Deployment method for secure communication of multi-drone MEC supporting dynamic role switching

Abstract

This invention discloses a multi-UAV MEC secure communication deployment method supporting dynamic role switching, relating to the field of UAV mobile edge computing technology. The method includes: S1, setting UAV roles; S2, calculating the data offloading rate; S3, calculating the leakage rate; S4, calculating the communication security capacity based on the offloading rate and leakage rate; calculating the edge computing latency; S5, calculating an optimized objective function constructed based on the communication security capacity and edge computing latency, and obtaining the objective function value; S6, optimizing all variables using an algorithm, and repeating S2-S5 with the optimized variable values ​​to obtain a new objective function value; S7, calculating the difference between the new objective function value and the previously calculated objective function value, stopping the iteration when the condition is met, otherwise repeating S6-S7. This invention improves communication security capacity while reducing edge computing latency by introducing UAVs that support dynamic role switching and using algorithms to optimize UAV role switching decisions and related variables.

Description

Technical Field

[0001] This invention relates to the field of mobile edge computing technology for unmanned aerial vehicles (UAVs), and more particularly to a method for deploying secure communication for multi-UAV MECs that supports dynamic role switching. Background Technology

[0002] Mobile edge computing is widely used in the Internet of Things (IoT) by offloading computationally intensive and latency-critical data to the network edge closer to terminal devices, thereby enhancing device performance and saving battery resources. However, due to long distances or damage caused by natural disasters, infrastructure coverage is limited in some areas. Drones, with their computing resources, offer computing services to areas not covered by fixed mobile edge computing infrastructure due to their rapid deployment and low cost. Therefore, drones are considered a mobile edge computing platform with enormous development potential and broad application prospects. For example, Chinese patent CN114372612B proposes a path planning and task offloading method based on deep reinforcement learning for drone mobile edge computing scenarios. This method leverages the complementarity of deep reinforcement learning and convex optimization in terms of complexity and accuracy to minimize drone energy consumption and task completion time, thereby improving energy efficiency in performing ground terminal tasks. Z. Yang et al. [Z. Yang, S. Bi and Y.-JA Zhang, "Online Trajectory and Resource Optimization for Stochastic UAV-Enabled MEC Systems," in IEEE Transactions on Wireless Communications, vol. 21, no. 7, pp. 5629-5643, July 2022, doi:10.1109 / TWC.2022.3142365.] proposed a resource allocation and trajectory planning method for UAV mobile edge computing platforms based on Lyapunov optimization and continuous convex approximation, which minimizes the average weighted total energy consumption of ground users under constraints of energy consumption and queue stability.

[0003] However, due to the line-of-sight wireless channel between the drone and the ground terminal, data offloaded at the edge in mobile edge computing networks for drones is easily intercepted by potentially malicious devices. Chinese Patent Publication No. CN114070451B provides a secure communication method for drone mobile edge computing based on non-orthogonal multiple access, relating to the field of secure communication at the physical layer of mobile edge computing. It achieves secure communication by broadcasting artificial noise through a ground jammer to interfere with unauthorized drones. R.Li et al. [R.Li, Z.Wei, L.Yang, DWKNg, J.Yuan and J.An, "Resource Allocation for Secure Multi-UAV Communication Systems With Multi-Eavesdropper," in IEEE Transactions on Communications, vol.68, no.7, pp.4490-4506, July 2020, doi:10.1109 / TCOMM.2020.2983040.] proposed a secure communication scheme based on a novel multi-purpose unmanned aerial vehicle (UAV). Idle UAV base stations can act as friendly jammers to transmit noise signals to unauthorized devices. The physical layer security of the communication system is improved by jointly optimizing resource allocation strategies and UAV flight trajectories. A. Gao et al. [A. Gao, Q. Wang, Y. Hu, W. Liang and J. Zhang, "Dynamic Role Switching Scheme With Joint Trajectory and Power Control for Multi-UAV Cooperative Secure Communication," in IEEE Transactions on Wireless Communications, vol. 23, no. 2, pp. 1260-1275, Feb. 2024, doi: 10.1109 / TWC. 2023. 3287849.] proposed a joint optimization method for UAV flight trajectory and transmit power based on role switching to ensure the security of multi-UAV cooperative communication. This method also allows each UAV to switch roles as a data collector or a friendly jammer.

[0004] Furthermore, the limited battery capacity of drones makes it difficult for them to provide long-term, stable edge computing services for large-scale IoT devices. Chinese Patent Publication No. CN115134370B discloses a multi-drone-assisted mobile edge computing offloading method, which centrally reduces the energy consumption of multi-drone and multi-user data processing by optimizing drone flight trajectories, the computational workload of each time slot, and some offloading variables. (J.Li et al. [J.Li, C.Yi, J.Chen, K.Zhu and J.Cai, "Joint..."))

[0005] Trajectory Planning, Application Placement, and Energy Renewal for UAV-Assisted MEC: A Triple-Learner-Based Approach," in IEEE Internet of ThingsJournal, vol.10, no.15, pp.

[0006] [13622-13636,1Aug.1,2023,doi:10.1109 / JIOT.2023.3262687.] This paper studies the energy-saving scheduling problem of mobile edge computing for multiple UAVs and proposes a new reinforcement learning method based on a triple learner to maximize the long-term energy efficiency of all UAVs.

[0007] To address the physical layer security communication issues in multi-UAV mobile edge computing, this invention also considers the energy and computational latency problems of the UAVs. It aims to dynamically switch between three roles of the UAV, thereby updating its edge application service layout, the transmission power of the UAV and ground equipment, and the UAV's flight trajectory when the UAV has sufficient power. This improves the system's communication security capacity and reduces edge computing latency. Specifically, by obtaining the latest variable information, the UAV can flexibly switch its role in the environment to achieve the goals of secure communication and reduced latency. This invention iteratively updates the UAV's role switching decisions, edge application service layout, the transmission power of the UAV and ground equipment, and the UAV's flight trajectory, providing real-time secure mobile edge computing services for multiple users in environments with potential eavesdroppers. Summary of the Invention

[0008] To address the above issues, this invention proposes a secure communication deployment method for multi-UAV (Mobile Edge Computing) MEC that supports dynamic role switching. By introducing multiple UAVs supporting dynamic role switching as communication receivers or friendly jammers, and considering secure edge data offloading in coordination with ground equipment when the UAVs have sufficient power, the method improves the efficiency of UAV secure communication. A weighted average of communication security capacity and edge computing latency is used as the objective, optimizing the target value under the worst-case scenario, thereby achieving collaborative optimization of the multi-UAV secure communication deployment method in the mobile edge computing system. This method employs block coordinate descent and continuous convex approximation algorithms to jointly optimize UAV role switching decisions and edge application service layout, UAV and ground equipment transmission power, and UAV flight trajectory, improving communication security capacity while reducing edge computing latency.

[0009] A method for deploying secure communication for multi-drone MEC that supports dynamic role switching, based on a multi-drone system including several drones, several ground devices, stations, and several eavesdroppers, includes the following steps:

[0010] S1, Set the role of the drone; the role includes communication receiver, friendly jammer, site updater, and idle drone; each drone holds a role and supports dynamic role switching;

[0011] S2, calculate the data offload rate of the ground equipment based on the ground equipment's transmit power;

[0012] S3 calculates the leakage rate of data intercepted by the eavesdropper based on the drone's transmission power, the ground equipment's transmission power, and the Boolean variables of the friendly jammer;

[0013] S4 calculates the communication security capacity based on the data offloading rate, leakage rate, edge application service type, and communication receiver Boolean variables; and calculates the edge computing latency based on the edge application service type and communication receiver Boolean variables.

[0014] S5, calculate the optimization objective function based on communication security capacity and edge computing latency, and obtain the objective function value;

[0015] S6, use block coordinate descent and continuous convex approximation algorithms to jointly optimize all variables, and repeat S2-S5 with the optimized variable values ​​to obtain new objective function values; the variables include communication receiver Boolean variables, friendly jammer Boolean variables, site updater Boolean variables, edge application service type, ground equipment transmission power, UAV transmission power and UAV location.

[0016] S7: Calculate the difference between the new objective function value and the previously calculated objective function value. If the difference is greater than the preset difference, repeat S6-S7; otherwise, stop the iteration.

[0017] Preferably, the dynamic role switching specifically means: when the remaining power of a drone reaches a preset power threshold, or when a drone has an edge application service update requirement, it switches to a site update drone; the three roles of communication receiver, idle drone, and friendly jammer can switch between each other.

[0018] Preferably, the remaining battery power of drone i Represented as:

[0019]

[0020] in, Indicates the energy consumption for propulsion and hovering; Indicates transmission power consumption; E represents the calculated energy consumption. CHa Indicates the amount of electricity added at the station; E ret Indicates the maximum flight energy consumption to the station; a ij [t] represents a Boolean variable indicating whether drone i acts as a communication receiver, collecting data from device j. A value of 1 indicates yes, and a value of 0 indicates no; b ij [t] A Boolean variable representing the friendly jammer role of UAV i, indicating whether UAV i acts as a friendly jammer, interfering with the nearest eavesdropper to ground equipment j. A value of 1 indicates yes, and a value of 0 indicates no; c i [t] represents the Boolean variable for the site updater of drone i, indicating whether drone i plays the role of the site updater. A value of 1 indicates yes, and a value of 0 indicates no; t represents the t-th time slot.

[0021] Preferably, the optimization objective function is a weighted average of the security capacity and edge computing latency, expressed as follows:

[0022]

[0023] Where α represents the weighting coefficient for communication security capacity; β represents the weighting coefficient for edge computing latency; T represents the number of time slots within the system time; R sec [t] represents the communication security capacity; L com [t] represents the edge computing latency; t represents the t-th time slot.

[0024] Preferably, the communication security capacity is specifically expressed as:

[0025]

[0026] Where N represents the number of ground devices; M represents the number of drones; A represents the type of mission data; E represents the number of eavesdroppers; z ja[t]∈{0,1} indicates whether the task data type of ground device j is a, with a value of 1 indicating yes and a value of 0 indicating no; z ia [t]∈{0,1} indicates whether the edge application service type of drone i is a, with a value of 1 indicating yes and a value of 0 indicating no; l je ∈{0,1} indicates whether the data unloaded by ground equipment j was intercepted by eavesdropper e. When the data unloaded by ground equipment j was intercepted by eavesdropper e, l je [t] = 1, otherwise, l je [t] = 0; a ij [t] represents a Boolean variable indicating whether drone i acts as a communication receiver, collecting data from device j; a value of 1 indicates yes, and a value of 0 indicates no; R ji [t] represents the unloading rate per Hertz from ground equipment j to drone i; R je [t] represents the leakage rate per hertz from ground device j to eavesdropper e.

[0027] Preferably, the unloading rate per hertz from ground equipment j to UAV i is specifically expressed as:

[0028]

[0029] Where, p ji [t] represents the transmit power received by UAV i from ground device j when ground device j and UAV i are performing data offloading. Its value is equal to the transmit power p of ground device j. j [t];g ji [t] represents the channel gain; σ 2 This represents the power of additive white Gaussian noise.

[0030] Preferably, the leakage rate per hertz from the ground device j to the eavesdropper e is expressed as:

[0031]

[0032] Where, p je [t] represents the transmission power received by the eavesdropper e from ground device j when ground device j is offloading data; its value is equal to the ground device transmission power p of ground device j. j [t];p i′j [t] represents the drone i ′ An eavesdropper emits artificial noise to interfere with ground equipment j; the eavesdropper receives the signal from drone i. ′ Transmission power, the value of which is equal to that of the UAV i ′ The drone's transmit power p i′ [t], drone i ′This refers to an idle drone that can be converted into a friendly jammer; b i′j [t] represents the friendly interference variable of drone i, indicating that drone i ′ Whether to act as a friendly jammer, interfering with the nearest eavesdropper to ground equipment j, with a value of 1 indicating yes and a value of 0 indicating no; g je Indicates the channel gain from ground device j to eavesdropper e; g i′e [t] represents the drone i ′ Channel gain to eavesdropper e; σ 2 This represents the power of additive white Gaussian noise.

[0033] Preferably, the edge computing latency is specifically expressed as follows:

[0034]

[0035] Where N represents the number of ground devices; M represents the number of drones; A represents the type of mission data; z ja [t]∈{0,1} indicates whether the task data type of ground equipment j is a, with a value of 1 indicating yes and a value of 0 indicating no; z ia [t]∈{0,1} indicates whether the edge application service type of drone i is a, with a value of 1 indicating yes and a value of 0 indicating no; D a The task data size of task data type 'a'; f i This represents the computing power of drone i; a ij [t] represents a Boolean variable for the communication receiver of drone i, indicating whether drone i acts as a communication receiver and collects data from device j. A value of 1 indicates yes, and a value of 0 indicates no.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] This invention utilizes dynamic role switching and inter-drone collaboration to jointly optimize drone role switching decisions, edge application service layout, drone and ground equipment transmission power, and drone flight trajectory. This improves the system's physical layer communication security capacity while reducing edge computing latency, and allows manual adjustment of their weights to achieve different optimization objectives. Furthermore, this invention addresses the drone's battery power issue in secure edge computing communication, a topic rarely discussed in existing technologies. It considers secure edge data offloading in collaboration with ground equipment when the drone has sufficient power, thereby improving the efficiency of secure drone communication. The innovative integration of charging functionality into the drone's dynamic role switching method demonstrates significant advantages and promising application prospects. Attached Figure Description

[0038] The present invention will now be described in further detail with reference to the accompanying drawings;

[0039] Figure 1 This is a system model diagram of a multi-UAV MEC secure communication deployment method supporting dynamic role switching, as described in an embodiment of the present invention.

[0040] Figure 2 This is a flowchart illustrating the multi-UAV MEC secure communication deployment method supporting dynamic role switching according to an embodiment of the present invention.

[0041] Figure 3 This is a timing diagram of the UAV operation of the multi-UAV MEC secure communication deployment method supporting dynamic role switching according to an embodiment of the present invention;

[0042] Figure 4 This diagram illustrates a comparison of the objective function optimization between the multi-UAV MEC secure communication deployment method supporting dynamic role switching in this invention and common UAV role fixing methods. Detailed Implementation

[0043] The present invention will be further described below through specific embodiments.

[0044] This embodiment is based on Figure 1 The diagram shows a multi-UAV system model; the multi-UAV system model includes several UAVs, several ground devices, stations, and several eavesdroppers.

[0045] See Figure 2 and Figure 3 As shown, the present invention provides a multi-UAV MEC secure communication deployment method that supports dynamic role switching, as detailed below.

[0046] Step 1: Determine the number of drones, ground equipment, and eavesdroppers, and set the system parameters accordingly. Specifically, place M drones, N ground equipment units, and E potential eavesdroppers in a 400m × 400m suburban area. The total system time T0 is discretized into T time slots δ of equal duration. t =10s. The system employs Orthogonal Frequency Division Multiple Access (OFDMA) technology with a bandwidth of 1MHz. All communication devices are equipped with a single antenna, with the UAV having only one transceiver and operating in half-duplex mode to avoid self-interference. Additive white Gaussian noise (AWGN) power spectral density σ 2=-120dBm, channel gain β0=-60dB at a reference distance of 1m. The roles a drone can play include communication receiver, friendly jammer, site updater, and idle drone. The communication receiver receives communication tasks from ground equipment, performs edge data offloading and calculations; the friendly jammer emits jamming signals to interfere with eavesdroppers; the site updater flies to the site to recharge or update edge application service types and returns to its original position; when the drone is not acting as a communication receiver, friendly jammer, or site updater, it is an idle drone, maintaining its original position without action. Each drone holds one role, and dynamic role switching is supported. When the remaining battery power of each role drone reaches a preset battery threshold, it switches to site updater; when each role drone has an edge application service update requirement, it switches to site updater. For example, idle drones and friendly jammers may go to the site in advance to update application types for later use as communication receivers; the three roles of communication receiver, idle drone, and friendly jammer can switch between each other to maximize the subsequent objective function value.

[0047] For a rotary-wing UAV with velocity V, the propulsion power consumption is modeled as follows:

[0048]

[0049] Step 2: Determine the onboard resources of each UAV, including memory size, computing power, battery capacity, and the scale of edge application services and mission data. Specifically, the ground equipment in each time slot randomly generates a mission z. ja [t], i.e., ∑ a∈A z ja [t] = 1, where a ∈ {1, ..., A} is the index of the task data type, representing different data types, z ja [t] = 1 indicates that device j requests to unload task data a; otherwise, z ja [t] = 0. When the drone's edge application service can calculate this data (i.e., the data type is the same), i.e., ∑ j∈N ∑ i∈M ∑ a∈A z ja [t]z ia Communication only occurs when [t] = 1, and the scale of different types of task data / edge application services varies. Furthermore, the memory size, computing power, and battery capacity of drones in a given scenario may differ, providing the system with greater room for optimization.

[0050] Step 3: Let the transmission power of ground equipment j be p. j [t], Ground equipment j transmits at power p j [t] performs edge data unloading, considering the worst-case scenario, i.e., p jData with a value of [t] > 0 will be intercepted by the nearest eavesdropper, within T0. j and w e Since the location is fixed, the communication link and channel gain between the ground equipment and the eavesdropper remain constant. ij [t]∈{0,1} represents a Boolean variable for the communication receiver of UAV i, i.e., whether UAV i acts as a communication receiver to collect data from device j; a ij [t] = 1 indicates that UAV i acts as a communication receiver, collecting data from ground device j; a ij [t] = 0 indicates that the drone i does not act as a communication receiver.

[0051] Step 4: Let the transmit power of UAV i be p. i [t], UAV i transmits at power p i [t] Transmit artificial noise to interfere with the eavesdropper. Since the drone can eliminate the interference signal from the received signal, the artificial noise only interferes with the eavesdropper. b ij [t]∈{0,1} represents the Boolean variable for the friendly jammer role of UAV i, indicating whether UAV i acts as a friendly jammer to interfere with the eavesdropper closest to device j. If UAV i chooses to interfere with the eavesdropper closest to ground device j, i.e., it assumes the role of a friendly jammer, then b ij [t] = 1, otherwise b ij [t] = 0. Let the transmit power of UAV i be p. i When there is artificial noise [t], since other drones acting as communication receivers are aware of the artificial noise in advance, the influence of the artificial noise can be eliminated when receiving mission data. The artificial noise will only interfere with the eavesdropper.

[0052] The unloading rate per hertz from ground equipment j to UAV i in time slot t is:

[0053]

[0054] Where, p ji [t] represents the transmit power received by UAV i from device j when device j performs data offloading with UAV i. Its value is equal to the transmit power p of device j. j [t], g ji [t] represents the channel gain, σ 2 This represents the power of additive white Gaussian noise.

[0055] Similarly, the leakage rate per hertz from ground device j to eavesdropper e is:

[0056]

[0057] Where, p je[t] represents the transmit power received by eavesdropper e from device j when device j is offloading data; its value is equal to the transmit power p of device j. j [t];p i′j [t] represents the drone i ′ An eavesdropper emitting artificial noise jamming device j, and the drone i received by the eavesdropper. ′ Transmission power, the value of which is equal to that of the UAV i ′ Transmit power p i′ [t], drone i ′ This refers to idle drones that, besides those currently functioning as communication receivers, can be used as friendly jammers. je and g i′e [t] represents device j and drone i, respectively. ′ The channel gain to the eavesdropper e. It is worth noting that, since all devices and the eavesdropper's location are fixed, g je It is a constant value.

[0058] Step 5: Let z j [t] = {z j1 [t],…,z jA [t]} represents the task offloading request of ground equipment j, where a∈{1,…,A} is the index of the task data type, z ja [t] = 1 indicates that device j requests to unload task data a; otherwise, z ja [t] = 0, each time slot device j randomly generates a task offloading request, i.e., ∑ a∈A z ja [t] = 1.

[0059] Let the edge application service layout of drone i be z. i [t] = {z i1 [t],…,z iA [t]}, where z ia [t] = 1 indicates that drone i is equipped with an edge application capable of calculating task data a; otherwise, z ia [t] = 0. This occurs when the edge application service type of the drone and the data type unloaded by the device are the same, and the unloaded data is selected to be received by drone i, i.e., a. ij When [t] = 1, i.e., ∑ j∈N ∑ i∈M ∑ a∈A z ja [t]z ia [t]·a ij For [t] to be 1, the two will communicate; this requires δ... t The edge data is unloaded and computed internally.

[0060] Considering the possibility of insufficient battery power for drone i, ci [t]∈{0,1} represents a boolean variable for updating the station, c i [t] = 1 indicates that drone i acts as a station updater, flying to the station to update its battery level or update z. i [t]; otherwise c i [t] = 0; to ensure the drone can operate at δ t Complete charging and return within the time limit, c i The flight speed when [t] = 1 is set to 60 m / s. Furthermore, the drone can only select one role to operate at a time.

[0061] The remaining battery power E of drone i i rem [t] is modeled as:

[0062]

[0063] in, These are the energy consumption for propulsion and hovering, transmission, and computing, respectively, with two constant values ​​E. cha and E ret These represent the additional battery power gained at the station and the maximum flight energy consumption to the station, respectively. This is because the drone's battery power must always be greater than the maximum flight energy consumption E to reach the station. ret The drone will run when the battery level drops to E. ret Before selecting to fly to the station to recharge, since only one character can be selected at a time, c i [t] = 1 and a ij [t],b ij [t] = 0, the drone's battery level is Furthermore, if drone i needs to process a certain type of task data and the edge application service placed on it cannot handle that type of task data, it will also choose to fly to the site to update the edge application service layout z. i [t]; when the drone does not choose to fly to a charging station, c i [t] = 0, the drone's battery level is

[0064] Step 6: Establish a multi-UAV mobile edge computing network and determine the locations of ground device j and eavesdropper e as w respectively. j =[x j ,y j ] and w e =[x e ,y e Let the position of drone i be q. i [t]{=x i [t],y i [t],H}, where {xi [t],y i [t]} represents the horizontal position of drone i, and H represents the fixed flight altitude of the drone in the system. Specifically, q i The horizontal coordinate of [t] and w j All follow a uniform distribution, w e They then follow a normal distribution within their respective regions. The drones maintain a flight altitude of H = 180m to ensure they can receive data from all devices in the area with a line-of-sight probability close to 1. δ t The drone's position is considered fixed. It flies in a straight line at a constant speed of 20m / s for a maximum of 100m every time slot, then hovers, and after hovering, it acts as a communication receiver or a friendly jammer.

[0065] Step 7: Calculate the communication security capacity and edge computing latency based on the existing system model. The system optimization objective is modeled as a weighted average of the communication security capacity and edge computing latency. This is a difficult nonconvex mixed-integer nonlinear programming problem. Based on the block coordinate descent method, the original problem is divided into four subproblems. All values ​​of 'a' are then alternately optimized according to the given initial values ​​of the optimization variables. ij [t]、b ij [t]、c i [t]、z i [t]、p j [t]、p i [t] and q i [t], and use the optimized variable value as the initial value of the optimized variable in the next iteration.

[0066] The communication security capacity R of the t-th time slot sec [t] is:

[0067]

[0068] Where z ja [t],z ia [t] = 1 indicates that the task data type and edge application service type are both a; otherwise, z ja [t],z ia [t] = 0. (This is related to g.) je Similarly, l je ={0,1} is a constant value, indicating whether the data unloaded by device j was intercepted by eavesdropper e.

[0069] The edge computation delay L of the t-th time slot com [t] is:

[0070]

[0071] Among them, D af is the task data size of type a. t This represents the computing power of drone i. The optimization objective is as follows:

[0072]

[0073] Where {α,β} are the weighting coefficients for communication security capacity and edge computing latency, respectively.

[0074] Step 8: Repeat steps 3-7 until the difference between two consecutive objective function values ​​is less than the iteration termination condition (obj-primal). obj ≤err th Stop iteration when ).

[0075] See Figure 4 As shown, the convergence process of the objective function value optimization of the dynamic role-switching method of this invention and the existing fixed role method in the same scenario is compared. Clearly, the dynamic role-switching method has better performance in terms of both communication security capacity and edge computing latency. This is because the fixed role method assigns a fixed role to each drone, thus failing to ensure that the drone acting as a communication receiver is always closest to the ground equipment, and the drone acting as a friendly jammer is always closest to the eavesdropper. The dynamic role-switching method offers greater flexibility, allowing drones to approach ground equipment or eavesdroppers with a shorter travel distance. Specifically, the communication security capacity component of the dynamic role-switching method has a weight of 0.6, and the edge computing latency component has a weight of 0.4.

[0076] This embodiment innovatively proposes a secure communication deployment method for multi-UAV mobile edge computing that supports dynamic role switching. By leveraging dynamic role switching and inter-UAV collaboration, it jointly optimizes UAV role switching decisions, edge application service layout, UAV and ground equipment transmission power, and UAV flight trajectories. This improves the system's physical layer communication security capacity while reducing edge computing latency, and allows manual adjustment of the weights of both to achieve different optimization objectives. Furthermore, this embodiment considers the UAV battery issue in secure communication for mobile edge computing by innovatively incorporating charging functionality into the dynamic role switching method, demonstrating significant advantages and promising application prospects.

[0077] The above are merely specific embodiments of the present invention, but the design concept of the present invention is not limited thereto. Any non-substantial modifications made to the present invention using this concept shall be considered as infringing upon the protection scope of the present invention.

Claims

1. A multi-UAV MEC secure communication deployment method supporting dynamic role switching, based on a multi-UAV system including several UAVs, several ground devices, stations, and several eavesdroppers, characterized in that, Includes the following steps: S1, Set the role of the drone; the role includes communication receiver, friendly jammer, site updater, and idle drone; each drone holds a role and supports dynamic role switching; S2, calculate the data offload rate of the ground equipment based on the ground equipment's transmit power; S3 calculates the leakage rate of data intercepted by the eavesdropper based on the drone's transmission power, the ground equipment's transmission power, and the Boolean variables of the friendly jammer; S4 calculates the communication security capacity based on data offloading rate, leakage rate, edge application service type, and communication receiver Boolean variables; Calculate edge computing latency based on edge application service type and communication receiver Boolean variables; S5, calculate the optimization objective function based on communication security capacity and edge computing latency, and obtain the objective function value; S6, use block coordinate descent and continuous convex approximation algorithms to jointly optimize all variables, and repeat S2-S5 with the optimized variable values ​​to obtain new objective function values; the variables include communication receiver Boolean variables, friendly jammer Boolean variables, site updater Boolean variables, edge application service type, ground equipment transmission power, UAV transmission power and UAV location. S7, calculate the difference between the new objective function value and the previously calculated objective function value. If the difference is greater than the preset difference, repeat S6-S7. Otherwise, stop iterating; The optimization objective function is a weighted average of communication security capacity and edge computing latency, expressed as follows: Where α represents the weighting coefficient for communication security capacity; β represents the weighting coefficient for edge computing latency; T represents the number of time slots within the system time; R sec [t] represents the communication security capacity; L com [t] represents the edge computing latency; t represents the t-th time slot.

2. The multi-UAV MEC secure communication deployment method supporting dynamic role switching according to claim 1, characterized in that, The dynamic role switching specifically refers to the following: when a drone's remaining battery power reaches a preset battery threshold, or when a drone has an edge application service update requirement, it switches to a site update drone; the three roles of communication receiver, idle drone, and friendly jammer can switch between each other.

3. The multi-UAV MEC secure communication deployment method supporting dynamic role switching according to claim 2, characterized in that, Remaining battery power of drone i Represented as: in, Indicates the energy consumption for propulsion and hovering; Indicates transmission power consumption; E represents the calculated energy consumption. cha Indicates the amount of electricity added at the station; E ret Indicates the maximum flight energy consumption to the station; a ij [t] represents a Boolean variable indicating whether drone i acts as a communication receiver, collecting data from device j. A value of 1 indicates yes, and a value of 0 indicates no; b ij [t] A Boolean variable representing the friendly jammer role of UAV i, indicating whether UAV i acts as a friendly jammer, interfering with the nearest eavesdropper to ground equipment j. A value of 1 indicates yes, and a value of 0 indicates no; c i [t] represents the Boolean variable for the site updater of drone i, indicating whether drone i plays the role of the site updater. A value of 1 indicates yes, and a value of 0 indicates no; t represents the t-th time slot.

4. The multi-UAV MEC secure communication deployment method supporting dynamic role switching according to claim 1, characterized in that, The communication security capacity is specifically expressed as follows: Where N represents the number of ground devices; M represents the number of drones; A represents the type of mission data; E represents the number of eavesdroppers; z ja [t]∈{0,1} indicates whether the task data type of ground equipment j is a, with a value of 1 indicating yes and a value of 0 indicating no; z ia [t]∈{0,1} indicates whether the edge application service type of drone i is a, with a value of 1 indicating yes and a value of 0 indicating no; l je ∈{0,1} indicates whether the data unloaded by ground equipment j was intercepted by eavesdropper e. When the data unloaded by ground equipment j was intercepted by eavesdropper e, l je [t] = 1, otherwise, l je [t] = 0; a ij [t] represents a Boolean variable indicating whether drone i acts as a communication receiver, collecting data from device j. A value of 1 indicates yes, and a value of 0 indicates no; r ji [t] represents the unloading rate per Hertz from ground equipment j to UAV i; r je [t] represents the leakage rate per hertz from ground device j to eavesdropper e.

5. The multi-UAV MEC secure communication deployment method supporting dynamic role switching according to claim 4, characterized in that, The unloading rate per Hertz from ground equipment j to UAV i is specifically expressed as: Where, p ji [t] represents the transmit power received by UAV i from ground device j when ground device j and UAV i are performing data offloading. Its value is equal to the transmit power p of ground device j. j [t];g ji [t] represents the channel gain; σ 2 This represents the power of additive white Gaussian noise.

6. The multi-UAV MEC secure communication deployment method supporting dynamic role switching according to claim 4, characterized in that, The leakage rate per hertz from the ground device j to the eavesdropper e is expressed as: Where, p je [t] represents the transmission power received by the eavesdropper e from ground device j when ground device j is offloading data; its value is equal to the ground device transmission power p of ground device j. j [t];p i′j [t] represents the eavesdropper whose drone i′ emits artificial noise to interfere with ground equipment j, and the eavesdropper receives the noise from drone i. ′ Transmission power, the value of which is equal to the transmission power p of UAV i′. i′ [t], where i′ represents an idle drone that can be converted into a friendly jammer; b i′j [t] represents the friendly interference variable of drone i, indicating that drone i ′ Whether to act as a friendly jammer, interfering with the nearest eavesdropper to ground equipment j, with a value of 1 indicating yes and a value of 0 indicating no; g je Indicates the channel gain from ground device j to eavesdropper e; g i′e [t] represents the channel gain from drone i′ to eavesdropper e; σ 2 This represents the power of additive white Gaussian noise.

7. The multi-UAV MEC secure communication deployment method supporting dynamic role switching according to claim 1, characterized in that, The edge computing latency is specifically expressed as follows: Where N represents the number of ground devices; M represents the number of drones; A represents the type of mission data; z ja [t]∈{0,1} indicates whether the task data type of ground equipment j is a, with a value of 1 indicating yes and a value of 0 indicating no; z ia [t]∈{0,1} indicates whether the edge application service type of drone i is a, with a value of 1 indicating yes and a value of 0 indicating no; d a The task data size of task data type 'a'; f i This represents the computing power of drone i; a ij [t] represents a Boolean variable for the communication receiver of drone i, indicating whether drone i acts as a communication receiver and collects data from device j. A value of 1 indicates yes, and a value of 0 indicates no.

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