Unmanned aerial vehicle charging scheduling method and system for wireless rechargeable sensor network

Abstract

The invention discloses a wireless rechargeable sensor network-oriented unmanned aerial vehicle charging scheduling method and system, and the method comprises the steps: taking the maximization of the service life of a cluster head as a target, calculating the priority of a deployed sensor node, and providing a basis for the selection of the cluster head in a network; secondly, selecting the cluster heads and evaluating the energy consumption of the cluster heads to form clusters by taking each cluster head as a target that each cluster head can undertake a data collection task in one period and does not die; and finally, in order to ensure that the tasks of the unmanned aerial vehicles are not overlapped, the base station allocates cluster head sets and a fixed period service mode to the unmanned aerial vehicles, and the unmanned aerial vehicles periodically execute charging and data collection tasks for the cluster head sets served by the unmanned aerial vehicles. The method gives full play to the convenience and flexibility of the multiple unmanned aerial vehicles as mobile data collection and charging equipment, and the nodes directly transmit the data to the corresponding cluster heads, thereby solving the problem of energy void, reducing the data uploading energy consumption, and prolonging the service life of the network.

Description

Technical Field

[0001] The present invention relates to a multi-UAV data collection and charging scheduling method for wireless rechargeable sensor networks, belonging to the field of wireless sensor networks and communication technologies. Background Art

[0002] At present, wireless rechargeable sensor networks have become an important part of the Internet of Things field and are widely used in fields such as environmental monitoring, smart home, and military reconnaissance. However, due to the limited energy, wide distribution, and complex and changeable working environment of nodes, how to improve the energy efficiency and data transmission efficiency of the network has always been the focus of attention in the academic and industrial circles.

[0003] To extend the network lifetime, mobile charging devices such as carts or UAVs can be used to supplement energy for nodes. However, due to the limitations of terrain factors such as obstacles, pools, and potholes, carts often cannot reach some special areas to serve nodes. In addition, the energy radiation range of carts is limited, resulting in low charging efficiency. On the contrary, using UAVs to perform wireless energy replenishment for nodes is not only basically unaffected by terrain but can also serve multiple nodes simultaneously. Considering the limited energy carried by UAVs themselves, in large-scale networks, multiple UAVs often work in parallel. However, if the areas to be served and service modes are not planned, it is very likely to cause uneven task loads, which directly affects the possibility of nodes being charged in a timely manner.

[0004] On the other hand, multi-hop data uploading in the Internet of Things often leads to excessive energy consumption differences among nodes, resulting in the "energy hole" phenomenon and the situation that "the nodes closer to the base station are more likely to die prematurely". "Using UAVs to collect the sensed data of nodes" is an effective means to solve the above problems. However, how to determine the hovering positions of UAVs is the key to ensuring their efficient data collection. Summary of the Invention

[0005] Object of the Invention: To solve the "energy hole" problem, the present invention provides a UAV charging scheduling method for wireless rechargeable sensor networks. This method aims to solve the "energy hole" problem, divides the network into multiple cluster head sets, and nodes directly transmit data to the corresponding cluster heads, avoiding the problem of uneven energy consumption rates of network nodes caused by nodes relaying data to the base station through multiple hops. At the same time, to ensure timely data collection, the energy of cluster heads can be replenished in a timely manner.

[0006] Technical Solution: To achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A UAV charging scheduling method for wireless rechargeable sensor networks, comprising the following steps:

[0008] Step (1), randomly deploy homogeneous sensor nodes in the planar area, and the base station deployed at the network center records the location and its attribute information of each node.

[0009] Step (2), calculate the priority priority of N nodes according to the attribute information of each node i , where i is an integer, i ∈ [1, N]), N represents the number of nodes, and let the number of cluster heads m = 1.

[0010] Step (3), select m cluster heads CH from all nodes according to the priority priority i , where j is an integer and j ∈ [1, m], and thus construct m clusters. j

[0011] Step (4), evaluate the energy consumption E(Cluster j ). If there is a cluster head with excessive energy consumption, update the value of m and jump to Step (3), otherwise execute Step (5).

[0012] Step (5), calculate the minimum number of drones X required to complete a round of charging and data collection tasks for all network nodes.

[0013] Step (6), the base station assigns the set of cluster heads to be served for each drone according to the distribution of nodes in each cluster and the minimum number of drones X required to complete a round of charging and data collection tasks for all network nodes.

[0014] Step (7), the base station sets a fixed periodic service mode for each drone, and the network starts to operate.

[0015] Step (8), during the operation of the network, all drones wirelessly charge the nodes and collect node data in turn according to the service mode set in Step (7).

[0016] Preferably: the priority priority in Step (2) i is calculated by the following formula:

[0017]

[0018] where, priority i represents the priority, represents the initial remaining energy, E max represents the maximum battery capacity of all nodes, B i represents the cache capacity of each node, B max represents the maximum cache capacity among all nodes, w 1 , w 2 , w 3 are three weight parameters and satisfy w​1 +w 2 +w 3 = 1, i, J, k ∈ [1, N], where N represents the number of nodes, and s i , s J , s k represents a node, and R max represents the maximum single-hop communication distance, and [·] represents a logical expression. If the condition within the square brackets is satisfied, the result is 1; otherwise, it is 0.

[0019] Preferably: The method for constructing m clusters in step (3) includes the following steps:

[0020] Step (3-1), arrange all the nodes in the network in descending order of the priority i value to form a candidate cluster head queue Q can .

[0021] Step (3-2), make the head of Q can become the first cluster head in the network. If m = 1 at this time, jump to step (3-3); otherwise, starting from the second node in Q can , find all the nodes where d(s i , CH j ) > R max in sequence as cluster heads until the number of cluster heads is equal to m. All the cluster heads are added to the cluster head queue Q CH in the selected order.

[0022] Step (3-3), initialize a cluster Cluster j for each cluster head CH j , and use CH j as the root node of the cluster.

[0023] Step (3-4), add all non-cluster head nodes to the root node CH j of the cluster that is closest to them.

[0024] Step (3-5), for each node s j within each cluster Cluster i , if its distance d(s i , CH j ) ≤

[0025] R max , then the node s i directly sends the data in one hop to the cluster head CH j , otherwise jump to step (3-6).

[0026] Step (3-6), if the node s iIf there is no neighbor node, then this node becomes an isolated point and no further operations will be performed on it. Otherwise, s i Select the node within the cluster that is the closest to it and is closer to the CH i than s j as its next-hop relay. If there are two or more such nodes, then select the node with a lower energy consumption rate) as its next-hop relay. If d(s k , CH k ) > R j , then s max continues to select its next-hop relay in this way until the selected relay is less than R k away from the CH j . max

[0027] Preferably: The energy consumption E(Cluster j ) of each cluster head in step (4) is calculated by the following formula:

[0028]

[0029] where l(s i ) is the size of the data packet sent by node s i to the cluster head within one cycle, p con represents the physical circuit power consumption of the cluster head, f(h) is a function related to the altitude h of the UAV that changes with the environment, and the UAV is always located directly above the cluster head to perform tasks; check in sequence whether the energy consumption of each cluster head in Q CH exceeds the standard. If , then it is considered that the energy consumption of cluster head CH j exceeds the standard; if there is a cluster head with excessive energy consumption, when m > 1, m will be updated to m + 1, otherwise, m will be updated to E(Cluster j ) represents the energy consumption of each cluster head, represents the remaining energy of the cluster head, E max represents the maximum battery capacity of all nodes, represents rounding up.

[0030] Preferably: The minimum number of UAVs X required to complete one round of charging and data collection tasks for all network nodes in step (5) is obtained by the following steps:

[0031] Step (5-1), taking the base station as the starting and ending point, and the hovering points at a height h directly above the m cluster heads as the traversal points, construct the shortest Hamiltonian cycle. Virtually schedule a UAV with infinite energy and cache space and a charging power of P charge ​The UAV starts from the base station BS and hovers at each traversal point along this loop in turn at a fixed flight altitude h and a fixed speed v. After fully charging the cluster head directly below the traversal point and collecting its data, it flies to the next traversal point until it returns to the base station BS.

[0032] Record the time t required for this UAV to complete the above tasks total and the total amount of data B received total .

[0033] Step (5-2), calculate the moving duration t of this UAV to complete the above tasks according to the following formula move :

[0034]

[0035] Step (5-3), calculate the energy consumption E of this UAV in this round of tasks by the following formula total :

[0036] E total = P move × t move +(P hover + P charge )×(t total - t move )

[0037] where P move is the moving power of the UAV, and P hover is the hovering power of the UAV, which is numerically equal to P when v = 0 move .

[0038] Step (5-4), calculate the minimum number of UAVs X required to complete all tasks in the network according to the following formula:

[0039]

[0040] where E max UAV is the maximum battery capacity of the UAV, and B max UAV is the maximum cache capacity of the UAV.

[0041] Preferably: The step of allocating the set of cluster heads to be served for the UAV in step (6) is:

[0042] Step (6-1), regard each cluster head as an independent cluster head set G l CH .

[0043] Step (6-2), let be the coordinates of the center point of each cluster head set, where:

[0044]

[0045] Among them, represents the abscissa of the center point of the cluster head set, represents the number of cluster heads in the cluster head set, and x(CH p ) represents the abscissa of the cluster head, represents the ordinate of the center point of the cluster head set, and y(CH p ) represents the ordinate of the cluster head.

[0046] Step (6-3), calculate the distance between the center points of any two cluster head sets l’ and G CH

[0047] Step (6-4), merge the two cluster head sets with the smallest distance into one cluster head set.

[0048] Step (6-5), if the number of cluster head sets at this time is not equal to X, jump to step (6-3), otherwise execute step (6-6).

[0049] Step (6-6), distribute the finally obtained X cluster head sets to X UAVs respectively.

[0050] Preferably: In step (7), the base station sets a fixed periodic service mode for each UAV, including:

[0051] Step (7-1), for each cluster head set, respectively establish a shortest Hamiltonian circuit with the base station BS as the starting and ending points and the hovering points h above the respective cluster heads in the set as the traversing points, and distribute the finally formed X paths to X UAVs respectively.

[0052] Step (7-2), the base station sets the first departure time of the UAV serving the cluster head set as:

[0053]

[0054] Among them, represents the first departure time of the UAV serving the cluster head set , is the time required for the cluster head CH j in j to start generating data until the data volume reaches B The calculation formula is j where B is the data volume of the cluster head, j is the data generation rate of CH For the cluster head CH j the death time, the calculation formula is represents the remaining energy of the cluster head, p j represents the energy consumption rate of the cluster head, α∈(0,1), β∈(0,1) are both adjustable parameters.

[0055] Step (7-3), set the task cycle of each UAV as:

[0056]

[0057] where represents the task cycle of the UAV, is the UAV charging each cluster head in the set

[0058] and the duration of collecting its data, the calculation formula is:

[0059]

[0060] where, R collect is the data collection rate of the UAV.

[0061] Step (7-4), the UAVs hover at each traversal point along their respective paths from the base station, and perform the charging and data collection tasks. After ΔT j (G l CH ) duration and then go to the next traversal point to perform the task, and finally return to the base station.

[0062] Step (7-5), after the UAVs replace the batteries at the base station, perform the tasks of the next round of cycle again according to Step (7-4).

[0063] Preferably: In step (1), N homogeneous sensor nodes are randomly deployed in a rectangular plane area of L×W, the node positions are fixed, and each node is marked as s i , i is an integer, and i∈[1,N]), N represents the number of nodes, L represents the length of the plane area, W represents the width of the plane area, and the base station deployed at the network center records the position and its attribute information of each node.

[0064] Another object of the present invention is to provide a UAV charging scheduling system for a wireless rechargeable sensor network to implement the above-mentioned UAV charging scheduling method for a wireless rechargeable sensor network, including sensor nodes, a base station, a priority calculation unit, a cluster construction unit, a cluster number update unit, and a minimum UAV number determination unit, where:

[0065] The sensor nodes are used for random deployment in the plane area.

[0066] The priority calculation unit is used to calculate the priorities priority of the N nodes according to the attribute information of each node i , where i is an integer and i ∈ [1, N]), N represents the number of nodes, and it is assumed that the number of cluster heads m = 1

[0067] The cluster construction unit is used to select m cluster heads CH from all nodes according to the priority priority of the nodes i , where j is an integer and j ∈ [1, m], and m clusters are constructed therefrom j The cluster number update unit is used to evaluate the energy consumption E(Cluster

[0068] ) of each cluster. If there is a cluster head with excessive energy consumption, the value of m is updated j The minimum number of drones determination unit is used to calculate the minimum number of drones X required to complete one round of charging and data collection tasks for all nodes in the network

[0069] The base station is deployed at the network center. The base station assigns a set of cluster heads to be served to each drone according to the distribution of nodes in each cluster and the minimum number of drones X required to complete one round of charging and data collection tasks for all nodes in the network. The base station sets a fixed periodic service mode for each drone, and the network starts to run. During the network operation, all drones wirelessly charge the nodes and collect node data in turn according to the set service mode

[0070] An electronic device includes: at least one processor, at least one memory, and a communication interface. The processor, the memory, and the communication interface communicate with each other. The memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the drone charging scheduling method for a wireless rechargeable sensor network

[0071] Compared with the prior art, the present invention has the following beneficial effects

[0072] Aiming at the problems of uneven energy consumption rate and multi-drone cooperative task allocation in a wireless rechargeable sensor network, the present invention realizes efficient collection of sensor network data and timely replenishment of node energy by designing a reasonable node priority mechanism, a perfect drone scheduling algorithm, and a periodic service mode, thereby significantly improving the energy utilization rate and stability of the network

[0073] Brief Description of the Drawings FIG. is the execution flowchart of the multi-drone data collection and charging scheduling method for a wireless rechargeable sensor network

[0074] Figure 1 is the execution flowchart of the multi-drone data collection and charging scheduling method for a wireless rechargeable sensor network

[0075] Figure 2 is a schematic diagram of cluster head selectionFigure 3 is Figure 2 An example of the information of sensor nodes deployed in the network.

[0076] Figure 4 It is a schematic diagram of the cluster distribution in the network before and after the initial cluster formation. (a) shows that each cluster head is an independent cluster before the initial cluster formation, and (b) shows that the surrounding nodes transmit data to the cluster head in the form of single-hop or multi-hop after the initial cluster formation.

[0077] Figure 5 It is a schematic diagram of the process of aggregating the cluster heads. (a) shows that each cluster head is a separate cluster head set initially, (b) shows the distribution of the network cluster head set after one aggregation, and (c) shows the distribution of the network cluster head set after two aggregations.

[0078] Figure 6 It is a schematic diagram of the base station constructing and allocating the traversal path of the unmanned aerial vehicle. Specific implementation manners

[0079] The following further clarifies the present invention in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. After reading the present invention, various equivalent modifications made by those skilled in the art to the present invention all fall within the scope defined by the appended claims of this application.

[0080] A method for charging and scheduling an unmanned aerial vehicle for a wireless rechargeable sensor network. First, with the goal of maximizing the lifetime of the cluster heads, calculate the priorities of the deployed sensor nodes to provide a basis for selecting the cluster heads in the network; secondly, with the goal that each cluster head can undertake the data collection task for one cycle without dying, select the cluster heads and evaluate the energy consumption of the cluster heads and then form clusters accordingly; finally, with the goal of ensuring that the tasks of the unmanned aerial vehicles do not overlap, the base station allocates the cluster head sets and fixed-period service modes to each unmanned aerial vehicle, and the unmanned aerial vehicles periodically execute charging and data collection tasks for the cluster head sets they serve. As Figure 1 shown, it includes the following steps:

[0081] Step (1): Randomly deploy N homogeneous sensor nodes in a rectangular plane area of L×W, and the node positions are fixed. Each node is marked as s i (where i is an integer and i ∈ [1, N]). After completing the node deployment, the base station deployed at the center of the network records the position and its attribute information of each node.

[0082] In another embodiment, the nodes in step (1) have sensing, communication, and computing capabilities, and their positions are known, and are configured with a wireless charging coil based on magnetic coupling resonance for receiving wireless energy supply from the unmanned aerial vehicle.

[0083] In another embodiment, the attribute information of the nodes in step (1) includes: the maximum battery capacity of all nodes is E max , the maximum single-hop communication distance is R max , and the cache capacity, initial remaining energy, and energy consumption rate of each node are B i , , and p i .

[0084] Step (2): Calculate the priority priority of these N nodes according to the attribute information of each node i , and set the number of cluster heads m = 1.

[0085] In another embodiment, the value of priority i is calculated by the following formula:

[0086]

[0087] Among them, priority i represents the priority, represents the initial remaining energy, E max represents the maximum battery capacity of all nodes, B i represents the cache capacity of each node, B max represents the maximum cache capacity among all nodes, w 1 , w 2 , w 3 are three weight parameters, and satisfy w 1 + w 2 + w 3 = 1, i, J, k ∈ [1, N], N represents the number of nodes, s i , s J , s k represents the node, R max represents the maximum single-hop communication distance, [·] represents a logical expression, if the condition in the square brackets is satisfied, the result is 1 otherwise 0.

[0088] Step (3): Select m cluster heads CH j (where j is an integer and j ∈ [1, m]) from all nodes, and construct m clusters therefrom.

[0089] In another embodiment, the process of selecting m cluster heads CH j (where j is an integer and j ∈ [1, m]) from all nodes and constructing m clusters therefrom is as follows:

[0090] Step (3-1) Arrange all the nodes in the network in descending order of the value of priority i into a candidate cluster head queue Q can .

[0091] Step (3-2) Let the head of Q can become the first cluster head in the network. If m = 1 at this time, jump to Step (3-3); otherwise, starting from the second node in Q can , find all nodes with d(s i , CH j ) > R max in sequence as cluster heads until the number of cluster heads is equal to m. All cluster heads are added to the cluster head queue Q CH in the selected order.

[0092] Step (3-3) Initialize a cluster Cluster j for each cluster head CH j , and use CH j as the root node of this cluster.

[0093] Step (3-4) All non-cluster head nodes are respectively added to the CH j nearest to them.

[0094] Step (3-5) For each node s j in each cluster Cluster i , if the distance d(s i , CH j ) ≤ R max , then node s i sends the data directly to the cluster head CH j in one hop; otherwise, jump to Step (3-6).

[0095] Step (3-6) If node s i has no neighbor nodes, then this node becomes an isolated point and no further operations will be performed on it subsequently. Otherwise, s i selects the node s i nearest to it within this cluster and closer to CH j than s k (if there are two or more such nodes, select the node with a lower energy consumption rate) as its next-hop relay. If d(s k , CH j ) > R max , then s k continues to select its next-hop relay in this way until the distance between the selected relay and CH j is less than R max .

[0096] Taking Figure 2 as an example, assume that m = 2 at this time and the maximum battery capacity E max of the nodes is 200 J. In network A, there are s 1 , s2 , s 3 , s 4 , s 5 The information of the five nodes is as follows Figure 3 shown. According to the cluster head selection method described above, the nodes finally selected as cluster heads are s 1 , s 5 , compared with other nodes, their priority i value is the largest, the initial remaining energy and cache capacity are larger, the number of neighbors is smaller, and it can better avoid the rapid depletion of node power and data cache overflow, thus maintaining the stable operation of the network.

[0097] Take Figure 4 as an example. Network B deploys 14 nodes, and 3 of them are selected as cluster heads. Before the initial clusters are formed, each cluster head forms a separate cluster, that is, there are 3 clusters, as Figure 4 (a) shown. After the initial clusters are formed, each node joins the cluster where the nearest cluster head is located. If it is within the transmission range of the cluster head, it communicates with the cluster head through single-hop; otherwise, the data of the node will be forwarded through multiple relay nodes, and the relay node is the nearest neighbor node to the node, as Figure 4 (b) shown.

[0098] Step (4): Evaluate the energy consumption E(Cluster j ) of each cluster. If there is a cluster head with excessive energy consumption, update the value of m and jump to step (3); otherwise, execute step (5).

[0099] In another embodiment, the energy consumption E(Cluster j ) of each cluster head is calculated by the following formula:

[0100]

[0101] where l(s i ) is the size of the data packet sent by node s i to the cluster head within one cycle, p con represents the physical circuit power consumption of the cluster head, f(h) is a function related to the height h of the UAV that changes with the environment, and the UAV is always located directly above the cluster head to perform tasks; check whether their energy consumption exceeds the standard in the order of each cluster head in Q CH in turn. If then it is considered that the energy consumption of cluster head CH j exceeds the standard; if there is a cluster head with excessive energy consumption, when m > 1, m will be updated to m + 1; otherwise, m will be updated to E(Cluster j ) represents the energy consumption of each cluster head, represents the remaining energy of the cluster head, E maxRepresents the maximum battery capacity of all nodes, Indicates rounding up.

[0102] Step (5): Calculate the minimum number of drones X required to complete one round of charging and data collection tasks for all nodes in the network.

[0103] In another embodiment, the minimum number of drones X required to complete all tasks in the network in step (5) is obtained by the following steps:

[0104] Step (5-1) Construct the shortest Hamiltonian cycle with the base station as the starting and ending point and the hovering points at a height h directly above the m cluster heads as the traversal points. Virtually schedule a drone with infinite energy, buffer space, and a charging power of P charge to start from the BS, hover at each traversal point along this cycle at a fixed flight height h and a fixed speed v, and after fully charging the cluster head directly below the traversal point and collecting its data, fly to the next traversal point until it returns to the BS. Record the time t required for this drone to complete the above tasks total and the total amount of data B received total .

[0105] Step (5-2) Calculate the moving duration t of this drone to complete the above tasks according to the following formula move :

[0106]

[0107] Step (5-3) Calculate the energy consumption E of this drone in this round of tasks according to the following formula total :

[0108] E total = P move ×t move +(P hover + P charge )×(t total - t move )

[0109] where P hover is the hovering power of the drone, which is numerically equal to P when v = 0 move , P move is the moving power of the drone, which is calculated by the following formula:

[0110]

[0111] where P 0 and P i are two constants, representing the blade profile power and induced power in the hovering state respectively, U tip is the rotor blade tip speed, v 0is the average rotor induced velocity during hovering, d 0 and s are the fuselage drag ratio and rotor robustness, and ρ and A represent the air density and the rotor disk area respectively.

[0112] Step (5-4) calculates the minimum number of UAVs X required to complete all tasks in the network, which can ensure that no node will die due to energy exhaustion or cache overflow during the mission execution of each UAV:

[0113]

[0114] where E max UAV is the maximum battery capacity of the UAV, and B max UAV is the maximum cache capacity of the UAV.

[0115] Step (6): The base station assigns the set of cluster heads that each UAV needs to serve according to the distribution of each cluster node.

[0116] In another embodiment, the step of assigning the set of cluster heads that each of the X UAVs needs to serve in step (6) is: Step (6-1) regards each cluster head as an independent set of cluster heads

[0117] Step (6-2) Let be the coordinates of the center point of each set of cluster heads, where:

[0118]

[0119] Step (6-3) calculates the distance between the center points of any two sets of cluster heads l’ CH and G

[0120] Step (6-4) merges the two sets of cluster heads with the smallest distance into one set of cluster heads.

[0121] Step (6-5) If the number of sets of cluster heads at this time is not equal to X, jump to step (6-3), otherwise execute step (6-6).

[0122] Step (6-6) assigns the finally obtained X sets of cluster heads to X UAVs respectively.

[0123] Taking Figure 5 as an example, network C deploys 16 nodes, and 4 of them are selected as cluster heads. Assume that the cluster heads of this network need to be finally merged into 2 sets of cluster heads. Before merging the cluster heads, each cluster head is a separate set of cluster heads, that is, there are 4 sets of cluster heads, as Figure 5(a) As shown. Subsequently, the two cluster heads with the smallest inter-cluster distance are selected and merged, the two cluster heads before merging are deleted, and the center points of the new cluster head set are marked, as Figure 5 (b) As shown. The final result of the cluster head set merging is as Figure 5 (c) As shown.

[0124] Step (7): The base station sets a fixed periodic service mode for each UAV, and the network starts to operate.

[0125] In another embodiment, the base station setting a fixed periodic service mode for each UAV in step (7) includes:

[0126] Step (7-1) For each cluster head set, a shortest Hamiltonian circuit is respectively established with BS as the starting and ending points and the hovering points h above the respective cluster heads in the set as the traversing points, and the finally formed X paths are respectively assigned to X UAVs.

[0127] Step (7-2) The base station sets the first departure time of the UAV in service cluster head set G l CH as:

[0128]

[0129] where is the time required for cluster head CH j to start generating data until the data volume reaches B j The calculation formula is where is the data generation rate of CH j . is the death time of cluster head CH j The calculation formula is Both α∈(0,1) and β∈(0,1) are adjustable parameters. If α is larger than β, it means that the departure time of the UAV is more inclined to be determined by the data volume collected by the cluster head. On the contrary, it means that its departure time is mainly affected by the remaining survival time of the cluster head.

[0130] Step (7-3) Set the task cycle of each UAV as:

[0131]

[0132] where is the time for the UAV to charge each cluster head in cluster head set and collect its data. The calculation formula is:

[0133]

[0134] where Rcollect is the data collection rate of the UAV.

[0135] In step (7-4), the UAVs hover from the base station to each traversal point along their respective paths, and perform charging and data collection tasks. After ΔT j (G l CH ) duration, they then go to the next traversal point to perform tasks, and finally return to the base station.

[0136] In step (7-5), after the UAVs replace the batteries at the base station, they perform the tasks of the next cycle again according to step (7-4).

[0137] Step (8): During the network operation, all UAVs wirelessly charge the nodes and collect node data in turn according to the service mode set in step (7).

[0138] In another embodiment, the execution process of the method is as Figure 1 shown, and includes: an initial wireless rechargeable sensor network, deploying sensor nodes, and recording the specific positions and attribute information of the nodes. Initialize m, and select m nodes as cluster heads and form clusters with the goal of the remaining energy of the cluster heads and the larger cache capacity and fewer neighbor nodes. Next, to ensure that the current number of cluster heads m in the network can undertake the current network task volume, it is necessary to compare the estimated energy consumption of the cluster heads with the remaining energy of the cluster heads as the basis for exceeding the energy consumption standard. If it exceeds the standard, update m and reselect the cluster heads. If it does not exceed the standard, with the goal of ensuring that the UAV tasks do not overlap, the base station assigns a cluster head set and a fixed-period service mode to each UAV, and the UAVs periodically perform charging and data collection tasks for the cluster head sets they serve.

[0139] The present invention gives full play to the convenience and flexibility of multiple UAVs as mobile data collection and charging devices. At the same time, the nodes directly transmit their data to the corresponding cluster heads, solving the "energy hole" problem, reducing the energy consumption of data upload, and extending the network lifetime.

[0140] In another embodiment, a UAV charging scheduling system for a wireless rechargeable sensor network is provided to implement the above-mentioned UAV charging scheduling method for a wireless rechargeable sensor network, including sensor nodes, a base station, a priority calculation unit, a cluster construction unit, a cluster number update unit, and a minimum UAV number determination unit, where:

[0141] The sensor nodes are used for random deployment in a planar area.

[0142] The priority calculation unit is used to calculate the priority priority of N nodes according to the attribute information of each node i , i is an integer, i ∈ [1, N]), N represents the number of nodes, and let the number of cluster heads m = 1.

[0143] The cluster construction unit is used to select m cluster heads CH from all nodes according to the priority of the nodes i where j is an integer and j ∈ [1, m], and m clusters are constructed accordingly. j

[0144] The cluster number update unit is used to evaluate the energy consumption E(Cluster j ). If there is a cluster head with excessive energy consumption, the value of m is updated.

[0145] The minimum number of drones determination unit is used to calculate the minimum number of drones X required to complete a round of charging and data collection tasks for all nodes in the network.

[0146] The base station is deployed at the network center. The base station assigns a set of cluster heads to be served to each drone according to the distribution of nodes in each cluster and the minimum number of drones X required to complete a round of charging and data collection tasks for all nodes in the network. The base station sets a fixed periodic service mode for each drone, and the network starts to operate. During the operation of the network, all drones wirelessly charge the nodes and collect node data in turn according to the set service mode.

[0147] In another embodiment, an electronic device is provided, including: at least one processor, at least one memory, and a communication interface. The processor, memory, and communication interface communicate with each other. The memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the drone charging scheduling method for a wireless rechargeable sensor network.

[0148] For Figure 6 example, there are two drones UAV 1 and UAV 2 , the set of cluster heads served by UAV 1 has 8 cluster heads, and the set of cluster heads served by UAV 2 has 7 cluster heads. According to the assigned set of cluster heads and the fixed periodic service mode, the two drones start from the base station along the constructed shortest Hamiltonian path, pass through the stationary points directly above the cluster heads in their respective sets of cluster heads, charge the cluster heads and collect cluster head data at each stationary point, and finally return to the base station.

[0149] By scheduling multiple drones for wireless charging and data collection, the present invention not only realizes the efficient collection of data in the sensor network, but also realizes the timely replenishment of node energy, thereby significantly improving the energy utilization rate and stability of the network and extending the life cycle of the wireless rechargeable sensor network.

[0150] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A charging scheduling method for unmanned aerial vehicles for wireless rechargeable sensor networks, characterized in that: The following steps are involved: Step (1), randomly deploy homogeneous sensor nodes in the plane area, and the base station deployed in the center of the network records the location and attribute information of each node; Step (2): Calculate the priority of N nodes based on the attribute information of each node. i , i is an integer, i∈[1,N]), N represents the number of nodes, and the number of cluster heads is set to m=1; Step (3), according to the node priority i Select m cluster heads CH from all nodes j , where j is an integer and j∈[1,m], and m clusters are constructed from it; Step (4), the energy consumption E(Cluster j ) for evaluation; if there is a cluster head with excessive energy consumption, update the value of m and jump to step (3), otherwise execute step (5); Step (5), calculate the minimum number of drones X required to complete a round of charging and data collection tasks for all network nodes; Step (6), the base station allocates a cluster head set to be served to each drone according to the distribution of each cluster node and the minimum number of drones X required to complete a round of charging and data collection tasks for all network nodes; Step (7), the base station sets a fixed periodic service mode for each drone, and the network starts running; Step (8), during the network operation, all drones wirelessly charge the nodes and collect node data in turn according to the service mode set in step (7).

2. The method for charging and scheduling unmanned aerial vehicles for wireless rechargeable sensor networks according to claim 1, characterized in that: The priority in step (2) i The value of is calculated by the following formula: Among them, priority i Indicates priority, E i r Represents the initial residual energy, E max represents the maximum battery capacity of all nodes, B i represents the cache capacity of each node, B max represents the maximum cache capacity of all nodes, w1, w2, w3 are three weight parameters, and satisfy w1+w2+w3=1, i,J,k∈[1,N], N represents the number of nodes, s i ,s J ,s k represents a node, R max represents the maximum single-hop communication distance, [·] represents a logical expression. If the condition in the square brackets is met, the result is 1, otherwise it is 0.

3. The method for charging and scheduling unmanned aerial vehicles for wireless rechargeable sensor networks according to claim 2, characterized in that: The method for constructing m clusters in step (3) includes the following steps: Step (3-1), sort all nodes in the network according to priority i The values ​​are arranged in descending order to form the candidate cluster head queue Q can ; Step (3-2), let Q can The team head becomes the first cluster head in the network; If m=1, jump to step (3-3), otherwise, start from Q can Starting from the second node in, find all d(s i ,CH j )>R max The nodes are used as cluster heads until the number of cluster heads is equal to m. All cluster heads are added to the cluster head queue Q in the order of selection. CH ; Step (3-3), for each cluster head CH j Initialize a cluster j , and CH j As the root node of the cluster; Step (3-4), all non-cluster head nodes join the root node CH of the cluster closest to them. j ; Steps (3-5), for each cluster j Each node s in i , if the distance between it and the cluster head is d(s i ,CH j )≤R max , then node s i Send the data directly to the cluster head CH in one hop j , otherwise jump to step (3-6); Step (3-6), if node s i If there is no neighbor node, the node becomes an isolated point and no subsequent operations will be performed on it; otherwise, s i Select the closest one in the cluster and compared to s i Closer to CH j Nodes k as its next hop relay; if d(s k ,CH j )>R max , then s k Continue to select the next hop relay in this way until the selected relay is consistent with CH j The distance is less than R max .

4. The method for charging and scheduling unmanned aerial vehicles for wireless rechargeable sensor networks according to claim 3 is characterized in that: The energy consumption E(Cluster j ) is calculated by the following formula: Among them, l(s i ) is node s i The size of the data packet sent to the cluster head in one cycle, p con represents the power consumption of the cluster head physical circuit, f(h) is a function related to the height h of the drone that changes with the environment, and the drone is always located directly above the cluster head to perform tasks; CH Check whether their energy consumption exceeds the standard in the order of E(Cluster j )>E j r , then the cluster head CH j Energy consumption exceeds the limit; if there is a cluster head with energy consumption exceeding the limit, when m>1, m will be updated to m+1, otherwise, m will be updated to E(Cluster j ) represents the energy consumption of each cluster head, E j r represents the residual energy of the cluster head, E max represents the maximum battery capacity of all nodes, Indicates rounding up.

5. The method for charging and scheduling unmanned aerial vehicles for wireless rechargeable sensor networks according to claim 4, characterized in that: The minimum number of drones X required to complete a round of charging and data collection tasks for all network nodes in step (5) is obtained by the following steps: Step (5-1), taking the base station as the starting and ending point, and the hovering point at a height h directly above the m cluster heads as the traversal point, construct the shortest Hamiltonian circuit; virtually dispatch a plane with infinite energy and cache space and a charging power of P charge The UAV is ordered to start from the base station BS, fly to each traversal point in turn along this loop at a fixed flight height h and a fixed speed v, and after fully charging the cluster head directly below the traversal point and collecting its data, fly to the next traversal point until it returns to the base station BS; Record the time t required for the drone to complete the above task total The total amount of data received B total ; Step (5-2): calculate the moving time t of the drone to complete the above task according to the following formula: move : Step (5-3), calculate the energy consumption E of this drone in this round of mission by the following formula: total : E total =P move ×t move +(P hover +P charge )×(t total -t move ) Among them, P move is the UAV's mobile power, P hover is the hovering power of the drone, which is numerically equal to P when v=0 move ; Step (5-4), calculate the minimum number of drones X required to complete all tasks in the network according to the following formula: Among them, E max UAV is the maximum battery capacity of the drone, B max UAV The maximum cache capacity of the drone.

6. The method for charging and scheduling unmanned aerial vehicles for wireless rechargeable sensor networks according to claim 5, characterized in that: The steps of allocating the cluster head set that needs to be served to the UAV in step (6) are: Step (6-1), treat each cluster head as an independent cluster head set G l CH ; Step (6-2), let (x(G l CH ),y(G l CH )) are the coordinates of the center points of each cluster head set, where: and in, represents the horizontal coordinate of the center point of the cluster head set, represents the number of cluster heads in the cluster head set, x(CH p ) represents the abscissa of the cluster head, The ordinate of the center point of the cluster head set, y(CH p ) represents the vertical coordinate of the cluster head; Step (6-3), calculate any two cluster head sets G l CH With G l’ CH The distance between the center points d(G l CH ,G l’ CH ); Step (6-4), merging the two cluster head sets with the smallest distance into one cluster head set; Step (6-5), if the number of cluster heads at this time is not equal to X, jump to step (6-3), otherwise execute step (6-6); In step (6-6), the resulting X cluster head sets are respectively allocated to X drones.

7. The method for charging and scheduling unmanned aerial vehicles for wireless rechargeable sensor networks according to claim 6, characterized in that: In step (7), the base station sets a fixed periodic service mode for each drone including: Step (7-1), for each cluster head set, establish a shortest Hamiltonian circuit with the base station BS as the starting and ending points and the hovering point at h directly above each cluster head in the set as the traversal point, and assign the resulting X paths to X drones respectively; Step (7-2), the base station sets the service cluster head set G l CH The first departure time of the drone is: in, Represents the service cluster head set G l CH The first departure moment of the drone, G l CH The cluster head CH j Start generating data until the amount of data reaches B j The time required is calculated as follows: Among them B j Indicates the amount of cluster head data, CH j The data generation rate, The cluster head CH j The time of death is calculated as represents the residual energy of the cluster head, p j represents the cluster head energy consumption rate, α∈(0,1), β∈(0,1) are both adjustable parameters; Step (7-3), set the mission cycle of each drone to: in, represents the mission cycle of the UAV, ΔT j (G l CH) The drone is the cluster head set G l CH The time for each cluster head to charge and collect its data is calculated as follows: Among them, R collect is the data collection rate of the drone; Step (7-4), the drones follow their respective paths from the base station to each traversal point and hover to perform charging and data collection tasks. j (G l CH ) time, then go to the next traversal point to perform the task, and finally return to the base station; Step (7-5), after the drone replaces the battery at the base station, it performs the next cycle of tasks again according to step (7-4).

8. The method for charging and scheduling unmanned aerial vehicles for wireless rechargeable sensor networks according to claim 7, characterized in that: In step (1), N homogeneous sensor nodes are randomly deployed in a L×W rectangular plane area. The node positions are fixed and each node is marked as s. i , i is an integer, and i∈[1,N]), N represents the number of nodes, L represents the length of the plane area, W represents the width of the plane area, and the base station deployed in the center of the network records the location of each node and its attribute information.

9. A scheduling system based on the UAV charging scheduling method for wireless rechargeable sensor network according to claim 1, characterized in that: It includes sensor nodes, base stations, priority calculation units, cluster building units, cluster quantity updating units, and minimum number of drones determination units, among which: The sensor nodes are used to be randomly deployed in a plane area; The priority calculation unit is used to calculate the priority of N nodes according to the attribute information of each node. i , i is an integer, i∈[1,N]), N represents the number of nodes, and the number of cluster heads is set to m=1; The cluster construction unit is used to construct the cluster according to the priority of the node. i Select m cluster heads CH from all nodes j , where j is an integer and j∈[1,m], and m clusters are constructed from it; The cluster quantity updating unit is used to update the energy consumption E (Cluster j ) is evaluated; if there is a cluster head with excessive energy consumption, the value of m is updated; The minimum number of drones determining unit is used to calculate the minimum number of drones X required to complete a round of charging and data collection tasks for all network nodes; The base station is used to be deployed in the network center. The base station allocates a cluster head set to be served to each drone according to the distribution of each cluster node and the minimum number of drones X required to complete a round of charging and data collection tasks for the entire network nodes; the base station sets a fixed periodic service mode for each drone, and the network starts to run; during the network operation, all drones wirelessly charge the nodes and collect node data in turn according to the set service mode.

10. An electronic device, characterized in that: include: at least one processor, at least one memory, and a communication interface; The processor, memory and communication interface communicate with each other; The memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the drone charging scheduling method for a wireless rechargeable sensor network according to any one of claims 1-8.

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