Optimization method of total system energy consumption in UAV-enabled wireless sensor networks

Abstract

The present invention relates to a method for optimizing total system energy consumption in a drone-enabled wireless sensor network, belonging to the field of drone-enabled data collection. The method comprises the following steps: S1: determining the optimal number of ground clusters based on the energy consumption models of drones and ground sensor nodes; S2: improving the fuzzy C-means algorithm to balance clusters and selecting the optimal ground cluster head using a backoff timer mechanism to reduce ground sensor node energy consumption; and S3: jointly optimizing the drone's descent altitude and the ground cluster head's transmit power to reduce the system's total energy consumption during data collection. While ensuring the integrity of data collection, the present invention minimizes the total system energy consumption in a drone-enabled wireless sensor network, thereby extending the network's lifecycle.

Description

Technical Field

[0001] The present invention belongs to the field of drone-enabled data collection and relates to a method for optimizing total system energy consumption in a drone-enabled wireless sensor network. Background Art

[0002] Drones (UAVs) have been frequently used as mobile data collectors in recent years due to their high maneuverability and flexible deployment. Together with randomly deployed ground-based sensor nodes, they enable the rapid construction of UAV-enabled wireless sensor networks (WSNs) to collect data in remote, dangerous, or inaccessible areas. Current research on UAV-enabled data collection focuses on three key areas, depending on the application requirements: minimizing task completion time, maximizing throughput, and minimizing energy consumption. In UAV-enabled WSNs, nodes often operate in harsh environments, making battery replacement inconvenient or impossible. This makes it difficult to recharge nodes, forcing them to exit the network when their batteries run out. Furthermore, UAVs are powered by onboard batteries, which, to minimize weight and size, have limited capacity, severely limiting their flight time and range. Therefore, reducing energy consumption is a fundamental challenge in UAV-enabled WSNs.

[0003] Current research has significant guiding value for the application of drone-assisted node communications. However, previous research has only considered the energy consumption of drones or ground sensor nodes, ignoring the impact of drones on ground sensor node energy consumption and vice versa. Specifically, considering only ground sensor node energy consumption often increases drone energy consumption, potentially preventing the drone from completing its data collection mission. Considering only drone energy consumption often increases ground sensor node energy consumption, leading to sensor node failure and shortening the network lifecycle. Furthermore, weighted minimization of both energy consumptions actually favors minimizing drone energy consumption while increasing ground sensor node energy consumption to minimize the weighted sum, which often fails to ensure that sensor node energy consumption remains within budget. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a method for optimizing the total energy consumption of the system in a drone-enabled wireless sensor network, which minimizes the total energy consumption of ground sensor nodes and drones while ensuring the data collection integrity of ground sensor nodes, so as to extend the network life cycle.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A method for optimizing total system energy consumption in a wireless sensor network enabled by drones, comprising the following steps:

[0007] S1: Determine the optimal number of ground node clusters based on the energy consumption model of UAVs and ground sensor nodes;

[0008] S2: The cluster is balanced by improving the fuzzy C-means algorithm and the backoff timer mechanism is used to select the optimal ground cluster head to reduce the energy consumption of ground sensor nodes;

[0009] S3: Jointly optimize the descent height of the UAV and the transmission power of the ground cluster head to reduce the total energy consumption of the system during data collection.

[0010] Furthermore, the step S1 specifically includes:

[0011] In a large-scale wireless sensor network enabled by drones, the ground sensor network is divided into K clusters, each cluster contains G k sensor nodes, 1≤k≤K, including a ground cluster head k and (G k -1) cluster member nodes, the drone acts as a data collector to collect perception data from ground sensor nodes and transmits the data to the data center for processing;

[0012] The total system energy consumption in the UAV-enabled wireless sensor network includes the total energy consumption of the ground nodes and the total energy consumption of the UAVs;

[0013] The total energy consumption of the ground sensor node E g Including the communication energy consumption between cluster member node i and ground cluster head k in each cluster And the communication energy consumption E between the ground cluster head k and the UAV k ,Right now:

[0014]

[0015]

[0016]

[0017] Among them, l represents the data size, E elec represents the energy consumption of the electronic system, ε fs represents the propagation loss coefficient, d ki represents the distance between the ground cluster head k and the cluster member node i; p k and R k represents the transmission power and data transmission rate of ground cluster head k.

[0018] The total energy consumption of the UAV is E uav Including the UAV flight energy consumption, hovering energy consumption and ascent and descent energy consumption, that is

[0019]

[0020] Among them, E flight Energy consumption for direct flight E flight , E hov The hovering energy consumption of a single ground node collected by the UAV, Edac The sum of the ascent and descent energy consumption when collecting data for a single ground node for the UAV.

[0021] Since the energy consumption of UAVs and ground nodes is at different levels, it is necessary to add an energy consumption compensation coefficient φ for ground sensor nodes; that is, the total energy consumption of the system in the UAV-enabled wireless sensor network E total Expressed as:

[0022]

[0023] N sensor nodes are randomly deployed in a square with a side length of M. The disk mathematical model is used to predict the distance between the cluster member node and the ground cluster head as E[d CH ]=1.262M 2 / 2πK, and the distance between ground cluster heads is

[0024] Assume that each cluster contains (N / K-1) cluster member nodes, and the energy consumption of the cluster member nodes communicating with the ground cluster head follows the free space model (d<d0), that is:

[0025]

[0026] Among them, P prop is the direct flight power of the UAV, E total It is a function containing only parameter K, which can be differentiated with respect to K:

[0027]

[0028] where v u is the speed of the UAV in straight line flight;

[0029] Finally, mathematical tools are used to solve the optimal number of ground cluster heads K opt .

[0030] Furthermore, in step S2, the fuzzy C-means algorithm is improved by using the second highest membership, including forming an initial cluster based on the fuzzy C-means algorithm, and implementing a less-in, more-out mechanism to balance the members within the cluster by using the second highest membership, as follows:

[0031] S21: Randomly generate K opt Initial centroids;

[0032] S22: Objective function based on the fuzzy C-means algorithm Update the membership matrix, calculate the new centroid and the new objective function, where μ ij and d ij are the membership degree of ground node i to cluster centroid j and the Euclidean distance between them, respectively, and m is a fuzzy index (m>0);

[0033] S23: Repeat step S22 until the iteration condition is met and an initial cluster is formed;

[0034] S24: Calculate the number of sensor nodes in each cluster, and form a node number greater than or equal to the threshold N / K opt Set A of and set B of which are less than the threshold;

[0035] S25: Calculate the number of nodes to be removed from each cluster in A to form set A e The number of nodes that need to be added to each cluster in B forms set B e ;

[0036] S26: Calculate the number of nodes in each cluster in A whose second highest membership belongs to the cluster in B to form a set F, and compare it with the number of nodes removed from the corresponding cluster, and update A, F, and A in ascending order according to the comparison results. e For A={c o ,c o+1 ...}、F={f o ,f o+1 ...}、A e ={e o ,e o+1 ...};

[0037] S27: Extract cluster c from A o , calculate the difference between the highest membership and the second highest membership of each node in the cluster, and arrange the differences in ascending order to form an array S;

[0038] S28: Extract cluster c o f o and e o For comparison, if f o <e o Execute steps S29-S210, otherwise execute step S211;

[0039] S29: Cluster c o Eliminate f o nodes to B, update B e Count in and delete the corresponding data in S;

[0040] S210: Cluster c o Eliminate e in sequence according to S o -f o nodes to A, update A e medium number;

[0041] S211: Cluster c o According to S, determine whether the node with the second highest membership belongs to B, and add the node belonging to B to B;

[0042] S212: Extract the next cluster in A and repeat steps S27-S211 until the last cluster in A completes node culling;

[0043] S213: Extract set B and repeat steps S24-S212; add conditions in both steps S210 and S211, and if the remaining clusters meet the threshold conditions, they are retained by the cluster itself;

[0044] S214: Calculate new centroids to form the final cluster.

[0045] Furthermore, in step S2, after the cluster formation phase is completed, the backoff timer mechanism is used to select the optimal ground cluster head for each cluster. The objective function F of the timer is defined by the membership degree of the sensor nodes in each cluster and the residual energy of the sensor nodes using the cluster centroid of each cluster, that is:

[0046]

[0047] Among them, α1, α2 are constant coefficients between 0 and 1, and α1+α2=1, E i is the node residual energy, E cap is the battery capacity, E i / E cap It means that the node with more residual energy is more likely to be selected as the ground cluster head; It means that the cluster with a higher membership degree of the cluster centroid to the node has a higher probability of being selected as the ground cluster head.

[0048] Furthermore, the joint optimization of the descent height of the UAV and the transmission power of the ground cluster head in step S3 to reduce the total energy consumption of the system during data collection includes the following steps:

[0049] The communication channel between the UAV and the ground node is a visual link, and its average path loss is L u,k , when the bandwidth is B and the Gaussian white noise is σ 2 In this case, given the path of the UAV when collecting data and the ground cluster head transmitting data at the hovering point, the UAV descent height h is jointly optimized. k and the ground cluster head transmission power p k The problem statement is:

[0050]

[0051] Among them (P d +P c ) is the descent and climb power of the UAV, (P i +P0) is the hovering power of the UAV, v v is the speed of the drone descending and climbing, h k To descend in height;

[0052] The constraints are:

[0053]

[0054]

[0055] in, is the ground cluster head transmission power p k The function is expressed as:

[0056]

[0057] in, where f c is the carrier frequency, c is the speed of light, is the probability of line-of-sight link, the average additional path loss of ηLoS and ηNLoS, and E0 is the energy budget of the ground cluster head;

[0058] because As p decreases, its definition The constraints are rewritten as:

[0059]

[0060] Given the transmission power vector P of the ground cluster head (r) , the subproblem is simplified to optimizing h, namely:

[0061]

[0062] The constraints are:

[0063]

[0064] By referring to the auxiliary variable ζ, the formula for optimizing h is transformed into:

[0065]

[0066] The constraints are:

[0067]

[0068]

[0069] make x=(Hh k ) 2 , f k (h k ) Using the first-order Taylor expansion, we can get the inequality:

[0070]

[0071] Will It is expressed as the vector of the optimized hovering point in the kth iteration, and the ground cluster head transmission power and the UAV descent height are obtained by solving the intelligent algorithm.

[0072] The beneficial effect of this invention is that when sensor nodes are deployed in harsh environments or high-risk areas where battery replacement is difficult, drones can act as mobile data collectors and communicate directly with the nodes, thereby reducing node energy consumption and extending the network lifecycle. While ensuring the integrity of data collection, this invention minimizes the total system energy consumption of drone-enabled wireless sensor networks, thereby extending the network lifecycle.

[0073] 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

[0074] 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:

[0075] Figure 1 This is a model diagram of the UAV ground node data acquisition system of the present invention. DETAILED DESCRIPTION

[0076] 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.

[0077] 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.

[0078] 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.

[0079] This embodiment is based on a method for optimizing the total energy consumption of a system in a wireless sensor network enabled by drones. Figure 1 As shown in Figure 1, a wireless communication system consists of a rotorcraft UAV and N ground sensor nodes. The ground sensor network is divided into K clusters, each containing G k sensor nodes, 1≤k≤K, including a ground cluster head k and (G k -1) cluster member nodes. The drone, as a mobile data collector, starts from the data center, flies to the top of the ground cluster head at a fixed height H, and descends a certain height h k The drone hovers to collect data from ground cluster head k. After completing the collection task, it climbs to altitude H and flies to the next ground cluster head k' to collect data. After collecting data from all ground cluster heads, it returns to the data center. To optimize the total energy consumption of drone-enabled wireless sensor networks, the system reduces total energy consumption by clustering ground sensor nodes and jointly optimizing the drone's descent altitude and the ground cluster head's transmit power. The specific steps are as follows:

[0080] 1. Determine the optimal number of ground node clusters based on the energy consumption model of drones and ground sensor nodes:

[0081] The number of ground cluster heads not only affects the lifecycle of the entire network but also the energy consumption of drones. If there are too many ground cluster heads, the drone's energy consumption will increase; if there are too few ground cluster heads, the data tasks they bear will increase, accelerating the death of ground cluster heads and affecting the operation of the entire network.

[0082] In a large-scale wireless sensor network enabled by drones, the ground sensor network is divided into K clusters, each cluster contains G k sensor nodes, 1≤k≤K, including a ground cluster head k and (G k -1) cluster member nodes, the drone acts as a data collector to collect perception data from ground sensor nodes and transmits the data to the data center for processing;

[0083] The energy consumption in UAV-enabled wireless sensor networks mainly includes the energy consumption of ground nodes and the total energy consumption of UAVs.

[0084] The total energy consumption of the ground sensor node E g Including the communication energy consumption between cluster member node i and ground cluster head k in each cluster And the communication energy consumption E between the ground cluster head k and the UAV k ,Right now:

[0085]

[0086]

[0087]

[0088] Among them, l represents the data size, E elec represents the energy consumption of the electronic system, ε fs represents the propagation loss coefficient, d ki represents the distance between the ground cluster head k and the cluster member node i; p k and R k represents the transmission power and data transmission rate of ground cluster head k.

[0089] The total energy consumption of the UAV is E uav Including the UAV flight energy consumption, hovering energy consumption and ascent and descent energy consumption, that is

[0090]

[0091] Among them, E flight Energy consumption for direct flight E flight , E hov The hovering energy consumption of a single ground node collected by the UAV, E dac The sum of the ascent and descent energy consumption when collecting data for a single ground node for the UAV.

[0092] Since the energy consumption of UAVs and ground nodes is at different levels, it is necessary to add an energy consumption compensation coefficient φ for ground sensor nodes; that is, the total energy consumption of the system in the UAV-enabled wireless sensor network E total Expressed as:

[0093]

[0094] N sensor nodes are randomly deployed in a square with a side length of M. The disk mathematical model is used to predict the distance between the cluster member node and the ground cluster head as E[d CH ]=1.262M 2 / 2πK, and the distance between ground cluster heads is

[0095] Assume that each cluster contains (N / K-1) cluster member nodes, and the energy consumption of the cluster member nodes communicating with the ground cluster head follows the free space model (d<d0), that is:

[0096]

[0097] Among them, P prop is the direct flight power of the UAV, E total It is a function containing only parameter K, which can be differentiated with respect to K:

[0098]

[0099] where v u is the speed of the UAV in straight line flight;

[0100] Finally, mathematical tools are used to solve the optimal number of ground cluster heads K opt .

[0101] 2. Improve the fuzzy C-means algorithm to balance the cluster and use the backoff timer mechanism to select the optimal ground cluster head to reduce the energy consumption of ground sensor nodes:

[0102] Ground sensor nodes are typically randomly deployed, and the fuzzy C-means algorithm can produce unbalanced clusters, leading to uneven energy consumption among ground sensor nodes and negatively impacting the network lifecycle. To form balanced clusters, the fuzzy C-means algorithm is improved using the sub-highest membership. This approach consists of two sub-stages: initial cluster formation based on the fuzzy C-means algorithm and balancing cluster members using a less-in, more-out mechanism using the sub-highest membership. The process is as follows:

[0103] Step 1: Randomly generate K opt Initial centroids;

[0104] Step 2: According to the objective function of the fuzzy C-means algorithm Update the membership matrix, calculate the new centroid and the new objective function, where μ ij and d ij are the membership degree of ground node i to cluster centroid j and the Euclidean distance between them, respectively, and m is a fuzzy index (m>0);

[0105] Step 3: Repeat step 2 until the iteration conditions are met and the initial cluster is formed;

[0106] Step 4: Calculate the number of sensor nodes in each cluster, and form a node number greater than or equal to the threshold N / K opt Set A of and set B of which are less than the threshold;

[0107] Step 5: Calculate the number of nodes to be removed from each cluster in A to form set A e The number of nodes that need to be added to each cluster in B forms set B e;

[0108] Step 6: Calculate the number of nodes in each cluster in A that have the second highest membership and belong to the cluster in B to form a set F, and compare it with the number of nodes removed from the corresponding cluster. Update A, F, and A according to the comparison results in ascending order. e For A={c o ,c o+1 ...}、F={f o ,f o+1 ...}、A e ={e o ,e o+1 ...};

[0109] Step 7: Extract cluster c from A o , calculate the difference between the highest membership and the second highest membership of each node in the cluster, and arrange the differences in ascending order to form an array S;

[0110] Step 8: Extract cluster c o f o and e o For comparison, if f o <e o Execute steps 9-10, otherwise go to step 11;

[0111] Step 9: Cluster c o Eliminate f o nodes to B, update B e Count in and delete the corresponding data in S;

[0112] Step 10: Cluster c o Eliminate e in sequence according to S o -f o nodes to A, update A e medium number;

[0113] Step 11: Cluster c o According to S, determine whether the node with the second highest membership belongs to B, and add the node belonging to B to B.

[0114] Step 12: Extract the next cluster in A and repeat steps 7-11 until the last cluster in A completes node culling;

[0115] Step 13: Extract set B and repeat steps 4 to 12. Add conditions in both steps 10 and 11. If the remaining clusters meet the threshold conditions, they will be retained by the cluster itself.

[0116] Step 14: Calculate new centroids to form the final clusters.

[0117] After the cluster formation phase, the backoff timer mechanism is used to select the optimal ground cluster head for each cluster. The objective function F of the timer is defined by the membership degree of the sensor nodes in each cluster and the residual energy of the sensor nodes using the cluster centroid of each cluster, that is:

[0118]

[0119] Wherein, α1 and α2 are constant coefficients between 0 and 1, and α1+α2=1. i , E cap are the node remaining energy and battery capacity respectively, and E i / E cap It means that the node with more residual energy is more likely to be selected as the ground cluster head. It means that the cluster with a higher membership degree of the cluster centroid to the node has a higher probability of being selected as the ground cluster head.

[0120] 3. Jointly optimize the drone's descent height and the ground cluster head's transmission power to reduce system energy consumption during data collection

[0121] The communication channel between the UAV and the ground sensor node is a visual link with an average path loss of L u,k , when the bandwidth is B and the Gaussian white noise is σ 2 In this case, given the path of the UAV when collecting data and the ground cluster head transmitting data at the hovering point, the UAV descent height h is jointly optimized. k and the ground cluster head transmission power p k The problem can be stated as:

[0122]

[0123] Among them (P d +P c ) is the descent and climb power of the UAV, (P i +P0) is the hovering power of the UAV, v v is the speed of the drone descending and climbing, h k To descend in height;

[0124] The constraints are:

[0125]

[0126]

[0127] in, It is a function of the ground cluster head transmission power p, and its expression is:

[0128]

[0129] in, where f c is the carrier frequency, c is the speed of light, is the probability of line-of-sight link, the average additional path loss of ηLoS and ηNLoS, and E0 is the energy budget of the ground cluster head. As p decreases, its definition The constraints are rewritten as:

[0130] Given the transmission power vector P of the ground cluster head (r) , the subproblem is simplified to optimizing h, namely:

[0131]

[0132] The constraints are:

[0133]

[0134] By referencing the auxiliary variable ζ, Equation (7) is transformed into:

[0135]

[0136] The constraints are:

[0137]

[0138]

[0139] Because k (h k ) is about (Hh k ) 2 Concave function, let x=(Hh k ) 2 , f k (h k ) Using the first-order Taylor expansion, we can get the inequality:

[0140]

[0141] Will It is expressed as the vector of the optimized hovering point in the kth iteration, and the ground cluster head transmission power and the UAV descent height are obtained by solving the intelligent algorithm.

[0142] 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 method for optimizing total system energy consumption in a wireless sensor network enabled by drones, characterized by: Including the following step: S1: Determine the optimal number of ground node clusters based on the energy consumption model of the UAV and ground sensor nodes; the step S1 specifically includes: In a large-scale wireless sensor network enabled by drones, the ground sensor network is divided into K clusters, each cluster contains G k sensor nodes, 1≤k≤K, including a ground cluster head k and (G k -1) cluster member nodes, the drone acts as a data collector to collect perception data from ground sensor nodes and transmits the data to the data center for processing; The total system energy consumption in the UAV-enabled wireless sensor network includes the total energy consumption of the ground nodes and the total energy consumption of the UAVs; The total energy consumption of the ground sensor node E g Including the communication energy consumption between cluster member node i and ground cluster head k in each cluster And the communication energy consumption E between the ground cluster head k and the UAV k ,Right now: Among them, l represents the data size, E elec represents the energy consumption of the electronic system, ε fs represents the propagation loss coefficient, d ki represents the distance between the ground cluster head k and the cluster member node i; p k and R k represents the transmission power and data transmission rate of ground cluster head k; The total energy consumption of the UAV is E uav Including the UAV flight energy consumption, hovering energy consumption and ascent and descent energy consumption, that is Among them, E flight Energy consumption for direct flight E flight , E hov The hovering energy consumption of a single ground node collected by the UAV, E dac The sum of the ascent and descent energy consumption when the UAV collects data from a single ground node; Since the energy consumption of UAVs and ground nodes is at different levels, it is necessary to add an energy consumption compensation coefficient φ for the ground sensor nodes, that is, the total energy consumption of the system in the UAV-enabled wireless sensor network E total Expressed as: N sensor nodes are randomly deployed in a square with a side length of M. The disk mathematical model is used to predict the distance between the cluster member node and the ground cluster head as E[d CH ]=1.262M 2 / 2πK, and the distance between ground cluster heads is Assume that each cluster contains (N / K-1) cluster member nodes, and the energy consumption of the cluster member nodes communicating with the ground cluster head follows the free space model (d<d0), that is: Among them, P prop is the direct flight power of the UAV, E total It is a function containing only parameter K, which can be differentiated with respect to K: where v u is the speed of the UAV in straight line flight; Finally, mathematical tools are used to solve the optimal number of ground cluster heads K opt ; S2: The cluster is balanced by improving the fuzzy C-means algorithm and the backoff timer mechanism is used to select the optimal ground cluster head to reduce the energy consumption of ground sensor nodes; In step S2, the fuzzy C-means algorithm is improved by using the second highest membership, including forming an initial cluster based on the fuzzy C-means algorithm, and implementing a less-in, more-out mechanism to balance the members within the cluster by using the second highest membership, as follows: S21: Randomly generate K opt Initial centroids; S22: Objective function based on the fuzzy C-means algorithm Update the membership matrix, calculate the new centroid and the new objective function, where μ ij and d ij are the membership degree of ground node i to cluster centroid j and the Euclidean distance between them, respectively, and m is a fuzzy index (m>0); S23: Repeat step S22 until the iteration condition is met and an initial cluster is formed; S24: Calculate the number of sensor nodes in each cluster, and form a node number greater than or equal to the threshold N / K opt Set A of and set B of which are less than the threshold; S25: Calculate the number of nodes to be removed from each cluster in A to form set A e The number of nodes that need to be added to each cluster in B forms set B e ; S26: Calculate the number of nodes in each cluster in A whose second highest membership belongs to the cluster in B to form a set F, and compare it with the number of nodes removed from the corresponding cluster, and update A, F, and A in ascending order according to the comparison results. e For A={c o ,c o+1 ...}、F={f o ,f o+1 ...}、A e ={e o ,e o+1 ...}; S27: Extract cluster c from A o , calculate the difference between the highest membership and the second highest membership of each node in the cluster, and arrange the differences in ascending order to form an array S; S28: Extract cluster c o f o and e o For comparison, if f o <e o Execute steps S29-S210, otherwise execute step S211; S29: Cluster c o Eliminate f o nodes to B, update B e Count in and delete the corresponding data in S; S210: Cluster c o Eliminate e in sequence according to S o -f o nodes to A, update A e medium number; S211: Cluster c o According to S, determine whether the node with the second highest membership belongs to B, and add the node belonging to B to B; S212: Extract the next cluster in A and repeat steps S27-S211 until the last cluster in A completes node culling; S213: Extract set B and repeat steps S24-S212; add conditions in both steps S210 and S211, and if the remaining clusters meet the threshold conditions, they are retained by the cluster itself; S214: Calculate new centroids to form the final cluster; In step S2, after the cluster formation phase is completed, the backoff timer mechanism is used to select the optimal ground cluster head for each cluster. The objective function F of the timer is defined by the membership degree of the sensor nodes in each cluster and the residual energy of the sensor nodes using the cluster centroid of each cluster, that is: Among them, α1, α2 are constant coefficients between 0 and 1, and α1+α2=1, E i is the node residual energy, E cap is the battery capacity, E i / E cap It means that the node with more residual energy is more likely to be selected as the ground cluster head; It means that the cluster with a high degree of membership of the cluster centroid to the node has a high probability of being selected as the ground cluster head; S3: Jointly optimizing the descent height of the UAV and the transmission power of the ground cluster head to reduce the total energy consumption of the system during data collection; Step S3, wherein the joint optimization of the descent height of the UAV and the transmission power of the ground cluster head to reduce the total energy consumption of the system during data collection, includes the following steps: The communication channel between the UAV and the ground node is a visual link, and its average path loss is L u,k , when the bandwidth is B and the Gaussian white noise is σ 2 In this case, given the path of the UAV when collecting data and the ground cluster head transmitting data at the hovering point, the UAV descent height h is jointly optimized. k and the ground cluster head transmission power p k The problem statement is: Among them (P d +P c ) is the descent and climb power of the UAV, (P i +P0) is the hovering power of the UAV, v v is the speed of the drone descending and climbing, h k To descend in height; The constraints are: in, is the ground cluster head transmission power p k The function is expressed as: in, where f c is the carrier frequency, c is the speed of light, is the line-of-sight link probability, ηLoS and ηNLoS are the average additional path losses, and E0 is the energy budget of the ground cluster head; because As p decreases, its definition The constraints are rewritten as: Given the transmission power vector P of the ground cluster head (r) , the subproblem is simplified to optimizing h, namely: The constraints are: By referring to the auxiliary variable ζ, the formula for optimizing h is transformed into: The constraints are: make x=(Hh k ) 2 , f k (h k ) Using the first-order Taylor expansion, we can get the inequality: Will It is expressed as the vector of the optimized hovering point in the kth iteration, and the ground cluster head transmission power and the UAV descent height are obtained by solving the intelligent algorithm.

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