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

Abstract

The invention discloses a sleep scheduling method and device for a three-dimensional heterogeneous rechargeable wireless sensor network. The method comprises the following steps: constructing a first objective function for maximizing k coverage quality in a monitoring period; decomposing the first objective function to obtain a second objective function for maximizing k coverage quality of a single time slot and a third objective function for minimizing a charging period of energy compensation; according to a second objective function, through a distributed iterative grouping algorithm, performing grouping operation on each network node, and distributing each group to each time slot in the time slot group in a circulating mode; and according to the third target function, the first target node of the time slot group is scheduled through an adaptive sampling method, and the three-dimensional heterogeneous rechargeable wireless sensor network with maximum k coverage quality is obtained. According to the invention, the continuous k coverage quality of the three-dimensional heterogeneous rechargeable wireless sensor network can be improved, and the method can be widely applied to the technical field of wireless sensor networks.

Description

Technical Field

[0001] The present invention relates to the technical field of wireless sensor networks, and in particular to a sleep scheduling method and device for a three-dimensional heterogeneous rechargeable wireless sensor network. Background Art

[0002] With the advancement of energy harvesting technology, rechargeable wireless sensor networks composed of passive sensors have gradually replaced traditional wireless sensor networks and become the infrastructure of the Internet of Things. Its sensor nodes can collect energy from the environment to charge and store it in capacitors for use, solving the problem of limited node energy in traditional wireless sensor networks. However, the charging rate of the node is affected by the uncertainty of environmental conditions and the deployment location. In addition, the charging rate of the node is usually lower than the consumption rate. Among them, the relevant research on the coverage problem of rechargeable wireless sensor networks is mostly focused on two-dimensional networks, but the application scenarios in the real world are mostly three-dimensional, and multiple different types of sensors are required to monitor the environment collaboratively. Different types of nodes can have different perception and / or communication ranges, different charging and consumption speeds, 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 energy consumption and charging rates, or focuses on networks composed of traditional sensors and passive sensors, and no research has been done on k-fold coverage. In addition, when an event is detected, sensors will consume a lot of energy to track the development of the event. For example, fire detection involves placing various sensors in the forest to monitor temperature, humidity, fog and other data. Once a fire is detected, sensors must frequently collect data and transmit it to remote terminals. Their energy will be consumed quickly and need to be recharged immediately. However, traditional methods do not take the above situations into account 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 purpose of the embodiments of the present invention is to provide a sleep scheduling method and device for a three-dimensional heterogeneous rechargeable wireless sensor network, so as to improve the continuous k-layer coverage quality of the three-dimensional heterogeneous rechargeable wireless sensor network.

[0005] To achieve the above object, an embodiment 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 the first objective function to maximize the k-fold coverage quality within the monitoring period;

[0007] Decomposing the first objective function to obtain a second objective function of maximizing the k-fold coverage quality of a single time slot and a third objective function of minimizing 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 assigned to each time slot in a time slot group in a cyclic manner; wherein the time slot group includes a plurality of time slots;

[0009] According to the third objective function, the first target node of the time slot group is scheduled by 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 for maximizing the k-fold coverage quality within a monitoring period includes the following steps:

[0011] Divide the total three-dimensional space area into a number of three-dimensional space sub-areas of equal size;

[0012] dividing the monitoring period into a plurality of said time slots;

[0013] Acquire, according to the volume of the three-dimensional space sub-area and the total number of the time slots, a first coverage quality covering the total area of ​​the three-dimensional space within the monitoring period;

[0014] The first objective function is constructed according to the first coverage quality.

[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] Obtaining a standard deviation between the coverage rates of the three-dimensional space sub-areas in a single time slot;

[0017] Acquire, according to the standard deviation, a second coverage quality of the set of network nodes in a single time slot;

[0018] The second objective function is constructed according to the second coverage quality.

[0019] In some embodiments, the 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 further includes the following steps:

[0020] Setting the initial sampling number of a single time slot;

[0021] Obtain the energy consumption of sending data, the energy consumption of receiving data, and the minimum energy consumption of one sampling;

[0022] The third objective function is constructed according to the initial sampling number, the energy consumption of sending data, the energy consumption of receiving data and the minimum energy consumption of one sampling.

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

[0024] Storing the set of network nodes into an initial group;

[0025] arbitrarily taking out a second target node in the network nodes from the initial group;

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

[0027] Receiving a second coverage weight of a neighbor node of the second target node;

[0028] According to the weight values ​​of the first coverage weight and the second coverage weight, the second target node and the neighbor nodes of the second target node are arranged in descending order, and the third target nodes corresponding to the first k largest weight values ​​are 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 randomly taking out a second target node from the network node from 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 randomly taking out a second target node from the network node from the initial group, until all the second target nodes in the initial group are taken out.

[0030] In some embodiments, the grouping operation on each network node by using a distributed iterative grouping algorithm according to the second objective function further includes the following steps:

[0031] Presetting a first intermediate parameter and a second intermediate parameter;

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

[0033] Determining 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 smaller 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 so, 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 not, add 1 to the second intermediate parameter, and return to the step of determining whether the second intermediate parameter is smaller 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, add 1 to the first intermediate parameter, and return to the step of determining whether the first intermediate parameter is less than or equal to the number of time slots in one of the time slot groups until the first intermediate parameter is greater than the number of time slots in one of the time slot groups.

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

[0037] Presetting the third intermediate parameter;

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

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

[0040] When the first charging rate is not equal to the second charging rate, obtaining a first sampling number of the active node;

[0041] When the first charging rate is equal to the second charging rate, determining whether the third intermediate parameter is greater than 0; if so, adjusting the first sampling number to a sampling threshold, and reducing the third intermediate parameter by 1; if not, setting the second charging rate equal to the first charging rate;

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

[0043] In some embodiments, scheduling the active node according to the third intermediate parameter, the first sampling number, and the first charging rate includes the following steps:

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

[0045] Acquire the additional energy consumption of the active node, and update the third intermediate parameter according to the additional energy consumption;

[0046] Determine whether the monitoring of the current time slot group is completed. 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 an active node. If so, set the first charging rate equal to 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 an active node, until the monitoring of all time slot groups is completed.

[0047] To achieve the above object, another aspect of an embodiment of the present invention provides 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 k-fold coverage quality within a monitoring period;

[0049] A 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] A third module is used to perform a grouping operation on each network node through a distributed iterative grouping algorithm according to the second objective function, and allocate each group to each time slot in a 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 through an adaptive sampling method to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes the k-fold coverage quality.

[0052] To achieve the above-mentioned purpose, another aspect of an embodiment of the present invention provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network described above.

[0053] To achieve the above-mentioned purpose, another aspect of an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network described above is implemented.

[0054] To achieve the above object, another aspect of an embodiment of the present invention provides a computer program product or a computer program, the computer program product or the computer program includes computer instructions, and the computer instructions are 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 the processor executes the computer instructions, so that the computer device executes 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 device for a three-dimensional heterogeneous rechargeable wireless sensor network, which constructs a first objective function that maximizes the k-fold coverage quality within the 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 of energy compensation; according to the second objective function, through a distributed iterative grouping algorithm, each network node is grouped, and each group is assigned to each time slot in a time slot group in a cyclic manner; wherein the time slot group includes several time slots; according to the third objective function, the first target node of the time slot group is scheduled through an adaptive sampling method to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes the k-fold coverage quality. The present invention can improve the detection accuracy and effectively improve the continuous k-fold coverage quality of the three-dimensional heterogeneous rechargeable wireless sensor network by combining the distributed iterative grouping algorithm and the adaptive sampling method for sleep scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

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

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

[0060] Figure 4 is a flow chart of a distributed iterative grouping algorithm provided by an embodiment of the present invention;

[0061] Figure 5 is a flow chart of an adaptive sampling times method provided by an embodiment of the present invention;

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

[0063] Figure 7 It is a schematic diagram of the number of sampling times and coverage quality in each time period when events occur in different time periods in the experiment provided by the embodiment of the present invention;

[0064] Figure 8 It is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present invention, and they are only examples of devices and methods consistent with some aspects of the embodiments of the present invention as detailed in the attached claims.

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

[0067] The terms "at least one", "multiple", "each", "any", etc. used in the present invention, at least one includes one, two or more, multiple includes two or more, 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 those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0069] Before describing the embodiments of the present invention in detail, some nouns and terms involved in the embodiments of the present invention are first described. The nouns and terms involved in the embodiments of the present invention are subject to the following explanations.

[0070] K-fold coverage is used to describe the situation in which each point in a given monitoring area 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 a three-dimensional Euclidean space. These nodes can sense, collect and transmit information in the space.

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

[0073] In a rechargeable wireless sensor network, its sensor nodes can collect energy from the environment to charge and store it in capacitors for use, which solves the problem of limited node energy in traditional wireless sensor networks. However, the charging rate of the node is affected by the uncertainty of environmental conditions and the deployment location. In addition, the charging rate of the node is usually less than the consumption rate. Therefore, more complex energy management of the network is required, including fine control of the energy collection, consumption and sensing of the nodes. However, 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 focuses on networks composed of traditional sensors and passive sensors, and traditional methods are difficult to ensure stable and high-quality coverage.

[0074] In view of this, if Figure 1 As shown, an embodiment of the present 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, constructing a first objective function for maximizing the k-fold coverage quality within a monitoring period;

[0076] Step S200, decomposing the first objective function to obtain a second objective function for maximizing the k-fold coverage quality of a single time slot and a third objective function for minimizing the charging cycle of energy compensation;

[0077] Step S300, 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 a time slot group in a cyclic manner; wherein the time slot group includes a plurality of time slots;

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

[0079] In steps S100 to S400 of some embodiments, Figure 2 As shown in the figure, the first objective function that maximizes the k-fold coverage quality within the monitoring period is constructed, and the 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 of energy compensation, and dynamically adjust the sampling rate through an adaptive sampling method to ensure that the activated nodes have enough energy for coverage.

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

[0081] Step S101, dividing the total three-dimensional space area into a plurality of three-dimensional space sub-areas of the same size;

[0082] Step S102, dividing the monitoring period into a plurality of time slots;

[0083] Step S103, obtaining a first coverage quality of the total area of ​​the three-dimensional space within the monitoring period according to the volume of the three-dimensional space sub-area and the total number of the time slots;

[0084] Step S104: construct the first objective function according to the first coverage quality.

[0085] In step S100 of some embodiments, the total three-dimensional space area is divided into a plurality of three-dimensional space sub-areas of the same size, and the monitoring period is divided into a plurality of time slots, thereby constructing a first objective function, the first objective function being to find a series of non-intersecting node subsets of the rechargeable wireless sensor network nodes, each subset covering the three-dimensional monitoring space area with the highest quality k times in each time slot. Exemplarily, the constraints of the first objective function may include:

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

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

[0088] 3) The remaining energy of the nodes in the active state is not less than the threshold.

[0089] 4) The number of sampling times of the node in each time slot is within the specified range.

[0090] In some embodiments, the k-fold coverage quality in the monitoring period is the ratio of the sum of the k-fold coverage quality of each time slot to the number of time slots plus the standard deviation of the total area coverage of the three-dimensional space in different time slots. The k-fold coverage quality of a certain time slot is the ratio of the sum of the k-fold coverage quality of each divided cuboid sub-region in the time slot to the number of cuboid sub-regions into which the total area of ​​the three-dimensional space is divided plus the standard deviation of the coverage of each cuboid sub-region in the time slot. The k-fold coverage quality of a certain cuboid sub-region in a certain time slot is the ratio of the volume covered by k sensor nodes in the cuboid sub-region to the volume of the cuboid sub-region.

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

[0092]

[0093] In the formula,

[0094]

[0095]

[0096] In some embodiments, the constraints are as follows:

[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, Indicates that the network node set V is in the monitoring period Total area of ​​3D space covered Quality of coverage; Represents the sum of k-fold coverage qualities of each time slot; represents the standard deviation of the total area coverage of the three-dimensional space in different time slots; m represents the total number of time slots; Cov ji (V) represents the sum of the k-fold coverage qualities of each divided three-dimensional space sub-region in the time slot; represents the k-fold covering volume of a subregion in three-dimensional space; |r i | represents the volume of a sub-region in three-dimensional space; Represents the total area of ​​3D space, which is divided into l cuboid sub-areas r of the same size i (i.e., a sub-region of three-dimensional space); represents the monitoring period, which is divided into m time slots t j ; V represents the set of network nodes, including t types of nodes, namely type 1, type 2, type a, ..., type b, ..., type t, among which the set of nodes of type b is V b , the node set of type a is V a ; rs(v) represents the perception radius of node v; rs(u) represents the perception radius of node u; B v (t j ) represents the 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 the node v at height z is in time slot t j The charging rate of B thre represents the energy threshold; z max Indicates the maximum height of the node's position; S v Indicates the number of sampling times in a time slot; S thre Represents the sampling threshold of each time slot; S max Indicates the maximum number of sampling times per time slot; |t j | represents time slot t jduration of the event.

[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 time slot and the problem of minimizing the charging cycle of energy compensation, that is, decomposing the first objective function, a second objective function of maximizing the k-fold coverage quality of a single time slot and a third objective function of minimizing 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, obtaining the standard deviation between the coverage rates of the three-dimensional space sub-areas in a single time slot;

[0107] Step S202: acquiring a second coverage quality of the set of network nodes in a single time slot according to the standard deviation;

[0108] Step S203: construct the second objective function according to the second coverage quality.

[0109] In steps S201 to S203 of some embodiments, charging and energy consumption are not considered, and a second objective function that maximizes the k-fold coverage quality of a single time slot is constructed in a time slot where 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-areas of the same size, and the monitoring period is divided into several time slots.

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

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

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

[0114]

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

[0116] B v (t j )≥B thre ,v∈V (12)

[0117] in, Represents the network node set V in time slot t j The inner k-fold coverage quality is defined as:

[0118]

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

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

[0121] Step S204, setting the initial sampling number of a single time slot;

[0122] Step S205, obtaining the energy consumption of sending data, the energy consumption of receiving data and the minimum energy consumption of one sampling;

[0123] Step S206: construct the third objective function according to the initial sampling times, the energy consumption of sending data, the energy consumption of receiving 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 of energy compensation is solved by constructing a third objective function of minimizing the charging cycle of energy compensation, that is, minimizing the gap 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-areas of the same size.

[0126] 2) The remaining energy of the nodes in the active state is not less than the threshold.

[0127] 3) The number of node sampling times in each time slot is greater than the threshold.

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

[0129]

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

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

[0132]

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

[0134] S v ≥S thre (16)

[0135] Among them, E t Represents the energy consumption of sending data; E r Represents the energy consumption of receiving data; e s Indicates the energy consumption of one sampling.

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

[0137] Step S301, storing the set of network nodes into an initial group;

[0138] Step S302, randomly selecting a second target node in the network node from the initial group;

[0139] Step S303, obtaining a first coverage weight of the second target node;

[0140] Step S304, receiving a second coverage weight of a neighbor node of the second target node;

[0141] Step S305: Arrange the second target node and the neighbor nodes of the second target node in descending order according to the weight values ​​of the first coverage weight and the second coverage weight, and obtain the third target nodes corresponding to the first 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 randomly taking out a second target node from the network node from 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 randomly taking out a second target node from the network node from the initial group, until all the second target nodes in the initial group are taken out.

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

[0144] Step S307, presetting the first intermediate parameter and the second intermediate parameter;

[0145] Step S308, determining whether the first intermediate parameter is less than or equal to the number of time slots in one of the time slot groups, and when the first intermediate parameter is less than or equal to the number of time slots in one of the time slot groups, taking a fourth target node from the current group;

[0146] Step S309, determining 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 smaller than the length of the neighbor information list of the fourth target node, determine whether the group number of the neighbor nodes of the fourth target node is the group number of the initial group; if so, store the neighbor nodes 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 not, add 1 to the second intermediate parameter, and return to the step of determining whether the second intermediate parameter is smaller 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, add 1 to the first intermediate parameter, and return to the step of determining whether the first intermediate parameter is less than or equal to the number of time slots in one of the time slot groups, until the first intermediate parameter is greater than the number of time slots in one of the time slot groups.

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

[0150] In some embodiments, 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 to G0=V, V is the set of all nodes in the network. The group number GN to which the node v belongs is vInitialized to 0. Each node v collects information about its neighbor nodes, including but not limited to the neighbor node ID, type, group number, distance between the neighbor node and node v, and the coverage weight of the neighbor node (i.e., the second coverage weight), and records the above neighbor node information in the neighbor information list. Based on the neighbor information list, the coverage weight W of each node v is calculated. v (i.e., the first coverage weight), collect the coverage weights of neighboring nodes. Select the k nodes with the largest weights from the coverage weights of any node v and the coverage weights of the neighboring nodes of the node v. If the node v is one of the k nodes, the group number of the node v is 1, that is, the node v is stored in the first group G1, and the node v is subtracted from the initial group G0, and then the group number GN is v =1 is sent to all neighboring nodes of node v.

[0153] Step 2: Form the next group:

[0154] Step 2 is to create a new group by reducing the difference in coverage quality between the previous group and the new group. p Each node v in 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. In the initial case, 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, presetting a third intermediate parameter;

[0157] Step S402, setting the first target node of the current group of the current time slot group to an active state to obtain an active node;

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

[0159] Step S404, when the first charging rate is not equal to the second charging rate, obtaining a first sampling number of the active node;

[0160] Step S405, when the first charging rate is equal to the second charging rate, determining whether the third intermediate parameter is greater than 0, if so, adjusting the first sampling number to a sampling threshold, and reducing the third intermediate parameter by 1, if not, setting the second charging rate equal to the first charging rate;

[0161] Step S406: scheduling the active nodes according to the third intermediate parameter, the first sampling times, and the first charging rate 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 warning information, the active node enters a warning state, obtains a warning node, and adjusts the first sampling number to a maximum sampling number;

[0164] Step S408, obtaining the additional energy consumption of the active node, and updating the third intermediate parameter according to the additional energy consumption;

[0165] Step S409, determine whether the monitoring of the current time slot group is completed. 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 an active node. If so, set the first charging rate equal to 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 an active node, until the monitoring of all time slot groups is completed.

[0166] In steps S401 to S409 of some embodiments, the sampling times are adaptively adjusted according to the charging rate and remaining energy of the node, and the event processing mechanism can be used to compensate the consumed energy as quickly as possible after the event is detected, thereby solving the charging cycle problem of minimizing energy compensation.

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

[0168] Step 3.1: Initialize each variable, 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 the 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 sampling times of node v; otherwise jump to step 3.6.

[0173] Step 3.6: If the variable RE is greater than 0, let the sampling number of node v be equal to the given sampling threshold, and the variable RE is reduced 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 sensed data is out of the normal range, or receives a warning message, the following event processing mechanism is executed:

[0176] 1) The node enters the "Warning" state, in which it adjusts its sampling times to the maximum value and sends a "Warning" message to its neighbors. Only neighbors in the "Active" state can receive the "Warning" message and change their own state to the "Warning" state. All "Active" nodes in the network will eventually intensively sense data about the event. These nodes will remain in the "Warning" state until the end of the current time slot.

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

[0178] 3) Calculate the value of variable RE based on the calculated additional energy consumption value. In the next RE time slots, the node that was originally in the "warning" state will enter the recovery phase to reduce energy consumption. That is, the node that was previously in the "warning" state switches to the "active" state, which will increase the sampling times S of node v. v Reduce to the threshold S thre To ensure that in time slot t j+1 The energy consumption is less than its energy collection, facilitating energy charging.

[0179] Otherwise proceed to step 3.9.

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

[0181] Step 3.10: Return to step 3.3 and execute until all nodes in the j+c*m1th group are traversed.

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

[0183] Step 3.12: If m1 can divide C, then add 1 to the value of variable m1. Otherwise, proceed to step 3.13.

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

[0185] For example, taking the three-dimensional space area to be covered as 80 meters × 80 meters × 80 meters as an example, the rechargeable wireless sensor network contains three types of sensors, namely: type 1, type 2 and type 3. Their sensing radius is 10 meters, 15 meters and 20 meters respectively, and the communication radius is 15 meters, 20 meters and 25 meters respectively. The ratio of type 1, type 2 and type 3 nodes is 1:1:1. The length of the time slot is set to 2 minutes. The energy parameters of each passive sensor are set as follows: voltage (E init ) is 3V, the battery capacity is 15kJ, and the energy threshold (B thre ) is 10 kJ. Each type of sensor unit senses energy consumption (E s ) are: Type 1 is 0.16 kJ, Type 2 is 0.284 kJ, and Type 3 is 0.64 kJ. The maximum number of sampling times per time slot (S max ) is 10, the threshold (S thre ) is 1. The charging energy of each time slot ranges from 1.25 kJ to 2.5 kJ. The value of k is set to 2.

[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 k-fold coverage quality 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 and the third objective function of minimizing the charging cycle of energy compensation

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

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

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

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

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

[0194] Step 4.4: Run the neighbor discovery algorithm to collect information about the neighbor 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 the node v v , and will cover the weight W v Sent to the neighboring nodes of node v, covering the weight W v The specific calculation is as follows:

[0196]

[0197] Among them, W init represents the initial coverage weight of node v, which is equal to the perceived volume of node v; W v represents the coverage weight of node v, that is, the first coverage weight; a u represents the perceived overlap volume between node v and its neighbor node u; represents the perceived overlap volume of the maximum k-1 neighbor nodes of node v; n represents the number of neighbor nodes of node v; Represents the perceived overlap volume of the other n-k+1 neighbor nodes of node v.

[0198] Furthermore, a u The specific calculation is as follows:

[0199]

[0200] Among them, d uv Represents the distance between neighbor node u and node v; represents the perception volume of neighbor node u; Represents the perceived volume of node v.

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

[0202] Step 4.7: Add the neighbor information list to v The neighbor nodes of are arranged in descending order according to their coverage weights.

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

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

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

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

[0207] Step 4.12: From group G p Take out the node v.

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

[0209] Step 4.14: If l <length(Nei v ), go to step 4.15, otherwise go to step 4.17.

[0210] Step 4.15: From the Neighbor Information List v Take out the neighbor node u from G. u = = 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 execute step 4.11.

[0213] Step 4.18: Take out each node v in the network node set V.

[0214] Step 4.19: If node v receives the group number of the neighbor node, it updates its neighbor information list Nei v The group number of the corresponding neighbor node in , otherwise execute step 4.20.

[0215] Step 4.20: If node v receives a value p from a neighbor node, then GN v =p+1, and the group number GN to which the node v belongs vSend to neighboring nodes, otherwise execute step 4.18 until all nodes are taken out and checked.

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

[0217] Step N6: S v =S max

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

[0219] Step N8: Execute an adaptive sampling method to solve the charging cycle problem of minimizing energy compensation, such as Figure 5 As shown, an optional specific step of the adaptive sampling method is as follows:

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

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

[0222] Step 8.3: For grouping For node v in , execute 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, that is, R1≠R2, then calculate the sampling times S of 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 the target event is detected and its sensor perception data is not within a given range [val l ,val h] (For example, in forest monitoring, the normal temperature range of the temperature sensor is [-20℃, 50℃]), or a "warning" message is received from a neighbor, and RE==0, then S v =S max , then calculate the node v in time slot t j The 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 in "warning" state; E c (v,t j-C ) indicates that node v is in time slot t j-C Energy consumption when in the "active" state.

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

[0233]

[0234] Send a "warning" message to each neighbor node of node v, otherwise execute 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", otherwise execute step 8.10.

[0236] Step 8.10: Execute step 8.3 until grouping The midpoints are removed.

[0237] Step 8.11: j++.

[0238] Step 8.12: If the remainder when m1 is divided by C is 0, then m1++, otherwise execute step 8.13.

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

[0240] In some optional 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 k-fold coverage quality within the monitoring period. In the distributed sleep scheduling algorithm, nodes can autonomously determine their state as "sleeping" or "active". The "active" state means that the sensor can sense and communicate. The "sleeping" state means that the sensor is turned off and all functions stop running. Optionally, initially, all nodes are set to the "active" state. The node can determine its group number through a distributed iterative grouping algorithm, and the grouping determines whether the node transitions to the "sleeping" state or remains in the "active" state. Optionally, the node initially sets its sampling rate to the maximum value. In a specific time slot, the nodes in a specific group switch to the "active" state, while the remaining nodes are in the "sleeping" state. During the monitoring period, scheduling is performed every C time slots. If the "active" node is in time slot t j-C The charging rate is related to the time slot t j-2C The charging rate of the node is different, then the node in time slot t j When an Activity 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 three-dimensional space area of ​​80 meters × 80 meters × 80 meters with 240 nodes as an example, the nodes are randomly distributed in the monitoring area 10 times, and in the period when no event is monitored, the average coverage quality obtained by running the method of the present invention reaches 85.333. Taking a given three-dimensional space area of ​​80 meters × 80 meters × 80 meters with 480 nodes, and divided into two groups, namely C=2 as an example, the nodes of these two groups monitor the three-dimensional area in turn by detecting the temperature. The normal temperature range is set to [-10℃, 40℃]. If the detected temperature exceeds this range, the event processing mechanism will be triggered. Figure 6 , Figure 7 As shown in Figure 1, in the 14th and 19th time slots, the temperature reaches 50°C and 45°C respectively. At this time, according to the adaptive sampling method, the number of samples of the node in this time slot reaches the maximum S max . Accordingly, energy consumption increases rapidly. Once the temperature returns to the normal range, the node will run the recovery phase in the event handling mechanism. For example, the node group that is active in the 14th time slot will enter the recovery phase in the 16th and 18th time slots. This phase reduces the sampling times and energy consumption, and the collected energy can compensate for the previous large consumption. The same operation will be performed in the 21st and 23rd time slots. Therefore, the coverage quality remains at a high level throughout the cycle.

[0242] The embodiment of the present invention further provides a sleep scheduling device for a three-dimensional heterogeneous rechargeable wireless sensor network, which can implement the sleep scheduling method for the 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 k-fold coverage quality within a monitoring period;

[0244] A 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] A third module is used to perform a grouping operation on each network node through a distributed iterative grouping algorithm according to the second objective function, and allocate each group to each time slot in a 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 through an adaptive sampling method to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes the k-fold coverage quality.

[0247] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically 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] The embodiment of the present invention further provides an electronic device, which includes a processor and a memory, wherein the memory stores a computer program, and when the processor executes the computer program, the sleep scheduling method of a three-dimensional heterogeneous rechargeable wireless sensor network is implemented. The electronic device can be any intelligent terminal including a tablet computer, a vehicle-mounted computer, etc.

[0249] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically 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.

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

[0251] The processor 501 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an 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 in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 502 can store an operating system and other applications. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 502, and the processor 501 calls and executes a sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to an embodiment of the present invention;

[0253] Input / output interface 503, used to implement information input and output;

[0254] Communication interface 504, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

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

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

[0257] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network is implemented.

[0258] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium 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.

[0259] The embodiment of the present invention also provides a computer program product or a computer program, which 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 the processor executes the computer instructions, so that the computer device executes the aforementioned sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network.

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

[0261] 1. The embodiment of the present invention proposes the problem of maximizing the k-fold coverage quality with constraints on energy and sampling rate, and decomposes the problem 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 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 of energy compensation, and propose an adaptive sampling method to dynamically adjust the sampling rate according to the time slot length, charging rate and energy consumption to ensure that the activated nodes have sufficient energy for coverage. Combining the algorithms of the above two sub-problems, a distributed sleep scheduling algorithm is proposed.

[0262] 2. Compared with the traditional two-dimensional wireless sensor network, the three-dimensional wireless sensor network of the embodiment of the present invention can provide more comprehensive and accurate monitoring capabilities, and is particularly suitable for scenarios that require perception requirements in the height direction, such as environmental monitoring, building structure monitoring, underground mine monitoring, etc.

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

[0264] 4. Coverage is one of the basic and key issues in rechargeable wireless sensor networks. The quality of network coverage is related to the accuracy and completeness of data collection, and will directly affect the performance of upper-layer applications. Due to monitoring quality and protocol requirements, some applications require the network to achieve k-fold coverage. The embodiment of the present invention maximizes the quality of k-fold coverage through a sleep scheduling method, and performs more complex energy management on the network, including fine control of node energy collection, consumption and sensing, so that the network can effectively utilize energy and provide continuous services, thereby achieving long-term and effective operation of the network.

[0265] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part for which is described as a larger operation is performed independently.

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

[0267] If the functions are implemented in the form of 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 the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

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

[0269] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0270] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0271] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

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

[0273] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the described embodiments. Those skilled in the art may make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in 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: The following steps are involved: Construct the first objective function to maximize the k-fold coverage quality within the monitoring period; Decomposing the first objective function to obtain a second objective function of maximizing the k-fold coverage quality of a single time slot and a third objective function of minimizing 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 assigned to each time slot in a time slot group in a cyclic manner; wherein the time slot group includes a plurality of time slots; According to the third objective function, the first target node of the time slot group is scheduled by an adaptive sampling method to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality.

2. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 1, characterized in that: The method of constructing a first objective function that maximizes the k-fold coverage quality within a monitoring period includes the following steps: Divide the total three-dimensional space area into a number of three-dimensional space sub-areas of equal size; dividing the monitoring period into a plurality of said time slots; Acquire, according to the volume of the three-dimensional space sub-area and the total number of the time slots, a first coverage quality covering the total area of ​​the three-dimensional space within the monitoring period; The first objective function is constructed according to the first coverage quality.

3. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 2, characterized in that: 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: Obtaining a standard deviation between the coverage rates of the three-dimensional space sub-areas in a single time slot; Acquire, according to the standard deviation, a second coverage quality of the set of network nodes in a single time slot; The second objective function is constructed according to the second coverage quality.

4. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 2, characterized in that: The decomposing the first objective function to obtain a second objective function for maximizing the k-fold coverage quality of a single time slot and a third objective function for minimizing the charging cycle of energy compensation also includes the following steps: Setting the initial sampling number of a single time slot; Obtain the energy consumption of sending data, the energy consumption of receiving data, and the minimum energy consumption of one sampling; The third objective function is constructed according to the initial sampling number, the energy consumption of sending data, the energy consumption of receiving data and the minimum energy consumption of one sampling.

5. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 1, characterized in that: The step of performing a grouping operation on each network node according to the second objective function by using a distributed iterative grouping algorithm comprises the following steps: Storing the set of network nodes into an initial group; arbitrarily taking out a second target node in the network nodes from the initial group; Obtaining a first coverage weight of the second target node; Receiving a second coverage weight of a neighbor node of the second target node; According to the weight values ​​of the first coverage weight and the second coverage weight, the second target node and the neighbor nodes of the second target node are arranged in descending order, and the third target nodes corresponding to the first k largest weight values ​​are 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 randomly taking out a second target node from the network node from 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 randomly taking out a second target node from the network node from the initial group, until all the second target nodes in the initial group are taken out.

6. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 1, characterized in that: The step of performing a grouping operation on each network node by using a distributed iterative grouping algorithm according to the second objective function further includes the following steps: Presetting a first intermediate parameter and a second intermediate parameter; Determine whether the first intermediate parameter is less than or equal to the number of time slots in one of the time slot groups, and when the first intermediate parameter is less than or equal to the number of time slots in one of the time slot groups, extract a fourth target node from the current group; 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 smaller 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 so, 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 not, add 1 to the second intermediate parameter, and return to the step of determining whether the second intermediate parameter is smaller 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, add 1 to the first intermediate parameter, and return to the step of determining whether the first intermediate parameter is less than or equal to the number of time slots in one of the time slot groups until the first intermediate parameter is greater than the number of time slots in one of the time slot groups.

7. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 1, characterized in that: The method of scheduling the first target node of the time slot group by an adaptive sampling method according to the third objective function to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality includes the following steps: Presetting the third intermediate parameter; Setting the first target node of the current group of the current time slot group to an active state to obtain an active node; Obtaining a first charging rate of the active node in a previous time slot group, and obtaining a second charging rate of the active node in a next time slot group; When the first charging rate is not equal to the second charging rate, obtaining a first sampling number of the active node; When the first charging rate is equal to the second charging rate, determining whether the third intermediate parameter is greater than 0; if so, adjusting the first sampling number to a sampling threshold, and reducing the third intermediate parameter by 1; if not, setting the second charging rate equal to the first charging rate; The active nodes are scheduled according to the third intermediate parameter, the first sampling times and the first charging rate to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes k-fold coverage quality.

8. The sleep scheduling method for a three-dimensional heterogeneous rechargeable wireless sensor network according to claim 7, characterized in that: The step of scheduling the active node according to the third intermediate parameter, the first sampling number, and the first charging rate comprises the following steps: When the active node detects a target event or receives warning information, the active node enters a warning state, obtains a warning node, and adjusts the first sampling number to a maximum sampling number; Acquire the additional energy consumption of the active node, and update the third intermediate parameter according to the additional energy consumption; Determine whether the monitoring of the current time slot group is completed. 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 an active node. If so, set the first charging rate equal to 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 an active node, until the monitoring of all time slot groups is completed.

9. 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 k-fold coverage quality within a monitoring period; A 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; A third module is used to perform a grouping operation on each network node through a distributed iterative grouping algorithm according to the second objective function, and allocate each group to each time slot in a 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 through an adaptive sampling method to obtain a three-dimensional heterogeneous rechargeable wireless sensor network that maximizes the k-fold coverage quality.

10. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 8.

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