Cooperative regulation and control method and system for communication anti-interference parameters

Abstract

The invention relates to the technical field of data communication, and particularly discloses a communication anti-interference parameter cooperative regulation and control method and system, and the method comprises the steps: obtaining energy state information; adjusting the frequency and fineness of environment perception activities, and perceiving and collecting environment perception information; determining a report format and a sending frequency according to the energy state information and the communication parameter set, and generating cooperative information according to the energy state information and the environment perception information based on the report format; the cooperative information is uploaded to the sink node based on the sending frequency, so that the sink node generates a parameter adjustment instruction according to the cooperative information and issues the parameter adjustment instruction to the corresponding sensor node; receiving a parameter adjustment instruction, and updating the communication parameter set based on the parameter adjustment instruction; according to the method, an energy sensing type self-adaptive closed-loop control mechanism is formed, and the problems of energy waste and response lag caused by a traditional fixed or non-energy sensing regulation and control mode when the energy of the wireless sensor network is limited and the environment is dynamically changed are effectively solved.

Description

Technical Field

[0001] This application relates to the field of data communication technologies, and in particular, to a method and system for collaborative regulation of communication anti-interference parameters. Background Art

[0002] Wireless sensor networks are widely used in large-scale environmental monitoring. Their nodes usually rely on battery power supply and are deployed in complex and changing wireless environments. Existing technologies achieve anti-interference transmission by dynamically adjusting the communication parameter set, and nodes collaborate to share channel state information to optimize network performance. This mechanism shows significant potential in improving communication reliability. With the expansion of network scale and the enhancement of environmental dynamics, how to maintain efficient collaborative regulation under energy constraints has become a key direction for technological development.

[0003] However, the existing collaborative regulation mechanism of wireless sensors faces multiple bottlenecks in energy-constrained scenarios. Traditional methods adopt an environmental perception mode with a fixed frequency and fail to dynamically adjust the perception intensity according to the remaining energy of nodes, resulting in premature failure of high-energy-consuming nodes. The information interaction between nodes lacks an energy perception mechanism and it is difficult to balance communication quality and energy consumption control when the channel state changes drastically. The massive data transmission requirements generated by the centralized decision-making mode exacerbate the energy burden of edge nodes, and the fixed-period parameter update strategy cannot match the real-time requirements of environmental changes, resulting in a contradictory situation where regulation lags and energy is wasted coexist.

[0004] In response to the above problems, there is currently no effective technical solution. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for collaborative regulation of communication anti-interference parameters to solve the problems of energy waste and response lag caused by traditional fixed or non-energy-aware regulation methods in wireless sensor networks under energy constraints and dynamic environmental changes.

[0006] In a first aspect, this application provides a method for collaborative regulation of communication anti-interference parameters, which is applied to sensor nodes in a wireless sensor network. The method includes the following steps: S1. Obtain the energy status information of the sensor node; S2. Adjust the frequency and fineness of the environmental perception activity according to the energy status information and the communication parameter set, and then sense and collect environmental perception information; S3. Determine the report format and transmission frequency according to the energy status information and the communication parameter set, and generate collaborative information based on the energy status information and the environmental perception information according to the report format; S4. Upload the collaborative information to the aggregation node based on the transmission frequency, so that the aggregation node generates a parameter adjustment instruction according to the collaborative information and issues it to the corresponding sensor node; S5. Receive a parameter adjustment instruction and update the communication parameter set based on the parameter adjustment instruction.

[0007] The method of the present application combines the energy state information of the sensor node with the adjustment of the environmental perception activity and the determination of the information reporting format and transmission frequency, and introduces the collaborative regulation of the sink node to form an energy-aware adaptive closed-loop control mechanism, effectively solving the problems of energy waste and response lag caused by traditional fixed or non-energy-aware regulation methods in wireless sensor networks under energy constraints and dynamic environmental changes.

[0008] The communication anti-interference parameter collaborative regulation method, wherein the communication parameter set includes a sensing frequency set and a sensing fineness set; Step S2 includes: S21. Determine the current energy level according to the energy state information based on a preset hierarchical rule; S22. Determine the frequency and fineness of the environmental perception activity according to the current energy level based on the sensing frequency set and the sensing fineness set.

[0009] Through the above processing, the present application provides a clear and structured mechanism for adjusting the frequency and fineness of the environmental perception activity according to the energy state information and the communication parameter set, solves the problem of lack of specific adjustment methods, makes the adjustment process more refined, fully considers the energy constraint, and improves the energy management efficiency.

[0010] The communication anti-interference parameter collaborative regulation method, wherein the communication parameter set further includes an anti-interference parameter set; Step S2 further includes: S23. Monitor the environment where the sensor node is located and determine whether there is an interference source; S24. When there is an interference source, obtain the interference type and interference intensity, and compensate and adjust the frequency and fineness of the environmental perception activity based on the anti-interference parameter set according to the interference type and the interference intensity.

[0011] The communication anti-interference parameter collaborative regulation method, wherein the anti-interference parameter set contains multiple interference types and corresponding frequency adjustment factors and fineness adjustment factors; in step S24, the step of compensating and adjusting the frequency and fineness of the environmental perception activity based on the anti-interference parameter set according to the interference type and the interference intensity includes: S241. Select the corresponding frequency adjustment factor and fineness adjustment factor from the anti-interference parameter set according to the interference type; S242. Normalize the interference intensity, and use the normalized interference intensity to weight the frequency adjustment factor and the fineness adjustment factor to obtain the frequency adjustment coefficient and the fineness adjustment coefficient; S243. Based on the frequency adjustment coefficient and the fineness adjustment coefficient, compensate and adjust the frequency and fineness of the environment perception activity.

[0012] The communication anti-interference parameter collaborative regulation method, wherein the communication parameter set includes a report format list and a transmission frequency set; step S3 includes: S31. Select a report format and a transmission frequency from a preset report format list and transmission frequency set based on the current energy level determined by using the energy state information; S32. Extract key environment perception parameters from the environment perception information according to the selected report format, and use a preset compression algorithm to perform compression encoding on the key environment perception parameters to obtain compressed environment perception data; S33. Package the energy state information and the compressed environment perception data according to the selected report format to generate collaborative information.

[0013] The communication anti-interference parameter collaborative regulation method, wherein there are multiple preset compression algorithms; in step S32, the step of using a preset compression algorithm to perform compression encoding on the key environment perception parameters to obtain compressed environment perception data includes: S321. Select a compression algorithm according to the current energy level and the data type of the key environment perception parameters; S322. Use the selected compression algorithm to perform compression encoding on the extracted key environment perception parameters to generate compressed environment perception data.

[0014] The communication anti-interference parameter collaborative regulation method, wherein the process of the aggregation node generating a parameter adjustment instruction according to the collaborative information and sending it to the corresponding sensor node includes: A1. The aggregation node receives the collaborative information uploaded by each sensor node, and analyzes the collaborative information to extract the energy state information and the environment perception information of each sensor node; A2. The aggregation node constructs a hierarchical transmission mechanism according to the extracted energy state information; A3. The aggregation node calculates the expected parameter adjustment amount of each sensor node for different types of parameters in the communication parameter set according to the extracted environment perception information, and then combines the extracted energy state information to correct the corresponding expected parameter adjustment amount to obtain the actual parameter adjustment amount; A4. The aggregation node generates a parameter adjustment instruction including the node identifier and the parameter adjustment value according to the actual parameter adjustment amount; A5. The sink node issues the parameter adjustment instruction to the corresponding sensor node according to the hierarchical transmission mechanism.

[0015] For the communication anti-interference parameter collaborative regulation method, in step A3, the process by which the sink node calculates the expected parameter adjustment amounts of each sensor node for different types of parameters in the communication parameter set according to the extracted environmental perception information includes: A31. For the environmental perception information of each sensor node extracted, use the fuzzy clustering algorithm for data fusion to obtain multiple information clusters, and each information cluster represents an environmental state region; A32. Calculate the weights of each environmental state region according to the number of nodes in the information cluster and the central position of the cluster; A33. For each sensor node, calculate the expected parameter adjustment amounts of the sensor node for different types of parameters in the communication parameter set according to the weight of the cluster to which it belongs and the deviation between the environmental perception information of the sensor node and the environmental perception information of the cluster center.

[0016] For the communication anti-interference parameter collaborative regulation method, in step A3, the process of combining the extracted energy state information to correct the corresponding expected parameter adjustment amount to obtain the actual parameter adjustment amount includes: A34. Extract the correction coefficient from the preset correction coefficient set according to the energy level interval where the extracted energy state information is located; A35. Multiply the correction coefficient by the expected parameter adjustment amount to obtain the actual parameter adjustment amount.

[0017] In a second aspect, the present application also provides a communication anti-interference parameter collaborative regulation system, which is applied to a wireless sensor network. The system includes: a plurality of sensor nodes and a sink node; The sensor node includes: A monitoring module, configured to obtain the energy state information of the sensor node; A sensing module, configured to adjust the frequency and fineness of the environmental perception activity according to the energy state information and the communication parameter set, and then sense and collect the environmental perception information; A report generation module, configured to determine the report format and transmission frequency according to the energy state information and the communication parameter set, and generate collaborative information based on the report format according to the energy state information and the environmental perception information; An upload module, configured to upload the collaborative information to the sink node based on the transmission frequency; An update module, configured to receive the parameter adjustment instruction and update the communication parameter set based on the parameter adjustment instruction; The aggregation node is used to generate a parameter adjustment instruction according to the collaboration information and send it to the corresponding sensor node.

[0018] The system of the present application combines the energy state information of the sensor node with the adjustment of the environmental perception activity and the determination of the information reporting format and sending frequency, and introduces the collaborative regulation of the aggregation node to form an energy-aware adaptive closed-loop control mechanism, effectively solving the problems of energy waste and response lag caused by traditional fixed or non-energy-aware regulation methods in wireless sensor networks under energy constraints and dynamic environmental changes.

[0019] As can be seen from the above, the present application provides a method and system for collaborative regulation of communication anti-interference parameters. Among them, the method of the present application enables the sensor node to sense its own energy state and adaptively adjust its sensing and communication behaviors, significantly reducing the energy consumption of the node and extending the overall survival time of the network. Through the global collaborative regulation of the aggregation node and the real-time issuance of parameter adjustment instructions, it can respond to environmental changes and interference more quickly and accurately, improving the reliability and anti-interference ability of communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a flowchart of the method for collaborative regulation of communication anti-interference parameters provided by the embodiment of the present application.

[0021] Figure 2 It is a schematic structural diagram of the system for collaborative regulation of communication anti-interference parameters provided by the embodiment of the present application.

[0022] Figure 3 It is a schematic structural diagram of the sensor node.

[0023] Reference numerals: 100, sensor node; 200, aggregation node; 101, monitoring module; 102, sensing module; 103, report generation module; 104, upload module; 105, update module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application claimed, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0025] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, terms such as "first" and "second" are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0026] In a first aspect, please refer to Figure 1 , some embodiments of this application provide a method for collaborative regulation of communication anti-interference parameters, which is applied to sensor nodes in a wireless sensor network. The method includes the following steps: S1. Obtain the energy state information of the sensor node; S2. Adjust the frequency and fineness of the environmental perception activity according to the energy state information and the communication parameter set, and then sense and collect environmental perception information; S3. Determine the report format and transmission frequency according to the energy state information and the communication parameter set, and generate collaborative information based on the report format, according to the energy state information and the environmental perception information; S4. Upload the collaborative information to the aggregation node based on the transmission frequency, so that the aggregation node generates a parameter adjustment instruction according to the collaborative information and issues it to the corresponding sensor node; S5. Receive the parameter adjustment instruction and update the communication parameter set based on the parameter adjustment instruction.

[0027] Specifically, the energy state information refers to the current energy reserve situation of the sensor node, which can be obtained in various ways, such as by monitoring the battery voltage, estimating the remaining battery percentage, or recording the energy consumption history. Its main purpose is to enable the node to perceive its own energy limitations and provide basic data for subsequent energy-aware behavior adjustment.

[0028] More specifically, the communication parameter set refers to a set of adjustable parameters that affect the environmental perception activities and information transmission behaviors of the sensor node. This parameter set can include parameter types such as perception frequency, perception fineness, report format, and transmission frequency. Its main purpose is to provide a control space that can be dynamically adjusted by the node and the aggregation node to adapt to different environmental conditions and energy states.

[0029] More specifically, adjusting the frequency and fineness of the environmental perception activity means changing the frequency and fineness of the sensor node for environmental data collection according to specific conditions. This adjustment can reduce or increase the energy consumption and data volume of the perception process. Its main purpose is to enable the node's perception behavior to be adaptive according to the energy state and environmental requirements, and balance the perception coverage and energy efficiency.

[0030] More specifically, the report format refers to the data structure or content organization method adopted by sensor nodes when sending environmental perception information and their own status information to the superior node (i.e., the sink node). Different report formats can contain different amounts or different levels of detail of information, mainly to affect communication energy consumption by controlling the amount of transmitted data and achieve energy-aware communication.

[0031] More specifically, the sending frequency refers to the rate or time interval at which sensor nodes send collaborative information to the superior node. Adjusting the sending frequency can control the number of communications, mainly to affect communication energy consumption by controlling the communication frequency and achieve energy-aware communication.

[0032] More specifically, the collaborative information refers to the information carrier generated by sensor nodes based on their own status and environmental perception results and uploaded to the sink node. This information usually includes the energy status information and environmental perception information of the nodes, mainly to converge the key information at the node level to the sink node and provide necessary data support for the global decision-making of the sink node.

[0033] More specifically, after analyzing and processing the received collaborative information, the sink node issues parameter adjustment instructions to each lower-level sensor node to guide it to update the communication parameter set. The parameter adjustment instructions are mainly to achieve centralized or distributed collaborative control of each node in the network by the sink node.

[0034] More specifically, updating the communication parameter set means that after receiving the parameter adjustment instructions, the sensor node modifies the corresponding parameter values in its locally stored communication parameter set. By updating the parameter set, the subsequent sensing and communication behaviors of the node will be executed according to the new parameters, mainly to enable the node to respond to the control of the sink node in real time and achieve dynamic adaptability.

[0035] More specifically, the working principle of the method of the present application is to construct a closed-loop system that combines an energy-aware and adaptive mechanism at the node level with a global collaborative regulation mechanism at the sink node level. First, each sensor node continuously obtains its own energy status information to understand the current energy reserve situation. Based on this energy status information and the current communication parameter set of the node, the node autonomously adjusts the frequency and fineness of its environmental perception activities. For example, when the energy is low, the perception intensity is reduced to save energy. At the same time, the node also determines the format and transmission frequency of reporting information to the superior node according to the energy status information and the communication parameter set. For example, when the energy is low, a more concise reporting format or a lower transmission frequency is adopted to reduce communication energy consumption. The node integrates the obtained energy status information and environmental perception information into collaborative information and uploads it to the sink node according to the determined transmission frequency. After receiving the collaborative information from each node in the network, the sink node comprehensively analyzes this information, evaluates the overall state of the network and the specific conditions of each node, and then generates parameter adjustment instructions for each sensor node. These instructions contain the optimization decisions made by the sink node based on global information, such as adjusting the perception parameters or communication parameters of the node. Finally, the sensor node receives the parameter adjustment instructions issued by the sink node and updates its communication parameter set according to the instructions. The updated parameters will affect the subsequent perception and communication behaviors of the node, enabling it to adapt to the regulation of the sink node and the changes in the environment, thus forming a continuous, energy-aware adaptive regulation loop.

[0036] The method of the present application combines the energy status information of the sensor node with the adjustment of environmental perception activities and the determination of the information reporting format and transmission frequency, and introduces the collaborative regulation of the sink node to form an energy-aware adaptive closed-loop control mechanism, effectively solving the problems of energy waste and response lag caused by traditional fixed or non-energy-aware regulation methods in wireless sensor networks under energy constraints and dynamic environmental changes; by enabling the sensor node to sense its own energy status and adaptively adjust its perception and communication behaviors accordingly, it significantly reduces the energy consumption of the node and extends the overall survival time of the network. Through the global collaborative regulation of the sink node and the real-time issuance of parameter adjustment instructions, it can respond more quickly and accurately to environmental changes and interferences, improving the reliability and anti-interference ability of communication.

[0037] In some preferred embodiments, the communication parameter set includes a set of sensing frequencies and a set of sensing finenesses; Step S2 includes: S21. Based on a preset hierarchical rule, determine the current energy level according to the energy status information; S22. Based on the set of sensing frequencies and the set of sensing finenesses, determine the frequency and fineness of the environmental perception activity according to the current energy level.

[0038] Specifically, the set of sensing frequencies refers to a set of pre-set and selectable execution frequency values for environmental sensing activities; the set of sensing fineness refers to a set of pre-set and selectable execution fineness values for environmental sensing activities; the pre-set grading rule refers to a rule for mapping the energy state information of sensor nodes to different energy levels; the energy level refers to a finite number of discrete levels divided according to the energy state information of sensor nodes, and each level represents a different energy reserve state of the node, which can be represented by integer values or predefined labels (such as high, medium, low).

[0039] More specifically, the method of the present application proposes to specify the communication parameter set to include the set of sensing frequencies and the set of sensing fineness, and by introducing the concept of energy levels, associate the energy state information with specific frequency and fineness adjustment values. Among them, step S21 provides a clear basis for subsequent parameter adjustment by mapping continuous or discrete energy state information to a finite and manageable energy level. Step S22 selects appropriate frequencies and fineness from these sets by using the current energy level as an index or basis, realizing the direct association between environmental sensing activities and the energy state of the node. The higher the energy level, the higher the sensing frequency and fineness can be selected to obtain more detailed environmental information; the lower the energy level, the lower the frequency and fineness are selected to save energy and extend the node life.

[0040] As a link in the entire communication anti-interference parameter collaborative regulation method, the adjustment result directly affects the subsequent acquisition of environmental sensing information (step S2), and further affects the generation of collaborative information (step S3) and energy consumption, while the energy consumption will change the energy state (step S1), forming a closed loop, enabling the entire system to dynamically adjust the sensing behavior according to the energy status of the node, so as to maintain the continuous operation and collaborative ability of the network in an energy-constrained environment.

[0041] Through the above processing, the present application provides a clear and structured mechanism for adjusting the frequency and fineness of environmental sensing activities according to the energy state information and the communication parameter set, solves the problem of lack of specific adjustment methods, makes the adjustment process more refined, fully considers the energy constraints, and improves the energy management efficiency.

[0042] In some preferred embodiments, the communication parameter set further includes an anti-interference parameter set; Step S2 further includes: S23. Monitor the environment where the sensor node is located and determine whether there is an interference source; S24. When there is an interference source, obtain the interference type and interference intensity, and based on the anti-interference parameter set, compensate and adjust the frequency and fineness of the environmental sensing activity according to the interference type and interference intensity.

[0043] Specifically, monitoring the environment where the sensor node is located and determining whether there is an interference source means that the sensor node detects whether there are signals or phenomena in its working environment that have an adverse impact on wireless communication or sensing activities through its own or external means. It can be achieved by using spectrum sensing technology, received signal strength indication (RSSI) monitoring, channel state information (CSI) analysis, or specific interference detection hardware.

[0044] More specifically, obtaining the interference type and interference intensity means that after detecting the interference source, further identifying the specific attributes of the interference signal (for example, whether it is from Wi-Fi, Bluetooth, microwave oven, or other sensor networks) and the degree of influence of this interference signal on the signal received or sent by the sensor node. It can be achieved by using signal spectrum analysis, pattern recognition algorithms, bit error rate (BER) or packet loss rate (PLR) measurement, or dedicated interference measurement equipment.

[0045] More specifically, the anti-interference parameter set refers to a set of data that is pre-configured or dynamically updated and is used to guide how the sensor node adjusts the frequency and fineness of its environmental sensing activities under different interference conditions. It can be achieved by using a lookup table, a set of rules, or a parameter mapping function.

[0046] More specifically, based on the sensor node adjusting the frequency and fineness of its environmental sensing activities according to the energy state information, the method of the present application adds the ability to sense and cope with wireless environment interference. Before or during the execution of the environmental sensing activity, the sensor node will actively monitor its wireless environment to determine whether there is an interference source. Once interference is detected, the node will further analyze the interference signal to obtain the specific type and intensity information of the interference. Subsequently, the node will consult or utilize the pre-set anti-interference parameter set. This parameter set contains coping strategies for different interference situations. The node finds the corresponding adjustment rules or parameters from the anti-interference parameter set according to the currently detected interference type and intensity, and uses this information to compensate or correct the sensing frequency and fineness determined based on the energy state previously. This adjustment is superimposed on the energy state adjustment, so that the final sensing strategy takes into account both the energy limitation of the node and the interference impact of the external environment.

[0047] Through the above solution, the present application solves the problem that in an energy-constrained wireless sensor network, adjusting the environmental sensing activity only based on the energy state fails to fully cope with the influence of interference factors in a complex and changeable wireless environment. By introducing the sensing of interference and the compensation adjustment mechanism based on the interference type and intensity, the sensor node can dynamically optimize the sensing strategy in the presence of interference, reduce data distortion and transmission failures caused by interference, avoid energy consumption caused by unnecessary retransmissions, thereby improving the effectiveness of the environmental sensing activity and more efficiently utilizing the node energy in an interference environment.

[0048] In some preferred embodiments, the anti-interference parameter set includes multiple types of interference and corresponding frequency adjustment factors and fineness adjustment factors; in step S24, the steps of compensating and adjusting the frequency and fineness of the environmental perception activity based on the anti-interference parameter set according to the interference type and interference intensity include: S241. Select the corresponding frequency adjustment factor and fineness adjustment factor from the anti-interference parameter set according to the interference type; S242. Normalize the interference intensity, and use the normalized interference intensity to weight the frequency adjustment factor and fineness adjustment factor to obtain a frequency adjustment coefficient and a fineness adjustment coefficient; S243. Based on the frequency adjustment coefficient and the fineness adjustment coefficient, compensate and adjust the frequency and fineness of the environmental perception activity.

[0049] Specifically, the frequency adjustment factor and the fineness adjustment factor are used to provide an initial adjustment direction and amplitude based on the interference type. Normalizing the interference intensity refers to the process of converting the monitored interference intensity value into a preset standard numerical range, which can be achieved by methods such as linear scaling or non-linear mapping. It is used to convert interference intensities of different dimensions or ranges into comparable and computable values.

[0050] More specifically, the weighting process enables the finally calculated frequency adjustment coefficient and fineness adjustment coefficient to dynamically reflect the influence of the interference intensity: the stronger the interference, the larger the weighted coefficient may be, indicating that a larger adjustment amplitude is required; the weaker the interference, the smaller the coefficient may be, and the adjustment amplitude is also correspondingly reduced.

[0051] Through the above solution, the present application provides a specific and quantifiable method for accurately compensating and adjusting the frequency and fineness of the environmental perception activity according to the interference type and interference intensity. This enables the adjustment of the perception parameters to fully reflect the actual influence degree of the interference and improves the anti-interference effect.

[0052] In some preferred embodiments, the communication parameter set includes a list of report formats and a set of transmission frequencies; step S3 includes: S31. Select a report format and a transmission frequency from a preset list of report formats and a set of transmission frequencies based on the current energy level determined using the energy state information; S32. Extract key environmental perception parameters from the environmental perception information according to the selected report format, and perform compression encoding on the key environmental perception parameters using a preset compression algorithm to obtain compressed environmental perception data; S33. Package the energy state information and the compressed environmental perception data according to the selected report format to generate collaborative information.

[0053] Specifically, the preset list of report formats refers to a collection of multiple predefined data report structures or templates. Each format specifies which environmental perception parameters the collaborative information should include, the order of these parameters, the data types, and the encapsulation methods, etc. Different report formats are designed to adapt to different energy levels. The lower the energy level, the fewer environmental perception information the corresponding report format usually contains to reduce the data volume. The preset set of transmission frequencies refers to a set of multiple optional data upload frequency values preset in advance. The node can select an appropriate transmission frequency according to its energy level. The lower the energy level, the lower transmission frequency is usually selected to reduce the number of transmissions.

[0054] More specifically, the environmental perception information refers to various data about the surrounding environment collected by the sensor node through its perception module, such as temperature, humidity, light, sound, vibration, air quality, etc. The key environmental perception parameters refer to a subset selected from all the environmental perception information that is of great significance for the sink node to make parameter adjustment decisions under a specific report format.

[0055] More specifically, the preset compression algorithm refers to an algorithm pre-stored in the sensor node for compressing environmental perception data, which can include various different compression techniques, such as lossless compression algorithms or lossy compression algorithms.

[0056] More specifically, the collaborative information refers to the data packet generated by the sensor node and uploaded to the sink node, which contains the energy state information of the node and the processed environmental perception data, and is used to support the sink node for collaborative regulation of network parameters.

[0057] Through the above solution, the method of the present application can dynamically adjust the content and frequency of data reports according to the energy state of the sensor node, reduce the data transmission volume and the number of transmissions when the energy reserve is low, thereby significantly reducing the energy consumption of the node, extending the working life of the sensor node, and improving the overall continuous operation ability and the stability of collaborative regulation of the wireless sensor network.

[0058] In some preferred embodiments, there are multiple preset compression algorithms. In step S32, the step of using the preset compression algorithm to perform compression encoding on the key environmental perception parameters to obtain the compressed environmental perception data includes: S321. Select a compression algorithm according to the current energy level and the data type of the key environmental perception parameters; S322. Use the selected compression algorithm to perform compression encoding on the extracted key environmental perception parameters to generate the compressed environmental perception data.

[0059] Specifically, this solution introduces multiple preset compression algorithms. When performing compression encoding on environmental perception data, it dynamically selects an appropriate compression algorithm according to the current energy state of the sensor node and the type of data to be compressed, avoiding the limitations of a single algorithm. This enables nodes with low energy reserves to select algorithms with low computational complexity, reducing energy consumption and extending the working time. Even if some compression ratio is sacrificed, it can ensure the effective transmission of data. At the same time, for nodes with sufficient energy or specific types of data, algorithms with high computational complexity but high compression ratio can be selected to improve data transmission efficiency. This adaptive compression strategy improves energy utilization efficiency, alleviates the energy bottleneck problem, and enhances the robustness and continuous working ability of the wireless sensor network in an energy-constrained environment.

[0060] In some preferred embodiments, the process by which the sink node generates a parameter adjustment instruction based on the collaboration information and distributes it to the corresponding sensor node includes: A1. The sink node receives the collaboration information uploaded by each sensor node, and parses the collaboration information to extract the energy state information and environmental perception information of each sensor node; A2. The sink node constructs a hierarchical transmission mechanism based on the extracted energy state information; A3. The sink node calculates the expected parameter adjustment amounts of each sensor node for different types of parameters in the communication parameter set according to the extracted environmental perception information, and then combines the extracted energy state information to correct the corresponding expected parameter adjustment amounts to obtain the actual parameter adjustment amounts; A4. The sink node generates a parameter adjustment instruction including the node identifier and the parameter adjustment value according to the actual parameter adjustment amount; A5. The sink node distributes the parameter adjustment instruction to the corresponding sensor node according to the hierarchical transmission mechanism.

[0061] Specifically, collaborative information refers to the data packets generated by sensor nodes based on their own energy state information and environmental perception information for uploading to the sink node, which can be implemented using a data frame structure containing specific fields. The hierarchical transmission mechanism means that when the sink node issues instructions to sensor nodes, different transmission strategies are adopted according to the energy state information of the nodes, which can be implemented using priority-based queue management, different retransmission times limits, or different transmission intervals. Among them, for the hierarchical transmission mechanism in step A2, lower priorities are assigned to nodes with higher energy levels, and higher priorities are assigned to nodes with lower energy levels. The expected parameter adjustment amount refers to the theoretical value initially calculated by the sink node based on environmental perception information for adjusting the communication parameters of sensor nodes, which can be implemented using a lookup table or calculation formula based on the mapping relationship between environmental parameters and communication parameters. The actual parameter adjustment amount refers to the final value for adjusting communication parameters obtained by correcting the expected parameter adjustment amount in combination with the energy state information of sensor nodes, which can be implemented by multiplying or adding the expected adjustment amount with a correction coefficient based on the energy state. The parameter adjustment instruction refers to the message generated by the sink node and sent to the sensor node to indicate the node to update its communication parameter set.

[0062] More specifically, by assigning higher priorities to nodes with lower energy levels and lower priorities to nodes with higher energy levels, the sink node can ensure that nodes with energy shortages can receive parameter adjustment instructions in a timely manner. This is crucial for maintaining the connectivity of low-energy nodes and extending their lifespan, effectively addressing the reliability issue of instruction transmission in energy-constrained scenarios.

[0063] More specifically, the correction process in step A3 embodies the core idea of energy-aware collaborative regulation, that is, while meeting the requirements of environmental adaptability, considering the energy tolerance of nodes to avoid rapid energy depletion caused by excessive adjustment.

[0064] More specifically, the sink node uses the previously constructed hierarchical transmission mechanism to send the parameter adjustment instruction to the corresponding sensor node, ensuring that the instruction can be efficiently delivered to the target node in an energy-aware manner, especially giving priority to ensuring the receipt of instructions by nodes with lower energy levels, thus completing the closed-loop process of the entire parameter collaborative regulation.

[0065] Through the above processing, the method of the present application can efficiently utilize the energy state information and environmental perception information uploaded by sensor nodes, collaboratively determine the adjustment amount of parameters, and optimize the process of issuing instructions according to the energy state of the nodes. This enables the sink node to dynamically adjust its communication parameters according to the actual conditions of each node in the network, thereby maintaining good network performance when the environment changes. At the same time, by considering the energy state of the nodes to correct the parameter adjustment amount and optimize the instruction transmission, the excessive burden on nodes with insufficient energy is avoided, effectively extending the life cycle of the nodes and the overall operation time of the network. This energy-aware and environment-adaptive collaborative control mechanism improves the robustness and energy efficiency of the wireless sensor network in complex environments.

[0066] In some preferred embodiments, in step A3, the process by which the sink node calculates the expected parameter adjustment amounts of different types of parameters in the communication parameter set for each sensor node according to the extracted environmental perception information includes: A31. For the extracted environmental perception information of each sensor node, use a fuzzy clustering algorithm for data fusion to obtain multiple information clusters, and each information cluster represents an environmental state region; A32. Calculate the weights of each environmental state region according to the number of nodes in the information cluster and the central position of the cluster; A33. For each sensor node, calculate the expected parameter adjustment amounts of different types of parameters in the communication parameter set for this sensor node according to the weight of the cluster to which it belongs and the deviation between the environmental perception information of this sensor node and the environmental perception information of the cluster center.

[0067] Specifically, the fuzzy clustering algorithm refers to a method of assigning data points to different clusters, allowing data points to belong to multiple clusters with different membership degrees, and it can be implemented by using a fuzzy C-means algorithm, a density-based fuzzy clustering algorithm, etc. An information cluster refers to a set formed by grouping sensor nodes with similar environmental perception characteristics through a clustering algorithm, and each information cluster represents a specific environmental state region within the network coverage area.

[0068] More specifically, the weight of an environmental state region refers to an importance measure value assigned to the environmental state region represented by each information cluster. This weight reflects the importance degree of the environmental state of the corresponding region, and it can be calculated based on factors such as the number of nodes contained in the region, the typical value (cluster center position) of the environmental state of the region, or its degree of change. The deviation between the environmental perception information of a sensor node and the environmental perception information of the cluster center refers to the degree of difference between the sensed data of a single sensor node and the typical environmental state (represented by the cluster center) of the information cluster to which it belongs. The expected parameter adjustment amount refers to the recommended adjustment amplitude or direction for the communication parameters of a specific sensor node calculated by the sink node based on the environmental perception information. Among them, the higher the weight of the cluster to which the node belongs, and the greater the deviation between the environmental perception information of the node and the environmental perception information of the cluster center, the greater the expected parameter adjustment amount of the sensor node, and the expected parameter adjustment amount is limited within a preset adjustment range.

[0069] More specifically, by applying the fuzzy clustering algorithm, the sink node classifies nodes with similar perception information into different information clusters, and each cluster corresponds to a specific environmental state region. This step abstracts a large amount of raw data into a limited number of environmental state categories, facilitating subsequent analysis. Then, the sink node evaluates the importance of these environmental state regions. It calculates the weight of each cluster based on the number of nodes contained in each information cluster (the more nodes, the more common or important the state may be) and the center position of the cluster (reflecting the typical characteristics or potential impacts of the state). Clusters with high weights represent environmental state regions that require more attention. Finally, the sink node calculates the expected parameter adjustment amount for each sensor node. This calculation comprehensively considers the importance of the environmental state region where the node is located (reflected by the weight of the cluster to which it belongs) and the difference between the node's own perception information and the typical state of the region (reflected by the deviation from the cluster center). If a node is in an important environmental area and its sensed data deviates significantly from the average level of the region, this may mean that the node has sensed local important changes or anomalies, and thus requires a larger parameter adjustment amplitude to adapt. The calculated expected adjustment amount is restricted within a preset range to avoid over-adjustment. In this way, the sink node can extract meaningful adjustment bases from the scattered sensed data, calculate the expected parameter adjustment amount that reflects both the overall environmental importance and individual node differences. These expected adjustment amounts are then corrected in combination with the energy state information of the nodes to generate the actual parameter adjustment amounts for guiding subsequent communication parameter updates.

[0070] Through the above processing, the method of the present application can effectively identify different environmental state regions and their importance from a large amount of scattered sensor environmental perception information, and calculate a more targeted and accurate expected parameter adjustment amount by combining the differences between the individual perception information of the nodes and the typical states. This enables the aggregation node to more effectively guide the sensor nodes to adjust communication parameters, improving the anti-interference performance and adaptability of the network in complex and changing environments.

[0071] In some preferred embodiments, in step A3, the process of correcting the corresponding expected parameter adjustment amount by combining the extracted energy state information to obtain the actual parameter adjustment amount includes: A34. According to the energy level interval where the extracted energy state information is located (which can be divided in the manner of the aforementioned current energy level or a more detailed division method), extract a correction coefficient from a preset correction coefficient set; A35. Multiply the correction coefficient by the expected parameter adjustment amount to obtain the actual parameter adjustment amount.

[0072] Specifically, the preset correction coefficient set refers to a set that stores the correction coefficient values corresponding to each energy level interval. This set can be pre-configured before system deployment or dynamically adjusted according to the overall network state during system operation. The correction coefficient is a numerical factor used to adjust the expected parameter adjustment amount. When the correction coefficient is 0, it means to completely cancel this parameter adjustment.

[0073] More specifically, the method of the present application corrects the expected parameter adjustment amount by introducing the energy state, which can avoid imposing excessive adjustment requirements on nodes with insufficient energy, thereby balancing environmental adaptability and node energy consumption.

[0074] More specifically, through multiplicative correction in step A35, the finally obtained actual parameter adjustment amount takes into account both the requirements of environmental changes (reflected in the expected parameter adjustment amount) and the energy-bearing capacity of the nodes (reflected in the correction coefficient), making the parameter adjustment instructions sent to the nodes more reasonable and feasible. Thus, it balances the adaptability requirements of the network to environmental changes and the energy consumption of the sensor nodes, helps to extend the effective working time of a single sensor node, and further improves the overall operating life and stability of the wireless sensor network.

[0075] In some preferred embodiments, step S5 includes: S51. Receive the parameter adjustment instruction, parse the parameter adjustment instruction to extract the node identifier and several actual parameter adjustment amounts; S52. Determine whether it matches the sensor node according to the node identifier. If so, execute S53; otherwise, discard the parameter adjustment instruction; S53. Evaluate the current energy state based on the latest acquired energy state information. If the energy state is lower than the preset threshold, suspend the execution of parameter adjustment; otherwise, execute S54. S54. Update the corresponding type of parameters in the communication parameter set according to the actual parameter adjustment amount.

[0076] Specifically, the node identifier refers to a label used to uniquely identify a specific sensor node in a wireless sensor network, which can be implemented using the MAC address, short address, serial number, or logical number of the node. The preset threshold refers to a predefined energy level boundary in the energy management strategy, which is used to determine whether the node's energy is in a low battery state.

[0077] More specifically, in step S51, the parameter adjustment instruction sent by the sink node is received and the instruction content is parsed to identify which node the instruction is sent to and the specific parameter adjustment value required. Then, in step S52, it is checked whether the node identifier in the instruction matches its own identifier to ensure that only the instructions sent to itself are processed. In step S53, after confirming that the instruction is valid and sent to itself, the parameter adjustment is not immediately executed. Instead, the current energy state of itself is first obtained and evaluated. If the evaluation result shows that the current energy level is lower than a preset threshold, it indicates that the node's energy is insufficient to support potential high-energy-consuming parameter adjustment operations. At this time, the node will choose to suspend the execution of the adjustment instruction to avoid failure due to energy exhaustion. Only when the node's energy state is good, that is, higher than the preset threshold, step S54 is executed to update the corresponding parameters in its communication parameter set according to the adjustment amount specified in the instruction. After the parameter update in step S54 is completed, the detailed information of this adjustment can also be recorded for subsequent performance analysis or problem tracing.

[0078] Through the above solution, the method of the present application enables the sensor node to intelligently decide whether to execute parameter adjustment according to its real-time energy state after receiving the parameter adjustment instruction from the sink node. This effectively avoids executing parameter adjustment operations that may cause rapid energy depletion when the node's energy reserve is insufficient, thereby significantly extending the effective working time of the sensor node and reducing the risk of premature node failure. As a result, the overall stability and lifespan of the wireless sensor network are improved, especially in deployment scenarios with energy constraints and dynamic environmental changes.

[0079] In a second aspect, please refer to Figure 2 and Figure 3 , some embodiments of the present application also provide a communication anti-interference parameter collaborative regulation system applied to a wireless sensor network. The system includes: a plurality of sensor nodes 100 and a sink node 200; The sensor node 100 includes: A monitoring module 101 for obtaining the energy state information of the sensor node; The sensing module 102 is configured to adjust the frequency and fineness of the environmental sensing activity according to the energy state information and the communication parameter set, and then sense and collect environmental sensing information; The report generation module 103 is configured to determine the report format and transmission frequency according to the energy state information and the communication parameter set, and generate collaboration information based on the report format according to the energy state information and the environmental sensing information; The uploading module 104 is configured to upload the collaboration information to the aggregation node 200 based on the transmission frequency; The updating module 105 is configured to receive a parameter adjustment instruction and update the communication parameter set based on the parameter adjustment instruction; The aggregation node 200 is configured to generate a parameter adjustment instruction according to the collaboration information and send it to the corresponding sensor node 100.

[0080] The system of the present application combines the energy state information of the sensor node 100 with the adjustment of the environmental sensing activity and the determination of the information report format and transmission frequency, and introduces the collaborative regulation of the aggregation node 200 to form an energy-aware adaptive closed-loop control mechanism, effectively solving the problems of energy waste and response lag caused by traditional fixed or non-energy-aware regulation methods in wireless sensor networks under energy constraints and dynamic environmental changes; by enabling the sensor node 100 to sense its own energy state and adaptively adjust its sensing and communication behaviors accordingly, it significantly reduces the energy consumption of the node and extends the overall survival time of the network. Through the global collaborative regulation of the aggregation node and the real-time sending of parameter adjustment instructions, it can respond to environmental changes and interferences more quickly and accurately, improving the reliability and anti-interference ability of communication.

[0081] In addition, the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0082] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist alone, or two or more modules can be integrated to form an independent part.

[0083] In this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations.

[0084] The above are only embodiments of the present application and are not intended to limit the protection scope of the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

Claims

1. A communication anti-jamming parameter collaborative regulation method, which is applied to sensor nodes in a wireless sensor network, and is characterized in that, The method includes the following steps: S1. Obtain the energy state information of the sensor node; S2. Adjust the frequency and fineness of the environmental perception activity according to the energy state information and the communication parameter set, and then sense and collect environmental perception information; S3. Determine the report format and transmission frequency according to the energy state information and the communication parameter set, and generate collaborative information based on the report format, the energy state information, and the environmental perception information; S4. Upload the collaborative information to the sink node based on the transmission frequency, so that the sink node generates a parameter adjustment instruction according to the collaborative information and issues it to the corresponding sensor node; S5. Receive the parameter adjustment instruction and update the communication parameter set based on the parameter adjustment instruction.

2. The communication anti-interference parameter collaborative regulation method according to claim 1, characterized in that The communication parameter set includes a perception frequency set and a perception fineness set; Step S2 includes: S21. Determine the current energy level according to the energy state information based on a preset grading rule; S22. Determine the frequency and fineness of the environmental perception activity according to the current energy level based on the perception frequency set and the perception fineness set.

3. The communication anti-interference parameter collaborative regulation method according to claim 2, wherein The communication parameter set further includes an anti-interference parameter set; Step S2 further includes: S23. Monitor the environment where the sensor node is located and determine whether there is an interference source; S24. When there is an interference source, obtain the interference type and interference intensity, and compensate and adjust the frequency and fineness of the environmental perception activity based on the anti-interference parameter set according to the interference type and the interference intensity.

4. The communication anti-interference parameter collaborative regulation method according to claim 3, characterized in that, The anti-interference parameter set contains multiple interference types and corresponding frequency adjustment factors and fineness adjustment factors; In step S24, the step of compensating and adjusting the frequency and fineness of the environmental perception activity based on the anti-interference parameter set according to the interference type and the interference intensity includes: S241. Select the corresponding frequency adjustment factor and fineness adjustment factor from the anti-interference parameter set according to the interference type; S242. Normalize the interference intensity, and use the normalized interference intensity to weight the frequency adjustment factor and the fineness adjustment factor to obtain a frequency adjustment coefficient and a fineness adjustment coefficient; S243. Compensate and adjust the frequency and fineness of the environmental perception activity based on the frequency adjustment coefficient and the fineness adjustment coefficient.

5. The communication anti-interference parameter collaborative regulation method according to claim 1, wherein The communication parameter set includes a report format list and a transmission frequency set; Step S3 includes: S31. Select a report format and a transmission frequency from a preset report format list and transmission frequency set based on the current energy level determined by using the energy state information; S32. Extract key environmental perception parameters from the environmental perception information according to the selected report format, and perform compression encoding on the key environmental perception parameters by using a preset compression algorithm to obtain compressed environmental perception data; S33. Package the energy state information and the compressed environmental perception data according to the selected report format to generate collaborative information.

6. The communication anti-interference parameter collaborative regulation method according to claim 5, characterized in that, There are multiple preset compression algorithms; in step S32, the step of performing compression encoding on the key environmental perception parameters by using a preset compression algorithm to obtain compressed environmental perception data includes: S321. Select a compression algorithm according to the current energy level and the data type of key environmental perception parameters; S322. Use the selected compression algorithm to compress and encode the extracted key environmental perception parameters to generate compressed environmental perception data.

7. The communication anti-interference parameter collaborative regulation method according to claim 1, wherein The process by which the aggregation node generates a parameter adjustment instruction according to the collaboration information and sends it to the corresponding sensor node includes: A1. The aggregation node receives the collaboration information uploaded by each sensor node, and parses the collaboration information to extract the energy state information and environmental perception information of each sensor node; A2. The aggregation node constructs a hierarchical transmission mechanism according to the extracted energy state information; A3. The aggregation node calculates the expected parameter adjustment amounts of each sensor node for different types of parameters in the communication parameter set according to the extracted environmental perception information, and then combines the extracted energy state information to correct the corresponding expected parameter adjustment amounts to obtain the actual parameter adjustment amounts; A4. The aggregation node generates a parameter adjustment instruction including the node identifier and the parameter adjustment value according to the actual parameter adjustment amount; A5. The aggregation node sends the parameter adjustment instruction to the corresponding sensor node according to the hierarchical transmission mechanism.

8. The communication anti-interference parameter collaborative regulation method according to claim 7, characterized in that In step A3, the process by which the aggregation node calculates the expected parameter adjustment amounts of each sensor node for different types of parameters in the communication parameter set according to the extracted environmental perception information includes: A31. For the environmental perception information of each sensor node extracted, use the fuzzy clustering algorithm for data fusion to obtain multiple information clusters, and each information cluster represents an environmental state area; A32. Calculate the weights of each environmental state area according to the number of nodes in the information cluster and the center position of the cluster; A33. For each sensor node, calculate the expected parameter adjustment amounts of the sensor node for different types of parameters in the communication parameter set according to the weight of the cluster to which it belongs and the deviation between the environmental perception information of the sensor node and the environmental perception information of the cluster center.

9. The communication anti-interference parameter collaborative regulation method according to claim 7, wherein In step A3, the process of combining the extracted energy state information to correct the corresponding expected parameter adjustment amount to obtain the actual parameter adjustment amount includes: A34. Extract a correction coefficient from a preset correction coefficient set according to the energy level interval where the extracted energy state information is located; A35. Multiply the correction coefficient by the expected parameter adjustment amount to obtain the actual parameter adjustment amount.

10. A communication anti-jamming parameter collaborative regulation system is applied to a wireless sensor network, and is characterized in that The system includes: a plurality of sensor nodes and an aggregation node; The sensor node includes: A monitoring module for obtaining the energy state information of the sensor node; A sensing module for adjusting the frequency and fineness of the environmental perception activity according to the energy state information and the communication parameter set, and then sensing and collecting environmental perception information; A report generation module for determining the report format and sending frequency according to the energy state information and the communication parameter set, and generating collaboration information based on the report format according to the energy state information and the environmental perception information; An upload module for uploading the collaboration information to the aggregation node based on the sending frequency; An update module for receiving a parameter adjustment instruction and updating the communication parameter set based on the parameter adjustment instruction; The aggregation node is used to generate a parameter adjustment instruction according to the collaboration information and send it to the corresponding sensor node.

Images