Self-adaptive energy consumption optimization control method and system of sensor network

Abstract

The invention discloses a self-adaptive energy consumption optimization control method and system of a sensor network, and is applied to the self-adaptive energy consumption optimization control system of the sensor network, and the self-adaptive energy consumption optimization control system of the sensor network comprises a plurality of sensor sub-networks. Each sensor sub-network comprises a plurality of sink nodes and a plurality of sensor nodes, and the plurality of sink nodes and the plurality of sensor nodes in each sensor sub-network are in communication connection. According to the embodiment of the invention, after the main sink node is intelligently determined in each sensor sub-network in the sensor network, the communication parameters of each sensor node are dynamically adjusted at the main sink node based on the communication power control strategy, so that self-adaptive energy consumption optimization control in the communication process is realized; and the communication quality between the sensor node and the main sink node is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption control of sensor networks, and in particular to a method and system for adaptive energy consumption optimization control of sensor networks. Background Art

[0002] A sensor network generally includes at least one sink node and multiple sensor nodes. Multiple sensor nodes are responsible for collecting data from monitored objects (such as equipment status, environmental status or security status, etc.) and uploading them to the sink node for data aggregation processing to support subsequent data analysis and application. However, in current sensor networks, when each sensor node communicates with the sink node, its communication parameters (such as transmission power) are generally pre-configured and fixed in the subsequent work process. This makes it impossible to flexibly adjust the data communication between the sensor node and the sink node when the communication effect is not good, thereby failing to ensure the continuity and stability of the data transmission process. Summary of the invention

[0003] The embodiments of the present invention provide an adaptive energy consumption optimization control method and system for a sensor network, aiming to solve the problem in the prior art that when each sensor node in a sensor network communicates data with a sink node, its communication parameters are pre-configured and remain fixed during subsequent work. This makes it impossible to flexibly adjust the data communication between the sensor node and the sink node when the communication effect is poor, thereby failing to ensure the continuity and stability of the data transmission process.

[0004] In a first aspect, an embodiment of the present invention provides an adaptive energy consumption optimization control method for a sensor network, which is applied to an adaptive energy consumption optimization control system for a sensor network, wherein the adaptive energy consumption optimization control system for the sensor network includes a plurality of sensor sub-networks, each of which includes a plurality of aggregation nodes and a plurality of sensor nodes, and the plurality of aggregation nodes and the plurality of sensor nodes in each sensor sub-network are communicatively connected; for each sensor sub-network in the adaptive energy consumption optimization control system for the sensor network, the adaptive energy consumption optimization control method for the sensor network includes: The plurality of sink nodes in the sensor sub-network determine one of the sink nodes as a master sink node based on a preset master node screening strategy; If the master aggregation node detects a node energy consumption optimization instruction, it obtains the current communication parameters of each sensor node in the multiple sensor nodes in the sensor subnetwork, obtains the adjusted communication parameters corresponding to each sensor node based on a preset communication power control strategy, and sends each adjusted communication parameter to the corresponding sensor node; wherein the current communication parameters include at least a communication distance and a communication channel; Each sensor node in the plurality of sensor nodes sends the collected node collection data to the main aggregation node based on the corresponding adjusted communication parameter.

[0005] In a second aspect, an embodiment of the present invention further provides an adaptive energy consumption optimization control system for a sensor network, which includes a plurality of sensor sub-networks, each of which includes a plurality of aggregation nodes and a plurality of sensor nodes, and the plurality of aggregation nodes and the plurality of sensor nodes in each sensor sub-network are communicatively connected; for each sensor sub-network in the adaptive energy consumption optimization control system of the sensor network: The plurality of sink nodes in the sensor sub-network determine one of the sink nodes as a master sink node based on a preset master node screening strategy; If the master aggregation node detects a node energy consumption optimization instruction, it obtains the current communication parameters of each sensor node in the multiple sensor nodes in the sensor subnetwork, obtains the adjusted communication parameters corresponding to each sensor node based on a preset communication power control strategy, and sends each adjusted communication parameter to the corresponding sensor node; wherein the current communication parameters include at least a communication distance and a communication channel; Each sensor node in the plurality of sensor nodes is used to send the collected node collection data to the main aggregation node based on the corresponding adjusted communication parameters.

[0006] The embodiment of the present invention provides an adaptive energy consumption optimization control method and system for a sensor network, which is applied to an adaptive energy consumption optimization control system for a sensor network. The adaptive energy consumption optimization control system for the sensor network includes multiple sensor sub-networks, each of which includes multiple aggregation nodes and multiple sensor nodes, and the multiple aggregation nodes and multiple sensor nodes in each sensor sub-network are all communicatively connected; for each sensor sub-network in the adaptive energy consumption optimization control system for the sensor network, the adaptive energy consumption optimization control method for the sensor network includes: the multiple aggregation nodes in the sensor sub-network determine one of the aggregation nodes as a main aggregation node based on a preset main node screening strategy; if the main aggregation node detects a node energy consumption optimization instruction, the current communication parameters of each sensor node in the multiple sensor nodes in the sensor sub-network are obtained, and the adjusted communication parameters corresponding to each sensor node are obtained based on a preset communication power control strategy, and each adjusted communication parameter is sent to the corresponding sensor node; wherein the current communication parameters at least include a communication distance and a communication channel; each sensor node in the multiple sensor nodes sends the collected node collection data to the main aggregation node based on the corresponding adjusted communication parameters. The embodiment of the present invention can first intelligently determine the main aggregation node in each sensor subnetwork in the sensor network, and then dynamically adjust the communication parameters of each sensor node based on the communication power control strategy at the main aggregation node to achieve adaptive energy consumption optimization control during the communication process and ensure the communication quality between the sensor node and the main aggregation node. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0008] Figure 1 A schematic diagram of an application scenario of the adaptive energy consumption optimization control method for a sensor network provided by an embodiment of the present invention; Figure 2 A schematic diagram of a flow chart of a method for adaptive energy consumption optimization control of a sensor network provided by an embodiment of the present invention; Figure 3 A schematic diagram of a sub-process of an adaptive energy consumption optimization control method for a sensor network provided by an embodiment of the present invention; Figure 4 A schematic diagram of a sub-process of an adaptive energy consumption optimization control method for a sensor network provided by an embodiment of the present invention; Figure 5 A schematic diagram of a sub-process of an adaptive energy consumption optimization control method for a sensor network provided by an embodiment of the present invention; Figure 6 A schematic block diagram of an adaptive energy consumption optimization control system for a sensor network provided by an embodiment of the present invention; Figure 7 A schematic block diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0010] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0011] It should also be understood that the terms used in this specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0012] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0013] Please also refer to Figure 1 and Figure 2 ,in Figure 1 is a schematic diagram of a scenario of an adaptive energy consumption optimization control method for a sensor network according to an embodiment of the present invention, Figure 2 FIG. 1 is a flow chart of a method for adaptive energy consumption optimization control of a sensor network provided by an embodiment of the present invention. Figure 1 As shown, the adaptive energy consumption optimization control method of the sensor network provided in the embodiment of the present invention is applied to the adaptive energy consumption optimization control system of the sensor network, wherein the adaptive energy consumption optimization control system of the sensor network includes multiple sensor sub-networks, each sensor sub-network 100 includes a plurality of aggregation nodes 110 and a plurality of sensor nodes 120, and the plurality of aggregation nodes and the plurality of sensor nodes 120 in each sensor sub-network 100 are all communicatively connected.

[0014] like Figure 2 As shown, the method includes the following steps S110-S130.

[0015] S110. Several sink nodes in the sensor sub-network determine one of the sink nodes as a master sink node based on a preset master node screening strategy.

[0016] In this embodiment, the adaptive energy consumption optimization control system of the sensor network includes multiple sensor sub-networks, and the adaptive energy consumption optimization control method of the sensor network can be executed in each sensor sub-network. At this time, the technical solution of the present application is described by taking the process of executing the adaptive energy consumption optimization control method of the sensor network in a sensor sub-network as an example.

[0017] Because a sensor sub-network includes several sink nodes and multiple sensor nodes, when the total number of sink nodes in the sensor sub-network is greater than 1, one of the sink nodes can be determined as the main sink node based on the preset main node screening strategy. After that, the sensor data collected by the sensor nodes in the sensor sub-network are transmitted to the main sink node.

[0018] In one embodiment, if Figure 3 As shown, step S110 includes: S111, for each of the plurality of sink nodes, the sink node obtains current data collection scene information and obtains a locally pre-configured scene preference processing tag; S112: If the sink node determines that the current data collection scenario information is the same as the scenario preference processing label, then the node information of the sink node is added to the candidate main sink node set; S113: If the sink node determines that the number of sink nodes included in the candidate main sink node set is one, the sink node included in the candidate main sink node set is used as the main sink node; S114: If the aggregation node determines that the number of aggregation nodes included in the candidate main aggregation node set is greater than one, it obtains system performance parameters corresponding to each aggregation node in the candidate main aggregation node set, and uses the aggregation node with the best system performance parameters as the main aggregation node.

[0019] In this embodiment, when initialization configuration, regular configuration or irregular configuration is performed in the sensor subnetwork, it is necessary to complete the determination of the main aggregation node during the configuration process. Specifically, first, a aggregation node is randomly selected in the sensor subnetwork as a temporary main aggregation node; then, each aggregation node in the sensor subnetwork first sends its own scene preference processing tags stored locally to the temporary main aggregation node (wherein, the scene preference processing tags of the temporary main aggregation node itself are also known); then, because the temporary main aggregation node receives the current data collection scene information (such as temperature data collection, working voltage data collection and other collection scenes) sent by the server (such as edge server or cloud server, etc.) or user terminal, it can also compare and match the scene preference processing tags of each aggregation node with the current data collection scene information. After determining that there is a aggregation node If the scenario preference processing label is the same as the current data collection scenario information, the node information of the aggregation node is added to the candidate main aggregation node set; finally, in the temporary main aggregation node, if it is determined that the number of aggregation nodes included in the candidate main aggregation node set is one, the aggregation node included in the candidate main aggregation node set is used as the main aggregation node; or if it is determined that the number of aggregation nodes included in the candidate main aggregation node set is greater than one, the system performance parameters (such as CPU utilization, memory utilization, request response time, etc.) corresponding to each aggregation node in the candidate main aggregation node set are obtained, and the aggregation node with the best system performance parameters is used as the main aggregation node.

[0020] In one embodiment, after step S110, the method further includes: If the main aggregation node detects the current data acquisition scene setting information, it determines the target data acquisition frequency corresponding to the current data acquisition scene setting information based on the locally preset acquisition scene and acquisition frequency mapping relationship, and sends the target data acquisition frequency to each sensor acquisition node in the multiple sensor acquisition nodes in the sensor subnetwork.

[0021] In this embodiment, after the selection process of the main convergence node is completed in the sensor sub-network, the current data acquisition scene setting information (such as temperature data acquisition, working voltage data acquisition and other acquisition scenes) from the server (such as edge server or cloud server, etc.) or user terminal (such as smart phone, tablet computer, etc.) can also be received. Then, the current data acquisition scene setting information is compared and matched with the acquisition scene and acquisition frequency mapping relationship including multiple acquisition scene and acquisition frequency mapping data, and the target acquisition scene and acquisition frequency mapping data with the same acquisition scene as the current data acquisition scene setting information is screened out, and the target data acquisition frequency in the target acquisition scene and acquisition frequency mapping data is sent to each of the multiple sensor acquisition nodes in the sensor sub-network.

[0022] In specific implementation, the mapping relationship between the acquisition scene and the acquisition frequency can also be set to include multiple acquisition scene priorities and acquisition frequency mapping data, and the current acquisition scene priority corresponding to it can also be determined in the main aggregation node based on the current data acquisition scene setting information, and then the target acquisition scene priority and acquisition frequency mapping data corresponding to the current acquisition scene priority are obtained from the multiple acquisition scene priorities and acquisition frequency mapping data, and the target data acquisition frequency corresponding to the target acquisition scene priority and acquisition frequency mapping data is sent to each sensor acquisition node in the multiple sensor acquisition nodes in the sensor subnetwork. For example, for data with low data acquisition priority, the acquisition frequency is reduced; for data with fast changes and high data acquisition priority, the acquisition frequency is increased.

[0023] S120. If the main aggregation node detects a node energy consumption optimization instruction, it obtains the current communication parameters of each sensor node in the multiple sensor nodes in the sensor subnetwork, obtains the adjusted communication parameters corresponding to each sensor node based on a preset communication power control strategy, and sends each adjusted communication parameter to the corresponding sensor node.

[0024] The current communication parameters include at least a communication distance and a communication channel.

[0025] In this embodiment, after the sensor subnetwork determines the main aggregation node, when it detects the node energy consumption optimization instruction at regular or irregular intervals, it is necessary to first obtain the current communication parameters of each sensor node among the multiple sensor nodes in the sensor subnetwork (this current communication parameter can be understood as the parameter saved by the sensor node after the most recent communication parameter adjustment is completed), and then obtain the adjusted communication parameters corresponding to each sensor node through the communication power control strategy and the current communication parameters, and send each adjusted communication parameter to the corresponding sensor node, so as to adjust the communication parameters of each sensor node and ensure that each sensor node maintains a communication connection with the main aggregation node with the optimal communication power.

[0026] In one embodiment, as a first embodiment of the communication power control strategy, as Figure 4 As shown, step S120 includes: S121A, for each sensor node among the multiple sensor nodes of the sensor subnetwork, obtaining a current bit error rate corresponding to the sensor node; S122A. If it is determined that the current bit error rate is greater than a preset bit error rate threshold, obtain a first current transmission power corresponding to the current communication parameter, increase the first current transmission power by a first preset adjustment power value to update the first current transmission power, and form the adjusted communication parameter with the current communication parameter.

[0027] In this embodiment, as the first embodiment of the communication power control strategy, the main convergence node may obtain the current bit error rate for each sensor node in the multiple sensor nodes of the sensor subnetwork (the larger the current bit error rate, the worse the communication quality of the sensor node, and the smaller the current bit error rate, the better the communication quality of the sensor node). For example, taking the current bit error rate of one of the sensor nodes as an example, when it is determined that the current bit error rate of the sensor node is greater than the preset bit error rate threshold, it means that the sensor node can neither ensure the stability of the data transmission process nor maintain the optimal energy consumption when the current transmission power is still maintained. It is necessary to directly obtain or determine the first current transmission power from the current communication parameter corresponding to the sensor node, and then increase the first current transmission power by the first preset adjustment power value to update the first current transmission power, and form the adjustment communication parameter with the current communication parameter. The above power adjustment process is a fine-tuning process, which can not only effectively improve the stability of the data transmission process, but also does not significantly increase the energy consumption of the sensor node.

[0028] In one embodiment, as a second embodiment of the communication power control strategy, as Figure 5 As shown, step S120 includes: S121B, for each sensor node among the multiple sensor nodes of the sensor subnetwork, obtaining a current signal-to-noise ratio corresponding to the sensor node; S122B, if it is determined that the current signal-to-noise ratio is less than a preset signal-to-noise ratio threshold, obtaining a current communication channel corresponding to the current communication parameter, and adjusting to any one of the multiple communication channels corresponding to the main convergence node except the current communication channel, so as to update the current communication channel corresponding to the current communication parameter; S123B, obtaining a second current transmission power corresponding to the current communication parameter, and when determining that the number of historical consecutive adjustments of the communication parameter of the sensor node exceeds a preset adjustment number threshold, increasing the second current transmission power by a second preset adjustment power value to update the second current transmission power, and forming the adjusted communication parameter with the current communication parameter.

[0029] In this embodiment, as a second embodiment of the communication power control strategy, the main aggregation node may obtain the current signal-to-noise ratio for each sensor node among the multiple sensor nodes of the sensor subnetwork (the larger the current signal-to-noise ratio, the better the communication quality of the sensor node, and the smaller the current bit error rate, the better the communication quality of the sensor node). For example, taking the current signal-to-noise ratio of one of the sensor nodes as an example, when it is determined that the current signal-to-noise ratio of the sensor node is less than a preset signal-to-noise ratio threshold, it means that when the sensor node still maintains the current transmission power, it can neither ensure the stability of the data sending process nor maintain the optimal energy consumption. It is necessary to first obtain the current communication channel corresponding to the current communication parameters, and adjust it to any other communication channel among the multiple communication channels corresponding to the main aggregation node except the current communication channel, so as to update the current communication channel corresponding to the current communication parameters. After completing the adjustment of the communication channel between the sensor node and the main aggregation node, the second current transmission power corresponding to the current communication parameter is obtained, and when it is determined that the number of historical continuous adjustments of the communication parameter of the sensor node exceeds the preset adjustment number threshold (such as the preset adjustment number threshold is set to a custom positive integer such as 3, 5, 10), the second current transmission power is increased by the second preset adjustment power value (in specific implementation, the second preset adjustment power value is greater than the first preset adjustment power value in the above example, and can be an integer multiple thereof, that is, the second preset adjustment power value = N1 * the first preset adjustment power value, N1 is a positive integer) to update the second current transmission power, and form the adjustment communication parameter with the current communication parameter. The above power adjustment process can also be a fine-tuning process, which adjusts the communication channel and the transmission power at the same time, which can not only effectively improve the stability of the data transmission process, but also does not significantly increase the energy consumption of the sensor node.

[0030] S130. Each sensor node in the plurality of sensor nodes sends the collected node collection data to the main aggregation node based on the corresponding adjusted communication parameter.

[0031] In this embodiment, after the main aggregation node completes the determination of the adjusted communication parameters of each sensor node in the multiple sensor nodes in the sensor subnetwork, each sensor node sends the collected node collection data to the main aggregation node based on the corresponding adjusted communication parameters, thereby completing the data transmission and aggregation processing process. It should be noted that when steps S120-S130 are executed once, it is an adaptive energy consumption optimization control process of the sensor network performed when the main aggregation node detects the node energy consumption optimization instruction this time and the next node energy consumption optimization instruction has not been detected. After that, each time a node energy consumption optimization instruction is detected, the control process of steps S120-S130 will be repeated. It can be seen that through the above control method, the adaptive energy consumption optimization control of each sensor node in the sensor network is realized.

[0032] In one embodiment, after step S130, the method further includes: If the multiple sensor nodes determine that the main sink node is in a fault state and determine that the total number of sink nodes included in the sensor subnetwork is greater than one, then obtain other sink nodes except the main sink node in the fault state from among the sink nodes in the sensor subnetwork to form a candidate main sink node set; The multiple sensor nodes send the node cache collection data cached locally to one of the sink nodes in the candidate main sink node set based on a preset data balance transmission strategy.

[0033] In this embodiment, when the multiple sensor nodes send node collection data to the corresponding main sink node, the main sink node may fail and cannot be restored to normal in a short time. At this time, when it is determined that the main sink node is in a fault state and the total number of sink nodes included in the sensor sub-network is greater than 1, other sink nodes other than the main sink node in the fault state among several sink nodes in the sensor sub-network are obtained to form a candidate main sink node set. Afterwards, all sink nodes in the candidate main sink node set can receive the node cache collection data previously cached locally by each sensor node in the sensor sub-network, wherein the node cache collection data cached locally by the sensor node can be understood as the data collected by the sensor node from the time when the sensor node detects that the previous main sink node is in a fault state until the candidate main sink node set is determined again. It can be seen that through the above method, when the main sink node fails, other sink nodes in the same sensor sub-network can be quickly used as data receiving nodes to ensure the continuity and stability of the data transmission process.

[0034] In one embodiment, the plurality of sensor nodes send the node cache collection data cached locally to one of the sink nodes in the candidate main sink node set based on a preset data balancing transmission strategy, including: For each sensor node in the multiple sensor nodes, the current real-time data receiving weight value corresponding to each sink node in the candidate main sink node set is obtained, and the sink node with the largest current real-time data receiving weight value is selected and the corresponding sending node cache collects data.

[0035] In this embodiment, when multiple sensor nodes send the node cache collection data cached locally to one of the aggregation nodes in the candidate main aggregation node set based on the preset data balancing transmission strategy, each sensor node can first determine the current real-time data receiving weight value corresponding to a aggregation node from the candidate main aggregation node set (for example, when each aggregation node in the candidate main aggregation node set receives a node collection data, the inverse of the total number of data received is used as the current real-time data receiving weight value), and select the aggregation node with the largest current real-time data receiving weight value and send the node cache collection data accordingly. It can be seen that through the above method, the data reception amount of each aggregation node in the candidate main aggregation node set can be effectively adjusted, so that each aggregation node can receive the node cache collection data from the sensor node in a balanced manner.

[0036] It can be seen that the implementation embodiment of this method can first intelligently determine the main aggregation node in each sensor subnetwork in the sensor network, and then dynamically adjust the communication parameters of each sensor node based on the communication power control strategy at the main aggregation node to achieve adaptive energy consumption optimization control during the communication process and ensure the communication quality between the sensor node and the main aggregation node.

[0037] Figure 6 is a schematic block diagram of an adaptive energy consumption optimization control system for a sensor network provided by an embodiment of the present invention. Figure 6 As shown, corresponding to the above sensor network adaptive energy consumption optimization control method, the present invention also provides a sensor network adaptive energy consumption optimization control system. Figure 1 and Figure 6 As shown, the adaptive energy consumption optimization control system 10 of the sensor network includes multiple sensor sub-networks 11, each sensor sub-network 100 in the multiple sensor sub-networks 11 includes a number of aggregation nodes 110 and a number of sensor nodes 120, and the several aggregation nodes and the multiple sensor nodes 120 in each sensor sub-network 100 are all communicatively connected.

[0038] The plurality of sink nodes 110 in the sensor sub-network 100 are used to determine one of the sink nodes as a master sink node based on a preset master node screening strategy.

[0039] In this embodiment, the adaptive energy consumption optimization control system of the sensor network includes multiple sensor sub-networks, and the adaptive energy consumption optimization control method of the sensor network can be executed in each sensor sub-network. At this time, the technical solution of the present application is described by taking the process of executing the adaptive energy consumption optimization control method of the sensor network in a sensor sub-network as an example.

[0040] Because a sensor sub-network includes several sink nodes and multiple sensor nodes, when the total number of sink nodes in the sensor sub-network is greater than 1, one of the sink nodes can be determined as the main sink node based on the preset main node screening strategy. After that, the sensor data collected by the sensor nodes in the sensor sub-network are transmitted to the main sink node.

[0041] In one embodiment, the plurality of sink nodes 110 in the sensor sub-network 100 are specifically used for: For each of the plurality of sink nodes, the sink node obtains current data collection scene information and obtains a locally pre-configured scene preference processing tag; If the sink node determines that the current data collection scenario information is the same as the scenario preference processing label, then the node information of the sink node is added to the candidate main sink node set; If the sink node determines that the number of sink nodes included in the candidate main sink node set is one, the sink node included in the candidate main sink node set is used as the main sink node; If the aggregation node determines that the number of aggregation nodes included in the candidate main aggregation node set is greater than one, it obtains the system performance parameters corresponding to each aggregation node in the candidate main aggregation node set, and uses the aggregation node with the best system performance parameters as the main aggregation node.

[0042] In this embodiment, when initialization configuration, regular configuration or irregular configuration is performed in the sensor subnetwork, it is necessary to complete the determination of the main aggregation node during the configuration process. Specifically, first, a aggregation node is randomly selected in the sensor subnetwork as a temporary main aggregation node; then, each aggregation node in the sensor subnetwork first sends its own scene preference processing tags stored locally to the temporary main aggregation node (wherein, the scene preference processing tags of the temporary main aggregation node itself are also known); then, because the temporary main aggregation node receives the current data collection scene information (such as temperature data collection, working voltage data collection and other collection scenes) sent by the server (such as edge server or cloud server, etc.) or user terminal, it can also compare and match the scene preference processing tags of each aggregation node with the current data collection scene information. After determining that there is a aggregation node If the scenario preference processing label is the same as the current data collection scenario information, the node information of the aggregation node is added to the candidate main aggregation node set; finally, in the temporary main aggregation node, if it is determined that the number of aggregation nodes included in the candidate main aggregation node set is one, the aggregation node included in the candidate main aggregation node set is used as the main aggregation node; or if it is determined that the number of aggregation nodes included in the candidate main aggregation node set is greater than one, the system performance parameters (such as CPU utilization, memory utilization, request response time, etc.) corresponding to each aggregation node in the candidate main aggregation node set are obtained, and the aggregation node with the best system performance parameters is used as the main aggregation node.

[0043] In one embodiment, the master aggregation node is used to: If the current data acquisition scene setting information is detected, the target data acquisition frequency corresponding to the current data acquisition scene setting information is determined based on the locally preset acquisition scene and acquisition frequency mapping relationship, and the target data acquisition frequency is sent to each sensor acquisition node in the multiple sensor acquisition nodes in the sensor subnetwork.

[0044] In this embodiment, after the selection process of the main convergence node is completed in the sensor sub-network, the current data acquisition scene setting information (such as temperature data acquisition, working voltage data acquisition and other acquisition scenes) from the server (such as edge server or cloud server, etc.) or user terminal (such as smart phone, tablet computer, etc.) can also be received. Then, the current data acquisition scene setting information is compared and matched with the acquisition scene and acquisition frequency mapping relationship including multiple acquisition scene and acquisition frequency mapping data, and the target acquisition scene and acquisition frequency mapping data with the same acquisition scene as the current data acquisition scene setting information is screened out, and the target data acquisition frequency in the target acquisition scene and acquisition frequency mapping data is sent to each of the multiple sensor acquisition nodes in the sensor sub-network.

[0045] In specific implementation, the mapping relationship between the acquisition scene and the acquisition frequency can also be set to include multiple acquisition scene priorities and acquisition frequency mapping data, and the current acquisition scene priority corresponding to it can also be determined in the main aggregation node based on the current data acquisition scene setting information, and then the target acquisition scene priority and acquisition frequency mapping data corresponding to the current acquisition scene priority are obtained from the multiple acquisition scene priorities and acquisition frequency mapping data, and the target data acquisition frequency corresponding to the target acquisition scene priority and acquisition frequency mapping data is sent to each sensor acquisition node in the multiple sensor acquisition nodes in the sensor subnetwork. For example, for data with low data acquisition priority, the acquisition frequency is reduced; for data with fast changes and high data acquisition priority, the acquisition frequency is increased.

[0046] The main aggregation node is used to obtain the current communication parameters of each sensor node in the multiple sensor nodes in the sensor subnetwork if a node energy consumption optimization instruction is detected, obtain the adjusted communication parameters corresponding to each sensor node based on a preset communication power control strategy, and send each adjusted communication parameter to the corresponding sensor node.

[0047] The current communication parameters include at least a communication distance and a communication channel.

[0048] In this embodiment, after the sensor subnetwork determines the main aggregation node, when it detects the node energy consumption optimization instruction at regular or irregular intervals, it is necessary to first obtain the current communication parameters of each sensor node among the multiple sensor nodes in the sensor subnetwork (this current communication parameter can be understood as the parameter saved by the sensor node after the most recent communication parameter adjustment is completed), and then obtain the adjusted communication parameters corresponding to each sensor node through the communication power control strategy and the current communication parameters, and send each adjusted communication parameter to the corresponding sensor node, so as to adjust the communication parameters of each sensor node and ensure that each sensor node maintains a communication connection with the main aggregation node with the optimal communication power.

[0049] In one embodiment, as a first embodiment of the communication power control strategy, the main convergence node is specifically used for: For each sensor node among the plurality of sensor nodes of the sensor subnetwork, obtaining a current bit error rate corresponding to the sensor node; If it is determined that the current bit error rate is greater than a preset bit error rate threshold, a first current transmission power corresponding to the current communication parameter is obtained, the first current transmission power is increased by a first preset adjustment power value to update the first current transmission power, and the first current transmission power is combined with the current communication parameter to form the adjusted communication parameter.

[0050] In this embodiment, as the first embodiment of the communication power control strategy, the main convergence node may obtain the current bit error rate for each sensor node in the multiple sensor nodes of the sensor subnetwork (the larger the current bit error rate, the worse the communication quality of the sensor node, and the smaller the current bit error rate, the better the communication quality of the sensor node). For example, taking the current bit error rate of one of the sensor nodes as an example, when it is determined that the current bit error rate of the sensor node is greater than the preset bit error rate threshold, it means that the sensor node can neither ensure the stability of the data transmission process nor maintain the optimal energy consumption when the current transmission power is still maintained. It is necessary to directly obtain or determine the first current transmission power from the current communication parameter corresponding to the sensor node, and then increase the first current transmission power by the first preset adjustment power value to update the first current transmission power, and form the adjustment communication parameter with the current communication parameter. The above power adjustment process is a fine-tuning process, which can not only effectively improve the stability of the data transmission process, but also does not significantly increase the energy consumption of the sensor node.

[0051] In one embodiment, as a second embodiment of the communication power control strategy, the main convergence node is specifically used for: For each sensor node among the plurality of sensor nodes of the sensor subnetwork, obtaining a current signal-to-noise ratio corresponding to the sensor node; If it is determined that the current signal-to-noise ratio is less than a preset signal-to-noise ratio threshold, the current communication channel corresponding to the current communication parameter is obtained, and adjusted to any one of the multiple communication channels corresponding to the main convergence node except the current communication channel, so as to update the current communication channel corresponding to the current communication parameter; Obtain a second current transmission power corresponding to the current communication parameter, and when it is determined that the number of historical continuous adjustments of the communication parameter of the sensor node exceeds a preset adjustment number threshold, increase the second current transmission power by a second preset adjustment power value to update the second current transmission power, and form the adjusted communication parameter with the current communication parameter.

[0052] In this embodiment, as a second embodiment of the communication power control strategy, the main aggregation node may obtain the current signal-to-noise ratio for each sensor node among the multiple sensor nodes of the sensor subnetwork (the larger the current signal-to-noise ratio, the better the communication quality of the sensor node, and the smaller the current bit error rate, the better the communication quality of the sensor node). For example, taking the current signal-to-noise ratio of one of the sensor nodes as an example, when it is determined that the current signal-to-noise ratio of the sensor node is less than a preset signal-to-noise ratio threshold, it means that when the sensor node still maintains the current transmission power, it can neither ensure the stability of the data sending process nor maintain the optimal energy consumption. It is necessary to first obtain the current communication channel corresponding to the current communication parameters, and adjust it to any other communication channel among the multiple communication channels corresponding to the main aggregation node except the current communication channel, so as to update the current communication channel corresponding to the current communication parameters. After completing the adjustment of the communication channel between the sensor node and the main aggregation node, the second current transmission power corresponding to the current communication parameter is obtained, and when it is determined that the number of historical continuous adjustments of the communication parameter of the sensor node exceeds the preset adjustment number threshold (such as the preset adjustment number threshold is set to a custom positive integer such as 3, 5, 10), the second current transmission power is increased by the second preset adjustment power value (in specific implementation, the second preset adjustment power value is greater than the first preset adjustment power value in the above example, and can be an integer multiple thereof, that is, the second preset adjustment power value = N1 * the first preset adjustment power value, N1 is a positive integer) to update the second current transmission power, and form the adjustment communication parameter with the current communication parameter. The above power adjustment process can also be a fine-tuning process, which adjusts the communication channel and the transmission power at the same time, which can not only effectively improve the stability of the data transmission process, but also does not significantly increase the energy consumption of the sensor node.

[0053] Each sensor node in the plurality of sensor nodes 120 is configured to send the collected node collection data to the main aggregation node based on the corresponding adjusted communication parameters.

[0054] In this embodiment, after the main aggregation node completes the determination of the adjusted communication parameters of each sensor node in the multiple sensor nodes in the sensor sub-network, each sensor node sends the collected node collection data to the main aggregation node based on the corresponding adjusted communication parameters, thereby completing the data transmission and aggregation processing process. It can be seen that through the above control method, adaptive energy consumption optimization control of each sensor node in the sensor network is realized.

[0055] In one embodiment, the plurality of sensor nodes 120 are further configured to: If it is determined that the main sink node is in a fault state and it is determined that the total number of sink nodes included in the sensor subnetwork is greater than one, then obtaining other sink nodes except the main sink node in the fault state from among a number of sink nodes in the sensor subnetwork to form a set of candidate main sink nodes; The multiple sensor nodes send the node cache collection data cached locally to one of the sink nodes in the candidate main sink node set based on a preset data balance transmission strategy.

[0056] In this embodiment, when the multiple sensor nodes send node collection data to the corresponding main sink node, the main sink node may fail and cannot be restored to normal in a short time. At this time, when it is determined that the main sink node is in a fault state and the total number of sink nodes included in the sensor sub-network is greater than 1, other sink nodes other than the main sink node in the fault state among several sink nodes in the sensor sub-network are obtained to form a candidate main sink node set. Afterwards, all sink nodes in the candidate main sink node set can receive the node cache collection data previously cached locally by each sensor node in the sensor sub-network, wherein the node cache collection data cached locally by the sensor node can be understood as the data collected by the sensor node from the time when the sensor node detects that the previous main sink node is in a fault state until the candidate main sink node set is determined again. It can be seen that through the above method, when the main sink node fails, other sink nodes in the same sensor sub-network can be quickly used as data receiving nodes to ensure the continuity and stability of the data transmission process.

[0057] In one embodiment, the plurality of sensor nodes send the node cache collection data cached locally to one of the sink nodes in the candidate main sink node set based on a preset data balancing transmission strategy, including: For each sensor node in the multiple sensor nodes, the current real-time data receiving weight value corresponding to each sink node in the candidate main sink node set is obtained, and the sink node with the largest current real-time data receiving weight value is selected and the corresponding sending node cache collects data.

[0058] In this embodiment, when multiple sensor nodes send the node cache collection data cached locally to one of the aggregation nodes in the candidate main aggregation node set based on the preset data balancing transmission strategy, each sensor node can first determine the current real-time data receiving weight value corresponding to a aggregation node from the candidate main aggregation node set (for example, when each aggregation node in the candidate main aggregation node set receives a node collection data, the inverse of the total number of data received is used as the current real-time data receiving weight value), and select the aggregation node with the largest current real-time data receiving weight value and send the node cache collection data accordingly. It can be seen that through the above method, the data reception amount of each aggregation node in the candidate main aggregation node set can be effectively adjusted, so that each aggregation node can receive the node cache collection data from the sensor node in a balanced manner.

[0059] It can be seen that the implementation example of the system can first intelligently determine the main aggregation node in each sensor subnetwork in the sensor network, and then dynamically adjust the communication parameters of each sensor node based on the communication power control strategy at the main aggregation node to achieve adaptive energy consumption optimization control during the communication process and ensure the communication quality between the sensor node and the main aggregation node.

[0060] The above-mentioned adaptive energy consumption optimization control system of the sensor network can be implemented in the form of a computer program. The computer program can be used in Figure 7 Runs on the computer device shown.

[0061] See also Figure 7 , Figure 7 The present invention provides a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device integrates any one of the sensor network adaptive energy consumption optimization control systems provided by an embodiment of the present invention.

[0062] See also Figure 7 The computer device 400 includes a processor 402 , a memory and a network interface 405 connected via a system bus 401 , wherein the memory may include a storage medium 403 and an internal memory 404 .

[0063] The storage medium 403 can store an operating system 4031 and a computer program 4032. The computer program 4032 includes program instructions, and when the program instructions are executed, the processor 402 can execute an adaptive energy consumption optimization control method for a sensor network.

[0064] The processor 402 is used to provide computing and control capabilities to support the operation of the entire computer device.

[0065] The internal memory 404 provides an environment for the operation of the computer program 4032 in the storage medium 403. When the computer program 4032 is executed by the processor 402, the processor 402 can execute the above-mentioned adaptive energy consumption optimization control method for the sensor network.

[0066] The network interface 405 is used to communicate with other devices over the network. Figure 7 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0067] The processor 402 is used to run a computer program 4032 stored in the memory to implement the above-mentioned adaptive energy consumption optimization control method for the sensor network.

[0068] It should be understood that in the embodiment of the present invention, the processor 402 may be a central processing unit (CPU), and the processor 402 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0069] It can be understood by those skilled in the art that all or part of the processes in the method for implementing the above embodiment can be completed by instructing the relevant hardware through a computer program. The computer program includes program instructions, and the computer program can be stored in a storage medium, which is a computer-readable storage medium. The program instructions are executed by at least one processor in the computer system to implement the process steps of the embodiment of the above method.

[0070] Therefore, the present invention also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor executes the above-mentioned adaptive energy consumption optimization control method for a sensor network.

[0071] The storage medium may be a USB flash drive, a mobile hard disk, a read-only memory (ROM), a magnetic disk, or an optical disk, etc., which are computer-readable storage media that can store program codes.

[0072] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0073] In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of each unit is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed.

[0074] The steps in the method of the embodiment of the present invention can be adjusted in order, combined and deleted according to actual needs. The units in the device of the embodiment of the present invention can be combined, divided and deleted according to actual needs. In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0075] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, terminal, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention.

[0076] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An adaptive energy consumption optimization control method for a sensor network, applied to an adaptive energy consumption optimization control system for a sensor network, characterized in that: The adaptive energy consumption optimization control system of the sensor network includes multiple sensor sub-networks, each of which includes multiple aggregation nodes and multiple sensor nodes, and the multiple aggregation nodes and multiple sensor nodes in each sensor sub-network are all communicatively connected; for each sensor sub-network in the adaptive energy consumption optimization control system of the sensor network, the adaptive energy consumption optimization control method of the sensor network includes: The plurality of sink nodes in the sensor sub-network determine one of the sink nodes as a master sink node based on a preset master node screening strategy; If the master aggregation node detects a node energy consumption optimization instruction, it obtains the current communication parameters of each sensor node in the multiple sensor nodes in the sensor subnetwork, obtains the adjusted communication parameters corresponding to each sensor node based on a preset communication power control strategy, and sends each adjusted communication parameter to the corresponding sensor node; wherein the current communication parameters include at least a communication distance and a communication channel; Each sensor node in the plurality of sensor nodes sends the collected node collection data to the main aggregation node based on the corresponding adjusted communication parameter.

2. The method according to claim 1, characterized in that: After the step of each sensor node in the plurality of sensor nodes sending the collected node collection data to the main aggregation node based on the corresponding adjusted communication parameter, the method further includes: If the multiple sensor nodes determine that the main sink node is in a fault state and determine that the total number of sink nodes included in the sensor subnetwork is greater than one, then obtain other sink nodes except the main sink node in the fault state from among the sink nodes in the sensor subnetwork to form a candidate main sink node set; The multiple sensor nodes send the node cache collection data cached locally to one of the sink nodes in the candidate main sink node set based on a preset data balance transmission strategy.

3. The method according to claim 1, characterized in that The plurality of sink nodes determine one of the sink nodes as the master sink node based on a preset master node screening strategy, including: For each of the plurality of sink nodes, the sink node obtains current data collection scene information and obtains a locally pre-configured scene preference processing tag; If the sink node determines that the current data collection scenario information is the same as the scenario preference processing label, then the node information of the sink node is added to the candidate main sink node set; If the sink node determines that the number of sink nodes included in the candidate main sink node set is one, the sink node included in the candidate main sink node set is used as the main sink node; If the aggregation node determines that the number of aggregation nodes included in the candidate main aggregation node set is greater than one, it obtains the system performance parameters corresponding to each aggregation node in the candidate main aggregation node set, and uses the aggregation node with the best system performance parameters as the main aggregation node.

4. The method according to claim 1, characterized in that: The step of obtaining the current communication parameters of each sensor node in the plurality of sensor nodes in the sensor subnetwork and obtaining the adjusted communication parameters corresponding to each sensor node based on a preset communication power control strategy includes: For each sensor node among the plurality of sensor nodes of the sensor subnetwork, obtaining a current bit error rate corresponding to the sensor node; If it is determined that the current bit error rate is greater than a preset bit error rate threshold, a first current transmission power corresponding to the current communication parameter is obtained, the first current transmission power is increased by a first preset adjustment power value to update the first current transmission power, and the first current transmission power is combined with the current communication parameter to form the adjusted communication parameter.

5. The method according to claim 1, characterized in that The step of obtaining the current communication parameters of each sensor node in the plurality of sensor nodes in the sensor subnetwork and obtaining the adjusted communication parameters corresponding to each sensor node based on a preset communication power control strategy includes: For each sensor node among the plurality of sensor nodes of the sensor subnetwork, obtaining a current signal-to-noise ratio corresponding to the sensor node; If it is determined that the current signal-to-noise ratio is less than a preset signal-to-noise ratio threshold, the current communication channel corresponding to the current communication parameter is obtained, and adjusted to any other communication channel among the multiple communication channels corresponding to the main convergence node except the current communication channel, so as to update the current communication channel corresponding to the current communication parameter; Obtain a second current transmission power corresponding to the current communication parameter, and when it is determined that the number of historical continuous adjustments of the communication parameter of the sensor node exceeds a preset adjustment number threshold, increase the second current transmission power by a second preset adjustment power value to update the second current transmission power, and form the adjusted communication parameter with the current communication parameter.

6. The method according to claim 1, characterized in that After the step of determining, by a plurality of sink nodes in the sensor subnetwork, one of the sink nodes as a master sink node based on a preset master node screening strategy, the method further comprises: If the main aggregation node detects the current data acquisition scene setting information, it determines the target data acquisition frequency corresponding to the current data acquisition scene setting information based on the locally preset acquisition scene and acquisition frequency mapping relationship, and sends the target data acquisition frequency to each sensor acquisition node in the multiple sensor acquisition nodes in the sensor subnetwork.

7. The method according to claim 2, characterized in that: The multiple sensor nodes send the node cache collection data cached locally to one of the sink nodes in the candidate main sink node set based on a preset data balancing transmission strategy, including: For each sensor node in the plurality of sensor nodes, the current real-time data receiving weight value corresponding to each sink node in the candidate main sink node set is obtained, and the sink node with the largest current real-time data receiving weight value is selected and the corresponding sending node cache collects data.

8. An adaptive energy consumption optimization control system for a sensor network, characterized in that: The invention comprises a plurality of sensor sub-networks, each of which comprises a plurality of sink nodes and a plurality of sensor nodes, and the plurality of sink nodes and the plurality of sensor nodes in each sensor sub-network are communicatively connected; for each sensor sub-network in the adaptive energy consumption optimization control system of the sensor network: The plurality of sink nodes in the sensor sub-network determine one of the sink nodes as a master sink node based on a preset master node screening strategy; If the master aggregation node detects a node energy consumption optimization instruction, it obtains the current communication parameters of each sensor node in the multiple sensor nodes in the sensor subnetwork, obtains the adjusted communication parameters corresponding to each sensor node based on a preset communication power control strategy, and sends each adjusted communication parameter to the corresponding sensor node; wherein the current communication parameters include at least a communication distance and a communication channel; Each sensor node in the plurality of sensor nodes is used to send the collected node collection data to the main aggregation node based on the corresponding adjusted communication parameters.

9. The adaptive energy consumption optimization control system of the sensor network according to claim 8, characterized in that: The plurality of sensor nodes are further configured to: If it is determined that the main sink node is in a fault state and it is determined that the total number of sink nodes included in the sensor subnetwork is greater than one, then obtaining other sink nodes except the main sink node in the fault state from among a number of sink nodes in the sensor subnetwork to form a candidate main sink node set; The node cache collection data cached locally is sent to one of the aggregation nodes in the candidate main aggregation node set based on a preset data balancing transmission strategy.

10. The adaptive energy consumption optimization control system of the sensor network according to claim 8, characterized in that: The plurality of sink nodes determine one of the sink nodes as the master sink node based on a preset master node screening strategy, including: For each of the plurality of sink nodes, the sink node obtains current data collection scene information and obtains a locally pre-configured scene preference processing tag; If the sink node determines that the current data collection scenario information is the same as the scenario preference processing label, then the node information of the sink node is added to the candidate main sink node set; If the sink node determines that the number of sink nodes included in the candidate main sink node set is one, the sink node included in the candidate main sink node set is used as the main sink node; If the aggregation node determines that the number of aggregation nodes included in the candidate main aggregation node set is greater than one, it obtains the system performance parameters corresponding to each aggregation node in the candidate main aggregation node set, and uses the aggregation node with the best system performance parameters as the main aggregation node.

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