Sensor node scheduling method and device

Abstract

The invention discloses a sensor node scheduling method and device. Comprising the following steps: acquiring hardware parameter information of sensor nodes in the wireless sensor network and node state information of the sensor nodes in a plurality of time slots in a target time period; determining a target scheduling problem which enables the total number of violation events of the sensor node in the target time period to be minimum and the available value of battery energy to be maximum, and determining a plurality of constraint conditions according to the hardware parameter information of the sensor node and the node state information in the plurality of time slots; based on the multiple constraint conditions, a preset algorithm is adopted to solve the target scheduling problem, a target scheduling strategy is obtained, and the target scheduling strategy is used for stipulating that data collected by the sensor nodes are reported to the aggregation node in the target time slot. According to the method and the device, the technical problems that the number of violation events is large and the network stability is poor due to the fact that dynamic changes of battery energy and channel states cannot be comprehensively considered when sensor nodes in a wireless sensor network are scheduled to report data in the prior art are solved.

Description

Technical Field

[0001] The present application relates to the technical field of Internet of Things, and in particular to a sensor node scheduling method and device. Background Art

[0002] With the rapid development of the Internet of Things (IoT), wireless sensor networks (WSN) are increasingly being used in various industries, such as agricultural irrigation systems, environmental monitoring, smart cities, industrial automation, and surveillance. Among them, wireless sensor networks are composed of a large number of low-power sensor nodes, which are distributed in the monitoring area to collect physical or chemical information in the environment, such as temperature, humidity, light intensity, pressure, etc., and transmit these data to the aggregation node (or central server) through wireless communication for further processing and decision-making.

[0003] In wireless sensor networks, sensor nodes usually rely on limited battery power or environmental energy (such as solar energy and wind energy) for data collection. If the sensor node cannot complete data transmission before the energy is exhausted, it will lead to data loss or transmission delay, which will affect the reliability and data accuracy of the system. Therefore, the key to ensure the long-term stable operation of wireless sensor networks is to reasonably design the scheduling algorithm of sensor nodes to maximize energy utilization efficiency and reduce violation events (such as data transmission timeout or loss). At present, the scheduling technology of sensor nodes mainly relies on greedy algorithms, heuristic-based energy allocation strategies or simple polling scheduling methods. However, these methods only focus on the optimal utilization of local energy, ignore the changes in future energy state and channel state, and may generate a large number of violation events (such as data transmission timeout or loss) in subsequent time slots. Therefore, it is impossible to achieve optimal scheduling of sensor nodes under multiple constraints.

[0004] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0005] The embodiments of the present application provide a sensor node scheduling method and device to at least solve the technical problem that the related technology fails to comprehensively consider the dynamic changes of battery energy and channel status when scheduling sensor node reporting data in a wireless sensor network, resulting in a large number of violation events and poor network stability.

[0006] According to one aspect of an embodiment of the present application, a sensor node scheduling method is provided, comprising: obtaining hardware parameter information of sensor nodes in a wireless sensor network and node status information of the sensor nodes in multiple time slots within a target time period; determining a target scheduling problem that minimizes the total number of violation events of the sensor nodes within the target time period and maximizes the available value of battery energy, and determining multiple constraints based on the hardware parameter information of the sensor nodes and the node status information in multiple time slots; based on the multiple constraints, using a preset algorithm to solve the target scheduling problem to obtain a corresponding target scheduling strategy, wherein the target scheduling strategy is used to stipulate that the data collected by the sensor nodes is reported to a sink node in the wireless sensor network within the target time slot; wherein the preset algorithm comprises: dividing the target scheduling problem into multiple sub-scheduling problems, and solving the sub-scheduling problem based on the multiple constraints corresponding to the sub-scheduling problem to obtain a sub-scheduling strategy to update the multiple constraints corresponding to the next sub-scheduling problem.

[0007] Optionally, the hardware parameter information includes at least: the maximum battery energy value of the battery equipped with the sensor node and the signal transmission power; the node status information includes at least: call status information, energy status information, and channel status information, wherein the call status information is used to reflect whether the sensor node is called in each time slot, wherein when the sensor node is called in any time slot, the call status information of the sensor node in the corresponding time slot is a valid value, and when the sensor node is not called in any time slot, the call status information of the sensor node in the corresponding time slot is an invalid value; the energy status information is used to reflect whether the battery equipped by the sensor node is in The energy status in each time slot, and the energy status information at least includes: the available battery energy value and the battery collected energy value; the channel status information is used to reflect the channel characteristic information of the transmission path between the sensor node and the aggregation node in each time slot, and the channel status information at least includes: the reception signal-to-noise ratio of the signal transmitted by the sensor node received by the aggregation node, the path distance of the transmission path, the path loss index, the channel coefficient and the noise, wherein the reception signal-to-noise ratio is the product of the signal transmission power of the sensor node in the corresponding time slot and the square of the channel coefficient divided by the product of the Nth power of the path distance and the variance of the noise, and N is equal to the path loss index.

[0008] Optionally, multiple constraints are determined based on the hardware parameter information of the sensor node and the node status information in multiple time slots, including: taking the reception signal-to-noise ratio of the sensor node transmitted signal received by the sink node in the target time slot to be solved not less than a preset signal-to-noise ratio threshold as a first constraint; taking the battery energy available value of the sensor node in the target time slot to be solved as the minimum value between the battery energy remaining value in the target time slot to be solved and the battery energy maximum value as a second constraint, wherein the battery energy remaining value is the sum of the battery energy available value and the battery collected energy value in the previous time slot of the target time slot to be solved minus the battery energy value of the sensor node in the target time slot to be solved. The product of the calling state information in the target time slot, the duration of the target time slot to be solved, and the signal transmission power in the previous time slot of the target time slot to be solved is taken as the third constraint condition. The calling state information of the sensor node in the target time slot to be solved is taken as the product of the available battery energy value in the target time slot to be solved and the duration of the target time slot to be solved multiplied by the calling state information in the target time slot to be solved. The fourth constraint condition is that the calling state information of the sensor node in the middle time slot between two adjacent target time slots to be solved is an invalid value. The fifth constraint condition is that the number of middle time slots between two adjacent target time slots to be solved is at least 1.

[0009] Optionally, based on multiple constraints, a preset algorithm is used to solve the target scheduling problem to obtain a corresponding target scheduling strategy, including: dividing the target time period into multiple sub-time periods of equal length in sequence, wherein each sub-time period includes at least one time slot; dividing the target scheduling problem into multiple sub-scheduling problems according to the multiple sub-time periods; initializing the total number of violation events; traversing each sub-scheduling problem in turn, and performing the following steps: adjusting multiple constraints corresponding to the current sub-scheduling problem according to the target time slot and the total number of violation events corresponding to the previous sub-scheduling problem; solving the current sub-scheduling problem based on the adjusted multiple constraints to obtain the corresponding target time slot and the number of violation events, and determining the total number of violation events corresponding to the current sub-scheduling problem in combination with the total number of violation events corresponding to the previous sub-scheduling problem.

[0010] Optionally, multiple constraints corresponding to the current sub-scheduling problem are adjusted based on the target time slot and the total number of violation events corresponding to the previous sub-scheduling problem, including: adjusting the fourth constraint and the fifth constraint corresponding to the current sub-scheduling problem based on the target time slot corresponding to the previous sub-scheduling problem; adjusting the second constraint and the third constraint corresponding to the current sub-scheduling problem based on the total number of violation events corresponding to the target time slot corresponding to the previous sub-scheduling problem.

[0011] Optionally, the second constraint and the third constraint corresponding to the current sub-scheduling problem are adjusted according to the total number of violation events corresponding to the target time slot corresponding to the previous sub-scheduling problem, including: determining the number of violation events between the target time slot corresponding to the previous sub-scheduling problem and the target time slot to be solved corresponding to the current sub-scheduling problem; determining the product of the signal transmission power, the signal-to-noise ratio threshold and the duration of the target time slot to be solved in the target time slot to be solved corresponding to the current sub-scheduling problem, and taking the quotient of the obtained product result and the received signal-to-noise ratio in the target time slot to be solved corresponding to the current sub-scheduling problem as the current sub-scheduling problem. The battery energy cost in the target time slot to be solved corresponding to the problem; calculate the difference between the battery energy remaining value in the target time slot corresponding to the previous sub-scheduling problem and the battery energy cost, and take the difference and the sum of the battery collected energy values ​​of each intermediate time slot between the target time slot to be solved corresponding to the current sub-scheduling problem and the target time slot corresponding to the previous sub-scheduling problem as the battery energy remaining value in the target time slot to be solved corresponding to the current sub-scheduling problem; adjust the second constraint and the third constraint corresponding to the current sub-scheduling problem according to the battery energy remaining value in the target time slot to be solved corresponding to the current sub-scheduling problem.

[0012] Optionally, determining the number of violation events between the target time slot corresponding to the previous sub-scheduling problem and the target time slot to be solved corresponding to the current sub-scheduling problem includes: determining the number of intermediate time slots between the target time slot corresponding to the previous sub-scheduling problem and the target time slot to be solved of the current sub-scheduling problem; taking the ratio of the number of intermediate time slots to a preset time slot number threshold as the number of violation events between the target time slot corresponding to the previous sub-scheduling problem and the target time slot to be solved corresponding to the current sub-scheduling problem, wherein the time slot number threshold is the maximum number of time slots between two consecutive calls of the sensor node.

[0013] According to another aspect of an embodiment of the present application, a sensor node scheduling device is also provided, including: an acquisition module, used to acquire hardware parameter information of sensor nodes in a wireless sensor network and node status information of sensor nodes in multiple time slots within a target time period; a determination module, used to determine a target scheduling problem that minimizes the total number of violation events of sensor nodes within the target time period and maximizes the available value of battery energy, and determines multiple constraints based on the hardware parameter information of the sensor nodes and the node status information in multiple time slots; a scheduling module, used to solve the target scheduling problem based on multiple constraints and using a preset algorithm to obtain a corresponding target scheduling strategy, wherein the target scheduling strategy is used to stipulate that the data collected by the sensor nodes shall be reported to the aggregation node in the wireless sensor network within the target time slot; wherein the preset algorithm includes: dividing the target scheduling problem into multiple sub-scheduling problems, and solving the sub-scheduling problem based on the multiple constraints corresponding to the sub-scheduling problem to obtain a sub-scheduling strategy to update the multiple sub-constraints corresponding to the next sub-scheduling problem.

[0014] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided. The non-volatile storage medium includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the above-mentioned sensor node scheduling method by running the computer program.

[0015] According to another aspect of an embodiment of the present application, a computer program product is further provided. The computer program product includes a stored computer program, wherein when the computer program is executed by a processor, the above-mentioned sensor node scheduling method is implemented.

[0016] In the embodiment of the present application, the target scheduling problem is decomposed into multiple sub-scheduling problems, and for each sub-problem, the multiple constraints of the sub-scheduling problem currently being analyzed are adjusted according to the sub-scheduling strategy of the previous sub-scheduling problem, so as to fully consider the changes in the channel state and energy consumption during the period, and the sub-scheduling problem currently being analyzed is solved based on the adjusted multiple constraints to obtain a scheduling strategy that minimizes the number of violation events and maximizes the available value of battery energy. If the wireless sensor network schedules each sensor node in the network according to the scheduling strategy, the network stability and data processing efficiency can be greatly improved. This solves the technical problem that the related technology fails to comprehensively consider the dynamic changes of battery energy and channel state when scheduling the sensor nodes in the wireless sensor network to report data, resulting in a large number of violation events and poor network stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 is a schematic diagram of an alternative scheduling system;

[0019] Figure 2 It is a hardware structure block diagram of a computer terminal (or mobile device) for implementing a sensor node scheduling method according to an embodiment of the present application;

[0020] Figure 3 is a flow chart of an optional sensor node scheduling method according to an embodiment of the present application;

[0021] Figure 4 is a schematic structural diagram of an optional sensor node scheduling device according to an embodiment of the present application;

[0022] Figure 5 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] In addition, the relevant information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. For example, an interface is set up between this system and the relevant user or organization. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or organization through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or organization.

[0026] Example 1

[0027] According to an embodiment of the present application, an embodiment of a sensor node scheduling method is provided, which can be applied to Figure 1 In the scheduling system shown, the scheduling system includes a sensor node with data collection capability, which can continuously monitor data and send it to the corresponding sink node. In addition, the scheduling system adopts a time slot mechanism, that is, the sensor node can only transmit data to the sink node once at most in each time slot within the target time period, and the duration of the time slot is fixed.

[0028] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0029] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 2 FIG. 1 shows a hardware structure block diagram of a computer terminal (or mobile device) for implementing a sensor node scheduling method. Figure 2 As shown, the computer terminal 20 (or mobile device 20) may include one or more (202a, 202b, ..., 202n are used to illustrate) processors 202 (the processor 202 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 204 for storing data, and a transmission device 206 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 2 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 2 More or fewer components as shown, or with Figure 2 Different configurations are shown.

[0030] It should be noted that the one or more processors 202 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the computer terminal 20 (or mobile device). As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0031] The memory 204 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the sensor node scheduling method in the embodiment of the present application. The processor 202 executes various functional applications and data processing by running the software programs and modules stored in the memory 204, that is, the sensor node scheduling method of the above-mentioned application is realized. The memory 204 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 204 may further include a memory remotely arranged relative to the processor 202, and these remote memories may be connected to the computer terminal 20 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0032] The transmission device 206 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the computer terminal 20. In one example, the transmission device 206 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 206 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 20 (or mobile device).

[0034] In the above operating environment, Figure 2 is a flow chart of an optional sensor node scheduling method according to an embodiment of the present application, such as Figure 3 As shown, the method at least includes steps S302-S306, wherein:

[0035] Step S302: acquiring hardware parameter information of sensor nodes in the wireless sensor network and node status information of the sensor nodes in multiple time slots within a target time period.

[0036] In the technical solution provided in step S302, the hardware parameter information is the capability parameter of the sensor node itself, and the hardware parameter information may at least include: the maximum battery energy value E of the battery equipped with the sensor node; max , signal transmission power P(t), processor speed, memory size, storage capacity, etc.

[0037] The node status information is the status information of the sensor node in different time slots, and the node status information at least includes: call status information, energy status information, channel status information, where:

[0038] The above-mentioned calling status information is used to reflect whether the sensor node is called in each time slot, which can be recorded as x(t), wherein when the sensor node is called in any time slot, the calling status information of the sensor node in the corresponding time slot is a valid value, such as x(t)=1; and when the sensor node is not called in any time slot, the calling status information of the sensor node in the corresponding time slot is an invalid value, such as x(t)=0.

[0039] The energy status information is used to reflect the energy status of the battery equipped with the sensor node in each time slot, and the energy status information at least includes: the battery energy available value E(t) and the battery collected energy value A(t).

[0040] The above-mentioned channel state information is used to reflect the channel characteristic information of the transmission path between the sensor node and the aggregation node in each time slot, and the channel state information at least includes: the reception signal-to-noise ratio SNR(t) of the aggregation node receiving the signal transmitted by the sensor node, the path distance d of the transmission path, the path loss index β, the channel coefficient h(t) and the noise σ.

[0041] Among them, the received signal-to-noise ratio SNR(t) is the product of the signal transmission power P(t) of the sensor node in the corresponding time slot and the square of the channel coefficient h(t) divided by the product of the path distance d to the power of N (N is equal to the path loss exponent β) and the variance of the noise σ. Therefore, the expression of the received signal-to-noise ratio SNR(t) can be written as:

[0042]

[0043] Step S304, determining a target scheduling problem that minimizes the total number of violation events of the sensor node within the target time period and maximizes the available value of battery energy, and determining multiple constraints based on the hardware parameter information of the sensor node and the node status information in multiple time slots.

[0044] In the technical solution provided in step S304, the above-mentioned violation event refers to the sensor node not being in U max The collected data will be transmitted to the sink node in the wireless sensor network within the duration of the time slot. max Refers to the maximum number of time slots between two consecutive calls of a sensor node. The available battery energy value refers to the remaining power of the battery configured for the sensor node, wherein the battery can be a photovoltaic cell, an electromagnetic induction battery, etc. that obtains electrical energy from the external environment, and the embodiments of the present application do not impose specific restrictions on this. Therefore, the above-mentioned target scheduling problem can be understood as, within the target time period T, considering the uncertainty of the channel conditions, to provide the wireless sensor node with the best (i.e., the total number of violation events is the smallest and the available battery energy value is the largest) scheduling in which time slot to make a decision. Therefore, the expression of the target scheduling problem can be written as:

[0045]

[0046] Among them, y(s,t) indicates whether the sensor node is scheduled in time slots s and t, U max It indicates the maximum number of time slots between two consecutive scheduling periods of a sensor node.

[0047] The multiple constraints determined based on the hardware parameter information of the sensor node and the node status information in multiple time slots specifically include:

[0048] The first constraint condition is that the signal-to-noise ratio of the sensor node transmitted signal received by the sink node in the target time slot to be solved is not lower than the preset signal-to-noise ratio threshold, which can be expressed as:

[0049]

[0050] The available battery energy value of the sensor node in the target time slot to be solved is the minimum value of the remaining battery energy value and the maximum battery energy value in the target time slot to be solved as the second constraint condition, and the remaining battery energy value is the sum of the available battery energy value E(t-1) in the previous time slot of the target time slot and the battery collection energy value A(t-1) minus the product of the call state information x(t) of the sensor node in the target time slot, the duration τ of the target time slot, and the signal transmission power P(t-1) in the previous time slot of the target time slot. Therefore, the second constraint condition can be expressed as:

[0051]

[0052] The signal transmission power of the sensor node in the target time slot to be solved is not higher than the quotient of the available battery energy value in the target time slot to be solved and the duration of the target time slot to be solved multiplied by the call state information in the target time slot to be solved as the third constraint condition, which can be expressed as:

[0053]

[0054] The fourth constraint condition is that the call state information of the sensor node in the middle time slot between two adjacent target time slots to be solved is an invalid value, which can be expressed as:

[0055]

[0056] Among them, the above When x(t) = 1, it indicates that the sensor node is called at time slot t, otherwise, when x(t) = 0, it indicates that the sensor node is not called at time slot t. When y(s, t) = 1, it indicates that the sensor node is called at time slots s and t, but not at the middle time slot. Otherwise, when y(s, t) = 0, it indicates that the sensor node is called at time slots s and t, and is still called at the middle time slot. In addition,

[0057] The fifth constraint condition is that the number of intermediate time slots between two adjacent target time slots to be solved is at least 1, which can be expressed as:

[0058]

[0059] According to the above constraints, we can count the number of time slots between two consecutive scheduling of sensor nodes that exceed U max At the same time, in order to prevent the extreme situation that the sensor node never sends a data packet during the entire scheduling process, it is also set that the sensor node must be scheduled to transmit data at time slots t=0 and t=T+1. This is because, without this setting condition, it will become the optimal strategy for the sensor node not to transmit a data packet at all. Therefore, in order to avoid such extreme and meaningless scheduling schemes, the model forces state updates at the beginning and end of the time range to ensure actual transmission behavior during the entire scheduling process.

[0060] Furthermore, considering that the above scheduling problem is a complex scheduling problem with multiple constraints and nonlinear objective functions, the common solutions to this type of problem currently include: brute force search algorithm, greedy algorithm and heuristic algorithm. Among them, although the brute force search algorithm can find the global optimal solution, its computational complexity is extremely high and cannot be promoted in practical applications, especially in the face of large-scale wireless sensor networks; although the greedy algorithm and heuristic algorithm have more advantages in computational efficiency, their solutions are often only approximately optimal, and when the energy of the battery equipped with the sensor node is uncertain and the channel state changes greatly, the accuracy of the solution results is often not very stable.

[0061] In order to overcome the shortcomings of the above-mentioned solution method, this application will specifically introduce the efficient solution algorithm proposed this time through the following step S306.

[0062] Step S306, based on multiple constraints, a preset algorithm is used to solve the target scheduling problem to obtain a corresponding target scheduling strategy.

[0063] In the technical solution provided in step S306, the preset algorithm is also called a dynamic programming algorithm, which divides the target scheduling problem into multiple sub-scheduling problems, and solves the sub-scheduling problem based on the multiple constraints corresponding to the sub-scheduling problem to obtain the sub-scheduling strategy to update the multiple constraints corresponding to the next sub-scheduling problem. Compared with the greedy algorithm or the heuristic algorithm, the dynamic programming ensures the global optimal solution, and can flexibly adjust the scheduling strategy according to the energy state and channel quality in different time periods, so that the final solution has higher solution accuracy and stability, which is particularly suitable for wireless sensor network environments with changing energy states. Finally, the solutions obtained from each sub-scheduling problem are summarized to obtain the target scheduling strategy corresponding to the target scheduling problem, and the target scheduling strategy stipulates that the data collected by the sensor in the target time slot is reported to the aggregation node (such as a central server) in the wireless sensor network.

[0064] Specifically, when the preset algorithm solves the target scheduling problem, the following steps can be followed:

[0065] Step S3061: divide the target time period into a plurality of sub-time periods of equal length in sequence, wherein each sub-time period includes at least one time slot.

[0066] Step S3062: Divide the target scheduling problem into multiple sub-scheduling problems according to multiple sub-time periods.

[0067] In the embodiment of the present application, each sub-scheduling problem is defined as OPT[t,v]: finding the optimal solution that can maximize the battery available energy E(t) among all solutions that satisfy the constraints.

[0068] Step S3063, initializing the total number of violation events.

[0069] It can be set according to the actual application scenario, and can be 0 or the total number of violation events in a time slot before the target time period T.

[0070] Step S3064, traverse each sub-scheduling problem in turn. For the first sub-scheduling problem, if t=0, v=0, then OPT[0,v]=0, t=0, v>0, then OPT[0,v]=-∞. For t>0, perform the following steps:

[0071] Step 1: Adjust multiple constraints corresponding to the current sub-scheduling problem according to the target time slot and the total number of violation events corresponding to the previous sub-scheduling problem.

[0072] Specifically, the above step 1 can be implemented by adjusting the rules as follows:

[0073] Adjust the fourth constraint condition and the fifth constraint condition corresponding to the current sub-scheduling problem according to the target time slot corresponding to the previous sub-scheduling problem;

[0074] The second constraint condition and the third constraint condition corresponding to the current sub-scheduling problem are adjusted according to the total number of violation events corresponding to the target time slot corresponding to the previous sub-scheduling problem.

[0075] Optionally, the second constraint condition and the third constraint condition corresponding to the current sub-scheduling problem may be adjusted according to the following steps, including:

[0076] Step 1: Determine the number of violation events between the target time slot corresponding to the solution of the previous sub-scheduling problem and the target time slot to be solved corresponding to the current sub-scheduling problem.

[0077] Among them, the number of intermediate time slots between the target time slot s corresponding to the previous sub-scheduling problem and the target time slot t to be solved in the current sub-scheduling problem can be determined first; then, the number of intermediate time slots and the preset time slot number threshold U max The ratio of (which represents the maximum number of time slots between two consecutive calls of the sensor node) is used as the number of violation events between the target time slot corresponding to the previous sub-scheduling problem and the target time slot to be solved corresponding to the current sub-scheduling problem. Therefore, the expression of the above probability can be written as:

[0078]

[0079] Step 2: Determine the product of the signal transmission power P(t) in the target time slot to be solved corresponding to the current sub-scheduling problem, the signal-to-noise ratio threshold γ0 and the duration τ of the target time slot to be solved, and take the quotient of the received signal-to-noise ratio SNR(t) in the target time slot to be solved corresponding to the current sub-scheduling problem as the battery energy cost in the target time slot to be solved corresponding to the current sub-scheduling problem. Therefore, the expression of the battery energy cost can be written as:

[0080]

[0081] Step 3: Calculate the difference between the remaining battery energy value OPT[s,vp] in the target time slot corresponding to the previous sub-scheduling problem and the battery energy cost e(t), and add the difference to the sum of the battery energy values ​​collected in each intermediate time slot between the target time slot to be solved corresponding to the current sub-scheduling problem and the target time slot corresponding to the previous sub-scheduling problem. As the remaining battery energy value in the target time slot to be solved corresponding to the current sub-scheduling problem, the expression of the remaining battery energy value can be written as:

[0082]

[0083] Step 4: Adjust the second constraint and the third constraint corresponding to the current sub-scheduling problem according to the remaining battery energy value in the target time slot to be solved corresponding to the current sub-scheduling problem.

[0084] Among them, the second constraint and the third constraint are directly or indirectly related to the remaining battery energy value, so the second constraint and the third constraint corresponding to the current sub-scheduling problem can be adjusted according to the remaining battery energy value solved in the third step.

[0085] Step 2: Solve the current sub-scheduling problem based on the adjusted multiple constraints to obtain the corresponding target time slot and number of violation events, and determine the total number of violation events corresponding to the current sub-scheduling problem in combination with the total number of violation events corresponding to the previous sub-scheduling problem.

[0086] Among them, the embodiment of the present application does not specifically limit the selection of the method for solving the current sub-scheduling problem in the above step 2.

[0087] Based on the scheme defined by the above steps S302 to S306, it can be known that, in an embodiment, by decomposing the target scheduling problem into multiple sub-scheduling problems, and for each sub-problem, the multiple constraints of the sub-scheduling problem currently being analyzed are adjusted according to the sub-scheduling strategy of the previous sub-scheduling problem, so as to fully consider the changes in channel state and energy consumption during the period, and solve the sub-scheduling problem currently being analyzed based on the adjusted multiple constraints to obtain a scheduling strategy that minimizes the number of violation events and maximizes the available battery energy value. If the wireless sensor network schedules each sensor node in the network according to the scheduling strategy, the network stability and data processing efficiency can be greatly improved.

[0088] Example 2

[0089] Based on the first embodiment of the present application, an embodiment of a sensor node scheduling device is also provided, and the sensor node scheduling method of the above embodiment is executed when the device is running. Figure 4 is a schematic diagram of the structure of an optional sensor node scheduling device according to an embodiment of the present application, such as Figure 4 As shown, the sensor node scheduling device at least includes an acquisition module 42, a determination module 44 and a scheduling module 46, wherein:

[0090] An acquisition module 42, used to acquire hardware parameter information of sensor nodes in the wireless sensor network and node status information of the sensor nodes in multiple time slots within a target time period;

[0091] A determination module 44 is used to determine a target scheduling problem that minimizes the total number of violation events of the sensor node within a target time period and maximizes the available value of battery energy, and determines multiple constraint conditions based on hardware parameter information of the sensor node and node state information within multiple time slots;

[0092] The scheduling module 46 is used to solve the target scheduling problem based on multiple constraints using a preset algorithm to obtain a corresponding target scheduling strategy, wherein the target scheduling strategy is used to specify that the data collected by the sensor node is reported to the aggregation node in the wireless sensor network within the target time slot;

[0093] Among them, the preset algorithm includes: dividing the target scheduling problem into multiple sub-scheduling problems, and solving the sub-scheduling problem based on multiple constraints corresponding to the sub-scheduling problem to obtain a sub-scheduling strategy to update the multiple sub-constraints corresponding to the next sub-scheduling problem.

[0094] It should be noted that the various modules in the above-mentioned sensor node scheduling device can be program modules (for example, a set of program instructions that implement a certain specific function) or hardware modules. For the latter, it can be expressed in the following forms, but is not limited to this: the expression form of each of the above-mentioned modules is a processor, or the functions of each of the above-mentioned modules are implemented by a processor.

[0095] Example 3

[0096] According to an embodiment of the present application, a non-volatile storage medium is further provided, in which a program is stored, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the sensor node scheduling method in embodiment 1.

[0097] Optionally, the device where the non-volatile storage medium is located implements the following steps by running the program: obtaining hardware parameter information of sensor nodes in the wireless sensor network and node status information of the sensor nodes in multiple time slots within a target time period; determining a target scheduling problem that minimizes the total number of violation events of the sensor nodes within the target time period and maximizes the available value of battery energy, and determining multiple constraints based on the hardware parameter information of the sensor nodes and the node status information in multiple time slots; based on the multiple constraints, using a preset algorithm to solve the target scheduling problem to obtain a corresponding target scheduling strategy, wherein the target scheduling strategy is used to stipulate that the data collected by the sensor nodes shall be reported to the aggregation node in the wireless sensor network within the target time slot; wherein the preset algorithm includes: dividing the target scheduling problem into multiple sub-scheduling problems, and solving the sub-scheduling problem based on the multiple constraints corresponding to the sub-scheduling problem to obtain a sub-scheduling strategy to update the multiple constraints corresponding to the next sub-scheduling problem.

[0098] According to an embodiment of the present application, a computer program product is further provided. The computer program product includes a stored computer program, wherein when the computer program is executed by a processor, the sensor node scheduling method in Embodiment 1 is implemented.

[0099] Optionally, the computer program executes the following steps: obtaining hardware parameter information of sensor nodes in the wireless sensor network and node status information of the sensor nodes in multiple time slots within a target time period; determining a target scheduling problem that minimizes the total number of violation events of the sensor nodes within the target time period and maximizes the available value of battery energy, and determining multiple constraints based on the hardware parameter information of the sensor nodes and the node status information in multiple time slots; based on the multiple constraints, using a preset algorithm to solve the target scheduling problem to obtain a corresponding target scheduling strategy, wherein the target scheduling strategy is used to stipulate that the data collected by the sensor nodes shall be reported to the aggregation node in the wireless sensor network within the target time slot; wherein the preset algorithm includes: dividing the target scheduling problem into multiple sub-scheduling problems, and solving the sub-scheduling problem based on the multiple constraints corresponding to the sub-scheduling problem to obtain the sub-scheduling strategy to update the multiple constraints corresponding to the next sub-scheduling problem.

[0100] According to an embodiment of the present application, a processor is further provided, and the processor is used to run a program, wherein the sensor node scheduling method in Embodiment 1 is executed when the program is run.

[0101] Optionally, the following steps are executed when the program is running: obtaining hardware parameter information of sensor nodes in the wireless sensor network and node status information of the sensor nodes in multiple time slots within a target time period; determining a target scheduling problem that minimizes the total number of violation events of the sensor nodes within the target time period and maximizes the available value of battery energy, and determining multiple constraints based on the hardware parameter information of the sensor nodes and the node status information in multiple time slots; based on the multiple constraints, using a preset algorithm to solve the target scheduling problem to obtain a corresponding target scheduling strategy, wherein the target scheduling strategy is used to stipulate that the data collected by the sensor nodes shall be reported to the aggregation node in the wireless sensor network within the target time slot; wherein the preset algorithm includes: dividing the target scheduling problem into multiple sub-scheduling problems, and solving the sub-scheduling problem based on the multiple constraints corresponding to the sub-scheduling problem to obtain a sub-scheduling strategy to update the multiple constraints corresponding to the next sub-scheduling problem.

[0102] According to an embodiment of the present application, an electronic device is also provided, wherein: Figure 5 is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application, such as Figure 5 As shown, the electronic device includes one or more processors; a memory for storing one or more programs, which, when the one or more programs are executed by the one or more processors, enables the one or more processors to run the programs, wherein the programs are configured to execute the sensor node scheduling method in the above-mentioned embodiment 1 when running.

[0103] Optionally, the processor is configured to execute the following steps through a computer program: obtaining hardware parameter information of sensor nodes in the wireless sensor network and node status information of the sensor nodes in multiple time slots within a target time period; determining a target scheduling problem that minimizes the total number of violation events of the sensor nodes within the target time period and maximizes the available value of battery energy, and determining multiple constraints based on the hardware parameter information of the sensor nodes and the node status information in multiple time slots; based on the multiple constraints, using a preset algorithm to solve the target scheduling problem to obtain a corresponding target scheduling strategy, wherein the target scheduling strategy is used to stipulate that the data collected by the sensor nodes shall be reported to the aggregation node in the wireless sensor network within the target time slot; wherein the preset algorithm includes: dividing the target scheduling problem into multiple sub-scheduling problems, and solving the sub-scheduling problem based on the multiple constraints corresponding to the sub-scheduling problem to obtain a sub-scheduling strategy to update the multiple constraints corresponding to the next sub-scheduling problem.

[0104] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0105] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0106] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units can be a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0107] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0108] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0109] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes 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 method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0110] The above is only a preferred implementation of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A sensor node scheduling method, characterized in that: include: Acquire hardware parameter information of sensor nodes in a wireless sensor network and node status information of the sensor nodes in multiple time slots within a target time period; Determine a target scheduling problem that minimizes the total number of violation events of the sensor node within a target time period and maximizes the available value of battery energy, and determine multiple constraint conditions based on hardware parameter information of the sensor node and node state information in multiple time slots; Based on the multiple constraints, a preset algorithm is used to solve the target scheduling problem to obtain a corresponding target scheduling strategy, wherein the target scheduling strategy is used to stipulate that the data collected by the sensor node is reported to the aggregation node in the wireless sensor network within a target time slot; Among them, the preset algorithm includes: dividing the target scheduling problem into multiple sub-scheduling problems, and solving the sub-scheduling problem based on multiple constraints corresponding to the sub-scheduling problem to obtain a sub-scheduling strategy to update the multiple constraints corresponding to the next sub-scheduling problem.

2. The method according to claim 1, characterized in that The hardware parameter information at least includes: the maximum battery energy of the battery equipped with the sensor node and the signal transmission power; the node status information at least includes: call status information, energy status information, channel status information, wherein, The calling state information is used to reflect whether the sensor node is called in each time slot, wherein when the sensor node is called in any time slot, the calling state information of the sensor node in the corresponding time slot is a valid value, and when the sensor node is not called in any time slot, the calling state information of the sensor node in the corresponding time slot is an invalid value; The energy status information is used to reflect the energy status of the battery equipped with the sensor node in each time slot, and the energy status information at least includes: the battery energy available value and the battery collection energy value; The channel state information is used to reflect the channel characteristic information of the transmission path between the sensor node and the aggregation node in each time slot, and the channel state information at least includes: a reception signal-to-noise ratio of the signal transmitted by the sensor node received by the aggregation node, a path distance of the transmission path, a path loss index, a channel coefficient and noise, wherein the reception signal-to-noise ratio is the product of the signal transmission power of the sensor node in the corresponding time slot and the square of the channel coefficient divided by the product of the Nth power of the path distance and the variance of the noise, and N is equal to the path loss index.

3. The method according to claim 2, characterized in that Determine multiple constraint conditions based on the hardware parameter information of the sensor node and the node status information in multiple time slots, including: The first constraint condition is that the signal-to-noise ratio of the signal transmitted by the sensor node received by the sink node in the target time slot to be solved is not lower than a preset signal-to-noise ratio threshold; The battery energy available value of the sensor node in the target time slot to be solved is the minimum value of the battery energy remaining value in the target time slot to be solved and the battery energy maximum value as the second constraint condition, wherein the battery energy remaining value is the sum of the battery energy available value and the battery collection energy value in the previous time slot of the target time slot to be solved minus the product of the calling state information of the sensor node in the target time slot to be solved, the duration of the target time slot to be solved, and the signal transmission power in the previous time slot of the target time slot to be solved; The signal transmission power of the sensor node in the target time slot to be solved is not higher than the quotient of the available battery energy value in the target time slot to be solved and the duration of the target time slot to be solved multiplied by the call state information in the target time slot to be solved as the third constraint condition; The calling state information of the sensor node in the middle time slot between two adjacent target time slots to be solved is an invalid value as a fourth constraint condition; The fifth constraint condition is that the number of intermediate time slots between the two adjacent target time slots to be solved is at least 1.

4. The method according to claim 3, characterized in that Based on the multiple constraints, a preset algorithm is used to solve the target scheduling problem to obtain a corresponding target scheduling strategy, including: Dividing the target time period into a plurality of sub-time periods of equal length in sequence, wherein each sub-time period includes at least one time slot; Dividing the target scheduling problem into the multiple sub-scheduling problems according to the multiple sub-time periods; Initialize the total number of violation events; Traverse each of the sub-scheduling problems in turn and perform the following steps: adjust the multiple constraints corresponding to the current sub-scheduling problem according to the target time slot and the total number of violation events corresponding to the previous sub-scheduling problem; solve the current sub-scheduling problem based on the adjusted multiple constraints to obtain the corresponding target time slot and the number of violation events, and determine the total number of violation events corresponding to the current sub-scheduling problem in combination with the total number of violation events corresponding to the previous sub-scheduling problem.

5. The method according to claim 4, characterized in that According to the target time slot and the total number of violation events corresponding to the previous sub-scheduling problem, multiple constraints corresponding to the current sub-scheduling problem are adjusted, including: Adjusting the fourth constraint condition and the fifth constraint condition corresponding to the current sub-scheduling problem according to the target time slot corresponding to the previous sub-scheduling problem; The second constraint condition and the third constraint condition corresponding to the current sub-scheduling problem are adjusted according to the total number of violation events corresponding to the target time slot corresponding to the previous sub-scheduling problem.

6. The method according to claim 5, characterized in that The second constraint condition and the third constraint condition corresponding to the current sub-scheduling problem are adjusted according to the total number of violation events corresponding to the target time slot corresponding to the previous sub-scheduling problem, including: Determine the number of violation events between the target time slot corresponding to the previous sub-scheduling problem and the target time slot to be solved corresponding to the current sub-scheduling problem; Determine the product of the signal transmission power in the target time slot to be solved corresponding to the current sub-scheduling problem, the signal-to-noise ratio threshold and the duration of the target time slot to be solved, and use the quotient of the obtained product result and the received signal-to-noise ratio in the target time slot to be solved corresponding to the current sub-scheduling problem as the battery energy cost in the target time slot to be solved corresponding to the current sub-scheduling problem; Calculate the difference between the remaining battery energy value in the target time slot corresponding to the previous sub-scheduling problem and the battery energy cost, and use the difference and the sum of the battery collected energy values ​​of each intermediate time slot between the target time slot to be solved corresponding to the current sub-scheduling problem and the target time slot corresponding to the previous sub-scheduling problem as the remaining battery energy value in the target time slot to be solved corresponding to the current sub-scheduling problem; The second constraint condition and the third constraint condition corresponding to the current sub-scheduling problem are adjusted according to the remaining battery energy value in the target time slot to be solved corresponding to the current sub-scheduling problem.

7. The method according to claim 6, characterized in that Determining the number of violation events between the target time slot corresponding to the previous sub-scheduling problem and the target time slot to be solved corresponding to the current sub-scheduling problem includes: Determine the number of intermediate time slots between the target time slot corresponding to the previous sub-scheduling problem and the target time slot to be solved for the current sub-scheduling problem; The ratio of the number of intermediate time slots to a preset time slot number threshold is used as the number of violation events between the target time slot corresponding to the previous sub-scheduling problem and the target time slot to be solved corresponding to the current sub-scheduling problem, wherein the time slot number threshold is the maximum number of time slots between two consecutive calls of the sensor node.

8. A sensor node scheduling device, characterized in that: include: An acquisition module, used to acquire hardware parameter information of sensor nodes in the wireless sensor network and node status information of the sensor nodes in multiple time slots within a target time period; A determination module, used to determine a target scheduling problem that minimizes the total number of violation events of the sensor node within a target time period and maximizes the available value of battery energy, and determines multiple constraint conditions based on hardware parameter information of the sensor node and node state information within multiple time slots; A scheduling module, used to solve the target scheduling problem based on the multiple constraints using a preset algorithm to obtain a corresponding target scheduling strategy, wherein the target scheduling strategy is used to stipulate that the data collected by the sensor node is reported to the aggregation node in the wireless sensor network within a target time slot; Among them, the preset algorithm includes: dividing the target scheduling problem into multiple sub-scheduling problems, and solving the sub-scheduling problem based on multiple constraints corresponding to the sub-scheduling problem to obtain a sub-scheduling strategy to update multiple sub-constraints corresponding to the next sub-scheduling problem.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a computer program, wherein the device where the non-volatile storage medium is located executes the sensor node scheduling method according to any one of claims 1 to 7 by running the computer program.

10. A computer program product, characterized in that include: A computer program, wherein when the computer program is executed by a processor, the sensor node scheduling method according to any one of claims 1 to 7 is implemented.

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