A UAV-assisted adaptive dual-mode data acquisition method

Abstract

The present invention relates to the field of communication technology, and specifically discloses a drone-assisted adaptive dual-mode data acquisition method, comprising: calculating the average remaining energy level of network nodes and the energy level of energy storage stations according to the current network state; determining a data acquisition mode according to the average remaining energy level of network nodes and the energy level of energy storage stations; wherein the data acquisition mode comprises a fixed data acquisition mode and a mobile data acquisition mode; if the average remaining energy level of network nodes is higher than the energy level of energy storage stations, the mobile data acquisition mode is adopted, otherwise the fixed data acquisition mode is adopted; if it is determined to adopt the mobile data acquisition mode, the optimal access point set of the drone is calculated and constructed; a high-energy-efficiency network topology structure is determined according to the optimal access point set; and network node data is collected according to the high-energy-efficiency network topology structure.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a drone-assisted adaptive dual-mode data acquisition method. Background Art

[0002] Wireless sensor networks consist of a group of rechargeable sensor nodes and base stations. As a bridge connecting the physical world and human society, they collect data extensively to support data mining and decision-making in the upper layer of the Internet of Things system. With the rapid development of information technology and the increasing maturity of modern wireless communication technology, the application scenarios of Internet of Things technology and 5G technology are constantly expanding, and the number of network nodes using sensors in the network is growing. In large-scale application scenarios, ground sensors and energy storage stations constitute the sensor network, but they are small in size and have very limited battery capacity. Their overall performance and effective operating time are limited by battery capacity, and they cannot support sensors to continuously obtain information for a long time. Therefore, extending the network life to support long-term uninterrupted data transmission has become one of the main research hotspots of wireless rechargeable sensor networks.

[0003] At present, in the data collection process of wireless rechargeable sensor networks, routing optimization is one of the main methods to improve its energy efficiency. When the base station is fixed, multi-hop routing and cluster routing are widely used in fixed data collection mode, but the average upload distance of fixed data collection is long, the energy consumed in the data transmission process is too large, and the aggregation node often consumes more energy. When a node in the network is in a "dead" state, the entire network will be paralyzed and data transmission will be interrupted, which is not conducive to extending the network life cycle.

[0004] Therefore, how to solve the problems caused by the fixed data collection mode in the prior art is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0005] To achieve the purpose of the present invention, the present application provides a drone-assisted adaptive dual-mode data collection method, comprising:

[0006] Step S1: Calculate the average remaining energy level of network nodes and the energy level of energy storage stations according to the current network status;

[0007] Step S2: determining a data collection mode according to the average remaining energy level of the network nodes and the energy level of the energy storage station; wherein the data collection mode includes a fixed data collection mode and a mobile data collection mode; if the average remaining energy level of the network nodes is higher than the energy level of the energy storage station, the mobile data collection mode is adopted, otherwise the fixed data collection mode is adopted;

[0008] Step S3: If it is determined to adopt the mobile data collection mode, then the optimal access point set of the UAV is calculated and constructed;

[0009] Step S4: determining a high energy efficiency network topology structure according to the optimal access point set;

[0010] Step S5: Collecting network node data according to the energy-efficient network topology structure.

[0011] In one specific embodiment, in step S1, the average remaining energy level of the network nodes is determined according to the following formula:

[0012]

[0013] Where, δ is the average remaining energy level of network nodes in the current network state, E i is the residual energy of network node i; E node,B is the battery capacity of the ground sensor, and N is the total number of network nodes.

[0014] In one specific embodiment, in step S1, the energy level of the energy storage station is determined according to the following formula:

[0015]

[0016] Where μ is the energy level of the network energy storage station, E Θ is the surplus energy of the network energy storage station, E Θ,B is the capacity of the network energy storage station.

[0017] In one specific embodiment, in step S3, the method for constructing the optimal access point set is as follows:

[0018]

[0019] Where C is the optimal access point set, Γ is the sequence generated by arranging all network nodes according to the order of current residual energy from low to high, Γ i Represents the first i network nodes.

[0020] In one embodiment, when i=N max When , the corresponding access point set is the optimal access point set of the drone, as shown below:

[0021]

[0022] Where N max is the maximum number of access points for the set Γ, When the access point set is Γ i The estimated remaining energy of the drone.

[0023] In one embodiment, when i=N max When the remaining energy is estimated The value of is the smallest, and the remaining energy is estimated As shown below:

[0024]

[0025] In the formula, When the access point set is Γ i The estimated remaining energy of the drone, E UAV,B is the total energy of the UAV; θ UAV,com is the energy consumption of UAV communication, Energy consumption for drone movement.

[0026] In one specific embodiment, the UAV movement energy consumption θ UAV,com Calculated by the following formula:

[0027]

[0028] Where M is the number of bits contained in a single data packet, e r is the energy consumption of the drone receiving bit data, e p The energy consumption of processing bit data for the drone, L i is the total number of data packets that the UAV needs to collect from network node i, e UAV,t The energy consumption of transmitting bit data from the energy storage station to the base station after the drone has collected all the data.

[0029] In one specific embodiment, the energy consumption of transmitting bit data from the energy storage station to the base station after the drone has collected all the data is UAV,t Calculated by the following formula:

[0030]

[0031] In the formula, e b,t , ε fs and ε mp is the energy consumption parameter of the first-order wireless communication energy consumption model, d UAV,s is the distance between the UAV and the base station, and d0 is the distance threshold of the first-order wireless communication energy consumption model.

[0032] In one specific embodiment, the step S4 includes: based on the energy-efficient network topology, determining the routing mode of the network node i by the following formula:

[0033]

[0034] In the formula, i is a network node, G is the set of record points of all available forward neighbors of network node i, G = {j|d j,s <d i,s ,d i.j <di,s ,j∈V},r i It is the network node routing mode.

[0035] In one specific embodiment, when r i = 0 indicates that the network node selects the direct transmission routing mode; when r i =1 indicates that the network node selects the multi-hop routing mode, and the next hop of the network node i is the network node j that maximizes the transmission efficiency when facing the real-time network topology Y. i,j,Υ The corresponding transmission energy efficiency is as follows:

[0036]

[0037] is the expected remaining energy of the network node, as follows:

[0038]

[0039] In the formula, E i,c,Υ is the communication energy consumption of network node i when the network topology is Y, as shown below:

[0040] E i,c,Υ =M[(e s -e r )·L0+(e r +e p +e i,t )·L i,Υ ]

[0041] Where L0 is the number of data packets generated by the network node, e s 、e r and e p represents the energy consumption of wireless sensor to collect, receive and process bit data, e i,t is the energy consumption of network node i in transmitting bit data towards the current network topology Y, and its calculation formula is:

[0042]

[0043] In the formula, e b,t , ε fs and ε mp is the energy consumption parameter of the first-order wireless communication energy consumption model, d0 is the distance threshold of the first-order wireless communication energy consumption model, is the current network node i and its next hop Y when the network topology is Y i The distance between

[0044] L i,Υ is the service load of network node i, and the calculation formula is as follows:

[0045]

[0046] In the formula, represents the number of data packets from other nodes that network node i needs to transfer, Γ i,Υ is the parent node set of network node i when the network topology is Y.

[0047] Beneficial effects of the above technical solution:

[0048] (1) The drone-assisted adaptive dual-mode high-energy-efficiency data collection method provided by the present invention for use in signal-free areas selects a suitable data collection mode for data collection by judging the energy levels of nodes and energy storage stations. Compared with a single fixed data collection mode, the addition of drones greatly improves the flexibility of network data collection, reduces the energy consumed by the fixed data collection mode due to the long propagation path, and thus extends the life cycle of the network.

[0049] (2) The UAV-assisted adaptive dual-mode high-energy-efficiency data collection method provided by the present invention for use in signal-free areas takes into account the limitation that nodes can only upload data through multi-hop routing. The probability of node death is reduced by determining the access point set of the UAV, and the next hop of node data transmission is determined by using the estimated remaining energy, thereby determining a high-energy-efficiency network topology, and maximizing network energy efficiency while performing data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0051] Figure 1 A schematic diagram of a flow chart of a drone-assisted adaptive dual-mode data acquisition method provided by an embodiment of the present invention;

[0052] Figure 2 A life cycle comparison result diagram of a drone-assisted adaptive dual-mode data collection method under the same network environment provided by an embodiment of the present invention;

[0053] Figure 3 A graph showing the comparison results of network energy levels in the same network environment for a drone-assisted adaptive dual-mode data acquisition method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0055] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar symbols throughout represent the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0056] Embodiment 1

[0057] An embodiment of the present invention provides a drone-assisted adaptive dual-mode data acquisition method, referring to Figure 1 As shown, including:

[0058] Step S1: Calculate the average remaining energy level of network nodes and the energy level of energy storage stations according to the current network status;

[0059] Step S2: determining a data collection mode according to the average remaining energy level of the network nodes and the energy level of the energy storage station; wherein the data collection mode includes a fixed data collection mode and a mobile data collection mode; if the average remaining energy level of the network nodes is higher than the energy level of the energy storage station, the mobile data collection mode is adopted, otherwise the fixed data collection mode is adopted;

[0060] Step S3: If it is determined to adopt the mobile data collection mode, then the optimal access point set of the UAV is calculated and constructed;

[0061] Step S4: determining a high energy efficiency network topology structure according to the optimal access point set;

[0062] Step S5: Collecting network node data according to the energy-efficient network topology structure.

[0063] In one specific embodiment, in step S1, the average remaining energy level of the network nodes is determined according to the following formula:

[0064]

[0065] Where, δ is the average remaining energy level of network nodes in the current network state, E i is the residual energy of network node i; E node,B is the battery capacity of the ground sensor, and N is the total number of network nodes.

[0066] In one specific embodiment, in step S1, the energy level of the energy storage station is determined according to the following formula:

[0067]

[0068] Where μ is the energy level of the network energy storage station, E Θ is the surplus energy of the network energy storage station, E Θ,B is the capacity of the network energy storage station.

[0069] Specifically, the data collection mode is determined according to the following formula:

[0070]

[0071] Compare the energy levels of the node and the energy storage station, λ is the node energy level, if the node energy level is higher, λ = 0, which means that the fixed data collection mode is selected. Otherwise, the mobile data collection mode is selected.

[0072] In one specific embodiment, in step S3, the method for constructing the optimal access point set is as follows:

[0073]

[0074] Where C is the optimal access point set, Γ is the sequence generated by arranging all network nodes according to the order of current residual energy from low to high, Γ i Represents the first i network nodes.

[0075] In one embodiment, when i=N max When , the corresponding access point set is the optimal access point set of the drone, as shown below:

[0076]

[0077] Where N max is the maximum number of access points for the set Γ, When the access point set is Γ i The estimated remaining energy of the drone.

[0078] In one embodiment, when i=N max When the remaining energy is estimated The value of is the smallest, and the remaining energy is estimated As shown below:

[0079]

[0080] In the formula, When the access point set is Γ i The estimated remaining energy of the drone at this time, E UAV,B is the total energy of the UAV; θ UAV,com is the energy consumption of UAV communication, Energy consumption for drone movement.

[0081] In one specific embodiment, the UAV movement energy consumption θ UAV,com Calculated by the following formula:

[0082]

[0083] Where M is the number of bits contained in a single data packet, e r is the energy consumption of the drone receiving bit data, e p The energy consumption of processing bit data for the drone, L i is the total number of data packets that the UAV needs to collect from network node i, e UAV,t The energy consumption of transmitting bit data from the energy storage station to the base station after the drone has collected all the data.

[0084] In one specific embodiment, the energy consumption of transmitting bit data from the energy storage station to the base station after the drone has collected all the data is UAV,t Calculated by the following formula:

[0085]

[0086] In the formula, e b,t , ε fs and ε mp is the energy consumption parameter of the first-order wireless communication energy consumption model, d UAV,s is the distance between the UAV and the base station, and d0 is the distance threshold of the first-order wireless communication energy consumption model.

[0087] In one specific embodiment, the step S4 includes: based on the energy-efficient network topology, determining the routing mode of the network node i by the following formula:

[0088]

[0089] In the formula, i is a network node, G is the set of record points of all available forward neighbors of network node i, G = {j|d j,s <d i,s ,d i.j <d i,s ,j∈V},r i It is the network node routing mode.

[0090] In one specific embodiment, when r i = 0 indicates that the network node selects the direct transmission routing mode; when r i =1 indicates that the network node selects the multi-hop routing mode, and the next hop of the network node i is the network node j that maximizes the transmission efficiency when facing the real-time network topology Y. i,j,Υ The corresponding transmission energy efficiency is as follows:

[0091]

[0092] is the expected remaining energy of the network node, as follows:

[0093]

[0094] In the formula, E i,c,Υ is the communication energy consumption of network node i when the network topology is Y, as shown below:

[0095] E i,c,Υ =M[(e s -e r )·L0+(e r +e p +e i,t )·L i,Υ ]

[0096] Where L0 is the number of data packets generated by the network node, e s 、e r and e p represents the energy consumption of wireless sensor to collect, receive and process bit data, e i,t is the energy consumption of network node i in transmitting bit data towards the current network topology Y, and its calculation formula is:

[0097]

[0098] In the formula, e b,t , ε fs and ε mp is the energy consumption parameter of the first-order wireless communication energy consumption model, d0 is the distance threshold of the first-order wireless communication energy consumption model, is the current network node i and its next hop Y when the network topology is Y i The distance between

[0099] L i,Υ is the service load of network node i, and the calculation formula is as follows:

[0100]

[0101] In the formula, represents the number of data packets from other nodes that network node i needs to transfer, Γ i,Υ is the parent node set of network node i when the network topology is Y.

[0102] In a specific embodiment of the present invention, in order to test the performance of the drone-assisted adaptive dual-mode high-energy-efficiency data collection method applied to signal-free areas proposed in the example of the present invention, the impact of different numbers of nodes on the network life cycle and network energy level under the same network environment is first tested. Then, under the same network environment, the data collection mode in this example is simulated and compared with other existing data collection modes, such as fixed data collection and mobile data collection. The simulation environment is a square two-dimensional plane with a side length of 500m. The network energy storage station is located in the center of the simulated square, and the base station is 1000m away from the network energy storage station. The specific simulation process is as follows:

[0103] First, all nodes are fully charged in the first round, and the network stops working when the first dead node appears.

[0104] Secondly, the algorithm is aimed at switching the network data collection mode under extreme weather in the no-signal area. In order to reflect the applicability of the network in extreme weather, it is assumed that the nodes and network energy storage stations are replenished with energy in each round, and the amount of energy replenishment can be measured by relevant methods and randomly distributed within a certain range. The network needs to forward all received data packets to the base station in each round, so that the energy consumption of each data collection method can be clearly calculated and the performance of different data collection methods can be compared.

[0105] Finally, MATLAB is used as the simulation tool, and the system simulation environment parameters are set as follows:

[0106] The peak power generation capacity of photovoltaic panels is 150W / square meter;

[0107] Given that the UAV capacity is 500000J, that is, E_UAV = 500000J;

[0108] Given node capacity E_node = 1000J, given node energy replenishment is U(0,6);

[0109] Given the network energy storage station capacity E_Ess = 30*E_UAV, the given node energy replenishment amount is U(0,750000)

[0110] The data packet size is 1MB;

[0111] Each node uploads one data packet per round, i.e., Ns = 1;

[0112] The moving speed of the drone is V = 10m / s;

[0113] The drone hovers for 3 seconds at each access point;

[0114] The drone's hovering power is 80W, and its moving power is 100W;

[0115] According to the above simulation process, the simulation results are as follows: Figure 1 and 2 shown.

[0116] Depend on Figure 2 It can be seen that the network life cycle corresponding to the data collection method proposed in the present invention is significantly higher than that of the fixed data collection method and the mobile data collection method.

[0117] Depend on Figure 3 It can be seen that the gap between the node energy level and the energy storage station energy level corresponding to the data collection method proposed in the present invention is smaller, and the values ​​are all close to 0.

[0118] Therefore, it can be learned from the above simulation results that the data collection method in the present invention optimizes system performance, greatly prolongs the life cycle of the network, and improves the stability and sustainability of the network.

[0119] The UAV-assisted adaptive dual-mode high-energy-efficiency data collection method provided by the present invention for use in signal-free areas takes into account the limitation that nodes can only upload data through multi-hop routing. The probability of node death is reduced by determining the access point set of the UAV, and the next hop of node data transmission is determined by estimating the remaining energy, thereby determining a high-energy-efficiency network topology, and maximizing network energy efficiency while performing data transmission.

[0120] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

[0121] Each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referenced to each other. The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the functions in the process. Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing terminal device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in one or more boxes. Although the preferred embodiments of the embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they know the basic creative concept. Therefore, the attached claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention. Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or terminal device including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0122] The method and device provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

[0123] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", "one specific embodiment" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A drone-assisted adaptive dual-mode data acquisition method, characterized in that: include: Step S1: Calculate the average remaining energy level of network nodes and the energy level of energy storage stations according to the current network status; Step S2: determining a data collection mode according to the average remaining energy level of the network nodes and the energy level of the energy storage station; wherein the data collection mode includes a fixed data collection mode and a mobile data collection mode; if the average remaining energy level of the network nodes is higher than the energy level of the energy storage station, the mobile data collection mode is adopted, otherwise the fixed data collection mode is adopted; Step S3: If it is determined to adopt the mobile data collection mode, then the optimal access point set of the UAV is calculated and constructed; Step S4: determining a high energy efficiency network topology structure according to the optimal access point set; Step S5: collecting network node data according to the energy-efficient network topology structure; In step S1, the energy level of the energy storage station is determined according to the following formula: Where μ is the energy level of the network energy storage station, E Θ is the surplus energy of the network energy storage station, E Θ,B is the capacity of the network energy storage station; In step S3, the method for constructing the optimal access point set is as follows: Where C is the optimal access point set, Γ is the sequence generated by arranging all network nodes according to the order of current residual energy from low to high, Γ i Represents the first i network nodes; When i=N max When , the corresponding access point set is the optimal access point set of the drone, as shown below: Where N max is the maximum number of access points for the set Γ, When the access point set is Γ i The estimated remaining energy of the drone at this time; When i=N max When the remaining energy is estimated The value of is the smallest, and the remaining energy is estimated As shown below: In the formula, When the access point set is Γ i The estimated remaining energy of the drone, E UAV,B is the total energy of the UAV; θ UAV,com is the energy consumption of UAV communication, Energy consumption for drone movement; The UAV moving energy consumption θ UAV,com Calculated by the following formula: Where M is the number of bits contained in a single data packet, e r is the energy consumption of the drone receiving bit data, e p The energy consumption of processing bit data for the drone, L i is the total number of data packets that the UAV needs to collect from network node i, e UAV,t The energy consumption of transmitting bit data from the energy storage station to the base station after the drone has collected all the data; The energy consumption of transmitting bit data from the energy storage station to the base station after the drone has collected all the data is e UAV,t Calculated by the following formula: In the formula, e b,t , ε fs and ε mp is the energy consumption parameter of the first-order wireless communication energy consumption model, d UAV,s is the distance between the UAV and the base station, and d0 is the distance threshold of the first-order wireless communication energy consumption model.

2. The UAV-assisted adaptive dual-mode data acquisition method according to claim 1, characterized in that: In step S1, the average remaining energy level of the network nodes is determined according to the following formula: Where, δ is the average remaining energy level of network nodes in the current network state, E i is the residual energy of network node i; E node,B is the battery capacity of the ground sensor, and N is the total number of network nodes.

3. The UAV-assisted adaptive dual-mode data acquisition method according to claim 1, characterized in that: The step S4 comprises: based on the energy-efficient network topology, determining the routing mode of the network node i by the following formula: In the formula, i is a network node, G is the set of record points of all available forward neighbors of network node i, G = {j|d j,s <d i,s ,d i.j <d i,s ,j∈V},r i It is the network node routing mode.

4. The UAV-assisted adaptive dual-mode data acquisition method according to claim 1, characterized in that: When i = 0 indicates that the network node selects the direct transmission routing mode; when r i =1 indicates that the network node selects the multi-hop routing mode, and the next hop of the network node i is the network node j that maximizes the transmission efficiency when facing the real-time network topology γ, ω i,j,γ The corresponding transmission energy efficiency is as follows: is the expected remaining energy of the network node, as follows: In the formula, E i,c,Υ is the communication energy consumption of network node i when the network topology is γ, as shown below: AND i,c,Υ =M[(and s -And r )·L0+(and r +e p +e i,t )·THE i,Υ ] Where L0 is the number of data packets generated by the network node, e s 、e r and e p represents the energy consumption of wireless sensor to collect, receive and process bit data, e i,t is the energy consumption of network node i in transmitting bit data towards the current network topology Y, and its calculation formula is: In the formula, e b,t , ε fs and ε mp is the energy consumption parameter of the first-order wireless communication energy consumption model, d0 is the distance threshold of the first-order wireless communication energy consumption model, is the current network node i and its next hop γ when the network topology is γ i The distance between L i,γ is the service load of network node i, and the calculation formula is as follows: In the formula, represents the number of data packets from other nodes that network node i needs to transfer, Γ i,γ is the parent node set of network node i when the network topology is Y.

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