CN110996304A - Method and system for inquiring and calculating edge data of low-power-consumption Internet of things - Google Patents
Method and system for inquiring and calculating edge data of low-power-consumption Internet of things Download PDFInfo
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- CN110996304A CN110996304A CN201911230415.7A CN201911230415A CN110996304A CN 110996304 A CN110996304 A CN 110996304A CN 201911230415 A CN201911230415 A CN 201911230415A CN 110996304 A CN110996304 A CN 110996304A
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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
- H04W4/70—Services for machine-to-machine communication [M2M] or machine type communication [MTC]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
- H04W16/225—Traffic simulation tools or models for indoor or short range network
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- 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
- H04W52/0225—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal
- H04W52/0248—Power saving arrangements in terminal devices using monitoring of external events, e.g. the presence of a signal dependent on the time of the day, e.g. according to expected transmission activity
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- 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
- H04W52/0251—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity
- H04W52/0258—Power saving arrangements in terminal devices using monitoring of local events, e.g. events related to user activity controlling an operation mode according to history or models of usage information, e.g. activity schedule or time of day
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- 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 method for inquiring and calculating edge data of a low-power-consumption Internet of things, which comprises the following steps: each network node in the low-power-consumption Internet of things acquires historical energy acquisition information, energy consumption data and environmental data of each network node, and predicts and calculates comprehensive energy acquisition, consumption data and working time length data of each node in preset time through a deep learning algorithm; determining a node with the longest predicted working time as a network center node; the other non-network central nodes collect self data, respectively send the collected self data to the network central nodes, and enter a sleep mode after sending; and the network center node receives the self data of each non-network center node, reports the service data to the server at regular time and actively, and responds to the query request issued by the server. The problem of among the prior art because do not control and lead to the uncontrollable energy consumption to whole node comprehensive energy consumption is solved.
Description
Technical Field
The invention relates to the field of communication, in particular to a method and a system for inquiring and calculating edge data of a low-power-consumption Internet of things.
Background
The low-power-consumption Internet of things is a network formed by connecting a plurality of scattered low-power-consumption network nodes through wireless communication, in the low-power-consumption Internet of things, each low-power-consumption network node collects data from the environment so as to monitor the environment in real time, aiming at data query of environment monitoring, environment real-time data are queried mainly from top to bottom by consuming less transmission bandwidth and energy, query information is sent to sensor nodes at corresponding positions according to processing requirements, the low-power-consumption network nodes feed back query results to a system center node, and meanwhile, the low-power-consumption network nodes also receive execution instructions so as to complete a specific control function.
However, due to the distribution characteristics, different sunlight irradiation, different working time lengths and different transmission architectures of the low-power-consumption network nodes, the low-power-consumption network nodes have high and low energy collection and energy consumption, the comprehensive energy consumption of each node can significantly affect the use efficiency of the low-power-consumption network nodes, in the prior art, only a single network node is subjected to independent energy-saving processing, the whole network is not subjected to comprehensive energy consumption control, the overall energy consumption of the low-power-consumption internet of things is uncontrollable, and the efficiency cannot be improved.
Disclosure of Invention
The invention aims to provide a data query method and a data query system of a low-power-consumption Internet of things, and solves the problem that in the prior art, energy consumption is uncontrollable because the comprehensive energy consumption of the whole node is not controlled.
In order to achieve the purpose, the invention provides the following technical scheme:
an edge data query and calculation method of a low-power-consumption Internet of things comprises the following steps:
each network node in the low-power-consumption Internet of things acquires historical energy acquisition information, energy consumption data and environmental data of each network node, and predicts and calculates comprehensive energy acquisition, consumption data and working time length data of each node in preset time through a deep learning algorithm;
determining a node with the longest predicted working time as a network center node;
the other non-network central nodes collect self data, respectively send the collected self data to the network central nodes, and enter a sleep mode after sending;
and the network center node receives the self data of each non-network center node, reports the service data to the server actively at regular time, and responds to the query request and the control execution instruction sent by the server.
Optionally, the self-history energy collection information includes: time-varying power generation data of solar panels, wind power generators, geothermal power generation equipment and hydroelectric power generation equipment.
Optionally, the self energy consumption data includes: the node comprises time-varying battery electric quantity data, energy consumption data of each functional unit of the node and work cycle period data of each functional unit, wherein the work cycle period data of each functional unit comprises a standby period, a work period and a shutdown period.
Optionally, each functional unit of the node includes a data acquisition unit, a control unit, an execution unit, a calculation unit, and a communication unit.
Optionally, the predicting and calculating the working time of each node of the internet of things includes:
and (3) taking historical energy acquisition data, historical energy consumption data and work cycle period data of the historical functional units of the nodes as input, and calculating a predicted value of the future work time length by adopting a nonlinear regression analysis algorithm and a deep neural network and/or a convolutional neural network.
Optionally, the method further comprises:
each node of the Internet of things broadcasts a predicted value of the working time of each node of the Internet of things, and the node with the longest predicted working time is used as a network center node;
when the preset data updating time point is reached, the central node performs statistical comparison processing on the collected predicted values of the working time of each node, and the non-central nodes ignore the broadcast data sent by other nodes.
Optionally, the method further comprises:
setting a switching threshold for the difference value of the working time length prediction values of all nodes of the Internet of things, triggering the switching of the network center node when the preset threshold is reached, and informing the alternative center node of working state switching by the center node.
The embodiment of the invention also provides an edge data query and calculation system of the low-power-consumption Internet of things, which comprises a processor and a memory, wherein the memory is used for storing a computer program capable of running on the processor; wherein the processor is configured to execute the above method when running the computer program.
Compared with the prior art, the invention has the following beneficial effects: the method and the system can improve the energy use efficiency of each node on the basis of reducing the total energy consumption in the network, thereby prolonging the service life of the whole Internet of things and improving the service efficiency.
Drawings
FIG. 1 is a topological diagram of a sensor network structure according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for querying and computing edge data of a low-power IOT according to an embodiment of the present invention;
FIG. 3 is another flowchart of a method for querying and computing edge data of a low power IOT according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a single-node nonlinear regression prediction computing network according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a single-node nonlinear regression prediction computation network structure according to an embodiment of the present invention;
fig. 6 is a schematic diagram of an edge data query and computation structure of the low-power internet of things in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
A typical topology of a sensor network is shown in fig. 1, and frequent battery replacement is not easy to implement due to the arrangement of the sensor nodes and the limitation of the application environment, so it is important to save energy consumption of the sensor nodes, thereby prolonging the network life. The main sources that the energy consumption of the sensor node can control are the transceivers of the sensor node, and the transceivers of the sensor node can be in three working states: a transmit state, a listen state, and a sleep state. The method comprises the steps that an engine of a sensor node is in a sending state when being opened to send data, a receiver of the sensor node is in a monitoring state when being opened to receive data and wait to receive data, a transmitter and the receiver of the sensor node are in a sleep state when being closed, the sensor node consumes more energy when being in the sending state and the monitoring state, and the energy consumed by the sensor node is less when being in the sleep state.
The service life of the sensor network is from the completion of the arrangement of the sensor nodes to the completion of the energy consumption of the first sensor node, so that the energy consumption among the sensors is balanced as much as possible while the total energy consumption of the sensor network is reduced, and the service life of the network is prolonged to the maximum extent.
To achieve the above object, as shown in fig. 1, an embodiment of the present invention provides a method for querying and calculating edge data of a low power consumption internet of things, where the method includes the following steps:
s101, each network node in the low-power-consumption Internet of things collects historical energy collection information, energy consumption data and environment data of each network node, and the comprehensive energy collection, consumption data and working time length data of each node in preset time are calculated through a deep learning algorithm in a prediction mode;
before monitoring the environment, the sensor nodes need to be arranged according to the specific application environment. The arrangement of the sensor nodes may be random or in a specific manner. The random mode can adopt the broadcasting sensor nodes such as airplanes, and the mode is mainly used for outdoor environments such as forests, grasslands and the like; the special mode can adopt artificial arrangement, is used for indoor environment mostly.
The number of the low-power-consumption internet-of-things nodes can be 2 or more than 2, so as to form different networking architectures, generally speaking, a star-shaped networking can be formed, and the star-shaped networking is composed of a central node and a plurality of non-central nodes, wherein the non-central nodes can send self-collected data to the central node, for example, in a broadcast communication or single-point communication mode.
The historical energy collection information comprises time-varying generated energy data of a solar cell panel, a wind driven generator, geothermal power generation equipment and hydroelectric power generation equipment.
The energy consumption data of the node comprises time-varying battery power data, energy consumption data of each functional unit of the node, and work cycle period data of each functional unit, wherein the work cycle period data of each functional unit comprises a standby period, a work period and a shutdown period.
In the embodiment of the invention, each functional unit of the node comprises a data acquisition unit, a control unit, an execution unit, a calculation unit and a communication unit.
The detection of energy consumption data and the monitoring of remaining operating time can be detected by arranging an energy detection module in each sensor node, and by the total service life and used time of the sensor node. Methods for detecting the remaining energy information of the network node are known in the prior art, and therefore are not described herein again.
Optionally, the predicting and calculating the working time of each node of the internet of things includes:
and (3) taking historical energy acquisition data, historical energy consumption data and work cycle period data of the historical functional units of the nodes as input, and calculating a predicted value of the future work time length by adopting a nonlinear regression analysis algorithm and a deep neural network and/or a convolutional neural network.
S102, determining a node with the longest predicted working time as a network center node;
and the network center node is used for controlling the sensor network, is responsible for communicating with a server at the rear end, and actively gathers and reports an acquisition result. The predicted working time can be predicted through a back-end server or through each node.
S103, collecting self data by other non-network central nodes, respectively sending the collected self data to the network central nodes, and entering a sleep mode after sending;
and S104, the network center node receives the self data of each non-network center node, reports the service data to the server actively at regular time, and responds to the query request issued by the server.
Optionally, the method further comprises:
each node of the Internet of things broadcasts a predicted value of the working time of each node of the Internet of things, and the node with the longest predicted working time is used as a network center node;
when the preset data updating time point is reached, the central node performs statistical comparison processing on the collected predicted values of the working time of each node, and the non-central nodes ignore the broadcast data sent by other nodes.
Optionally, the method further comprises:
setting a switching threshold for the difference value of the working time length prediction values of all nodes of the Internet of things, triggering the switching of the network center node when the preset threshold is reached, and informing the alternative center node of working state switching by the center node.
Fig. 3 is a flow chart for illustrating the operation principle of the central node switching in the embodiment of the present invention. FIG. 4 is a diagram of a single-node nonlinear regression prediction computing network operating principle; FIG. 5 is a schematic diagram of a single-node nonlinear regression prediction calculation network structure according to an embodiment of the present invention.
As shown in fig. 3, the input parameters are divided into three categories: 1. acquiring data by energy; 2. energy consumption data; 3. the history function unit duty cycle duration.
Energy acquisition data includes, but is not limited to: a. the intensity of solar illumination; b. wind strength; c. air temperature; d. geothermal temperature; e. the water flow rate; f. average generated electricity quantity; g. and energy conversion efficiency of each energy acquisition unit.
Energy consumption data includes, but is not limited to: a. a battery voltage difference; b. power consumption of each data acquisition unit; c. power consumption of each control unit; d. power consumption of each execution unit; e. calculating unit power consumption; f. each communication unit consumes power.
Functional unit duty cycle durations include, but are not limited to: a. the actual working time of each data acquisition unit; b. the actual working time of each control unit; c. the actual working time of each execution unit; d. calculating the actual working time of the unit; e. the actual working time of each communication unit; f. and (4) actual working time period of the node.
The data label is the actual working time of the node.
In the embodiment of the invention, the three types of data are used as tensor data and input into a prediction calculation network. The prediction calculation network adopts a convolutional neural network and a nonlinear regression analysis algorithm, the network structure comprises three convolutional layers, the characteristics of the three input parameters are respectively extracted, and a plurality of fully-connected networks are followed. The system utilizes the parameter data collected historically to perform model training.
When the timer 2 times out, the system inputs the predictive computation network by using the historical collected data (including labels, namely, actual working duration data), and then calculates the relation between the working duration Y and the multivariable (namely, input parameter x) by the training learning algorithm, so as to obtain a network model Y ═ f (x). And then carrying out nonlinear regression analysis by using the currently acquired data (without labels) to calculate the predicted working time of the node, namely inputting the current input parameter x into the training network model Y (h) (x), and carrying out regression to calculate the predicted working time Y. (as shown in FIG. 4)
The input parameters are historical energy acquisition data, historical energy consumption data and work cycle period data of the historical functional units of the nodes, and a plurality of tensor data of 32X32X3 are formed after the tensor data are sequentially arranged. And successively passing through 3 convolutional layers, successively performing feature enhancement on the data and reducing data noise (as shown in fig. 5). In the embodiment of the invention, convolution layer 1 can adopt convolution kernels with the size of 3X3, an input channel of 3 and an output channel of 5, 0 filling is adopted during convolution calculation so as to keep the sizes of input data and output data consistent, and the convolution step length is 1; convolution layer 2 can adopt convolution kernels with the size of 3X3, an input channel of 5 and an output channel of 5, 0 filling can be adopted during convolution calculation, and the convolution step length is 1; convolution layer 3 uses convolution kernels with the size of 3X3, the input channel of 5 and the output channel of 5, and 0 padding is used in convolution calculation during convolution calculation, and the convolution step is 1.
Pooling layers between convolutional layers are used to reduce the overfitting degree of the network training parameters and models, and the dimension reduction is performed by using average pooling with the size of 2X2 in the embodiment of the invention.
In the embodiment of the present invention, the output of the convolutional layer 3 is a tensor of 8X5, and after data stretching and sorting, the input enters a full-connection network. The fully-connected network in the embodiment of the invention is a network of 320 input nodes, two hidden layers and 1 output node, wherein the first hidden layer has 512 neurons, and the second hidden layer has 128 neurons.
The loss function adopted during the training of the fully-connected network model is Mean Square Error (MSE):
the learning rate in the model training process is set to be 0.001, and the random gradient descent method is used for calculation. In the training process, if the total loss rate in the loss function iteration process is less than 0.002 or the change rate of the total loss rate obtained by continuous 10 times of calculation is less than 1%, the training frequency is reduced (i.e. the timing duration of the timer 3 is increased), so as to achieve the purposes of further reducing the system power consumption and increasing the network life cycle.
When the timer 3 times out, the system will trigger the node historical data acquisition process. The single-node nonlinear regression prediction calculation network input parameters are current energy acquisition data, historical energy consumption data and work cycle period data of the historical functional units of the nodes, and are sequentially arranged to form tensor data of 32X32X 3.
When the timer 1 times out, the system will trigger the data broadcasting process. And outputting the predicted working time length parameter of the node through regression analysis calculation of the prediction calculation network, and using the predicted working time length parameter for the network node to carry out working state switching judgment.
Fig. 6 is a schematic internal structure diagram of the low-power consumption internet of things node system in one embodiment. As shown in fig. 6, the system includes a processor, memory, and a network interface connected by a system bus. Wherein, the processor is used for providing calculation and control capability and supporting the operation of the whole electronic equipment. The memory is used for storing data, programs and the like, and the memory stores at least one computer program which can be executed by the processor to realize the wireless network communication method suitable for the electronic device provided by the embodiment of the application. The memory may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The computer program can be executed by a processor for implementing a method provided in the following embodiments. The internal memory provides a cached execution environment for the operating system computer programs in the non-volatile storage medium. The network interface may be an ethernet card or a wireless network card, etc. for communicating with an external electronic device. The electronic device may be a wireless network sensor, an intelligent system, or the like.
The embodiment of the application also provides a computer readable storage medium. One or more non-transitory computer-readable storage media containing computer-executable instructions that, when executed by one or more processors, cause the processors to perform the steps of the method for edge data querying and computing for low power internet of things.
A computer program product containing instructions which, when run on a computer, cause the computer to perform a method of edge data querying and computing for low power internet of things.
It should be understood that, in the various embodiments of the present application, the size of the serial number of each process does not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative modules and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system, the system and the module described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
All parts of the specification are described in a progressive mode, the same and similar parts of all embodiments can be referred to each other, and each embodiment is mainly introduced to be different from other embodiments. In particular, the system and system embodiments are substantially similar to the method embodiments and therefore are described in a relatively simple manner, where relevant, reference may be made to the description of the method embodiments.
Finally, it is to be noted that: the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. To the extent that such modifications and variations of the present application fall within the scope of the claims and their equivalents, they are intended to be included within the scope of the present application.
Claims (8)
1. An edge data query and calculation method of a low-power-consumption Internet of things is characterized by comprising the following steps:
each network node in the low-power-consumption Internet of things acquires historical energy acquisition information, energy consumption data and environmental data of each network node, and predicts and calculates comprehensive energy acquisition, consumption data and working time length data of each node in preset time through a deep learning algorithm;
determining a node with the longest predicted working time as a network center node;
the other non-network central nodes collect self data, respectively send the collected self data to the network central nodes, and enter a sleep mode after sending;
and the network center node receives the self data of each non-network center node, reports the service data to the server at regular time and actively, and responds to the query request issued by the server.
2. The method of claim 1, wherein: the self historical energy acquisition information comprises: time-varying power generation data of solar panels, wind power generators, geothermal power generation equipment and hydroelectric power generation equipment.
3. The method of claim 1, wherein: the self energy consumption data includes: the node comprises time-varying battery electric quantity data, energy consumption data of each functional unit of the node and work cycle period data of each functional unit, wherein the work cycle period data of each functional unit comprises a standby period, a work period and a shutdown period.
4. The method of claim 3, wherein: each functional unit of the node comprises a data acquisition unit, a control unit, an execution unit, a calculation unit and a communication unit.
5. The method of claim 1, wherein: the method for predicting and calculating the working time of each node of the Internet of things comprises the following steps:
and (3) taking historical energy acquisition data, historical energy consumption data and work cycle period data of the historical functional units of the nodes as input, and calculating a predicted value of the future work time length by adopting a nonlinear regression analysis algorithm and a deep neural network and/or a convolutional neural network.
6. The method of claim 1, further comprising:
each node of the Internet of things broadcasts a predicted value of the working time of each node of the Internet of things, and the node with the longest predicted working time is used as a network center node;
when the preset data updating time point is reached, the central node performs statistical comparison processing on the collected predicted values of the working time of each node, and the non-central nodes ignore the broadcast data sent by other nodes.
7. The method of claim 1, further comprising:
setting a switching threshold for the difference value of the working time length prediction values of all nodes of the Internet of things, triggering the switching of the network center node when the preset threshold is reached, and informing the alternative center node of working state switching by the center node.
8. An edge data query and computation system of a low power consumption internet of things, the system comprising a processor and a memory for storing a computer program capable of running on the processor; wherein the processor is configured to perform the method of any one of claims 1 to 7 when running the computer program.
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