A method and apparatus for selecting jump nodes of wireless sensors

Abstract

This invention discloses a method and apparatus for selecting hop nodes for wireless sensors. The method includes: extracting node information of the current node corresponding to the wireless sensor within a communication network; calculating node energy efficiency, node data packet transmission success rate, and node overall load based on the current node information; calculating neighbor node values ​​using the node energy efficiency, data packet transmission success rate, and node overall load; calculating a node overall value based on the neighbor node values; selecting the node with the largest overall value from several node overall values; and using the communication node with the largest overall value as the hop node. This invention calculates the node overall value through various node information and then selects nodes based on the overall value. This can filter nodes with short paths, low packet loss rates, and high communication performance, improving the accuracy and practicality of the selection process and avoiding serious problems such as data redundancy and radio channel contention.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method and apparatus for selecting jump nodes of a wireless sensor. Background Technology

[0002] Wireless sensor networks are large-scale networks formed by the organization and combination of numerous sensor nodes based on communication technology. With the increasing maturity and rapid development of wireless communication technology, sensor manufacturing processes, integrated chip design, and electronic component manufacturing, low-power, multifunctional, and highly adaptable wireless sensor nodes have driven the popularization and application of large-scale wireless sensor networks.

[0003] During node communication, communication may be interrupted due to different environmental factors. To restore communication in a timely manner, the commonly used method is to call the WSN routing protocol to filter available nodes from the idle nodes and jump from the current node to the filtered node so that communication can continue.

[0004] However, the commonly used methods have the following technical problems: WSN routing protocols only filter idle nodes based on the usage status of nodes. The selected idle nodes may have long paths, high packet loss rates, and low communication performance after use, which increases communication time and often requires re-filtering. This will lead to serious problems such as data redundancy and radio channel contention, thereby greatly shortening the life cycle of wireless sensor networks. Summary of the Invention

[0005] This invention proposes a method and apparatus for selecting jump nodes of wireless sensors, which can solve one or more of the above-mentioned technical problems.

[0006] A first aspect of this invention provides a method for filtering hop nodes of a wireless sensor, the method comprising:

[0007] Extract node information of the current node of the wireless sensor within the communication network, wherein the communication network contains multiple communication nodes;

[0008] Based on the current node information, calculate the node energy efficiency, node data packet transmission success rate, and node overall load respectively;

[0009] The neighbor node value is calculated using the node energy efficiency, the data packet transmission success rate, and the node's overall load.

[0010] A node composite value is calculated based on the neighbor node values. The node composite value with the largest value is selected from several node composite values, and the communication node with the largest value is used as the jump node. The node composite value is a selectable evaluation value of the communication node adjacent to the current node of the wireless sensor.

[0011] In one possible implementation of the first aspect, the node energy efficiency is calculated as follows:

[0012] E j =CC j ;

[0013] In the above formula, E j Let C be the node's energy efficiency, and C be the node's expected forwarding cost. j This represents the actual energy consumption of the node.

[0014] In one possible implementation of the first aspect, the data packet transmission success rate is calculated as follows:

[0015]

[0016] In the above formula, S j Let N be the node data packet transmission success rate, and N be the total number of data packets that need to be transmitted within the time period T. j This represents the number of data packets successfully received by the node within the time period T.

[0017] In one possible implementation of the first aspect, the calculation of the node's overall load is shown in the following formula:

[0018] CL=U butter *N dp ;

[0019] In the formula, CL represents the node's total load, U butter The utilization rate of the node buffer, N dp This represents the maximum number of data packets a node can process per unit of time.

[0020] The utilization rate U of the node buffer butter The calculation is shown in the following formula:

[0021]

[0022] In the above formula, butter used For the amount of cache already used, butter total This represents the total cache size.

[0023] In one possible implementation of the first aspect, calculating the neighbor node value using the node energy efficiency, the data packet transmission success rate, and the node's overall load includes:

[0024] A learning reward function is constructed using the node energy efficiency, the data packet transmission success rate, and the node's overall load.

[0025] Numerical initialization is performed using the learning reward function to obtain the initialization function value;

[0026] The values ​​of neighboring nodes are obtained by iteratively updating the initialization function value using the Bellman equation.

[0027] In one possible implementation of the first aspect, the learning reward function is calculated as follows:

[0028] R = W1 - E j +W2*S j -W3*CL-W4*H P ;

[0029] Where W1, W2, W3, and W4 are the weighting factors for different factors, and E j For the energy efficiency of the node, S j CL represents the node's data packet transmission success rate, CL represents the node's overall load, and HP represents the node's hop count.

[0030] In one possible implementation of the first aspect, the neighbor node value is calculated as follows:

[0031] Q(s,a)←Q(s,a)+α*(R+γ*max(Q(s′,a′))-Q(s′,a′));

[0032] Where Q(s, a) is the neighbor node value, α is the learning rate, γ is the discount factor, s represents the current state, a is the selected action, s' is the new state, and a' is the best action in the new state.

[0033] In one possible implementation of the first aspect, calculating the node composite value based on the neighbor node values ​​includes:

[0034] Determine the communication distance value, which is the distance between the adjacent communication node and the destination node;

[0035] The node composite value is calculated using the neighbor node value and the communication distance value.

[0036] In one possible implementation of the first aspect, the communication distance value is calculated as follows:

[0037]

[0038] In the above formula, D(j, d) is the communication distance value, (X j Y j (X) represents the location coordinates of adjacent communication nodes. d Y d () represents the position coordinates of the destination node;

[0039] The calculation of the node's comprehensive value is shown in the following formula:

[0040] OP j =ε*Q(s, a)-μD(j, d);

[0041] In the above formula, ε and μ are the weighting factors of different factors, and Q(s, a) is the neighbor node value.

[0042] A second aspect of the present invention provides a hop node screening device for wireless sensors, the device comprising:

[0043] The node information extraction module is used to extract the node information of the current node corresponding to the wireless sensor within the communication network, wherein the communication network contains multiple communication nodes.

[0044] The node information calculation module is used to calculate the node energy efficiency, node data packet transmission success rate, and node overall load based on the current node information.

[0045] The node value calculation module is used to calculate the neighbor node value using the node energy efficiency, the data packet transmission success rate, and the node's overall load.

[0046] The node filtering module is used to calculate the node comprehensive value based on the neighbor node values, filter the node comprehensive value with the largest value from several node comprehensive values, and use the communication node with the largest value as the jump node. The node comprehensive value is the filterable evaluation value of the communication node adjacent to the current node of the wireless sensor.

[0047] In one possible implementation of the second aspect, the node energy efficiency is calculated as follows:

[0048] E j =CC j ;

[0049] In the above formula, E j Let C be the node's energy efficiency, and C be the node's expected forwarding cost. j This represents the actual energy consumption of the node.

[0050] In one possible implementation of the second aspect, the data packet transmission success rate is calculated as follows:

[0051]

[0052] In the above formula, S j Let N be the node data packet transmission success rate, and N be the total number of data packets that need to be transmitted within the time period T. j This represents the number of data packets successfully received by the node within the time period T.

[0053] In one possible implementation of the second aspect, the calculation of the node's overall load is shown in the following formula:

[0054] CL=U butter *N dp ;

[0055] In the formula, CL represents the node's total load, U butter The utilization rate of the node buffer, N dp This represents the maximum number of data packets a node can process per unit of time.

[0056] The utilization rate U of the node buffer butter The calculation is shown in the following formula:

[0057]

[0058] In the above formula, butter used For the amount of cache already used, butter total This represents the total cache size.

[0059] In one possible implementation of the second aspect, calculating the neighbor node value using the node energy efficiency, the data packet transmission success rate, and the node's overall load includes:

[0060] A learning reward function is constructed using the node energy efficiency, the data packet transmission success rate, and the node's overall load.

[0061] Numerical initialization is performed using the learning reward function to obtain the initialization function value;

[0062] The values ​​of neighboring nodes are obtained by iteratively updating the initialization function value using the Bellman equation.

[0063] In one possible implementation of the second aspect, the learning reward function is calculated as follows:

[0064] R = W1 * E j +W2*S j -W3*CL-W4*HP;

[0065] Where W1, W2, W3, and W4 are the weighting factors for different factors, and E j For the energy efficiency of the node, S j CL represents the node's data packet transmission success rate, CL represents the node's overall load, and HP represents the node's hop count.

[0066] In one possible implementation of the second aspect, the neighbor node value is calculated as follows:

[0067] Q(s, a)←Q(s, a)+α*(R+γ*max(Q(s′, a))-Q(s′, a′));

[0068] Where Q(s, a) is the neighbor node value, α is the learning rate, γ is the discount factor, s represents the current state, a is the selected action, s' is the new state, and a' is the best action in the new state.

[0069] In one possible implementation of the second aspect, calculating the node composite value based on the neighbor node values ​​includes:

[0070] Determine the communication distance value, which is the distance between the adjacent communication node and the destination node;

[0071] The node composite value is calculated using the neighbor node value and the communication distance value.

[0072] In one possible implementation of the second aspect, the communication distance value is calculated as shown in the following formula:

[0073]

[0074] In the above formula, D(j, d) is the communication distance value, (X j Y j (X) represents the location coordinates of adjacent communication nodes. d Y d () represents the position coordinates of the destination node;

[0075] The calculation of the node's comprehensive value is shown in the following formula:

[0076] OP j =ε*Q(s, a)-μD(j, d);

[0077] In the above formula, ε and μ are the weighting factors of different factors, and Q(s, a) is the neighbor node value.

[0078] Compared to existing technologies, the present invention provides a method and apparatus for selecting jump nodes for wireless sensors, which has the following advantages: The present invention can extract node information of the current node corresponding to the wireless sensor within the communication network; calculate a comprehensive node value based on the current node information and neighbor node values; select the node with the largest comprehensive node value from several comprehensive node values; and use the communication node with the largest comprehensive node value as the jump node. By calculating the comprehensive node value through various node information and then selecting nodes based on the comprehensive node value, nodes with short paths, low packet loss rates, and high communication performance can be selected, improving the accuracy and practicality of the selection process and avoiding serious problems such as data redundancy and radio channel contention. Attached Figure Description

[0079] Figure 1 This is a flowchart illustrating a method for selecting jump nodes for a wireless sensor according to an embodiment of the present invention.

[0080] Figure 2 This is a schematic diagram illustrating the interaction between multiple agents and environmental information in a location-based wireless sensor network routing method provided in an embodiment of the present invention.

[0081] Figure 3 This is a schematic diagram of the structure of a wireless sensor jump node screening device provided in an embodiment of the present invention. Detailed Implementation

[0082] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0083] To address the aforementioned issues, the following specific embodiments will provide a detailed description and explanation of a wireless sensor hop node selection method provided in this application.

[0084] Reference Figure 1 The diagram shows a flowchart of a method for selecting jump nodes for a wireless sensor according to an embodiment of the present invention.

[0085] In one embodiment, the hop node filtering method for wireless sensors is applicable to routing in wireless sensor networks.

[0086] Reference Figure 2 This diagram illustrates the interaction between multiple agents and environmental information in a location-based wireless sensor network routing method according to an embodiment of the present invention.

[0087] Wireless sensor network routing enables communication and detection with wireless sensors within a communication network.

[0088] As an example, the method for selecting hop nodes for the wireless sensor may include:

[0089] S11. Extract the node information of the current node corresponding to the wireless sensor within the communication network, wherein the communication network contains multiple communication nodes.

[0090] In one embodiment, a communication network of wireless sensors for multiple agents can be pre-built.

[0091] In one specific implementation, a suitable hardware platform can be selected first, such as a microcontroller based on the ARM Cortex-M series, which integrates multiple sensors such as temperature, humidity, light, and sound, and is configured with a low-power wireless communication module, such as IEEE802.15.4 / Zigbee or LoRa.

[0092] Furthermore, the design of wireless sensor nodes may include equipping each wireless sensor node with a positioning module to acquire its own geographical location information; equipping each wireless sensor node with a communication module to receive data acquisition information and status information and to communicate with other nodes; and equipping each wireless sensor with an autonomous learning module for making autonomous decisions.

[0093] Furthermore, a communication network for multi-agent wireless sensors is constructed based on the specific circumstances of the implementation scenario.

[0094] In one specific implementation, each wireless sensor node is equipped with temperature, smoke concentration, and humidity sensors, as well as a GPS module for positioning; a low-power wireless communication module, LoRa, is selected to meet the requirements of long distance and low power consumption; and a Q-learning module is set up for autonomous decision-making.

[0095] By utilizing GIS geographic information system and remote sensing technology, a forest density map is drawn to distinguish low, medium and high density areas. Each node is laid out according to the forest distribution density, and different density deployment strategies are adopted in areas with different forest densities. The node density is increased in high-density forest areas and appropriately reduced in low-density areas, thus constructing a communication network of wireless sensors for multiple agents.

[0096] Next, node information of each wireless sensor can be obtained in the communication network of the wireless sensor;

[0097] Specifically, each node of the wireless sensor can periodically collect its own state information, which serves as node information. In one embodiment, node information may include energy consumption data, the number of successful data packet transmissions and receptions, the current task buffer usage, node hop count, and geographical location information.

[0098] S12. Calculate the node energy efficiency, node data packet transmission success rate, and node overall load based on the current node information.

[0099] In an optional embodiment, the energy consumption of a node during packet forwarding can be calculated based on the node's energy consumption data, and the expected forwarding cost can be compared with the actual energy consumption to evaluate the node's energy efficiency.

[0100] Specifically, the node energy efficiency is calculated as follows:

[0101] Ej =CC j ;

[0102] In the above formula, E j Let C be the node's energy efficiency, and C be the node's expected forwarding cost. j This represents the actual energy consumption of the node.

[0103] In an optional embodiment, the node data packet transmission success rate can be calculated based on the number of data packets successfully received by the node within time period T and the total number of data packets that need to be transmitted within time period T.

[0104] Specifically, the data packet transmission success rate is calculated as follows:

[0105]

[0106] In the above formula, S j Let N be the node data packet transmission success rate, and N be the total number of data packets that need to be transmitted within the time period T. j This represents the number of data packets successfully received by the node within the time period T.

[0107] In an alternative embodiment, the utilization rate U of the node buffer can be used as a basis. butter The maximum number of data packets N that a node can process per unit of time. dp To calculate the node's overall load CL.

[0108] Specifically, the calculation of the node's overall load is shown in the following formula:

[0109] CL=U butter *N dp ;

[0110] In the above formula, CL represents the node's total load, and U represents the total load of the node. butter The utilization rate of the node buffer, N dp This represents the maximum number of data packets a node can process per unit of time.

[0111] Among them, the utilization rate U of the node buffer butter The calculation is shown in the following formula:

[0112]

[0113] In the above formula, butter used For the amount of cache already used, butter total This represents the total cache size.

[0114] S13. Calculate the neighbor node value using the node energy efficiency, the data packet transmission success rate, and the node comprehensive load.

[0115] In one embodiment, the neighbor node value can be the Q value of the neighbor node.

[0116] In one embodiment, step S13 may include the following sub-steps:

[0117] S131. Construct a learning reward function using the node energy efficiency, the data packet transmission success rate, and the node comprehensive load.

[0118] S132. Use the learning reward function to perform numerical initialization and obtain the initialization function value.

[0119] S133. The initialization function value is iteratively updated using the Bellman equation to obtain the neighbor node value.

[0120] In one embodiment, the learning reward function is the reward given to the node. Specifically, the higher the node's energy efficiency, the higher the reward; the higher the packet transmission success rate, the higher the reward; the lower the node's overall load, the higher the reward; and the lower the node's hop count, the higher the reward.

[0121] In one embodiment, the learning reward function is calculated as follows:

[0122] R = W1 * E j +W2*S j -W3*CL-W4*HP;

[0123] Where W1, W2, W3, and W4 are the weighting factors for different factors, and E j For the energy efficiency of the node, S j CL represents the node's data packet transmission success rate, CL represents the node's overall load, and HP represents the node's hop count.

[0124] By designing a reward function that considers node energy efficiency, nodes in the network are encouraged to choose low-energy paths for data transmission, thereby extending the overall network lifespan and reducing maintenance costs. Furthermore, packet transmission success rate is incorporated into the reward function, ensuring data is delivered accurately and promptly to its destination, improving network reliability and quality of service. Additionally, setting a comprehensive load in the reward function helps distribute data processing pressure across the network, preventing performance bottlenecks caused by overloaded nodes and enhancing overall network stability and processing efficiency.

[0125] Specifically, numerical initialization can be the initialization of Q values: assigning an initial Q value to all state-action pairs, typically 0 or a small positive number;

[0126] Furthermore, when selecting actions, an ε-greedy strategy can usually be adopted, which means randomly selecting actions with a certain probability ε to explore new paths, and selecting the action corresponding to the currently known maximum Q value with a probability of 1-ε.

[0127] In one embodiment, the Bellman equation can be used for iterative updates of the Q-values ​​of neighboring nodes. In one embodiment, the calculation of the neighboring node values ​​is shown in the following formula:

[0128] Q(s, a)←Q(s, a)+α*(R+γ*max(Q(s', a'))-Q(s', a'));

[0129] Where Q(s, a) is the neighbor node value, α is the learning rate, γ is the discount factor, s represents the current state, a is the selected action, s' is the new state, and a' is the best action in the new state.

[0130] This embodiment combines geographic location information and Q-value iteration to dynamically select the optimal path, reduce node hops, and shorten data transmission time.

[0131] It should be noted that the learning rate α can be used to control the rate of fusion of new and old information, that is, the trade-off between the newly obtained reward or estimate and the current estimate when updating the reward function. When starting Q-value iteration updates, a relatively large learning rate is usually set to accelerate the fusion rate of new and old information in the early stages of learning. The learning rate is gradually reduced to 0 over time to promote function convergence. The learning rate α can be specifically set according to the specific implementation scenario. Furthermore, by combining Q-learning to optimize routing decisions, intelligent management of wireless sensor networks can be achieved.

[0132] In addition, the Q-learning algorithm enables the network to learn and adjust its routing strategy according to environmental changes, improving the robustness and adaptability of the system and maintaining efficient operation without human intervention. Through intelligent decision-making processes, network resources are effectively utilized, especially by considering buffer usage, which avoids data congestion, reduces data packet loss rate, and improves the overall network performance.

[0133] It should also be noted that the discount factor γ is used to measure the importance of future rewards in the current value estimation. When γ is close to 1, the algorithm focuses more on long-term rewards and is suitable for tasks that require long-term planning. When γ is close to 0, it focuses more on immediate rewards and is suitable for short-term decision-making problems. The discount factor γ can be set according to the specific task planning cycle.

[0134] S14. Calculate the node comprehensive value based on the neighbor node value, select the node comprehensive value with the largest value from several node comprehensive values, and use the communication node with the largest value as the jump node. The node comprehensive value is the filterable evaluation value of the communication node adjacent to the current node of the wireless sensor.

[0135] As an example, step S14 may include the following sub-steps:

[0136] S141. Determine the communication distance value, which is the distance between the adjacent communication node and the destination node.

[0137] S142. Calculate the node composite value using the neighbor node value and the communication distance value.

[0138] In one embodiment, Euclidean distance can be used to evaluate the distance between the node and the destination node.

[0139] In one embodiment, the communication distance value is calculated as follows:

[0140]

[0141] In the above formula, D(j, d) is the communication distance value, (X j Y j (X) represents the location coordinates of adjacent communication nodes. d Y d () represents the position coordinates of the destination node;

[0142] In one embodiment, the node composite value is calculated as follows:

[0143] OP j =ε*Q(s, a)-μD(j, d);

[0144] In the above formula, ε and μ are the weighting factors of different factors, and Q(s, a) is the neighbor node value.

[0145] Specifically, after calculating the node composite value using the above formula, multiple node composite values ​​can be obtained. Then, the node composite value with the largest value is selected from these multiple values, and the communication node with the largest composite value is used as the jump node. When the current node is faulty or communication is not smooth, the jump node can be switched from the current node, and communication can continue using the jump node.

[0146] The above method balances path cost and distance factors through comprehensive optionality calculation, achieving more efficient routing decisions.

[0147] In this embodiment, the present invention provides a method for filtering jump nodes for wireless sensors. Its advantages are as follows: the present invention can extract the node information of the current node corresponding to the wireless sensor within the communication network; calculate the node comprehensive value based on the neighbor node values ​​and the current node information; filter the node with the largest comprehensive value from several node comprehensive values; and use the communication node with the largest comprehensive value as the jump node. By calculating the node comprehensive value through various node information and then filtering nodes based on the comprehensive value, nodes with short paths, low packet loss rates, and high communication performance can be selected, improving the accuracy and practicality of the filtering process and avoiding serious problems such as data redundancy and radio channel contention.

[0148] This invention also provides a wireless sensor hop node screening device, see [link to relevant documentation]. Figure 3 The diagram shows a schematic of the structure of a wireless sensor jump node screening device according to an embodiment of the present invention.

[0149] As an example, the wireless sensor's jump node screening device may include:

[0150] The node information extraction module 301 is used to extract the node information of the current node corresponding to the wireless sensor within the communication network, wherein the communication network includes multiple communication nodes.

[0151] The node information calculation module 302 is used to calculate the node energy efficiency, node data packet transmission success rate and node comprehensive load based on the current node information.

[0152] The node value calculation module 303 is used to calculate the neighbor node value using the node energy efficiency, the data packet transmission success rate, and the node's overall load.

[0153] The node filtering module 304 is used to calculate the node comprehensive value based on the neighbor node value, filter the node comprehensive value with the largest value from a number of node comprehensive values, and use the communication node with the largest value as the jump node. The node comprehensive value is the filterable evaluation value of the communication node adjacent to the current node of the wireless sensor.

[0154] Optionally, the node energy efficiency is calculated as follows:

[0155] E j =CC j ;

[0156] In the above formula, E j Let C be the node's energy efficiency, and C be the node's expected forwarding cost. j This represents the actual energy consumption of the node.

[0157] Optionally, the data packet transmission success rate is calculated as follows:

[0158]

[0159] In the above formula, S j Let N be the node data packet transmission success rate, and N be the total number of data packets that need to be transmitted within the time period T. j This represents the number of data packets successfully received by the node within the time period T.

[0160] Optionally, the calculation of the node's overall load is shown in the following formula:

[0161] CL=U butter *N dp ;

[0162] In the formula, CL represents the node's total load, U butter The utilization rate of the node buffer, N dp This represents the maximum number of data packets a node can process per unit of time.

[0163] The utilization rate U of the node buffer butter The calculation is shown in the following formula:

[0164]

[0165] In the above formula, butter used For the amount of cache already used, butter total This represents the total cache size.

[0166] Optionally, the step of calculating the neighbor node value using the node energy efficiency, the data packet transmission success rate, and the node's overall load includes:

[0167] A learning reward function is constructed using the node energy efficiency, the data packet transmission success rate, and the node's overall load.

[0168] Numerical initialization is performed using the learning reward function to obtain the initialization function value;

[0169] The values ​​of neighboring nodes are obtained by iteratively updating the initialization function value using the Bellman equation.

[0170] Optionally, the learning reward function is calculated as follows:

[0171] R=W1*E j +W2*S j -W3*CL-W4*HP;

[0172] Where W1, W2, W3, and W4 are the weighting factors for different factors, and E jFor the energy efficiency of the node, S j CL represents the node's data packet transmission success rate, CL represents the node's overall load, and HP represents the node's hop count.

[0173] Optionally, the neighbor node value is calculated as follows:

[0174] Q(s,a)←Q(s,a)+α*(R+γ*max*Q(s',a'))-Q(s',a'));

[0175] Where Q(s, a) is the neighbor node value, α is the learning rate, γ is the discount factor, s represents the current state, a is the selected action, s' is the new state, and a' is the best action in the new state.

[0176] Optionally, calculating the node composite value based on the neighbor node values ​​includes:

[0177] Determine the communication distance value, which is the distance between the adjacent communication node and the destination node;

[0178] The node composite value is calculated using the neighbor node value and the communication distance value.

[0179] Optionally, the communication distance value is calculated as follows:

[0180]

[0181] In the above formula, D(j, d) is the communication distance value, (X j Y j (X) represents the location coordinates of adjacent communication nodes. d Y d () represents the position coordinates of the destination node;

[0182] The calculation of the node's comprehensive value is shown in the following formula:

[0183] OP j =ε*Q(s, a)-μD(j, d);

[0184] In the above formula, ε and μ are the weighting factors of different factors, and Q(s, a) is the neighbor node value.

[0185] Those skilled in the art will understand that, for ease of description and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0186] Furthermore, this application also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the wireless sensor jump node screening method as described in the above embodiments.

[0187] Furthermore, embodiments of this application also provide a computer-readable storage medium storing a computer-executable program for causing a computer to execute the wireless sensor jump node screening method as described in the above embodiments.

[0188] In the description of the embodiments of the present invention, it should be noted that the terms "above," "below," etc., indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. When an element such as a layer, region, or substrate is referred to as being "above" or "on top of" another element, it may be directly on the other element, or there may be an intermediate element. Conversely, when an element is referred to as being "directly on" or "above" another element, there is no intermediate element. It should also be understood that when an element is referred to as being "below" or "under" another element, it may be directly below or under the other element, or there may be an intermediate element. Conversely, when an element is referred to as being "directly below" or "under" another element, there is no intermediate element. Unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0189] Those skilled in the art will understand that embodiments of this application may also include computer program products. Therefore, this application may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0190] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), devices, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0191] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0192] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0193] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for selecting jump nodes in a wireless sensor, characterized in that, The method includes: Extract node information of the current node corresponding to the wireless sensor within the communication network, wherein the communication network contains multiple communication nodes; Based on the current node information, calculate the node energy efficiency, node data packet transmission success rate, and node overall load respectively; The neighbor node value is calculated using the node energy efficiency, the data packet transmission success rate, and the node's overall load. The node comprehensive value is calculated based on the neighbor node values. The node comprehensive value with the largest value is selected from several node comprehensive values, and the communication node with the largest value is used as the jump node. The node comprehensive value is the filterable evaluation value of the communication node adjacent to the current node of the wireless sensor. The calculation of neighbor node values ​​using the node's energy efficiency, data packet transmission success rate, and overall node load includes: A learning reward function is constructed using the node energy efficiency, the data packet transmission success rate, and the node's overall load. Numerical initialization is performed using the learning reward function to obtain the initialization function value; The values ​​of the initialization function are iteratively updated using the Bellman equation to obtain the values ​​of the neighboring nodes; The learning reward function is calculated as follows: R=W1*E j +W2*S j -W3*C L -W4*H P ; Where W1, W2, W3, and W4 are the weighting factors for different factors, and E j For the energy efficiency of the node, S j CL represents the node's data packet transmission success rate, CL represents the node's overall load, and HP represents the node's hop count; The communication distance value is calculated as follows: In the above formula, D(j, d) is the communication distance value, (X j Y j (X) represents the location coordinates of adjacent communication nodes. d Y d () represents the position coordinates of the destination node; The calculation of the node's comprehensive value is shown in the following formula: OP j =ε*Q(s,a)-μD(j,d); In the above formula, ε and μ are the weighting factors of different factors, and Q(s, a) is the neighbor node value.

2. The method for selecting jump nodes of a wireless sensor according to claim 1, characterized in that, The node energy efficiency is calculated as follows: E j =C-C j ; In the above formula, E j Let C be the node's energy efficiency, and C be the node's expected forwarding cost. j This represents the actual energy consumption of the node.

3. The method for selecting jump nodes of a wireless sensor according to claim 1, characterized in that, The success rate of data packet transmission is calculated as follows: In the above formula, S j Let N be the node data packet transmission success rate, and N be the total number of data packets that need to be transmitted within the time period T. j This represents the number of data packets successfully received by the node within the time period T.

4. The method for selecting jump nodes for wireless sensors according to claim 1, characterized in that, The calculation of the node's overall load is shown in the following formula: CL=U butter *N dp ; In the formula, CL represents the node's total load, U butter The utilization rate of the node buffer, N dp This represents the maximum number of data packets a node can process per unit of time. The utilization rate U of the node buffer butter The calculation is shown in the following formula: In the above formula, butter used For the amount of cache already used, butter total This represents the total cache size.

5. The method for selecting jump nodes for wireless sensors according to claim 1, characterized in that, The neighbor node value is calculated as follows: Q(s,a)←Q(s,a)+α*(R+γ*max(Q(s′,a′))-Q(s′,a′)); Where Q(s, a) is the neighbor node value, α is the learning rate, γ is the discount factor, s represents the current state, a is the selected action, s' is the new state, and a' is the best action in the new state.

6. A wireless sensor jump node screening device, characterized in that, The device includes: The node information extraction module is used to extract the node information of the current node corresponding to the wireless sensor within the communication network, wherein the communication network contains multiple communication nodes. The node information calculation module is used to calculate the node energy efficiency, node data packet transmission success rate, and node overall load based on the current node information. The node value calculation module is used to calculate the neighbor node value using the node energy efficiency, the data packet transmission success rate, and the node's overall load. The node filtering module is used to calculate the node comprehensive value based on the neighbor node value, filter the node comprehensive value with the largest value from several node comprehensive values, and use the communication node with the largest value as the jump node. The node comprehensive value is the filterable evaluation value of the communication node adjacent to the current node of the wireless sensor. The calculation of neighbor node values ​​using the node's energy efficiency, data packet transmission success rate, and overall node load includes: A learning reward function is constructed using the node energy efficiency, the data packet transmission success rate, and the node's overall load. Numerical initialization is performed using the learning reward function to obtain the initialization function value; The values ​​of the initialization function are iteratively updated using the Bellman equation to obtain the values ​​of the neighboring nodes; The learning reward function is calculated as follows: R=W1*E j +W2*S j -W3*CL-W4*HP; Where W1, W2, W3, and W4 are the weighting factors for different factors, and E j For the energy efficiency of the node, S j CL represents the node's data packet transmission success rate, CL represents the node's overall load, and HP represents the node's hop count; The communication distance value is calculated as follows: In the above formula, D(j, d) is the communication distance value, (X j Y j (X) represents the location coordinates of adjacent communication nodes. d Y d () represents the position coordinates of the destination node; The calculation of the node's comprehensive value is shown in the following formula: OP j =ε*Q(s,a)-μD(j,d); In the above formula, ε and μ are the weighting factors of different factors, and Q(s, a) is the neighbor node value.

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