the interchange.

TABLE 8

1	2	3	6	9	0	0	0	0
									1	2	5	8	9	0	0	0	0
5	8	9	0	0	0	0	0	0
									5	6	9	0	0	0	0	0	0

TABLE 9

1	2	3	6	9	0	0	0	0
									1	4	7	8	9	0	0	0	0
5	8	9	0	0	0	0	0	0
									5	6	9	0	0	0	0	0	0

In this embodiment, since the antenna allocation table and the channel allocation table both correspond to the nodes and links in the path allocation table, the intersection processing of the antenna allocation table and the channel allocation table is consistent with the intersection processing of the path allocation table, for example, the antenna information of the row in which the paths 1-4-7-8-9 and 1-2-5-8-9 are located in the antenna allocation table is exchanged, and the channel numbers of the row in which the paths 1-4-7-8-9 and 1-2-5-8-9 are located in the channel allocation table is exchanged

In step 503, a node on a path may be selected, a predetermined routing algorithm is used to generate a first path from the selected node to a destination node, or a second path from a source node to the node, the first path is used to replace an original path from the node to the destination node, or the second path is used to replace an original path from the source node to the node.

In this embodiment, since the antenna allocation table and the channel allocation table are both associated with the nodes and links in the path allocation table, the mutation process of the antenna allocation table and the channel allocation table corresponds to the mutation process of the path allocation table. The following examples are given.

For example, the antenna information for the path 1 → 2 → 3 → 6 → 9, the links 1 → 2, 2 → 3,3 → 6, 6 → 9 are (2,0), (-3,2), (0,1), (3,0), the channel number of the links 1 → 2, 2 → 3,3 → 6, 6 → 9 is 1,2,3,1, the node 3 is selected during the mutation process, the path from the node 1 to the node 3 is generated according to the routing algorithm and may be 1 → 5 → 4 → 3, i.e. 1 → 5 → 4 → 3 replaces the original 1 → 2 → 3, the path after the mutation is 1 → 5 → 4 → 3 → 6 → 9, the corresponding antenna information is also correspondingly mutated due to the addition of the new link 1 → 5, 5 → 4, 4 → 3, but the mutation may be random, or an existing table is obtained, for example, the antenna information of the link 1 → 5 is (2,1) the antenna information (2,0), (3,1) of the link 5 → 4, 4 → 3 is randomly generated, that is, the antenna information after the variation is (2,1), (2,0), (3,1), (0,1), (3, 0); in addition, because new links 1 → 5, 5 → 4, 4 → 3 are added, optionally, corresponding channel information is also correspondingly mutated, but the mutation may be random, or the existing table is looked up, for example, the channel of the link 1 → 5 is 5 in the existing table, and the channel 1,3 of the link 5 → 4, 4 → 3 is randomly generated, that is, the channel number after mutation is 5,1,3,3,1.

For example, in step 503, the path may not be changed, and only the antenna information or channel number may be changed, for example, for the path 1 → 2 → 3 → 6 → 9, the antenna information or channel number of each link 1 → 2, 2 → 3,3 → 6, 6 → 9 is (2,0), (-3,2), (0,1), (3,0), the channel number of each link 1 → 2, 2 → 3,3 → 6, 6 → 9 is 1,2,3,1, and in the case of changing, the path 1 → 2 → 3 → 6 → 9 is not changed, but the antenna information or channel number of the changed link 1 → 2 (optionally one or more links) is changed to (2,0) randomly as (2,1), or the channel number 1 is changed to 2 randomly, and the like, which are not given as examples.

In this embodiment, the deployment scenario of the variation performed in step 503 may be the deployment scenario after the intersection in step 502, or may also be the deployment scenario in which the first deployment scenario is not involved in the intersection process, which is not limited in this embodiment, the degree of the variation is determined according to the variation rate, the greater the variation rate is, the more the content of the variation is, and the specific implementation of the variation rate may refer to the prior art, and is not described herein again.

In step 302 of this embodiment, the predetermined condition may be that i +1 is equal to a preset first threshold, or that each deployment scenario in a set of consecutive m-generation deployment scenarios in the set of i + 1-generation deployment scenarios is the same, where m is a preset second threshold, and if it is satisfied that i +1 is equal to the preset first threshold, or that each deployment scenario in the set of consecutive m-generation deployment scenarios in the set of i + 1-generation deployment scenarios is the same, the set of i + 1-generation deployment scenarios is determined as a final deployment scenario set. When the preset condition is not met, processing the (i + 1) th generation deployment scheme set until a final deployment scheme set is obtained; for example, when the first threshold is set to 100, if i +1 is 100, determining the resulting i +1 th generation deployment scenario set as a final deployment scenario set; or when the second threshold is set to 5, if each deployment scheme in the successive 5-generation deployment scheme sets is the same, that is, each deployment scheme in the i-3 th, i-2 th, i-1 th, i + 1-th generation deployment scheme sets is the same, determining the obtained i + 1-th generation scheme set as a final deployment scheme set.

In step 302 of this embodiment, the predetermined condition may also be that a deployment scenario in the deployment scenario set satisfies that a network capability is greater than a third threshold, and when the predetermined condition is satisfied, the (i + 1) th generation deployment scenario set is determined as a final deployment scenario set. When the predetermined condition is not satisfied, the processing is performed on the (i + 1) th generation deployment scenario set, and a determination method of the network capability is as described in embodiment 3, which is not described herein again.

Example 2

Fig. 8 is a flowchart of a multi-hop wireless network deployment method in this embodiment, and as shown in fig. 8, the method includes:

step 801, discretizing a region to be deployed to generate candidate node positions; numbering each node in sequence;

step 802, generating an initial deployment scheme set, and setting a current initial deployment scheme set as an ith generation, wherein i is 0;

in this embodiment, each deployment scenario in the initial deployment scenario set is encoded by using the encoding method in embodiment 1, and this is not repeated here.

Step 803, selecting a first predetermined number of deployment schemes from the current ith generation deployment scheme set to obtain a first deployment scheme set;

step 804, selecting a second predetermined number of groups of deployment schemes from the first deployment scheme set, wherein each group of deployment schemes comprises two deployment schemes, and performing cross processing on the two deployment schemes in each group of deployment schemes;

step 805, selecting a third predetermined number of deployment plans from the first deployment plan set after performing interchange processing on the deployment plans of the second predetermined number of groups to obtain a second deployment plan set, and performing variation processing on the deployment plans in the second deployment plan set to obtain the i +1 th generation deployment plan set;

the specific implementation of steps 803-805 is the same as steps 301-303 in example 1, and is not repeated here;

step 806, determining whether a predetermined condition is satisfied, if yes, executing step 807, otherwise executing step 808; the predetermined condition may refer to the implementation of step 302, which is not described herein again.

Step 807, assigning i +1 to i, and returning to step 803;

step 808, determining the (i + 1) th generation deployment scheme set as a final deployment scheme set.

By the method of the embodiment, when the multi-hop wireless network deployment is carried out, the channel allocation is considered, so that the wireless network deployment scheme can be optimized, and the network performance of the deployment scheme is improved.

Example 3

This embodiment 3 provides a method for determining network capability, fig. 9 is a flowchart of the method for determining network capability in this embodiment, and as shown in fig. 9, the method includes:

step 901, determining the network capability according to the currently transmitted data volume and the data volume which can be transmitted by the link with the minimum transmission potential in the network; wherein the transmission potential of the link is determined according to the relation between the residual capacity and the occupied capacity of the link.

In this embodiment, the network capability can be used as an index for evaluating the network performance, and in step 901, the network capability may be determined by a transmission potential of a link in the network, where the transmission potential represents a data amount (load) that the link remaining bandwidth can also support transmission, for example, the larger the data amount that the link remaining bandwidth can also support transmission, the larger the link transmission potential is, that is, the stronger the network capability is.

In this embodiment, the transmission potential of the link is determined according to a relationship between the remaining capacity of the link (Mbps), which represents the amount of data that can be transmitted in a unit time, and the occupied capacity (Mbps), which represents the amount of data that has been transmitted in a unit time; wherein, the occupied capacity is equal to the sum of the capacity occupied by data transmission and the capacity occupied by interference transmission, and the capacity occupied by interference transmission represents the capacity occupied by the interference link in the capacity of the current data transmission link; the transmission potential of the link is equal to the ratio of the remaining capacity of the link to the occupied capacity, i.e. represents a multiple of the existing load (occupied capacity) that the remaining capacity of the link can also support, i.e. a multiple by which the amount of source (currently transmitted) data can be increased. The more the multiple, the stronger the network capability.

The following describes how to calculate the capacity occupied by the interfering transmission.

In the calculation of the transmission potential of a link i, a link j, T is determined which is within the interference range of the link i_jRepresents the occupation amount (%) of the interference link j in the interference range of the link i to the time, C_iDenotes the channel capacity, T, of the link i per unit time_j·C_iThe capacity occupied by the interference link j in the capacity of the link i is shown, and when n interference links j exist, the capacity occupied by the interference transmission is

Determining a link j in an interference range of a link i by setting a first threshold Q, calculating a distance between the link i and another link, and determining a link with a distance smaller than Q as the link j in the interference range, where the first threshold Q may be determined according to an interference radius and a node communication radius, and this embodiment is not limited thereto; the inter-link distance is equal to the distance between the two nodes with the smallest distance in the different links. Wherein, T_j＝q_j/C_j·CHS_ijThe amount of data to be transmitted in a unit time of the link j is q_j，C_jIndicating the channel capacity, CHS, of the link j per unit time_ijThe channel separation degree between channels used by the link i and the link j is used to indicate the degree of interference between the channels used by the link j, where the larger the interference is, the larger the channel separation degree is, for example, the value of the channel separation degree is 0-1, and table 10 below is a corresponding relationship between the channel separation degree and the interference (taking 802.11b as an example).

Watch 10

Channel number	1	2	3	4	5	6	7	8	9	10	11
												1	1	0.8	0.6	0.4	0.2	0	0	0	0	0	0
2	0.8	1	0.8	0.6	0.4	0.2	0	0	0	0	0
												3	0.6	0.8	1	0.8	0.6	0.4	0.2	0	0	0	0
4	0.4	0.6	0.8	1	0.8	0.6	0.4	0.2	0	0	0
												5	0.2	0.4	0.6	0.8	1	0.8	0.6	0.4	0.2	0	0
6	0	0.2	0.4	0.6	0.8	1	0.8	0.6	0.4	0.2	0
												7	0	0	0.2	0.4	0.6	0.8	1	0.8	0.6	0.4	0.2
8	0	0	0	0.2	0.4	0.6	0.8	1	0.8	0.6	0.4
												9	0	0	0	0	0.2	0.4	0.6	0.8	1	0.8	0.6
10	0	0	0	0	0	0.2	0.4	0.6	0.8	1	0.8
												11	0	0	0	0	0	0	0.2	0.4	0.6	0.8	1

As shown in table 10, the rows and columns of table 10 respectively indicate the channel numbers used by link i and link j, for example, when the channel used by link i is the same as the channel used by link j, the channel separation degree is 1, i.e., the interference is maximum; when the channel used by the link i is 1 and the channel used by the link j is 4, the channel separation degree is 0.4; when the channel used by link i is 2 and the channel used by link j is 7, the channel separation is 0, i.e. the interference is ignored, which is not illustrated here, and the channel separation is not limited by table 10 above.

The amount of data to be transmitted in unit time of the link i is q_i(occupied capacity for data transmission or currently transmitted data amount), the occupied capacity of the link i is

The remaining capacity of link i is equal to

The transmission potential CLi of the link is equal to

In this embodiment, the link with the smallest transmission potential in each link in a network (e.g., a wireless multi-hop network) is determined to be the link z, and the transmission potential CLz of the link z is obtained, where the link z can transmit the maximum data amount of (1+ CLz) × Qs, where Qs is the known data transmission rate of the source node, and the network capability is equal to the maximum data amount (1+ CLz) × Qs) that can be transmitted by the link z in a unit time.

By the embodiment, the mutual influence among the neighbor nodes is considered, the network capacity is determined according to the relation between the occupied capacity and the residual capacity of data transmission in the network, and the network capacity is used as an index for evaluating the network performance, so that the network performance of the multi-hop wireless network can be evaluated more accurately.

Example 4

Embodiment 4 further provides a multi-hop wireless network deployment apparatus, and since the principle of the apparatus for solving the problem is similar to the method in embodiment 1, the specific implementation thereof may refer to the implementation of the method in embodiment 1, and repeated details are not repeated.

Fig. 10 is a schematic diagram of an embodiment of a multi-hop wireless network deployment apparatus in this embodiment, as shown in fig. 10, the apparatus 1000 includes:

a first processing unit 1001, configured to process, by using a genetic algorithm, deployment schemes in an ith generation deployment scheme set obtained in advance, and generate an i +1 th generation deployment scheme set;

a first determining unit 1002, configured to determine the i +1 th generation deployment scenario set generated by the first processing unit as a final deployment scenario set when a predetermined condition is satisfied; and when the preset condition is not met, processing the deployment schemes in the (i + 1) th generation deployment scheme set until a final deployment scheme is obtained.

Wherein, the deployment scheme set includes a plurality of deployment schemes, each deployment scheme includes more than one path from at least one source node to at least one destination node and antenna information configured by nodes on the path, optionally, the deployment scheme may further include: for the channel information of each link on the path, the specific representation manner of the deployment scenario may refer to embodiment 1, which is incorporated herein and is not described herein again.

In this embodiment, reference may be made to step 301-302 in embodiment 1 for specific implementations of the first processing unit 1001 and the first determining unit 1002, and repeated descriptions are omitted here.

Fig. 11 is a schematic diagram of the first processing unit 1001, and as shown in fig. 11, the first processing unit 1001 includes:

a selecting unit 1101, configured to calculate at least two objective functions of each deployment scenario in the ith-generation route node deployment scenario set, calculate a fitness function of each deployment scenario in the ith-generation route node deployment scenario set according to the at least two objective functions of each deployment scenario, and select a deployment scenario that meets a first predetermined condition to obtain a first deployment scenario set; wherein the fitness function is inversely proportional to the minimum Euclidean distance of the pareto solution in each deployment scenario and the (i-1) th generation deployment scenario set;

an intersection unit 1102, configured to select a second predetermined number of groups of deployment plans from the first deployment plan set, where each group of deployment plans includes two deployment plans, and perform intersection processing on the two deployment plans in each group of deployment plans;

a variation unit 1103, configured to select a third predetermined number of deployment schemes from the first deployment scheme set after performing cross processing on the deployment schemes of the second predetermined number of groups, to obtain a second deployment scheme set, and perform variation processing on the deployment schemes in the second deployment scheme set, so as to obtain the i +1 th generation deployment scheme set.

For example, in one aspect, the selection unit 1101 normalizes at least two (m) objective functions; mapping each deployment scheme and the (i-1) th generation deployment scheme set pareto solution into an m-dimensional space coordinate system established according to the at least two objective functions; calculating Euclidean distances between each deployment scheme and each pareto solution in the i-1 generation deployment scheme set in the coordinate system; selecting a minimum distance of the distances; taking the reciprocal of the minimum distance as the fitness function, the specific implementation thereof can refer to fig. 6, which is not described herein again.

For example, in an aspect, the selecting unit 1101 equally divides a difference between a maximum value and a minimum value of each objective function of each deployment scenario into N intervals, selects a first predetermined number (m1) of deployment scenarios in each interval, and/or selects a second predetermined number (m2) of deployment scenarios with a largest fitness function, and/or selects a third predetermined number (m3) of deployment scenarios with at least one objective function being an optimal value in the objective functions, so as to obtain the first deployment scenario set.

In this embodiment, the detailed implementation of the selection unit 1101, the crossover unit 1102 and the mutation unit 1103 can refer to steps 501 to 503, which are not described herein again.

In this embodiment, optionally, the apparatus may further include:

a second determining unit (not shown) for determining the capability of the network according to the data amount currently transmitted in the network deployed in the final deployment scheme and the data amount that can be transmitted by the link with the minimum transmission potential in the network deployed in the final deployment scheme; the transmission potential of the link is determined according to the relationship between the remaining capacity and the occupied capacity of the link, and the specific implementation manner of the method may refer to step 901 in embodiment 3, which is not described herein again.

Fig. 12 is a schematic diagram of a hardware configuration of a multi-hop wireless network deployment apparatus according to an embodiment of the present invention, and as shown in fig. 12, an apparatus 1200 may include: an interface (not shown), a Central Processing Unit (CPU)1220 and a memory 1210; the memory 1210 is coupled to the central processor 1220. Wherein memory 1210 may store various numbers; further, a program of the multi-hop wireless network deployment is stored, and the program is executed under the control of the central processor 1220, and various preset values and predetermined conditions, etc. are stored.

In one embodiment, the functionality of the multi-hop wireless network deployment device may be integrated into central processor 1220. Wherein the central processor 1220 may be configured to: processing deployment schemes in an ith generation deployment scheme set obtained in advance by using a genetic algorithm to generate an (i + 1) th generation deployment scheme set; the deployment scheme set comprises a plurality of deployment schemes, each deployment scheme comprises more than one path from at least one source node to at least one destination node and antenna information configured by nodes on the paths, and i is an integer greater than or equal to zero;

when a preset condition is met, determining the (i + 1) th generation deployment scheme set generated by the first processing unit as a final deployment scheme set; and when the preset condition is not met, processing the deployment schemes in the (i + 1) th generation deployment scheme set until a final deployment scheme is obtained.

For example, the central processor 1220 may also be configured to: calculating at least two objective functions of each deployment scheme in the ith generation of routing node deployment scheme set, calculating a fitness function of each deployment scheme in the ith generation of routing node deployment scheme set according to the at least two objective functions of each deployment scheme, and selecting the deployment scheme meeting a first preset condition to obtain a first deployment scheme set; wherein the fitness function is inversely proportional to a minimum Euclidean distance of the pareto solution in each deployment scenario and the i-1 th generation deployment scenario set.

For example, the central processor 1220 may also be configured to: normalizing at least two (m) objective functions; mapping each deployment scheme and the (i-1) th generation deployment scheme set pareto solution into an m-dimensional space coordinate system established according to the at least two objective functions; calculating Euclidean distances between each deployment scheme and each pareto solution in the i-1 generation deployment scheme set in the coordinate system; selecting a minimum distance of the distances; the inverse of the minimum distance is taken as the fitness function.

For example, the central processor 1220 may also be configured to: equally dividing the difference value of the maximum value and the minimum value of each objective function of each deployment scheme into N intervals, selecting a first preset number m1 deployment schemes in each interval, and/or selecting a second preset number m2 deployment schemes with the maximum fitness function, and/or selecting a third preset number m3 deployment schemes with at least one objective function of the objective functions being an optimal value, so as to obtain the first deployment scheme set.

For example, the central processor 1220 may also be configured to: determining the capacity of the network according to the data volume currently transmitted in the network deployed in the final deployment scheme and the data volume which can be transmitted by the link with the minimum transmission potential in the network deployed in the final deployment scheme; wherein the transmission potential of the link is determined according to the relation between the residual capacity and the occupied capacity of the link.

In this embodiment, reference may be made to embodiment 1 for a specific implementation of the central processing unit 1220, which is not described herein again.

In another embodiment, the multi-hop wireless network deployment apparatus may also be configured on a chip (not shown) connected to the central processor 1220, and the function of the multi-hop wireless network deployment apparatus is realized under the control of the central processor 1020.

It is to be noted that the apparatus 1200 does not necessarily include all the components shown in fig. 12, for example, the communication module 1204 may be included, and in addition, the apparatus 1200 may include components not shown in fig. 12, which may refer to the prior art.

This embodiment also provides a node (not shown), where the node may be a common node or a gateway node, and may include modules such as a sensor, a CPU, a communication module, and a power supply, and in addition, the node may further include the multi-hop wireless network deployment apparatus shown in fig. 12, and may also integrate functions of the multi-hop wireless network deployment apparatus into the CPU of the node, which is not described herein again.

By the device of the embodiment, when multi-hop wireless network deployment is performed, channel allocation is considered, so that a wireless network deployment scheme can be optimized, and network performance of the deployment scheme is improved.

Example 5

Embodiment 5 further provides a network capability determining apparatus, and since the principle of the apparatus for solving the problem is similar to the method in embodiment 3, the specific implementation thereof may refer to the implementation of the method in embodiment 3, and repeated details are not repeated.

Fig. 13 is a schematic diagram of an implementation of the network capability determining apparatus in this embodiment, and as shown in fig. 13, the apparatus 1300 includes:

a third determining unit 1301, configured to determine the network capability according to the amount of data currently transmitted and the amount of data that can be transmitted by the link with the smallest transmission potential in the network; wherein the transmission potential of the link is determined according to the relation between the residual capacity and the occupied capacity of the link.

In this embodiment, the occupied capacity is equal to the sum of the capacity occupied by data transmission and the capacity occupied by interference transmission, and the capacity occupied by interference transmission represents the capacity occupied by the interference link in the capacity of the link currently transmitting data; the transmission potential of the link is equal to the ratio of the remaining capacity of the link to the occupied capacity.

In this embodiment, reference may be made to step 901 in embodiment 3 for a specific implementation of the third determining unit 1301, and details are not described here.

Fig. 14 is a schematic diagram of a hardware configuration of a network capability determining apparatus according to an embodiment of the present invention, and as shown in fig. 14, an apparatus 1400 may include: an interface (not shown), a Central Processing Unit (CPU)1420 and a memory 1410; the memory 1410 is coupled to the central processor 1420. Wherein memory 1410 may store various numbers; further, a program for network capability determination is stored, and the program is executed under the control of the central processor 1420, and various preset values, predetermined conditions, and the like are stored.

In one embodiment, the functionality of the network capability determination apparatus may be integrated into central processor 1420. Wherein the central processor 1420 may be configured to: determining the network capacity according to the current transmitted data volume and the data volume which can be transmitted by the link with the minimum transmission potential in the network; wherein the transmission potential of the link is determined according to the relation between the residual capacity and the occupied capacity of the link.

In this embodiment, reference may be made to embodiment 3 for a specific implementation of the central processing unit 1420, which is not described herein again.

In another embodiment, the network capability determining apparatus may be configured on a chip (not shown) connected to the central processor 1420, and the function of the network capability determining apparatus may be implemented by the control of the central processor 1420.

It is noted that the apparatus 1400 does not necessarily include all the components shown in fig. 14, for example, the communication module 1404 may be included, and the apparatus 1400 may further include components not shown in fig. 14, which may be referred to in the prior art.

This embodiment also provides a node (not shown), which may be a common node or a gateway node, and may include modules such as a sensor, a CPU, a communication module, and a power supply, and in addition, it may further include a network capability determining device shown in fig. 14, and may also integrate the functions of the network capability determining device into the CPU of the node, which is not described herein again.

An embodiment of the present invention further provides a computer-readable program, where when the program is executed in a multi-hop wireless network deployment apparatus, the program causes a computer to execute the multi-hop wireless network deployment method in the multi-hop wireless network deployment apparatus as in embodiment 1 above.

An embodiment of the present invention further provides a storage medium storing a computer-readable program, where the computer-readable program enables a computer to execute the multi-hop wireless network deployment method in the multi-hop wireless network deployment apparatus in embodiment 1 above.

An embodiment of the present invention also provides a computer-readable program, where when the program is executed in a network capability determination device, the program causes a computer to execute the network capability determination method in the network capability determination device as in embodiment 3 above.

An embodiment of the present invention further provides a storage medium storing a computer-readable program, where the computer-readable program enables a computer to execute the network capability determining method in embodiment 3 above in a network capability determining device.

The multi-hop wireless network deployment, network capability determination apparatus, multi-hop wireless network deployment, network capability determination methods described in connection with embodiments of the present invention may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. For example, one or more of the functional block diagrams and/or one or more combinations of the functional block diagrams illustrated in fig. 10-14 may correspond to individual software modules of a computer program flow or individual hardware modules. These software modules may correspond to the steps shown in fig. 3,5,6,8, and 9, respectively. These hardware modules may be implemented, for example, by solidifying these software modules using a Field Programmable Gate Array (FPGA).

A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. A storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium; or the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC. The software module can be stored in the memories of the multi-hop wireless network deployment and network capability determining devices, and can also be stored in the memory cards which can be inserted into the multi-hop wireless network deployment and network capability determining devices.

One or more of the functional block diagrams and/or one or more combinations of the functional block diagrams described with respect to fig. 10-14 may be implemented as a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any suitable combination thereof designed to perform the functions described herein. One or more of the functional block diagrams and/or one or more combinations of the functional block diagrams described with respect to fig. 3,5,6,8,9 may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in communication with a DSP, or any other such configuration.

While the invention has been described with reference to specific embodiments, it will be apparent to those skilled in the art that these descriptions are illustrative and not intended to limit the scope of the invention. Various modifications and adaptations of the present invention will become apparent to those skilled in the art in view of the foregoing description, which are also within the scope of the present invention.

With regard to the embodiments including the above embodiments, the following remarks are also disclosed.

Supplementary note 1, a multi-hop wireless network deployment apparatus, wherein the apparatus comprises:

the first processing unit is used for processing deployment schemes in an ith generation deployment scheme set which is obtained in advance by using a genetic algorithm to generate an i +1 th generation deployment scheme set; the deployment scheme set comprises a plurality of deployment schemes, each deployment scheme comprises more than one path from at least one source node to at least one destination node and antenna information configured by nodes on the paths, and i is an integer greater than or equal to zero;

Supplementary note 2, the apparatus according to supplementary note 1, wherein each of the deployment scenarios further comprises: channel information for each link on the path.

Supplementary note 3, the apparatus according to supplementary note 1, wherein the antenna information includes: beam width of the antenna and/or directional information of the antenna.

Note 4 that the apparatus according to note 3 is configured to further include direction information including uplink direction information and/or downlink direction information, where the uplink direction information and/or the downlink direction information is a degree of deviation between the uplink direction and/or the downlink direction and the link line.

Supplementary note 5, the apparatus according to supplementary note 1, wherein the first processing unit includes:

the selection unit is used for calculating at least two objective functions of each deployment scheme in the ith generation of route node deployment scheme set, calculating a fitness function of each deployment scheme in the ith generation of route node deployment scheme set according to the at least two objective functions of each deployment scheme, and selecting the deployment scheme meeting a first preset condition to obtain a first deployment scheme set; wherein the fitness function is inversely proportional to the minimum Euclidean distance of the pareto solution in each deployment scenario and the (i-1) th generation deployment scenario set;

a crossing unit, configured to select a second predetermined number of groups of deployment plans from the first deployment plan set, where each group of deployment plans includes two deployment plans, and cross-process the two deployment plans in each group of deployment plans;

and a variation unit, configured to select a third predetermined number of deployment schemes from the first deployment scheme set after performing cross processing on the deployment schemes of the second predetermined number of groups, to obtain a second deployment scheme set, and perform variation processing on the deployment schemes in the second deployment scheme set, to obtain the i +1 th generation deployment scheme set.

Supplementary note 6, the apparatus according to supplementary note 5, wherein the selecting unit normalizes at least two (m) objective functions; mapping each deployment scheme and the (i-1) th generation deployment scheme set pareto solution into an m-dimensional space coordinate system established according to the at least two objective functions; calculating Euclidean distances in the coordinate system between each deployment scheme and each pareto solution in the i-1 generation deployment scheme set; selecting a minimum distance of the distances; and taking the reciprocal of the minimum distance as the fitness function.

Supplementary note 7, the apparatus according to supplementary note 5, wherein the selecting unit equally divides a difference between a maximum value and a minimum value of each objective function of each deployment scenario into N intervals, selects a first predetermined number of deployment scenarios in each interval, and/or selects a second predetermined number of deployment scenarios with a largest fitness function, and/or selects a third predetermined number of deployment scenarios with at least one objective function being an optimal value in the objective functions, to obtain the first deployment scenario set.

Supplementary note 8, the apparatus according to supplementary note 1, wherein, the apparatus further includes:

a second determining unit, configured to determine a capability of the network according to a currently transmitted data amount in the network deployed in the final deployment scheme and a data amount that can be transmitted by a link with a minimum transmission potential in the network deployed in the final deployment scheme; wherein the transmission potential of the link is determined according to the relation between the residual capacity and the occupied capacity of the link.

Supplementary note 9, a network capability determining apparatus, wherein the apparatus comprises:

Supplementary note 10, the apparatus according to supplementary note 9, wherein the occupied capacity is equal to a sum of a capacity occupied by data transmission and a capacity occupied by interference transmission, the capacity occupied by interference transmission indicating a capacity occupied by an interference link among capacities of links currently transmitting data; the transmission potential of the link is equal to the ratio of the remaining capacity of the link to the occupied capacity.

Supplementary note 11, a multi-hop wireless network deployment method, wherein the method comprises:

processing deployment schemes in an ith generation deployment scheme set obtained in advance by using a genetic algorithm to generate an (i + 1) th generation deployment scheme set; the deployment scheme set comprises a plurality of deployment schemes, and each deployment scheme comprises more than one path from at least one source node to at least one destination node and antenna information configured by nodes on the paths;

when a preset condition is met, determining the generated (i + 1) th generation deployment scheme set as a final deployment scheme set; and when the preset condition is not met, processing the deployment schemes in the (i + 1) th generation deployment scheme set until a final deployment scheme is obtained.

Supplementary note 12, the method according to supplementary note 11, wherein each of the deployment scenarios further comprises:

channel information for each link on the path.

Supplementary note 13, the method according to supplementary note 11, wherein the antenna information includes: beam width of the antenna and/or directional information of the antenna.

Reference numeral 14, the method according to reference numeral 13, wherein the direction information includes uplink direction information and/or downlink direction information, and the uplink direction information and/or the downlink direction information is a degree of deviation between the uplink direction and/or the downlink direction and the link line.

Supplementary note 15, the method according to supplementary note 11, wherein processing the deployment scenario in the ith generation deployment scenario set comprises: selecting, crossing and mutating, wherein the selecting comprises:

calculating at least two objective functions of each deployment scheme in the ith generation of routing node deployment scheme set;

a deployment scenario is selected that satisfies a first predetermined condition.

Supplementary note 16, the method according to supplementary note 15, wherein the selecting process further comprises:

calculating a fitness function of each deployment scheme in the ith generation of routing node deployment scheme set;

wherein the fitness function is inversely proportional to a minimum Euclidean distance of the pareto solution in each deployment scenario and the i-1 th generation deployment scenario set.

Supplementary note 17, the method according to supplementary note 16, wherein calculating the fitness function of each deployment scenario in the ith-generation routing node deployment scenario set comprises:

normalizing at least two (m) objective functions;

mapping each deployment scheme and the (i-1) th generation deployment scheme set pareto solution into an m-dimensional space coordinate system established according to the at least two objective functions;

calculating Euclidean distances in the coordinate system between each deployment scheme and each pareto solution in the i-1 generation deployment scheme set;

selecting a minimum distance of the distances;

and taking the reciprocal of the minimum distance as the fitness function.

Supplementary note 18, the method according to supplementary note 15 or 16, wherein selecting a deployment scenario that satisfies a first predetermined condition comprises:

equally dividing the difference between the maximum value and the minimum value of each objective function of each deployment plan into N intervals, selecting a first predetermined number of deployment plans within each interval, and/or,

selecting a second predetermined number of deployment scenarios with a maximum fitness function, and/or,

a third predetermined number of deployment scenarios are selected in which at least one of the objective functions is an optimal value.

Supplementary note 19, the method according to supplementary note 11, wherein the method further comprises:

determining the capacity of the network according to the data quantity currently transmitted in the network deployed in the final deployment scheme and the data quantity which can be transmitted by the link with the minimum transmission potential in the network deployed in the final deployment scheme; wherein the transmission potential of the link is determined according to the relation between the residual capacity and the occupied capacity of the link.

A method according to supplementary note 20, 19, wherein the occupied capacity is equal to the sum of the capacity occupied by data transmission and the capacity occupied by interference transmission, and the capacity occupied by interference transmission represents the capacity occupied by the interference link among the capacities of the links currently transmitting data; the transmission potential of the link is equal to the ratio of the remaining capacity of the link to the occupied capacity.

Claims

1. A multi-hop wireless network deployment apparatus, wherein the apparatus comprises:

a first determining unit, configured to determine the i +1 th generation deployment scenario set generated by the first processing unit as a final deployment scenario set when a predetermined condition is satisfied; when the preset conditions are not met, processing the deployment schemes in the (i + 1) th generation deployment scheme set by using the genetic algorithm until a final deployment scheme is obtained;

wherein the first processing unit comprises:

2. The apparatus of claim 1, wherein the each deployment scenario further comprises: channel information for each link on the path.

3. The apparatus of claim 1, wherein the antenna information comprises: beam width of the antenna and/or directional information of the antenna.

4. The apparatus according to claim 3, wherein the direction information includes uplink direction information and/or downlink direction information, and the uplink direction information and/or the downlink direction information is a deviation degree of an uplink direction and/or a downlink direction from a link line, and the deviation degree has a corresponding relationship with a direction angle of the uplink direction and/or the downlink direction and a deviation angle of the link line.

5. The apparatus according to claim 1, wherein the selection unit normalizes the m objective functions; mapping each deployment scheme and the (i-1) th generation deployment scheme set pareto solution into an m-dimensional space coordinate system established according to the at least two objective functions; calculating Euclidean distances in the coordinate system between each deployment scheme and each pareto solution in the i-1 generation deployment scheme set; selecting a minimum distance of the distances; and taking the reciprocal of the minimum distance as the fitness function, wherein m is an integer greater than or equal to 2.

6. The apparatus according to claim 1, wherein the selecting unit equally divides a difference between a maximum value and a minimum value of each objective function of each deployment scenario into N intervals, selects a first predetermined number of deployment scenarios in each interval, and/or selects a second predetermined number of deployment scenarios with a largest fitness function, and/or selects a third predetermined number of deployment scenarios with at least one objective function being an optimal value in the objective functions, to obtain the first deployment scenario set.

7. The apparatus of claim 1, wherein the apparatus further comprises:

8. The apparatus of claim 7, wherein the occupied capacity is equal to a sum of a capacity occupied by data transmission and a capacity occupied by interference transmission, the capacity occupied by interference transmission representing a capacity occupied by an interference link among capacities of links currently transmitting data; the transmission potential of the link is equal to the ratio of the remaining capacity of the link to the occupied capacity.