Information processing device, information processing method and program

Abstract

To provide an information processing device, an information processing method and a program capable of determining a base node device from many installed node devices.SOLUTION: An information processing device includes a clustering part for clustering a plurality of node devices into a plurality of clusters by a clustering algorithm on the basis of each position information of the plurality of node devices arranged according to a mesh type topology, a centrality index derivation part for deriving each medium centrality of the plurality of node devices, a selection part for selecting a plurality of clusters on the basis of the number of node devices included in each of the plurality of clusters, and a determination part for determining a node device having the highest medium centrality from the plurality of node devices included in each of the plurality of clusters selected by the selection part as a base node device.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] In recent years, as mobile communication speeds have increased, the use of microcells and nanocells has been considered in order to shorten the distance between mobile terminals and base stations. In the 6th Generation Mobile Communication System, the next generation of mobile communication, base stations will need to be installed every 50 to 100 meters. However, it is economically and geographically difficult to connect such densely-located base stations and base stations all with optical fibers.

[0003] Therefore, a network system that connects densely-arranged remote stations and base stations in a mesh pattern using multi-hop wireless is being considered. Since the capacity of the lines connecting these remote stations is expected to reach 100Gbps to 1Tbsp, it is expected that wireless systems using millimeter waves and terahertz waves, which are electromagnetic waves with frequencies from 30GHz to 1THz, will be used to achieve this transmission speed.

[0004] However, millimeter and terahertz waves are significantly attenuated by rain. In recent years, there have been many localized torrential rains in Japan, with the instantaneous rainfall intensity exceeding 90 mm / hr. During such torrential rains, even millimeter and terahertz radio links with transmission distances of only a few hundred meters may be cut off due to rain attenuation. As a result, there is a risk that mobile communications will be interrupted throughout the area covered by the center station.

[0005] As a means for solving such problems in wireless communication systems, Patent Document 1 discloses a technology for controlling the output of wireless devices according to rainfall conditions. In the wireless communication system disclosed in Patent Document 1, when the received power decreases due to attenuation caused by rainfall, fog, etc., the line is maintained by switching the transmission frequency and using adaptive modulation. However, this method significantly reduces the transmission speed of the wireless line, which may cause congestion in the mobile communication network.

[0006] Patent Document 2 also discloses a method for improving line availability during torrential rains by using network configuration technology. In the wireless communication system disclosed in Patent Document 2, a plurality of node devices and a base node device that transmits and receives information between these node devices are connected in a ring shape via a wireless device provided for each node device. In the wireless communication system, the node devices are arranged in the ring so that the line margin calculated by taking into account the propagation distance and rain attenuation is larger between node devices closer to the base node device than between node devices farther from the base node device, with reference to the line margin calculated by taking into account the propagation distance and rain attenuation. However, in order to maintain line margins even during torrential downpours exceeding 90 mm / hr, the distance of the wireless lines connecting the node devices needs to be several tens of meters. However, the radius of the area where the rainfall intensity exceeds 90 mm / hr during torrential downpours is approximately 1 km, so connecting this entire area with multi-hop wireless links of several tens of meters is difficult from both an economic and location standpoint.

[0007] Furthermore, Patent Document 3 discloses a method for selecting, as wireless communication routes, multiple routes that have low spatial correlation in quality degradation of the wireless communication environment, such as rain attenuation and radio wave interference, in a network in which node devices are arranged in a mesh topology and each node device is connected wirelessly. However, even with this method, if a sudden downpour with a rainfall intensity exceeding 90 mm / hr occurs in the area where the base node device is located, there is a risk that all wireless links connecting the base node device to the surrounding node devices will be cut off, and all remote stations will not be able to connect to the base station. [Prior art documents] [Patent documents]

[0008] [Patent Document 1] JP 2007-37029 A [Patent Document 2] Patent No. 3538044 [Patent Document 3] Patent No. 3693164 Summary of the Invention [Problem to be solved by the invention]

[0009] In the wireless communication system described above, only one base node device is installed in the area covered by the base node device. Therefore, if a sudden downpour occurs around the area where the base node device is installed, all wireless lines connecting the base node device and the surrounding node devices may be cut off, and all node devices may lose connection with the base node device. A sudden downpour is a type of torrential rain, and refers to a localized heavy rain that occurs suddenly and is difficult to accurately predict by weather forecasting. In addition, if a sudden downpour occurs in the area where the base node device is located, the transmission speed of the wireless lines connecting the base node device and the surrounding node devices may drop significantly, which may cause congestion.

[0010] If multiple base node devices are installed in the area to be covered, even if all wireless links connecting one base node device to the surrounding node devices are cut off, there is a possibility that links between the other base node devices and the surrounding node devices may be maintained, which is expected to improve line availability. However, it is not clear which of the many installed node devices should be set as a base node device in order to improve line availability. An object of the present invention is to provide an information processing apparatus, an information processing method, and a program capable of determining a base node apparatus from a large number of installed node apparatuses. [Means for solving the problem]

[0011] (1) One aspect of the present invention is an information processing device comprising: a clustering unit that clusters a plurality of node devices into a plurality of clusters using a clustering algorithm based on position information of each of the plurality of node devices arranged according to a mesh topology; a centrality index derivation unit that derives a betweenness centrality of each of the plurality of node devices; a selection unit that selects a plurality of clusters based on the number of node devices included in each of the plurality of clusters; and a determination unit that determines a node device with the highest betweenness centrality as a base node device from the plurality of node devices included in each of the plurality of clusters selected by the selection unit. (2) One aspect of the present invention is an information processing device as described in (1) above, wherein the determination unit selects one or more node devices to serve as cutting points from a plurality of node devices included in each of a plurality of the clusters, and determines the node device with the highest betweenness centrality from the selected one or more node devices as the base node device. (3) One aspect of the present invention is the information processing device according to (1) or (2) above, wherein the clustering unit performs clustering into a plurality of clusters using a DBSCAN algorithm. (4) One aspect of the present invention is an information processing device as described in (1) or (2) above, wherein the selection unit selects the cluster with the largest number of node devices and the cluster with the second largest number of node devices, or the cluster with the second largest number of node devices and the cluster with the third largest number of node devices. (5) One aspect of the present invention is an information processing device as described in (1) above, further comprising a distance derivation unit that derives a distance between multiple base node devices, and a determination unit that determines whether the distance between multiple base node devices is greater than or equal to a threshold, wherein the selection unit changes one of the selected clusters to an unselected cluster if the distance is less than the threshold, and the determination unit determines the node device with the highest betweenness centrality as the base node device from the multiple node devices included in the cluster changed by the selection unit. (6) One aspect of the present invention is the information processing device described in (5) above, in which the threshold value is determined based on an area of ​​sudden localized heavy rain. (7) One aspect of the present invention is an information processing device as described in (1) above, wherein the selection unit selects multiple clusters based on a ratio of the number of node devices included in each of the multiple clusters to the number of all node devices, and the determination unit acquires a node device with the highest betweenness centrality from the multiple node devices included in each of the multiple clusters selected by the selection unit, and determines the two node devices that are furthest away from the acquired multiple node devices as base node devices. (8) One aspect of the present invention is an information processing device as described in (7) above, wherein the determination unit selects one or more node devices to be cutting points from a plurality of node devices included in each of a plurality of the clusters, obtains a node device with the highest betweenness centrality from the selected one or more node devices, and determines two node devices that are furthest away from the obtained plurality of node devices as base node devices. (9) One aspect of the present invention is an information processing method executed by a computer, comprising the steps of: clustering a plurality of node devices arranged according to a mesh topology into a plurality of clusters using a clustering algorithm based on position information of each of the plurality of node devices; deriving a betweenness centrality of each of the plurality of node devices; selecting a plurality of clusters based on the number of node devices included in each of the plurality of clusters; and determining, from the plurality of node devices included in each of the selected plurality of clusters, a node device with the highest betweenness centrality as a base node device. (10) One aspect of the present invention is a program that causes a computer to execute the steps of: clustering a plurality of node devices arranged according to a mesh topology into a plurality of clusters using a clustering algorithm based on position information of each of the plurality of node devices; deriving a betweenness centrality of each of the plurality of node devices; selecting a plurality of clusters based on the number of node devices included in each of the plurality of clusters; and determining a node device with the highest betweenness centrality from the plurality of node devices included in each of the selected plurality of clusters as a base node device. Effect of the Invention

[0012] According to the present invention, an effect is obtained in that a base node device can be determined from a large number of installed node devices. [Brief description of the drawings]

[0013] [Figure 1] FIG. 1 is a diagram showing an example of a configuration diagram of a wireless communication system 1 created by an information processing device according to the present embodiment. [Diagram 2] FIG. 1 is a diagram illustrating an example of an information processing device 10 according to the present embodiment. [Diagram 3] 4 is a flowchart showing an example of the operation of the information processing device 10 according to the present embodiment. [Figure 4]FIG. 13 is a diagram showing an example of a configuration diagram of a wireless communication system 1a created by an information processing device according to a first modified example of an embodiment. [Diagram 5] FIG. 13 illustrates an example of an information processing device 10a according to a first modified example of the embodiment. [Figure 6] 13 is a flowchart showing an example of the operation of the information processing device 10a according to the first modified example of the embodiment. [Figure 7] FIG. 13 is a diagram showing an example of a configuration diagram of a wireless communication system 1aa created by an information processing device 10a according to a first modified example of the embodiment. [Figure 8] FIG. 11 is a diagram showing an example of a configuration diagram of a wireless communication system 1b created by an information processing device according to a second modified example of the embodiment. [Figure 9] FIG. 11 illustrates an example of an information processing device 10b according to a second modified example of the embodiment. [Figure 10] 13 is a flowchart showing an example of the operation of the information processing device 10b according to the second modification of the embodiment. [Figure 11] FIG. 11 is a diagram showing an example of a configuration diagram of a wireless communication system 1c created by an information processing device according to a third modified example of the embodiment. [Figure 12] FIG. 11 illustrates an example of an information processing device 10c according to a third modified example of the embodiment. [Figure 13] 13 is a flowchart showing an example of the operation of an information processing device 10c according to a third modified example of the embodiment. [Figure 14] 10A to 10C are diagrams for explaining an example of the effects of the present embodiment and first to third modified examples of the embodiment. [Figure 15] 10A to 10C are diagrams for explaining an example of the effects of the present embodiment and first to third modified examples of the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Next, an information processing apparatus, an information processing method, and a program according to the present embodiment will be described with reference to the drawings. The embodiment described below is merely an example, and the embodiment to which the present invention is applied is not limited to the following embodiment. In addition, in all the drawings for explaining the embodiments, the same reference numerals are used for the parts having the same functions, and the repeated explanation is omitted. In addition, "based on XX" in this application means "based on at least XX," and includes cases where it is based on other elements in addition to XX. Furthermore, "based on XX" is not limited to cases where XX is used directly, but also includes cases where it is based on XX that has been calculated or processed. "XX" is any element (for example, any information).

[0015] (Embodiment) Hereinafter, an embodiment of the present invention will be described with reference to the drawings. 1 is a diagram showing an example of a configuration diagram of a wireless communication system 1 created by an information processing device according to this embodiment. The wireless communication system 1 includes a monitor control station 100, a center station 200, a base station 300-1, a base station 300-2, and a plurality of remote stations. An example of an arrangement of the monitor control station 100, the center station 200, the base station 300-1, the base station 300-2, and the plurality of remote stations is a mesh topology.

[0016] The monitoring control station 100 monitors and controls the entire wireless communication system 1. The center station 200 connects the monitoring control station 100 and a network monitored and controlled by the monitoring control station 100. The base station 300-1 and the base station 300-2 transmit and receive information between the center station and a plurality of remote stations. The remote stations are arranged in a mesh pattern in the network. The base station 300-1 and the base station 300-2 are connected to each of the remote stations by a multi-hop wireless link.

[0017] Each of the base station 300-1, the base station 300-2, and the multiple remote stations includes a wireless device (not shown). Each of the base station 300-1, the base station 300-2, and the multiple remote stations can establish a bidirectional wireless link with the corresponding one of the base station 300-1, the base station 300-2, and the multiple remote stations by using the wireless device. In FIG. 1, the stations that can establish a bidirectional wireless link are connected by a line. The same applies to the following figures. For example, the base station 300-1, the base station 300-2, and the multiple remote stations perform wireless communication using millimeter waves and terahertz waves. By configuring in this way, the base station 300-1, the base station 300-2, and the multiple remote stations can be connected by a mesh-shaped network.

[0018] The base station 300-1 and the base station 300-2 included in the wireless communication system 1 are determined by the information processing device 10. Station position information, which is information associating identification information with position information for each of the monitor control station 100, the center station 200, and the multiple remote stations, is input to the information processing device 10. For example, a user determines the positions of the monitor control station 100, the center station 200, and the multiple remote stations. An example of the position information is represented by longitude and latitude, and coordinates on a display unit (not shown) displaying the wireless communication system 1. The information processing device 10 clusters each of the multiple remote stations into multiple clusters using a clustering algorithm based on the location information of the multiple remote stations. An example of the clustering algorithm is DBSCAN (Density-based spatial clustering of applications with noise). DBSCAN is a density-based clustering algorithm, and it is determined whether or not to make an area into a cluster based on the number of points contained within a radius. In this embodiment, as a result of clustering into a plurality of clusters by the DBSCAN algorithm, a plurality of remote stations are clustered into a first cluster, a second cluster, a third cluster, a fourth cluster, and a fifth cluster, as shown in Fig. 1. The number of remote stations included in each cluster is, in descending order, the first cluster, the second cluster, the third cluster, the fourth cluster, and the fifth cluster.

[0019] The information processing device 10 derives a centrality index of each of the multiple remote stations included in each of the first cluster, the second cluster, the third cluster, the fourth cluster, and the fifth cluster. An example of the centrality index is betweenness centrality. Below, the case where the centrality index is betweenness centrality will be described. The information processing device 10 selects a cluster that includes a number of remote stations equal to or greater than a threshold from among the first cluster, the second cluster, the third cluster, the fourth cluster, and the fifth cluster. The information processing device 10 determines the remote station with the highest betweenness centrality among the multiple remote stations included in the selected cluster as the base station. The information processing device 10 according to this embodiment will be described in detail below.

[0020] (Information processing device 10) 2 is a diagram showing an example of an information processing device 10 according to the present embodiment. The information processing device 10 is realized by a device such as a personal computer, a server, a smartphone, a tablet computer, or an industrial computer. The information processing device 10 includes an input unit 12, a clustering unit 14, a centrality index derivation unit 16, a selection unit 18, a determination unit 20, and a storage unit 22. The storage unit 22 is configured with a non-volatile storage medium such as a hard disk, a flash memory, etc. In the storage unit 22, programs executed by the information processing device 10 are stored.

[0021] The input unit 12 inputs information. As an example, the input unit 12 may have an operation unit such as a keyboard and a mouse. In this case, the input unit 12 inputs information according to an operation performed by a user on the operation unit. As another example, the input unit 12 may input information from an external device. The external device may be, for example, a portable storage medium. Station location information is input to the input unit 12. The input unit 12 stores the input station location information in the storage unit 22.

[0022] The clustering unit 14 acquires station location information stored in the storage unit 22. Based on the location information of the remote stations included in the acquired station location information, the clustering unit 14 clusters each of the remote stations into a plurality of clusters using a clustering algorithm. The clustering unit 14 creates cluster information that associates identification information of each of the plurality of clusters with identification information of one or more remote stations included in each of the plurality of clusters. For example, the clustering unit 14 clusters into a plurality of clusters using a DBSCAN algorithm.

[0023] The centrality index derivation unit 16 acquires cluster information from the clustering unit 14. Based on the acquired cluster information, the centrality index derivation unit 16 identifies identification information of multiple remote stations included in a cluster for each of the identification information of multiple clusters included in the cluster information. Based on the identified identification information of the multiple remote stations, the centrality index derivation unit 16 acquires location information of the multiple remote stations from the station location information in the storage unit 22. Based on the acquired location information of the multiple remote stations, the centrality index derivation unit 16 derives a centrality index for each of the multiple remote stations. For each of the multiple remote stations, the centrality index derivation unit 16 associates the identification information of the remote station with a centrality index. For example, the centrality index derivation unit 16 derives the betweenness centrality of each of the multiple remote stations included in each of the multiple clusters. The betweenness centrality is a numerical representation of the percentage of the node device of interest that is included in the shortest path between two other node devices, and the higher the value, the more likely it is to be involved in information distribution.

[0024] The selection unit 18 acquires cluster information from the clustering unit 14. The selection unit 18 selects identification information of a cluster including a number of remote stations equal to or greater than a threshold value from the identification information of multiple clusters included in the acquired cluster information. A cluster corresponding to the identification information of the cluster selected by the selection unit 18 is referred to as a selected cluster. Below, a description will be given of a case where the selection unit 18 selects multiple selected clusters.

[0025] The determination unit 20 acquires identification information of the multiple selected clusters from the selection unit 18. The determination unit 20 specifies identification information of multiple remote stations associated with each of the multiple selected clusters based on the acquired identification information of the multiple selected clusters, and acquires a betweenness centrality associated with each of the specified identification information of the multiple remote stations from the centrality index derivation unit 16. For each of the multiple selected clusters, the determination unit 20 determines, as a base station, a remote station corresponding to the identification information of a remote station having the highest associated betweenness centrality among the identification information of the multiple remote stations associated with the identification information of the selected cluster. For example, for each of a plurality of selected clusters, the determination unit 20 ranks the identification information of a plurality of remote stations associated with the selected cluster in order of the highest associated betweenness centrality, and determines the remote station corresponding to the identification information of the remote station with the highest betweenness centrality as the base station.

[0026] The input unit 12, the clustering unit 14, the centrality index derivation unit 16, the selection unit 18 and the determination unit 20 are realized, for example, by a hardware processor such as a CPU (Central Processing Unit) executing a computer program (software) stored in the memory unit 22.

[0027] Furthermore, some or all of these functional units may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by a combination of software and hardware. The computer program may be stored in advance in a storage device such as a HDD or flash memory, or may be stored on a removable storage medium such as a DVD (Digital Versatile Disc) or CD-ROM, and installed by inserting the storage medium into a drive device.

[0028] (Operation of information processing device 10) Fig. 3 is a flowchart showing an example of the operation of the information processing device 10 according to the present embodiment. A process of creating a network configuration diagram of the wireless communication system 1, specifically, a process of determining a base station from among a plurality of remote stations, will be described with reference to Fig. 3. (Step S1-1) Station location information, which associates the identification information and location information of the monitor control station 100, the center station 200, and each of the multiple remote stations, is input to the input unit 12. The input unit 12 stores the input station location information in the storage unit 22.

[0029] (Step S2-1) The clustering unit 14 acquires station location information stored in the storage unit 22. Based on the acquired station location information, the clustering unit 14 clusters each of the multiple remote stations into multiple clusters using the DBSCAN algorithm. The clustering unit 14 creates cluster information that associates identification information of each of the multiple clusters with identification information of one or more remote stations included in each of the multiple clusters.

[0030] (Step S3-1) The centrality index derivation unit 16 acquires cluster information from the clustering unit 14. The centrality index derivation unit 16 derives, for each of the multiple clusters, a betweenness centrality of each of the multiple remote stations included in the cluster based on the acquired cluster information. The centrality index derivation unit 16 associates, for each of the multiple remote stations, the identification information of the remote station with a centrality index.

[0031] (Step S4-1) The selection unit 18 acquires cluster information from the clustering unit 14. The selection unit 18 selects identification information of a selected cluster including a number of remote stations equal to or greater than a threshold from identification information of multiple clusters included in the acquired cluster information. For example, the selection unit 18 selects, as the selected clusters, a first cluster including the largest number of remote stations and a second cluster including the second largest number of remote stations.

[0032] (Step S5-1) The determination unit 20 acquires identification information of a plurality of selected clusters from the selection unit 18. For example, the determination unit 20 acquires identification information of a first cluster and identification information of a second cluster from the selection unit 18. The determination unit 20 identifies identification information of a plurality of remote stations associated with the identification information of each of the plurality of selected clusters from the centrality index derivation unit 16 based on the identification information of the plurality of selected clusters obtained, and obtains the betweenness centrality associated with the identification information of the plurality of remote stations identified. For example, the determination unit 20 identifies, from the centrality index derivation unit 16, identification information of a plurality of remote stations associated with the identification information of the first cluster, identifies identification information of a plurality of remote stations associated with the identification information of the second cluster, and obtains the betweenness centrality associated with each of the identification information of the identified plurality of remote stations. The determination unit 20 determines, for each of the multiple selected clusters, a remote station with the highest betweenness centrality among multiple remote stations included in the selected cluster as a base station. For example, the determination unit 20 determines, as a base station, a remote station corresponding to the identification information of a remote station with the highest associated betweenness centrality among the identification information of multiple remote stations associated with the identification information of a first cluster, and determines, as a base station, a remote station corresponding to the identification information of a remote station with the highest associated betweenness centrality among the identification information of multiple remote stations associated with the identification information of a second cluster.

[0033] 3, in step S4-1, the selection unit 18 selects the first cluster including the largest number of remote stations and the second cluster including the second largest number of remote stations, but the present invention is not limited to this example. For example, the selection unit 18 may select the second cluster including the second largest number of remote stations and the third cluster including the third largest number of remote stations, or may select a cluster other than the above.

[0034] In the above embodiment, the case has been described where the station position information in which the identification information and the position information of each of the monitoring control station 100, the center station 200, and the multiple remote stations are associated with each other is input to the input unit 12. However, the present invention is not limited to this example. For example, in the case where a base station is selected from the multiple remote stations, the input unit 12 may be configured to input the position information of the multiple remote stations.

[0035] According to the information processing device 10 of this embodiment, the information processing device 10 includes a clustering unit 14 that clusters a plurality of node devices (in the embodiment, remote stations) arranged according to a mesh topology into a plurality of clusters using a clustering algorithm based on the position information of each of the plurality of node devices, a centrality index derivation unit 16 that derives the betweenness centrality of each of the plurality of node devices, a selection unit 18 that selects a plurality of clusters based on the number of node devices included in each of the plurality of clusters, and a determination unit 20 that determines the node device with the highest betweenness centrality as a base node device (in the embodiment, the base station) from the plurality of node devices included in each of the plurality of clusters selected by the selection unit 18. In the information processing device 10, the selection unit 18 selects the cluster with the largest number of node devices and the cluster with the second largest number of node devices.

[0036] By configuring in this way, the remote station with the highest betweenness centrality among the selected clusters can be determined as the base station, and the base station can connect to each node included in the cluster with as few hops as possible. As a result, the number of remote stations that lose connection to the base station due to a sudden downpour can be reduced compared to when a remote station other than the one with the highest betweenness centrality is determined as the base station.

[0037] In the information processing device 10, the clustering unit 14 performs clustering into a plurality of clusters using the DBSCAN algorithm. With this configuration, a plurality of node devices can be classified into clusters by a density-based clustering algorithm based on points included within a radius of the cluster.

[0038] (First Modification of the Embodiment) 4 is a diagram showing an example of a configuration diagram of a wireless communication system 1a created by an information processing device according to the first modification of the embodiment. The wireless communication system 1a includes a monitor control station 100, a center station 200, a base station 300-1, a base station 300-2, and a plurality of remote stations. An example of an arrangement of the monitor control station 100, the center station 200, the base station 300-1, the base station 300-2, and the plurality of remote stations is a mesh topology.

[0039] The base station 300-1 and the base station 300-2 of the wireless communication system 1 are determined by the information processing device 10a. Station location information is input to the information processing device 10a. The information processing device 10a clusters each of the multiple remote stations into multiple clusters using a clustering algorithm based on the location information of the multiple remote stations included in the station location information. An example of the clustering algorithm is DBSCAN.

[0040] As shown in Fig. 4, multiple remote stations are clustered into a first cluster, a second cluster, a third cluster, a fourth cluster, and a fifth cluster. The number of remote stations included in each cluster is, in descending order, the first cluster, the second cluster, the third cluster, the fourth cluster, and the fifth cluster.

[0041] The information processing device 10a derives a centrality index of each of the multiple remote stations included in each of the first cluster, the second cluster, the third cluster, the fourth cluster, and the fifth cluster. One example of the centrality index is betweenness centrality. The information processing device 10a selects a cluster including a number of remote stations equal to or greater than a threshold from among the first, second, third, fourth, and fifth clusters. The information processing device 10a selects one or more remote stations to be cut points from among the multiple remote stations included in the selected cluster, and determines the remote station with the highest betweenness centrality from among the selected one or more remote stations as the base station. The cut point refers to a node device that would split the connected graph into two if it were removed. The information processing device 10a according to this embodiment will be described in detail below.

[0042] (Information processing device 10a) 5 is a diagram illustrating an example of an information processing device 10a according to a first modified example of an embodiment. The information processing device 10a is realized by a device such as a personal computer, a server, a smartphone, a tablet computer, or an industrial computer. The information processing device 10a includes an input unit 12, a clustering unit 14, a centrality index derivation unit 16, a selection unit 18, a determination unit 20a, and a storage unit 22.

[0043] The determination unit 20a acquires identification information of the multiple selected clusters from the selection unit 18. The determination unit 20a identifies identification information of multiple remote stations associated with each of the multiple selected clusters based on the acquired identification information of the multiple selected clusters, and acquires betweenness centrality associated with each of the identified identification information of the multiple remote stations from the centrality index derivation unit 16. For each of the multiple selected clusters, the determination unit 20a selects identification information corresponding to one or more remote stations that will be a disconnection point, based on the position information of each of the acquired identification information of the multiple remote stations, from the identification information of the multiple remote stations associated with the identification information of the selected cluster. The determination unit 20a determines, as the base station, the remote station corresponding to the identification information of the remote station with the highest associated betweenness centrality from the identification information of the selected one or more remote stations. For example, the determination unit 20a acquires location information associated with the identification information of a plurality of remote stations associated with the identification information of the selected cluster from the station location information, and selects identification information corresponding to one or more remote stations that will be disconnection points based on the location information associated with the acquired identification information of the plurality of remote stations. The determination unit 20a ranks the identification information of the plurality of remote stations associated with the selected cluster in descending order of associated betweenness centrality for each of the plurality of selected clusters, and determines the remote station corresponding to the identification information of the remote station with the highest betweenness centrality as the base station.

[0044] The determination unit 20a is realized by, for example, a hardware processor such as a CPU executing a computer program (software) stored in the storage unit 22. Furthermore, some or all of these functional units may be realized by hardware (including circuitry) such as an LSI, ASIC, FPGA, or GPU, or may be realized by a combination of software and hardware. The computer program may be stored in advance in a storage device such as a HDD or flash memory, or may be stored on a removable storage medium such as a DVD or CD-ROM and installed by inserting the storage medium into a drive device.

[0045] (Operation of the information processing device 10a) Fig. 6 is a flowchart showing an example of the operation of the information processing device 10a according to the first modified example of the embodiment. A process of creating a network configuration diagram of the wireless communication system 1a, specifically, a process of determining a base station from a plurality of remote stations, will be described with reference to Fig. 6. Steps S1-2 to S4-2 can be applied to steps S1-1 to S4-1 described with reference to Fig. 3, so the description will be omitted.

[0046] (Step S5-2) The determination unit 20a acquires identification information of a plurality of selected clusters from the selection unit 18. For example, the determination unit 20a acquires identification information of a first cluster and a second cluster from the selection unit 18. The determining unit 20a obtains from the centrality index derivation unit 16 the betweenness centrality associated with the identification information of each of the selected clusters, based on the obtained identification information of the selected clusters. For example, the determination unit 20a identifies, from the centrality index derivation unit 16, identification information of a plurality of remote stations associated with the identification information of the first cluster, identifies identification information of a plurality of remote stations associated with the identification information of the second cluster, and obtains the betweenness centrality associated with each of the identification information of the identified plurality of remote stations.

[0047] The determination unit 20a selects, for each of a plurality of selected clusters, identification information corresponding to one or more remote stations that will be the disconnection points from the identification information of a plurality of remote stations associated with the identification information of the selected cluster, and determines, from the identification information of the one or more selected remote stations, the remote station corresponding to the identification information of the remote station with the highest associated betweenness centrality as the base station.

[0048] For example, the determination unit 20a selects identification information of one or more remote stations to be disconnection points from the identification information of multiple remote stations associated with the identification information of the first cluster, and determines the remote station corresponding to the identification information of the remote station with the highest betweenness centrality from the identification information of the selected one or more remote stations as the base station. For example, the determination unit 20a selects identification information of one or more remote stations to be disconnection points from the identification information of multiple remote stations associated with the identification information of the second cluster, and determines the remote station corresponding to the identification information of the remote station with the highest betweenness centrality from the identification information of the selected one or more remote stations as the base station.

[0049] 6, in step S4-2, the selection unit 18 selects the first cluster including the largest number of remote stations and the second cluster including the second largest number of remote stations, but the present invention is not limited to this example. For example, the selection unit 18 may select the second cluster including the second largest number of remote stations and the third cluster including the third largest number of remote stations, or may select a cluster other than the above.

[0050] 7 is a diagram showing an example of a configuration diagram of a wireless communication system 1aa created by an information processing device 10a according to a first modified example of the embodiment. The wireless communication system 1aa includes a monitor control station 100, a center station 200, a base station 300-1, a base station 300-2, and a plurality of remote stations. An example of an arrangement of the monitor control station 100, the center station 200, the base station 300-1, the base station 300-2, and the plurality of remote stations is a mesh topology.

[0051] When the first and second base stations are installed in the first and second clusters, respectively, there is a high possibility that the first and second base stations will be installed near the border between the first and second clusters. In this case, when the first and second base stations are installed near the border between the first and second clusters, the distance between the first and second base stations becomes shorter, and there is a high possibility that the first and second base stations will be simultaneously included in a heavy rain area where the rainfall exceeds 90 mm / hr locally due to a sudden downpour. If the first and second base stations are simultaneously included in a heavy rain area, there is a risk that the lines from all remote stations to the center station will be cut off.

[0052] On the other hand, when the first base station and the second base station are installed in the second cluster and the third cluster, respectively, the first base station is installed near the boundary between the first cluster and the second cluster, and the second base station is installed near the boundary between the first cluster and the third cluster. When the first base station and the second base station are installed in the second cluster and the third cluster, respectively, the distance between the first base station and the second base station is longer than when the first base station and the second base station are installed in the first cluster and the second cluster, respectively. Therefore, when the first base station and the second base station are installed in the second cluster and the third cluster, respectively, even if heavy rain exceeding 90 mm / hr occurs locally, there is a high possibility that one of the two base stations will be outside the heavy rain area. This makes it possible to increase the ratio of remote stations that maintain a connection with the center station.

[0053] According to the information processing device 10a relating to the first variant of the embodiment, in the information processing device 10a, the determination unit 20a selects one or more node devices to be the disconnection points from a plurality of node devices (remote stations in the first variant of the embodiment) included in each of a plurality of clusters, and determines the node device with the highest betweenness centrality from the selected one or more node devices as the base node device (base station in the first variant of the embodiment). In the information processing device 10a, the selection unit 18 selects the cluster having the second largest number of node devices and the cluster having the third largest number of node devices. With this configuration, the remote station that is the cut point and has the highest betweenness centrality in the selected clusters can be set as the base station, so that the base station can reduce the possibility of the connection graph being split into two and can connect to each remote station in the cluster with as few hops as possible. As a result, the number of remote stations that lose their connection to the base station due to a sudden downpour can be reduced.

[0054] (Modification 2 of the embodiment) 8 is a diagram showing an example of a configuration diagram of a wireless communication system 1b created by an information processing device according to the second modification of the embodiment. The wireless communication system 1b includes a monitor control station 100, a center station 200, a base station 300-1, a base station 300-2, and a plurality of remote stations. An example of an arrangement of the monitor control station 100, the center station 200, the base station 300-1, the base station 300-2, and the plurality of remote stations is a mesh topology.

[0055] The base station 300-1 and the base station 300-2 of the wireless communication system 1b are determined by the information processing device 10b. Station location information is input to the information processing device 10b. Based on the location information of the multiple remote stations included in the station location information, the information processing device 10b clusters each of the multiple remote stations into multiple clusters by a clustering algorithm. An example of the clustering algorithm is DBSCAN.

[0056] As shown in Fig. 8, multiple remote stations are clustered into a first cluster, a second cluster, a third cluster, a fourth cluster, and a fifth cluster. The number of remote stations included in each cluster is, in descending order, the first cluster, the second cluster, the third cluster, the fourth cluster, and the fifth cluster.

[0057] The information processing device 10b derives a centrality index for each of the multiple remote stations included in each of the first, second, third, fourth, and fifth clusters. One example of the centrality index is betweenness centrality. The information processing device 10b selects a cluster including a number of remote stations equal to or greater than a threshold from among the first cluster, the second cluster, the third cluster, the fourth cluster, and the fifth cluster. The information processing device 10b selects one or more remote stations to be disconnection points from among the remote stations included in the selected cluster, and provisionally determines the remote station with the highest betweenness centrality from among the selected one or more remote stations as the base station.

[0058] The information processing device 10b derives the distance between the multiple base stations. The information processing device 10b judges whether the distance between the multiple base stations is equal to or less than a threshold. When the distance between the multiple base stations is equal to or less than the threshold, the information processing device 10b changes one of the selected clusters to an unselected cluster. From the multiple remote stations included in the changed cluster, the information processing device 10b determines the remote station with the highest betweenness centrality as the base station. When the distance between the multiple base stations is longer than the threshold, the information processing device 10b determines the remote station provisionally determined as the base station as the base station. Hereinafter, the information processing device 10b according to the modified example 2 of the embodiment will be described in detail.

[0059] (Information processing device 10b) 9 is a diagram illustrating an example of an information processing device 10b according to a second modification of the embodiment. The information processing device 10b is realized by a device such as a personal computer, a server, a smartphone, a tablet computer, or an industrial computer. The information processing device 10b includes an input unit 12, a clustering unit 14, a centrality index derivation unit 16, a selection unit 18b, a determination unit 20b, a storage unit 22, a distance derivation unit 24b, and a determination unit 26b.

[0060] The selection unit 18b acquires cluster information from the clustering unit 14. The selection unit 18b selects identification information of a plurality of selected clusters including a threshold or more of remote stations from the identification information of a plurality of clusters included in the acquired cluster information.

[0061] The determination unit 20b acquires identification information of the selected clusters from the selection unit 18b. The determination unit 20b specifies identification information of the remote stations associated with each of the selected clusters from the centrality index derivation unit 16 based on the acquired identification information of the selected clusters, and acquires betweenness centrality associated with each of the specified identification information of the remote stations. The determination unit 20b selects, for each of a plurality of selected clusters, identification information corresponding to one or more remote stations that will be the disconnection points from the identification information of a plurality of remote stations associated with the identification information of the selected cluster, and provisionally determines, from the identification information of the one or more selected remote stations, the remote station corresponding to the identification information of the remote station with the highest associated betweenness centrality as the base station.

[0062] The distance derivation unit 24b acquires identification information of the multiple base stations provisionally determined by the determination unit 20b. Based on the acquired identification information of the multiple base stations, the distance derivation unit 24b acquires position information of each of the multiple base stations from the station position information in the storage unit 22. Based on the acquired position information of each of the multiple base stations, the distance derivation unit 24b derives distances between the multiple base stations. The determination unit 26b acquires the derivation results of the distances between the multiple base stations from the distance derivation unit 24b. Based on the acquired derivation results of the distances between the multiple base stations, the determination unit 26b determines whether the distances are equal to or greater than a threshold value.

[0063] If the determining unit 26b determines that the distances between the plurality of base stations are equal to or greater than a threshold, the determining unit 20b determines the provisionally determined plurality of base stations as base stations. When the determining unit 26b determines that the distance is not equal to or greater than the threshold, the selecting unit 18b changes one of the selected clusters to a non-selected cluster. When the determining unit 26b determines that the distance between the base stations is not equal to or greater than the threshold, the deciding unit 20b acquires identification information of the changed cluster from the selecting unit 18b. Based on the acquired identification information of the cluster, the deciding unit 20b specifies identification information of the remote stations associated with the cluster from the centrality index derivation unit 16, and acquires betweenness centrality associated with each of the identified identification information of the remote stations. The deciding unit 20b provisionally determines, as a base station, a remote station corresponding to the identification information of the remote station with the highest betweenness centrality associated with the identification information of the cluster.

[0064] The selection unit 18b, the decision unit 20b, the distance derivation unit 24b, and the determination unit 26b are realized by, for example, a hardware processor such as a CPU executing a computer program (software) stored in the storage unit 22. Furthermore, some or all of these functional units may be realized by hardware (including circuitry) such as an LSI, ASIC, FPGA, or GPU, or may be realized by a combination of software and hardware. The computer program may be stored in advance in a storage device such as a HDD or flash memory, or may be stored on a removable storage medium such as a DVD or CD-ROM and installed by inserting the storage medium into a drive device.

[0065] (Operation of information processing device 10b) Fig. 10 is a flowchart showing an example of the operation of the information processing device 10b according to the modified example 2 of the embodiment. A process of creating a network configuration diagram of the wireless communication system 1b, specifically, a process of determining a base station from among a plurality of remote stations, will be described with reference to Fig. 10. Steps S1-3 to S3-3 can be implemented by steps S1-1 to S3-1 described with reference to FIG. 3, and therefore a description thereof will be omitted. (Step S4-3) The selection unit 18b acquires cluster information from the clustering unit 14. The selection unit 18b selects identification information of a selected cluster including a number of remote stations equal to or greater than a threshold from identification information of multiple clusters included in the acquired cluster information. For example, the selection unit 18b selects the second cluster including the second largest number of remote stations and the third cluster including the third largest number of remote stations.

[0066] (Step S5-3) The determination unit 20b acquires identification information of the multiple selected clusters from the selection unit 18b. For example, the determination unit 20b acquires identification information of the second cluster and the third cluster from the selection unit 18b. The determination unit 20b acquires a betweenness centrality associated with each of the identification information of the multiple selected clusters from the centrality index derivation unit 16 based on the acquired identification information of the multiple selected clusters. For example, the determination unit 20b specifies identification information of a plurality of remote stations associated with the identification information of the second cluster from the centrality index derivation unit 16, and obtains a betweenness centrality associated with each of the specified identification information of the plurality of remote stations. The determination unit 20b specifies identification information of a plurality of remote stations associated with the identification information of the third cluster from the centrality index derivation unit 16, and obtains a betweenness centrality associated with each of the specified identification information of the plurality of remote stations.

[0067] The determination unit 20b selects, for each of a plurality of selected clusters, identification information corresponding to one or more remote stations that will be the disconnection points from the identification information of a plurality of remote stations associated with the identification information of the selected cluster, and provisionally determines, from the identification information of the one or more selected remote stations, the remote station corresponding to the identification information of the remote station with the highest associated betweenness centrality as the base station.

[0068] For example, the determination unit 20b selects one or more remote stations to be disconnection points from among the multiple remote stations included in the second cluster, and determines the remote station with the highest betweenness centrality from the selected one or more remote stations as the base station. For example, the determination unit 20b selects one or more remote stations to be disconnection points from among the multiple remote stations included in the third cluster, and determines the remote station with the highest betweenness centrality from the selected one or more remote stations as the base station.

[0069] (Step S6-3) The distance derivation unit 24b acquires identification information of the multiple base stations provisionally determined by the determination unit 20b. Based on the acquired identification information of the multiple base stations, the distance derivation unit 24b acquires position information of each of the multiple base stations from the storage unit 22. Based on the acquired position information of each of the multiple base stations, the distance derivation unit 24b derives distances between the multiple base stations. For example, the distance derivation unit 24b acquires identification information of a base station provisionally determined from a plurality of remote stations included in the second cluster and a base station provisionally determined from a plurality of remote stations included in the third cluster. Based on the acquired identification information of the two base stations, the distance derivation unit 24b acquires position information of each of the two base stations from the storage unit 22. Based on the acquired position information of each of the two base stations, the distance derivation unit 24b derives the distance between the two base stations. The determination unit 26b acquires the derivation results of the distances between the multiple base stations from the distance derivation unit 24b. Based on the acquired derivation results of the distances between the multiple base stations, the determination unit 26b determines whether the distances are equal to or greater than a threshold value. For example, the determination unit 26b acquires the derivation result of the distance between two base stations from the distance derivation unit 24b. Based on the acquired derivation result of the distance between the two base stations, the determination unit 26b determines whether the distance is equal to or greater than a threshold value.

[0070] (Step S7-3) If the determining unit 26b determines that the distances between the plurality of base stations are equal to or greater than a threshold, the determining unit 20b determines the provisionally determined plurality of base stations as base stations. (Step S8-3) When the determining unit 26b determines that there is a plurality of base stations whose distances with each other are less than a threshold, the selecting unit 18b changes one of the plurality of selected clusters to an unselected cluster. For example, when the determining unit 26b determines that the distance between the two base stations is less than the threshold, the selecting unit 18b changes the cluster having the fewer number of remote stations among the two selected clusters to the fourth cluster having the next largest number of clusters among the unselected clusters. Then, the process proceeds to step S5-3.

[0071] In step S5-3, the determination unit 20b acquires information indicating the fourth cluster from the selection unit 18b. The determination unit 20b acquires betweenness centralities of the multiple remote stations included in the fourth cluster from the centrality index derivation unit 16 based on the acquired information indicating the fourth cluster. The determination unit 20b selects, for the fourth cluster, one or more remote stations to serve as disconnection points from among the multiple remote stations included in the fourth cluster, and provisionally determines, from the selected one or more remote stations, the remote station with the highest betweenness centrality as the base station.

[0072] In the second modification of the embodiment, when the distance between the multiple base stations is not equal to or greater than a threshold, the determination unit 20b provisionally determines, as a base station, a remote station corresponding to the identification information of the remote station with the highest associated betweenness centrality among the identification information of the multiple remote stations associated with the identification information of the cluster based on the identification information of the cluster acquired from the selection unit 18b, but this is not limited to the example. For example, the determination unit 20b may determine without provisionally determining again, or a threshold value for the number of provisional determinations may be set. By configuring in this way, it is possible to prevent the process of provisionally determining a base station from being repeated.

[0073] According to the information processing device 10b according to the second modification of the embodiment, the information processing device 10b further includes a distance derivation unit 24b that derives distances between a plurality of base node devices (base stations in the modification of the embodiment) in the information processing device 10, and a determination unit 26b that determines whether the distances between the plurality of base node devices are equal to or greater than a threshold. If the distance is not equal to or greater than the threshold, the selection unit 18b changes one of the selected clusters to a cluster that has not been selected. The determination unit 20b determines, as a base station, a remote station with the highest betweenness centrality from among the remote stations included in the cluster changed by the selection unit 18b. In the information processing device 10b, the threshold value is determined based on an area of ​​sudden localized heavy rain.

[0074] By configuring in this way, even if the distance between multiple base stations falls below the threshold, a new base station can be selected, so the distance between the base stations can be made equal to or greater than the threshold. This significantly reduces the probability that all of the multiple base stations are included in the heavy rain area of ​​a sudden downpour. Even if a torrential downpour of more than 90 mm / hr occurs locally, the proportion of remote stations that maintain a connection with the center station can be increased.

[0075] For example, when the distance between multiple base stations falls below a separation distance set based on the radius of a heavy rain area in a sudden downpour, a new base station can be determined, so that the distance between the base stations can be set to be equal to or greater than the separation distance. One example of the separation distance is the diameter of an area where an instantaneous rainfall intensity occurs that exceeds the receiving power margin of a wireless line having an average length among wireless lines connecting remote stations. For example, during the torrential rains that hit Tokyo on August 13, 2020, the diameter of the area where the one-minute instantaneous rainfall intensity exceeded 90 mm / hr was approximately 1 km. This separation distance can be set based on a meteorological database of instantaneous rainfall intensity in the area where the wireless network will be installed. The meteorological database of instantaneous rainfall intensity is provided by the Data Integration and Analysis System (DIAS), among others.

[0076] (Modification 3 of the embodiment) 11 is a diagram showing an example of a configuration diagram of a wireless communication system 1c created by an information processing device according to a third modification of the embodiment. The wireless communication system 1c includes a monitor control station 100, a center station 200, a base station 300-1, a base station 300-2, and a plurality of remote stations. An example of an arrangement of the monitor control station 100, the center station 200, the base station 300-1, the base station 300-2, and the plurality of remote stations is a mesh topology.

[0077] The base station 300-1 and the base station 300-2 of the wireless communication system 1c are determined by the information processing device 10c. Station location information is input to the information processing device 10c. Based on the location information of the multiple remote stations included in the station location information, the information processing device 10c clusters each of the multiple remote stations into multiple clusters by a clustering algorithm. An example of the clustering algorithm is DBSCAN.

[0078] As shown in Fig. 11, multiple remote stations are clustered into a first cluster, a second cluster, a third cluster, a fourth cluster, and a fifth cluster. The number of remote stations included in each cluster is, in descending order, the first cluster, the second cluster, the third cluster, the fourth cluster, and the fifth cluster.

[0079] The information processing device 10c derives a centrality index of each of the multiple remote stations included in each of the first cluster, the second cluster, the third cluster, the fourth cluster, and the fifth cluster. One example of the centrality index is betweenness centrality. The information processing device 10c selects all clusters including the number of remote stations that is equal to or greater than a threshold from the first cluster, the second cluster, the third cluster, the fourth cluster, and the fifth cluster. An example of the threshold is 10%, and the threshold can be changed appropriately based on at least one of the number of remote stations included in the wireless communication system 1c and the number of clusters clustered by the DBSCAN algorithm. For example, the threshold may be 5% to 20%, or 7.5% to 15%.

[0080] For each of the selected clusters, the information processing device 10c selects one or more remote stations to serve as disconnection points from the multiple remote stations included in the cluster, selects the remote station with the highest betweenness centrality from the selected one or more remote stations, and sets the selected remote station as a temporary base station. The information processing device 10c determines the two most distant temporary base stations as base stations among the selected temporary base stations. The information processing device 10c returns the temporary base stations other than the temporary base stations determined as base stations to remote stations. The information processing device 10c according to the third modification of the embodiment will be described in detail below.

[0081] (Information processing device 10c) 12 is a diagram illustrating an example of an information processing device 10c according to a third modification of the embodiment. The information processing device 10c is realized by a device such as a personal computer, a server, a smartphone, a tablet computer, or an industrial computer. The information processing device 10c includes an input unit 12, a clustering unit 14, a centrality index derivation unit 16, a selection unit 18c, a determination unit 20c, a storage unit 22, and a distance derivation unit 24c.

[0082] The selection unit 18c acquires cluster information from the clustering unit 14. The selection unit 18b selects, from the identification information of multiple clusters included in the acquired cluster information, all identification information of clusters including a number of remote stations whose ratio to the total number of remote stations is equal to or greater than a threshold value.

[0083] The determination unit 20c acquires identification information of the selected clusters from the selection unit 18c. Based on the acquired identification information of the selected clusters, the determination unit 20c identifies identification information of the remote stations associated with each of the selected clusters from the centrality index derivation unit 16, and acquires betweenness centrality associated with each of the identified identification information of the remote stations. The determination unit 20c selects, for each of a plurality of selected clusters, a remote station corresponding to the identification information of the remote station with the highest associated betweenness centrality among the identification information of a plurality of remote stations associated with the identification information of the selected cluster, as a provisional base station.

[0084] The distance derivation unit 24b acquires identification information of the multiple temporary base stations selected by the determination unit 20b. Based on the acquired identification information of the multiple temporary base stations, the distance derivation unit 24b acquires position information of each of the multiple temporary base stations from the station position information in the storage unit 22. Based on the acquired position information of each of the multiple temporary base stations, the distance derivation unit 24b derives distances between the multiple temporary base stations. The determination unit 20c acquires the derivation results of the distances between the plurality of temporary base stations from the distance derivation unit 24b. The determination unit 20c determines the two temporary base stations that are the furthest apart as base stations based on the acquired derivation results of the distances between the plurality of temporary base stations. The determination unit 20c returns the temporary base stations other than the temporary base station determined as the base station to the remote station.

[0085] The selection unit 18c, the determination unit 20c, and the distance derivation unit 24c are realized by, for example, a hardware processor such as a CPU executing a computer program (software) stored in the storage unit 22. Furthermore, some or all of these functional units may be realized by hardware (including circuitry) such as an LSI, ASIC, FPGA, or GPU, or may be realized by a combination of software and hardware. The computer program may be stored in advance in a storage device such as a HDD or flash memory, or may be stored on a removable storage medium such as a DVD or CD-ROM and installed by inserting the storage medium into a drive device.

[0086] (Operation of information processing device 10c) Fig. 13 is a flowchart showing an example of the operation of the information processing device 10c according to the third modification of the embodiment. A process of creating a network configuration diagram of the wireless communication system 1c, specifically, a process of determining a base station from among a plurality of remote stations, will be described with reference to Fig. 13. Steps S1-4 to S3-4 can be implemented by steps S1-1 to S3-1 described with reference to FIG. 3, and therefore a description thereof will be omitted. (Step S4-4) The selection unit 18c acquires cluster information from the clustering unit 14. The selection unit 18b selects, from the identification information of multiple clusters included in the acquired cluster information, all identification information of clusters including a number of remote stations whose ratio to the total number of remote stations is equal to or greater than a threshold value.

[0087] (Step S5-4) The determination unit 20c acquires identification information of the selected clusters from the selection unit 18c. Based on the acquired identification information of the selected clusters, the determination unit 20c identifies identification information of the remote stations associated with each of the selected clusters from the centrality index derivation unit 16, and acquires betweenness centrality associated with each of the identified identification information of the remote stations. The determination unit 20c selects, for each of a plurality of selected clusters, a remote station corresponding to the identification information of the remote station with the highest associated betweenness centrality among the identification information of a plurality of remote stations associated with the identification information of the selected cluster, as a provisional base station. The distance derivation unit 24b acquires identification information of the multiple temporary base stations selected by the determination unit 20b. Based on the acquired identification information of the multiple temporary base stations, the distance derivation unit 24b acquires position information of each of the multiple temporary base stations from the station position information in the storage unit 22. Based on the acquired position information of each of the multiple temporary base stations, the distance derivation unit 24b derives distances between the multiple temporary base stations. The determination unit 20c acquires the derivation results of the distances between the plurality of temporary base stations from the distance derivation unit 24b. The determination unit 20c determines the two temporary base stations that are the furthest apart as base stations based on the acquired derivation results of the distances between the plurality of temporary base stations. The determination unit 20c returns the temporary base stations other than the temporary base station determined as the base station to the remote station.

[0088] In the information processing device 10c, the determination unit 20c may select one or more remote stations to be the disconnection points from a plurality of remote stations included in each of a plurality of clusters, obtain a remote station with the highest betweenness centrality from the selected one or more remote stations, and determine the two remote stations that are the furthest apart from the obtained plurality of remote stations as the base station.

[0089] According to the information processing device 10c according to the third modification of the embodiment, in the information processing device 10c, the selection unit 18c selects multiple clusters based on the ratio of the number of node devices (remote stations in the embodiment) included in each of the multiple clusters to all node devices. The determination unit 20c acquires a node device with the highest betweenness centrality from the multiple node devices included in each of the multiple clusters selected by the selection unit 18c, and determines two node devices that are the furthest away from the acquired multiple node devices as base node devices (base stations in the embodiment). In the information processing device 10c, the determination unit 20c selects one or more node devices to serve as cutting points from a plurality of node devices included in each of a plurality of clusters, acquires a node device with the highest betweenness centrality from the selected one or more node devices, and determines the two node devices that are furthest away from the acquired plurality of node devices as base node devices.

[0090] With this configuration, the two remote stations that are the furthest apart among multiple remote stations can be determined as base stations, which significantly reduces the probability that both base stations are included in the heavy rain area expected in a sudden downpour. Even if heavy rain of more than 90 mm / hr occurs locally, the proportion of remote stations that maintain connection with the center station can be increased.

[0091] 14 and 15 are diagrams for explaining an example of the effects of this embodiment and modified examples 1 to 3 of the embodiment. FIG. 14 shows the ratio of remote stations connected to a center station during a torrential downpour in a mesh network connected by 300 GHz band wireless in the center of Tokyo. FIG. 15 is a diagram showing an example of a mesh network. Data on instantaneous rainfall intensity during a torrential downpour was used from 16:30 to 16:59 on August 13, 2020.

[0092] When one remote station was selected as the base station, the rate at which the remote station was connected to the center station was 40.0% (1). When two arbitrary remote stations were selected as base stations, the rate at which the remote station was connected to the center station was 72.0%. When two arbitrary remote stations were selected as base stations, the rate was significantly higher than when one remote station was selected as the base station (2). When two remote stations with the highest betweenness centrality but that were not cut points were selected from the remote stations included in the second and third clusters, the rate at which the remote station was connected to the center station was 75.7% (3).

[0093] When a remote station that is an arbitrary disconnection point is selected from the remote stations included in the second and third clusters, the rate at which the remote station is connected to the center station is 73.6% (4). When two remote stations that are disconnection points and have the highest betweenness centrality are selected from the remote stations included in the first and second clusters, the rate at which the remote station is connected to the center station is 73.5% (5). When two remote stations that are disconnection points and have the highest betweenness centrality are selected from the remote stations included in the second and third clusters, the rate at which the remote station is connected to the center station is 77.2% (6). When two remote stations that are disconnection points and have the highest betweenness centrality and whose distance is greater than or equal to a threshold are selected from the remote stations included in the second and third clusters, the rate at which the remote station is connected to the center station is 77.6% (7).

[0094] When two remote stations that are disconnection points and have the highest betweenness centrality are selected from the remote stations included in the second and third clusters, the ratio of remote stations being connected to the center station is higher than when two remote stations that have the highest betweenness centrality but are not disconnection points are selected from the remote stations included in the second and third clusters, or when a remote station that is an arbitrary disconnection point is selected from the remote stations included in the second and third clusters. It can be seen that the ratio of remote stations connected to the center station is higher when the cluster with the second and third largest number of remote stations is selected than when the cluster with the first and second largest number of remote stations is selected.

[0095] The above describes in detail the embodiments and modified examples of the present invention with reference to the drawings. However, the specific configuration is not limited to this embodiment, and also includes design changes and the like that do not deviate from the gist of the present invention. For example, a computer program for implementing the functions of each of the above-mentioned devices may be recorded on a computer-readable recording medium, and the program recorded on the recording medium may be read into a computer system and executed. Note that the "computer system" referred to here may include hardware such as an OS and peripheral devices.

[0096] In addition, the term "computer-readable recording medium" refers to writable non-volatile memory such as a flexible disk, an optical magnetic disk, a ROM, or a flash memory, a portable medium such as a DVD (Digital Versatile Disc), or a storage device such as a hard disk built into a computer system. Furthermore, the term "computer-readable recording medium" includes devices that retain a program for a certain period of time, such as volatile memory (e.g., DRAM (Dynamic Random Access Memory)) within a computer system that serves as a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line.

[0097] The program may be transmitted from a computer system in which the program is stored in a storage device or the like to another computer system via a transmission medium, or by a transmission wave in the transmission medium. Here, the "transmission medium" that transmits the program refers to a medium that has a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may be for realizing part of the functions described above. Furthermore, the above-mentioned functions may be realized in combination with a program already recorded in the computer system, that is, a so-called differential file (differential program). [Explanation of symbols]

[0098] 1, 1a, 1aa, 1b, 1c... wireless communication system, 10, 10a, 10c... information processing device, 12... input unit, 14... clustering unit, 16... centrality index derivation unit, 18, 18b, 18c... selection unit, 20, 20a, 20b, 20c... decision unit, 22... storage unit, 24b, 24c... distance derivation unit, 26b... judgment unit, 100... monitor and control station, 200... center station, 300-1, 300-2... base station

Claims

1. a clustering unit that performs clustering of a plurality of node devices into a plurality of clusters by a clustering algorithm based on position information of each of the plurality of node devices arranged according to a mesh topology; a centrality index derivation unit that derives a betweenness centrality of each of a plurality of node devices; A selection unit that selects a plurality of clusters based on the number of node devices included in each of the plurality of clusters; a determination unit that determines a node device having the highest betweenness centrality as a base node device from among a plurality of node devices included in each of the plurality of clusters selected by the selection unit; An information processing device comprising:

2. The information processing device according to claim 1, wherein the determination unit selects one or more node devices to be disconnection points from a plurality of node devices included in each of a plurality of the clusters, and determines a node device having the highest betweenness centrality from the selected one or more node devices as a base node device.

3. The information processing device according to claim 1 , wherein the clustering unit performs clustering into a plurality of clusters using a DBSCAN algorithm.

4. 3 . The information processing device according to claim 1 , wherein the selection unit selects the cluster having the largest number of node devices and the cluster having the second largest number of node devices, or the cluster having the second largest number of node devices and the cluster having the third largest number of node devices.

5. a distance derivation unit that derives distances between a plurality of base node devices; a determination unit that determines whether the distance between a plurality of base node devices is equal to or greater than a threshold; Further equipped with When the distance is not equal to or greater than a threshold, the selection unit changes one of the selected clusters to an unselected cluster; and The information processing device according to claim 1 , wherein the determination unit determines, as a base node device, a node device having a highest betweenness centrality from among the plurality of node devices included in the cluster changed by the selection unit.

6. The information processing device according to claim 5 , wherein the threshold value is determined based on an area of ​​sudden localized heavy rain.

7. the selection unit selects a plurality of clusters based on a ratio of the number of node devices included in each of the plurality of clusters to all of the node devices; 2. The information processing device according to claim 1, wherein the determination unit acquires a node device with the highest betweenness centrality from a plurality of node devices included in each of the plurality of clusters selected by the selection unit, and determines two node devices that are furthest away from the acquired plurality of node devices as base node devices.

8. The information processing device according to claim 7, wherein the determination unit selects one or more node devices to be cutting points from a plurality of node devices included in each of a plurality of the clusters, obtains a node device with the highest betweenness centrality from the selected one or more node devices, and determines two node devices that are furthest away from the obtained plurality of node devices as base node devices.

9. A step of clustering the plurality of node devices into a plurality of clusters by a clustering algorithm based on position information of each of the plurality of node devices arranged according to a mesh topology; A step of deriving a betweenness centrality of each of a plurality of node devices; selecting a plurality of clusters based on the number of node devices included in each of the plurality of clusters; determining a node device having the highest betweenness centrality as a base node device from among a plurality of node devices included in each of the selected plurality of clusters; 2. A computer-implemented information processing method comprising:

10. On the computer, A step of clustering the plurality of node devices into a plurality of clusters by a clustering algorithm based on position information of each of the plurality of node devices arranged according to a mesh topology; A step of deriving a betweenness centrality of each of a plurality of node devices; selecting a plurality of clusters based on the number of node devices included in each of the plurality of clusters; determining a node device having the highest betweenness centrality as a base node device from among a plurality of node devices included in each of the selected plurality of clusters; A program to execute.

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