Sleep scheduling method and device for three-dimensional heterogeneous rechargeable wireless sensor network

Abstract

The application discloses a sleep scheduling method and device of a three-dimensional heterogeneous rechargeable wireless sensor network, and the method comprises the following steps: constructing a first target function of maximizing k-fold coverage quality in a monitoring period; decomposing the first target function to obtain a second target function of maximizing k-fold coverage quality of a single time slot and a third target function of minimizing energy compensation of a charging period; grouping each network node through a distributed iterative grouping algorithm according to the second target function, and distributing each group to each time slot in a time slot group in a cyclic manner; and scheduling a first target node of the time slot group through an adaptive sampling method according to the third target function to obtain a three-dimensional heterogeneous rechargeable wireless sensor network with maximized k-fold coverage quality. The application can improve the continuous k-fold coverage quality of the three-dimensional heterogeneous rechargeable wireless sensor network, and can be widely applied in the technical field of wireless sensor networks.

Description

Technical Field

[0001] This invention relates to the field of wireless sensor network technology, and in particular to a sleep scheduling method and apparatus for a three-dimensional heterogeneous rechargeable wireless sensor network. Background Technology

[0002] With advancements in energy harvesting technology, rechargeable wireless sensor networks (RSNs) composed of passive sensors are gradually replacing traditional RSNs as the infrastructure of the Internet of Things (IoT). Their sensor nodes can harvest energy from the environment for charging and storage in capacitors for later use, solving the problem of limited node energy in traditional RSNs. However, the charging rate of nodes is affected by uncertainties in environmental conditions and deployment location. Furthermore, the charging rate is typically lower than the consumption rate. Most research on the coverage problem of rechargeable wireless sensor networks focuses on two-dimensional networks; however, real-world applications are mostly three-dimensional and require the use of various types of sensors to collaboratively monitor the environment. Different types of nodes can have different sensing and / or communication ranges, and different charging and consumption rates, forming a three-dimensional heterogeneous rechargeable wireless sensor network.

[0003] Current research on the coverage of heterogeneous two-dimensional rechargeable wireless sensor networks either focuses on sensors with different power consumption and charging rates, or on networks composed of traditional and passive sensors, but neglects k-fold coverage. Furthermore, when an event is detected, sensors consume significant energy to track its development; for example, fire detection involves placing various sensors in a forest to monitor temperature, humidity, fog, and other data. Once a fire is detected, the sensors must frequently collect data and transmit it to a remote terminal. Their energy is quickly depleted, requiring immediate recharging. However, traditional methods do not consider these factors in the coverage problem, making it difficult to ensure stable and high-quality coverage. Summary of the Invention

[0004] In view of this, the main objective of the embodiments of the present invention is to provide a sleep scheduling method and apparatus for a three-dimensional heterogeneous rechargeable wireless sensor network, so as to improve the continuous k-fold coverage quality of the three-dimensional heterogeneous rechargeable wireless sensor network.

[0005] To achieve the above objectives, one aspect of the present invention provides a sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network, the method comprising the following steps:

[0006] Construct a first objective function that maximizes the quality of k-fold coverage within the monitoring period;

[0007] The first objective function is decomposed to obtain a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging cycle of energy compensation.

[0008] According to the second objective function, a distributed iterative grouping algorithm is used to group each network node, and each group is allocated to each time slot in the time slot group in a cyclic manner; wherein, the time slot group includes a number of time slots;

[0009] Based on the third objective function, the first target node of the time slot group is scheduled using an adaptive sampling method to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality.

[0010] In some embodiments, constructing a first objective function that maximizes the quality of k-fold coverage within a monitoring period includes the following steps:

[0011] Divide the total three-dimensional space into several three-dimensional space sub-regions of the same size;

[0012] The monitoring period is divided into multiple time slots;

[0013] Based on the volume of the three-dimensional spatial sub-region and the total number of time slots, obtain the first coverage quality covering the total three-dimensional spatial region within the monitoring period;

[0014] Based on the first coverage quality, construct the first objective function.

[0015] In some embodiments, decomposing the first objective function to obtain a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging cycle of energy compensation includes the following steps:

[0016] Obtain the standard deviation between the coverage rates of the three-dimensional spatial sub-regions in a single time slot;

[0017] Based on the standard deviation, obtain the second coverage quality of the set of network nodes in a single time slot;

[0018] Based on the second coverage quality, construct the second objective function.

[0019] In some embodiments, the decomposition of the first objective function to obtain a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging cycle of energy compensation further includes the following steps:

[0020] Set the initial number of samples for each of the aforementioned time slots;

[0021] Obtain the energy consumption for sending data, the energy consumption for receiving data, and the minimum energy consumption for a single sampling;

[0022] The third objective function is constructed based on the initial number of samples, the energy consumption of the transmitted data, the energy consumption of the received data, and the minimum energy consumption of a single sample.

[0023] In some embodiments, the grouping operation of each network node according to the second objective function using a distributed iterative grouping algorithm includes the following steps:

[0024] Store the set of network nodes in the initial packet;

[0025] Randomly select a second target node from the network nodes in the initial group;

[0026] Obtain the first coverage weight of the second target node;

[0027] Receive the second coverage weight of the neighboring nodes of the second target node;

[0028] Based on the weight values ​​of the first coverage weight and the second coverage weight, the second target node and its neighboring nodes are sorted in descending order, and the third target node corresponding to the k largest weight values ​​is obtained.

[0029] Determine whether the second target node belongs to the third target node. If the second target node does not belong to the third target node, return to the step of arbitrarily retrieving the second target node from the network node in the initial group. If the second target node belongs to the third target node, store the second target node in the first group and return to the step of arbitrarily retrieving the second target node from the network node in the initial group, until all the second target nodes in the initial group have been retrieved.

[0030] In some embodiments, the step of grouping network nodes according to the second objective function using a distributed iterative grouping algorithm further includes the following steps:

[0031] Preset the first intermediate parameter and the second intermediate parameter;

[0032] Determine whether the first intermediate parameter is less than or equal to the number of time slots in a time slot group. If the first intermediate parameter is less than or equal to the number of time slots in a time slot group, extract a fourth target node from the current group.

[0033] Determine whether the second intermediate parameter is less than the length of the neighbor information list of the fourth target node;

[0034] When the second intermediate parameter is less than the length of the neighbor information list of the fourth target node, determine whether the group number of the neighbor node of the fourth target node is the group number of the initial group. If yes, store the neighbor node of the fourth target node in the next group and return to the step of retrieving a fourth target node from the current group. If no, increment the second intermediate parameter by 1 and return to the step of determining whether the second intermediate parameter is less than the length of the neighbor information list of the fourth target node.

[0035] When the second intermediate parameter is greater than or equal to the length of the neighbor information list of the fourth target node, the first intermediate parameter is incremented by 1, and the step of determining whether the first intermediate parameter is less than or equal to the number of time slots in a time slot group is returned, until the first intermediate parameter is greater than the number of time slots in a time slot group.

[0036] In some embodiments, scheduling the first target node of the time slot group according to the third objective function using an adaptive sampling method to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality includes the following steps:

[0037] Preset a third intermediate parameter;

[0038] Set the first target node of the current group in the current time slot group to the active state to obtain the active node;

[0039] Obtain the first charging rate of the active node in the previous time slot group, and obtain the second charging rate of the active node in the next time slot group;

[0040] When the first charging rate is not equal to the second charging rate, the first sampling count of the active node is obtained;

[0041] When the first charging rate is equal to the second charging rate, determine whether the third intermediate parameter is greater than 0. If yes, adjust the first sampling number to the sampling threshold and decrement the third intermediate parameter by 1. If no, set the second charging rate to equal the first charging rate.

[0042] Based on the third intermediate parameter, the first sampling number, and the first charging rate, the active nodes are scheduled to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality.

[0043] In some embodiments, scheduling the active node based on the third intermediate parameter, the first sampling count, and the first charging rate includes the following steps:

[0044] When the active node detects a target event or receives a warning message, the active node enters a warning state, becomes a warning node, and adjusts the first sampling number to the maximum sampling number;

[0045] Obtain the additional energy consumption of the active node, and update the third intermediate parameter based on the additional energy consumption;

[0046] Determine whether the monitoring of the current time slot group has ended. If not, return to the step of setting the first target node of the current group of the current time slot group to an active state to obtain the active node. If yes, set the first charging rate to equal the third charging rate of the current warning node in the current time slot group, set the warning node to a sleep state, and enter the next time slot group. Return to the step of setting the first target node of the current group of the current time slot group to an active state to obtain the active node, until the monitoring of all time slot groups has ended.

[0047] To achieve the above objectives, another aspect of the present invention proposes a sleep scheduling device for a three-dimensional heterogeneous rechargeable wireless sensor network, the device comprising:

[0048] The first module is used to construct a first objective function that maximizes the quality of k-fold coverage within the monitoring period;

[0049] The second module is used to decompose the first objective function to obtain a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging cycle of energy compensation.

[0050] The third module is used to group each network node according to the second objective function using a distributed iterative grouping algorithm, and to allocate each group to each time slot in the time slot group in a cyclic manner; wherein, the time slot group includes a plurality of time slots;

[0051] The fourth module is used to schedule the first target node of the time slot group according to the third objective function and through an adaptive sampling method, so as to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes the k-fold coverage quality.

[0052] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network described above.

[0053] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network described above.

[0054] To achieve the above objectives, another aspect of the present invention provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network.

[0055] The embodiments of the present invention include at least the following beneficial effects: The present invention provides a sleep scheduling method and apparatus for a three-dimensional heterogeneous rechargeable wireless sensor network. This scheme constructs a first objective function that maximizes the k-fold coverage quality within a monitoring period; decomposes the first objective function to obtain a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging period for energy compensation; according to the second objective function, a distributed iterative grouping algorithm is used to group each network node, and each group is allocated to each time slot in the time slot group in a cyclic manner; wherein, the time slot group includes several time slots; according to the third objective function, an adaptive sampling method is used to schedule the first target node of the time slot group to obtain a three-dimensional heterogeneous rechargeable wireless sensor network with maximized k-fold coverage quality. The present invention, by combining a distributed iterative grouping algorithm and an adaptive sampling method for sleep scheduling, can improve detection accuracy and effectively improve the continuous k-fold coverage quality of the three-dimensional heterogeneous rechargeable wireless sensor network. Attached Figure Description

[0056] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0057] Figure 1 This is a flowchart of a sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network provided in an embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the distributed sleep scheduling algorithm framework provided in an embodiment of the present invention;

[0059] Figure 3 This is a schematic diagram of network node grouping and allocation provided in an embodiment of the present invention;

[0060] Figure 4 This is a flowchart of the distributed iterative grouping algorithm provided in an embodiment of the present invention;

[0061] Figure 5 This is a flowchart of the adaptive sampling number method provided in the embodiments of the present invention;

[0062] Figure 6 This is a schematic diagram of temperature and energy consumption in an experiment provided in an embodiment of the present invention;

[0063] Figure 7 This is a schematic diagram showing the number of samples and coverage quality for each time period when events occur at different time periods in the experiment provided by the embodiments of the present invention;

[0064] Figure 8 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with those of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0066] It should be noted that although functional modules are divided in the system diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first / S100" and "second / S200" in the specification, claims, and the foregoing drawings may be used herein to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are used only to distinguish one concept from another. For example, first information may also be referred to as second information without departing from the scope of the embodiments of the invention, and similarly, second information may also be referred to as first information. Depending on the context, the words "if" or "when" as used herein may be interpreted as "when," "in response to a determination," or "in the event of a determination."

[0067] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing embodiments of the invention only and is not intended to limit the invention.

[0069] Before providing a detailed description of the embodiments of the present invention, some of the nouns and terms involved in the embodiments of the present invention will be explained first. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.

[0070] k-fold coverage describes a situation where, within a given monitoring area, each point is covered by the detection range of at least k sensors.

[0071] A three-dimensional wireless sensor network is a network that deploys sensor nodes in three-dimensional Euclidean space, which can sense, collect, and transmit information within the space.

[0072] Passive sensors are wireless sensors that do not have a battery power supply.

[0073] In rechargeable wireless sensor networks, sensor nodes can harvest energy from the environment to charge themselves and store it in capacitors for use, solving the problem of limited node energy in traditional wireless sensor networks. However, the charging rate of nodes is affected by the uncertainty of environmental conditions and deployment location. Furthermore, the charging rate of nodes is usually lower than their consumption rate. Therefore, more complex energy management of the network is required, including fine control over node energy harvesting, consumption, and sensing. Current research on the coverage of heterogeneous two-dimensional rechargeable wireless sensor networks either focuses on sensors with different energy consumption and charging rates or on networks composed of traditional and passive sensors, and traditional methods struggle to ensure stable and high-quality coverage.

[0074] In view of this, such as Figure 1 As shown, this embodiment of the invention provides a sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network, which may include, but is not limited to, steps S100 to S400:

[0075] Step S100: Construct a first objective function that maximizes the quality of k-fold coverage within the monitoring period;

[0076] Step S200: Decompose the first objective function to obtain a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging cycle of energy compensation.

[0077] Step S300: According to the second objective function, the network nodes are grouped using a distributed iterative grouping algorithm, and each group is assigned to a time slot in the time slot group in a cyclic manner; wherein, the time slot group includes a plurality of time slots;

[0078] Step S400: Based on the third objective function, the first target node of the time slot group is scheduled using an adaptive sampling method to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality.

[0079] In some embodiments, steps S100 to S400, such as Figure 2 As shown, a first objective function is constructed to maximize the k-fold coverage quality within a monitoring period, and this first objective function is decomposed into two sub-problems for solution. The first sub-problem is to maximize the k-fold coverage quality of a time slot without considering charging and energy consumption, and a distributed iterative grouping algorithm is proposed to form a group with optimal coverage quality for each time slot. The second sub-problem is to minimize the charging cycle for energy compensation, and the sampling rate is dynamically adjusted through an adaptive sampling method to ensure that activated nodes have sufficient energy for coverage.

[0080] In some embodiments, step S100 may include, but is not limited to, steps S101 to S104:

[0081] Step S101: Divide the total three-dimensional space region into several three-dimensional space sub-regions of the same size;

[0082] Step S102: Divide the monitoring period into multiple time slots;

[0083] Step S103: Based on the volume of the three-dimensional spatial sub-region and the total number of time slots, obtain the first coverage quality covering the total three-dimensional spatial region within the monitoring period;

[0084] Step S104: Construct the first objective function based on the first coverage quality.

[0085] In step S100 of some embodiments, the total three-dimensional spatial region is divided into several three-dimensional spatial sub-regions of equal size, the monitoring period is divided into multiple time slots, and a first objective function is constructed. The first objective function is to find a series of disjoint subsets of rechargeable wireless sensor network nodes, each subset covering the three-dimensional monitoring spatial region with the highest quality k times in each time slot. For example, the constraints of the first objective function may include:

[0086] 1) The monitored three-dimensional cuboid space is divided into several cuboid sub-regions of the same size, and the monitoring period is divided into several time slots.

[0087] 2) Different types of passive sensors have different sensing radii and communication radii.

[0088] 3) The remaining energy of the active node is not lower than the threshold.

[0089] 4) The number of samples taken by the node in each time slot is within the specified range.

[0090] In some embodiments, the k-fold coverage quality within the monitoring period is the ratio of the sum of the k-fold coverage qualities of each time slot to the number of time slots plus the standard deviation of the coverage rate of the total 3D spatial area within different time slots. The k-fold coverage quality of a given time slot is the ratio of the sum of the k-fold coverage qualities of each cuboid sub-region within that time slot to the number of cuboid sub-regions in the total 3D spatial area plus the standard deviation of the coverage rate of each cuboid sub-region within that time slot. The k-fold coverage quality of a given cuboid sub-region within a given time slot is the ratio of the volume of the cuboid sub-region covered by k sensor nodes to the volume of the cuboid sub-region itself.

[0091] As an optional implementation, a first objective function is constructed to maximize the quality of k-fold coverage within the monitoring period. The expression for the first objective function is:

[0092]

[0093] In the formula,

[0094]

[0095]

[0096] In some embodiments, the constraints are as follows (5)-(10):

[0097]

[0098]

[0099] V1∩V2∩…∩V t (7)

[0100]

[0101] B v (t j )-E c (v,t j )+hvj (z)|t j |≥B thre ,z∈[0,z max (9)

[0102] S v ∈[S thre ,S max (10)

[0103] in, This indicates that the set of network nodes V is in the monitoring period. The total area of ​​the inner three-dimensional space is covered. Coverage quality; This represents the sum of the k-fold coverage quality for each time slot; The standard deviation of the total 3D spatial coverage within different time slots; m represents the total number of time slots; Cov ji (V) represents the sum of k-fold coverage quality of each divided three-dimensional spatial sub-region in the time slot; Represents the k-fold cover volume of a three-dimensional spatial sub-region; |r i | represents the volume of a sub-region in three-dimensional space; The total three-dimensional space is divided into l rectangular sub-regions r of equal size. i (i.e., a three-dimensional spatial sub-region); The monitoring period is represented by m time slots t. j V represents the set of network nodes, containing t nodes of different types: type 1, type 2, type a, ..., type b, ..., type t, where the set of nodes of type b is V. b The set of nodes of type a is V a rs(v) represents the sensing radius of node v; rs(u) represents the sensing radius of node u; B v (t j ) represents time slot t j The remaining energy of node v at the beginning; E c (v,t j ) indicates that node v is in time slot t j Energy consumption; h vj (z) indicates that node v at height z is in time slot t. j The charging rate; B thre Indicates the energy threshold; z max S represents the maximum height of the node's position. v S represents the number of samples in a time slot; thre S represents the sampling threshold for each time slot; max Indicates the maximum number of samples per time slot; |t j | represents time slot t jDuration.

[0104] In step S200 of some embodiments, by decomposing the problem of maximizing the k-fold coverage quality within the monitoring period into the problem of maximizing the k-fold coverage quality of a single time slot and the problem of minimizing the charging cycle of energy compensation, i.e., decomposing the first objective function, a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging cycle of energy compensation can be obtained.

[0105] In some embodiments, step S200 may include, but is not limited to, steps S201 to S203:

[0106] Step S201: Obtain the standard deviation between the coverage rates of the three-dimensional spatial sub-regions in a single time slot;

[0107] Step S202: Based on the standard deviation, obtain the second coverage quality of the set of network nodes in a single time slot;

[0108] Step S203: Construct the second objective function based on the second coverage quality.

[0109] In steps S201 to S203 of some embodiments, without considering charging and energy consumption, a second objective function is constructed to maximize the k-fold coverage quality of a single time slot while the node energy remains stable. Optionally, the constraints of the second objective function may include:

[0110] 1) The monitored three-dimensional cuboid space is divided into several cuboid sub-regions of the same size, and the monitoring period is divided into several time slots.

[0111] 2) Different types of passive sensors have different sensing radii and communication radii.

[0112] 3) The initial energy of the active node is not lower than the threshold.

[0113] In some embodiments, the expression for the second objective function is:

[0114]

[0115] Alternatively, the constraints are equations (5)-(8) and the following equation (12):

[0116] B v (t j )≥B thre , v∈V (12)

[0117] in, This represents the set of network nodes V in time slot t. j Inner k-fold coverage quality is defined as:

[0118]

[0119] In the formula, Indicates time slot t j The standard deviation between the coverage rates of three-dimensional spatial sub-regions.

[0120] In some embodiments, step S200 may also include, but is not limited to, steps S204 to S206:

[0121] Step S204: Set the initial sampling number for a single time slot;

[0122] Step S205: Obtain the energy consumption for sending data, the energy consumption for receiving data, and the minimum energy consumption for one sampling.

[0123] Step S206: Construct the third objective function based on the initial number of samplings, the energy consumption of the transmitted data, the energy consumption of the received data, and the minimum energy consumption of one sampling.

[0124] In steps S204 to S206 of some embodiments, the problem of minimizing the charging cycle with energy compensation is solved by constructing a third objective function that minimizes the charging cycle with energy compensation, i.e., minimizing the difference between the number of time slots required for charging and the C value. Optionally, the constraints of the third objective function may include:

[0125] 1) The monitored three-dimensional cuboid space is divided into several cuboid sub-regions of the same size.

[0126] 2) The remaining energy of the active node is not lower than the threshold.

[0127] 3) The number of times the nodes in each time slot are sampled is greater than the threshold.

[0128] Furthermore, the C value is defined as:

[0129]

[0130] Where C represents the strategy parameter; This indicates the maximum energy consumption for transmitting data; This indicates the maximum energy consumption for receiving data; h represents the minimum energy consumption for a single sampling. vj (z max ) indicates that network node v is in time slot t j Maximum charging rate.

[0131] In some embodiments, the expression for the third objective function is:

[0132]

[0133] Alternatively, the constraints are equations (5), (9) and the following equation (16):

[0134] S v ≥S thre (16)

[0135] Among them, E t E represents the energy consumption for transmitting data. r Indicates the energy consumption for receiving data; e s This indicates the energy consumption of a single sampling.

[0136] In some embodiments, step S300 may include, but is not limited to, steps S301 to S306:

[0137] Step S301: Store the set of network nodes in the initial packet;

[0138] Step S302: Randomly select a second target node from the network nodes in the initial group;

[0139] Step S303: Obtain the first coverage weight of the second target node;

[0140] Step S304: Receive the second coverage weight of the neighboring nodes of the second target node;

[0141] Step S305: Based on the weight values ​​of the first coverage weight and the second coverage weight, sort the second target node and the neighbor nodes of the second target node in descending order, and obtain the third target node corresponding to the k largest weight values.

[0142] Step S306: Determine whether the second target node belongs to the third target node. If the second target node does not belong to the third target node, return to the step of arbitrarily extracting the second target node from the network node in the initial group. If the second target node belongs to the third target node, store the second target node in the first group and return to the step of arbitrarily extracting the second target node from the network node in the initial group, until all the second target nodes in the initial group have been extracted.

[0143] In some embodiments, step S300 may also include, but is not limited to, steps S307 to S311:

[0144] Step S307: Preset the first intermediate parameter and the second intermediate parameter;

[0145] Step S308: Determine whether the first intermediate parameter is less than or equal to the number of time slots in a time slot group. When the first intermediate parameter is less than or equal to the number of time slots in a time slot group, extract a fourth target node from the current group.

[0146] Step S309: Determine whether the second intermediate parameter is less than the length of the neighbor information list of the fourth target node;

[0147] Step S310: When the second intermediate parameter is less than the length of the neighbor information list of the fourth target node, determine whether the group number of the neighbor node of the fourth target node is the group number of the initial group. If yes, store the neighbor node of the fourth target node in the next group and return to the step of taking out a fourth target node from the current group. If no, increment the second intermediate parameter by 1 and return to the step of determining whether the second intermediate parameter is less than the length of the neighbor information list of the fourth target node.

[0148] Step S311: When the second intermediate parameter is greater than or equal to the length of the neighbor information list of the fourth target node, increment the first intermediate parameter by 1, and return to the step of determining whether the first intermediate parameter is less than or equal to the number of time slots in a time slot group, until the first intermediate parameter is greater than the number of time slots in a time slot group.

[0149] In some embodiments, steps S301 to S311, such as Figure 3 As shown, to solve the problem of maximizing the k-fold coverage quality of a time slot, based on the second objective function, an iterative method is used to divide the network nodes into C disjoint groups {G1,…,G...}. C}, where each group is a coverage set that satisfies the requirement of maximizing k-fold coverage quality, and the value of C is calculated by formula (14). Each group is assigned to one of the C time slots in a cyclic manner, where the C time slots form a time slot group, and there are several time slot groups.

[0150] In some embodiments, such as Figure 4 As shown, the distributed iterative grouping algorithm process may include:

[0151] Step 1: Generate the first group:

[0152] Initialize the initial group G0 and the first group G1 as G0 = V, respectively. V is the set of all nodes in the network. The group number GN to which node v belongs is... vInitialize to 0. Each node v collects information about its neighbors, including but not limited to the neighbor's ID, type, group number, distance between the neighbor and node v, and the neighbor's coverage weight (i.e., the second coverage weight), and records this information in a neighbor information list. Based on this neighbor information list, calculate the coverage weight W for each node v. v (i.e., the first coverage weight), collect the coverage weights of neighboring nodes. From the coverage weight of any node v and the coverage weights of its neighboring nodes, select the k nodes with the largest weight values. If node v is one of these k nodes, then set the group number of node v to 1, that is, store node v in the first group G1, and subtract node v from the initial group G0, then change the group number GN. v =1 is sent to all neighboring nodes of node v.

[0153] Step 2: Form the next group:

[0154] Step 2 creates a new group by reducing the coverage quality difference between the previous group and the new rent. For example, group G... p Each node v in the group identifies its nearest neighbor node u from group G0 and requests that the group number of neighbor node u be updated to p+1. Subsequent groups are formed based on the previous groups, and step 2 is repeated until p = C. Initially, the first intermediate parameter p = 1.

[0155] In some embodiments, step S400 may include, but is not limited to, steps S401 to S406:

[0156] Step S401: Preset the third intermediate parameter;

[0157] Step S402: Set the first target node of the current group of the current time slot group to the active state to obtain the active node;

[0158] Step S403: Obtain the first charging rate of the active node in the previous time slot group, and obtain the second charging rate of the active node in the next time slot group;

[0159] Step S404: When the first charging rate is not equal to the second charging rate, obtain the first sampling number of the active node;

[0160] Step S405: When the first charging rate is equal to the second charging rate, determine whether the third intermediate parameter is greater than 0. If yes, adjust the first sampling number to the sampling threshold and decrement the third intermediate parameter by 1. If no, set the second charging rate to equal the first charging rate.

[0161] Step S406: Based on the third intermediate parameter, the first sampling number, and the first charging rate, schedule the active nodes to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality.

[0162] In some embodiments, step S400 may also include, but is not limited to, steps S407 to S409:

[0163] Step S407: When the active node detects a target event or receives a warning message, the active node enters a warning state, becomes a warning node, and adjusts the first sampling number to the maximum sampling number;

[0164] Step S408: Obtain the additional energy consumption of the active node, and update the third intermediate parameter based on the additional energy consumption;

[0165] Step S409: Determine whether the monitoring of the current time slot group has ended. If not, return to the step of setting the first target node of the current group of the current time slot group to an active state to obtain the active node. If yes, set the first charging rate to equal the third charging rate of the current warning node in the current time slot group, set the warning node to a sleep state, and enter the next time slot group. Return to the step of setting the first target node of the current group of the current time slot group to an active state to obtain the active node, until the monitoring of all time slot groups ends.

[0166] In steps S401 to S409 of some embodiments, the number of samplings is adaptively adjusted according to the node's charging rate and remaining energy, and the event handling mechanism can be used to maximize the rapid compensation of consumed energy after an event is detected, thereby solving the problem of minimizing the charging cycle for energy compensation.

[0167] In some embodiments, such as Figure 5 As shown, the adaptive sampling method process may include:

[0168] Step 3.1: Initialize all variables, i.e., j = 1, m1 = 0, R1 = R2 = 0, RE = 0.

[0169] Step 3.2: Take the time slot t within the monitoring period. j Initially, j = 1.

[0170] Step 3.3: Take node v in the group with group number j+c*m1.

[0171] Step 3.4: Set the state of node v to "active".

[0172] Step 3.5: If variable R1 is not equal to R2, calculate the number of samples for node v; otherwise, skip to step 3.6.

[0173] Step 3.6: If the variable RE is greater than 0, then make the number of samples of node v equal to the given sampling threshold, and decrease the variable RE by 1; otherwise, jump to step 3.7.

[0174] Step 3.7: Set variable R2 equal to variable R1.

[0175] Step 3.8: When a node detects an event and its perceived data exceeds the normal range, or receives a warning message, execute the following event handling mechanism:

[0176] 1) A node enters an "alert" state, in which it adjusts its sampling count to the maximum value and sends an "alert" message to its neighbors. Only neighbors in an "active" state can receive the "alert" message and change their own state to "alert". All "active" nodes in the network will eventually be densely aware of data about the event. These nodes will remain in the "alert" state until the current time slot ends.

[0177] 2) Due to the increase in sampling rate, the node will calculate the additional energy consumption resulting therefrom.

[0178] 3) Calculate the value of variable RE based on the calculated additional energy consumption. In the next RE time slots, nodes previously in the "warning" state will enter a recovery phase to reduce energy consumption. That is, nodes previously in the "warning" state will switch to the "active" state, and this will reduce the sampling count S of node v. v Reduced to threshold S thre To ensure that in time slot t j+1 Its energy consumption is less than its energy collection, which promotes energy charging.

[0179] Otherwise, proceed to step 3.9.

[0180] Step 3.9: If the current time slot ends, set the variable R1 to the charging rate of the node in the current time slot and set its own state to "sleep"; otherwise, proceed to step 3.10.

[0181] Step 3.10: Return to step 3.3 until all nodes in group j+c*m1 have been traversed.

[0182] Step 3.11: Increment the value of variable j by 1.

[0183] Step 3.12: If m1 is divisible by C, increment the value of variable m1 by 1. Otherwise, proceed to step 3.13.

[0184] Step 3.13: Return to step 3.2 until the monitoring ends.

[0185] For example, taking a three-dimensional spatial area of ​​80m × 80m × 80m to be covered as an example, the rechargeable wireless sensor network includes three types of sensors: Type 1, Type 2, and Type 3. Their sensing radii are 10m, 15m, and 20m, respectively, while their communication radii are 15m, 20m, and 25m, respectively. The node ratio of Type 1, Type 2, and Type 3 is 1:1:1. The time slot length is set to 2 minutes. The energy parameters of each passive sensor are set as follows: voltage (E... init The voltage is 3V, the battery capacity is 15kJ, and the energy threshold (B) is... thre The energy consumption per unit sensing time (E) is 10 kJ. s The values ​​are as follows: Type 1 is 0.16 kJ, Type 2 is 0.284 kJ, and Type 3 is 0.64 kJ. The maximum number of samples per time slot (S) max The threshold (S) is 10. thre The value of k is 1. The charging energy range for each time slot is 1.25 kJ to 2.5 kJ.

[0186] As an optional implementation, a sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network may include, but is not limited to, the following steps:

[0187] Step N1: Construct the first objective function to maximize the quality of k-fold coverage within the monitoring period.

[0188] Step N2: Decompose the first objective function into a second objective function that maximizes the k-fold coverage quality of a single time slot. The third objective function is to minimize the charging cycle of energy compensation.

[0189] Step N3: Initialize the state of all network nodes to "active".

[0190] Step N4: Use a distributed iterative grouping algorithm to solve the problem of maximizing the k-fold coverage quality of a time slot, obtaining C groups {G1,…,G}. C},like Figure 4 As shown, one possible step of this distributed iterative grouping algorithm is as follows:

[0191] Step 4.1: Place all network nodes into the initial packet G0, and each node v has a neighbor information list Nei. v The neighbor information list stores information including, but not limited to, the neighbor node's ID, type, group number, distance between the neighbor node and node v, and the neighbor node's coverage weight. The neighbor information list is initialized to empty.

[0192] Step 4.2: Remove node v from the initial group G0.

[0193] Step 4.3: Set the group number Gn of node v. v It is 0.

[0194] Step 4.4: Run the neighbor discovery algorithm to collect information about the neighboring nodes of node v and store it in the neighbor information list Nei. v middle.

[0195] Step 4.5: Calculate the coverage weight W of node v. v and will cover weight W v Send to the neighboring nodes of node v, overwriting the weight W v The specific calculations are as follows:

[0196]

[0197] Among them, W init W represents the initial coverage weight of node v, which is equal to the perceived volume of node v; v This represents the coverage weight of node v, i.e., the first coverage weight; a u This represents the perceptual overlap volume between node v and its neighbor node u; represents the perceptual overlap volume of node v's largest k-1 neighbor nodes; n represents the number of neighbor nodes of node v. This represents the perceived overlap volume of node v's other n-k+1 neighboring nodes.

[0198] Furthermore, a u The specific calculations are as follows:

[0199]

[0200] Where, d uv This represents the distance between neighboring node u and node v; Represents the perceived volume of neighbor node u; This represents the perceived volume of node v.

[0201] Step 4.6: Node v receives the coverage weights from its neighboring nodes and stores these weights in Nei. v .

[0202] Step 4.7: List the neighbor information (Nei) v The neighboring nodes are arranged in descending order according to their coverage weight.

[0203] Step 4.8: If the coverage weight W of node v v If the value of node v is among the first k values ​​in its descending list of neighboring node information, then let the group number GN of node v be... v=1, then G1 = G1∪{v}, G0 = G0-{v}, and GN v Send it to the neighboring node; otherwise, proceed to step 4.2.

[0204] Step 4.9: Repeat steps 4.2 to 4.8 until all nodes v in the initial group G0 have been taken.

[0205] Step 4.10: Initialize the first intermediate parameter p = 1.

[0206] Step 4.11: If p≤C, then proceed to step 4.12; otherwise, proceed to step 4.18.

[0207] Step 4.12: From group G p Extract node v from the data.

[0208] Step 4.13: The second intermediate parameter l = 0, and the neighbor information list Nei is... v The neighboring nodes of node v are sorted in descending order according to the distance between node v and its neighboring nodes.

[0209] Step 4.14: If l <length(Nei v If the condition is met, proceed to step 4.15; otherwise, proceed to step 4.17.

[0210] Step 4.15: From the Neighbor Information List Nei v Take the neighbor node u from G, if G u If the value is 0, then send p to neighbor node u, G p+1 =G p+1 ∪{u}, G0=G0-{u}, and execute step 4.12; otherwise, execute step 4.16.

[0211] Step 4.16: l++, execute step 4.14.

[0212] Step 4.17: p = p + 1, return to step 4.11.

[0213] Step 4.18: Extract each node v from the network node set V.

[0214] Step 4.19: If node v receives the group number of a neighbor node, then update its neighbor information list Nei. v If the group number of the corresponding neighbor node is not specified, proceed to step 4.20.

[0215] Step 4.20: If node v receives a value p from a neighboring node, then GN v =p+1, and set the group number GN to which node v belongs. vSend to neighboring nodes; otherwise, proceed to step 4.18 until all nodes have been retrieved and examined.

[0216] Step N5: For all network nodes, proceed to step N6.

[0217] Step N6: S v =S max

[0218] Step N7: If node v does not belong to group G1, set the state of node v to "sleep"; otherwise, execute step N5 until all nodes have been processed.

[0219] Step N8: Execute an adaptive sampling method to address the problem of minimizing the charging cycle for energy compensation, such as... Figure 5 As shown, one possible specific step of this adaptive sampling method is as follows:

[0220] Step 8.1: Initialize the variables, i.e., j = 1, m1 = 0, R1 = R2 = 0, RE = 0.

[0221] Step 8.2: For the monitoring cycle The time slot t in j Proceed to step 8.3.

[0222] Step 8.3: For grouping For node v in the sequence, proceed to step 8.4.

[0223] Step 8.4: Set the state of node v to "active".

[0224] Step 8.5: If the first charging rate R1 is not equal to the second charging rate R2, i.e., R1 ≠ R2, then calculate the number of samplings S for node v. v The calculation formula is as follows:

[0225]

[0226] Otherwise, proceed to step 8.6.

[0227] Step 8.6: If the third intermediate parameter RE > 0, then S v =S thre RE--, otherwise proceed to step 8.7.

[0228] Step 8.7: R1 = R2.

[0229] Step 8.8: If a target event is detected and its sensor data is not within a given range [val] l ,val h(For example, in forest monitoring, the normal temperature range of a temperature sensor is [-20℃, 50℃]), or if a "warning" message is received from a neighbor, and RE == 0, then S v =S max Then calculate node v in time slot t j Additional energy consumption E add (v,t j The calculation formula is as follows:

[0230] E add (v,t j ) = E c (v,t j )-E c (v,t j-C (20)

[0231] Among them, E c (v,t j ) indicates that node v is in time slot t j Energy consumption when the system is in a "warning" state; E c (v,t j-C ) indicates that node v is in time slot t j-C Energy consumption when the body is in an "active" state.

[0232] The formula for calculating the value of the updated third intermediate parameter RE is as follows:

[0233]

[0234] Send a "warning" message to all neighboring nodes of node v; otherwise, proceed to step 8.9.

[0235] Step 8.9: The current time slot ends, then R1 = h vj (z) Set the state of node v to "sleep" state; otherwise, proceed to step 8.10.

[0236] Step 8.10: Execute step 8.3 until grouping. All nodes in the middle have been extracted.

[0237] Step 8.11: j++.

[0238] Step 8.12: If the remainder of m1 divided by C is 0, then m1++; otherwise, proceed to step 8.13.

[0239] Step 8.13: Execute step 8.2 until the monitoring cycle ends.

[0240] In some alternative embodiments, such as Figure 2As shown, a distributed sleep scheduling algorithm combining a distributed iterative grouping algorithm and an adaptive sampling method is used to solve the problem of maximizing the quality of k-fold coverage within a monitoring period. In this distributed sleep scheduling algorithm, nodes can autonomously determine their state as "sleep" or "active". The "active" state means the sensor can perform sensing and communication. The "sleep" state means the sensor is off and all functions cease operation. Optionally, initially, all nodes are set to the "active" state. Nodes can determine their group number using the distributed iterative grouping algorithm; the group determines whether a node transitions to the "sleep" state or remains in the "active" state. Optionally, nodes initially set their sampling rate to the maximum value. In a specific time slot, nodes within a specific group switch to the "active" state, while the remaining nodes remain in the "sleep" state. During the monitoring period, scheduling is performed every C time slots. If an "active" node is in time slot t... j-C The charging rate and the charging rate in time slot t j-2C If the charging rates are different, then the node in time slot t j The adaptive sampling method is executed accordingly. When the "Active" node detects an event or receives a warning message, it triggers the event handling mechanism in the adaptive sampling method.

[0241] Optionally, taking a given 80m × 80m × 80m three-dimensional spatial region with 240 nodes as an example, the nodes are randomly distributed in the monitoring area 10 times. During the period when no events are detected, the average coverage quality obtained by running the method of this invention reaches 85.333. Taking a given 80m × 80m × 80m three-dimensional spatial region with 480 nodes, which have been divided into two groups, i.e., C=2, as an example, the nodes in these two groups take turns monitoring the three-dimensional region by detecting temperature. The normal temperature range is set to [-10℃, 40℃]. If the detected temperature exceeds this range, an event handling mechanism will be triggered. Figure 6 , Figure 7 As shown, in the 14th and 19th time slots, the temperature reached 50℃ and 45℃, respectively. At this point, according to the adaptive sampling method, the number of samples taken by the node in that time slot reached its maximum S. max Correspondingly, energy consumption increases rapidly. Once the temperature returns to normal, the nodes will run the recovery phase of the event handling mechanism. For example, a group of nodes active in time slot 14 will enter the recovery phase in time slots 16 and 18. This phase reduces sampling times and energy consumption, allowing the collected energy to compensate for the previous high energy consumption. The same operation will occur in time slots 21 and 23. Therefore, coverage quality remains at a high level throughout the cycle.

[0242] This invention also provides a sleep scheduling device for a three-dimensional heterogeneous rechargeable wireless sensor network, which can implement the above-mentioned sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network. The device includes:

[0243] The first module is used to construct a first objective function that maximizes the quality of k-fold coverage within the monitoring period;

[0244] The second module is used to decompose the first objective function to obtain a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging cycle of energy compensation.

[0245] The third module is used to group each network node according to the second objective function using a distributed iterative grouping algorithm, and to allocate each group to each time slot in the time slot group in a cyclic manner; wherein, the time slot group includes a plurality of time slots;

[0246] The fourth module is used to schedule the first target node of the time slot group according to the third objective function and through an adaptive sampling method, so as to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes the k-fold coverage quality.

[0247] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0248] This invention also provides an electronic device, which includes a processor and a memory. The memory stores a computer program, and when the processor executes the computer program, it implements the aforementioned sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0249] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0250] refer to Figure 8 , Figure 8 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0251] The processor 501 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention.

[0252] The memory 502 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 502 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 502 and is called and executed by the processor 501 to implement a sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to an embodiment of the present invention.

[0253] The input / output interface 503 is used to implement information input and output;

[0254] The communication interface 504 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0255] Bus 505 transmits information between various components of the device (e.g., processor 501, memory 502, input / output interface 503, and communication interface 504);

[0256] The processor 501, memory 502, input / output interface 503, and communication interface 504 are connected to each other within the device via bus 505.

[0257] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network.

[0258] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0259] This invention also provides a computer program product or computer program that includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network.

[0260] In summary, the sleep scheduling method and apparatus for a three-dimensional heterogeneous rechargeable wireless sensor network according to embodiments of the present invention have the following advantages:

[0261] 1. This invention proposes a problem for maximizing k-fold coverage quality with constraints on energy and sampling rate, and decomposes this problem into two sub-problems. The first sub-problem is to maximize the k-fold coverage quality of a time slot without considering charging and energy consumption, and proposes a distributed iterative grouping algorithm to form a group with optimal coverage quality for each time slot. The second sub-problem is to minimize the charging cycle for energy compensation, and proposes an adaptive sampling method to dynamically adjust the sampling rate based on the time slot length, charging rate, and energy consumption to ensure that activated nodes have sufficient energy for coverage. Combining the algorithms for the above two sub-problems, a distributed sleep scheduling algorithm is proposed.

[0262] 2. Compared with traditional two-dimensional wireless sensor networks, the three-dimensional wireless sensor network of this invention can provide more comprehensive and accurate monitoring capabilities, and is especially suitable for scenarios that require sensing in the vertical direction, such as environmental monitoring, building structure monitoring, and underground mine monitoring.

[0263] 3. A distributed sleep scheduling algorithm combining a distributed iterative grouping algorithm and an adaptive sampling method in an embodiment of the present invention enables nodes to communicate only with their neighbors and does not require the location information of the nodes. This can improve detection accuracy and ensure that the excess energy consumed by the nodes during event detection is compensated for within a certain period of time, effectively improving the continuous k-fold coverage quality of the three-dimensional heterogeneous rechargeable wireless sensor network.

[0264] 4. Coverage is one of the fundamental and critical issues in rechargeable wireless sensor networks. The quality of network coverage directly affects the accuracy and completeness of data acquisition and the performance of upper-layer applications. Due to monitoring quality and protocol requirements, some applications require k-fold coverage. This invention utilizes a sleep scheduling method to maximize k-fold coverage quality and implements more complex energy management for the network. This includes fine-grained control over node energy harvesting, consumption, and sensing, enabling the network to effectively utilize energy and provide continuous service, thus achieving long-term effective network operation.

[0265] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0266] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0267] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0268] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0269] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0270] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0271] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0272] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0273] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network, characterized in that, Includes the following steps: Construct a first objective function that maximizes the quality of k-fold coverage within the monitoring period; The first objective function is decomposed to obtain a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging cycle of energy compensation. According to the second objective function, a distributed iterative grouping algorithm is used to group each network node, and each group is allocated to each time slot in the time slot group in a cyclic manner; wherein, the time slot group includes a number of time slots; Based on the third objective function, the first target node of the time slot group is scheduled using an adaptive sampling method to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality. The construction of the first objective function that maximizes the quality of k-fold coverage within the monitoring period includes the following steps: Divide the total three-dimensional space into several three-dimensional space sub-regions of the same size; The monitoring period is divided into multiple time slots; Based on the volume of the three-dimensional spatial sub-region and the total number of time slots, obtain the first coverage quality covering the total three-dimensional spatial region within the monitoring period; Based on the first coverage quality, construct the first objective function; The process of decomposing the first objective function to obtain a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging cycle for energy compensation includes the following steps: Obtain the standard deviation between the coverage rates of the three-dimensional spatial sub-regions in a single time slot; Based on the standard deviation, obtain the second coverage quality of the set of network nodes in a single time slot; Based on the second coverage quality, construct the second objective function; Set the initial number of samples for each of the aforementioned time slots; Obtain the energy consumption for sending data, the energy consumption for receiving data, and the minimum energy consumption for a single sampling; The third objective function is constructed based on the initial number of samples, the energy consumption of the transmitted data, the energy consumption of the received data, and the minimum energy consumption of a single sample.

2. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 1, characterized in that, The step of grouping network nodes according to the second objective function using a distributed iterative grouping algorithm includes the following steps: Store the set of network nodes in the initial packet; Randomly select a second target node from the network nodes in the initial group; Obtain the first coverage weight of the second target node; Receive the second coverage weight of the neighboring nodes of the second target node; Based on the weight values ​​of the first coverage weight and the second coverage weight, the second target node and its neighboring nodes are sorted in descending order, and the third target node corresponding to the k largest weight values ​​is obtained. Determine whether the second target node belongs to the third target node. If the second target node does not belong to the third target node, return to the step of arbitrarily retrieving the second target node from the network node in the initial group. If the second target node belongs to the third target node, store the second target node in the first group and return to the step of arbitrarily retrieving the second target node from the network node in the initial group, until all the second target nodes in the initial group have been retrieved.

3. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 2, characterized in that, The step of grouping network nodes according to the second objective function using a distributed iterative grouping algorithm further includes the following steps: Preset the first intermediate parameter and the second intermediate parameter; Determine whether the first intermediate parameter is less than or equal to the number of time slots in a time slot group. If the first intermediate parameter is less than or equal to the number of time slots in a time slot group, extract a fourth target node from the current group. Determine whether the second intermediate parameter is less than the length of the neighbor information list of the fourth target node; When the second intermediate parameter is less than the length of the neighbor information list of the fourth target node, determine whether the group number of the neighbor node of the fourth target node is the group number of the initial group. If yes, store the neighbor node of the fourth target node in the next group and return to the step of retrieving a fourth target node from the current group. If no, increment the second intermediate parameter by 1 and return to the step of determining whether the second intermediate parameter is less than the length of the neighbor information list of the fourth target node. When the second intermediate parameter is greater than or equal to the length of the neighbor information list of the fourth target node, the first intermediate parameter is incremented by 1, and the step of determining whether the first intermediate parameter is less than or equal to the number of time slots in a time slot group is returned, until the first intermediate parameter is greater than the number of time slots in a time slot group.

4. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 1, characterized in that, The step of scheduling the first target node of the time slot group according to the third objective function and through an adaptive sampling method to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality includes the following steps: Preset a third intermediate parameter; Set the first target node of the current group in the current time slot group to the active state to obtain the active node; Obtain the first charging rate of the active node in the previous time slot group, and obtain the second charging rate of the active node in the next time slot group; When the first charging rate is not equal to the second charging rate, the first sampling count of the active node is obtained; When the first charging rate is equal to the second charging rate, determine whether the third intermediate parameter is greater than 0. If yes, adjust the first sampling number to the sampling threshold and decrement the third intermediate parameter by 1. If no, set the second charging rate to equal the first charging rate. Based on the third intermediate parameter, the first sampling number, and the first charging rate, the active nodes are scheduled to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality.

5. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 4, characterized in that, The scheduling of the active node based on the third intermediate parameter, the first sampling count, and the first charging rate includes the following steps: When the active node detects a target event or receives a warning message, the active node enters a warning state, becomes a warning node, and adjusts the first sampling number to the maximum sampling number; Obtain the additional energy consumption of the active node, and update the third intermediate parameter based on the additional energy consumption; Determine whether the monitoring of the current time slot group has ended. If not, return to the step of setting the first target node of the current group of the current time slot group to an active state to obtain the active node. If yes, set the first charging rate to equal the third charging rate of the current warning node in the current time slot group, set the warning node to a sleep state, and enter the next time slot group. Return to the step of setting the first target node of the current group of the current time slot group to an active state to obtain the active node, until the monitoring of all time slot groups has ended.

6. A sleep scheduling device for a three-dimensional heterogeneous rechargeable wireless sensor network, characterized in that, include: The first module is used to construct a first objective function that maximizes the quality of k-fold coverage within the monitoring period; The second module is used to decompose the first objective function to obtain a second objective function that maximizes the k-fold coverage quality of a single time slot and a third objective function that minimizes the charging cycle of energy compensation. The third module is used to group each network node according to the second objective function using a distributed iterative grouping algorithm, and to allocate each group to each time slot in the time slot group in a cyclic manner; wherein, the time slot group includes a plurality of time slots; The fourth module is used to schedule the first target node of the time slot group according to the third objective function and through an adaptive sampling method, so as to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes the k-fold coverage quality. Specifically, the first module is used for: Divide the total three-dimensional space into several three-dimensional space sub-regions of the same size; The monitoring period is divided into multiple time slots; Based on the volume of the three-dimensional spatial sub-region and the total number of time slots, obtain the first coverage quality covering the total three-dimensional spatial region within the monitoring period; Based on the first coverage quality, construct the first objective function; The second module is specifically used for: Obtain the standard deviation between the coverage rates of the three-dimensional spatial sub-regions in a single time slot; Based on the standard deviation, obtain the second coverage quality of the set of network nodes in a single time slot; Based on the second coverage quality, construct the second objective function; Set the initial number of samples for each of the aforementioned time slots; Obtain the energy consumption for sending data, the energy consumption for receiving data, and the minimum energy consumption for a single sampling; The third objective function is constructed based on the initial number of samples, the energy consumption of the transmitted data, the energy consumption of the received data, and the minimum energy consumption of a single sample.

7. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 5.

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