Data collection method and device for energy harvesting internet of things, equipment and medium

Abstract

This invention discloses a data acquisition method, apparatus, device, and medium for an energy-harvesting Internet of Things (IoT), addressing the technical problem of high complexity in power allocation optimization and high requirements for the computational and processing capabilities of devices during sensor data acquisition in IoT. The method includes: determining the current acquisition area of ​​the energy-harvesting IoT; pre-allocating power in the current acquisition area to obtain the cross-coherence value of the current power allocation matrix; determining whether the cross-coherence value is less than a preset cross-coherence threshold; if not, dividing the current acquisition area into four equal parts to obtain several sub-acquisition areas, sequentially using each sub-acquisition area as the current acquisition area, and returning to the step of pre-allocating power in the current acquisition area to obtain the cross-coherence value of the current power allocation matrix, wherein several energy-harvesting sensors are deployed in the current acquisition area; if yes, sequentially acquiring sensor data from the energy-harvesting sensors in each sub-acquisition area using a drone.

Description

Technical Field

[0001] This invention relates to the field of data collection technology, and in particular to a data collection method, apparatus, device and medium for an energy-harvesting Internet of Things. Background Technology

[0002] Wireless sensor networks (WSNs), as a special type of functional network, have been widely applied in fields such as environmental monitoring, medical surveillance, and military tracking. Unmanned aerial vehicle (UAV)-assisted data acquisition, by combining UAVs with sensor networks, enables efficient, accurate, and real-time data collection over vast areas. UAV-based sensor network data acquisition leverages the rapid movement and autonomous control capabilities of UAVs to quickly complete large-scale data collection tasks, improving data acquisition efficiency. Because UAVs can achieve omnidirectional coverage of the target area, their application in sensor network data acquisition can avoid data blind spots or dead zones, thus improving the reliability of data collection.

[0003] In large-scale, complex sensor deployments, the energy supply of sensor nodes is limited. Traditional batteries cannot provide a long-term, stable energy supply, restricting the operational lifespan of sensor nodes and the network lifespan. Therefore, sensor energy supply has always been a challenge. Energy harvesting has been identified as one of the important solutions for extending the lifespan of sensor nodes. By continuously harvesting energy from solar, thermal, kinetic, electromagnetic, or other sources, energy harvesters provide almost permanent energy. However, due to variations in conditions such as sunlight propagation and wind speed, sensors typically have different energy harvesting rates, resulting in unreliable and intermittent available energy. Therefore, a reasonable power allocation strategy is needed to achieve efficient utilization of sensor energy and extend the sensor's operational lifespan.

[0004] Due to the dense deployment of nodes in sensor networks, the sensed data exhibits strong correlations and offers significant potential for compression. Employing compressed sensing algorithms to compress data is an effective means of reducing network communication energy consumption and extending network lifespan. Traditional data transmission suffers from data loss and transmission delays, impacting the accuracy and real-time performance of data acquisition. Furthermore, traditional solutions present challenges for the management and maintenance of large-scale sensor networks.

[0005] In summary, existing sensor network data acquisition schemes mainly suffer from the following problems:

[0006] Regarding system energy consumption, most methods focus on the energy constraints of UAVs and the low energy consumption of data transmission, while ignoring the energy constraints of sensors.

[0007] In scenarios involving energy harvesting sensors, existing methods do not take into account the different energy conditions in different time slots of the sensor and the issue of data compression, making them insufficient for practical application.

[0008] In view of this, existing technologies consider the dynamic change of available energy in each time slot of energy-harvesting sensors and propose to transform the power allocation problem into the construction problem of the measurement matrix. This solves the power allocation optimization problem for each time slot of energy-harvesting sensors in IoT systems, i.e., solving the power optimization allocation problem under the constraint of available energy of each sensor. This method also considers the constraint of available energy of sensors and the compression problem of data transmission, providing a new approach for data acquisition applications in real-world energy-harvesting sensor scenarios. However, this algorithm has high time complexity and may not be optimized for networks with a large number of sensors. For sensors far from the fusion center, the energy consumption of transmitting data may be very high, and the large number of sensors will lead to excessively high complexity in the power allocation optimization process, placing high demands on the computing and processing capabilities of the equipment. Summary of the Invention

[0009] This invention provides a data acquisition method, apparatus, device, and medium for energy-harvesting Internet of Things (IoT), which addresses the technical problems of high complexity in power allocation optimization and high requirements for the computing and processing capabilities of devices during sensor data acquisition in IoT.

[0010] This invention provides a data acquisition method for an energy-harvesting Internet of Things (IoT), comprising:

[0011] Determine the current data collection area for energy harvesting IoT;

[0012] Power is pre-allocated to the current acquisition area to obtain the mutual coherence value of the current power allocation matrix. Several energy harvesting sensors are deployed in the current acquisition area.

[0013] Determine whether the mutual interference value is less than a preset mutual interference threshold;

[0014] If not, the current acquisition area is divided into four equal parts to obtain several sub-acquisition areas. Each of the sub-acquisition areas is used as the current acquisition area in turn. The power pre-allocation of the current acquisition area is then performed to obtain the mutual coherence value of the current power allocation matrix. The current acquisition area is equipped with several energy harvesting sensors.

[0015] If so, the energy harvesting sensor data in each of the sub-collection areas is collected sequentially by a drone.

[0016] Optionally, the step of pre-allocating power to the current acquisition area to obtain the cross-coherence value of the current power allocation matrix includes:

[0017] The number of sensors and the maximum acceptable mean square value of the reconstructed signal error of the energy harvesting sensor are obtained.

[0018] The number of sensors and the mean square value of the maximum acceptable reconstructed signal error are input into a pre-trained multilayer perceptron (MLP), and the number of time slots is output.

[0019] The energy consumption of the energy harvesting sensor is calculated based on the number of time slots.

[0020] Construct a power allocation optimization problem for the current acquisition area with the goal of minimizing mutual interference and the energy consumption as a constraint;

[0021] Solve the power allocation optimization problem to obtain the mutual interference value of the current power allocation matrix.

[0022] Optionally, the step of sequentially collecting sensor data from the energy harvesting sensor in each of the sub-collection areas using a drone includes:

[0023] The drone sequentially sends activation signals to the energy harvesting sensors in each of the sub-collection areas;

[0024] Receive sensor data returned by the energy harvesting sensor in response to the activation signal.

[0025] Optionally, the function of the power allocation optimization problem is as follows:

[0026]

[0027]

[0028]

[0029] in, The mutual coherence value of the current power allocation matrix. Power allocation for energy harvesting sensors, The energy harvested by the j-th energy harvesting sensor in time slot l. For time slot index, For node indexing of energy harvesting sensors, The number of time slots, The number of sensors in an energy harvesting sensor. , For the preset threshold, This is the i-th element in the measurement matrix.

[0030] The present invention also provides a data acquisition device for an energy harvesting Internet of Things, comprising:

[0031] The current data collection area determination module is used to determine the current data collection area of ​​the energy harvesting Internet of Things (IoT).

[0032] The mutual coherence calculation module is used to pre-allocate power in the current acquisition area to obtain the mutual coherence value of the current power allocation matrix. The current acquisition area is equipped with several energy harvesting sensors.

[0033] The judgment module is used to determine whether the mutual interference value is less than a preset mutual interference threshold.

[0034] The return module is used to divide the current acquisition area into four equal parts to obtain several sub-acquisition areas, sequentially taking each of the sub-acquisition areas as the current acquisition area, and returning the steps of performing power pre-allocation on the current acquisition area to obtain the mutual coherence value of the current power allocation matrix, wherein several energy harvesting sensors are deployed in the current acquisition area.

[0035] The data acquisition module is used, if so, to sequentially acquire sensor data from the energy harvesting sensors in each of the sub-acquisition areas using a drone.

[0036] Optionally, the mutual interference value calculation module includes:

[0037] The sensor quantity and maximum acceptable mean square error of the reconstructed signal are obtained by the submodule, which is used to obtain the sensor quantity and the maximum acceptable mean square error of the reconstructed signal of the energy harvesting sensor.

[0038] The time slot number calculation submodule is used to input the number of sensors and the mean square value of the acceptable maximum reconstruction signal error into the pre-trained multilayer perceptron (MLP) and output the time slot number.

[0039] An energy consumption calculation submodule is used to calculate the energy consumption of the energy harvesting sensor based on the number of time slots.

[0040] The power allocation optimization problem construction submodule is used to construct the power allocation optimization problem of the current acquisition area with the goal of minimizing mutual interference value and the energy consumption as the constraint.

[0041] The mutual coherence value solving submodule is used to solve the power allocation optimization problem and obtain the mutual coherence value of the current power allocation matrix.

[0042] Optionally, the data acquisition module includes:

[0043] An activation signal transmission submodule is used to send activation signals sequentially to the energy harvesting sensors in each of the sub-collection areas via a drone;

[0044] The data receiving submodule is used to receive sensor data returned by the energy harvesting sensor in response to the activation signal.

[0045] Optionally, the function of the power allocation optimization problem is as follows:

[0046]

[0047]

[0048]

[0049] in, The mutual coherence value of the current power allocation matrix. Power allocation for energy harvesting sensors, The energy harvested by the j-th energy harvesting sensor in time slot l. For time slot index, For node indexing of energy harvesting sensors, The number of time slots, The number of sensors in an energy harvesting sensor. , For the preset threshold, This is the i-th element in the measurement matrix.

[0050] The present invention also provides an electronic device, the device comprising a processor and a memory:

[0051] The memory is used to store program code and transmit the program code to the processor;

[0052] The processor is used to execute the data acquisition method for the energy harvesting Internet of Things as described above, according to the instructions in the program code.

[0053] The present invention also provides a computer-readable storage medium for storing program code for executing the data acquisition method of the energy harvesting Internet of Things as described in any of the preceding claims.

[0054] As can be seen from the above technical solution, the present invention has the following advantages: The present invention discloses a data acquisition method for an energy-harvesting Internet of Things (IoT), specifically disclosing: determining the current acquisition area of ​​the energy-harvesting IoT; pre-allocating power in the current acquisition area to obtain the cross-coherence value of the current power allocation matrix, wherein the current acquisition area is equipped with several energy-harvesting sensors; determining whether the cross-coherence value is less than a preset cross-coherence threshold; if not, dividing the current acquisition area into four equal parts to obtain several sub-acquisition areas, sequentially using each sub-acquisition area as the current acquisition area, and returning to the steps of pre-allocating power in the current acquisition area to obtain the cross-coherence value of the current power allocation matrix, wherein the current acquisition area is equipped with several energy-harvesting sensors; if yes, sequentially collecting sensor data from the energy-harvesting sensors in each sub-acquisition area using a drone. The present invention, by optimizing power allocation for a network composed of multiple sensors, decomposes the optimization algorithm into sub-problems, greatly reducing the complexity of the optimization algorithm and alleviating the requirements on computing equipment. Attached Figure Description

[0055] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 A flowchart illustrating the steps of a data acquisition method for an energy-harvesting Internet of Things (IoT) according to an embodiment of the present invention;

[0057] Figure 2 A flowchart illustrating the steps of a data acquisition method for an energy-harvesting Internet of Things (IoT) according to another embodiment of the present invention;

[0058] Figure 3 A schematic diagram of a scenario model for drone data collection;

[0059] Figure 4 This is a structural block diagram of an energy harvesting Internet of Things (IoT) data acquisition device provided in an embodiment of the present invention. Detailed Implementation

[0060] This invention provides a data acquisition method, apparatus, device, and medium for an energy-harvesting Internet of Things (IoT), which addresses the technical problems of high complexity in power allocation optimization and high requirements for the computing and processing capabilities of devices during sensor data acquisition in the IoT.

[0061] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0062] Please see Figure 1 , Figure 1 A flowchart illustrating the steps of a data acquisition method for an energy-harvesting Internet of Things (IoT) according to an embodiment of the present invention.

[0063] The present invention provides a data acquisition method for an energy-harvesting Internet of Things (IoT), which may specifically include the following steps:

[0064] Step 101: Determine the current data collection area of ​​the energy harvesting IoT;

[0065] Step 102: Perform power pre-allocation on the current acquisition area to obtain the mutual coherence value of the current power allocation matrix. Several energy harvesting sensors are deployed in the current acquisition area.

[0066] The current data collection area refers to the Internet of Things (IoT) area where several energy harvesting sensors are deployed.

[0067] In this embodiment of the invention, power pre-allocation can be performed on the current acquisition area, and the mutual interference value of the current power allocation matrix can be calculated.

[0068] Step 103: Determine whether the mutual interference value is less than the preset mutual interference threshold;

[0069] In this embodiment of the invention, a threshold value of μ-th, which ensures that the MSE is not greater than a specified value, can be obtained by performing neural network learning on an artificial dataset. This threshold is used as a mutual interference threshold to determine whether the current acquisition area needs to be optimized by block segmentation.

[0070] Mean Squared Error (MSE) is a commonly used loss function used to evaluate the difference between a model's predictions and the true values. MSE measures the model's predictive performance by calculating the average of the squared errors between the predicted and true values.

[0071] Step 104: If not, divide the current acquisition area into four equal parts to obtain several sub-acquisition areas, take each sub-acquisition area as the current acquisition area in turn, and return to perform power pre-allocation on the current acquisition area to obtain the mutual interference value of the current power allocation matrix. The current acquisition area is equipped with several energy harvesting sensors.

[0072] Step 105: If so, use a drone to sequentially collect sensor data from the energy harvesting sensors in each sub-collection area.

[0073] In this embodiment of the invention, if the mutual coherence value of the current acquisition area is not less than the preset mutual coherence value, the current acquisition area is divided into four equal parts, and the above-mentioned judgment, segmentation, and acquisition operations are performed one by one in a clockwise direction until all sensor nodes have been acquired; if the mutual coherence value of the area to be acquired is less than the preset mutual coherence value, the sensor data of the energy harvesting sensor in each sub-acquisition area are acquired sequentially by the UAV.

[0074] This invention optimizes the power allocation of a network composed of multiple sensors, breaking down the optimization algorithm into sub-problems, which greatly reduces the complexity of the optimization algorithm and alleviates the requirements on computing devices.

[0075] Please see Figure 2 , Figure 2 A flowchart illustrating the steps of a data acquisition method for an energy-harvesting Internet of Things (IoT) according to another embodiment of the present invention. Specifically, it may include the following steps:

[0076] Step 201: Determine the current data collection area of ​​the energy harvesting IoT;

[0077] Step 202: Perform power pre-allocation on the current acquisition area to obtain the mutual coherence value of the current power allocation matrix. Several energy harvesting sensors are deployed in the current acquisition area.

[0078] In this embodiment of the invention, power pre-allocation can be performed on the current acquisition area, and the mutual interference value of the current power allocation matrix can be calculated.

[0079] In one example, the step of pre-allocating power to the current acquisition area to obtain the cross-coherence value of the current power allocation matrix may include the following sub-steps:

[0080] S21, obtain the number of sensors in the energy harvesting sensor and the mean square value of the maximum acceptable reconstruction signal error;

[0081] S22, input the number of sensors and the mean square value of the maximum acceptable reconstruction signal error into the pre-trained multilayer perceptron (MLP), and output the number of time slots;

[0082] In this embodiment of the invention, a dataset containing 15,600 data points—including the number of sensors n, the number of acquisition time slots m, the corresponding current power allocation matrix cross-coherence value μ, and the acceptable maximum mean square error of the reconstructed signal MSE—can be artificially constructed as training data for the time slots required for prediction. Since the artificial dataset has the characteristics of low dimensionality, single type, simple data, and non-linear data relationships, after comparing the Random Forest algorithm, Support Vector Machine (SVM) algorithm, Decision Tree algorithm, and Multilayer Perceptron (MLP) algorithm, based on the robustness of the results, an MLP network with two hidden layers and 20 neurons in each layer is selected as the training network model.

[0083] In this embodiment of the invention, the input of the time slot prediction network is the number of sensors n and the acceptable maximum mean square error of the reconstructed signal (MSE). The output is the number of time slots m required for the n sensor networks to ensure that the acceptable maximum mean square error of the reconstructed signal (MSE) does not exceed a specified value (preset mutual interference threshold).

[0084] The acceptable maximum mean square error of the reconstructed signal refers to the maximum acceptable mean square error value that is expected to be achieved.

[0085] S23, calculate the energy consumption of the energy harvesting sensor based on the number of time slots;

[0086] S24, construct a power allocation optimization problem for the current acquisition area with the objective of minimizing mutual interference and energy consumption as the constraint;

[0087] S25, solve the power allocation optimization problem to obtain the mutual interference value of the current power allocation matrix.

[0088] In a concrete implementation, a cluster consisting of n energy-harvesting IoT nodes (energy-harvesting sensors) and a fusion node (drone) can be constructed. The nodes send their observations (energy-harvesting sensor signal vectors) to the fusion center via a shared MAC. In the MAC communication model, the transmit power of all nodes in different time slots is coupled with channel effects, forming an effective measurement matrix for the standard CS problem. CS projected observations are simultaneously calculated through radio superposition and transmitted directly from the nodes in the network to the fusion center via an air interface using amplitude-modulated coherent transmission of randomly weighted sense values. Energy-harvesting IoT nodes can harvest energy from the environment, which can be modeled as a communication channel enhanced by energy-harvesting batteries. Therefore, this embodiment defines an energy arrival matrix E, where elements... Let x ∈ Rn be the energy harvested by the j-th IoT node in time slot l. Assume that energy harvesting and power consumption at each node occur in blocks: within each time slot, it is assumed that the energy harvested in the current time slot can be used for transmission in future time slots, and consumes a given transmit power. Let x ∈ Rn be a vector containing observations from all nodes, where x j Let x represent the observations collected by the j-th node, where 1 ≤ j ≤ n. Assuming x is compressible and fixed, we can model it as sparse relative to a fixed orthogonal basis. =[ It is sparse, that is:

[0089]

[0090] Regarding channel characteristics, it is assumed that there are channel coefficients in a single time slot. The channel is a flat fading channel, where 1 ≤ i ≤ m represents the slot index and 1 ≤ j ≤ n represents the node index. Further, it is assumed that the channel follows Rayleigh fading, where the channel coefficients for different slots follow a Gaussian distribution. In each slot, nodes simultaneously amplify their data and forward it to the fusion center, so that the signals from the nodes are linearly combined in the air. Specifically, node j amplifies its data x... j Multiply by a certain magnitude Then it is transmitted in the i-th time slot. Therefore, the fusion center receives:

[0091]

[0092] Among them, y i Let e ​​represent the measurement value in the i-th time slot. i This is a noise or error term. In our framework, the fusion center determines the power allocation for each node in each time slot, i.e. Then, the sensor nodes are notified. At time slot M (i.e., one frame), the fusion center receives the measurement vectors:

[0093]

[0094] Where H represents the channel coefficient matrix, Φ represents the power allocation matrix, Z is the element-wise product of the two matrices, i.e., Z = (H ⊙ Φ), and e is the additive noise or error term.

[0095] Therefore, by imposing full energy consumption constraints on each IoT node, the power allocation optimization problem can be constructed as follows:

[0096]

[0097]

[0098]

[0099] in, The mutual coherence value of the current power allocation matrix. Power allocation for energy harvesting sensors, The energy harvested by the j-th energy harvesting sensor in time slot l. For time slot index, For node indexing of energy harvesting sensors, The number of time slots, The number of sensors in an energy harvesting sensor. , For the preset threshold, This is the i-th element in the measurement matrix.

[0100] Step 203: Determine whether the mutual interference value is less than the preset mutual interference threshold;

[0101] Step 204: If not, divide the current acquisition area into four equal parts to obtain several sub-acquisition areas, take each sub-acquisition area as the current acquisition area in turn, and return to perform power pre-allocation on the current acquisition area to obtain the mutual interference value of the current power allocation matrix. The current acquisition area is equipped with several energy harvesting sensors.

[0102] After calculating the cross-coherence value of the current power allocation matrix, the cross-coherence value can be compared with the preset cross-coherence threshold. When the cross-coherence value of the current power allocation matrix is ​​not less than the preset cross-coherence threshold, the current acquisition area is divided into four equal parts, and the above judgment, segmentation, and acquisition operations are performed on small blocks in a clockwise direction until all sensor nodes have been acquired.

[0103] Step 205: If so, send activation signals sequentially to the energy harvesting sensors in each sub-collection area via the drone;

[0104] Step 206: Receive sensor data returned by the energy harvesting sensor in response to the activation signal.

[0105] When the mutual interference value of the current power distribution matrix is ​​less than the preset mutual interference threshold, the UAV sequentially sends an activation signal to the energy harvesting sensor in each sub-collection area and receives the sensor data returned by the energy harvesting sensor in response to the activation signal.

[0106] In one example, a drone data collection scenario model is as follows: Figure 3As shown, assume the sensors are randomly distributed within a square area. When the drone wants to collect data from the sensors in the current area, it flies to above the center point of the area and sends an activation signal to the designated sensors within that area. Sensors receiving the signal immediately begin transmitting data; sensors that do not receive the activation signal will not transmit data. Throughout the process, the drone's energy consumption is disregarded. It is assumed that the drone has sufficient battery power, does not need to return to recharge during the entire data collection process, and maintains the same altitude while flying and hovering.

[0107] This invention optimizes the power allocation of a network composed of multiple sensors, breaking down the optimization algorithm into sub-problems, which greatly reduces the complexity of the optimization algorithm and alleviates the requirements on computing devices.

[0108] Please see Figure 4 , Figure 4 This is a structural block diagram of an energy harvesting Internet of Things (IoT) data acquisition device provided in an embodiment of the present invention.

[0109] This invention provides a data acquisition device for an energy-harvesting Internet of Things (IoT) system, comprising:

[0110] The current acquisition area determination module 401 is used to determine the current acquisition area of ​​the energy harvesting Internet of Things.

[0111] The mutual coherence calculation module 402 is used to pre-allocate power in the current acquisition area to obtain the mutual coherence value of the current power allocation matrix. Several energy harvesting sensors are deployed in the current acquisition area.

[0112] The judgment module 403 is used to determine whether the mutual interference value is less than the preset mutual interference threshold.

[0113] Return module 404 is used to divide the current acquisition area into four equal parts to obtain several sub-acquisition areas, take each sub-acquisition area as the current acquisition area in turn, and return the steps of pre-allocating power to the current acquisition area to obtain the mutual interference value of the current power allocation matrix and deploying several energy harvesting sensors in the current acquisition area.

[0114] The data acquisition module 405 is used to sequentially collect sensor data from the energy harvesting sensors in each sub-acquisition area via a drone.

[0115] In this embodiment of the invention, the mutual interference value calculation module 402 includes:

[0116] The sensor quantity and maximum acceptable mean square error of the reconstructed signal are obtained by the submodule, which is used to obtain the sensor quantity and the maximum acceptable mean square error of the reconstructed signal of the energy harvesting sensor.

[0117] The time slot number calculation submodule is used to input the number of sensors and the mean square value of the maximum acceptable reconstruction signal error into the pre-trained multilayer perceptron (MLP) and output the number of time slots.

[0118] The energy consumption calculation submodule is used to calculate the energy consumption of energy-harvesting sensors based on the number of time slots.

[0119] The power allocation optimization problem construction submodule is used to construct the power allocation optimization problem of the current acquisition area with the objective of minimizing mutual interference and the constraint of energy consumption.

[0120] The cross-coherence value solving submodule is used to solve the power allocation optimization problem and obtain the cross-coherence value of the current power allocation matrix.

[0121] In this embodiment of the invention, the data acquisition module 405 includes:

[0122] The activation signal transmission submodule is used to send activation signals sequentially to the energy harvesting sensors in each sub-collection area via the UAV.

[0123] The data receiving submodule is used to receive sensor data returned by the energy harvesting sensor in response to the activation signal.

[0124] In this embodiment of the invention, the function of the power allocation optimization problem is as follows:

[0125]

[0126]

[0127]

[0128] in, The mutual coherence value of the current power allocation matrix. Power allocation for energy harvesting sensors, The energy harvested by the j-th energy harvesting sensor in time slot l. For time slot index, For node indexing of energy harvesting sensors, The number of time slots, The number of sensors in an energy harvesting sensor. , For the preset threshold, This is the i-th element in the measurement matrix.

[0129] This invention also provides an electronic device, which includes a processor and a memory:

[0130] The memory is used to store program code and transfer the program code to the processor;

[0131] The processor is used to execute the data acquisition method of the energy harvesting Internet of Things according to the instructions in the program code of this invention.

[0132] This invention also provides a computer-readable storage medium for storing program code for executing the data acquisition method for an energy-harvesting Internet of Things according to this invention.

[0133] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0134] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0135] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0137] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0139] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0140] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0141] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An energy-harvesting Internet-of-Things data collection method, characterized by, The method comprises the following steps: determining a current collection area of an energy collection type Internet of Things; performing power pre-allocation on the current collection area to obtain a mutual coherence value of a current power allocation matrix, the current collection area being provided with a plurality of energy collection type sensors; determining whether the mutual coherence value is less than a preset mutual coherence threshold value; if not, performing four equal division on the current collection area to obtain a plurality of sub-collection areas, and sequentially taking each of the sub-collection areas as a current collection area and returning to the step of performing power pre-allocation on the current collection area to obtain a mutual coherence value of a current power allocation matrix, the current collection area being provided with a plurality of energy collection type sensors; if yes, sequentially collecting sensor data of the energy collection type sensors in each of the sub-collection areas by using a drone.

2. The method of claim 1, wherein, The step of performing power pre-allocation on the current collection area to obtain a mutual coherence value of a current power allocation matrix comprises the following steps: obtaining a sensor quantity and an acceptable maximum reconstructed signal error mean square value of the energy collection type sensors; inputting the sensor quantity and the acceptable maximum reconstructed signal error mean square value into a pre-trained multi-layer perceptron (MLP) to output a time slot number; calculating energy consumption of the energy collection type sensors according to the time slot number; constructing a power allocation optimization problem of the current collection area with the minimum mutual coherence value as a target and the energy consumption as a constraint condition; solving the power allocation optimization problem to obtain a mutual coherence value of a current power allocation matrix.

3. The method of claim 1, wherein, The step of sequentially collecting sensor data of the energy collection type sensors in each of the sub-collection areas by using a drone comprises the following steps: sequentially sending an activation signal to the energy collection type sensors in each of the sub-collection areas by using a drone; receiving sensor data returned by the energy collection type sensors in response to the activation signal.

4. The method of claim 2, wherein, The function of the power allocation optimization problem is as follows: wherein, is a mutual coherence value of the current power allocation matrix, is the allocated power for the energy harvesting sensor, is the energy harvested by the jthenergy harvesting sensor in the time slot l, is the time slot index, is the node index of the energy harvesting sensor, is the number of time slots, is the number of sensors of the energy harvesting sensor, , is a pre-set threshold value, is the ith element in the measurement matrix.

5. An energy-harvesting Internet-of-Things data collection device, comprising: The method comprises the following steps: a current collection area determination module is configured to determine a current collection area of an energy collection type Internet of Things; a mutual coherence value calculation module is configured to perform power pre-allocation on the current collection area to obtain a mutual coherence value of a current power allocation matrix, the current collection area being provided with a plurality of energy collection type sensors; a determination module is configured to determine whether the mutual coherence value is less than a preset mutual coherence threshold value; a return module is configured to, if not, perform four equal division on the current collection area to obtain a plurality of sub-collection areas, and sequentially take each of the sub-collection areas as a current collection area and return to the step of performing power pre-allocation on the current collection area to obtain a mutual coherence value of a current power allocation matrix, the current collection area being provided with a plurality of energy collection type sensors; a data collection module is configured to, if yes, sequentially collect sensor data of the energy collection type sensors in each of the sub-collection areas by using a drone.

6. The apparatus of claim 5, wherein, The mutual coherence value calculation module comprises the following steps: a sensor quantity and an acceptable maximum reconstructed signal error mean square value obtaining sub-module is configured to obtain a sensor quantity and an acceptable maximum reconstructed signal error mean square value of the energy collection type sensors; The time slot number calculation submodule is configured to input the sensor number and the acceptable maximum reconstructed signal error mean square value into a pre-trained multi-layer perceptron (MLP), and output a time slot number. The energy consumption calculation submodule is configured to calculate energy consumption of the energy harvesting sensor according to the time slot number. The power allocation optimization problem construction submodule is configured to construct a power allocation optimization problem of a current acquisition area, with a minimum mutual coherence value as a target and the energy consumption as a constraint condition. The mutual coherence value solving submodule is configured to solve the power allocation optimization problem to obtain a mutual coherence value of a current power allocation matrix.

7. The apparatus of claim 5, wherein, The data acquisition module comprises: The activation signal sending submodule is configured to send an activation signal to the energy harvesting sensor in each of the sub-acquisition areas by the unmanned aerial vehicle in sequence. The data receiving submodule is configured to receive sensor data returned by the energy harvesting sensor in response to the activation signal.

8. The apparatus of claim 6, wherein, The power allocation optimization problem has a function as follows: wherein, is a mutual coherence value of the current power allocation matrix, is the allocated power for the energy harvesting sensor, is the energy harvested by the jthenergy harvesting sensor in the time slot l, is a time slot index, is a node index of the energy harvesting sensor, is a number of time slots, is a number of sensors of the energy harvesting sensor, , is a preset threshold value, is the ith element in the measurement matrix.

9. An electronic device, comprising: The device comprises a processor and a memory: The memory is configured to store program code and transmit the program code to the processor. The processor is configured to execute the energy harvesting Internet of Things data acquisition method according to instructions in the program code.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium is configured to store program code, and the program code is configured to execute the energy harvesting Internet of Things data acquisition method.

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