CN111970654A - Sensor node dynamic energy-saving sampling method based on data characteristics - Google Patents

Sensor node dynamic energy-saving sampling method based on data characteristics Download PDF

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CN111970654A
CN111970654A CN202010650939.8A CN202010650939A CN111970654A CN 111970654 A CN111970654 A CN 111970654A CN 202010650939 A CN202010650939 A CN 202010650939A CN 111970654 A CN111970654 A CN 111970654A
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CN111970654B (en
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杨鲲
万维祥
梅海波
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Chengdu Huijianlian Information Technology Co ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0209—Power saving arrangements in terminal devices
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • 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

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Abstract

The invention discloses a dynamic energy-saving sampling method of a sensor node based on data characteristics, which aims at the problem that energy is wasted due to excessive sampling when a wireless sensor node is sampled at a fixed sampling interval; the sampling environment variation amplitude is judged through the exponential smoothing algorithm and the weighted exponential smoothing algorithm, the sampling interval of the node is dynamically adjusted through the node sampling step length control window, the sampling interval of the node is gradually increased when the environment variation amplitude is gradual, the energy consumption of the node is reduced, and the sampling interval of the node is timely shortened when the environment variation is severe, so that the node can timely capture sudden variation of the sampling environment.

Description

Sensor node dynamic energy-saving sampling method based on data characteristics
Technical Field
The invention belongs to the technical field of low-power-consumption short-distance wireless networking communication, and particularly relates to a dynamic energy-saving sampling method for a sensor node based on data characteristics, which is used for reducing the energy consumption of the wireless sensor node.
Background
The wireless sensor wireless communication technology is widely applied to the fields of environmental monitoring, industrial automation and intelligent home with the network characteristics of low power consumption, low speed and low cost, and although the wireless sensor technology greatly improves the production efficiency and reduces the maintenance cost in the fields, the energy consumption problems of limited power supply, limited memory computing resources and the like bring huge challenges to the application and development of the wireless sensor network due to the network characteristics. The most important factors influencing the energy consumption of the wireless sensor node mainly comprise two aspects, namely a process of acquiring data and processing data by the node sensor and a process of executing a communication task and transmitting data by the node. Therefore, reducing the energy consumption requires reducing the sampling times and data transmission times of the nodes and increasing the sleep time of the nodes.
Data acquisition and transmission of wireless sensor nodes are often periodic, and if a sampling task is performed at a fixed period, the nodes transmitting data at fixed time intervals inevitably have the condition of data transmission redundant energy waste. On one hand, when the sampling environment changes slowly, if the high-frequency sampling is still kept, the energy of the node battery is inevitably wasted greatly. On the other hand, when the sampling precision is required to be high due to severe sampling environment change, if the low-frequency sampling is kept, the service quality of the node is affected. Therefore, the sampling interval of the wireless sensor node is dynamically adjusted according to the real-time sampling environment change, when the sampling environment change is gradual, the sampling interval of the node is prolonged, the energy consumption of the node is reduced, and when the sampling environment change is severe, the sampling interval is shortened, and the sampling precision is ensured. Therefore, the requirement of the wireless sensor node for low-power-consumption sampling in the sampling process can be met.
Disclosure of Invention
Aiming at the problem of energy waste of a wireless sensor node in the sampling process, the node dynamic sampling scheduling scheme is provided, the change amplitude of the sampling environment is judged by using the data change characteristics of the node sampling process, and the sampling interval of the node is adjusted. When the sampling environment changes to be stable, the sampling interval of the node is gradually increased, so that the sleep time of the node is increased, the energy consumption of the node is reduced, and the sampling precision requirement can be met because the sampling with too high frequency is not needed at the moment. Meanwhile, based on the trend change characteristics and autocorrelation of the sampled data along with time, the severe change of the sampling environment is detected in time, the sampling frequency is increased, and the omission factor is reduced.
The technical scheme of the invention is as follows: the dynamic energy-saving sampling method of the sensor node based on the data characteristics dynamically adjusts the sampling interval of the node according to the variation amplitude of the sampling environment, and reduces the energy consumption of the node. The method specifically comprises the following steps:
a1, constructing a time-series-based sampling data prediction model of the node, and predicting sampling data of the node at different moments under k step length;
a2, comparing the error between the predicted data and the measured value, so as to judge whether the difference between the predicted value and the measured value is compared with the error tolerance under the condition that the maximum sampling interval is not reached, and determining the variation amplitude of the sampling environment;
a3, constructing a long-short term sampling weighted exponential mobile data prediction model of the node, calculating a long-short term mobile average value of the node, and capturing sudden change data of the environment when the node reaches the maximum sampling interval. Detecting sudden changes in a steady sampling environment;
and A4, dynamically adjusting the sampling interval and the sleep duration of the node according to the change amplitude of the environment through the node sampling step length control window.
The method of the invention has the following advantages:
1. in the node sampling process, the sampling interval can be dynamically adjusted according to the change degree of the environment, so that the energy consumption of the node can be effectively reduced when the environment change is slow, and redundant sampling is avoided;
2. the data prediction model can be constructed without off-line training and operated on each node. The model building is done by the coordinator without sending the data to the coordinator. Therefore, the algorithm is essentially low in communication requirement;
3. the algorithm calculation and storage requirements are low, the calculation of the exponential smoothing prediction algorithm can be completed through a simple recursion formula, and extra calculation burden can not be brought to the node, so that the method is suitable for the wireless sensor node with very limited calculation and memory resources;
4. the dynamic sampling step length is used for controlling the window to dynamically adjust the node sampling interval and the dormancy duration, so that the requirements of node energy consumption reduction can be met, the requirements on the sampling data precision under different conditions can be met, and different sampling precision requirements can be met.
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The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure without limiting the disclosure. In the drawings:
fig. 1 is a step of node dynamic sampling scheduling provided in an embodiment of the present invention;
fig. 2 is a flowchart of node dynamic sampling scheduling provided in the embodiment of the present invention;
fig. 3 is a step of detecting a sudden change of a node sampling environment according to an embodiment of the present invention;
fig. 4 is a flow chart of detecting a sudden change in a node sampling environment according to an embodiment of the present invention;
fig. 5 is a step diagram of a dynamic energy-saving sampling method for a sensor node based on data characteristics according to the present invention.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
As shown in fig. 5, the present invention provides a dynamic energy-saving sampling method for sensor nodes based on data characteristics, and the implementation steps of the present invention are specifically described below with reference to the accompanying drawings.
The node constructs a sampling data model based on a time sequence according to the collected original data, predicts the sampling data of the node by using a quadratic exponential smoothing model, adjusts the sampling interval of the node according to the prediction error, calculates a long-term moving average value and a short-term moving average value by using an exponential weighted moving average algorithm to judge the possible emergency of the sampling environment when the step length reaches the maximum value, and dynamically adjusts the sampling interval of the node through a node sampling step length control window to reduce the energy consumption of the node. The method specifically comprises the following implementation processes:
a1, constructing a data prediction model of wireless sensor node sampling by using the time sequence, predicting data of the time sequence at a future moment based on autocorrelation and trend change characteristics of the sampling data, and predicting data S of a next sampling pointtWith measured data ytAnd comparing, wherein the error can be used as the basis for judging the change intensity of the sampling environment. Defining a time series model as:
S＝{St|t＝t1，t2，...，tn}
a2, as shown in FIG. 1, a node dynamic sampling scheduling step is shown, node sampling data are predicted through a prediction model, the error between the sampling data and measured data is compared to obtain the amplitude of environmental change, and the sampling interval of a node is adjusted according to the speed of the environmental change, as shown in FIG. 2, an algorithm flow diagram of the step is shown.
A21, predicting the node sampling prediction value under k step length by using quadratic exponential smoothing algorithm, and predicting the node sampling prediction value under k step length at k intervaltThe sampling prediction value of the next sampling point at the time t under the step length can be represented by a formula
Figure BDA0002574938790000031
To give StIs the predicted value at time t, btIs the trend prediction value at time t, ktIs the step size (sampling interval), k, at time ttDetermines the sleep time of the node and thus also the energy consumption of the node.
Defining the measured value y at time t-1t-1Trend value bt-1Step length kt-1And a smoothing coefficient beta (beta is more than or equal to 0 and less than or equal to 1) and an actual measured value y at the time ttAverage sampling interval q, trend value b at time ttThis can be obtained by the following equation:
Figure BDA0002574938790000032
defining the predicted value S at time t-1t-1And a smoothing coefficient alpha (alpha is more than or equal to 0 and less than or equal to 1) and a predicted value S at the time ttCan be obtained by the following formulaAnd (3) discharging:
St＝(1-(1-α)q)yt-1+(1-α)q(St-1+kt-1bt-1)
and A22, calculating the error of the predicted value and the measured value, judging the change amplitude of the node sampling environment, and adjusting the sampling interval of the node. Definition feSetting the initial sampling step length of the node to be 1 and setting the step length k to be the error between the predicted value and the measured valuetChanges with the speed of the environmental change.
At each sampling point, f is judgedeIn contrast to a predetermined prediction error margin sigma, if it is lower than the prediction error margin, ktAdding and increasing gradually, wherein each increase is 1 larger than the step length of the previous sampling point, and repeating until ktReaches the maximum value when the node reaches the maximum sampling interval kmaxWhen k istThe increase is stopped, the total sampling time and the data transmission time of the nodes are reduced in the process, and the sampling interval of the nodes is prolonged, so that the sleep time is increased, the consumption is reduced, and a good energy-saving effect is achieved.
Once f iseGreater than the prediction error margin σ, ktWill be reduced to half the current step size until feLess than σ or ktAnd reducing the node step size to the initial value, and simultaneously, the node step size is increased and the calculation is started from 1 again.
A3, FIG. 3 shows the sudden change detection step of the node sampling environment, when the sampling control window reaches the maximum value and the prediction error feWhen the sampling time is less than the prediction error tolerance sigma, in order to avoid the sudden change of the sampling environment from being missed, the sudden change of the sampling environment is detected by using a weighted index moving average algorithm, and the sampling interval of the node is shortened in time to ensure that the node sends the sampling data during the sudden change to the coordinator in time. An algorithmic flow chart for this step is shown in fig. 4.
A31, two different smoothing coefficients alpha are definedshortAnd alphalongCorrespondingly calculating a short-term exponentially weighted moving average SshortAnd a long-term exponentially weighted moving average Slong. When the step size reaches the maximum value, the data meter is used for each sampling dataCalculating the ratio eta of the two average values, which is expressed by the following formula:
Figure BDA0002574938790000041
a32, if the ratio exceeds the threshold value tau, it indicates that there is a sudden change in the sampling environment, at this moment, the sampling interval is shortened immediately, the sampling step controls the window size, namely the step ktSet to an initial value of 1 and the amplification is again incremented starting with 1.
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Various modifications and changes may be made by those skilled in the art based on the present invention and are within the scope of the appended claims.

Claims (4)

1. A dynamic energy-saving sampling method for a sensor node based on data characteristics is characterized by comprising the following steps:
a1, constructing a node sampling data prediction model, and predicting sampling data of nodes at different moments;
a2, comparing the error between the predicted data and the measured value to judge the change range of the sampling environment when the maximum sampling interval is not reached;
a3, constructing a weighted exponential moving prediction model of the node, calculating a long-term and short-term moving average value of the node, and capturing sudden change data of the environment when the node reaches the maximum sampling interval. (ii) a
And A4, dynamically adjusting the sampling interval and the sleep duration of the node according to the change amplitude of the environment through the node sampling step length control window.
2. The method as claimed in claim 1, wherein a quadratic exponential smoothing algorithm is used to predict the sampled data of the sampled data prediction model, and the variation range of the sampling environment is determined according to the error between the predicted value and the measured value, the sampling interval of the node is dynamically adjusted according to the variation range of the sampling environment, and when the variation of the sampling environment is gradual, the sampling interval is extended, and the sleep time of the node is increased.
3. The dynamic energy-saving sampling method for the sensor node based on the data characteristics as claimed in claim 1, wherein a long-term and short-term moving average of the node is predicted by using a weighted exponential moving average algorithm, so as to obtain the burst change data of the node when the maximum sampling step is reached.
4. The dynamic energy-saving sampling method of the sensor node based on the data characteristics as claimed in claim 1, wherein the sampling data characteristics based on the node sense the change of the sampling environment by using a quadratic exponential smoothing algorithm and a weighted exponential moving average algorithm, a sampling step control window of the wireless sensor node is designed, when the sampling environment changes violently, the sampling step of the node is shortened, the sleep time of the node is reduced, and the sampling data during the violent change is obtained; when the change of the sampling environment is slow, the sampling step length of the node is increased, and the sleep time of the node is prolonged.
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CN117630309B (en) * 2023-10-17 2024-05-28 水利部珠江水利委员会水文局 Intelligent river channel water quality monitoring method and system
CN117978240A (en) * 2024-01-29 2024-05-03 中国人民解放军军事科学院系统工程研究院 Material state monitoring method and device based on high-flux satellite Internet of things

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