A communication anti-jamming parameter cooperative regulation method and system

Abstract

The application relates to the technical field of data communication, and particularly discloses a communication anti-interference parameter cooperative regulation method and system, wherein the method comprises the following steps: acquiring energy state information; adjusting the frequency and fineness of environment sensing activities, and sensing and collecting environment sensing information; determining a report format and a sending frequency according to the energy state information and a communication parameter set, and generating cooperative information according to the energy state information and the environment sensing information based on the report format; uploading the cooperative information to a convergence node based on the sending frequency, so that the convergence node generates a parameter regulation instruction according to the cooperative information, and the parameter regulation instruction is delivered to a corresponding sensor node; and receiving the parameter regulation instruction, and updating the communication parameter set based on the parameter regulation instruction; the method forms an energy-sensing type adaptive closed-loop control mechanism, and effectively solves the problems of energy waste and response lag caused by the traditional fixed or non-energy-sensing regulation mode of a wireless sensor network under the conditions of energy limitation and dynamic environment change.

Description

Technical Field

[0001] The present application relates to the field of data communication technology, and more specifically, to a method and system for collaboratively controlling communication anti-interference parameters. Background Art

[0002] Wireless sensor networks are widely used in large-scale environmental monitoring. Their nodes are typically battery-powered and deployed in complex and changing wireless environments. Existing technologies achieve interference-resistant transmission by dynamically adjusting communication parameter sets, and collaboratively share channel state information between nodes to optimize network performance. This mechanism has shown significant potential for improving communication reliability. As network scale expands and environmental dynamics increase, maintaining efficient collaborative control under energy-constrained conditions has become a key area of ​​technological development.

[0003] However, existing collaborative control mechanisms for wireless sensors face multiple bottlenecks in energy-constrained scenarios. Traditional methods employ fixed-frequency environmental sensing patterns and fail to dynamically adjust sensing intensity based on the remaining energy of nodes, leading to premature failure of high-energy-consuming nodes. Information exchange between nodes lacks an energy-aware mechanism, making it difficult to balance communication quality and energy consumption control when channel states fluctuate dramatically. The massive data transmission demands generated by centralized decision-making models exacerbate the energy burden on edge nodes, while fixed-period parameter update strategies cannot keep pace with the real-time requirements of environmental changes, resulting in a paradoxical situation where control lags and energy waste coexist.

[0004] There is currently no effective technical solution to the above problems. Summary of the Invention

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

[0006] In a first aspect, the present application provides a method for collaboratively controlling communication anti-interference parameters, which is applied to sensor nodes in a wireless sensor network. The method comprises the following steps:

[0007] S1, obtain the energy status information of the sensor node;

[0008] S2. Adjusting the frequency and precision of the environment sensing activity according to the energy state information and the communication parameter set, and then sensing and collecting the environment sensing information;

[0009] S3. Determine a report format and a sending frequency according to the energy status information and the communication parameter set, and generate collaborative information according to the energy status information and the environmental perception information based on the report format;

[0010] S4, transmitting the coordination information to a sink node based on the sending frequency, so that the sink node generates parameter adjustment instructions according to the coordination information, and the parameter adjustment instructions are sent to the corresponding sensor node;

[0011] S5, receiving the parameter adjustment instructions, and updating the communication parameter set based on the parameter adjustment instructions.

[0012] The method of the 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 the sending frequency, and introduces the coordinated regulation of the sink node, to form an energy-aware adaptive closed-loop control mechanism, which effectively solves the problems of energy waste and response lag caused by the traditional fixed or non-energy-aware regulation mode under the conditions of energy limitation and dynamic change of the environment.

[0013] The communication anti-interference parameter coordinated regulation method, wherein the communication parameter set comprises a perception frequency set and a perception precision set;

[0014] Step S2 comprises:

[0015] S21, determining a current energy level according to the energy state information based on a preset hierarchical rule;

[0016] S22, determining the frequency and precision of the environmental perception activity according to the current energy level based on the perception frequency set and the perception precision set.

[0017] Through the above processing, the application provides a clear and structured mechanism for adjusting the frequency and precision of the environmental perception activity according to the energy state information and the communication parameter set, solves the problem of lacking specific adjustment methods, makes the adjustment process more precise, fully considers the energy constraint, and improves the energy management efficiency.

[0018] The communication anti-interference parameter coordinated regulation method, wherein the communication parameter set further comprises an anti-interference parameter set;

[0019] Step S2 further comprises:

[0020] S23, monitoring the environment in which the sensor node is located, and determining whether there is an interference source;

[0021] S24, obtaining the interference type and the interference intensity when there is an interference source, and compensating and adjusting the frequency and precision of the environmental perception activity according to the interference type and the interference intensity based on the anti-interference parameter set.

[0022] The method for coordinated control of communication anti-interference parameters, wherein the anti-interference parameter set includes 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:

[0023] S241. Select a corresponding frequency adjustment factor and a fineness adjustment factor from the anti-interference parameter set according to the interference type;

[0024] 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;

[0025] S243: Based on the frequency adjustment coefficient and the fineness adjustment coefficient, compensate and adjust the frequency and fineness of the environment perception activity.

[0026] The communication anti-interference parameter coordinated control method, wherein the communication parameter set includes a report format list and a transmission frequency set; step S3 includes:

[0027] S31. Selecting a report format and a transmission frequency from a preset report format list and a transmission frequency set based on a current energy level determined using the energy status information;

[0028] S32. Extracting key environmental perception parameters from the environmental perception information according to the selected report format, and compressing and encoding the key environmental perception parameters using a preset compression algorithm to obtain compressed environmental perception data;

[0029] S33. Encapsulate the energy status information and the compressed environmental perception data according to the selected report format to generate collaborative information.

[0030] In the method for collaboratively controlling communication anti-interference parameters, wherein there are multiple preset compression algorithms, in step S32, the step of compressing and encoding the key environmental perception parameters using the preset compression algorithm to obtain compressed environmental perception data includes:

[0031] S321. Select a compression algorithm based on the current energy level and the data type of the key environmental perception parameter;

[0032] S322: Using the selected compression algorithm, compress and encode the extracted key environmental perception parameters to generate compressed environmental perception data.

[0033] In the method for collaborative control of communication anti-interference parameters, the process in which the sink node generates a parameter adjustment instruction based on the collaborative information and sends it to the corresponding sensor node includes:

[0034] A1. The sink node receives the collaborative information uploaded by each sensor node and parses the collaborative information to extract the energy state information and environmental perception information of each sensor node;

[0035] A2, the sink node builds a hierarchical transmission mechanism based on the extracted energy state information;

[0036] A3. The sink node calculates the expected parameter adjustment values ​​of each sensor node for different types of parameters in the communication parameter set based on the extracted environmental perception information. Then, it modifies the corresponding expected parameter adjustment values ​​based on the extracted energy state information to obtain the actual parameter adjustment values.

[0037] A4. The aggregation node generates a parameter adjustment instruction including a node identifier and a parameter adjustment value according to the actual parameter adjustment amount;

[0038] A5. The sink node sends the parameter adjustment instruction to the corresponding sensor node according to the hierarchical transmission mechanism.

[0039] In the method for collaboratively controlling communication anti-interference parameters, in step A3, the process in which the sink node calculates the expected parameter adjustment amounts of each sensor node for different types of parameters in the communication parameter set based on the extracted environmental perception information includes:

[0040] A31. Based on the environmental perception information extracted from each sensor node, a fuzzy clustering algorithm is used to perform data fusion to obtain multiple information clusters, each of which represents an environmental state area;

[0041] A32. Calculate the weight of each environmental state region based on the number of nodes in the information cluster and the center position of the cluster;

[0042] A33. For each sensor node, calculate the expected parameter adjustment amount 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.

[0043] In the method for collaboratively controlling communication anti-interference parameters, 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:

[0044] A34. Extracting a correction coefficient from a preset correction coefficient set according to the energy level interval of the extracted energy state information;

[0045] A35. Multiply the correction coefficient by the expected parameter adjustment amount to obtain the actual parameter adjustment amount.

[0046] In a second aspect, the present application further provides a communication anti-interference parameter collaborative control system, which is applied to a wireless sensor network, the system comprising: a plurality of sensor nodes and a sink node;

[0047] The sensor node comprises:

[0048] Monitoring module, used to obtain energy status information of sensor nodes;

[0049] A perception module, configured to adjust the frequency and precision of environmental perception activities according to the energy state information and the communication parameter set, and then perceive and collect environmental perception information;

[0050] a report generation module, configured to determine a report format and a sending frequency according to the energy status information and the communication parameter set, and generate collaborative information according to the energy status information and the environmental perception information based on the report format;

[0051] an uploading module, configured to upload the collaborative information to a sink node based on the sending frequency;

[0052] an updating module, configured to receive a parameter adjustment instruction and update the communication parameter set based on the parameter adjustment instruction;

[0053] The aggregation node is used to generate a parameter adjustment instruction according to the collaborative information and send it to the corresponding sensor node.

[0054] The system of the present application forms an energy-aware adaptive closed-loop control mechanism by combining the energy status information of sensor nodes with the adjustment of environmental perception activities and the determination of information reporting format and sending frequency, and introduces collaborative regulation of aggregation nodes. This effectively solves the energy waste and response lag problems caused by traditional fixed or non-energy-aware regulation methods in wireless sensor networks under energy constraints and dynamic environmental changes.

[0055] From the above, it can be seen that the present application provides a method and system for collaborative control of communication anti-interference parameters. The method of the present application significantly reduces the energy consumption of nodes and extends the overall survival time of the network by enabling sensor nodes to perceive their own energy status and adaptively adjust perception and communication behaviors accordingly. Through the global collaborative control of aggregation nodes and the real-time issuance of parameter adjustment instructions, it can respond to environmental changes and interference more quickly and accurately, thereby improving the reliability and anti-interference capability of communication. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 Flowchart of the method for collaborative control of communication anti-interference parameters provided in an embodiment of the present application.

[0057] Figure 2 A schematic diagram of the structure of the communication anti-interference parameter collaborative control system provided in an embodiment of the present application.

[0058] Figure 3 Schematic diagram of the sensor node structure.

[0059] Reference numerals: 100, sensor node; 200, sink node; 101, monitoring module; 102, perception module; 103, report generation module; 104, upload module; 105, update module. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the 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 of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely 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 making creative work fall within the scope of protection of the present application.

[0061] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0062] First, please refer to Figure 1 Some embodiments of the present application provide a method for collaboratively controlling communication anti-interference parameters, which is applied to sensor nodes in a wireless sensor network. The method includes the following steps:

[0063] S1, obtain the energy status information of the sensor node;

[0064] S2. Adjust the frequency and precision of the environmental perception activity according to the energy state information and the communication parameter set, and then perceive and collect environmental perception information;

[0065] S3. Determine a report format and a sending frequency according to the energy status information and the communication parameter set, and generate collaborative information according to the energy status information and the environmental perception information based on the report format;

[0066] 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 sends it to the corresponding sensor node;

[0067] S5. Receive a parameter adjustment instruction, and update the communication parameter set based on the parameter adjustment instruction.

[0068] Specifically, energy status information refers to the current energy reserve status of the sensor node, which can be obtained in a variety of ways, such as by monitoring the battery voltage, estimating the remaining power 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 adjustments.

[0069] More specifically, the communication parameter set refers to a collection of adjustable parameters that affect the environmental perception activities and information transmission behaviors of sensor nodes. The parameter set can include parameter types such as perception frequency, perception fineness, report format, and sending frequency. Its main purpose is to provide a control space that can be dynamically adjusted by nodes and aggregation nodes to adapt to different environmental conditions and energy states.

[0070] More specifically, adjusting the frequency and precision of environmental perception activities refers to changing the frequency and precision of environmental data collection by sensor nodes according to specific conditions. This adjustment can reduce or increase the energy consumption and data volume of the perception process. It is mainly to enable the node's perception behavior to adapt according to the energy status and environmental requirements, and balance perception coverage and energy efficiency.

[0071] More specifically, the report format refers to the data structure or content organization method used by the sensor node when sending environmental perception information and its own status information to the superior node (i.e., the sink node). Different report formats can contain different amounts or levels of information. Its main purpose is to affect communication energy consumption by controlling the amount of transmitted data and realize energy-aware communication.

[0072] More specifically, the sending frequency refers to the rate or time interval at which the sensor node sends collaborative information to the superior node. Adjusting the sending frequency can control the number of communications. Its main purpose is to affect the communication energy consumption by controlling the communication frequency and realize energy-aware communication.

[0073] More specifically, collaborative information refers to the information carrier generated by sensor nodes based on their own status and environmental perception results and uploaded to the aggregation node. This information usually contains the node's energy status information and environmental perception information. Its main purpose is to aggregate key information at the node level to the aggregation node and provide necessary data support for the global decision-making of the aggregation node.

[0074] More specifically, after analyzing the received coordination information, the sink node issues parameter adjustment instructions to each sensor node to guide the update of its communication parameter set. The parameter adjustment instructions are mainly used to achieve centralized or distributed coordination and control of the sink node over each node in the network.

[0075] More specifically, 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 node's subsequent sensing and communication behavior will be executed according to the new parameters, which is mainly to enable the node to respond to the sink node's control in real time and achieve dynamic adaptation.

[0076] More specifically, the working principle of the method of the present application is to build a closed-loop system that combines an energy-aware and adaptive mechanism at the node level with a global coordination and control mechanism at the sink node level. First, each sensor node continuously acquires its own energy state information to understand its current energy reserve. Based on this energy state information and the node's current communication parameter set, the node autonomously adjusts the frequency and precision of its environmental sensing activities, such as reducing the sensing intensity to save energy when the energy is low. At the same time, the node also determines the format and sending frequency of information reported to the upper node based on the energy state information and the communication parameter set, such as using a more concise reporting format or reducing the sending frequency to reduce communication energy consumption when the energy is low. The node integrates the acquired energy state information and environmental sensing information into coordination information and uploads it to the sink node according to the determined sending frequency. After receiving the coordination information from each node in the network, the sink node comprehensively analyzes these information, evaluates the overall state of the network and the specific situation 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 node's sensing parameters or communication parameters. 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 node's subsequent sensing and communication behavior, enabling it to adapt to the sink node's control and environmental changes, thereby forming a continuous, energy-aware adaptive control cycle.

[0077] The method of the present application combines the energy status information of sensor nodes with the adjustment of environmental perception activities and the determination of information reporting format and sending frequency, and introduces the collaborative regulation of aggregation nodes to form an energy-aware adaptive closed-loop control mechanism, which effectively solves the energy waste and response lag problems caused by traditional fixed or non-energy-aware regulation methods in wireless sensor networks under energy constraints and dynamic environmental changes; it enables sensor nodes to perceive their own energy status and adaptively adjust perception and communication behaviors accordingly, significantly reducing the energy consumption of nodes and extending the overall survival time of the network; through the global collaborative regulation of aggregation nodes and the real-time issuance of parameter adjustment instructions, it can respond to environmental changes and interference more quickly and accurately, thereby improving the reliability and anti-interference ability of communication.

[0078] In some preferred embodiments, the communication parameter set includes a sensing frequency set and a sensing fineness set;

[0079] Step S2 includes:

[0080] S21. Determine the current energy level based on the energy status information based on a preset classification rule;

[0081] S22. Based on the perception frequency set and the perception fineness set, and according to the current energy level, determine the frequency and fineness of the environmental perception activity.

[0082] Specifically, the perception frequency set refers to a set of pre-set, optional frequency values ​​for executing environmental perception activities; the perception fineness set refers to a set of pre-set, optional fineness values ​​for executing environmental perception activities; the preset grading rules refer to the rules used to map the energy status information of sensor nodes to different energy levels; the energy level refers to a finite number of discrete levels divided according to the energy status information of sensor nodes, each level represents a different energy reserve state of the node, which can be represented by an integer value or a predefined label (such as high, medium, low).

[0083] More specifically, the method of the present application proposes to concretize the communication parameter set into a set containing a perception frequency set and a perception fineness set, and by introducing the concept of energy level, the energy state information is associated 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 limited and easy-to-manage energy level. Step S22 realizes the direct association between environmental perception activities and node energy status by selecting appropriate frequencies and fineness from these sets based on the current energy level as an index or basis. The higher the energy level, the higher the perception 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 life of the node.

[0084] The adjustment process is a link in the whole communication anti-interference parameter cooperative regulation method, and the adjustment result directly affects the subsequent environment perception information collection (step S2), and then affects the generation of cooperative information (step S3) and energy consumption, and the energy consumption changes the energy state (step S1), forming a closed loop, so that the whole system can dynamically adjust the perception behavior according to the energy state of the node, thereby maintaining the continuous operation and cooperative ability of the network in the energy-constrained environment.

[0085] Through the above processing, the application provides a clear and structured mechanism for adjusting the frequency and fineness of environment perception activities according to energy state information and a communication parameter set, solves the problem of lack of specific adjustment method, makes the adjustment process more precise, fully considers the energy constraint, and improves the energy management efficiency.

[0086] In some preferred embodiments, the communication parameter set further includes an anti-interference parameter set;

[0087] Step S2 further includes:

[0088] S23, monitoring the environment where the sensor node is located to determine whether there is an interference source;

[0089] S24, when there is an interference source, obtaining the interference type and interference intensity, and based on the anti-interference parameter set, compensating and adjusting the frequency and fineness of the environment perception activities according to the interference type and interference intensity.

[0090] Specifically, monitoring the environment where the sensor node is located to determine whether there is an interference source means that the sensor node detects whether there is a signal or phenomenon in its working environment that has an adverse effect on wireless communication or perception activities by itself or external means, which can use spectrum sensing technology, received signal strength indication (RSSI) monitoring, channel state information (CSI) analysis or specific interference detection hardware to achieve.

[0091] More specifically, obtaining the interference type and interference intensity means that after detecting the interference source, the specific attributes of the interference signal (for example, from Wi-Fi, Bluetooth, microwave oven or other sensor networks) and the degree of influence of the interference signal on the reception or transmission signal of the sensor node are further identified, which can use signal spectrum analysis, pattern recognition algorithm, bit error rate (BER) or packet loss rate (PLR) measurement, or special interference measurement equipment to achieve.

[0092] More specifically, the anti-interference parameter set refers to a set of data that is pre-configured or dynamically updated, which is used to guide the sensor node to adjust the frequency and fineness of its environment perception activities under different interference conditions, which can use lookup table, rule set, or parameter mapping function to achieve.

[0093] More specifically, the method of the present application increases the ability of the sensor node to perceive and cope with wireless environment interference on the basis of adjusting the frequency and fineness of the environmental perception activity according to the energy state information. Before or during the execution of the environmental perception activity, the sensor node actively monitors the wireless environment in which it is located to determine whether there is an interference source. Once interference is detected, the node further analyzes the interference signal to obtain specific type and intensity information of the interference. Subsequently, the node consults or utilizes a preset 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 type and intensity of the interference currently detected, and uses this information to compensate or correct the perception frequency and fineness previously determined based on the energy state. This adjustment is superimposed on the energy state adjustment, so that the final perception strategy takes into account both the energy limitations of the node and the interference influence of the external environment.

[0094] Through the above scheme, the present application solves the problem that adjusting environmental perception activity based only on energy state cannot adequately cope with the influence of interference factors in a complex and variable wireless environment in a wireless sensor network with limited energy. By introducing the perception of interference and compensation adjustment mechanism based on the type and intensity of interference, the sensor node can dynamically optimize the perception strategy in the presence of interference, reduce data distortion and transmission failures caused by interference, avoid unnecessary energy consumption caused by retransmission, thereby improving the effectiveness of environmental perception activity and more efficiently utilizing node energy in an interference environment.

[0095] In some preferred embodiments, the anti-interference parameter set contains a plurality of 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 interference intensity comprises:

[0096] S241, selecting corresponding frequency adjustment factors and fineness adjustment factors from the anti-interference parameter set according to the interference type;

[0097] S242, normalizing the interference intensity and weighting the frequency adjustment factors and fineness adjustment factors using the normalized interference intensity to obtain a frequency adjustment coefficient and a fineness adjustment coefficient;

[0098] S243, compensating and adjusting the frequency and fineness of the environmental perception activity based on the frequency adjustment coefficient and the fineness adjustment coefficient.

[0099] 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. The normalization of 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 linear scaling or nonlinear mapping, etc. It is used to convert the interference intensity of different dimensions or ranges into comparable and calculable values.

[0100] More specifically, the weighting processing enables the finally calculated frequency adjustment factor and the fineness adjustment factor to dynamically reflect the influence of the interference intensity: the stronger the interference, the larger the weighted coefficient, indicating that a larger amplitude of adjustment is needed; the weaker the interference, the smaller the coefficient, and the adjustment amplitude is also reduced accordingly.

[0101] Through the above scheme, the present application provides a specific and quantitative method for accurately compensating and adjusting the frequency and fineness of the environmental perception activity according to the interference type and the interference intensity. This enables the adjustment of the perception parameters to fully reflect the actual influence degree of the interference, thereby improving the anti-interference effect.

[0102] In some preferred embodiments, the set of communication parameters includes a report format list and a set of transmission frequencies; step S3 includes:

[0103] S31, selecting a report format and a transmission frequency from the preset report format list and the set of transmission frequencies based on the current energy level determined using the energy state information;

[0104] S32, extracting key environmental perception parameters from the environmental perception information according to the selected report format, and compressively encoding the key environmental perception parameters using a preset compression algorithm to obtain compressed environmental perception data;

[0105] S33, encapsulating the energy state information and the compressed environmental perception data according to the selected report format to generate the coordination information.

[0106] Specifically, the preset report format list refers to a set of predefined multiple data report structures or templates, each format specifying which environmental perception parameters the coordination information should contain, the order of these parameters, the data type, and the encapsulation method, etc. Different report formats are designed to adapt to different energy levels, and the lower the energy level, the less 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 selectable data upload frequency values, which can be selected by the node according to its energy level. The lower the energy level, the lower the transmission frequency is usually selected to reduce the transmission times.

[0107] More specifically, environmental perception information refers to various data about the surrounding environment collected by sensor nodes through their perception modules, such as temperature, humidity, light, sound, vibration, and air quality. Key environmental perception parameters are subsets of all environmental perception information, selected in a specific reporting format, that are important for sink nodes to make parameter adjustment decisions.

[0108] More specifically, the preset compression algorithm refers to an algorithm pre-stored in the sensor node for compressing the environmental perception data, and may include a variety of different compression technologies, such as a lossless compression algorithm or a lossy compression algorithm.

[0109] More specifically, collaborative information refers to the data packet generated by the sensor node and uploaded to the sink node. The data packet contains the node's energy status information and processed environmental perception data, which is used to support the sink node in collaboratively regulating network parameters.

[0110] Through the above scheme, the method of the present application can dynamically adjust the content and frequency of data reports according to the energy status of the sensor node, reducing the amount of data transmission 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 capability of the wireless sensor network and the stability of collaborative regulation.

[0111] In some preferred embodiments, there are multiple preset compression algorithms; in step S32, the steps of compressing and encoding the key environmental perception parameters using the preset compression algorithm to obtain compressed environmental perception data include:

[0112] S321. Select a compression algorithm based on the current energy level and the data type of the key environmental perception parameter;

[0113] S322: Using the selected compression algorithm, compress and encode the extracted key environmental perception parameters to generate compressed environmental perception data.

[0114] Specifically, this solution avoids the limitations of a single algorithm by introducing multiple preset compression algorithms and dynamically selecting the appropriate compression algorithm based on the sensor node's current energy state and the type of data to be compressed during environmental perception data compression encoding. This allows nodes with lower energy reserves to select algorithms with lower computational complexity, reducing energy consumption and extending operating time. Even at the expense of some compression rate, this ensures effective data transmission. Furthermore, for nodes with sufficient energy or for specific types of data, algorithms with higher computational complexity but higher compression rates can be selected to improve data transmission efficiency. This adaptive compression strategy improves energy utilization, alleviates energy bottlenecks, and enhances the robustness and sustainable operation of wireless sensor networks in energy-constrained environments.

[0115] In some preferred embodiments, the process in which the sink node generates a parameter adjustment instruction based on the collaborative information and sends it to the corresponding sensor node includes:

[0116] A1. The sink node receives the collaborative information uploaded by each sensor node and parses the collaborative information to extract the energy state information and environmental perception information of each sensor node;

[0117] A2, the sink node builds a hierarchical transmission mechanism based on the extracted energy state information;

[0118] A3. The sink node calculates the expected parameter adjustment values ​​of each sensor node for different types of parameters in the communication parameter set based on the extracted environmental perception information. Then, it modifies the corresponding expected parameter adjustment values ​​based on the extracted energy state information to obtain the actual parameter adjustment values.

[0119] A4. The aggregation node generates a parameter adjustment instruction including a node identifier and a parameter adjustment value according to the actual parameter adjustment amount;

[0120] A5. The sink node sends the parameter adjustment instructions to the corresponding sensor nodes according to the hierarchical transmission mechanism.

[0121] Specifically, collaborative information refers to data packets generated by sensor nodes based on their own energy status information and environmental perception information for upload to the sink node. This information can be implemented using a data frame structure containing specific fields. A hierarchical transmission mechanism refers to the sink node adopting different transmission strategies based on the energy status information of the sensor nodes when issuing instructions to them. This can be implemented through priority-based queue management, different retransmission limits, or different transmission intervals. The hierarchical transmission mechanism in step A2 assigns lower priorities to nodes with higher energy levels and higher priorities to nodes with lower energy levels. The expected parameter adjustment value refers to the theoretical value initially calculated by the sink node based on environmental perception information for adjusting the communication parameters of the sensor nodes. This can be implemented using a lookup table or calculation formula based on the mapping between environmental parameters and communication parameters. The actual parameter adjustment value refers to the final value used to adjust the communication parameters, obtained by correcting the expected parameter adjustment value based on the energy status information of the sensor nodes. This can be implemented by multiplying or adding the expected adjustment value with a correction factor based on the energy status. A parameter adjustment instruction refers to a message generated by the sink node and sent to the sensor node, instructing the node to update its communication parameter set.

[0122] More specifically, the sink node can ensure that the nodes with low energy level can timely receive the parameter adjustment instruction by assigning higher priority to the nodes with low energy level and lower priority to the nodes with high energy level. This is crucial for maintaining the connectivity of low-energy nodes and prolonging their life cycle, effectively addressing the reliability of instruction transmission in energy-constrained scenarios.

[0123] More specifically, the correction process of step A3 embodies the core idea of energy-aware collaborative regulation, that is, while meeting the environmental adaptability requirements, the energy bearing capacity of the nodes is considered to avoid rapid energy depletion due to excessive adjustment.

[0124] More specifically, the sink node uses the previously constructed hierarchical transmission mechanism to issue the parameter adjustment instruction to the corresponding sensor nodes, ensuring that the instruction can be efficiently delivered to the target nodes in an energy-aware manner, especially prioritizing the instruction reception of low-energy nodes, thereby completing the closed-loop process of the entire parameter collaborative regulation.

[0125] Through the above processing, the method of the present application can efficiently utilize the energy state information and environmental perception information uploaded by the sensor nodes to collaboratively determine the adjustment amount of the parameters and optimize the instruction delivery process according to the energy state of the nodes. This enables the sink node to dynamically adjust its communication parameters according to the actual situation 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 overburdening of nodes with insufficient energy is avoided, effectively prolonging the life cycle of the nodes and the overall operation time of the network. This energy-aware and environment-adaptive collaborative regulation mechanism improves the robustness and energy efficiency of wireless sensor networks in complex environments.

[0126] In some preferred embodiments, in step A3, the process of calculating the expected parameter adjustment amount of each sensor node with respect to different types of parameters in the set of communication parameters based on the extracted environmental perception information includes:

[0127] A31, for the extracted environmental perception information of each sensor node, data fusion is performed using a fuzzy clustering algorithm to obtain multiple information clusters, each information cluster representing an environmental state region;

[0128] A32, according to the number of nodes in the information cluster and the center position of the cluster, the weight of each environmental state region is calculated;

[0129] A33, for each sensor node, according to the weight of the cluster to which it belongs and the deviation of the environmental perception information of the sensor node from the environmental perception information of the cluster center, the expected parameter adjustment amount of the sensor node with respect to different types of parameters in the set of communication parameters is calculated.

[0130] 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, which can be implemented by fuzzy C-means algorithm, density-based fuzzy clustering algorithm, etc. The information cluster refers to a set formed by grouping sensor nodes with similar environmental perception characteristics through clustering algorithm, and each information cluster represents a specific environmental state region in the network coverage area.

[0131] More specifically, the weight of the environmental state region refers to an importance measure value given to the environmental state region represented by each information cluster, which reflects the importance degree of the corresponding regional environmental state, which can be calculated based on the number of nodes contained in the region, the typical value (cluster center position) of the regional environmental state or the degree of change, etc. The deviation of the sensor node's environmental perception information and the cluster center's environmental perception information refers to the difference between the perception 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 range or direction of the communication parameter of a specific sensor node calculated by the sink node according to the environmental perception information. Among them, the higher the weight of the node's cluster and the greater the deviation between the node's environmental perception information and the cluster center's environmental perception information, the greater the expected parameter adjustment amount of the sensor node, and the expected parameter adjustment amount is limited within the preset adjustment range.

[0132] More specifically, by applying a fuzzy clustering algorithm, the sink node groups nodes with similar perception information into different information clusters, each of which corresponds to a specific environmental state region. This step abstracts the massive raw data into limited environmental state categories, facilitating subsequent analysis. Next, the sink node evaluates the importance of these environmental state regions. It calculates the weight of each information cluster according to the number of nodes contained in the 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 impact of the state). Clusters with high weights represent environmental state regions that require more attention. Finally, the sink node calculates the desired parameter adjustment amount for each sensor node. This calculation takes into account the importance of the environmental state region in which 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 region and its perception data deviates significantly from the average level of the region, it may mean that the node has perceived a local important change or abnormal situation, and therefore requires a larger parameter adjustment range to adapt. The calculated desired adjustment amount will be limited within a pre-set range to avoid excessive adjustment. In this way, the sink node can extract meaningful adjustment basis from scattered perception data and calculate desired parameter adjustment amounts that reflect both the overall environmental importance and individual node differences. These desired adjustment amounts will then be modified in conjunction with the node's energy state information to generate actual parameter adjustment amounts, which will be used to guide subsequent communication parameter updates.

[0133] 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 more targeted and accurate desired parameter adjustment amounts by considering the difference between individual node perception information and typical state. This enables the sink node to more effectively guide sensor nodes to adjust communication parameters, improving the network's anti-interference performance and adaptability in complex and variable environments.

[0134] In some preferred embodiments, in step A3, the process of modifying the corresponding desired parameter adjustment amount in combination with the extracted energy state information to obtain the actual parameter adjustment amount includes:

[0135] A34, according to the energy level interval (which can be divided in the manner of the aforementioned current energy level, or a more detailed division manner) of the extracted energy state information, extract a correction coefficient from a pre-set correction coefficient set;

[0136] A35, multiply the correction coefficient with the desired parameter adjustment amount to obtain the actual parameter adjustment amount.

[0137] Specifically, a preset correction coefficient set stores correction coefficient values ​​corresponding to various energy level intervals. This set can be pre-configured before system deployment or dynamically adjusted during system operation based on the overall network status. A correction coefficient is a numerical factor used to adjust the desired parameter adjustment. A correction coefficient of 0 completely cancels the parameter adjustment.

[0138] More specifically, the method of the present application introduces energy status to correct the expected parameter adjustment amount, which can avoid making excessively high adjustment requirements for nodes with insufficient energy, thereby balancing environmental adaptability and node energy consumption.

[0139] More specifically, the actual parameter adjustment calculated through multiplication in step A35 takes into account both the requirements of environmental changes (reflected in the expected parameter adjustment) and the energy tolerance of the node (reflected in the correction coefficient), making the parameter adjustment instructions issued to the node more reasonable and feasible. This balances the network's adaptability to environmental changes with the energy consumption of the sensor nodes, helping to extend the effective operating time of individual sensor nodes and, in turn, improving the overall operational lifespan and stability of the wireless sensor network.

[0140] In some preferred embodiments, step S5 includes:

[0141] S51, receiving a parameter adjustment instruction, parsing the parameter adjustment instruction to extract a node identifier and several actual parameter adjustment amounts;

[0142] S52, judging whether the node matches the sensor node according to the node identifier, if so, executing S53, otherwise, discarding the parameter adjustment instruction;

[0143] S53, evaluating the current energy state according to the latest acquired energy state information. If the energy state is lower than a preset threshold, temporarily suspending parameter adjustment. Otherwise, executing S54;

[0144] S54: Update the corresponding type of parameters in the communication parameter set according to the actual parameter adjustment amount.

[0145] Specifically, a node identifier is a tag used to uniquely identify a specific sensor node in a wireless sensor network. It can be implemented using the node's MAC address, short address, serial number, or logical number. A preset threshold is an energy level limit set in the energy management strategy to determine whether a node is in a low-battery state.

[0146] More specifically, step S51 receives a parameter adjustment instruction from a convergence node and parses the instruction content to identify the node to which the instruction is addressed and the specific parameter adjustment values ​​that need to be made. Next, step S52 checks whether the node identifier in the instruction matches its own identifier, ensuring that only instructions addressed to itself are processed. After confirming that the instruction is valid and addressed to itself, step S53 does not immediately perform parameter adjustments. Instead, it first obtains and evaluates its current energy status. If the evaluation result shows that the current energy level is below a pre-set threshold, indicating that the node's energy is insufficient to support potential high-energy parameter adjustment operations, the node will choose to temporarily suspend execution of the adjustment instruction to avoid failure due to energy depletion. Only when the node's energy status is good, that is, above the pre-set threshold, is step S54 executed to update the corresponding parameters in its communication parameter set according to the adjustment amount specified in the instruction. After the parameter update is completed in step S54, detailed information about the adjustment can also be recorded for subsequent performance analysis or problem tracing.

[0147] Through the above scheme, the method of the present application enables sensor nodes to intelligently decide whether to perform parameter adjustments based on their own real-time energy status after receiving parameter adjustment instructions from the aggregation node. This effectively avoids performing parameter adjustment operations that could lead to rapid energy depletion when the node's energy reserves are insufficient, thereby significantly extending the effective operating 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 limited energy and dynamically changing environments.

[0148] Second, please refer to Figure 2 and Figure 3 ,Some embodiments of the present application also provide a communication anti-interference parameter collaborative control system, which is applied to a wireless sensor network. The system includes: a plurality of sensor nodes 100 and a sink node 200;

[0149] The sensor node 100 includes:

[0150] Monitoring module 101, used to obtain energy status information of sensor nodes;

[0151] A sensing module 102 is configured to adjust the frequency and precision of environmental sensing activities based on energy state information and a communication parameter set, and then sense and collect environmental sensing information;

[0152] A report generation module 103 is configured to determine a report format and a sending frequency according to the energy status information and the communication parameter set, and generate collaborative information according to the energy status information and the environmental perception information based on the report format;

[0153] The uploading module 104 is configured to upload the coordination information to the sink node 200 based on the transmission frequency;

[0154] An updating module 105 is configured to receive a parameter adjustment instruction and update a communication parameter set based on the parameter adjustment instruction;

[0155] The sink node 200 is used to generate parameter adjustment instructions according to the collaborative information and send them to the corresponding sensor nodes 100 .

[0156] The system of the present application combines the energy status information of the sensor node 100 with the adjustment of the environmental perception activity and the determination of the information report format and the sending frequency, and introduces the collaborative regulation of the aggregation node 200 to form an energy-aware adaptive closed-loop control mechanism, which effectively solves the energy waste and response lag problems caused by traditional fixed or non-energy-aware regulation methods in wireless sensor networks under energy constraints and dynamic environmental changes; it enables the sensor node 100 to perceive its own energy status and adaptively adjust the perception and communication behavior accordingly, which 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 issuance of parameter adjustment instructions, it can respond to environmental changes and interference more quickly and accurately, thereby improving the reliability and anti-interference ability of communication.

[0157] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] 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 independently, or two or more modules can be integrated to form an independent part.

[0159] In this document, relational terms such as first and second, etc. are used merely to distinguish one entity or operation from another entity or operation, but do not necessarily require or imply any actual relationship or order between these entities or operations.

[0160] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A communication anti-interference parameter coordinated control method, applied to sensor nodes in a wireless sensor network, characterized in that: The method comprises the following steps: S1, obtain the energy status information of the sensor node; S2. Adjusting the frequency and precision of the environment sensing activity according to the energy state information and the communication parameter set, and then sensing and collecting the environment sensing information; S3. Determine a report format and a sending frequency according to the energy status information and the communication parameter set, and generate collaborative information according to the energy status information and the environmental perception information based on the report format; S4. Uploading the collaborative information to a sink node based on the sending frequency, so that the sink node generates a parameter adjustment instruction according to the collaborative information and sends it to the corresponding sensor node; S5. Receive a parameter adjustment instruction, and update the communication parameter set based on the parameter adjustment instruction; The process of the sink node generating a parameter adjustment instruction according to the collaborative information and sending the instruction to the corresponding sensor node includes: A1. The sink node receives the collaborative information uploaded by each sensor node and parses the collaborative information to extract the energy state information and environmental perception information of each sensor node; A2, the sink node builds a hierarchical transmission mechanism based on the extracted energy state information; A3. The sink node calculates the expected parameter adjustment values ​​of each sensor node for different types of parameters in the communication parameter set based on the extracted environmental perception information. Then, it modifies the corresponding expected parameter adjustment values ​​based on the extracted energy state information to obtain the actual parameter adjustment values. A4. The aggregation node generates a parameter adjustment instruction including a node identifier and a parameter adjustment value according to the actual parameter adjustment amount; A5. The sink node sends the parameter adjustment instruction to the corresponding sensor node according to the hierarchical transmission mechanism; Step S5 includes: S51, receiving a parameter adjustment instruction, parsing the parameter adjustment instruction to extract a node identifier and several actual parameter adjustment amounts; S52, judging whether the node matches the sensor node according to the node identifier, if so, executing S53, otherwise, discarding the parameter adjustment instruction; S53, evaluating the current energy state according to the latest acquired energy state information. If the energy state is lower than a preset threshold, temporarily suspending parameter adjustment. Otherwise, executing S54; S54: Update the corresponding type of parameters in the communication parameter set according to the actual parameter adjustment amount.

2. The method for collaborative control of communication anti-interference parameters according to claim 1, characterized in that: 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 status information based on a preset classification rule; S22. Based on the perception frequency set and the perception fineness set, and according to the current energy level, determine the frequency and fineness of the environment perception activity.

3. The method for collaboratively controlling communication anti-interference parameters according to claim 2, wherein: The communication parameter set also includes an anti-interference parameter set; Step S2 further includes: S23, monitoring the environment of the sensor node to determine whether there is an interference source; S24. When an interference source exists, obtain the interference type and interference intensity, and based on the anti-interference parameter set, compensate and adjust the frequency and fineness of the environment perception activity according to the interference type and the interference intensity.

4. The method for collaborative control of communication anti-interference parameters according to claim 3, characterized in that: The anti-interference parameter set includes 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 environment perception activity according to the interference type and the interference intensity based on the anti-interference parameter set includes: S241. Select a corresponding frequency adjustment factor and a 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: Based on the frequency adjustment coefficient and the fineness adjustment coefficient, compensate and adjust the frequency and fineness of the environment perception activity.

5. The method for collaborative control of communication anti-interference parameters according to claim 1, characterized in that: The communication parameter set includes a report format list and a transmission frequency set; step S3 includes: S31. Selecting a report format and a transmission frequency from a preset report format list and a transmission frequency set based on a current energy level determined using the energy status information; S32. Extracting key environmental perception parameters from the environmental perception information according to the selected report format, and compressing and encoding the key environmental perception parameters using a preset compression algorithm to obtain compressed environmental perception data; S33. Encapsulate the energy status information and the compressed environmental perception data according to the selected report format to generate collaborative information.

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

7. The method for collaboratively controlling communication anti-interference parameters according to claim 1, wherein: In step A3, the process in which the sink node calculates the expected parameter adjustment amount of each sensor node with respect to different types of parameters in the communication parameter set according to the extracted environmental perception information includes: A31. Based on the environmental perception information extracted from each sensor node, a fuzzy clustering algorithm is used to perform data fusion to obtain multiple information clusters, each of which represents an environmental state area; A32. Calculate the weight of each environmental state region based on 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 amount 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.

8. The method for collaboratively controlling communication anti-interference parameters according to claim 1, wherein: In step A3, the process of combining the extracted energy state information, correcting the corresponding expected parameter adjustment amount, and obtaining the actual parameter adjustment amount includes: A34. Extracting a correction coefficient from a preset correction coefficient set according to the energy level interval of the extracted energy state information; A35. Multiply the correction coefficient by the expected parameter adjustment amount to obtain the actual parameter adjustment amount.

9. A communication anti-interference parameter coordinated control system, applied to wireless sensor networks, characterized in that: The system includes: a plurality of sensor nodes and a sink node; The sensor node comprises: Monitoring module, used to obtain energy status information of sensor nodes; A perception module, configured to adjust the frequency and precision of environmental perception activities according to the energy state information and the communication parameter set, and then perceive and collect environmental perception information; a report generation module, configured to determine a report format and a sending frequency according to the energy status information and the communication parameter set, and generate collaborative information according to the energy status information and the environmental perception information based on the report format; an uploading module, configured to upload the collaborative information to a sink node based on the sending frequency; an updating module, configured to receive a parameter adjustment instruction and update the communication parameter set based on the parameter adjustment instruction; The aggregation node is used to generate parameter adjustment instructions according to the collaborative information and send them to the corresponding sensor nodes; The process of the sink node generating a parameter adjustment instruction according to the collaborative information and sending the instruction to the corresponding sensor node includes: A1. The sink node receives the collaborative information uploaded by each sensor node and parses the collaborative information to extract the energy state information and environmental perception information of each sensor node; A2, the sink node builds a hierarchical transmission mechanism based on the extracted energy state information; A3. The sink node calculates the expected parameter adjustment values ​​of each sensor node for different types of parameters in the communication parameter set based on the extracted environmental perception information. Then, it modifies the corresponding expected parameter adjustment values ​​based on the extracted energy state information to obtain the actual parameter adjustment values. A4. The aggregation node generates a parameter adjustment instruction including a node identifier and a parameter adjustment value according to the actual parameter adjustment amount; A5. The sink node sends the parameter adjustment instruction to the corresponding sensor node according to the hierarchical transmission mechanism; The step of receiving a parameter adjustment instruction and updating the communication parameter set based on the parameter adjustment instruction includes: S51, receiving a parameter adjustment instruction, parsing the parameter adjustment instruction to extract a node identifier and several actual parameter adjustment amounts; S52, judging whether the node matches the sensor node according to the node identifier, if so, executing S53, otherwise, discarding the parameter adjustment instruction; S53, evaluating the current energy state according to the latest acquired energy state information. If the energy state is lower than a preset threshold, temporarily suspending parameter adjustment. Otherwise, executing S54; S54: Update the corresponding type of parameters in the communication parameter set according to the actual parameter adjustment amount.

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