CN111800829B - LoRaWAN communication self-adaptive rate adjustment method, system and network server - Google Patents
LoRaWAN communication self-adaptive rate adjustment method, system and network server Download PDFInfo
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- CN111800829B CN111800829B CN202010637335.XA CN202010637335A CN111800829B CN 111800829 B CN111800829 B CN 111800829B CN 202010637335 A CN202010637335 A CN 202010637335A CN 111800829 B CN111800829 B CN 111800829B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
- H04W28/18—Negotiating wireless communication parameters
- H04W28/22—Negotiating communication rate
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a self-adaptive rate adjustment method, a system and a network server for LoRaWAN communication, wherein the method comprises the steps of obtaining the signal-to-noise ratio of a wireless signal, filtering the obtained signal-to-noise ratio by adopting a one-dimensional Kalman filtering algorithm, and dynamically adjusting related parameters in the one-dimensional Kalman filtering algorithm according to environmental changes to obtain an optimal estimated value of the signal-to-noise ratio; and carrying out hierarchical speed regulation according to the optimal estimated value of the signal-to-noise ratio, and controlling the upward speed regulation request in the hierarchical speed regulation process through a preset upward speed regulation rule. The filter can adapt to environmental changes by dynamically adjusting relevant parameters in the Kalman filter, so that the environmental changes can be tracked in real time, and the optimal speed is obtained; meanwhile, the upward speed regulation time and the upward speed regulation grade span in the hierarchical speed regulation process are controlled by using a preset hierarchical speed regulation rule, so that the speed regulation process is more gentle, the packet loss rate is reduced, the communication quality is more reliable, and the communication quality and the power consumption are both considered.
Description
Technical Field
The invention relates to the technical field of the Internet of things, in particular to a LoRaWAN communication self-adaptive rate adjustment method, a system and a network server.
Background
With the rise of the internet of things, the lorewan is an emerging high-efficiency stable LPWAN (low-power consumption wide area network) technology, and has been widely used in products of various industries. In the LoRaWAN communication system, the ADR rate adjusting function is used for dynamically adjusting the uplink rate of the terminal node, so that the performance of the whole system is optimal, and therefore, the speed regulating algorithm has a crucial influence on the performances of the system, such as power consumption, communication quality and the like.
The currently mainstream ADR rate adjustment manner is basically to perform hierarchical speed adjustment according to the SNR (SIGNAL NOISE RATIO, signal-to-noise ratio) of the signal, and a specific example can be seen in the following table 1, that is, under the premise of ensuring the current rate link budget, the optimal rate is calculated according to the margin.
Table 1 SNR example of staged speed regulation
It is not difficult to find that under the condition of environmental interference, SNR fluctuates greatly, the existing speed regulation technology can cause frequent speed regulation, the specific gravity of the communication bandwidth occupied by speed regulation is large, and the normal communication of a user is adversely affected. And the speed regulation is not smooth enough, so that the system is easy to lose connection, the power consumption cannot be optimized, and the method is difficult to popularize in practical application.
Therefore, how to provide an efficient and reliable adaptive rate adjustment method for the lorewan communication is a problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a self-adaptive rate adjustment method, a system and a network server for LoRaWAN communication, which can greatly ensure that the system is not in disconnection and simultaneously has optimal overall power consumption, and solve the problems that the existing speed regulation method is poor in anti-interference capability, frequent in speed regulation of the whole life cycle of the system, severe in amplitude, easy in disconnection of the system and meanwhile cannot achieve optimal power consumption.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for adjusting an adaptive rate of a lorewan communication, the method comprising the steps of:
acquiring a signal-to-noise ratio of a wireless signal, filtering the acquired signal-to-noise ratio by adopting a one-dimensional Kalman filtering algorithm, and dynamically adjusting related parameters in the one-dimensional Kalman filtering algorithm according to environmental changes to obtain an optimal estimated value of the signal-to-noise ratio;
and carrying out hierarchical speed regulation according to the optimal estimated value of the signal-to-noise ratio, and controlling an upward speed regulation request in the hierarchical speed regulation process through a preset upward speed regulation rule.
Further, the process of dynamically adjusting the relevant parameters in the one-dimensional Kalman filtering algorithm according to the environmental change specifically comprises:
presetting the minimum values of the variance and standard deviation of the optimal estimated value;
when the difference between the current N signal-to-noise ratio measurement average value and the optimal estimated value exceeds a preset threshold value, setting the measurement noise standard deviation as a minimum value;
when the difference between the current N signal-to-noise ratio measurement average value and the optimal estimated value continuously exceeds the preset threshold value for a set number of times, the initial input rate and the signal-to-noise ratio of the wireless signal are set as minimum values.
Further, the preset upward speed regulation rule includes:
maintaining an upward speed regulation request counter, and increasing the upward speed regulation request counter by 1 when the calculated target speed is greater than the current speed;
when the current measured value is larger than the optimal estimated value and the upward speed regulation request counter exceeds a preset counting threshold value, allowing upward speed regulation;
the maximum level span of upward speed regulation is 2 levels.
In a second aspect, the invention also provides a LoRaWAN communication self-adaptive rate adjustment system, which comprises a filtering processing module and a hierarchical speed regulation module;
the filtering processing module is used for acquiring the signal-to-noise ratio of the wireless signal, filtering the acquired signal-to-noise ratio by adopting a one-dimensional Kalman filtering algorithm, and dynamically adjusting related parameters in the one-dimensional Kalman filtering algorithm according to environmental changes to obtain an optimal estimated value of the signal-to-noise ratio;
the hierarchical speed regulation module is used for carrying out hierarchical speed regulation according to the optimal estimated value of the signal to noise ratio obtained by the filtering processing module, and controlling an upward speed regulation request in the hierarchical speed regulation process through a preset upward speed regulation rule.
Further, the filtering processing module dynamically adjusts relevant parameters in the one-dimensional Kalman filtering algorithm according to environmental changes, and the following steps are executed:
presetting the minimum values of the variance and standard deviation of the optimal estimated value;
when the difference between the current N signal-to-noise ratio measurement average value and the optimal estimated value exceeds a preset threshold value, setting the measurement noise standard deviation as a minimum value;
when the difference between the current N signal-to-noise ratio measurement average value and the optimal estimated value continuously exceeds the preset threshold value for a set number of times, the initial input rate and the signal-to-noise ratio of the wireless signal are set as minimum values.
Further, the upward speed regulation rule executed by the hierarchical speed regulation module includes:
maintaining an upward speed regulation request counter, and increasing the upward speed regulation request counter by 1 when the calculated target speed is greater than the current speed;
when the current measured value is larger than the optimal estimated value and the upward speed regulation request counter exceeds a preset counting threshold value, allowing upward speed regulation;
the maximum level span of upward speed regulation is 2 levels.
In a third aspect, the present invention also provides a network server, the network server including a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to execute the computer program stored in the memory, so that the network server executes a method for adaptive rate adjustment of lorewan communication as described above.
Compared with the prior art, the invention discloses a method, a system and a network server for adjusting the self-adaptive rate of LoRaWAN communication, wherein the filter can adapt to environmental changes by dynamically adjusting relevant parameters in a Kalman filter, so that the environmental changes can be tracked in real time, and the optimal rate can be obtained; meanwhile, the upward speed regulation time and the upward speed regulation grade span in the hierarchical speed regulation process are controlled by using a preset hierarchical speed regulation rule, so that the speed regulation process is more gentle, the packet loss rate is reduced, the communication quality is more reliable, and the communication quality and the power consumption are both considered.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a self-adaptive rate adjustment method for the lorewan communication provided by the invention;
FIG. 2 is a schematic diagram showing the step response effect of the filter before and after parameter adjustment in an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a adaptive rate adjustment system for lorewan communication according to the present invention;
fig. 4 is a schematic structural diagram of a network server according to the present invention;
fig. 5 is a schematic diagram of a data packet transmission sequence in an embodiment of the present invention;
fig. 6 is a schematic diagram of packet loss rate at each rate in the embodiment of the present invention;
FIG. 7 is a schematic diagram of the data output after the collected data is imported into the algorithm in the simulation test stage according to the embodiment of the present invention;
fig. 8 is a schematic diagram of an analysis result based on actual measurement performed by a certain LoRaWAN temperature and humidity sensor in the field in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In a first aspect, referring to fig. 1, an embodiment of the present invention discloses a method for adjusting an adaptive rate of a lorewan communication, the method includes the following steps:
s1: acquiring a signal-to-noise ratio of a wireless signal, filtering the acquired signal-to-noise ratio by adopting a one-dimensional Kalman filtering algorithm, and dynamically adjusting related parameters in the one-dimensional Kalman filtering algorithm according to environmental changes to obtain an optimal estimated value of the signal-to-noise ratio;
s2: and carrying out hierarchical speed regulation according to the optimal estimated value of the signal-to-noise ratio, and controlling an upward speed regulation request in the hierarchical speed regulation process through a preset upward speed regulation rule.
Since the SNR of a wireless signal is easily interfered by the environment, the acquired SNR needs to be filtered to calculate an optimal estimated value.
In the lowwan system, the number of uplink data packets of each terminal product is generally small in the whole life cycle, and the uplink period is not fixed, so that the effect of a commonly used frequency domain filtering algorithm (such as low-pass filtering) is limited. Considering that the SNR behaves the same at each rate (which has been verified by sweep testing) with a relatively stable environment, and that the ambient noise can be approximated as gaussian white noise. Therefore, the optimal estimation algorithm can adopt a one-dimensional Kalman filtering algorithm which is more practical in industry. The principle of the one-dimensional kalman filter algorithm is as follows:
predictive value x 0 [k]Uncertainty alpha 0 [k]；
Real measurement x 1 [k]Uncertainty alpha 1 [k]。
The optimal estimate is as follows:
x[k]＝x 0 [k]+w[k](x 1 [k]-x[0]) Wherein the weight coefficient
α 2 [k]＝w[k]×α 1 [k] 2 ＝(1-w[k])×α 0 [k] 2
Since the entire environment can be considered relatively stable after the installation of the LoRaWAN system is completed, the prediction model can be established as follows:
the last optimal estimate of SNR is used as the current predictor of the kalman filter.
However, in practical application, the environment cannot be unchanged, but can only be said to be stable for a period of time, the above prediction model has serious hysteresis and poor dynamic performance. In order to increase the robustness of the prediction model, a kalman filter is required to be able to adapt to environmental changes. Therefore, an environment detection function needs to be added in the Kalman filter, and the specific implementation is to dynamically adjust relevant parameters in the Kalman filter, and the specific contents are as follows:
presetting a range of an optimal estimated value variance and a standard deviation; the minimum values of the variance and standard deviation of the optimal estimated value are mainly limited, because the smaller the value is, the higher the weight of the optimal estimated value is, and the filtering effect cannot quickly follow the environmental change.
When the difference between the current N times of SNR measurement average value and the optimal estimated value exceeds a preset threshold value, setting the measurement noise standard deviation as a minimum value;
when the difference between the average value of the current N SNR measurements and the optimal estimated value continuously exceeds the preset threshold value M times, the Kalman filter is reset ((i.e. the initial input rate and SNR are set as minimum values, and for the LoRaWAN system, the initial values of the rate and SNR can be respectively selected from DR0 and-20).
The effect of the step response of the filter before parameter adjustment is shown in fig. 2 (a), and the effect of the step response of the filter after parameter adjustment is shown in fig. 2 (b). Origin_snr in the figure represents the original SNR value, kalman_snr represents the SNR value of the filter output. As is obvious from the comparison of the two graphs, the dynamic performance of the filter after parameter adjustment is remarkably improved.
After the Kalman filtering is performed in the last step, the SNR is stable, and most of requirements can be met by combining the existing hierarchical speed regulation scheme. However, there are still severe cases of speed regulation, such as changing directly from DR0 to DR5, and especially when the environment changes from excellent to very poor, there is a high possibility that the nodes are disconnected. In addition, SNR can cause frequent speed regulation when the critical zone of two rates fluctuates, affecting the normal communication of users.
In order to avoid the occurrence of the above situation, the existing step-by-step speed regulation scheme needs to be optimized, namely, the larger the speed is, the higher the damping coefficient is, and the specific scheme is as follows:
maintaining an upward speed regulation request counter, and increasing the upward speed regulation request counter by 1 when the calculated target speed is greater than the current speed;
when the current measured value is larger than the optimal estimated value and the upward speed regulation request counter exceeds a preset counting threshold value, upward speed regulation can be performed;
the maximum level span of upward speed regulation is limited to 2 levels.
In a second aspect, referring to fig. 3, the embodiment of the present invention further discloses a system for adaptive rate adjustment of lorewan communication, where the system includes a filtering processing module 11 and a hierarchical speed regulation module 12;
the filtering processing module 11 is used for obtaining the signal-to-noise ratio of the wireless signal, filtering the obtained signal-to-noise ratio by adopting a one-dimensional Kalman filtering algorithm, and dynamically adjusting related parameters in the one-dimensional Kalman filtering algorithm according to environmental changes to obtain an optimal estimated value of the signal-to-noise ratio;
the hierarchical speed regulation module 12 is used for performing hierarchical speed regulation according to the optimal estimated value of the signal to noise ratio obtained by the filtering processing module 11, and controlling an upward speed regulation request in the hierarchical speed regulation process through a preset upward speed regulation rule.
In a specific embodiment, the filtering processing module dynamically adjusts relevant parameters in the one-dimensional kalman filtering algorithm according to environmental changes, and performs the following steps:
presetting the ranges of the variance and standard deviation of the optimal estimated value (respectively limiting the minimum value of the variance and the standard deviation);
when the difference between the current N signal-to-noise ratio measurement average value and the optimal estimated value exceeds a preset threshold value, setting the measurement noise standard deviation as a minimum value;
when the difference between the current N signal-to-noise ratio measurement average value and the optimal estimated value continuously exceeds the preset threshold value for a set number of times, the initial input rate and the signal-to-noise ratio of the wireless signal are set as minimum values.
In one particular embodiment, the upward pacing rules performed by the hierarchical pacing module include:
maintaining an upward speed regulation request counter, and increasing the upward speed regulation request counter by 1 when the calculated target speed is greater than the current speed;
when the current measured value is larger than the optimal estimated value and the upward speed regulation request counter exceeds a preset counting threshold value, allowing upward speed regulation;
the maximum level span of upward speed regulation is 2 levels.
In a third aspect, referring to fig. 4, the embodiment of the present invention further discloses a web server, where the web server includes a processor 21 and a memory 22;
the memory 22 is for storing a computer program;
the processor 21 is configured to execute a computer program stored in the memory 22, so that the network server performs a method for adaptive rate adjustment of the lorewan communication described above.
The following simulation tests were performed on the above scheme:
in order to accurately reflect the communication conditions of different rates in a period of time, the data collection adopts a sweep frequency mode, and the specific method is as follows:
the terminal device loops transmitting data packets at different rates.
Assuming that the node uplink rate supports DR0-DR5, the packet transmission sequence is shown in fig. 5. The packet loss rate at each rate is shown in fig. 6 by cleaning and analyzing the collected data. As can be seen from fig. 6, in the present test period, the target rate needs to be adjusted to DR0 or DR1 to ensure the communication quality. The collected data (part of the data can be seen in table 2 below) is imported into the algorithm and the output data is shown in figure 7.
Table 2 partial data acquisition table
The information in fig. 7 is explained below:
the horizontal axis represents packet transmission sequence numbers, which may be equivalent to time, and the vertical axis represents SNR values.
The original _ SNR line represents the original SNR value,
the kalman _ sr line represents the filtered SNR values,
the kalman ndr line represents the target output rate (for ease of illustration, the rate value is shifted up by 10 units, i.e. 10 represents DR 0),
up-1 indicates that the speed is adjusted upward 1 time,
down-1 indicates that the speed is adjusted downward 1 time,
and per-5.84 indicates a packet loss rate of 5.84%.
Through the above simulation test, the following conclusion can be reached:
the rate converges to DR0 and DR1, consistent with the actual expected value.
The speed regulation is smooth, and the phenomenon of intense shaking is avoided.
The speed regulation times are extremely small, and the uplink 4000 multi-packet data are only regulated twice, so that the normal communication bandwidth is hardly occupied.
The dynamic performance is better, and the filtered SNR can track the change of the environment in real time.
The practical effect of the above-described scheme is specifically verified by an example.
In order to further verify the practical effect of the above scheme, the embodiment performs actual measurement based on a certain LoRaWAN temperature and humidity sensor (firmware accelerates the data packet sending frequency) on site, and the analysis result of the actual measurement can be seen in fig. 8, the horizontal axis represents the data packet sending sequence number, the equivalent is time, and the vertical axis represents the SNR value.
The original _ SNR line represents the original SNR value,
the server_snrm and the server_dr are mainly used for verifying whether the realization of the Server algorithm is correct in the debugging process,
the kalman _ sr line represents the filtered SNR values,
up-14 indicates that the speed is adjusted upward 14 times,
down-13 indicates that the speed is adjusted down 13 times,
and per-0.94 indicates a packet loss rate of 0.94%.
As can be seen from fig. 8:
the packet loss rate is less than 1%, and the communication quality is reliable.
The speed adjustment is gentle, the frequency is low, and the downlink bandwidth occupancy rate is extremely low.
The dynamic response is good, and the target rate can track environmental changes in real time.
It is not difficult to find that through a large number of simulation and measured data analysis, compared with the prior art, the speed regulation technical scheme disclosed by the embodiment has the following advantages:
1. the speed regulation is stable and smooth, and the risk of node disconnection is greatly reduced.
2. The speed regulation frequency is low, and the user communication bandwidth is extremely small.
3. The dynamic performance is good, the environment change can be tracked in real time, the optimal speed is obtained, and the communication quality and the power consumption are both considered.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. A method for adaptive rate adjustment for a lorewan communication, comprising:
acquiring a signal-to-noise ratio of a wireless signal, filtering the acquired signal-to-noise ratio by adopting a one-dimensional Kalman filtering algorithm, and dynamically adjusting related parameters in the one-dimensional Kalman filtering algorithm according to environmental changes to obtain an optimal estimated value of the signal-to-noise ratio;
step speed regulation is carried out according to the optimal estimated value of the signal to noise ratio, and an upward speed regulation request in the step speed regulation process is controlled through a preset upward speed regulation rule;
the process for dynamically adjusting the relevant parameters in the one-dimensional Kalman filtering algorithm according to the environmental change specifically comprises the following steps:
presetting the minimum values of the variance and standard deviation of the optimal estimated value;
when the difference between the current N signal-to-noise ratio measurement average value and the optimal estimated value exceeds a preset threshold value, setting the measurement noise standard deviation as a minimum value;
when the difference between the current N signal-to-noise ratio measurement average value and the optimal estimated value continuously exceeds the preset threshold value for a set number of times, setting the initial input rate and the signal-to-noise ratio of the wireless signal as minimum values;
the preset upward speed regulation rule comprises the following steps:
maintaining an upward speed regulation request counter, and increasing the upward speed regulation request counter by 1 when the calculated target speed is greater than the current speed;
when the current measured value is larger than the optimal estimated value and the upward speed regulation request counter exceeds a preset counting threshold value, allowing upward speed regulation;
the maximum level span of upward speed regulation is 2 levels.
2. The LoRaWAN communication self-adaptive rate adjustment system is characterized by comprising a filtering processing module and a hierarchical speed regulation module;
the filtering processing module is used for acquiring the signal-to-noise ratio of the wireless signal, filtering the acquired signal-to-noise ratio by adopting a one-dimensional Kalman filtering algorithm, and dynamically adjusting related parameters in the one-dimensional Kalman filtering algorithm according to environmental changes to obtain an optimal estimated value of the signal-to-noise ratio;
the hierarchical speed regulation module is used for carrying out hierarchical speed regulation according to the optimal estimated value of the signal to noise ratio obtained by the filtering processing module, and controlling an upward speed regulation request in the hierarchical speed regulation process through a preset upward speed regulation rule;
the filtering processing module dynamically adjusts relevant parameters in a one-dimensional Kalman filtering algorithm according to environmental changes, and the following steps are executed:
presetting the minimum values of the variance and standard deviation of the optimal estimated value;
when the difference between the current N signal-to-noise ratio measurement average value and the optimal estimated value exceeds a preset threshold value, setting the measurement noise standard deviation as a minimum value;
when the difference between the current N signal-to-noise ratio measurement average value and the optimal estimated value continuously exceeds the preset threshold value for a set number of times, setting the initial input rate and the signal-to-noise ratio of the wireless signal as minimum values;
the upward speed regulation rule executed by the hierarchical speed regulation module comprises the following steps:
maintaining an upward speed regulation request counter, and increasing the upward speed regulation request counter by 1 when the calculated target speed is greater than the current speed;
when the current measured value is larger than the optimal estimated value and the upward speed regulation request counter exceeds a preset counting threshold value, allowing upward speed regulation;
the maximum level span of upward speed regulation is 2 levels.
3. A network server, comprising a processor and a memory;
the memory is used for storing a computer program;
the processor is configured to execute the computer program stored in the memory, so that the network server executes a method for adaptive rate adjustment for lorewan communication according to claim 1.
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