A method and system for mobile edge computing network data processing and energy consumption optimization

Abstract

This invention discloses a method and system for data processing and energy consumption optimization in mobile edge computing networks. It formalizes the network energy consumption optimization problem as an optimization problem related to UAV flight trajectory, user access control, task offloading, and resource allocation. The method jointly optimizes user scheduling and UAV trajectory, and optimizes task offloading and resource allocation while ensuring timely completion of computing tasks. It utilizes the dynamic flight of UAVs to relay tasks for IoT devices, while simultaneously using UAVs to carry edge servers and make task offloading decisions, assisting the central server in processing data. This invention can effectively balance network load, improve resource utilization, and reduce network energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) communication technology, specifically relating to a method and system for data processing and energy consumption optimization in mobile edge computing networks. Background Technology

[0002] Currently, the closest existing technology: In recent years, with the development of low-latency and high-reliability mobile communication networks, mobile edge computing has brought new developments and opportunities to the Internet of Things (IoT). Mobile edge computing provides users with stable and efficient services by deploying computing resources near users and implementing computation offloading, transferring tasks from user devices to servers. However, with the continuous development of new-generation information technologies and the expanding scale of emerging applications, the complexity of computing tasks and the requirements for communication quality are further increasing, posing severe challenges to network resource management. Traditional mobile edge computing struggles to meet these demands. Furthermore, IoT devices are typically located in complex environments, making it difficult to obtain good coverage from base stations. Communicating with base stations or offloading tasks to edge servers consumes a significant amount of energy, thus limiting their lifespan.

[0003] Existing technology 1 proposes a drone-assisted mobile edge computing network architecture, in which computational data generated by IoT devices can be computed locally or relayed to a central server via a drone for computation. This technology improves performance by optimizing the device's computational task offloading strategy and uplink bandwidth resource allocation. However, this technology only considers the relay function of drones and does not take into account the potential of drones as edge servers for collaborative computing. In practical applications, when the number of users increases, drone relay nodes may face network congestion problems. Therefore, when the amount of computational tasks increases, it may lead to an increase in sensor task offloading latency, thereby affecting the user device's offloading strategy to favor local computation, resulting in excessive device power consumption.

[0004] Existing technology two utilizes drones carrying edge servers to collect data from IoT devices. The drones make offloading decisions, with some data processed on the edge servers and the remainder transmitted to a central server for processing. This technology also optimizes user scheduling and drone deployment locations. However, this technology assumes sufficient computing resources for the central server and only considers the energy consumption and latency associated with offloading and processing data from IoT devices to the edge servers, without taking into account the central server's computational consumption and resource allocation strategies. In practice, an unreasonable computing resource allocation strategy can lead to difficulties in guaranteeing the completion time of differentiated computing tasks, resulting in resource waste and high remote computing energy consumption.

[0005] Existing technology three utilizes drones carrying edge servers to fly within a coverage area, collecting data and assisting a central server in processing computing tasks. This technology optimizes the drone's flight path, bandwidth allocation, and computing resource allocation. However, this technology does not consider user access strategies in its design. When a large number of user devices simultaneously offload data, interference and conflicts can occur, making it difficult to ensure fairness among users. Some devices may be unable to complete task offloading and must process a large number of tasks locally, significantly reducing flight time.

[0006] In summary, the problems with existing technologies are:

[0007] (1) The existing technology is a mobile edge computing network structure assisted by drones. It does not take into account the problems of deploying edge servers and relay congestion under high load, which may lead to excessive local computing energy consumption.

[0008] (2) The existing technology 2 uses drones to carry edge servers and make task offloading decisions. However, it does not take into account the remote computing cost and resource allocation of the central server, which makes it difficult to guarantee the task completion time and the energy consumption of remote computing is high.

[0009] (3) Existing technology three uses drones to collect data and process it in coordination with the central server without considering user access strategy, which will cause task unloading conflicts and excessive local computing power consumption of some devices.

[0010] The difficulty in solving the above technical problems:

[0011] Existing technology 1: When the amount of computational tasks increases, it may lead to an increase in the sensor task offloading latency, thereby affecting the user device's offloading strategy to favor local computation, resulting in excessive device power consumption. Ensuring task processing latency will inevitably lead to an increase in computational power consumption.

[0012] Existing technology two: This technology assumes that the central server has sufficient computing resources and only considers the energy consumption and latency caused by IoT devices and edge servers offloading and computing, without considering the computing consumption and resource allocation strategy of the central server. In practice, an unreasonable computing resource allocation strategy will make it difficult to guarantee the completion time of differentiated computing tasks, while also causing resource waste and high remote computing energy consumption.

[0013] Existing technology 3: This technology does not consider user access strategies. When a large number of user devices simultaneously offload data, interference and conflicts will occur, making it difficult to ensure fairness among users. Some devices will be unable to complete task offloading and will have to process a large number of tasks locally, greatly reducing battery life. Summary of the Invention

[0014] The technical problem to be solved by the present invention is to provide a method and system for data processing and energy consumption optimization in mobile edge computing networks, which addresses the shortcomings of the prior art and solves the technical problems of weak computing power of IoT devices and heavy load on central servers in traditional terrestrial wireless networks.

[0015] The present invention adopts the following technical solution:

[0016] A method for data processing and energy consumption optimization in mobile edge computing networks includes the following steps:

[0017] S1. Construct a drone-assisted mobile edge computing network model consisting of one ground base station connected to a central server, one drone equipped with an edge server, and K IoT devices.

[0018] S2. Based on the UAV-assisted mobile edge computing network model obtained in step S1, construct communication models between UAVs and IoT devices, as well as between base stations and UAVs.

[0019] S3. Data processing flow of UAV-assisted mobile edge computing network based on communication model between nodes;

[0020] S4. Based on the communication model obtained in step S2 and the data processing flow obtained in step S3, construct the objectives and optimization conditions for data collection and data computation of UAV-assisted mobile edge computing network.

[0021] S5. Based on the data collection objectives and optimization conditions obtained in step S4, a dynamic deployment and IoT device access control strategy for edge servers is constructed by using the block coordinate descent method and continuous convex approximation solution.

[0022] S6. Based on the data obtained in step S4, calculate the objectives and optimization conditions, and the edge server deployment and device access strategies obtained in step S5. Solve the problem using variable substitution and the binary search method to construct a task unloading and resource allocation strategy.

[0023] Specifically, step S1 is as follows:

[0024] S101. Construct a mobile edge computing network model containing 1+1+K communication nodes. Each IoT device has a task of size L that needs to be computed within time T.

[0025] S102. The IoT device sends computing tasks to the drone. The edge server on the drone, together with the central server connected to the ground base station, calculates these tasks and sends them back to the IoT device.

[0026] S103. Let w be the horizontal position of the kth IoT device. k =(x k ,yk The ground base station is located at (0,0); time T is discretized into N uniform time slots, each time slot having a duration of t = T / N; the set of N time slots is described as N = {1,2,...,N}, the horizontal position of the UAV in the nth time slot is q[n]; the height of the UAV relative to the ground base station is H;

[0027] S104. Assume the UAV must return to its starting position in the last time slot, and the UAV's flight trajectory is constrained by speed. Determine the UAV's flight energy consumption E. fly [n].

[0028] Furthermore, the energy consumption of drone flight E fly [n] is:

[0029] E fly [n]=κ||q[n+1]-q[n]|| 2

[0030] Where n = 1, ..., N-1, and κ represents the weight parameter.

[0031] Specifically, step S2 is as follows:

[0032] S201. Determine the channel power gain h from IoT device k to the drone within time slot n. k [n];

[0033] S202. Determine the channel gain h of the link between the UAV and the ground base station within time slot n. u [n];

[0034] S203. A drone is associated with only one IoT device in a time slot. Define a binary IoT device access variable a. k [n], when a k When [n] = 1, the k-th IoT device connects to the drone in the n-th time slot; otherwise, a k [n] = 0;

[0035] S204. Express the transmission power of IoT device k as P. k We obtain the device transmission rate in the nth time slot and the UAV transmission rate in the nth time slot.

[0036] Furthermore, in step S204, the device transmission rate R in the nth time slot... k [n] is represented as:

[0037]

[0038] The energy consumption of a drone flight is defined as:

[0039] E fly[n]=κ||q[n+1]-q[n]|| 2

[0040] Where n = 1, ..., N-1, d max κ represents the distance the drone flies at its maximum speed within a time slot, and κ represents the weighting parameter.

[0041] Specifically, step S3 is as follows:

[0042] S301. The data processing flow for each time slot includes two stages: data collection and data computation.

[0043] During the data collection phase, drones fly above IoT devices and collect data.

[0044] During the data computation phase, the drone adopts an offloading strategy, processing a portion of the data locally while offloading the remaining data to the central server for remote computation.

[0045] S302. Determine the energy consumption E of IoT devices uploading data during the data collection phase. tr [n] represents the amount of data L[n] collected by the drone from IoT devices;

[0046] S303. Describe the data offloading ratio of the UAV as α[n]. The (1-α[n]) part of the task is processed directly on the UAV, and the energy consumption E of the UAV calculated locally at time slot n is obtained. loc [n] represents the mission unloading time t of the drone in time slot n. off [n], the energy consumption E of the drone unloading the task at time slot n. off [n], the central server remotely calculates time t at time slot n. rem Energy consumption E for remote computation at [n] and time slot n rem [n].

[0047] Furthermore, in step S303, the energy consumption E of the UAV is locally calculated at time slot n. loc [n] is:

[0048]

[0049] The unloading time t of the drone in time slot n off [n] is:

[0050]

[0051] Energy consumption E of the drone unloading the mission in time slot n off [n] is:

[0052] E off [n] = P ut off [n]

[0053] The central server remotely calculates time t in time slot n. rem [n] is:

[0054]

[0055] Energy consumption E for remote computing in time slot n rem [n] is:

[0056]

[0057] Among them, f u [n] represents the CPU frequency allocated to the UAV in time slot n, ψ represents the effective capacitance coefficient, L[n] represents the amount of data collected by the UAV from IoT devices, and f s [n] represents the CPU frequency allocated to the central server for time slot n, P u R is the transmit power of the drone. u [n] represents the transmission rate of the UAV in time slot n. Where is the effective capacitance coefficient of the ground base station, and C represents the number of computation cycles required to process 1 bit of task.

[0058] Specifically, in step S4, the energy consumption of the drone-assisted mobile edge computing network data collection phase includes the drone's flight energy consumption and the transmission energy consumption of data uploaded by IoT devices. The energy consumption of the data computing phase is the sum of the energy consumption of local computing, drone unloading, and remote computing.

[0059] Furthermore, the data collection objectives and optimization conditions are as follows:

[0060]

[0061]

[0062]

[0063] C3:q[1]=q[N]

[0064] C4:||q[n+1]-q[n]||≤d max

[0065]

[0066] The objectives and optimization conditions for data computation are as follows:

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] Where n = 1, ..., N-1, E fly [n] represents the energy consumption of the drone flight, a k [n] represents the IoT device access variable, q[N] represents the drone's position in time slot N, and d represents the drone's position in time slot N. max E represents the distance a drone can fly at maximum speed within a time slot. loc [n] represents the energy consumption of the UAV calculated locally at time slot n, E off [n] represents the energy consumption of the UAV unloading the task at time slot n, E rem [n] represents the energy consumption for remote computation in time slot n, f u [n] represents the CPU frequency allocated to the UAV during time slot n, f s [n] represents the CPU frequency allocated to the central server for time slot n, t loc [n] represents the local computation time, α[n] represents the data unloading ratio of the UAV in time slot n, and F u F is the maximum CPU frequency of the drone. s t is the maximum CPU frequency of the central server. off [n] represents the task unloading time of the UAV in time slot n, t rem [n] represents the time remotely calculated by the central server at time slot n, where t is the time. tr Let L[n] be the transmission time length for each time slot, L[n] be the amount of data collected by the UAV from the IoT device, and a, q, f, α be the set of variables for IoT device access, UAV location, CPU frequency allocated to the UAV and the central server, and UAV data offloading ratio, respectively.

[0074] Secondly, embodiments of the present invention provide a mobile edge computing network data processing and energy consumption optimization system, comprising:

[0075] The module is built to construct a drone-assisted mobile edge computing network model consisting of one ground base station connected to a central server, one drone equipped with an edge server, and K IoT devices.

[0076] The building module, based on the drone-assisted mobile edge computing network model obtained from the building module, constructs communication models between drones and IoT devices, as well as between base stations and drones;

[0077] The processing module constructs a data processing flow for a drone-assisted mobile edge computing network based on the communication model between nodes;

[0078] The optimization module, based on the communication model obtained from the construction module and the data processing flow obtained from the processing module, constructs the goals and optimization conditions for data collection and data computation of the UAV-assisted mobile edge computing network.

[0079] The control module, based on the data collection objectives and optimization conditions obtained from the optimization module, constructs a control strategy for the dynamic deployment of edge servers and the access of IoT devices through block coordinate descent and continuous convex approximation.

[0080] The output module, based on the data obtained from the optimization module, calculates the target and optimization conditions, and the edge server deployment and device access strategies obtained from the control module. It then constructs task unloading and resource allocation strategies by solving the problem using variable substitution and the binary search method.

[0081] Compared with the prior art, the present invention has at least the following beneficial effects:

[0082] A mobile edge computing network data processing and energy consumption optimization method and system is proposed. Addressing the problems of existing technologies, the network energy consumption optimization problem is formalized as an optimization problem related to UAV flight trajectory, user access control, task offloading, and resource allocation. The method jointly optimizes user scheduling and UAV trajectory, and optimizes task offloading and resource allocation while ensuring timely completion of computing tasks. UAVs are used for dynamic flight to relay tasks for IoT devices, while simultaneously carrying edge servers and making task offloading decisions to assist the central server in data processing. This method can effectively balance network load, improve resource utilization, and reduce network energy consumption.

[0083] Furthermore, the purpose of constructing a drone-assisted mobile edge computing network model consisting of one ground base station connected to a central server, one drone equipped with an edge server, and K IoT devices is that most IoT devices are distributed in complex ground environments, facing obstacles such as environmental obstruction that hinder communication with base stations. Moreover, IoT devices have relatively weak computing capabilities, making it difficult for them to process their own data. By leveraging the dynamic flight of drones to collect device data and assist in data computation, the data transmission rate of IoT devices can be improved, and mobile edge computing technology can significantly reduce the energy consumption of data computation.

[0084] Furthermore, the purpose of constructing communication models between drones and IoT devices, as well as between base stations and drones, is that, since the location of drones changes with time slots, it is necessary to represent the channel models in the network separately according to time slots; stipulating that drones only associate with one device in the same time slot can avoid interference, and the association relationship between devices and drones is represented by the defined IoT device access variables.

[0085] Furthermore, the purpose of constructing the data processing flow of the drone-assisted mobile edge computing network is to define the network's operation process and the parameters involved in the two stages of data collection and data computation, so as to illustrate the specific steps of the data processing flow.

[0086] Furthermore, by constructing a drone-assisted mobile edge computing network data processing flow, a method and system for optimizing mobile edge computing network data processing and energy consumption were designed. Compared with existing mechanisms, this method can effectively ensure processing latency, reduce network energy consumption, and extend device lifespan. With the development of the Internet of Things (IoT) industry, emerging IoT applications such as smart healthcare, autonomous driving, and virtual reality are constantly emerging. IoT devices, relying on their limited energy and computing resources, struggle to support these applications with low latency and high computing demands. The joint optimization method, by deploying edge servers on drones for dynamic flight, controls user devices to access and collect data from IoT devices. Through task offloading and resource allocation, it can effectively utilize network resources to complete computing tasks and reduce overall network energy consumption.

[0087] It is understandable that the beneficial effects of the second aspect mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0088] In summary, this invention can effectively balance network load, improve resource utilization, and reduce network energy consumption.

[0089] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0090] Figure 1 This is a flowchart of the method of the present invention;

[0091] Figure 2 This is a diagram illustrating the effect of the UAV trajectory optimization of this invention on the tolerance time for mission completion.

[0092] Figure 3This is a performance comparison chart showing the total network energy consumption as the data computation stage duration varies for the following IoT systems provided in this embodiment: Remote Computation (with a central server for remote computation), Local Computation (with an edge server for local computation), Circular Trajectory (with a drone's circular trajectory), and Random Access (with IoT devices for random access).

[0093] Figure 4 This is a performance comparison chart showing the total network power consumption as the sensor workload changes for the following IoT systems provided in this embodiment: Remote Computation (with remote computing from a central server), Local Computation (with local computing from an edge server), Circular Trajectory (with a circular trajectory from a drone), and Random Access (with random access from IoT devices).

[0094] Figure 5 A schematic diagram of a computer device provided in an embodiment of the present invention;

[0095] Figure 6 This is a block diagram of a chip provided according to an embodiment of the present invention. Detailed Implementation

[0096] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0097] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0098] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0099] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0100] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0101] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0102] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0103] This invention provides a method for data processing and energy consumption optimization in mobile edge computing networks. It formalizes the network energy consumption optimization problem as an optimization problem related to UAV flight trajectory, user access control, task offloading, and resource allocation. The method jointly optimizes user scheduling and UAV trajectory, and optimizes task offloading and resource allocation while ensuring timely completion of computing tasks. It utilizes the dynamic flight of UAVs to relay tasks for IoT devices, while simultaneously using UAVs to carry edge servers and make task offloading decisions, assisting the central server in processing data. This method can effectively balance network load, improve resource utilization, and reduce network energy consumption.

[0104] Please see Figure 1 This invention discloses a method for data processing and energy consumption optimization in mobile edge computing networks, comprising the following steps:

[0105] S1. Construct a drone-assisted mobile edge computing network model consisting of one ground base station connected to a central server, one drone equipped with an edge server, and K IoT devices.

[0106] S101. Construct a mobile edge computing network model containing 1+1+K communication nodes. Each IoT device has a task of size L that needs to be computed within time T.

[0107] S102. The IoT device sends computing tasks to the drone. The edge server on the drone, together with the central server connected to the ground base station, calculates these tasks and sends them back to the IoT device.

[0108] S103. Let w be the horizontal position of the kth IoT device. k =(x k ,y k The ground base station's horizontal position is (0,0); further, time T is discretized into N uniform time slots, each time slot having a duration of t = T / N; the set of N time slots is described as N = {1,2,...,N}, and the UAV's horizontal position in the nth time slot is represented as q[n] = (x u [n],y u [n]); In addition, the altitude of the drone and the ground base station is expressed as a constant H;

[0109] S104. Assume that the UAV must fly back to the starting position in the last time slot, and the flight trajectory of the UAV is limited by speed constraints. Determine the flight energy consumption of the UAV.

[0110] The flight trajectory of a drone is limited by speed constraints, specifically:

[0111] q[1]=q[N]

[0112] ||q[n+1]-q[n]||≤d max

[0113] Where n = 1, ..., N-1, d max This indicates the distance a drone can travel in a time slot at its maximum speed.

[0114] The energy consumption of a drone flight is defined as:

[0115] E fly [n]=κ||q[n+1]-q[n]|| 2

[0116] Where n = 1, ..., N-1, and κ represents the weight parameter.

[0117] S2. Based on the UAV-assisted mobile edge computing network model obtained in step S1, construct communication models between UAVs and IoT devices, as well as between base stations and UAVs.

[0118] S201. Let d represent the distances from IoT device k to the drone and from the drone to the ground base station in time slot n. k [n] = ||q[n] - w k ||and d u [n] = ||q[n]||;

[0119] The channel power gain from IoT device k to drone within time slot n is defined as:

[0120]

[0121] Where ρ0 represents the reference channel gain constant.

[0122] S202. Determine the channel gain h of the link between the UAV and the ground base station within time slot n. u [n];

[0123] Channel gain h u [n] is:

[0124]

[0125] S203. A drone can only be associated with one IoT device in a time slot. Define a binary IoT device access variable a. k [n], that is, when a k When [n] = 1, the k-th IoT device connects to the drone in the n-th time slot; otherwise, a k [n] = 0;

[0126] S204. Express the transmission power of IoT device k as P. k We obtain the device transmission rate in the nth time slot and the UAV transmission rate in the nth time slot.

[0127] The device transmission rate R in the nth time slot k [n] is represented as:

[0128]

[0129] Among them, B k For device bandwidth, σ 2 This represents noise power.

[0130] The transmission rate R of the UAV in the nth time slot u [n] is:

[0131]

[0132] Among them, B u For over-the-air network bandwidth, P u This refers to the drone's launch power.

[0133] S3. Data processing flow of UAV-assisted mobile edge computing network based on communication model between nodes;

[0134] S301. The data processing flow for each time slot includes two stages: data collection and data computation. During the data collection stage, the drone flies above the IoT device and collects data. During the data computation stage, the drone employs an offloading strategy, processing a portion of the data locally while offloading the remaining data to the central server for remote computation.

[0135] S302. Determine the energy consumption E of IoT devices uploading data during the data collection phase. tr [n] represents the amount of data L[n] collected by the drone from IoT devices;

[0136] Transmission energy consumption E tr [n] is:

[0137]

[0138] Among them, t tr For the transmission time length of each time slot, a k [n] represents the IoT device access variable, P k This refers to the transmit power of IoT devices.

[0139] The amount of data L[n] collected by the drone from IoT devices is defined as:

[0140]

[0141] Where K is the number of IoT devices, a k [n] represents the IoT device access variable, R k [n] represents the transmission rate of device k in time slot n.

[0142] S303. Describe the data offloading ratio of the UAV as α[n]. The (1-α[n]) part of the task is processed directly on the UAV, and the energy consumption E of the UAV calculated locally at time slot n is obtained. loc [n] represents the mission unloading time t of the drone in time slot n. off [n], the energy consumption E of the drone unloading the task at time slot n. off [n], the central server remotely calculates time t at time slot n. rem Energy consumption E for remote computation at [n] and time slot n rem [n].

[0143] Local computation time t loc [n] is:

[0144]

[0145] Energy consumption E of the drone calculated locally in time slot n loc [n] is:

[0146]

[0147] The unloading time t of the drone in time slot n off [n] is:

[0148]

[0149] Energy consumption E of the drone unloading the mission in time slot n off [n] is:

[0150] E off [n] = P u t off [n];

[0151] The central server remotely calculates time t in time slot n. rem [n] is:

[0152]

[0153] Energy consumption E for remote computing in time slot n rem [n] is:

[0154]

[0155] Among them, f u [n] represents the CPU frequency allocated to the UAV in time slot n, ψ represents the effective capacitance coefficient, and f s [n] represents the CPU frequency allocated to the central server for time slot n, P u R is the transmit power of the drone. u [n] represents the transmission rate of the UAV in time slot n. Where is the effective capacitance coefficient of the ground base station, and C represents the number of computation cycles required to process 1 bit of task.

[0156] S4. Based on the communication model obtained in step S2 and the data processing flow obtained in step S3, construct the objectives and optimization conditions for data collection and data computation of UAV-assisted mobile edge computing network.

[0157] Energy consumption during the data collection phase includes the flight energy consumption of the drone and the transmission energy consumption of data uploaded by IoT devices. The goal and optimization conditions for data collection are determined.

[0158] The data collection objectives and optimization conditions are as follows:

[0159]

[0160]

[0161]

[0162] C3:q[1]=q[N]

[0163] C4:||q[n+1]-q[n]||≤d max

[0164]

[0165] Where n = 1, ..., N-1, E fly [n] represents the energy consumption of the drone flight, a k [n] represents the IoT device access variable, q[N] represents the drone's position in time slot N, and d represents the drone's position in time slot N. max This refers to the distance a drone can fly at its maximum speed within a time slot.

[0166] S402. The energy consumption of the data computing phase is the sum of the energy consumption of local computing, drone unloading and remote computing. The goal and optimization conditions of data computing are determined.

[0167] The objectives and optimization conditions for data computation are as follows:

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174] Among them, E loc [n] represents the energy consumption of the UAV calculated locally at time slot n, E off [n] represents the energy consumption of the UAV unloading the task at time slot n, E rem [n] represents the energy consumption for remote computation in time slot n, f u [n] represents the CPU frequency allocated to the UAV during time slot n, f s [n] represents the CPU frequency allocated to the central server for time slot n, t loc[n] represents the local computation time, α[n] represents the data unloading ratio of the UAV in time slot n, and F u F is the maximum CPU frequency of the drone. s t is the maximum CPU frequency of the central server. off [n] represents the task unloading time of the UAV in time slot n, t rem [n] represents the time remotely calculated by the central server at time slot n, where t is the time. tr Let L[n] be the transmission time length for each time slot, L[n] be the amount of data collected by the UAV from the IoT device, and a, q, f, α be optimization variables, representing the set of variables for IoT device access, UAV location, CPU frequency allocated to the UAV and the central server, and UAV data offloading ratio, respectively.

[0175] S5. Based on the data collection objectives and optimization conditions obtained in step S4, a dynamic deployment and IoT device access control strategy for edge servers is constructed by using the block coordinate descent method and continuous convex approximation solution.

[0176] The data collection problem includes two sets of variables: UAV flight trajectory and user access control. The block coordinate descent method is used to decouple the variables and the problem is transformed into a convex problem by using continuous convex approximation.

[0177] By solving for the drone flight trajectory and user access separately, and converting the non-convex constraints into convex constraints through Taylor expansion, the dynamic deployment of edge servers and the control strategy for IoT device access can be obtained.

[0178] S6. Based on the data obtained in step S4, calculate the objectives and optimization conditions, and the edge server deployment and device access strategies obtained in step S5. Solve the problem using variable substitution and the binary search method to construct a task unloading and resource allocation strategy.

[0179] After variable substitution, the data computation problem becomes a convex problem with respect to α[n], which is solved using the bisection method. The upper and lower bounds of α[n] are calculated, and the optimal solution is approximated by continuously updating the value of α[n] to the average of the upper and lower bounds. f is then calculated using the optimal solution for α[n]. u [n] and f s [n], which yields the task unloading and resource allocation strategy.

[0180] The mobile edge computing network data processing and energy consumption optimization method of this invention can be applied to the Internet of Things.

[0181] In another embodiment of the present invention, a mobile edge computing network data processing and energy consumption optimization system is provided. This system can be used to implement the above-mentioned mobile edge computing network data processing and energy consumption optimization method. Specifically, the mobile edge computing network data processing and energy consumption optimization system includes a construction module, a building module, a processing module, an optimization module, a control module, and an output module.

[0182] The module includes a drone-assisted mobile edge computing network model consisting of a ground base station connected to a central server, a drone equipped with an edge server, and K IoT devices.

[0183] The building module, based on the drone-assisted mobile edge computing network model obtained from the building module, constructs communication models between drones and IoT devices, as well as between base stations and drones;

[0184] The processing module constructs a data processing flow for a drone-assisted mobile edge computing network based on the communication model between nodes;

[0185] The optimization module, based on the communication model obtained from the construction module and the data processing flow obtained from the processing module, constructs the goals and optimization conditions for data collection and data computation of the UAV-assisted mobile edge computing network.

[0186] The control module, based on the data collection objectives and optimization conditions obtained from the optimization module, constructs a control strategy for the dynamic deployment of edge servers and the access of IoT devices through block coordinate descent and continuous convex approximation.

[0187] The output module, based on the data obtained from the optimization module, calculates the target and optimization conditions, and the edge server deployment and device access strategies obtained from the control module. It then constructs task unloading and resource allocation strategies by solving the problem using variable substitution and the binary search method.

[0188] In another embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement corresponding method flows or corresponding functions. The processor described in this embodiment of the present invention can be used for the operation of mobile edge computing network data processing and energy consumption optimization methods, including:

[0189] This paper constructs a drone-assisted mobile edge computing network model, consisting of a ground base station connected to a central server, a drone equipped with an edge server, and K IoT devices. Based on this model, communication models are built between the drone and IoT devices, and between the base station and the drone. A data processing flow for the drone-assisted mobile edge computing network is constructed based on the node communication model. Based on the communication model and data processing flow, the objectives and optimization conditions for data collection and computation in the drone-assisted mobile edge computing network are established. Based on the data collection objectives and optimization conditions, a dynamic deployment strategy for edge servers and IoT device access is constructed using block coordinate descent and continuous convex approximation. Based on the data computation objectives and optimization conditions, as well as the edge server deployment and device access strategies, a task offloading and resource allocation strategy is constructed using variable substitution and bisection.

[0190] Please see Figure 5 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the fluid composition calculation method in the reservoir stimulation wellbore of this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the mobile edge computing network data processing and energy consumption optimization system of this embodiment. To avoid repetition, these details are not elaborated here.

[0191] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 5 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0192] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0193] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0194] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0195] Please see Figure 6 The terminal device is a chip. In this embodiment, the chip 600 includes a processor 622, which may be one or more, and a memory 632 for storing computer programs executable by the processor 622. The computer program stored in the memory 632 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processor 622 may be configured to execute the computer program to perform the aforementioned mobile edge computing network data processing and energy consumption optimization methods.

[0196] Additionally, chip 600 may also include a power supply component 626 and a communication component 650. The power supply component 626 can be configured to perform power management of chip 600, and the communication component 650 can be configured to enable communication of chip 600, such as wired or wireless communication. Furthermore, chip 600 may also include an input / output (I / O) interface 658. Chip 600 can operate on an operating system stored in memory 632.

[0197] In another embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (memory). This computer-readable storage medium is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device.

[0198] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the mobile edge computing network data processing and energy consumption optimization methods in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps:

[0199] This paper constructs a drone-assisted mobile edge computing network model, consisting of a ground base station connected to a central server, a drone equipped with an edge server, and K IoT devices. Based on this model, communication models are built between the drone and IoT devices, and between the base station and the drone. A data processing flow for the drone-assisted mobile edge computing network is constructed based on the node communication model. Based on the communication model and data processing flow, the objectives and optimization conditions for data collection and computation in the drone-assisted mobile edge computing network are established. Based on the data collection objectives and optimization conditions, a dynamic deployment strategy for edge servers and IoT device access is constructed using block coordinate descent and continuous convex approximation. Based on the data computation objectives and optimization conditions, as well as the edge server deployment and device access strategies, a task offloading and resource allocation strategy is constructed using variable substitution and bisection.

[0200] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0201] The technical effects of the present invention will be described in detail below with reference to simulation.

[0202] This experiment simulates a mobile edge computing network data processing and energy consumption optimization method and system in the Internet of Things (IoT) and an existing mechanism based on the same network parameters to verify the superiority of the proposed method. The specific steps are as follows: A 1km x 1km area with identical network parameters is used. IoT devices are located at specific positions within the simulation area. The ground base station is at a height of 30m, and the drone flies at a height of 30m. In this environment, there are 8 IoT devices, the maximum drone speed is 24m / s, the data computation phase duration varies between 3.6s and 4.4s, the task load of IoT devices varies between 26Mbits and 36Mbits, and the task completion tolerance time varies between 75s and 125s. The overall network energy consumption data is statistically analyzed. The results are the average values ​​after 100 simulations.

[0203] The drone trajectory optimization effect of the present invention is as follows: Figure 2 As shown, the performance of this invention is compared with that of remote computing via a central server, local computing via edge servers, mobile edge computing networks using circular trajectory drones, and mobile edge computing networks with random access from IoT devices. Figure 3 , Figure 4 As shown. Figure 2 , Figure 3 , Figure 4The images show the variation of the UAV trajectory optimization effect of the present invention with the tolerance time for task completion, and compare the total network energy consumption of the present invention with that of Remote Computation, Local Computation, Circular Trajectory, and Random Access in terms of data computation phase duration and sensor workload.

[0204] In summary, this invention provides a method and system for mobile edge computing network data processing and energy consumption optimization. It formalizes the network energy consumption optimization problem as an optimization problem related to UAV flight trajectory, user access control, task offloading, and resource allocation. The method jointly optimizes user scheduling and UAV trajectory, and optimizes task offloading and resource allocation while ensuring timely completion of computing tasks. It utilizes the dynamic flight of UAVs to relay tasks for IoT devices, while simultaneously using UAVs to carry edge servers and make task offloading decisions, assisting the central server in processing data. This method can effectively balance network load, improve resource utilization, and reduce network energy consumption.

[0205] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0206] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0207] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0208] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0209] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0210] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0211] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random-access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0212] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0213] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0214] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0215] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for data processing and energy consumption optimization in mobile edge computing networks, characterized in that, Includes the following steps: S1, constructing from 1 A ground base station connected to the central server, 1 A drone equipped with an edge server, K A drone-assisted mobile edge computing network model consisting of IoT devices; S2. Based on the UAV-assisted mobile edge computing network model obtained in step S1, construct communication models between UAVs and IoT devices, as well as between base stations and UAVs. S3. Data processing flow of UAV-assisted mobile edge computing network based on communication model between nodes; S4. Based on the communication model obtained in step S2 and the data processing flow obtained in step S3, construct the objectives and optimization conditions for data collection and data computation in the UAV-assisted mobile edge computing network. The objectives and optimization conditions for data collection are as follows: The objectives and optimization conditions for data computation are as follows: in, , For the energy consumption of drone flight, For IoT device access variables, For time slots The location of the drone at that time For time slots The location of the drone at that time The distance a drone can fly at its maximum speed within a time slot. For drones in time slots Energy consumption calculated locally. For drones in time slots Energy consumption of unloading tasks during operation. For time slots Energy consumption for remote computing For time slots The CPU frequency allocated to the drone at that time For time slots The CPU frequency allocated to the time center server, Calculate the time locally. For time slots The data unloading ratio of the drone at that time This is the maximum CPU frequency of the drone. The maximum CPU frequency of the central server, For drones in time slots Task unloading time at that time For the central server in time slots Remote calculation time, The transmission time length for each time slot, The amount of data that drones collect from IoT devices, These are sets of variables, namely, IoT device access, drone location, CPU frequency allocated to drones and central server, and drone data offloading ratio. S5. Based on the data collection objectives and optimization conditions obtained in step S4, a dynamic deployment and IoT device access control strategy for edge servers is constructed by using the block coordinate descent method and continuous convex approximation solution. S6. Based on the data obtained in step S4, calculate the objectives and optimization conditions, and the edge server deployment and device access strategies obtained in step S5. Solve the problem using variable substitution and the binary search method to construct a task unloading and resource allocation strategy.

2. The mobile edge computing network data processing and energy consumption optimization method according to claim 1, characterized in that, Step S1 is as follows: S101, Constructing includes 1 + 1 + K A mobile edge computing network model with communication nodes, where each IoT device has a size of [missing information]. The task is to The calculation must be completed within the specified time. S102. The IoT device sends computing tasks to the drone. The edge server on the drone, together with the central server connected to the ground base station, calculates these tasks and sends them back to the IoT device. S103, Let the first The horizontal position of the IoT devices is The horizontal position of the ground base station is Time Discretized There are 3 uniform time slots, each time slot having a duration of 1 ; The set of time slots is described as follows , No. The horizontal position of the drone in each time slot is ; The altitude between the drone and the ground base station is ; S104. Assume the UAV must return to its starting position in the last time slot, and the UAV's flight trajectory is constrained by speed. Determine the UAV's flight energy consumption. .

3. The mobile edge computing network data processing and energy consumption optimization method according to claim 2, characterized in that, Drone flight energy consumption for: in, , This represents the weighting parameter.

4. The mobile edge computing network data processing and energy consumption optimization method according to claim 1, characterized in that, Step S2 is as follows: S201. Determine the time slot Internal IoT devices Channel power gain to UAV ; S202, Determine the time slot Channel gain of the link between the UAV and the ground base station ; S203. A drone is associated with only one IoT device per time slot; define a binary IoT device access variable. ,when At that time, the first The first IoT device connected to the A drone in a time slot, otherwise ; S204, IoT devices The transmission power is expressed as , obtained the The device transmission rate of the time slot and the drone in the first time slot The transmission rate of each time slot.

5. The mobile edge computing network data processing and energy consumption optimization method according to claim 4, characterized in that, In step S204, the first Time slot device transmission rate Represented as: The energy consumption of a drone flight is defined as: in, , This indicates the distance a drone can travel in a time slot at its maximum speed. This represents the weighting parameter.

6. The mobile edge computing network data processing and energy consumption optimization method according to claim 1, characterized in that, Step S3 is as follows: S301. The data processing flow for each time slot includes two stages: data collection and data computation. During the data collection phase, drones fly above IoT devices and collect data. During the data computation phase, the drone adopts an offloading strategy, processing a portion of the data locally while offloading the remaining data to the central server for remote computation. S302. Determine the energy consumption of IoT devices uploading data during the data collection phase. The amount of data collected by the drone from IoT devices. ; S303, The data offloading ratio of the drone is expressed as... , Some tasks are processed directly on the drone, obtaining data from the drone within the time slot. Energy consumption calculated locally Drones in time slots Task unloading time Drones in time slots Energy consumption of unloading tasks The central server is in the time slot Remote calculation time and time slot Energy consumption of remote computing .

7. The mobile edge computing network data processing and energy consumption optimization method according to claim 6, characterized in that, In step S303, the drone is in the time slot Energy consumption calculated locally for: Drones in time slots Task unloading time for: Drones in time slots Energy consumption of unloading tasks for: The central server in the time slot Remote calculation time for: Time slot Energy consumption of remote computing for: in, For time slots The CPU frequency allocated to the drone at that time Indicates the effective capacitance coefficient. The amount of data that drones collect from IoT devices, For time slots The CPU frequency allocated to the time center server, For the drone's transmission power, For time slots The transmission rate of the drone The effective capacitance coefficient of the ground base station. This represents the number of computation cycles required to process a 1-bit task.

8. The mobile edge computing network data processing and energy consumption optimization method according to claim 1, characterized in that, In step S4, the energy consumption of the drone-assisted mobile edge computing network data collection phase includes the drone's flight energy consumption and the transmission energy consumption of data uploaded by IoT devices. The energy consumption of the data computing phase is the sum of the energy consumption of local computing, drone unloading, and remote computing.

9. A mobile edge computing network data processing and energy consumption optimization system, characterized in that, include: Build modules, construct from 1 A ground base station connected to the central server, 1 A drone equipped with an edge server, K A drone-assisted mobile edge computing network model consisting of IoT devices; The building module, based on the drone-assisted mobile edge computing network model obtained from the building module, constructs communication models between drones and IoT devices, as well as between base stations and drones; The processing module constructs a data processing flow for a drone-assisted mobile edge computing network based on the communication model between nodes; The optimization module, based on the communication model obtained from the construction module and the data processing flow obtained from the processing module, constructs the objectives and optimization conditions for data collection and computation in the UAV-assisted mobile edge computing network. The objectives and optimization conditions for data collection are as follows: The objectives and optimization conditions for data computation are as follows: in, , For the energy consumption of drone flight, For IoT device access variables, For time slots The location of the drone at that time For time slots The location of the drone at that time The distance a drone can fly at its maximum speed within a time slot. For drones in time slots Energy consumption calculated locally. For drones in time slots Energy consumption of unloading tasks during operation. For time slots Energy consumption for remote computing For time slots The CPU frequency allocated to the drone at that time For time slots The CPU frequency allocated to the time center server, Calculate the time locally. For time slots The data unloading ratio of the drone at that time This is the maximum CPU frequency of the drone. The maximum CPU frequency of the central server, For drones in time slots Task unloading time at that time For the central server in time slots Remote calculation time, The transmission time length for each time slot, The amount of data that drones collect from IoT devices, These are sets of variables, namely, IoT device access, drone location, CPU frequency allocated to drones and central server, and drone data offloading ratio. The control module, based on the data collection objectives and optimization conditions obtained from the optimization module, constructs a control strategy for the dynamic deployment of edge servers and the access of IoT devices through block coordinate descent and continuous convex approximation. The output module, based on the data obtained from the optimization module, calculates the target and optimization conditions, and the edge server deployment and device access strategies obtained from the control module. It then constructs task unloading and resource allocation strategies by solving the problem using variable substitution and the binary search method.

Images