CN114760709B - LoRa node distribution model construction method, node distribution method and device - Google Patents

Abstract

The invention discloses a LoRa node allocation model construction method, a node allocation method and a device. The invention initializes the LoRa physical layer and determines the calculation method of each data amount in the model; according to the characteristics of the random access network, the data generated by the nodes As a Poisson distribution, according to the Poisson distribution and the calculation method of each data volume, determine the first mathematical model of the transmission success rate; construct the second mathematical model in which the output received power is affected by the transmission power of the signal and the path loss; according to the first The mathematical model and the second mathematical model determine the objective function in the third mathematical model; according to the objective function and the spreading factor, the third mathematical model is solved by a nonlinear programming method; finally the present invention can generate the target scene according to the third mathematical model According to the node allocation and node deployment scheme, the present invention can improve the reliability of node transmission, reduce the overall system power and maintenance cost, and can be widely used in the field of communication technology.

Description

LoRa node allocation model construction method, node allocation method and device

technical field

The invention relates to the field of communication technology, in particular to a method for constructing a LoRa node allocation model, a node allocation method and a device.

Background technique

With the development of communication and network technologies, the Internet of Things (IoT, Internet of Things) has received extensive attention and rapid development. Using network information technology and a large number of deployed sensors and nodes, the Internet of Things can monitor various environmental and sign information in real time, thereby reducing manual intervention, improving efficiency and economic benefits, so it has attracted extensive attention from academia and industry.

Therefore, low power wide area network (LPWAN) came into being. The working principle of LPWAN is similar to the existing cellular mobile network: the base station is responsible for providing coverage of wireless signals, while the access device realizes long-distance transmission under extremely low power consumption by adopting specific modulation methods, transmission rates and power. LoRa (Long Range) has become one of the most widely used LPWAN network technologies due to its advantages of high sensitivity, low power consumption, long-distance transmission, easy deployment, and long battery life. It has been successfully applied to such as target tracking, Water level monitoring, fire warning, smart city and other scenarios.

However, the traditional LoRa network cannot guarantee the fairness of information transmitted by each node, and the nodes cannot dynamically adjust the power according to the distance. This makes the power of the node itself always in a high state, which greatly increases its power consumption, requires frequent maintenance of the node, and the reliability of transmission cannot be guaranteed, which makes it difficult to use in UAV forest fires in the early warning system. This requires us to take into account the LoRa node allocation model of low power consumption and fairness.

On the one hand, the system power supply must not only supply power to the flight control system of the UAV to maintain the monitoring array, but also supply power to the sensing system and communication system and retain enough return energy.

On the other hand, since the gateway cannot receive signals with the same spreading factor at the same time, when nodes are densely deployed in the same network, the collision problem of signals with the same spreading factor will inevitably occur. This not only increases transmission energy consumption, reduces system power supply life, but also affects real-time and synchronization of data.

Contents of the invention

In view of this, the embodiments of the present invention provide a LoRa node allocation model construction method, node allocation method and device with low power consumption and high fairness.

One aspect of the present invention provides LoRa node allocation model construction method, including:

Initialize the configuration of the LoRa physical layer to determine the calculation method of each data volume in the model;

According to the characteristics of the random access network, the data generated by the nodes is used as a Poisson distribution, and according to the calculation method of the Poisson distribution and the amount of each data, determine the first mathematical model of the transmission success rate;

Constructing the second mathematical model that the output received power is affected by the transmitted power of the signal and the path loss;

According to the first mathematical model and the second mathematical model, determine an objective function in a third mathematical model that takes into account both low power consumption and fairness;

Solving the third mathematical model through a nonlinear programming method according to the objective function and the spreading factor.

Optionally, the LoRa physical layer is initialized and configured to determine the calculation method of each data volume in the model, including:

Randomly arrange the LoRa nodes in the LoRa monitoring network to obtain a monitoring array;

According to the monitoring array, configure the average sending time interval of the data packet length and the node;

According to the lowest frequency and the highest frequency of each node in the LoRa monitoring network, determine the network bandwidth;

The LoRa monitoring network encodes different information on different starting frequencies, and uses a spreading factor to represent the number of bits that can be encoded in each symbol;

After determining the start frequency type of the spreading factor, encoding the bit information of the spreading factor;

Based on the symbol rate and the bit rate of the spreading factor, the transmission time of the data packet is determined.

Optionally, according to the characteristics of the random access network, the data generated by the nodes is used as a Poisson distribution, and the first mathematical model of the transmission success rate is determined according to the Poisson distribution and the calculation method of each data amount, include:

Calculate the load according to the packet length of the node, the number of nodes and the average time interval;

Calculate the transmission success rate of each node according to the transmission time of the data packet and the load;

According to the spreading factor corresponding to each node, a first mathematical model of the transmission success rate of each node with the spreading factor selected is obtained.

Optionally, said constructing a second mathematical model that the output received power is affected by the transmitted power of the signal and the path loss includes:

When the received power of the signal is greater than the sensitivity of the receiving end, it is determined that the signal can be successfully received; the sensitivity of the receiving end depends on the bandwidth and spreading factor of the sending end;

Use the path loss model of logarithmic distance to determine the corresponding path loss;

The received power is determined according to the transmitted power of the signal and the path loss.

Optionally, in the step of determining the objective function in the third mathematical model that takes into account both low power consumption and fairness according to the first mathematical model and the second mathematical model, the expression of the objective function for:

Among them, α represents the weight of power consumption; P _tx,i represents the minimum transmit power when the spreading factor SF _i is selected to ensure the gateway receives it; P _s,i represents the transmission success rate of the selected spreading factor SF _i ; β Represents the weight of unfairness; minQ represents the minimized objective function value.

Another aspect of the embodiments of the present invention also provides a LoRa node allocation method, including:

obtaining a third mathematical model as previously described;

According to the third mathematical model, a node allocation and a node deployment scheme in a target scenario are generated.

Another aspect of the embodiments of the present invention also provides a LoRa node allocation model construction device, including:

The first module is used to initialize the configuration of the LoRa physical layer and determine the calculation method of each data volume in the model;

The second module is used to use the data generated by the nodes as a Poisson distribution according to the characteristics of the random access network, and determine the first mathematical model of the transmission success rate according to the Poisson distribution and the calculation method of each data volume;

The third module is used to construct a second mathematical model that the output received power is affected by the transmitted power of the signal and the path loss;

A fourth module, configured to determine an objective function in a third mathematical model that takes into account low power consumption and fairness according to the first mathematical model and the second mathematical model;

The fifth module is configured to solve the third mathematical model through a nonlinear programming method according to the objective function and the spreading factor.

Another aspect of the embodiments of the present invention also provides a LoRa node allocation device, including:

The sixth module is used to obtain the third mathematical model as described above;

The seventh module is configured to generate node allocation and node deployment schemes in the target scenario according to the third mathematical model.

Another aspect of the embodiments of the present invention also provides an electronic device, including a processor and a memory;

The memory is used to store programs;

The processor executes the program to implement the method as described above.

Another aspect of the embodiments of the present invention also provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the aforementioned method.

The embodiment of the present invention also discloses a computer program product or computer program, where the computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the 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 above method.

The embodiment of the present invention carries out initial configuration to LoRa physical layer, determines the calculation mode of each data amount in the model; The calculation method of each data amount determines the first mathematical model of the transmission success rate; constructs the second mathematical model that the output received power is affected by the transmission power of the signal and the path loss; according to the first mathematical model and the second mathematical model model, determine the objective function in the third mathematical model that takes into account both low power consumption and fairness; according to the objective function and the spreading factor, solve the third mathematical model through a nonlinear programming method; finally, the present invention can solve the third mathematical model according to the The third mathematical model is used to generate node allocation and node deployment schemes in the target scenario. The present invention can improve the reliability of node transmission, reduce the overall power of the system, and reduce the maintenance cost of the system.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, other drawings can also be obtained based on these drawings without creative effort.

FIG. 1 is a schematic diagram of an implementation environment in a specific application scenario provided by an embodiment of the present invention;

Fig. 2 is a flow chart of steps provided by the embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

One aspect of the present invention provides LoRa node allocation model construction method, including:

Initialize the configuration of the LoRa physical layer to determine the calculation method of each data volume in the model;

According to the characteristics of the random access network, the data generated by the nodes is used as a Poisson distribution, and according to the calculation method of the Poisson distribution and the amount of each data, determine the first mathematical model of the transmission success rate;

Constructing the second mathematical model that the output received power is affected by the transmitted power of the signal and the path loss;

According to the first mathematical model and the second mathematical model, determine an objective function in a third mathematical model that takes into account both low power consumption and fairness;

Solving the third mathematical model through a nonlinear programming method according to the objective function and the spreading factor.

Optionally, the LoRa physical layer is initialized and configured to determine the calculation method of each data volume in the model, including:

Randomly arrange the LoRa nodes in the LoRa monitoring network to obtain a monitoring array;

According to the monitoring array, configure the average sending time interval of the data packet length and the node;

According to the lowest frequency and the highest frequency of each node in the LoRa monitoring network, determine the network bandwidth;

The LoRa monitoring network encodes different information on different starting frequencies, and uses a spreading factor to represent the number of bits that can be encoded in each symbol;

After determining the start frequency type of the spreading factor, encoding the bit information of the spreading factor;

Based on the symbol rate and the bit rate of the spreading factor, the transmission time of the data packet is determined.

Optionally, according to the characteristics of the random access network, the data generated by the nodes is used as a Poisson distribution, and the first mathematical model of the transmission success rate is determined according to the Poisson distribution and the calculation method of each data amount, include:

Calculate the load according to the packet length of the node, the number of nodes and the average time interval;

Calculate the transmission success rate of each node according to the transmission time of the data packet and the load;

According to the spreading factor corresponding to each node, a first mathematical model of the transmission success rate of each node with the spreading factor selected is obtained.

Optionally, said constructing a second mathematical model that the output received power is affected by the transmitted power of the signal and the path loss includes:

When the received power of the signal is greater than the sensitivity of the receiving end, it is determined that the signal can be successfully received; the sensitivity of the receiving end depends on the bandwidth and spreading factor of the sending end;

Use the path loss model of logarithmic distance to determine the corresponding path loss;

The received power is determined according to the transmitted power of the signal and the path loss.

Optionally, in the step of determining the objective function in the third mathematical model that takes into account both low power consumption and fairness according to the first mathematical model and the second mathematical model, the expression of the objective function for:

Among them, α represents the weight of power consumption; P _tx,i represents the minimum transmit power when the spreading factor SF _i is selected to ensure the gateway receives it; P _s,i represents the transmission success rate of the selected spreading factor SF _i ; β Represents the weight of unfairness; minQ represents the minimized objective function value.

Another aspect of the embodiments of the present invention also provides a LoRa node allocation method, including:

obtaining a third mathematical model as previously described;

According to the third mathematical model, a node allocation and a node deployment scheme in a target scenario are generated.

Another aspect of the embodiments of the present invention also provides a LoRa node allocation model construction device, including:

The first module is used to initialize the configuration of the LoRa physical layer and determine the calculation method of each data volume in the model;

The second module is used to use the data generated by the nodes as a Poisson distribution according to the characteristics of the random access network, and determine the first mathematical model of the transmission success rate according to the Poisson distribution and the calculation method of each data volume;

The third module is used to construct a second mathematical model that the output received power is affected by the transmitted power of the signal and the path loss;

A fourth module, configured to determine an objective function in a third mathematical model that takes into account low power consumption and fairness according to the first mathematical model and the second mathematical model;

The fifth module is configured to solve the third mathematical model through a nonlinear programming method according to the objective function and the spreading factor.

Another aspect of the embodiments of the present invention also provides a LoRa node allocation device, including:

The sixth module is used to obtain the third mathematical model as described above;

The seventh module is configured to generate node allocation and node deployment schemes in the target scenario according to the third mathematical model.

Another aspect of the embodiments of the present invention also provides an electronic device, including a processor and a memory;

The memory is used to store programs;

The processor executes the program to implement the method as described above.

Another aspect of the embodiments of the present invention also provides a computer-readable storage medium, where the storage medium stores a program, and the program is executed by a processor to implement the aforementioned method.

The embodiment of the present invention also discloses a computer program product or computer program, where the computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the 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 above method.

Below in conjunction with accompanying drawing of description, the specific implementation principle of the present invention is described in detail:

Aiming at the problems existing in the prior art, the present invention provides a LoRa node allocation model that takes into account both low power consumption and fairness. This model can be used in LoRa node allocation, thereby improving the reliability of node transmission, reducing the overall power of the system and reducing the maintenance cost of the system.

Specifically, the method of the present invention comprises the following steps:

S1: Make basic assumptions on the LoRa physical layer, and give the calculation method of the quantity used in the model;

S2: According to the characteristics of the random access network, the data generated by the node under the load λ can be regarded as a Poisson distribution. Considering that there is only one spreading factor SF for data transmission, when there is no data during [-T,T] When transferring, the data can reach the gateway successfully. According to the Poisson distribution, combined with the data formula in S1, the mathematical model of the transmission success rate P _s is given;

S3: Give the mathematical model that the input received power P _rx is affected by the transmitted power P _tx of the signal and the path loss L _p ;

S4: According to the mathematical models in S2 and S3, give the objective function Q in the mathematical model that takes into account both low power consumption and fairness;

S5: According to the ratio sum of the spreading factor SF being 1, combined with the objective function in S4, a mathematical model that takes into account both low power consumption and fairness is given, and a nonlinear programming method (such as penalty function method, evolutionary algorithm, particle swarm algorithm) is used etc.) to solve the nonlinear constraint model and give a deployment scheme.

S6: In the following calculations, specific calculation results are given for specific deployment scenarios, and it can be proved that the node allocation ratio is roughly the same when only the number of nodes is changed.

S7: Specifically, the deployment scenario in S6 is the UAV forest fire early warning system. After deployment according to this model, the reliability, real-time and synchronization of data transmission can be guaranteed while greatly improving the service life of the system.

Further, the basic assumptions and calculation methods in the step S1 include:

A LoRa monitor consists of N LoRa nodes and 1 LoRa gateway. The nodes are randomly arranged in a monitoring array, and the network is in a star topology. The length of the data packets to be sent is L, the average sending time interval is T _avg , and the sending time is evenly distributed on the time interval T _avg , which conforms to the characteristics of a random access network (Random Access Network). Assume that all nodes in the network are reachable at the lowest rate (that is, the highest spreading factor and highest sensitivity), the bandwidth BW is consistent with the code rate CR, and the transmission channel is the same.

BW＝ _fmax - _fmin

Among them, f _max represents the highest frequency; f _min represents the lowest frequency; BW represents the broadband, that is, the difference between the maximum frequency and the minimum frequency.

LoRa encodes different information on different start frequencies, and uses a spreading factor (SF, SpreadingFactor) to represent the number of bits that can be encoded in each symbol. For a LoRa node with a spreading factor of SF, one symbol has 2 different starting frequencies of ^SF , so it can encode SF bit information. And, because of the different spreading factors are linear. In spread spectrum modulation, it is orthogonal, so the gateway can simultaneously demodulate signals with different spreading factors. So the value range of SF is as follows:

SF ∈ {7, 8, 9, 10, 11, 12}

For a node with a spreading factor of SF, the symbol rate R _s of LoRa is

Wherein, BW is the bandwidth of the signal, BW∈{125, 250, 500} (unit: kHz).

SF means that there is SF bit information in a symbol, so the bit rate R _b is:

Among them, CR represents code rate.

For a data packet with a length of L bits, the transmission time T of the data packet is:

Further, the mathematical model in step S2 is derived as:

First, according to the Poisson distribution, the transmission success rate _PS is:

P _S ＝P(X>2T)＝e ^-2λT

Wherein, λ represents the load, and T represents the time required to send a data packet of length L under the spreading factor SF.

The load λ is:

Among them, L represents the length of the data packet, N represents the number of nodes, and T _avg represents the average time interval. According to S1, it is easy to get T as:

Based on the above formula, the transmission success rate P _S can be obtained:

Then analyze the success rate of each node:

For nodes that can choose any spreading factor for transmission network, if the ratio of a certain spreading factor SF _i is represented by R _i , since each node can only choose one spreading factor, the ratio of all spreading factors and is 1, that is:

Then the load λ _i is:

λ _i =R _i λ

Then the transmission time T _i is:

Combining the above formulas, the mathematical model of the transmission success rate P _s of each node with the spread spectrum factor SF _i obtained is:

Specifically, the mathematical model derivation method of step S3 is:

When the received power P _rx of the signal is greater than the sensitivity P _sens of the receiving end, the signal can be successfully received. The receiving sensitivity P _sens is related to the bandwidth BW and spreading factor SF selected by the sending end. The sensitivity P _sens is given by LoRa official, as shown in Table 1. Table 1 describes the relationship between sensitivity, spreading factor and bandwidth:

Table 1

The received power P _rx is affected by the transmitted power P _tx of the signal and the path loss L _P , expressed as:

P _rx =P _tx +GL-L _P

Among them, GL represents the influence of other factors, such as the gain of the antenna, the influence brought by the non-impedance matching circuit, etc. However, for the same network, the same equipment is generally used for deployment, so for the transmission of the same network, the value of GL is basically kept at the same level.

For the path loss L _P , the path loss model of the logarithmic distance is adopted, and the path loss L _P (d) at a distance d from the gateway is:

表示在参考距离d0的平均路径损耗，γ表示路径损耗指数，X_σ表示信道的随机衰减，用满足标准差σ的高斯分布表示。in,

Represents the average path loss at the reference distance d0, γ represents the path loss index, and X _σ represents the random attenuation of the channel, represented by a Gaussian distribution that satisfies the standard deviation σ.

Specifically, the target model of step S4 is:

Among them, α represents the weight of power consumption; P _tx,i represents the minimum transmit power when the spreading factor SF _i is selected to ensure that the gateway receives it, which can be determined according to step S3; P _s,i represents the selection of the spreading factor SF _i The transmission success rate of is given by step S2; β represents the unfairness weight; N represents the total number of nodes. Therefore, the first term of the objective function represents the failure transmission power under the weighted condition, and the second term represents the sum of squares of the probability difference under the weighted condition, and thus represents the degree of unfairness.

Specifically, the nonlinear constraint model of step S5 is:

Taking a specific application scenario as an example, the specific implementation process of the method of the present invention is described in detail below:

Suppose there are 60 nodes in this embodiment, the length L of the data packet to be transmitted is 20*8bit, the code rate CR is 0.8, the bandwidth BW is 125kHz, and the frequency modulation factor SF can be {7, 8, 9, 10, 11, 12} , the transmission power P _tx can take {2, 5, 8, 11, 14, 17} dBm, the schematic diagram of the problem is shown in Figure 1, and the solution is performed according to the flow of the method of the present invention (as shown in Figure 2).

以及比特率

以及数据包的传输时间

平均时间间隔T_avg＝60s。Step 1: According to the calculation method of LoRa quantity, the symbol rate of each spreading factor can be given

and bitrate

and the transmission time of the packet

The average time interval T _avg =60s.

Step 2: Give the mathematical model of the transmission success rate P _s,i of each node with the spread spectrum factor SF _i selected:

负载

Among them, R _i is the ratio of a certain spreading factor

load

Step 3: Give the power mathematical model:

P _rx =P _tx +GL-L _P

Step 4: Give the optimization objective function Q:

Here, in this embodiment, it is considered that fairness and power are equally important, and α=1, β=1.

Step 5: Give a mathematical model that takes into account low power consumption and fairness:

Then, the nonlinear programming method is used to solve this model, and the spreading factor SF is obtained as shown in Table 2:

Table 2

SF 7 8 9 10 11 12 R_i 0.2179 0.2015 0.1803 0.1538 0.1287 0.1178

In summary, the present invention initializes the LoRa physical layer to determine the calculation method of each data volume in the model; according to the characteristics of the random access network, the data generated by the node is used as a Poisson distribution, and according to the Poisson distribution and The calculation method of each data volume is to determine the first mathematical model of the transmission success rate; construct the second mathematical model that the output received power is affected by the transmission power of the signal and the path loss; according to the first mathematical model and the first mathematical model Two mathematical models, determining the objective function in the third mathematical model that takes into account both low power consumption and fairness; according to the objective function and the spreading factor, solve the third mathematical model through a nonlinear programming method; finally, the present invention can According to the third mathematical model, the node allocation and node deployment schemes in the target scene are generated, and the present invention can improve the reliability of node transmission, reduce the power of the whole system and reduce the maintenance cost of the system.

In some alternative implementations, the functions/operations noted in the block diagrams may occur out of the order noted in the operational diagrams. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/operations involved. Furthermore, the embodiments presented and described in the flowcharts of the present invention are provided by way of example in order to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logical flow presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are performed independently.

Furthermore, although the invention has been described in the context of functional modules, it should be understood that one or more of the described functions and/or features may be integrated into a single physical device and/or unless stated to the contrary. or software modules, or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to understand the present invention. Rather, given the attributes, functions and internal relationships of the various functional blocks in the devices disclosed herein, the actual implementation of the blocks will be within the ordinary skill of the engineer. Accordingly, those skilled in the art can implement the present invention set forth in the claims without undue experimentation using ordinary techniques. It is also to be understood that the particular concepts disclosed are illustrative only and are not intended to limit the scope of the invention which is to be determined by the appended claims and their full scope of equivalents.

If the functions described above are realized in the form of software function units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the essence of 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, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments 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), magnetic disk or optical disk and other media that can store program codes. .

The logic and/or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced listing of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium, For use with instruction execution systems, devices, or devices (such as computer-based systems, systems including processors, or other systems that can fetch instructions from instruction execution systems, devices, or devices and execute instructions), or in conjunction with these instruction execution systems, devices or equipment for use. For the purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate or transmit a program for use in or in conjunction with an instruction execution system, device or device.

More specific examples (non-exhaustive list) of computer-readable media include the following: electrical connection with one or more wires (electronic device), portable computer disk case (magnetic device), random access memory (RAM), Read Only Memory (ROM), Erasable and Editable Read Only Memory (EPROM or Flash Memory), Fiber Optic Devices, and Portable Compact Disc Read Only Memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program can be printed, since the program can be read, for example, by optically scanning the paper or other medium, followed by editing, interpretation or other suitable processing if necessary. The program is processed electronically and stored in computer memory.

It should be understood that various parts of the present invention can be realized by hardware, software, firmware or their combination. In the embodiments described above, various steps or methods may be implemented by software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented by any one or combination of the following techniques known in the art: Discrete logic circuits, ASICs with suitable combinational logic gates, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

In the description of this specification, descriptions referring to the terms "one embodiment", "some embodiments", "example", "specific examples", or "some examples" mean that specific features described in connection with the embodiment or example , structure, material or characteristic is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the invention is defined by the claims and their equivalents.

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, and those skilled in the art can also make various equivalent deformations or replacements without violating the spirit of the present invention. These equivalent modifications or replacements are all within the scope defined by the claims of the present application.

Claims (9)

1. LoRa node allocation model construction method is characterized in that, comprising: Initialize the configuration of the LoRa physical layer to determine the calculation method of each data volume in the model; According to the characteristics of the random access network, the data generated by the nodes is regarded as a Poisson distribution, and the first mathematical model of the transmission success rate is determined according to the Poisson distribution and the calculation method of each data volume; wherein, the random access The characteristics of the incoming network include: node load, data packet transmission time; Constructing the second mathematical model that the output received power is affected by the transmitted power of the signal and the path loss; According to the first mathematical model and the second mathematical model, determine an objective function in a third mathematical model that takes into account both low power consumption and fairness; wherein, the expression of the objective function is:

Among them, α represents the weight of power consumption; P _tx,i represents the minimum transmit power when the spreading factor SF _i is selected to ensure the gateway receives it; P _s,i represents the transmission success rate of the selected spreading factor SF _i ; β Represents the weight of unfairness; minQ represents the minimized objective function value; Solving the third mathematical model through a nonlinear programming method according to the objective function and the spreading factor. 2. LoRa node allocation model construction method according to claim 1, is characterized in that, described LoRa physical layer is carried out initialization configuration, determines the computing mode of each data amount in the model, comprises: Randomly arrange the LoRa nodes in the LoRa monitoring network to obtain a monitoring array; According to the monitoring array, configure the average sending time interval of the data packet length and the node; According to the lowest frequency and the highest frequency of each node in the LoRa monitoring network, determine the network bandwidth; The LoRa monitoring network encodes different information on different starting frequencies, and uses a spreading factor to represent the number of bits that can be encoded in each symbol; After determining the start frequency type of the spreading factor, encoding the bit information of the spreading factor; Based on the symbol rate and the bit rate of the spreading factor, the transmission time of the data packet is determined. 3. LoRa node distribution model construction method according to claim 1, is characterized in that, described according to the characteristic of random access network, the data that node produces is as Poisson distribution, according to described Poisson distribution and described each The calculation method of the amount of data, the first mathematical model to determine the transmission success rate, including: Calculate the load according to the packet length of the node, the number of nodes and the average time interval; Calculate the transmission success rate of each node according to the transmission time of the data packet and the load; According to the spreading factor corresponding to each node, a first mathematical model of the transmission success rate of each node with the spreading factor selected is obtained. 4. LoRa node allocation model construction method according to claim 1, is characterized in that, the second mathematical model that the described construction output receiving power is affected by the transmitting power of signal and path loss, comprises: When the received power of the signal is greater than the sensitivity of the receiving end, it is determined that the signal can be successfully received; the sensitivity of the receiving end depends on the bandwidth and spreading factor of the sending end; Use the path loss model of logarithmic distance to determine the corresponding path loss; The received power is determined according to the transmitted power of the signal and the path loss. 5. LoRa node distribution method, is characterized in that, comprises: Obtain the third mathematical model as described in any one of claims 1-4; According to the third mathematical model, a node allocation and a node deployment scheme in a target scenario are generated. 6.LoRa node allocation model construction device is characterized in that, comprising: The first module is used to initialize the configuration of the LoRa physical layer and determine the calculation method of each data volume in the model; The second module is used to use the data generated by the nodes as a Poisson distribution according to the characteristics of the random access network, and determine the first mathematical model of the transmission success rate according to the Poisson distribution and the calculation method of each data volume; Wherein, the characteristics of the random access network include: node load, transmission time of data packets; The third module is used to construct a second mathematical model that the output received power is affected by the transmitted power of the signal and the path loss; The fourth module is configured to determine an objective function in a third mathematical model that takes into account both low power consumption and fairness according to the first mathematical model and the second mathematical model; wherein, the expression of the objective function is:

Among them, α represents the weight of power consumption; P _tx,i represents the minimum transmit power when the spreading factor SF _i is selected to ensure the gateway receives it; P _s,i represents the transmission success rate of the selected spreading factor SF _i ; β Represents the weight of unfairness; minQ represents the minimized objective function value; The fifth module is configured to solve the third mathematical model through a nonlinear programming method according to the objective function and the spreading factor. 7.LoRa node allocation device is characterized in that, comprising: A sixth module, configured to obtain the third mathematical model according to any one of claims 1-4; The seventh module is configured to generate node allocation and node deployment schemes in the target scenario according to the third mathematical model. 8. An electronic device, comprising 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 4 or claim 5. 9. A computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the method according to any one of claims 1 to 4 or claim 5.

Images