Multi-unmanned aerial vehicle Internet of Things data acquisition method and device based on LoRa communication

Abstract

The invention discloses a multi-unmanned aerial vehicle Internet of Things data acquisition method and device based on LoRa communication, and aims at a large-scale network connection limited area. The method comprises the following steps: firstly, deploying a group of full-electric-quantity unmanned aerial vehicles from a ground station, and considering the energy consumption of the unmanned aerial vehicles in a data acquisition process; secondly, the unmanned aerial vehicle positions a target according to a GPS signal of the data acquisition end and hovers at the target position; and then, the unmanned aerial vehicle wakes up the data acquisition end and receives data sent by the data acquisition end. After data acquisition is completed, the unmanned aerial vehicle continues to fly to the next target point. As the target area is wide, a plurality of unmanned aerial vehicles need to work cooperatively so as to optimize the execution efficiency of a data acquisition task; and finally, returning the unmanned aerial vehicle to the ground station, and comprehensively outputting a data acquisition result and energy consumption data of all unmanned aerial vehicles. The invention aims to reduce the energy consumption of the unmanned aerial vehicle and the data acquisition end, improve the data acquisition efficiency to maintain the data freshness, help to improve the decision quality and response speed, and is suitable for application scenarios with low power consumption and limited network connection.

Description

Technical Field

[0001] The present invention relates to a multi-UAV Internet of Things data collection method and device based on LoRa communication, belonging to the field of UAV-Internet of Things. Background Art

[0002] In recent years, with the rapid development of IoT technology and the deepening of the concept of "Internet of Everything", researchers around the world have continued to pay more attention to the data collection technology of IoT devices. The sharp increase in the number of sensor devices has put forward higher requirements for data collection and transmission, especially in data-intensive application scenarios, the efficiency and flexibility of the system have become particularly critical. For IoT devices deployed in remote, harsh environments or dangerous areas, traditional manual data collection methods are inefficient and difficult to ensure real-time and security.

[0003] In order to solve the bottleneck of communication data transmission, drone-assisted communication technology has gradually become an effective way to solve the problem of data collection for large-scale IoT devices. Compared with the traditional fixed base station deployment mode, drones can flexibly adjust flight trajectories and dynamically optimize coverage to provide efficient and low-latency data transmission services for IoT devices in complex environments (such as deserts and mountainous areas). This method not only improves the efficiency of data collection, but also greatly enhances the flexibility and adaptability of the system in various uncertain environments. Summary of the invention

[0004] The present invention provides a multi-UAV Internet of Things data collection method and device based on LoRa communication, which are used to solve the technical problem of information transmission in areas where stable energy cannot be provided and network connection is limited.

[0005] The present invention provides the following technical solutions:

[0006] The method comprises the following steps:

[0007] The ground station system allocates a mission target area to a drone group, the drone group includes at least one drone, the mission target area includes several target collection areas, and each target collection area has at least one drone.

[0008] S1. Construct a multi-UAV-assisted IoT data acquisition system based on LoRa wireless communication. The multi-UAV-assisted IoT data acquisition system based on LoRa wireless communication includes a data collection and processing server of a ground station; an STM32 single chip, a LoRa wireless communication module, a data acquisition sensor and a power supply device at the data acquisition end; and a UAV that communicates with the data acquisition device.

[0009] S2. When the drone flies over the area where the data collection terminal is deployed, the location of the data collection terminal is found based on the GPS signal of the data collection terminal;

[0010] S3, sending a wake-up command to the data acquisition terminal, waiting for the data acquisition to be awakened and receiving the data sent by the data acquisition terminal;

[0011] S4, after the data acquisition terminal is activated, the data acquisition terminal transmits the current signal strength of the data acquisition sensor detected to the drone group, establishes communication, and transmits the data to the drone group;

[0012] S5, the drone returns to the ground station, and the data collection results and energy consumption data of all drones are comprehensively output;

[0013] The step S3 specifically comprises the following steps:

[0014] S31, the drone group broadcasts a wake-up instruction to the location of the data collection terminal device based on the collected location information of the data collection terminal device;

[0015] S41, when the wake-up instruction is verified to be successful, activating the data acquisition terminal;

[0016] S42, the drone group establishes a data communication channel between the drone group and the data collection end based on the signal strength;

[0017] S43, the sensor sends the collected data to the drone group;

[0018] In step S42, the data collection end is set to transmit at a constant power P only when it is activated by the drone. m otherwise, the data acquisition terminal will remain in sleep mode to optimize energy consumption. The sensing capability of a drone is defined as its sensing range Ω. Any sensor node (m∈M) within the coverage range is considered to have been sensed, awakened by the wake-up command and upload its data to the drone. In a time slot, each data acquisition terminal can only be used by one drone for data collection. In time slot n, if the data acquisition terminal m is activated by drone u, the maximum transmission rate that can be achieved between the two can be expressed as:

[0019]

[0020] Where B is the system bandwidth; σ 2 is the received noise power; the amount of data λ successfully uploaded by UAV u from sensor node m in time slot n u,m (n) is:

[0021]

[0022] Step S5 specifically includes the following contents: The energy consumption of the drone is divided into two parts: communication energy and propulsion energy. Since the communication-related energy is relatively small, it can usually be ignored. The consumption of propulsion energy mainly depends on the flight speed and acceleration of the drone. In the model, it is assumed that the energy consumption during acceleration or deceleration can be ignored. Therefore, for a drone with a speed of V, its propulsion power consumption can be modeled as follows:

[0023]

[0024] Where: P0 is the blade profile power in the hovering state; Pi is the induced power in the hovering state; U tip is the tip speed of the rotor blade; v0 is the average rotor induced speed when hovering; d0 is the fuselage drag ratio; s is the rotor solidity; ρ is the air density; A is the area of ​​the rotor disk; V is the flight speed of the drone. In the hovering state, the flight speed V of the drone is 0, and the tip speed of the rotor blade U tip is equal to the average rotor induced speed v0 during hovering, so the parasitic power P parasite is also 0, because it is proportional to the square of the flight speed; at this time, the total power consumption only includes the blade profile power P0 and half of the induced power Pi, because is 0, so the power in the hovering state is:

[0025]

[0026] In drone-assisted IoT data collection, data freshness (Age of Information, AoI) is modeled by the following formula to more accurately reflect the delay and update rate generated during data transmission. The detailed formula can be expressed as:

[0027]

[0028] Among them: indicator variable a i (t): If data source i updates data at time t, then a i (t) = 1; if data source i does not update data at time t, then a i (t) = 0; τ i (t) is the time when data i was last updated; N is the total number of data sources. Using this model can more accurately capture and optimize the freshness of information in a multi-data source system and ensure the real-time transmission and processing of data. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a schematic diagram of the process structure of the present invention;

[0030] Figure 2 It is a schematic diagram of the unit structure of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention is further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and should not be used as a limitation of the present invention.

[0032] In the drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of description and simplified description of the present invention, and does not mean that the described device or element must have a specific orientation, be constructed or operate in a specific orientation. Therefore, the positional relationship described in the drawings is only for illustrative purposes and cannot be understood as a limitation of the present invention. A person of ordinary skill in the art can understand the actual meaning of the above terms according to the specific circumstances.

[0033] See also Figure 1-2 The present invention provides a method and device for collecting data from multiple UAVs in the Internet of Things based on LoRa communication, which specifically comprises the following steps:

[0034] The ground station system allocates a mission target area to a drone group; the drone group includes at least one drone; the mission target area includes several target collection areas; each target collection area has at least one drone.

[0035] Step 1: Construct a multi-UAV assisted IoT data acquisition system based on LoRa wireless communication, wherein the multi-UAV assisted IoT data acquisition system based on LoRa wireless communication includes a data collection and processing server of a ground station; an STM32 single chip, a LoRa wireless communication module, a data acquisition sensor and a power supply device at the data acquisition end; and a UAV that communicates with the data acquisition device;

[0036] Step 2: When the UAV flies over the area where the data collection terminal is deployed, the location of the data collection terminal is found based on the GPS signal of the data collection terminal;

[0037] Step 3: Send a wake-up command to the data acquisition terminal, wait for the data acquisition to be awakened and receive the data sent by the data acquisition terminal;

[0038] Step 4: After the data acquisition terminal is activated, the data acquisition terminal transmits the current signal strength of the data acquisition sensor detected to the drone group, establishes communication, and transmits data to the drone group;

[0039] Step 5: The drone returns to the ground station, which outputs the data collection results and the energy consumption data of all drones;

[0040] The specific steps include:

[0041] Step 2.1: Consider that the drone flies at a fixed altitude H, then its two-dimensional position coordinates in the nth time slot are q u (n) = [x u (n),y u (n)] UAV u moves a distance l in a certain direction. u (n)∈[0,l max ], where l max Indicates the maximum distance the drone can move in a time slot.

[0042] Step 3.1: The drone group broadcasts a wake-up instruction to the location of the data collection terminal device based on the collected location information of the data collection terminal device;

[0043] Step 4.1: When the wake-up instruction is verified to be successful, activating the data acquisition terminal;

[0044] Step 4.2: The drone group establishes a data communication channel between the drone group and the data collection terminal based on the signal strength;

[0045] Step 4.3: The sensor sends the collected data to the drone group;

[0046] In step 4.2, the data acquisition end is set to transmit at a constant power P only when it is activated by the drone. m otherwise, the data acquisition terminal will remain in sleep mode to optimize energy consumption. The sensing capability of a drone is defined as its sensing range Ω. Any sensor node (m∈M) within the coverage range is considered to have been sensed, awakened by the wake-up command and upload its data to the drone. In a time slot, each data acquisition terminal can only be used by one drone for data collection. In time slot n, if the data acquisition terminal m is activated by drone u, the maximum transmission rate that can be achieved between the two can be expressed as:

[0047]

[0048] Where B is the system bandwidth; σ 2 is the received noise power; the amount of data λ successfully uploaded by UAV u from sensor node m in time slot n u,m (n) is:

[0049]

[0050] Step 5 specifically includes the following: The energy consumption of the drone is divided into communication energy and propulsion energy. Since the communication-related energy is relatively small, it can usually be ignored. The propulsion energy consumption mainly depends on the flight speed and acceleration of the drone. In the model, it is assumed that the energy consumption during acceleration or deceleration can be ignored. Therefore, for a drone with a speed of V, its propulsion power consumption can be modeled as follows:

[0051]

[0052] Where: P0 is the blade profile power in the hovering state; Pi is the induced power in the hovering state; U tip is the tip speed of the rotor blade; v0 is the average rotor induced speed when hovering; d0 is the fuselage drag ratio; s is the rotor solidity; ρ is the air density; A is the area of ​​the rotor disk; V is the flight speed of the drone. In the hovering state, the flight speed V of the drone is 0, and the tip speed of the rotor blade U tip is equal to the average rotor induced speed v0 during hovering, so the parasitic power P parasite is also 0, because it is proportional to the square of the flight speed; at this time, the total power consumption only includes the blade profile power P0 and half of the induced power Pi, because is 0, so the power in the hovering state is:

[0053]

[0054] In drone-assisted IoT data collection, data freshness (Age of Information, AoI) is modeled by the following formula to more accurately reflect the delay and update rate generated during data transmission. The detailed formula can be expressed as:

[0055]

[0056] Among them: indicator variable a i (t): If data source i updates data at time t, then a i (t) = 1. If data source i does not update data at time t, then a i (t) = 0; τ i (t) is the time when data i was last updated; N is the total number of data sources. Using this model can more accurately capture and optimize the freshness of information in a multi-data source system and ensure the real-time transmission and processing of data. Therefore, one of the goals of the present invention is to minimize Δ(t).

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention, rather than to limit it. Although the present invention has been described in detail through the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solution, and these modifications or replacements should be included in the scope of the claims of the present invention.

Claims

1. A multi-UAV Internet of Things data collection method and device based on LoRa communication, characterized in that: The ground station system allocates a mission target area to a drone group, the drone group includes at least one drone, the mission target area includes a plurality of target collection areas, each target collection area has at least one drone; the method comprises the following steps: S1. Construct a multi-UAV-assisted IoT data acquisition system based on LoRa wireless communication. The multi-UAV-assisted IoT data acquisition system based on LoRa wireless communication includes a data collection and processing server of a ground station; an STM32 single chip, a LoRa wireless communication module, a data acquisition sensor and a power supply device at the data acquisition end; and a UAV that communicates with the data acquisition device. S2. When the drone flies over the area where the data collection terminal is deployed, the location of the data collection terminal is found based on the GPS signal of the data collection terminal; S3, sending a wake-up command to the data acquisition terminal, waiting for the data acquisition to be awakened and receiving the data sent by the data acquisition terminal; S4, after the data acquisition terminal is activated, the data acquisition terminal transmits the current signal strength of the data acquisition sensor detected to the drone group, establishes communication, and transmits the data to the drone group; S5. The UAV returns to the ground station and comprehensively outputs the data collection results and the energy consumption data of all UAVs.

2. A multi-UAV Internet of Things data collection method and device based on LoRa communication according to claim 1, characterized in that: The step S3 specifically comprises the following steps: S31. The drone group broadcasts a wake-up instruction to the location of the data collection terminal device based on the collected location information of the data collection terminal device.

3. A method and device for collecting data of multiple UAV Internet of Things based on LoRa communication according to claim 1, characterized in that: The step S4 specifically comprises the following steps: S41, when the wake-up instruction is verified to be successful, activating the data acquisition terminal; S42, the drone group establishes a data communication channel between the drone group and the data collection end based on the signal strength; S43: The sensor sends the collected data to the drone group.

4. According to step S42 of claim 3, the drone group establishes a data communication channel between the drone group and the data collection terminal based on the signal strength, characterized in that: In step S42, the data collection end is set to transmit at a constant power P only when it is activated by the drone. m otherwise, the data acquisition terminal will remain in sleep mode to optimize energy consumption; the perception capability of the drone is defined as its perception range Ω, and any sensor node (m∈M) within the coverage range is considered to have been sensed, awakened by the wake-up command and upload its data to the drone. In a time slot, each data acquisition terminal can only be used by one drone for data collection. In time slot n, if the data acquisition terminal m is activated by drone u, the maximum transmission rate that can be achieved between the two can be expressed as: Where B is the system bandwidth; σ 2 is the received noise power; the amount of data λ successfully uploaded by UAV u from sensor node m in time slot n u,m (n) is:

5. A method and device for collecting data of multiple UAV Internet of Things based on LoRa communication according to claim 1, characterized in that: step S5 specifically includes the following contents: The energy consumption of drones is divided into communication energy and propulsion energy. Since the communication-related energy is relatively small, it can usually be ignored; the consumption of propulsion energy mainly depends on the flight speed and acceleration of the drone. In the model, it is assumed that the energy consumption during acceleration or deceleration can be ignored. Therefore, for a drone with a speed of V, its propulsion power consumption can be modeled as follows: Where: P0 is the blade profile power in the hovering state; Pi is the induced power in the hovering state; U tip is the tip speed of the rotor blade; v0 is the average rotor induced speed when hovering; d0 is the fuselage drag ratio; s is the rotor solidity; ρ is the air density; A is the area of ​​the rotor disk; V is the flight speed of the drone. In the hovering state, the flight speed V of the drone is 0, and the tip speed of the rotor blade U tip is equal to the average rotor induced speed v0 during hovering, so the parasitic power P parasite is also 0, because it is proportional to the square of the flight speed; at this time, the total power consumption only includes the blade profile power P0 and half of the induced power Pi, because is 0, so the power in the hovering state is: In drone-assisted IoT data collection, the data freshness (Age of Information, AoI) is modeled by the following formula to more accurately reflect the delay and update rate generated during data transmission. The detailed formula can be expressed as: Among them: indicator variable a i (t): If data source i updates data at time t, then a i (t) = 1, if data source i does not update data at time t, then a i (t) = 0; τ i (t) is the time when data i was last updated; N is the total number of data sources. Using this model can more accurately capture and optimize the freshness of information in a multi-data source system and ensure the real-time transmission and processing of data.

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