Method for calculating energy consumption of rechargeable unmanned aerial vehicle at mobile edge

Abstract

The invention belongs to the technical field of wireless communication, relates to a method for calculating energy consumption of a rechargeable unmanned aerial vehicle at a mobile edge, and relates to a mobile edge calculation system assisted by the unmanned aerial vehicle, a power station for supplying power to the unmanned aerial vehicle, Internet of Things equipment and an edge cloud, and MEC service is provided for ground users through the synergistic effect of the mobile edge calculation system, the power station, the Internet of Things equipment and the edge cloud. In order to optimize the service performance of the charging unmanned aerial vehicle in edge calculation, two optimization problems are formulated, including maximizing the residual energy of the unmanned aerial vehicle after the whole journey by finding the optimal position of the unmanned aerial vehicle; the energy consumption of the unmanned aerial vehicle is minimized by jointly optimizing the hovering position of the unmanned aerial vehicle, the communication and computing resource allocation and the task segmentation decision. Through cooperation of the unmanned aerial vehicle and the edge element, energy consumption of the unmanned aerial vehicle is effectively reduced, and network performance of the unmanned aerial vehicle is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wireless communications, and relates to a method for calculating energy consumption of a rechargeable UAV in a mobile edge computing system, which is applicable to a method for maximizing battery performance of a rechargeable UAV in a mobile edge computing network system based on wireless power transmission. Background Art

[0002] Mobile edge computing networks are considered a promising key technology to improve the quality of service for computationally intensive and delay-critical tasks. Mobile edge computing networks equipped with drones can take full advantage of the flexible deployment of drones to provide extended communication coverage and reliable connections to mobile users in a timely manner, especially when the ground communication infrastructure is destroyed or a large number of users surge. However, due to the limitations of drones' energy and computing resources, deploying more drones in mobile edge computing networks requires high costs and power consumption.

[0003] In addition, with the growth of global logistics business and the increasing demand of consumers for instant delivery, the efficiency and speed of logistics services have become an important issue. Drones are increasingly considered to be the main mode of transportation in various logistics application industries in the future. In this context, drone logistics services have emerged as an emerging market with great potential, using drone technology to deliver items to urban and rural areas to provide users with fast and efficient logistics services. However, as the size and power of traditional rotor drones decrease, the battery capacity limits the cruising distance and mission duration. Therefore, a wirelessly rechargeable drone can solve the problem of limited battery capacity of traditional drones, thereby improving communication capabilities, reducing the energy consumption of mobile edge computing networks, and improving the transmission reliability of MEC networks and expanding wireless coverage.

[0004] Since the resources of drones are limited, the user's computing tasks cannot be fully performed on the drone. Therefore, the drone's hovering position, communication resources and computing resources as well as the task segmentation decision are jointly optimized. However, the joint optimization problem is a complex non-convex problem. The successive convex approximation algorithm can effectively solve various optimization problems in wireless communication networks. Summary of the invention

[0005] The present invention relates to a method for optimizing high power consumption of unmanned aerial vehicles in edge computing. With the advent of the 5G era, the emerging field of computer technology - mobile edge computing has come into people's view, moving computing and storage capabilities to the edge of the network to achieve faster response speed, lower and better data privacy and security. However, with the surge in the number of mobile users, uneven distribution of network facilities and ground signal blocking, IoT devices cannot directly access edge servers. UAVs are widely used due to their good mobility and easy deployment. Therefore, it is considered to introduce drones into mobile edge computing networks, and the drone-mobile edge computing network greatly improves the performance of ground wireless communication systems. The present invention studies a drone logistics service system model, involving a drone-assisted mobile edge computing system, a power station for powering drones, and the synergy between IoT devices and edge clouds to provide MEC services for ground users. In order to optimize the service performance of charging drones in edge computing, two optimization problems are formulated, including maximizing the remaining energy of the drone after the entire journey by finding the optimal position of the drone; and minimizing the energy consumption of the drone by jointly optimizing the hovering position of the drone, communication and computing resource allocation, and task segmentation decisions. The present invention effectively reduces the energy consumption of drones and improves the network performance of drones through the collaboration of drones and edge elements.

[0006] A method for calculating energy consumption of a rechargeable drone at a mobile edge, the characteristics of which include: (1) First, a UAV mobile edge computing network system model for wireless power transmission is constructed; (2) Secondly, a UAV mobile edge computing network system model for wireless power transmission is constructed to build the communication model, mobility model and energy consumption model; (3) Finally, the minimum UAV energy consumption is constructed based on the communication model, maneuver model and energy consumption model, and the minimum UAV energy consumption is optimized according to the successive convex approximation method; the optimal UAV hovering position, communication and computing resource allocation, and task segmentation decision results are obtained; The charging drone mobile edge computing network system model for wireless power transmission includes: a rotorcraft drone auxiliary, a charging station, K ground users and a group of edge clouds; when the user's computing processor cannot meet the needs of the computing task, the drone is used as a relay to realize sensing, communication and computing in different scenarios; at the same time, the line-of-sight link method is adopted to solve the problem that the ground user cannot establish communication with the nearby cellular base station due to signal blocking or shadow of obstructions; the user partially offloads the computing task through the drone, and some tasks are calculated locally and some tasks are offloaded to the edge cloud; the user to the drone is the uplink communication link, and the drone to the edge cloud is represented as the downlink communication, forming a three-dimensional wireless communication network; because there are obstacles between the drone and the power station when the drone is performing computing services, the drone needs to collect energy at close range, so a line-of-sight link transmission is established between the drone and the power station.

[0007] The present invention proposes a wireless power transmission charging drone in a mobile edge computing network system, which combines the drone location, communication and computing resource allocation, and task segmentation decision method to reduce the drone's energy consumption. By solving the problem of drone residual energy and minimum energy consumption in steps, the optimal communication and computing resource allocation and task segmentation decision method is obtained. After simulation, the drone computing power, transmission power and system cost are compared with other scenarios. Compared with the existing baseline solution, the proposed method effectively saves drone energy consumption and improves system performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of the method for reducing energy consumption of mobile edge computing assisted by a charging drone of the present invention; Figure 2 This is a system model diagram of the mobile edge computing rechargeable drone of the present invention; Figure 3 Comparison of UAV computing power and system cost in different application scenarios; Figure 4 Comparison of UAV transmission power and system cost for different application scenarios of UAV. DETAILED DESCRIPTION

[0009] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. The present invention can also be implemented or applied through other different specific implementation methods. It should be noted that the following embodiments are provided only to illustrate the basic concept of the present invention in a schematic manner. In the absence of conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. The details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention.

[0010] The key technical requirements and guidance methods adopted by the application of rechargeable drones in mobile edge computing (MEC) scenarios in this invention, especially in the role of drones as mobile computing nodes, are as follows: Wireless charging technology: Design drones to charge in the air through near-field wireless power transfer technology.

[0011] Mobile edge computing integrated computing module: The drone carries an embedded server or high-performance computing unit to enable instant processing and analysis of data, reducing the time it takes for data to travel to and from the cloud center.

[0012] Mobile edge computing task offloading and resource allocation: Use algorithms (such as reinforcement learning, genetic algorithms, etc.) to dynamically optimize the allocation of computing tasks, decide which tasks to process locally and which to upload to the cloud, and balance computing resources and energy consumption.

[0013] 5G / 6G communication: Adopt the latest mobile communication technology to ensure high-speed, low-latency data transmission and meet the needs of MEC real-time interaction.

[0014] These technical requirements and guidance methods together constitute the technical framework of rechargeable drones in mobile edge computing application scenarios, aiming to improve the autonomy, efficiency, reliability and safety of drone systems.

[0015] To achieve the above object, the present invention adopts the following technical solutions: A low energy consumption method for mobile edge computing of a rechargeable drone based on wireless power transmission technology, comprising: (1) First, a UAV mobile edge computing network system model for wireless power transmission is constructed; (2) Secondly, a UAV mobile edge computing network system model for wireless power transmission is constructed to build the communication model, mobility model and energy consumption model; (3) Finally, the minimum UAV energy consumption problem is constructed based on the communication model, maneuver model and energy consumption model. The minimum UAV energy consumption problem is optimized using the successive convex approximation method; the optimized problem is solved to obtain the optimal UAV hovering position, communication and computing resource allocation, and task segmentation decision results.

[0016] The charging drone mobile edge computing network system model for wireless power transmission includes: a rotorcraft drone, a charging station, K ground users and a group of edge clouds. The user's small computing processor cannot meet the demand for excessive computing tasks, and the drone is used as a relay to realize sensing, communication and computing in different scenarios. At the same time, the present invention adopts a line-of-sight link method to solve the problem that ground users cannot establish communication with nearby cellular base stations due to signal blocking or shadows of obstructions. Since the user's computing power is limited and does not have the ability to perform local computing offloading, all tasks are offloaded to the drone. The user unloads the computing task to the drone, and the drone adopts a partial offloading strategy, with part of the task being calculated locally and part of the task being offloaded to the edge cloud. The ground user to the drone is an uplink communication link, and the drone to the edge cloud is represented as a downlink communication, forming a three-dimensional wireless communication network. Because there are obstacles between the drone and the power station when the drone is performing computing services, the drone needs to collect energy at close range, so a line-of-sight link transmission is established between the drone and the power station.

[0017] In order to solve the problems existing in the above-mentioned prior art, the present invention provides a method for maximizing the battery performance of a rechargeable drone in a mobile edge computing network system based on wireless power transmission. The method comprises: Stp1: Construct a UAV mobile edge computing network system model for wireless power transmission; The uplink channel gain and information rate are expressed as: in, is the path loss factor, It's distance. It's the location. is the drone location; in, is the bandwidth, is the transmit power, is the noise power, is the signal-to-noise ratio; The channel gain and information rate of the downlink are expressed as: in, It is the location; in, is the UAV transmission power, is the signal-to-noise ratio; Stp2: The UAV mobile edge computing network system model based on wireless power transmission builds the communication model, maneuver model and energy consumption model; Building a communication model includes: obtaining the channel gain of the link between the user and the drone according to the location information of the user and the drone; obtaining the channel gain between the drone and the power station according to the location information of the drone and the power station; obtaining the channel gain between the drone and the edge cloud according to the location information of the drone and the edge cloud; The expression of the communication model is: in, is some kind of coefficient, is the proportionality coefficient, is the frequency, is power, is the amount of data, is the capacity, is the drone power, and They are the communication rate; The expression of the maneuver model is: in, is the static power consumption, is the hover power consumption, Yes Dynamic power consumption, is the efficiency coefficient, is the static power consumption, It's time. is the speed-dependent power, is the acceleration, It's speed. is the time-dependent power; The expression of energy consumption model is: Stp3: Construct the minimum UAV energy consumption based on the communication model, maneuver model and energy consumption model, and optimize the minimum UAV energy consumption according to the successive convex approximation method; obtain the optimal UAV hovering position, communication and computing resource allocation, and task segmentation decision results; maximize the remaining energy of the UAV after the entire journey by solving the optimal UAV position, and take the solution obtained in the first step as the known solution; Jointly optimize the UAV's position, communication and computing resource allocation, and task segmentation decisions to minimize the UAV's communication energy consumption; Due to the non-convexity of the objective function and constraints, the non-convex objective function and constraints are converted into suitable convex approximations to iteratively solve the resulting optimization; by introducing non-negative auxiliary variables , ,Heavy The new objective function is obtained; in, is the coefficient, is a system parameter; Given a feasible solution for the i-th iteration of SCA, the optimization problem is represented by a set of decision variables. , and the reconstruction of the i-th iteration convex approximation is obtained Optimization: in, is the weight coefficient, is the iteration parameter.

[0018] Specific beneficial effects 1. Technical points 1.1 The process of reducing energy consumption by mobile edge computing: By establishing algorithms and processes, optimizing the location of drones, communication and computing resource allocation, and task segmentation decisions, the energy consumption of drones is minimized and the energy carried by drones is maximized.

[0019] 1.2 System model of mobile edge computing rechargeable drone: Build a mobile edge computing network system model for drones with wireless power transmission to build communication model, maneuver model and energy consumption model.

[0020] 1.3 Model Optimization Algorithm: An SCA-based algorithm is designed and developed to obtain the optimal communication and computing resource allocation and task segmentation decision method by solving the problem of minimizing the energy consumption of the UAV in steps.

[0021] 2. Effect The present invention proposes a wireless power transmission charging drone in a mobile edge computing network system, which combines the drone position, communication and computing resource allocation, and task segmentation decision method to reduce the energy consumption of the drone. By solving the remaining energy and energy consumption of the drone in steps and solving the problem of minimizing the energy consumption of the drone, the optimal communication and computing resource allocation and task segmentation decision method is obtained. Compared with the existing baseline solution, the proposed method effectively saves the energy consumption of the drone and improves the performance of the system.

[0022] 3. Possible application scenarios in the future In the future, the present invention may have typical applications in drone logistics services, drone emergency communication support and rescue, drone large-scale agricultural operations, drone photography, etc.

[0023] 3.1 Drone logistics services: With the rapid development of e-commerce and express delivery industries, drone logistics has become an effective means to improve delivery efficiency and reduce costs. They can provide fast delivery in remote areas or congested urban areas, especially in the "last mile" delivery. For example, through automatic flight path planning, drones can deliver packages directly from warehouses to customers, shortening delivery time and reducing carbon emissions.

[0024] 3.2 UAV emergency communication support and rescue: When natural disasters such as earthquakes and floods occur, traditional communication infrastructure may be destroyed, resulting in interruption of communication between the disaster area and the outside world. UAVs equipped with communication relay equipment can be quickly deployed to establish a temporary air communication network to provide emergency communication services for rescue teams and disaster-stricken people. At the same time, UAVs can also be used for emergency rescue tasks such as disaster reconnaissance, personnel search and rescue, and precise delivery of materials to improve rescue efficiency and response speed.

[0025] 3.3 Drones for large-scale agricultural operations: In modern agriculture, drones are widely used in sowing, fertilizing, spraying pesticides, crop monitoring, etc. They can accurately apply pesticides and fertilizers according to the actual conditions of the farmland, reduce the excessive use of chemicals, and collect data through high-definition cameras and sensors to help farmers achieve precision agricultural management, improve crop yields and quality, and reduce production costs.

[0026] 3.4 Drone photography: can be widely used in film and television production, news reporting, topographic surveying and mapping, real estate promotion, live broadcast of events and other fields.

[0027] These applications all rely on the continuous innovation of UAV technology, including longer endurance, higher payload, smarter autonomous navigation system and more stable communication technology. The results of this invention can effectively reduce the energy consumption of UAVs and improve the network performance of UAVs, which has great potential in promoting social and economic development and service improvement.

[0028] This paper first analyzes and derives the optimization of the UAV position, communication and computing resource allocation, and task segmentation decision in the WPT system under better channel conditions to minimize the UAV energy consumption and maximize the energy carried by the UAV. For the non-convexity of the optimization problem, an SCA-based algorithm is developed to solve the non-convexity of the original problem, which first transforms the original non-convex problem into its approximate convex form and then solves it efficiently.

[0029] Phase 1: The drone collects energy through the power station and flies above the user with the collected energy. The optimal position of the drone is solved to maximize the energy carried by the drone. Therefore, the solution to this problem is to maximize the energy carried by the drone. However, it is difficult to find the optimal solution for this problem, so we optimize the position of the drone to find the optimal position of the drone so that the energy carried by the drone is maximized.

[0030] Phase 2: Optimizing UAV positions, communication and computational resource allocation, and task splitting decisions employs a successive convex approximation algorithm that solves a sequence of (separable) strongly convex problems and maintains feasibility at each iteration. Convergence to a fixed solution of the original non-convex optimization is established. More importantly, unlike current SCA methods, it naturally leads to distributed and parallelizable implementations for a large class of non-convex problems. The non-convex constraints and non-convex objective functions are transformed into suitable approximations. Phase 3: According to the above algorithm, Matlab is used to simulate and draw a comparison chart of the drone's computing power, transmission power and system cost with other scenarios.

[0031] Figure 3It is a simulation result diagram showing the relationship between the computing power of drones and system costs and comparing it with other scenarios. As can be seen from the figure, when the computing power of drones increases from 3GHZ to 30GHZ, for the edge cloud only solution, the system cost does not change with the increase of the computing power of drones. For the same computing power of drones, the system cost is ranked from large to small as follows: only drone solution, only edge cloud solution, drone edge cloud collaborative solution, and the other three solutions all show a downward trend as the computing power of drones increases, because there are more computing resources available to reduce the computing delay of drone tasks.

[0032] Figure 4 The simulation result diagram shows the relationship between the transmission capacity of the drone and the system cost and compares it with other scenarios. Only the system cost of the drone solution remains unchanged as the transmission power of the drone increases, because all users offload all computing tasks to the drone for processing. The system costs of the other three solutions increase accordingly as the transmission power of the drone increases, because in the transmission link between the drone and the edge cloud, the transmission energy consumption of the drone is proportional to the transmission power. The proposed solution is better than the baseline solution in reducing system cost, which verifies the advantages of collaborative drones and edge clouds in the task offloading process.

[0033] Glossary MEC network: Multi-access Edge Computing is a new network architecture. It combines with the operator network and uses the wireless access network to provide IT services and cloud computing functions required by telecom users nearby, thus creating a telecom-grade service environment with high performance, low latency and high bandwidth.

[0034] SCA Method: The SCA algorithm is an optimization algorithm used to solve problems such as aircraft wing design. The algorithm is based on the concept of sine / cosine function graphs and consists of two phases: exploration and exploitation. In the exploration phase, the algorithm expands into unknown areas, while in the exploitation phase, it tries to find the global optimal solution. The steps of the SCA algorithm include initializing parameters, updating parameters to expand the search space, and probability-based update rules.

[0035] MATLAB: MATLAB is a commercial mathematical software produced by MathWorks, an American company, which is used in data analysis, wireless communications, deep learning, image processing and computer vision, signal processing, quantitative finance and risk management, robotics, control systems and other fields.

Claims

1. A method for calculating energy consumption of a rechargeable drone at a mobile edge, characterized in that The steps include: (1) First, a UAV mobile edge computing network system model for wireless power transmission is constructed; (2) Secondly, a UAV mobile edge computing network system model for wireless power transmission is constructed to build the communication model, mobility model and energy consumption model; (3) Finally, the minimum UAV energy consumption is constructed based on the communication model, maneuver model and energy consumption model, and the minimum UAV energy consumption is optimized according to the successive convex approximation method; the optimal UAV hovering position, communication and computing resource allocation, and task segmentation decision results are obtained; The wireless power transmission charging drone mobile edge computing network system model includes: a rotor drone, a charging power station, K ground users and a group of edge clouds; when the user's computing processor cannot meet the needs of the computing task, the drone is used as a relay to realize sensing, communication and computing in different scenarios; at the same time, the line-of-sight link method is adopted to solve the problem that the ground users cannot establish communication with the nearby cellular base stations due to signal blocking or shadows of obstructions; the user partially offloads the computing task through the drone, some tasks are calculated locally, and some tasks are offloaded to the edge cloud; the user to the drone is the uplink communication link, and the drone to the edge cloud is the downlink communication, forming a three-dimensional wireless communication network; because the drone has obstacles between it and the power station when performing computing services, the drone needs to collect energy at close range, so a line-of-sight link transmission is established between the drone and the power station.

2. According to claim 1, a method for calculating energy consumption of a rechargeable drone at a mobile edge is characterized in that Includes the following step: Stp1: Construct a UAV mobile edge computing network system model for wireless power transmission; The uplink channel gain and information rate are expressed as: in, is the path loss factor, It's distance. It's the location. is the drone location; in, is the bandwidth, is the transmit power, is the noise power, is the signal-to-noise ratio; The channel gain and information rate of the downlink are expressed as: in, It is the location; in, is the UAV transmission power, is the signal-to-noise ratio; Stp2: The UAV mobile edge computing network system model based on wireless power transmission builds the communication model, maneuver model and energy consumption model; Building a communication model includes: obtaining the channel gain of the link between the user and the drone according to the location information of the user and the drone; obtaining the channel gain between the drone and the power station according to the location information of the drone and the power station; obtaining the channel gain between the drone and the edge cloud according to the location information of the drone and the edge cloud; The expression of the communication model is: in, is some kind of coefficient, is the proportionality coefficient, is the frequency, is power, is the amount of data, is the capacity, is the drone power, and They are the communication rate; The expression of the maneuver model is: in, is the static power consumption, is the hover power consumption, is the mobile power consumption, is the efficiency coefficient, is the static power consumption, It's time. is the speed-dependent power, is the acceleration, It's speed. is the time-dependent power; The expression of energy consumption model is: Stp3: Construct the minimum UAV energy consumption according to the communication model, maneuver model and energy consumption model, and optimize the minimum UAV energy consumption according to the successive convex approximation method; obtain the optimal UAV hovering position, communication and computing resource allocation, and task segmentation decision results; By solving the optimal position of the drone to maximize the remaining energy of the drone after the entire journey, the solution obtained in the first step is taken as the known solution; Jointly optimize the UAV's position, communication and computing resource allocation, and task segmentation decisions to minimize the UAV's communication energy consumption; Due to the non-convexity of the objective function and constraints, the non-convex objective function and constraints are converted into suitable convex approximations to iteratively solve the resulting optimization; by introducing non-negative auxiliary variables , , and regain the objective function; in, is the coefficient, is a system parameter; Given a feasible solution for the i-th iteration of SCA, the optimization problem is represented by a set of decision variables. , and the reconstruction of the i-th iteration convex approximation is obtained Optimization: in, is the weight coefficient, is the iteration parameter.

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