Self-organizing communication method and device for intelligent devices in urban simulation field

Abstract

The present invention proposes a self-organizing communication method and device for intelligent devices in the field of urban simulation, including: collecting location data of Internet of Things nodes and access points, clustering and dividing into different groups; if there is no access point in the group, selecting a most central Internet of Things node from the group as a management node; if there is an access point in the group, selecting a management node based on the distance between the Internet of Things node and other Internet of Things nodes and the distance with the belonging access point; if there are multiple access points in the group, each access point and the Internet of Things node selected as the belonging access point are grouped into the same group. The present invention can improve communication efficiency, enhance network stability, and reduce energy consumption.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban simulation, and in particular relates to a self-organizing communication method and device for intelligent equipment in the field of urban simulation. Background Art

[0002] In the field of urban simulation, the widespread use of smart devices as IoT nodes has greatly improved the efficiency and accuracy of information collection. However, the communication environment of these devices is complex and changeable, involving various terrains and buildings such as high-rise buildings, underground pipe networks, and parks and green spaces. Traditional wireless communication methods (such as fixed base station communication) often cannot meet all needs. Therefore, a common practice at present is to achieve communication through self-organizing networks between devices (such as wireless sensor networks).

[0003] This self-organizing network usually relies on the device's geographic location information and wireless signal strength to automatically establish and optimize communication links. Devices communicate with each other, share location information, and select the best communication path based on signal quality. However, this conventional approach has the following technical problems:

[0004] 1. Low communication efficiency: In complex environments, signal interference between devices is serious, resulting in unstable communication quality, which in turn affects the efficiency and accuracy of data transmission.

[0005] 2. Poor network stability: Frequent self-organization processes between devices will increase the instability of the network, especially when the device moves or the environment changes, the network topology is prone to major changes.

[0006] 3. High energy consumption: Frequent self-organizing communication and signal detection consume a lot of energy and reduce the endurance of the device. Summary of the invention

[0007] The present invention proposes a self-organizing communication method and device for intelligent devices in the field of urban simulation, which can improve communication efficiency, enhance network stability and reduce energy consumption.

[0008] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0009] A self-organizing communication method for intelligent devices in the field of urban simulation, comprising:

[0010] S1, collect the location data of IoT nodes in the urban simulation field and the location data of wireless signal access points, and convert them as input data for clustering algorithm;

[0011] S2, performing K-means++ algorithm on the input data to obtain multiple cluster centers;

[0012] S3, dividing all IoT nodes and access points into different groups according to the cluster centers;

[0013] S4. If there is no access point in the group, execute step S5; if there is one access point in the group, execute step S6; if there are multiple access points in the group, execute step S7;

[0014] S5. Select a most central IoT node from the group as a management node. Each IoT node in the group selects the nearest access point from the entire network access points outside the group as the home access point. The management node, as the center of the group, coordinates the communication between IoT nodes, assumes the role of data aggregation and forwarding, enhances the information flow within the group, and optimizes the communication path.

[0015] S6, each IoT node in the group selects the one access point as the home access point, and selects a management node based on the distance between the IoT node and other IoT nodes and the distance between the IoT node and the home access point; the management node plays a relay role between the IoT node and the access point, and acts as a transfer station to realize communication between the IoT node and the access point;

[0016] S7. Each IoT node in the group selects the closest access point as the home access point; each access point and the IoT node that selects it as the home access point are grouped into the same group, thereby forming a subgroup.

[0017] Furthermore, step S5 specifically includes:

[0018] S51, calculating the central node of the group: for each IoT node in the group, respectively calculate the distance between it and other IoT nodes and sum them up as the centrality score of the IoT node, and select the IoT node with the highest centrality score as the central node of the group.

[0019] S52, selecting a home access point: for each IoT node in the group, calculate the distance between it and the global access point, and select the nearest access point as its home access point according to the distance; the global access point is the access point in other groups.

[0020] Furthermore, the method for selecting the management node in step S6 includes:

[0021] S601. For each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes;

[0022] S602, calculating the distance between each IoT node and the home access point;

[0023] S603, calculating a weighted sum of the distances of each IoT node to other IoT nodes and the distance to the home access point with the same weight as the comprehensive distance of the IoT node;

[0024] S604: Select the IoT node with the smallest comprehensive distance as the management node of the group.

[0025] Furthermore, the method for selecting the management node in step S6 includes:

[0026] S611. For each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes, and set the weight to 1;

[0027] S612, calculating the distance between each IoT node and the home access point, and setting the weight to 10;

[0028] S613, calculating a weighted sum of the distances and weights of each IoT node with other IoT nodes, and the distances and weights of each IoT node with the home access point, as a comprehensive distance of the IoT node;

[0029] S614: Select the IoT node with the smallest comprehensive distance as the management node of the group.

[0030] Furthermore, the method for selecting the management node in step S6 includes:

[0031] S621. For each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes;

[0032] S622, selecting the top N IoT nodes in terms of distance from small to large as candidate nodes;

[0033] S623: Select, from the candidate nodes, a candidate node with the shortest distance to the home access point as a management node of the group.

[0034] Another aspect of the present invention also provides a self-organizing communication device for intelligent devices in the field of urban simulation, comprising:

[0035] Collection module: collects the location data of IoT nodes in the urban simulation field and the location data of wireless signal access points, and converts them into input data for clustering algorithms;

[0036] Cluster center module: performing K-means++ algorithm on the input data to obtain multiple cluster centers;

[0037] Group module: divides all IoT nodes and access points into different groups according to the cluster centers;

[0038] Judgment module: if there is no access point in the group, enter the no access point module; if there is one access point in the group, enter the one access point module; if there are multiple access points in the group, enter the multiple access point module;

[0039] No access point module: select the most central IoT node from the group as the management node, and each IoT node in the group selects the nearest access point from the entire network access points outside the group as the home access point; the management node acts as the center of the group, coordinates the communication between IoT nodes, assumes the role of data aggregation and forwarding, enhances the information flow within the group, and optimizes the communication path;

[0040] An access point module: each IoT node in the group selects the one access point as the home access point, and selects a management node based on the distance between the IoT node and other IoT nodes and the distance between the IoT node and the home access point; the management node plays a relay role between the IoT node and the access point, and acts as a transfer station to realize communication between the IoT node and the access point;

[0041] Multi-access point module: Each IoT node in the group selects the closest access point as its home access point; each access point and the IoT node that selects it as its home access point are grouped into the same group, thereby forming a subgroup.

[0042] Furthermore, the access point-free module specifically includes:

[0043] Central node unit: For each IoT node in the group, the distances between it and other IoT nodes are calculated and summed up as the centrality score of the IoT node, and the IoT node with the highest centrality score is selected as the central node of the group.

[0044] Home access point unit: For each IoT node in the group, the distance between it and the global access point is calculated, and the nearest access point is selected as its home access point according to the distance; the global access point is the access point in other groups.

[0045] Furthermore, the access point module includes:

[0046] Distance and unit: for each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes;

[0047] Access point distance unit: calculates the distance between each IoT node and the home access point;

[0048] Comprehensive distance unit: calculates the weighted sum of the distance between each IoT node and other IoT nodes and the distance between each IoT node and the home access point with the same weight as the comprehensive distance of the IoT node;

[0049] Management node unit: Select the IoT node with the smallest comprehensive distance as the management node of the group.

[0050] Furthermore, the access point module includes:

[0051] Distance sum and weight unit: for each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes, and set the weight to 1;

[0052] Access point distance and weight unit: calculates the distance between each IoT node and the home access point, and sets the weight to 10;

[0053] Weighted sum unit: calculates the weighted sum of the distances and weights of each IoT node with other IoT nodes, and the distance with the home access point and its weight, as the comprehensive distance of the IoT node;

[0054] Management node selection unit: selects the IoT node with the smallest comprehensive distance as the management node of the group.

[0055] Furthermore, the access point module includes:

[0056] Distance sum calculation unit: for each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes;

[0057] Candidate node unit: select the top N IoT nodes from the smallest to the largest distance as candidate nodes;

[0058] The selection unit selects, from the candidate nodes, a candidate node with the shortest distance from the home access point as a management node of the group.

[0059] The present invention proposes a self-organizing communication method and device for intelligent devices in the field of urban simulation, which can improve communication efficiency, enhance network stability and reduce energy consumption.

[0060] To achieve the above object, the technical solution of the present invention is achieved as follows:

[0061] A self-organizing communication method for intelligent devices in the field of urban simulation, comprising:

[0062] S1, collect the location data of IoT nodes in the urban simulation field and the location data of wireless signal access points, and convert them as input data for clustering algorithm;

[0063] S2, performing K-means++ algorithm on the input data to obtain multiple cluster centers;

[0064] S3, dividing all IoT nodes and access points into different groups according to the cluster centers;

[0065] S4. If there is no access point in the group, execute step S5; if there is one access point in the group, execute step S6; if there are multiple access points in the group, execute step S7;

[0066] S5. Select a most central IoT node from the group as a management node. Each IoT node in the group selects the nearest access point from the entire network access points outside the group as the home access point. The management node, as the center of the group, coordinates the communication between IoT nodes, assumes the role of data aggregation and forwarding, enhances the information flow within the group, and optimizes the communication path.

[0067] S6, each IoT node in the group selects the one access point as the home access point, and selects a management node based on the distance between the IoT node and other IoT nodes and the distance between the IoT node and the home access point; the management node plays a relay role between the IoT node and the access point, and acts as a transfer station to realize communication between the IoT node and the access point;

[0068] S7. Each IoT node in the group selects the closest access point as the home access point; each access point and the IoT node that selects it as the home access point are grouped into the same group, thereby forming a subgroup.

[0069] Furthermore, step S5 specifically includes:

[0070] S51, calculating the central node of the group: for each IoT node in the group, respectively calculating the distance between it and other IoT nodes and summing them up as the centrality score of the IoT node, and selecting the IoT node with the highest centrality score as the central node of the group;

[0071] S52, selecting a home access point: for each IoT node in the group, calculate the distance between it and the global access point, and select the nearest access point as its home access point according to the distance; the global access point is the access point in other groups.

[0072] Furthermore, the method for selecting the management node in step S6 includes:

[0073] S601. For each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes;

[0074] S602, calculating the distance between each IoT node and the home access point;

[0075] S603, calculating a weighted sum of the distances of each IoT node to other IoT nodes and the distance to the home access point with the same weight as the comprehensive distance of the IoT node;

[0076] S604: Select the IoT node with the smallest comprehensive distance as the management node of the group.

[0077] Furthermore, the method for selecting the management node in step S6 includes:

[0078] S611. For each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes, and set the weight to 1;

[0079] S612, calculating the distance between each IoT node and the home access point, and setting the weight to 10;

[0080] S613, calculating a weighted sum of the distances and weights of each IoT node with other IoT nodes, and the distances and weights of each IoT node with the home access point, as a comprehensive distance of the IoT node;

[0081] S614: Select the IoT node with the smallest comprehensive distance as the management node of the group.

[0082] Furthermore, the method for selecting the management node in step S6 includes:

[0083] S621. For each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes;

[0084] S622, selecting the top N IoT nodes in terms of distance from small to large as candidate nodes;

[0085] S623: Select, from the candidate nodes, a candidate node with the shortest distance to the home access point as a management node of the group.

[0086] Another aspect of the present invention also provides a self-organizing communication device for intelligent devices in the field of urban simulation, comprising:

[0087] Collection module: collects the location data of IoT nodes in the urban simulation field and the location data of wireless signal access points, and converts them into input data for clustering algorithms;

[0088] Cluster center module: performing K-means++ algorithm on the input data to obtain multiple cluster centers;

[0089] Group module: divides all IoT nodes and access points into different groups according to the cluster centers;

[0090] Judgment module: if there is no access point in the group, enter the no access point module; if there is one access point in the group, enter the one access point module; if there are multiple access points in the group, enter the multiple access point module;

[0091] No access point module: select the most central IoT node from the group as the management node, and each IoT node in the group selects the nearest access point from the entire network access points outside the group as the home access point; the management node acts as the center of the group, coordinates the communication between IoT nodes, assumes the role of data aggregation and forwarding, enhances the information flow within the group, and optimizes the communication path;

[0092] An access point module: each IoT node in the group selects the one access point as the home access point, and selects a management node based on the distance between the IoT node and other IoT nodes and the distance between the IoT node and the home access point; the management node plays a relay role between the IoT node and the access point, and acts as a transfer station to realize communication between the IoT node and the access point;

[0093] Multi-access point module: Each IoT node in the group selects the closest access point as its home access point; each access point and the IoT node that selects it as its home access point are grouped into the same group, thereby forming a subgroup.

[0094] Furthermore, the access point-free module specifically includes:

[0095] Central node unit: for each IoT node in the group, the distance between it and other IoT nodes is calculated and summed up as the centrality score of the IoT node, and the IoT node with the highest centrality score is selected as the central node of the group;

[0096] Home access point unit: For each IoT node in the group, the distance between it and the global access point is calculated, and the nearest access point is selected as its home access point according to the distance; the global access point is the access point in other groups.

[0097] Furthermore, the access point module includes:

[0098] Distance and unit: for each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes;

[0099] Access point distance unit: calculates the distance between each IoT node and the home access point;

[0100] Comprehensive distance unit: calculates the weighted sum of the distance between each IoT node and other IoT nodes and the distance between each IoT node and the home access point with the same weight as the comprehensive distance of the IoT node;

[0101] Management node unit: Select the IoT node with the smallest comprehensive distance as the management node of the group.

[0102] Furthermore, the access point module includes:

[0103] Distance sum and weight unit: for each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes, and set the weight to 1;

[0104] Access point distance and weight unit: calculates the distance between each IoT node and the home access point, and sets the weight to 10;

[0105] Weighted sum unit: calculates the weighted sum of the distances and weights of each IoT node with other IoT nodes, and the distance with the home access point and its weight, as the comprehensive distance of the IoT node;

[0106] Management node selection unit: selects the IoT node with the smallest comprehensive distance as the management node of the group.

[0107] Furthermore, the access point module includes:

[0108] Distance sum calculation unit: for each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes;

[0109] Candidate node unit: select the top N IoT nodes from the smallest to the largest distance as candidate nodes;

[0110] The selection unit selects, from the candidate nodes, a candidate node with the shortest distance from the home access point as a management node of the group.

[0111] Compared with the prior art, the present invention has the following beneficial effects:

[0112] 1. Improve communication efficiency: The present invention divides smart devices into multiple relatively concentrated groups through access point clustering, which reduces the communication distance and signal interference and improves the efficiency and accuracy of data transmission.

[0113] 2. Enhanced network stability: The present invention, through the setting of management nodes, enables the devices and management nodes in the group to form a relatively stable network structure, reducing the impact of frequent self-organization processes on network stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0114] Figure 1 It is a schematic diagram of a flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0115] It should be noted that, in the absence of conflict, the embodiments of the present invention and the features in the embodiments may be combined with each other.

[0116] In order to make the purpose and features of the present invention more obvious and understandable, the present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0117] The city simulation field intelligent device self-organizing communication method implemented in this embodiment is as follows: Figure 1 As shown; including the following steps:

[0118] Step 1: Collect the location data of IoT nodes and access points as input for the clustering algorithm.

[0119] The Internet of Things nodes refer to smart devices in the Internet of Things network in the field of urban simulation.

[0120] The specific implementation method of this step is:

[0121] Step 11, data preparation: First, collect the location data of IoT nodes and access points. The location data can be in the form of latitude and longitude coordinates, geographic location descriptions, or other forms that can represent location information. Ensure that there is enough data to represent the IoT nodes in the IoT network and the access points of the network (mainly wireless network access points).

[0122] Step 12, data conversion: Convert the collected location data into a form suitable for clustering algorithm processing. A common representation method is to convert the location data into a feature vector form. For example, the distance, angle or other geometric attributes between the node and the access point. Organize these features into a vector form as input data for the subsequent clustering algorithm (K-means++ algorithm).

[0123] Step 2: Perform K-means++ algorithm to obtain multiple cluster centers.

[0124] Specific implementation:

[0125] Step 21, initialize the first cluster center: randomly select one from all input data as the first cluster center.

[0126] Step 22, select the next cluster center: for each input data, calculate its minimum distance with the selected cluster center, for example, calculate the minimum distance by Euclidean distance, first calculate the Euclidean distance between the input data and each cluster center, and then select the minimum distance.

[0127] Step 23, weighted selection: Select the next cluster center based on the result of the minimum distance calculation. In order to increase the probability of data points that are far away from the previously selected cluster center being selected as the cluster center, a weighted probability method called "K-means++" can be used. In this method, the probability of each data point being the next cluster center is calculated, and the probability is proportional to the square of its distance from the selected cluster center.

[0128] Step 24, repeat steps 22 and 23 until K cluster centers are selected.

[0129] Step 25, cluster center initialization completed: complete the selection of K cluster centers, which will be used as the initial centers of the K-means algorithm.

[0130] The K-means++ algorithm is an improved version of the K-means algorithm, which is used to better select the initial cluster centers. By using the K-means++ algorithm to select cluster centers, the performance and convergence speed of the K-means algorithm can be improved.

[0131] When implementing the above steps, you can use a programming language (such as Python) and a corresponding machine learning library (such as scikit-learn) to implement the K-means++ algorithm. These libraries usually provide implementations of clustering algorithms, including K-means and K-means++ algorithms.

[0132] Step 3: Divide all IoT nodes and access points into cluster groups.

[0133] The calculations described in this step and the following steps, including distance calculations, similarity calculations, etc., are all calculated using feature vectors converted from the location data of IoT nodes and access points, and will not be specifically explained later.

[0134] Specific implementation:

[0135] Step 31, calculate the distance or similarity: for each IoT node or access point, calculate the distance or similarity between it and each cluster center. The distance metric can use Euclidean distance, Manhattan distance, etc., and the similarity metric can use cosine similarity, correlation coefficient, etc.

[0136] Step 32, assign to the nearest cluster: assign each IoT node or access point to the cluster center with the closest distance or similarity. Usually, the cluster center with the smallest distance or similarity is selected as the basis for assignment.

[0137] Step 33, update the cluster center: update the location of the cluster center according to the IoT nodes and access points that have been assigned to each cluster center. The update can be performed by calculating the average location of each cluster or recalculating the cluster center.

[0138] Step 34, repeat iteration: Repeat step 32, step 33 and step 34 until the position of the cluster center no longer changes significantly or a predetermined number of iterations is reached.

[0139] Step 4: If there is no access point in a group, execute step 5; if there is only one access point, execute step 6; if there are multiple access points, execute step 7.

[0140] Step 5: Select a most central IoT node from the group as the management node, and each IoT node in the group selects the nearest access point from the entire network access points as the home access point.

[0141] Specific methods:

[0142] Step 51, calculate the central node of the group: for each IoT node in the group, calculate the distance between it and other nodes, you can use Euclidean distance, Manhattan distance, etc., sum up the distances of all nodes as the centrality score of each IoT node. Select the IoT node with the highest centrality score as the central node of the group.

[0143] Step 52, select the home access point: For each IoT node in the group, calculate the distance between it and the global access point. Select the access point closest to each node as its home access point based on the distance. Since there is no access point in this group, the global access point refers to the access point in other groups.

[0144] Thus, for a group without an access point, a most central IoT node will be selected as the management node, and the nearest access point will be selected as the home access point for each IoT node in the group.

[0145] In this group, since the IoT nodes are all connected to the access points of other groups, the distance between these access points and the IoT nodes of this group is relatively large, and there is a high possibility that they cannot communicate normally, so a management node is needed. The management node can serve as the center of the group to coordinate the communication between IoT nodes and ensure the effective transmission of information. For example, when the IoT node and the access point cannot communicate, the management node can intervene, especially by itself or by designating other IoT nodes as relays to complete the communication with the access point. The management node can also assume the role of data aggregation and forwarding, enhance the flow of information within the group, and improve the overall communication efficiency and stability. For example, the communication between IoT nodes in the group is often forwarded through access points in the prior art, but in the present invention, it can be forwarded by the management node.

[0146] Step 6: Each IoT node in the group selects the one access point as the home access point, and selects a management node from the IoT nodes.

[0147] There are three preferred implementation methods for selecting a management node from IoT nodes, which are described below.

[0148] Method 1: The sum of the distances between the IoT node and all other IoT nodes accounts for 50% of the reference weight, and the distance between the IoT node and the access point accounts for 50% of the reference weight.

[0149] Specifically:

[0150] For each IoT node:

[0151] Calculate the sum of the distances between the IoT node and other IoT nodes: The calculation method is to calculate the distance between the IoT node and each other IoT node in the group, and sum up all the distances.

[0152] Calculate the distance between the IoT node and the home access point.

[0153] Calculate the comprehensive distance: multiply the sum of the distances between the IoT node and other IoT nodes by a weight factor (50%), and multiply the distance between the IoT node and the home access point by a weight factor (50%). Calculate the weighted sum of the two as the comprehensive distance of the IoT node.

[0154] The IoT node with the smallest comprehensive distance is selected as the management node of the group.

[0155] This method optimizes the communication path, reduces signal interference, and enhances network adaptability and stability by balancing the distance between IoT nodes and access points. At the same time, it promotes load balancing and improves overall communication efficiency. It is simple to implement and easy to expand, suitable for smart device networks of different scales and distributions, and provides an efficient and reliable solution for self-organizing communication of smart devices in the field of urban planning.

[0156] Method 2: The distance and weight of the IoT node to other IoT nodes is 1, and the distance weight of the IoT node to the access point is 10.

[0157] Specifically:

[0158] For each IoT node:

[0159] Calculate the sum of the distances between the IoT node and other IoT nodes: The calculation method is to calculate the distance between the IoT node and each other IoT node in the group, and sum up all the distances, with the weight set to 1.

[0160] The distance between the IoT node and the home access point is calculated, and the weight is set to 10.

[0161] Calculate the weighted sum of the two as the comprehensive distance of the IoT node.

[0162] The IoT node with the smallest comprehensive distance is selected as the management node of the group.

[0163] This method prioritizes ensuring close connection between nodes and access points by significantly increasing the weight of the distance between IoT nodes and access points (10 times the distance between nodes). This helps reduce communication delays and signal attenuation, and improves the stability and efficiency of data transmission. At the same time, the method is simple to implement and highly adaptable, and can effectively optimize access point selection, providing stable and reliable access services for self-organizing communication of smart devices.

[0164] Method 3: Calculate the top N candidate nodes with the smallest sum based on the distance between the IoT node and other IoT nodes, and select the one with the smallest distance to the access point from these N candidate nodes as the management node.

[0165] Specifically:

[0166] For each IoT node:

[0167] Calculate the sum of the distances between the IoT node and other IoT nodes: the calculation method is to calculate the distance between the IoT node and each other IoT node in the group, and sum up all the distances;

[0168] Select the first N nodes with the smallest distance sum: that is, select the first N nodes with the smallest distance sum from all nodes as candidate nodes.

[0169] Among the candidate nodes, the candidate node with the shortest distance to the home access point is selected as the management node of the group.

[0170] This method combines local and global optimality by first selecting the first N nodes with the smallest distance to other nodes as candidates, and then selecting the node closest to the home access point from the candidate nodes as the management node. This method helps to reduce the communication cost between nodes while ensuring the close connection between nodes and access points, improving communication efficiency and network stability. In addition, the method is highly flexible and the N value can be adjusted according to the network scale, which is suitable for networks of different scales.

[0171] By any of the three methods in step 6, a management node of a group with only one access point can be selected. Since there is only one access point, there is indeed a possibility that this access point cannot directly and effectively serve all IoT nodes in the group, especially when the access point is remote or has limited signal coverage. Therefore, selecting a suitable management node as a relay can ensure smooth communication between the IoT node and the access point, and improve communication efficiency and stability. The management node as a relay means that the management node not only assumes the conventional functions of the IoT node, but also assumes the additional relay role between the IoT node and the access point. This means that if there is a problem with the direct connection between the IoT node and the access point (such as weak signal, interference, etc.), the management node can act as a transfer station to help the IoT node forward data to the access point to ensure the continuity and integrity of the data. In addition, the management node is also responsible for the management of mutual communication between IoT nodes in the group.

[0172] The management node selection methods mentioned in the above methods (such as centrality scoring, comprehensive distance calculation, etc.) are all aimed at finding the optimal management node in this situation.

[0173] Step 7: Each IoT node selects the closest access point as its home access point; each access point and the IoT node that selects it as its home access point are grouped into the same group, thereby forming a subgroup.

[0174] Specific implementation method:

[0175] Select the nearest home access point: For each IoT node, calculate the distance between it and all access points in the group. Select the closest access point as the home access point of the node.

[0176] Split a group into subgroups: Split a group into multiple subgroups based on the access point to which each node belongs. Nodes with the same access point are placed in the same subgroup.

[0177] When there are multiple access points in a group, each access point has the ability to provide services to the surrounding IoT nodes, thus reducing the risk of communication interruption caused by a single access point failure. In this case, the IoT node can directly select the closest access point as the home access point without the need for an additional management node to undertake the relay task. It only needs to divide the group into reasonable subgroups.

[0178] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A self-organizing communication method for intelligent devices in the field of urban simulation, characterized in that: include: S1, collect the location data of IoT nodes in the urban simulation field and the location data of wireless signal access points, and convert them as input data for clustering algorithm; S2, performing K-means++ algorithm on the input data to obtain multiple cluster centers; S3, dividing all IoT nodes and access points into different groups according to the cluster centers; S4. If there is no access point in the group, execute step S5; If there is one access point in the group, execute step S6; if there are multiple access points in the group, execute step S7; S5. Select a most central IoT node from the group as a management node. Each IoT node in the group selects the nearest access point from the entire network access points outside the group as the home access point. The management node, as the center of the group, coordinates the communication between IoT nodes, assumes the role of data aggregation and forwarding, enhances the information flow within the group, and optimizes the communication path. S6, each IoT node in the group selects the one access point as the home access point, and selects a management node based on the distance between the IoT node and other IoT nodes and the distance between the IoT node and the home access point; the management node plays a relay role between the IoT node and the access point, and acts as a transfer station to realize communication between the IoT node and the access point; S7. Each IoT node in the group selects the closest access point as the home access point; each access point and the IoT node that selects it as the home access point are grouped into the same group, thereby forming a subgroup.

2. The method for self-organizing communication of intelligent devices in the field of urban simulation according to claim 1, characterized in that: Step S5 specifically includes: S51, calculating the central node of the group: for each IoT node in the group, respectively calculating the distance between it and other IoT nodes and summing them up as the centrality score of the IoT node, and selecting the IoT node with the highest centrality score as the central node of the group; S52, selecting a home access point: for each IoT node in the group, calculate the distance between it and the global access point, and select the nearest access point as its home access point according to the distance; the global access point is the access point in other groups.

3. The method for self-organizing communication of intelligent devices in the field of urban simulation according to claim 1, characterized in that: The method for selecting the management node in step S6 includes: S601. For each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes; S602, calculating the distance between each IoT node and the home access point; S603, calculating a weighted sum of the distances of each IoT node to other IoT nodes and the distance to the home access point with the same weight as the comprehensive distance of the IoT node; S604: Select the IoT node with the smallest comprehensive distance as the management node of the group.

4. The method for self-organizing communication of intelligent devices in the field of urban simulation according to claim 1, characterized in that: The method for selecting the management node in step S6 includes: S611. For each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes, and set the weight to 1; S612, calculating the distance between each IoT node and the home access point, and setting the weight to 10; S613, calculating a weighted sum of the distances and weights of each IoT node with other IoT nodes, and the distances and weights of each IoT node with the home access point, as a comprehensive distance of the IoT node; S614: Select the IoT node with the smallest comprehensive distance as the management node of the group.

5. The method for self-organizing communication of intelligent devices in the field of urban simulation according to claim 1, characterized in that: The method for selecting the management node in step S6 includes: S621. For each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes; S622, selecting the top N IoT nodes in terms of distance from small to large as candidate nodes; S623: Select, from the candidate nodes, a candidate node with the shortest distance to the home access point as a management node of the group.

6. A self-organizing communication device for intelligent devices in the field of urban simulation, characterized in that: include: Collection module: collects the location data of IoT nodes in the urban simulation field and the location data of wireless signal access points, and converts them into input data for clustering algorithms; Cluster center module: performing K-means++ algorithm on the input data to obtain multiple cluster centers; Group module: divides all IoT nodes and access points into different groups according to the cluster centers; Judgment module: If there is no access point in the group, enter the no access point module; If there is one access point in the group, enter the one access point module; if there are multiple access points in the group, enter the multiple access point module; No access point module: select the most central IoT node from the group as the management node, and each IoT node in the group selects the nearest access point from the entire network access points outside the group as the home access point; the management node acts as the center of the group, coordinates the communication between IoT nodes, assumes the role of data aggregation and forwarding, enhances the information flow within the group, and optimizes the communication path; An access point module: each IoT node in the group selects the one access point as a home access point, and selects a management node based on the distance between the IoT node and other IoT nodes and the distance between the IoT node and the home access point; The management node plays a relay role between the IoT node and the access point, and as a transfer station, realizes the communication between the IoT node and the access point; Multi-access point module: Each IoT node in the group selects the closest access point as its home access point; each access point and the IoT node that selects it as its home access point are grouped into the same group, thereby forming a subgroup.

7. The self-organizing communication device for intelligent devices in the field of urban simulation according to claim 6, characterized in that: The access point-free module specifically includes: Central node unit: for each IoT node in the group, the distance between it and other IoT nodes is calculated and summed up as the centrality score of the IoT node, and the IoT node with the highest centrality score is selected as the central node of the group; Home access point unit: For each IoT node in the group, the distance between it and the global access point is calculated, and the nearest access point is selected as its home access point according to the distance; the global access point is the access point in other groups.

8. The self-organizing communication device for intelligent devices in the field of urban simulation according to claim 6, characterized in that: The access point module comprises: Distance and unit: for each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes; Access point distance unit: calculates the distance between each IoT node and the home access point; Comprehensive distance unit: calculates the weighted sum of the distance between each IoT node and other IoT nodes and the distance between each IoT node and the home access point with the same weight as the comprehensive distance of the IoT node; Management node unit: Select the IoT node with the smallest comprehensive distance as the management node of the group.

9. The self-organizing communication device for intelligent devices in the field of urban simulation according to claim 6, characterized in that: The access point module comprises: Distance sum and weight unit: for each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes, and set the weight to 1; Access point distance and weight unit: calculates the distance between each IoT node and the home access point, and sets the weight to 10; Weighted sum unit: calculates the weighted sum of the distances and weights of each IoT node with other IoT nodes, and the distances and weights of each IoT node with the home access point, as the comprehensive distance of the IoT node; Management node selection unit: selects the IoT node with the smallest comprehensive distance as the management node of the group.

10. The self-organizing communication device for intelligent devices in the field of urban simulation according to claim 6, characterized in that: The access point module comprises: Distance sum calculation unit: for each IoT node in the group, calculate the sum of the distances between the IoT node and other IoT nodes; Candidate node unit: select the top N IoT nodes from the smallest to the largest distance as candidate nodes; The selection unit selects, from the candidate nodes, a candidate node with the shortest distance from the home access point as a management node of the group.

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