Internet of things communication system, data aggregator deployment method, device and storage medium

Abstract

The present invention discloses an Internet of Things communication system, a data aggregator deployment method, a device and a storage medium. Among them, the data aggregator deployment method includes: clustering each Internet of Things node in the deployment area based on the information density of the Internet of Things data, and deploying a data aggregator in each cluster; determining the target position of the data aggregator in the corresponding cluster based on the geographical location of each Internet of Things node in each cluster; the data aggregator is used to send the Internet of Things data collected by each Internet of Things node in the cluster to a satellite. Since the data aggregator can collect the Internet of Things data of each Internet of Things node within the coverage area and send the collected Internet of Things data to the satellite, it can effectively avoid the Internet of Things node from sending information to the satellite, which is conducive to reducing the power consumption of the Internet of Things node. In addition, on the basis of reasonable deployment of the data aggregator, the power consumption of each Internet of Things node in the Internet of Things communication system can be further reduced.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things, and in particular to an Internet of Things communication system, a data aggregator deployment method, a device and a storage medium. Background Art

[0002] In related technologies, for IoT applications in remote areas, such as emergency disaster reporting, ground-based networks are often not enough. Satellite-assisted communications are considered to be one of the key technologies for future 6G IoT communications, which can greatly expand the coverage of the network and the services provided. Therefore, satellite networks are usually used to collect IoT data, and then the information is transmitted to the ground cellular network through the satellite ground system to complete subsequent work.

[0003] Since the use environment of IoT devices cannot meet the demand for long-term power supply, IoT devices used for data collection in remote areas need to meet the demand for low power consumption to ensure a longer service life. However, in the current satellite-assisted IoT communication system, IoT devices need to send information to satellites, which consumes a lot of power and is difficult to maintain and manage. Summary of the invention

[0004] In view of this, embodiments of the present invention provide an Internet of Things communication system, a data aggregator deployment method, an apparatus, and a storage medium, aiming to improve the maintainability of the Internet of Things communication system.

[0005] The technical solution of the embodiment of the present invention is achieved as follows:

[0006] An embodiment of the present invention provides a method for deploying a data aggregator in an Internet of Things communication system, including:

[0007] Cluster the IoT nodes in the deployment area based on the information density of IoT data, and deploy a data aggregator in each cluster;

[0008] Determining a target location of the data aggregator in each cluster based on the geographic location of each IoT node in the corresponding cluster;

[0009] The data aggregator is used to send the IoT data collected by each IoT node in the cluster to the satellite.

[0010] The embodiment of the present invention further provides an Internet of Things communication system, including:

[0011] A plurality of IoT nodes, each of which is used to collect IoT data;

[0012] A data aggregator, wherein the data aggregator is deployed based on the method described in the embodiment of the present invention, and the data aggregator is used to send the Internet of Things data collected by each of the Internet of Things nodes in the cluster to a satellite.

[0013] The embodiment of the present invention further provides a data collection method, which is applied to a data aggregator in an Internet of Things communication system of the embodiment of the present invention. The method includes:

[0014] Receiving IoT data periodically reported by each IoT node connected to the data aggregator;

[0015] Performing redundancy removal processing on each of the received IoT data;

[0016] Send the de-redundant IoT data to the satellite.

[0017] The embodiment of the present invention further provides a data aggregator deployment device in an Internet of Things communication system, comprising:

[0018] The clustering module is used to cluster the IoT nodes in the deployment area based on the information density of IoT data, and a data aggregator is deployed in each cluster;

[0019] a location determination module, configured to determine a target location of the data aggregator in each cluster based on the geographic location of each IoT node in the cluster;

[0020] The data aggregator is used to send the IoT data collected by each IoT node in the cluster to the satellite.

[0021] An embodiment of the present invention further provides a data aggregator deployment device in an Internet of Things communication system, comprising: a processor and a memory for storing a computer program that can be run on the processor, wherein the processor is used to execute the steps of the data aggregator deployment method described in the embodiment of the present invention when running the computer program.

[0022] An embodiment of the present invention also provides a data aggregator, which is deployed based on the data aggregator deployment method described in the embodiment of the present invention, and is used to send the Internet of Things data collected by each of the Internet of Things nodes in the cluster to a satellite. When the data aggregator is used to run a computer program, it executes the steps of the data collection method described in the embodiment of the present invention.

[0023] An embodiment of the present invention further provides a storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the data aggregator deployment method or the data collection method described in the embodiment of the present invention are implemented.

[0024] The technical solution provided by the embodiment of the present invention deploys a data aggregator in the Internet of Things communication system. The data aggregator collects the Internet of Things data of each Internet of Things node within the coverage area and sends the collected Internet of Things data to the satellite. This can effectively avoid the Internet of Things node from sending information to the satellite, which is beneficial to reducing the power consumption of the Internet of Things node. In addition, each Internet of Things node is clustered based on the information density of the Internet of Things data, and the target position of the data aggregator in the corresponding cluster is determined based on the geographical location of each Internet of Things node in each cluster. On the basis of reasonable deployment of the data aggregator, the power loss of each Internet of Things node when transmitting Internet of Things data can be reduced, thereby further reducing the power consumption of each Internet of Things node in the Internet of Things communication system, which is beneficial to reducing the subsequent maintenance requirements of each Internet of Things node in the Internet of Things system. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a schematic diagram of the structure of the Internet of Things communication system according to an embodiment of the present invention;

[0026] Figure 2 It is a flow chart of a method for deploying a data aggregator in an Internet of Things communication system according to an embodiment of the present invention;

[0027] FIG. 3A to FIG. 3D Schematic diagram of the principle of clustering IoT nodes in an embodiment of the present invention;

[0028] Figure 4 A schematic diagram of a data collection method according to an embodiment of the present invention;

[0029] Figure 5 It is a flow chart of a data collection method according to an application embodiment of the present invention;

[0030] Figure 6 This is a flow chart of a data collection method according to another application embodiment of the present invention;

[0031] Figure 7 This is a structural schematic diagram of a data aggregator deployment device in an Internet of Things communication system according to an embodiment of the present invention;

[0032] Figure 8 This is a structural diagram of a data aggregator deployment device according to an embodiment of the present invention;

[0033] Fig. 9 Schematic diagram of the structure of a data aggregator according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0036] An embodiment of the present invention provides a data aggregator deployment method in an Internet of Things communication system. Before introducing the deployment method, a brief introduction is first given to the Internet of Things communication system in the embodiment of the present invention.

[0037] like Figure 1 As shown, the IoT communication system includes: multiple IoT device nodes (i.e., IoT nodes), satellites, multiple IoT nodes are divided into different IoT node clusters, and a data aggregator is deployed in each IoT node cluster, which can collect IoT data within its coverage area. The data aggregator sends the collected IoT data of the IoT nodes to the satellite, which is then forwarded by the satellite to the ground-based network. Among them, multiple satellites constitute a satellite network, and the ground-based network includes: satellite ground antennas and base stations. The satellite ground antennas can receive IoT data forwarded by the satellite and forward it to the cellular network formed by the base station via wired or wireless means.

[0038] Since the number of data aggregators is much smaller than the number of IoT nodes, IoT nodes do not need to directly send IoT data to satellites, which can reduce the power consumption of IoT nodes and only requires regular maintenance of data aggregators. In some embodiments, the battery of the data aggregator can be replaced manually or wirelessly charged by a drone, which is not specifically limited in the embodiments of the present invention.

[0039] Since the data aggregator is a core in the entire satellite-assisted IoT communication system, the reasonable deployment of the data aggregator directly affects the performance of the entire system. Based on this, an embodiment of the present invention provides a method for deploying a data aggregator in an IoT communication system, such as Figure 2 As shown, including:

[0040] Step 201, clustering each IoT node in the area to be deployed based on the information density of IoT data, and deploying a data aggregator in each cluster;

[0041] Here, there can be multiple types of IoT nodes in the area to be deployed, and accordingly, there can be multiple types of IoT data collected. For example, taking fire monitoring as an example, the IoT nodes can be temperature sensors for collecting ambient temperature, smoke detectors for collecting smoke, etc., and the IoT data can be first data reflecting the ambient temperature, second data reflecting the ambient smoke concentration, etc.

[0042] Here, information density refers to the amount of IoT data that needs to be sent per unit time, and the data aggregator is used to send the IoT data collected by each IoT node in the cluster to the satellite.

[0043] In some embodiments, step 201 specifically includes:

[0044] Obtaining the information density of IoT data of each IoT node in the area to be deployed and the geographical location of each IoT node;

[0045] The IoT nodes are traversed based on the neighboring relationship of the geographical locations of the IoT nodes, a plurality of the IoT nodes whose sum of information density reaches a set density threshold are grouped into a cluster, and the clustering result is updated until all the IoT nodes are allocated to the cluster.

[0046] In this way, each IoT node can be clustered based on information density, so that IoT nodes with adjacent geographical locations and whose sum of information density reaches the set density threshold form a cluster, and the clustering results are continuously updated until each IoT node is assigned to a cluster, thereby determining the number and approximate area of ​​data aggregators that need to be deployed.

[0047] Step 202: Determine the target location of the data aggregator in each cluster based on the geographic location of each IoT node in each cluster.

[0048] Here, a data aggregator is deployed in each cluster so that the IoT data of each IoT node in the cluster can be sent to the satellite via the data aggregator. In practical applications, the data aggregator can also be a primary-backup structure, so that the backup data aggregator can be automatically started when the primary data aggregator fails to meet the monitoring needs of IoT data.

[0049] Since there is path loss in the communication between each IoT node and the data aggregator in the same cluster, in order to further optimize the power consumption of each IoT node in the same cluster, the embodiment of the present invention also optimizes the target position of the data aggregator based on the geographical location of each IoT node in each cluster, so as to minimize the power consumption of each IoT node and extend the service life of each IoT node, thereby enhancing the maintainability of the IoT communication system.

[0050] In some embodiments, step 202 includes:

[0051] Determine the position within the cluster where the sum of distances from the geographical locations of the Internet of Things nodes is the smallest as the target position of the data aggregator.

[0052] In this way, the comprehensive power consumption of each IoT node in the cluster can be optimized, and the maintainability of the IoT communication system can be greatly improved.

[0053] The embodiments of the present invention, by deploying a data aggregator in an Internet of Things communication system, can effectively prevent Internet of Things nodes from sending information to satellites, which is beneficial to reducing the power consumption of Internet of Things nodes. Each Internet of Things node is clustered based on the information density of Internet of Things data, and based on the geographical location of each Internet of Things node in each cluster, the target position of the data aggregator in the corresponding cluster is determined. On the basis of reasonable deployment of the data aggregator, the power consumption of each Internet of Things node can be further optimized, so that the maintainability of the Internet of Things communication system is greatly improved.

[0054] In an application example, each IoT node in the deployment area is clustered based on the information density of IoT data, including:

[0055] 1) Generate a two-dimensional graph for all IoT nodes;

[0056] Here, a two-dimensional map of the distribution of IoT nodes can be generated based on the geographical location of each IoT node, such as Figure 3A shown.

[0057] 2) Determine the center of the two-dimensional graph as the origin O(0,0), establish a rectangular coordinate system, and divide the entire two-dimensional graph into four equal parts;

[0058] Here, we can select the origin in the center of the two-dimensional graph to establish a rectangular coordinate system. Then we divide all IoT nodes into four parts, such as Figure 3B It is understood that this is only an example, and in actual applications, all IoT nodes can be divided into other numbers of parts.

[0059] 3) Each part starts from the origin O(0,0) and traverses from near to far, gradually adding up the data volume from small to large distances. When D is reached max When , it stops, and all IoT nodes before this form a cluster;

[0060] Here, the information density of the IoT data of each IoT node can be comprehensively considered based on the data transmission frequency f and the amount of data sent each time d. For example, the data amount D of an IoT node is expressed by the following formula 1:

[0061] D=f*t*d Formula 1

[0062] Each part sums the data volume by the distance from the IoT node to the origin of the coordinate system, traversing all IoT nodes from near to far (such as Figure 3C As shown), when the sum of the data volume of the IoT nodes reaches the set value D max When , see Formula 2, it is considered that the previous n IoT nodes form a cluster C.

[0063]

[0064] 4) Repeat step 3) to divide all IoT nodes into corresponding clusters.

[0065] like Figure 3D As shown, the IoT nodes shown in the figure are divided into clusters C1 to C6.

[0066] In this way, the clustering operation of each IoT node in the area to be deployed is completed, and the number and approximate area of ​​the data aggregators that need to be deployed can be determined.

[0067] In practical applications, due to the problem of spatial loss of signals, as shown in the following formula 3 (the ideal form of the Friis transmission equation, which only considers the transmission of electromagnetic waves and does not consider the gain of the transmitting and receiving antennas):

[0068]

[0069] Among them, P t is the transmission power, P r is the received power, f is the transmission signal frequency, in MHz, and d is the distance between the transmitter and the receiver, in km. It can be seen that when the communication frequency f within a cluster is the same, the path loss is directly related to the distance. Assume that in a cluster, the deployment location of the data aggregator is (X0, Y0), and the locations of other IoT device nodes are (X n , Y n ), the target position of the data aggregator can be solved by an unconstrained optimization algorithm. The main idea is to reach the optimal point of the objective function by "step by step", "step by step", or "step by step".

[0070] In an application example, determining a target location of a data aggregator within each cluster includes:

[0071] 1) Select an initial point X that is as close as possible to the minimum point of the objective function. (0) , from X (0) Start by finding a feasible direction and initial step length according to certain principles, and take a step forward to reach X (1) point;

[0072] Here, it is assumed that the center of each cluster is the initial point, and the function D(x, y) is (X0, Y0) to (X n , Y n ) distances, then the objective function minD(x, y) is solved based on the optimization algorithm.

[0073] 2) Get the new point X (1) Then, choose a new direction and appropriate step size to make the function value decrease rapidly, starting from X (1) Start from the point and take another step to reach X (2)point, and so on, exploring forward step by step and repeating numerical calculations, and finally reaching the optimal point of the objective function. The iterative form of the intermediate process is shown in Formula 4:

[0074]

[0075] Among them, X (k) The point obtained by the k-th step iterative calculation is called the k-th step iteration point, also known as the k-th step design solution; a (k) S is the step length of the k-th iteration calculation; (k) The exploration direction calculated for the k-th iteration.

[0076] 3) Each time you take a step forward, you should check whether the new point you get meets the predetermined calculation accuracy ε. If it does, that is, the decrease in the function value has reached the accuracy requirement, then X is considered (k+1) is the local minimum point, otherwise it should be X (k+1) As the new initial point, continue the step-by-step exploration according to the above method to solve the objective function minD(x, y).

[0077] Here, the final (X0, Y0) is the geographical location where the data aggregator is actually deployed, as shown in the following formula 5:

[0078]

[0079] In practical applications, the geographical location of each IoT node can be obtained based on GPS (Global Positioning System) positioning.

[0080] In this way, the embodiment of the present invention can realize the reasonable deployment of the data aggregator based on the information density and power consumption of each IoT node, thereby maximally reducing the maintenance workload of the IoT communication system.

[0081] Based on the aforementioned data aggregator deployment method, an embodiment of the present invention further provides an Internet of Things communication system, such as Figure 1 As shown, it includes: multiple Internet of Things nodes, each of which is used to collect Internet of Things data; a data aggregator, which is deployed based on the data aggregator deployment method described in an embodiment of the present invention, and the data aggregator is used to send the Internet of Things data collected by each of the Internet of Things nodes in the cluster to a satellite.

[0082] The embodiment of the present invention further provides a data collection method, which is applied to the data aggregator in the Internet of Things communication system described in the embodiment of the present invention, such as Figure 4 As shown, the data collection method includes:

[0083] Step 401, receiving IoT data periodically reported by each IoT node connected to the data aggregator;

[0084] Step 402, performing redundancy removal processing on each of the received IoT data;

[0085] Step 403: Send the IoT data after redundancy removal to the satellite.

[0086] Here, the data aggregator receives the IoT data reported by each IoT node within its coverage area, and sends the received IoT data to the satellite, thereby reducing the power consumption of each IoT node, which is conducive to extending the service life of each IoT node, meeting the low-power deployment requirements of IoT nodes in remote areas, and reducing the maintenance work on IoT nodes. In addition, the data aggregator can perform de-redundancy processing based on the repetitiveness of the IoT data of each IoT node. For example, if multiple IoT nodes in the same cluster use the same type of IoT data and all are normal information, it can be determined that the IoT data of the multiple IoT nodes can be de-redundantly processed, thereby reducing the repetitiveness of information transmission and avoiding wasting satellite resources.

[0087] In actual applications, before receiving the IoT data reported by each IoT node, the data aggregator also needs to perform initial authentication operations and node management operations with each IoT node.

[0088] Based on this, in some embodiments, the data collection method further includes:

[0089] Receiving status information of each IoT node connected to the data aggregator;

[0090] The IoT nodes connected to the data aggregator are managed based on the received status information.

[0091] Exemplarily, the data aggregator performs an initial authentication operation with each IoT node. For example, the data aggregator and each IoT node can perform a two-way security authentication based on a pre-authentication protocol. After the authentication is confirmed to be successful, the data aggregator establishes a connection with each IoT node that has passed the authentication, and receives status information sent by each IoT node. The status information may include: IoT node ID (identity identifier), data reporting frequency, geographic location information, etc. The data aggregator updates the local node list based on the received status information of each IoT node, that is, it realizes the management of each connected IoT node.

[0092] In some embodiments, the data collection method further comprises:

[0093] Determining that the received IoT data is abnormal information;

[0094] Sending a first instruction to a first IoT node corresponding to the abnormal information, and / or sending a second instruction to a second IoT node adjacent to the first IoT node, wherein the first instruction is used to instruct the first IoT node to increase the reporting frequency of IoT data, and the second instruction is used to instruct the second IoT node to increase the reporting frequency of IoT data;

[0095] Determining whether the abnormal information is reliable based on the IoT data sent by the first IoT node and / or the second IoT node after the reporting frequency is increased;

[0096] If yes, the abnormal information is sent to the satellite first;

[0097] If not, the confidence of the first IoT node is reduced.

[0098] Here, abnormal information refers to the monitored IoT data reaching or exceeding the preset safety threshold, indicating that there is an abnormal situation in the monitored environment, such as a sudden rise in temperature, and there may be a fire. If the IoT data received by the data aggregator is abnormal information, the data aggregator sends a first instruction to the first IoT node corresponding to the abnormal information, and / or sends a second instruction to the second IoT node adjacent to the first IoT node, so that the corresponding IoT node increases the frequency of IoT data collection and reporting, and verifies whether the abnormal information is reliable based on high-frequency IoT data. If the IoT data reported by the first IoT node and the second IoT node are both abnormal information, the abnormal information is determined to be reliable, and the abnormal information is sent to the satellite first; if the IoT data reported by the first IoT node / or the second IoT node is normal information, it is determined that the first IoT node has reported false information, and the confidence of the first IoT node is reduced.

[0099] Here, the confidence of the IoT node is explained below. Since satellite-assisted IoT communication systems are usually used for natural disaster warnings in dangerous environments or information monitoring in unmanned areas, it is difficult to determine the accuracy of information by other means. Therefore, the reliability of the information itself has a great impact on the reliability of the entire system and the operating efficiency. In an embodiment of the present invention, the reliability of the satellite-assisted IoT communication system is improved by deploying a data aggregator and interacting with the IoT node. The data aggregator can manage the confidence of each IoT node based on the aforementioned local node list. The confidence serves as a basis for judging the reliability of the IoT data of the IoT node. For example, the confidence can be divided into multiple reliability levels. During initialization, the confidence of each IoT node is set to the same initial level. If the IoT node has the aforementioned behavior of reporting false information, the confidence level of the corresponding IoT node can be reduced.

[0100] In some embodiments, the data collection method further comprises:

[0101] IoT nodes with confidence levels lower than a set threshold are determined to be malicious nodes.

[0102] Here, the data aggregator can manage the IoT nodes based on the confidence level. For example, if the confidence level is lower than the set level, the IoT node is determined to be a malicious node. Exemplarily, the data aggregator can delay the processing of IoT data of malicious nodes, or can also block IoT data reported by malicious nodes, or generate a message to prompt malicious nodes to maintenance personnel, so as to perform manual maintenance on the corresponding IoT nodes.

[0103] The data collection method of the embodiment of the present invention is further described in detail below in conjunction with application examples.

[0104] like Figure 5 As shown, in an application embodiment, the data aggregator collects data for normal information including:

[0105] Step 501 to step 502, initialization;

[0106] Here, the data aggregator performs identity authentication with nodes 1 and 2 to ensure the security of information collection. The specific authentication method can follow the unified requirements of the Internet of Things system.

[0107] Step 503 to step 504, status information (ID, location, information reporting frequency)

[0108] Node 1 and Node 2 respectively send basic status information to the data aggregator, such as ID, geographic location, information reporting frequency, etc.

[0109] Step 505 to step 506, reply information received;

[0110] After receiving the status information, the data aggregator replies a received message to the corresponding node.

[0111] Step 507, update the local node list;

[0112] The data aggregator stores the received status information of each node in the local node list. In the initial state, the confidence of all nodes is the same.

[0113] Step 508 to step 509, the sensor collects information;

[0114] Node 1 and Node 2 periodically report sensor collection information, that is, report IoT data. Here, the reporting frequency of each node can be the same or different.

[0115] Step 510, analyzing, processing, integrating and removing redundancy of the node information;

[0116] The data aggregator performs a preliminary analysis on the sensor information collected by nodes 1 and 2, determines the duplication of the information collected between the nodes, and deletes the redundant information to avoid wasting satellite transmission resources.

[0117] Step 511: Periodically report the collected information.

[0118] The data aggregator reports the de-redundant collected information to the satellite at a set frequency.

[0119] like Figure 6 As shown, in an application embodiment, the data aggregator collects data for abnormal information including:

[0120] Step 601, node 1 reports sensor collection information;

[0121] Node 1 reports the sensor collection information according to the set reporting frequency.

[0122] Step 602, determining whether the information contains warning information;

[0123] The data aggregator determines that the sensor collection information uploaded by node 1 is abnormal information. For example, the temperature value of node 1 exceeds the preset temperature threshold, and there may be a fire.

[0124] Step 603, requesting information collection from node 1;

[0125] The data aggregator immediately sends a data collection request message to node 1, requesting node 1 to report the collected data at a high frequency.

[0126] Step 604, requesting information collection from node 2;

[0127] The data aggregator immediately sends a data collection request message to node 2, requesting node 2 to report the collected data at a high frequency.

[0128] Step 605, node 1 reports sensor collection information (high frequency);

[0129] Node 1 adjusts the data collection frequency and reporting frequency, and reports the collected information to the data aggregator at a high frequency.

[0130] Step 606, node 2 reports sensor collection information (high frequency);

[0131] Node 2 adjusts the data collection frequency and reporting frequency, and reports the collected information to the data aggregator at a high frequency.

[0132] Step 607, determining whether an emergency situation occurs based on the collected information;

[0133] The data aggregator determines whether an emergency situation occurs based on the latest collection information reported by nodes 1 and 2. If the collection information reported by nodes 1 and 2 are both abnormal information, an emergency situation is determined to have occurred. If no abnormal information is found in the collection information within the set time period, an error occurs at the node.

[0134] Step 608: Update the node list and update the node confidence.

[0135] If step 607 determines that an emergency occurs, the abnormal information is immediately reported to the satellite, and the node list is updated to increase the reporting frequency of node 1 and node 2; if step 607 determines that node 1 has an error, the node list is updated to lower the confidence of node 1.

[0136] In this way, the data collection method of the embodiment of the present invention, on the one hand, can collect the collection information of each Internet of Things node by the data aggregator, and upload the collection information to the satellite, thereby reducing the power consumption of each Internet of Things node; on the other hand, the data aggregator analyzes and processes the collection information, reduces the transmission of redundant information, and improves the utilization rate of satellite resources; at the same time, the data aggregator can also realize the role of preliminary verification of information, ensure the reliability of emergency messages, avoid false reports and the existence of malicious nodes, and improve the reliability of satellite-assisted Internet of Things communication systems.

[0137] In order to implement the data aggregator deployment method of the embodiment of the present invention, the embodiment of the present invention also provides a data aggregator deployment device in an Internet of Things communication system. The data aggregator deployment device corresponds to the above-mentioned data aggregator deployment method, and each step in the above-mentioned data aggregator deployment method embodiment is also fully applicable to the data aggregator deployment device embodiment.

[0138] like Figure 7 As shown, the data aggregator deployment device 700 includes: a clustering module 701 and a position determination module 702, wherein the clustering module 701 is used to cluster each IoT node in the deployment area based on the information density of IoT data, and deploy a data aggregator in each cluster; the position determination module 702 is used to determine the target position of the data aggregator in the corresponding cluster based on the geographical location of each IoT node in each cluster; the data aggregator is used to send the IoT data collected by each IoT node in the cluster to the satellite.

[0139] In some embodiments, the clustering module 701 is specifically used for:

[0140] Obtaining the information density of the IoT data of each IoT node in the area to be deployed and the geographical location of each IoT node, wherein the information density is the amount of IoT data to be sent per unit time;

[0141] The IoT nodes are traversed based on the neighboring relationship of the geographical locations of the IoT nodes, a plurality of the IoT nodes whose sum of information density reaches a set density threshold are grouped into a cluster, and the clustering result is updated until all the IoT nodes are allocated to the cluster.

[0142] In some embodiments, the location determination module 702 is specifically configured to:

[0143] Determine the position within the cluster where the sum of distances from the geographical locations of the Internet of Things nodes is the smallest as the target position of the data aggregator.

[0144] In actual application, the clustering module 701 and the location determination module 702 can be implemented by a processor in the data aggregator deployment device 700. Of course, the processor needs to run the computer program in the memory to implement its functions.

[0145] It should be noted that: the data aggregator deployment device provided in the above embodiment only uses the division of the above program modules as an example when deploying the data aggregator. In actual applications, the above processing can be assigned to different program modules as needed, that is, the internal structure of the device is divided into different program modules to complete all or part of the processing described above. In addition, the data aggregator deployment device provided in the above embodiment and the data aggregator deployment method embodiment belong to the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0146] Based on the hardware implementation of the above program modules and in order to implement the method of the embodiment of the present invention, the embodiment of the present invention also provides a data aggregator deployment device. Figure 8 Only an exemplary structure of the data aggregator deployment device is shown, not all structures, and can be implemented as needed. Figure 8 Partial or complete structure shown.

[0147] like Figure 8 As shown, the data aggregator deployment device 800 provided in the embodiment of the present invention includes: at least one processor 801, a memory 802, a user interface 803 and at least one network interface 804. The various components in the data aggregator deployment device 800 are coupled together through a bus system 805. It can be understood that the bus system 805 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 805 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, in Figure 8 Various buses are labeled as bus system 805 .

[0148] The user interface 803 may include a display, a keyboard, a mouse, a trackball, a click wheel, keys, buttons, a touch pad or a touch screen.

[0149] The memory 802 in the embodiment of the present invention is used to store various types of data to support the operation of the data aggregator deployment device. Examples of such data include: any computer program used to operate on the data aggregator deployment device.

[0150] The data aggregator deployment method disclosed in the embodiment of the present invention can be applied to the processor 801, or implemented by the processor 801. The processor 801 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the data aggregator deployment method can be completed by the hardware integrated logic circuit or software instructions in the processor 801. The above-mentioned processor 801 may be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 801 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general-purpose processor may be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in the memory 802. The processor 801 reads the information in the memory 802 and completes the steps of the data aggregator deployment method provided in the embodiment of the present invention in combination with its hardware.

[0151] In an exemplary embodiment, the data aggregator deployment device may be implemented by one or more application specific integrated circuits (ASIC), DSP, programmable logic device (PLD), complex programmable logic device (CPLD), FPGA, general purpose processor, controller, microcontroller (MCU), microprocessor, or other electronic components to perform the aforementioned method.

[0152] Based on the hardware implementation of the above program modules and in order to implement the data collection method of the embodiment of the present invention, the embodiment of the present invention further provides a data aggregator. Fig. 9 Only an exemplary structure of the data aggregator is shown, not all structures, and can be implemented as needed. Fig. 9 Partial or complete structure shown.

[0153] like Fig. 9As shown, the data aggregator 900 provided in the embodiment of the present invention includes: at least one processor 901, a memory 902, a user interface 903 and at least one network interface 904. The various components in the data aggregator 900 are coupled together through a bus system 905. It can be understood that the bus system 905 is used to realize the connection and communication between these components. In addition to the data bus, the bus system 905 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, in Fig. 9 Various buses are labeled as bus system 905.

[0154] The user interface 903 may include a display, a keyboard, a mouse, a trackball, a click wheel, keys, buttons, a touch pad or a touch screen.

[0155] The memory 902 in the embodiment of the present invention is used to store various types of data to support the operation of the data aggregator. Examples of such data include: any computer program used to operate on the data aggregator.

[0156] The data acquisition method disclosed in the embodiment of the present invention can be applied to the processor 901, or implemented by the processor 901. The processor 901 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the data acquisition method can be completed by the hardware integrated logic circuit in the processor 901 or the instruction in the form of software. The above-mentioned processor 901 can be a general-purpose processor, a digital signal processor (DSP, Digital Signal Processor), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The processor 901 can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiment of the present invention. The general-purpose processor can be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiment of the present invention, it can be directly embodied as a hardware decoding processor to execute, or it can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium, which is located in the memory 902. The processor 901 reads the information in the memory 902 and completes the steps of the data acquisition method provided in the embodiment of the present invention in combination with its hardware.

[0157] In an exemplary embodiment, the data aggregator 900 may be implemented by one or more ASICs, DSPs, PLDs, CPLDs, FPGAs, general purpose processors, controllers, MCUs, Microprocessors, or other electronic components to perform the aforementioned methods.

[0158] It can be understood that the memory 802, 902 can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a magnetic random access memory (FRAM), a flash memory, a magnetic surface memory, an optical disk, or a compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk memory or a tape memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM, SyncLink Dynamic Random Access Memory), and direct RAM bus random access memory (DRRAM, Direct Rambus Random Access Memory).The memories described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memories.

[0159] In an exemplary embodiment, the embodiment of the present invention further provides a storage medium, namely a computer storage medium, which can be a computer-readable storage medium, for example, a memory 802 storing a computer program, which can be executed by a processor 801 of a data aggregator deployment device 800 to complete the steps described in the data aggregator deployment method of the embodiment of the present invention; for another example, a memory 902 storing a computer program, which can be executed by a processor 901 of a data aggregator 900 to complete the steps described in the data collection method of the embodiment of the present invention. The computer-readable storage medium can be a memory such as a ROM, a PROM, an EPROM, an EEPROM, a Flash Memory, a magnetic surface memory, an optical disk, or a CD-ROM.

[0160] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0161] In addition, the technical solutions described in the embodiments of the present invention can be arbitrarily combined without conflict.

[0162] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for deploying a data aggregator in an Internet of Things communication system, characterized in that: include: Cluster the IoT nodes in the deployment area based on the information density of IoT data, and deploy a data aggregator in each cluster; Determining a target location of the data aggregator in each cluster based on the geographic location of each IoT node in the corresponding cluster; The data aggregator is used to send the IoT data collected by each IoT node in the cluster to the satellite; The clustering of each IoT node in the area to be deployed based on the information density of IoT data includes: Obtaining the information density of the IoT data of each IoT node in the area to be deployed and the geographical location of each IoT node, wherein the information density is the amount of IoT data to be sent per unit time; The IoT nodes are traversed based on the neighboring relationship of the geographical locations of the IoT nodes, a plurality of the IoT nodes whose sum of information density reaches a set density threshold are grouped into a cluster, and the clustering result is updated until all the IoT nodes are allocated to the cluster.

2. The method according to claim 1, characterized in that The determining, based on the geographical location of each IoT node in each cluster, a target location of the data aggregator in the corresponding cluster includes: Determine the position within the cluster where the sum of distances from the geographical locations of the Internet of Things nodes is the smallest as the target position of the data aggregator.

3. An Internet of Things communication system, characterized in that: include: A plurality of IoT nodes, each of which is used to collect IoT data; A data aggregator, wherein the data aggregator is deployed based on the method described in any one of claims 1 to 2, and the data aggregator is used to send the Internet of Things data collected by each of the Internet of Things nodes in the cluster to a satellite.

4. A data collection method, characterized in that: Applied to the data aggregator in the Internet of Things communication system as claimed in claim 3, the method comprising: Receiving IoT data periodically reported by each IoT node connected to the data aggregator; Performing redundancy removal processing on each of the received IoT data; Send the de-redundant IoT data to the satellite.

5. The method according to claim 4, characterized in that The method further comprises: Receiving status information of each IoT node connected to the data aggregator; The IoT nodes connected to the data aggregator are managed based on the received status information.

6. The method according to claim 4, characterized in that The method further comprises: Determining that the received IoT data is abnormal information; Sending a first instruction to a first IoT node corresponding to the abnormal information, and / or sending a second instruction to a second IoT node adjacent to the first IoT node, wherein the first instruction is used to instruct the first IoT node to increase the reporting frequency of IoT data, and the second instruction is used to instruct the second IoT node to increase the reporting frequency of IoT data; Determining whether the abnormal information is reliable based on the IoT data sent by the first IoT node and / or the second IoT node after the reporting frequency is increased; If yes, the abnormal information is sent to the satellite first; If not, the confidence of the first IoT node is reduced.

7. The method according to claim 6, characterized in that The method further comprises: IoT nodes with confidence levels lower than a set threshold are determined to be malicious nodes.

8. A data aggregator deployment device in an Internet of Things communication system, characterized in that: include: The clustering module is used to cluster the IoT nodes in the deployment area based on the information density of IoT data, and a data aggregator is deployed in each cluster; a location determination module, configured to determine a target location of the data aggregator in each cluster based on the geographic location of each IoT node in the cluster; The data aggregator is used to send the IoT data collected by each IoT node in the cluster to the satellite; The clustering module is specifically used for: Obtaining the information density of the IoT data of each IoT node in the area to be deployed and the geographical location of each IoT node, wherein the information density is the amount of IoT data to be sent per unit time; The IoT nodes are traversed based on the neighboring relationship of the geographical locations of the IoT nodes, a plurality of the IoT nodes whose sum of information density reaches a set density threshold are grouped into a cluster, and the clustering result is updated until all the IoT nodes are allocated to the cluster.

9. A data aggregator deployment device in an Internet of Things communication system, characterized in that: include: A processor and a memory for storing a computer program that can be executed on the processor, wherein: The processor is used to execute the steps of the method according to any one of claims 1 to 2 when running a computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method described in any one of claims 1 to 2 or claims 4 to 7 are implemented.

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