CSMA / CA-based fallback method suitable for unmanned aerial vehicle auxiliary data acquisition

Abstract

The invention relates to a CSMA / CA-based fallback method suitable for unmanned aerial vehicle auxiliary data acquisition, and belongs to the technical field of communication. The problems that a traditional CSMA / CA protocol is high in channel collision rate and reduced in throughput due to a fixed backspace window in a high-mobility unmanned aerial vehicle scene are solved. According to the technical scheme, the method comprises the steps that a ground node calculates the priority in real time, and the priority fuses the difference value of the maximum communication duration and the to-be-uploaded data volume; the unmanned aerial vehicle dynamically allocates an initial rollback window value according to the global priority normal distribution parameter; and the nodes optimize rollback probability distribution through congestion weight iteration, and adaptively select a rollback value by taking throughput maximization as a target. Differentiated channel competition scheduling is achieved, the collision rate is remarkably reduced, the system throughput is improved, and meanwhile the adaptability to high-speed movement of the unmanned aerial vehicle is enhanced.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology and relates to a fallback method based on Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA) suitable for unmanned aerial vehicle (UAV) assisted data acquisition. Background Art

[0002] Drone-assisted data collection systems are widely used in fields such as the Internet of Things (IoT), smart agriculture, and smart homes. In this system, sensor nodes independently complete data collection and processing tasks, while drones act as mobile base stations, flying above ground nodes and performing data collection on demand.

[0003] Although the flexibility of drones significantly improves data collection efficiency, their high mobility, strong adaptability, and tolerance to harsh environments also bring new challenges to communication links. Specifically:

[0004] The rapid movement of drones causes continuous changes in transmission range, signal strength, and communication link stability;

[0005] The geographical distribution of ground nodes is uneven, and the amount of data to be transmitted varies significantly;

[0006] The traditional Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA) protocol cannot effectively adapt to these dynamic conditions. Its equal contention mechanism exacerbates channel conflicts in high-node-density scenarios, resulting in reduced throughput and increased transmission latency.

[0007] The existing standard CSMA / CA protocol uses a fixed backoff window and a random backoff strategy. This static design ignores two key factors: the maximum communication duration varies among nodes due to the movement of drones; and the disparity in the amount of data to be transmitted between nodes leads to significant differences in transmission time.

[0008] When multiple nodes simultaneously request access to a channel, traditional methods are prone to collisions and retransmissions, increasing energy consumption and potentially leading to data loss if the drone quickly moves out of communication range. This limitation severely limits system reliability in real-time scenarios, such as agricultural monitoring and disaster response.

[0009] Therefore, there is an urgent need for a dynamic fallback method that can combine the node's real-time communication window and data load characteristics to optimize the channel competition mechanism, thereby improving the communication performance of the highly mobile UAV-assisted data acquisition system. Summary of the Invention

[0010] In view of this, the purpose of the present invention is to provide a CSMA / CA-based fallback method suitable for drone-assisted data acquisition. According to the difference between the maximum communication time of each node and the amount of data to be uploaded, a different initial backoff window value is assigned to each node. Specifically, a smaller initial backoff window value is assigned to a node with a short communication time and a large amount of data to be uploaded, which initially helps it to obtain access opportunities as soon as possible. Subsequently, the fallback probability of the node is optimized according to the competition intensity of each fallback value of each competing node within the fallback window. The node falls back according to the optimized fallback probability distribution to reduce access collisions and improve network throughput.

[0011] In order to achieve the above object, the present invention provides the following technical solutions:

[0012] A CSMA / CA-based fallback method for UAV-assisted data collection includes the following steps:

[0013] S1: The drone flies at a fixed altitude H and broadcasts a beacon frame to activate ground nodes within the communication range; each node calculates its own priority PR i (t) and broadcast;

[0014] S2: The drone collects priority data, calculates the mean μ and standard deviation σ according to the normal distribution function, and broadcasts them; each node determines the initial backoff window value w based on μ and σ i (t), calculate the initial fallback probability

[0015] S3: With the goal of maximizing system throughput, each node iteratively optimizes the fallback probability q i,j , get the optimal fallback probability Then select the fallback value to access the channel.

[0016] Furthermore, in S1, the priority PR i The calculation formula for (t) is:

[0017] PR i (t) = T max_i (t)-T req_i (t)

[0018] in, is the maximum communication duration of node i;

[0019] The time required to upload data to node i;

[0020] r is the ground projection radius of the UAV communication range, v t is the speed of the drone, C i (t) is the amount of data to be uploaded, R i(t) is the upload rate.

[0021] Furthermore, the distance d i The calculation formula for (t) is:

[0022] d i (t)=|(x i -x t )sinθ t -(y i -y t )cosθ t |

[0023] Where (x t ,y t ,H) is the coordinate of the drone, (x i ,y i ,0) is the coordinate of node i, θ t is the flight direction angle.

[0024] Furthermore, in S2, the initial fallback window value w i The calculation formula for (t) is:

[0025]

[0026] Where z is the upper limit of the backoff window value, and CDF is the cumulative distribution function of the normal distribution.

[0027] Furthermore, in S3, the iterative optimization includes:

[0028] Define node exit probability Where δ is the time slot width, is the antenna radiation angle;

[0029] By congestion weight Suppress high contention backoff value, where r is the iteration round, j is the integer value in the initial backoff window, j = 1, 2, ..., w i (t), β∈(0.1,10] is the weight coefficient, is the channel congestion degree when node i takes the integer j in the r-1th round.

[0030] Furthermore, the fallback probability update formula is:

[0031]

[0032] Furthermore, the iteration termination condition is:

[0033] Reach the maximum iteration round r max ,or

[0034] Throughput increase Where ε is the preset threshold.

[0035] Furthermore, the upload rate R i The calculation formula for (t) is:

[0036]

[0037] Among them, P t_i (t) is the transmit power of node i in time slot t; λ is the wavelength; B is the system bandwidth; N0 is the noise power spectral density; G t ,G r are the transmitting and receiving antenna gains respectively; g i (t)~CN(0,1) is the Rayleigh fading coefficient;

[0038]

[0039] Furthermore, in S3, the system throughput The calculation formula is:

[0040]

[0041] where E[T t ] is the expected duration of the competition phase, T t Indicates the total duration of a competition phase; P s (r) is the probability of successful transmission in round r.

[0042] A drone-assisted data acquisition system, comprising:

[0043] UAV communication control module: configured to fly at a fixed altitude and periodically broadcast beacon frames via a wireless communication unit;

[0044] Ground node processing module: configured to receive the beacon frame broadcast by the UAV communication control module, calculate and broadcast priority data after being activated

[0045] Normal parameter processing module: integrated into the UAV communication control module, used to collect priority data and calculate normal distribution parameters μ and σ, and broadcast them to the ground node processing module through the omnidirectional antenna;

[0046] Fallback decision module: integrated in each ground node processing module, determines the initial fallback window value according to the received μ and σ, and selects the fallback value through iterative optimization;

[0047] A two-way wireless communication connection is established between the UAV communication control module and each ground node processing module, wherein:

[0048] Downlink transmission beacon frame and normal distribution parameters;

[0049] The uplink transmits priority data and service data based on the CSMA / CA protocol;

[0050] Priority broadcast channels are established between the ground node processing modules to realize distributed priority data exchange.

[0051] The beneficial effects of the present invention are:

[0052] (1) Based on the real-time difference between the maximum communication duration of a node and the amount of data to be uploaded, a smaller initial fallback window value is assigned to high-priority nodes. This ensures that nodes with limited communication windows but large amounts of data are given priority to seize the channel, significantly reducing the data loss rate. By dynamically quantifying the urgency of nodes through a priority calculation formula, the system breaks the egalitarian competition model of the traditional carrier sense multiple access conflict avoidance protocol and achieves load-balanced channel allocation.

[0053] (2) The drone collects global priority data and calculates normal distribution parameters, which the node then generates a personalized initial backoff window. This design simplifies centralized decision-making into distributed execution, avoiding the risk of single point failure. Channel congestion weights are introduced to dynamically suppress the probability of selecting a high-contention backoff value. Through multiple rounds of probabilistic iterative updates, the backoff distribution automatically adapts to the real-time network status.

[0054] (3) The optimized fallback probability distribution effectively disperses the node access timing, reduces the conflict probability of multiple nodes simultaneously occupying the channel, and reduces the number of data retransmissions. Taking system throughput as the optimization goal, the expected value of the contention phase duration is simultaneously compressed and the probability of successful transmission is improved, achieving a leap in the effective data transmission volume per unit time. The node exit probability is defined to quantify the impact of drone movement, and the risk of communication link interruption is predicted in the fallback decision, ensuring transmission reliability in high-mobility scenarios.

[0055] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0057] Figure 1 Schematic diagram of the system model of the CSMA / CA fallback method suitable for UAV-assisted data acquisition;

[0058] Figure 2 Schematic diagram of plane projection for calculating node communication duration;

[0059] Figure 3 Iterative optimization flow chart for node fallback probability. DETAILED DESCRIPTION

[0060] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0061] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0062] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0063] See also Figure 1 、 Figure 2 and Figure 3 , is a CSMA / CA-based fallback method suitable for UAV-assisted data collection, specifically:

[0064] A CSMA / CA-based fallback method for UAV-assisted data collection includes the following steps:

[0065] S1. A data acquisition system consists of a drone and ground nodes. The drone flies at a fixed altitude, H, and periodically broadcasts beacon frames to activate nearby ground nodes. The activated ground nodes upload data to the drone using the Carrier Sense Multiple Access with Collision Avoidance (CSMA / CA) protocol. If multiple nodes simultaneously request access to the channel, a collision occurs. To minimize the impact of collisions on system communication performance, competing nodes enter a fallback state. At this point, each node calculates its priority and broadcasts it.

[0066] S2: The drone places the received priority value into the priority sample library, calculates the mean and standard deviation of the sample library using the normal distribution function, and broadcasts the updated mean and standard deviation. Each node determines its own initial backoff window value based on the received mean and standard deviation, and calculates the initial backoff probability when each integer within the initial backoff window is used as the backoff value.

[0067] S3. With the goal of maximizing throughput, each node optimizes each initial backoff probability to obtain the optimal backoff probability for each backoff value, and selects the corresponding backoff value based on the optimized backoff probability.

[0068] Each node in S1 calculates its own priority, specifically:

[0069] like Figure 1 As shown, the UAV is flying in the air at a height of H, and the radiation angle of its antenna is The drone collects data from ground nodes. The projection of its communication range on the ground is a circle with a radius of r. A three-dimensional coordinate system is established, and the drone's flight time is discretized into time slots of width δ. In time slot t, t=1,2,...,t, the drone's flight speed is v t , the flight direction angle is θ t , the position coordinate is (x t ,y t ,H).

[0070] Assume that in time slot t, there are K t The ground nodes are within the communication range of the UAV, where the coordinates of the i-th node are (x i ,y i ,0),i=1,2,...,K t , the priority of node i is calculated as follows:

[0071] The flight trajectory of the UAV at time slot t is vertically projected onto the ground, as Figure 2 As shown, we get the straight line l t , whose expression is:

[0072] sinθ t ·(xx t )-cosθt ·(yy t )=0

[0073] Then nodes i to l t The distance d i (t) is:

[0074] d i (t)=|(x i -x t )sinθ t -(y i -y t )cosθ t |

[0075] Therefore, the maximum communication time between node i and the UAV is the time from when the UAV communication range just covers node i to when it is about to leave node i, that is, Figure 2 The time it takes for the drone to move between the two communication ranges:

[0076]

[0077] In order to calculate the data upload rate of node i at this time, a channel model between the drone and node i is established. Then, the data upload rate of node i is obtained according to the Shannon formula. The specific calculation process is as follows.

[0078] At time slot t, the three-dimensional space distance between node i and the UAV is:

[0079]

[0080] The path loss of the communication channel between the UAV and node i at time slot t is:

[0081]

[0082] Where λ is the wavelength.

[0083] Then the channel gain between node i and the UAV in time slot t is:

[0084]

[0085] Where G t , G r are the transmitting and receiving antenna gains respectively; g i (t) is the Rayleigh fading coefficient, which obeys the complex Gaussian distribution g i (t)~CN(0,1).

[0086] The received power of the UAV communicating with node i in time slot t is expressed as:

[0087] P r_i (t) = Pt_i (t)·|h i (t)| 2 +σ 2

[0088] Where, P t_i (t) is the transmit power of node i in time slot t; σ 2 is the additive white Gaussian noise power, satisfying σ 2 =N0B, N0 is the noise power spectral density, and B is the system bandwidth.

[0089] At this time, the signal-to-noise ratio (SNR) between the UAV and the ground node i is:

[0090]

[0091] Then the rate at which node i uploads data in time slot t is:

[0092]

[0093] Therefore, the time required for node i to upload data at time slot t is T req_i (t) is:

[0094]

[0095] Among them, C i (t) is the amount of data to be uploaded by node i at time slot t.

[0096] The priority of node i in time slot t is defined as the maximum communication time T max_i (t) and T req_i The difference between (t) is expressed as:

[0097] PR i (t) = T max_i (t)-T req_i (t)

[0098] Among them, PR i (t) represents the priority value of node i in time slot t.

[0099] In S2, each initial backoff window value is determined, and the initial backoff probability when each integer in the initial backoff window is taken as the backoff value is calculated, specifically:

[0100] Each node broadcasts the calculated priority value, and the drone collects the priority value of each node and compares it with the previous priority data according to the normal distribution function P~N(μ,σ 2), calculate its mean μ and standard deviation σ. The drone broadcasts the calculated μ and σ through beacon frames. Nodes that receive the beacon frames map the priority to the initial backoff window value using the normal distribution probability density function. The initial backoff window value of node i at time slot t is:

[0101]

[0102] Where z is the upper limit of the backoff window value, which is an integer greater than 0 and is used to limit the range of the initial backoff window value.

[0103] Node i determines w i (t) will be at (0,w i (t)] randomly selects an integer as the backoff value and sets it as the initial value of the counter. When the counter is reduced to 0, node i will request to access the channel. In the random case, node i selects (0, w i (t)] any integer j (j=1,2,...w i The initial probability of (t)) as the fallback value is:

[0104]

[0105] The sum of the probabilities that a node chooses each integer as a fallback value should be 1, i.e.

[0106] In S3, with the goal of maximizing throughput, each node optimizes each initial fallback probability. The specific steps are as follows:

[0107] (1) Due to the continuous movement of the UAV, the node may exit the UAV’s coverage during the competition phase. Define Q i (t) is the probability that node i is within the transmission range of the drone at time slot t but exits the transmission range in the next time slot, which is related to the speed of the drone v t , height H and antenna radiation angle They are all related. i The expression of (t) is:

[0108]

[0109] (2) Assume that the initial throughput of node i in time slot t is The initial probability of selecting an integer j within the initial backoff window as the backoff value is Its initial cumulative probability distribution function value Set the maximum number of optimization rounds to r max .

[0110] (3) The cumulative probability distribution function value of the return probability when the integer j is selected in the r-1th round (r≥1) is:

[0111]

[0112] Where, represents the probability that the fallback value selected by node i in the r-1th round is ≤ j.

[0113] (4) Calculate the channel congestion when node i takes integer j as the backoff value in round r-1:

[0114]

[0115] It indicates the probability that a node will roll back with the rollback value j in the r-1th round, reflecting the competition intensity of the rollback value j. The larger the value, the more nodes are likely to use the rollback value as the initial value of the counter under the current rollback probability distribution, and therefore the collision risk is also higher.

[0116] (5) In order to smoothly map the congestion degree to the congestion weight used in the optimization, an exponential decay function is introduced for processing. Based on the channel congestion degree when node i takes the integer j as the fallback value in the r-1th round, the congestion weight when node i takes the integer j as the fallback value in the rth round is calculated:

[0117]

[0118] Where exp is an exponential function; β∈(0.1,10] is a weight coefficient that controls the suppression strength. A larger β value significantly reduces the congestion weight, thereby more strongly suppressing the probability of taking the current fallback value. This weight is used to suppress congestion.

[0119] (6) Update the rollback probability when node i takes integer j in round r:

[0120]

[0121] (7) Calculate the system throughput value when node i takes j as the fallback value in round r:

[0122] In a competition phase, node i starts the backoff countdown with backoff value j, and the probability of successful transmission is:

[0123]

[0124] Where, represents the probability that node i chooses j as the fallback value; Indicates that other nodes have not completed the rollback before node i, l≠i.

[0125] When an access conflict occurs in time slot t, the total duration of the entire contention phase includes the backoff time and transmission time, and its expected value is expressed as:

[0126]

[0127] The expected value of the total duration of the competition phase refers to the average length of time from the beginning to the end of the entire competition phase. t Indicates the total duration of a competition phase. max =max w i (t) represents the maximum value of the initial backoff window in the competing node.

[0128] Normalizing the throughput of round r, we get:

[0129]

[0130] (8) If r ≥ r max or (ε is a very small constant), then the optimal fallback probability is obtained Node i is in the backoff window w i The backoff probability optimization process within (t) ends; otherwise, r=r+1, and go to step (3).

[0131] Get the optimal fallback probability After that, the node backs off according to the backoff probability distribution to reduce access collisions and improve network throughput. Figure 3 shown.

[0132] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A CSMA / CA-based fallback method suitable for drone-assisted data acquisition, characterized by: The following steps are involved: S1: The drone flies at a fixed altitude H and broadcasts a beacon frame to activate ground nodes within the communication range; each node calculates its own priority PR i (t) and broadcast; S2: The drone collects priority data, calculates the mean μ and standard deviation σ according to the normal distribution function, and broadcasts them; Each node determines the initial fallback window value w based on μ and σ i (t), calculate the initial fallback probability S3: With the goal of maximizing system throughput, each node iteratively optimizes the fallback probability q i,j , get the optimal fallback probability Then select the fallback value to access the channel.

2. The CSMA / CA-based fallback method for UAV-assisted data acquisition according to claim 1, characterized in that: In S1, the priority PR i The calculation formula for (t) is: PR i (t)=T max_i (t)-T req_i (t) in, is the maximum communication duration of node i; The time required to upload data to node i; r is the ground projection radius of the UAV communication range, v t is the speed of the drone, C i (t) is the amount of data to be uploaded, R i (t) is the upload rate.

3. The CSMA / CA-based fallback method for drone-assisted data acquisition according to claim 2, characterized in that: The distance d i The calculation formula for (t) is: d i (t)=|(x i -x t )sinθ t -(y i -y t )cosθ t | Where (x t ,y t ,H) is the coordinate of the drone, (x i ,y i ,0) is the coordinate of node i, θ t is the flight direction angle.

4. The CSMA / CA-based fallback method for UAV-assisted data acquisition according to claim 1, characterized in that: In S2, the initial fallback window value w i The calculation formula for (t) is: Where z is the upper limit of the backoff window value, and CDF is the cumulative distribution function of the normal distribution.

5. The CSMA / CA-based fallback method for UAV-assisted data acquisition according to claim 1, characterized in that: In S3, iterative optimization includes: Define node exit probability Where δ is the time slot width, is the antenna radiation angle; By congestion weight Suppress high contention backoff value, where r is the iteration round, j is the integer value in the initial backoff window, j = 1, 2, ..., w i (t), β∈(0.1,10] is the weight coefficient, is the channel congestion degree when node i takes the integer j in the r-1th round.

6. The CSMA / CA-based fallback method for UAV-assisted data acquisition according to claim 5, characterized in that: The fallback probability update formula is:

7. The CSMA / CA-based fallback method for UAV-assisted data acquisition according to claim 5, characterized in that: The iteration termination condition is: Reach the maximum iteration round r max ,or Throughput increase Where ε is the preset threshold.

8. The CSMA / CA-based fallback method for drone-assisted data acquisition according to claim 1, characterized in that: The upload rate R i The calculation formula for (t) is: Among them, P t_i (t) is the transmit power of node i in time slot t; λ is the wavelength; B is the system bandwidth; N0 is the noise power spectral density; G t ,G r are the transmitting and receiving antenna gains respectively; g i (t)~CN(0,1) is the Rayleigh fading coefficient; 9. The CSMA / CA-based fallback method for UAV-assisted data acquisition according to claim 1, characterized in that: In S3, the system throughput The calculation formula is: where E[T t ] is the expected duration of the competition phase, T t Indicates the total duration of a competition phase; P s (r) is the probability of successful transmission in round r.

10. A drone-assisted data acquisition system, characterized by: include: UAV communication control module: configured to fly at a fixed altitude and periodically broadcast beacon frames via a wireless communication unit; Ground node processing module: configured to receive the beacon frame broadcast by the UAV communication control module, calculate and broadcast priority data after being activated Normal parameter processing module: integrated into the UAV communication control module, used to collect priority data and calculate normal distribution parameters μ and σ, and broadcast them to the ground node processing module through the omnidirectional antenna; Fallback decision module: integrated in each ground node processing module, determines the initial fallback window value according to the received μ and σ, and selects the fallback value through iterative optimization; A two-way wireless communication connection is established between the UAV communication control module and each ground node processing module, wherein: Downlink transmission beacon frame and normal distribution parameters; The uplink transmits priority data and service data based on the CSMA / CA protocol; Priority broadcast channels are established between the ground node processing modules to realize distributed priority data exchange.

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