Unmanned aerial vehicle auxiliary unloading method based on fairness and information age

Abstract

The invention provides an unmanned aerial vehicle auxiliary unloading method based on fairness and information age, belongs to the technical field of computer networks, and solves the technical problem of data transmission fairness of an existing task unloading algorithm in a high vehicle speed difference scene. According to the technical scheme, the method comprises the following steps: firstly, establishing a vehicle position, channel link, fairness and information age model, and obtaining a fairness-information age dual-objective optimization model; secondly, solving the dual-objective optimization problem through a multi-objective grey wolf algorithm; the method has the beneficial effects that the fairness of the unloaded data volume is remarkably improved in a high-speed-difference scene, the load rate of the unmanned aerial vehicle can be stabilized to be about 0.625, the increase of information age is controlled, and an efficient and fair task unloading scheme is provided for vehicles with different speeds.

Description

Technical Field

[0001] The present invention relates to the field of computer network technology, and in particular to a drone-assisted unloading method based on fairness and information age. Background Art

[0002] The application of sensors in the transportation sector has driven advancements in intelligent transportation technology. As the core elements of an intelligent transportation system, roads, vehicles, traffic lights, monitoring equipment, and their interconnected 5G communication networks together constitute the Internet of Vehicles (IoV). The IoV is a specialized extension of IoT technology in the transportation sector, designed to enable real-time interaction between vehicle and environmental data and intelligent decision-making, ultimately achieving the core goals of safety, efficiency, and energy optimization. Compared to the ubiquitous connectivity of the Internet of Things, the IoV focuses on the deep integration of vehicles and transportation elements, making it a key technology for intelligent transportation systems.

[0003] With the development of connected vehicle (IoV) technology, applications such as intelligent traffic management, in-vehicle entertainment, remote vehicle monitoring, and autonomous driving are becoming increasingly popular. As a specific application of IoV technology in the field of autonomous driving, platooning technology, based on advanced communication and control systems, enables the coordinated operation of multiple unmanned vehicles on the road. Platooning vehicles require periodic state synchronization and coordinated control command transmission to achieve safe and reliable operation, which places stringent requirements on the fairness and timeliness of IoV task offloading. Fairness reflects the rationality of resource allocation and task scheduling, while information age represents the timeliness of information transmission. In terms of optimization strategies, research focus in recent years has gradually shifted to the introduction of drones as a supplementary communication method for IoV to further enhance the system's communication coverage.

[0004] After searching domestic and international literature, Wu et al. (2016) found that, in their paper "Velocity-Adaptive Access Scheme for MEC-Assisted Platooning Networks: Access Fairness Via Data Freshness," they address the issue of unfair network access for vehicles of varying speeds by establishing a joint optimization model for fairness and information age, and devising a multi-objective optimization scheme. First, they define a fairness index (a function of speed and minimum contention window) to reflect fairness among vehicles of varying speeds and propose a transmission fairness algorithm. Second, they employ a stochastic hybrid system (SHS) approach to simulate the changes in information age during vehicle access, characterizing the relationship between the average age of network data and the minimum contention window. Finally, they employ a multi-objective particle swarm optimization (MOPSO) algorithm to achieve this multi-objective optimization by adjusting the minimum contention window of each lane.

[0005] However, the above research has the following problems. First, the optimization strategy of adjusting the minimum contention window of the lane is only applicable to the case where the speed difference is small. However, the adjustment of the minimum contention window is limited in range. When the speed difference between the two lanes is large, adjusting the minimum contention window cannot effectively ensure the fairness of vehicle transmission. Secondly, as a classic particle swarm optimization algorithm, MOPSO is prone to fall into local optimal solutions. Especially in multi-objective optimization problems, the global search ability between particles may be limited due to early convergence, and the multi-objective optimization solution obtained still has room for optimization. In response to the above problems, the present invention proposes a UAV-assisted unloading method based on fairness and information age, which adjusts the UAV mobile deployment position according to the vehicle speed to ensure communication fairness and a relatively low information age. Summary of the Invention

[0006] The purpose of this invention is to achieve data transmission fairness in high vehicle speed difference scenarios and propose a UAV-assisted unloading method based on fairness and information age.

[0007] The inventive concept of this invention is as follows: First, a channel link, fairness, and information age model is established to obtain a dual-objective "fairness-information age" optimization model. Second, a multi-objective Grey Wolf Algorithm is used to solve this dual-objective optimization problem, thereby controlling the growth of information age and providing an efficient and fair task offloading solution for vehicles of different speeds.

[0008] In order to achieve the above-mentioned object of the invention, the present invention adopts a technical solution specifically as follows: a UAV-assisted unloading method based on fairness and information age, comprising the following steps:

[0009] S1: Establish a channel link model; the link quality of the channel is a key factor affecting task offloading;

[0010] S2: Establish a fairness model; fairness means that the amount of data sent by vehicles of different speeds should remain similar when crossing the coverage area of ​​the base station;

[0011] S3: Build an information age model; information age is a performance indicator to measure the “freshness” of data;

[0012] S4: Establish an optimization model for latency and total energy consumption; study the task offloading strategy in the cloud-sidecar collaborative system, with the goal of minimizing the total system consumption by optimizing the offloading strategy;

[0013] S5: Solve the above dual-objective optimization problem using the multi-objective grey wolf algorithm.

[0014] Furthermore, the step S1 includes the following steps:

[0015] The task offloading scenario of the present invention is as follows Figure 1 As shown in Figure 1, consider a two-lane, one-way highway. Base stations are deployed on the side of the highway. The lane closer to the base station is the fast lane. The lane farther from the base station is the slow lane. Multiple convoys, each in its own lane, travel through the base station's signal coverage area. Each convoy consists of a leader vehicle and several member vehicles. The leader vehicle controls and manages the movement of the entire convoy, with the member vehicles following the leader vehicle. The last vehicle in the convoy is defined as the tail vehicle. The intra-convoy spacing is defined as the distance between two adjacent vehicles in the convoy, while the inter-convoy spacing is the distance between the tail vehicle of the previous convoy and the leader of the next neighboring convoy. It is assumed that the speed, intra-convoy spacing, and inter-convoy spacing of vehicles in the same lane are the same. However, the speed, intra-convoy spacing, and inter-convoy spacing of vehicles in different lanes vary. Convoys in each lane travel past the base station according to a Poisson process. Within the base station's signal coverage area, each vehicle can transmit data directly to the base station. Drones fly horizontally within the base station's signal coverage area, acting as mobile relay nodes between vehicles and the base station. When the vehicle leaves the base station signal coverage area and cannot communicate directly with the base station, it transmits the data to the relay drone, which further forwards the data to the base station.

[0016] The link quality of the channel is a key factor affecting task offloading, so the various communication links that may be involved in the scenario are modeled. In the traditional V2B communication environment, since vehicles of different speeds have different communication durations with the base station when crossing the base station signal coverage area, there is a difference in the amount of data unloaded from the fast and slow vehicles to the base station, which raises the issue of offloading fairness. Consider introducing drones to extend the communication duration between fast lane vehicles and the base station. Figure 2 As shown in the figure, when the express train leaves the base station's signal coverage, the drone will act as a relay node to maintain communication between the express train and the base station until the slow train completely leaves the base station's signal coverage. Application data (such as music, video streaming, and news push) can be transmitted via V2B and V2U links respectively.

[0017] First, within the coverage of the base station signal, the vehicle unloads data to the base station through the V2B link. Secondly, when the express train leaves the base station signal range, it transmits the data to the drone through the V2U link. The drone acts as a relay node to receive the signal from the vehicle, and then amplifies and forwards the signal to the target base station through the U2B link. In this process, an amplification and forwarding strategy is adopted to integrate the V2U and U2B links into a V2U2B link. However, there are signal fading and interference problems in wireless communication links, which will seriously affect the data transmission rate of the above three wireless links, and thus affect the task unloading of vehicles at different speeds. For the two communication modes of traditional V2B link and V2U2B link after the introduction of drones, the present invention establishes corresponding channel gain models, and calculates the maximum data transmission rate under the two modes based on the Shannon formula.

[0018] (1) One-hop V2B communication

[0019] When a vehicle enters the coverage area of ​​a base station, it sends a data packet to the base station via a V2B link.

[0020] Based on Shannon's formula, the maximum data transmission rate of the fast lane V2B link is

[0021]

[0022] Where B represents the vehicle communication bandwidth. represents the signal-to-noise ratio between fast lane vehicles and BS.

[0023] The maximum data transmission rate of the slow lane V2B link is

[0024]

[0025] in, represents the signal-to-noise ratio between slow lane vehicles and BS.

[0026] (2) Two-hop V2U2B communication based on UAV relay

[0027] After the vehicle leaves the base station's signal coverage area, a drone can be positioned between the vehicle and the base station. The vehicle sends data to the drone via the V2U link, and the drone then forwards the data to the base station via the U2B link. First, calculate the signal-to-noise ratio of the V2U link. Second, calculate the signal-to-noise ratio of the U2B link. Third, employ an amplify-and-forward (AF) relay strategy to combine the V2U and U2B links into a V2U2B link and calculate the signal-to-noise ratio of the V2U2B link. Based on the Shannon equation, derive the maximum data transmission rate of the V2U2B link.

[0028] ①V2U link

[0029] When the vehicle leaves the communication range of the base station, it transmits a signal to the drone.

[0030] The signal-to-noise ratio of the vehicle-to-UAV link can be written as

[0031]

[0032] Among them, PR V2U Indicates the signal power received by the drone. P V2U,noise is the noise power of the V2U link.

[0033] ②U2B link

[0034] The UAV transmits data to the BS. According to the document "Age of Information in a Cellular Internet of UAVs: Sensing and Communication Trade-off Design", this process can be regarded as an air-to-ground propagation model.

[0035] The signal-to-noise ratio from the drone to the base station can be written as

[0036]

[0037] Among them, PR U2B Indicates the signal power received by BS from UAV. U2B,noise is the noise power of the U2B link.

[0038] ③Amplify and forward strategy

[0039] Data is sent from the vehicle to the drone via the V2U link, and then sent from the drone to the base station via U2B. The drone uses a relay protocol to convert the two-hop link into a V2U2B link. This invention uses the mainstream amplification and forwarding protocol. According to the amplification and forwarding principle, the signal-to-noise ratio of V2U2B can be calculated using the following formula:

[0040]

[0041] in, is a constant representing the relay gain.

[0042] Based on Shannon's formula, the maximum data transmission rate of the V2U2B link is

[0043]

[0044] S2 specifically includes the following steps:

[0045] Fairness means that vehicles of different speeds should maintain a similar amount of data transmission when traveling through a base station's coverage area. However, in a connected vehicle environment, the different speeds of vehicles on two lanes lead to differences in the amount of data transmitted, creating fairness issues.

[0046] In order to achieve fairness in data transmission between lanes, the absolute value of the difference in the amount of data unloaded to the base station by vehicles in the two lanes needs to be as small as possible.

[0047] min|C q -C s |(30) Among them, C q Indicates the amount of data unloaded to the base station by vehicles in the express lane. s Indicates the amount of data unloaded from slow lane vehicles to the base station.

[0048] The amount of data unloaded by the vehicle to the base station can be calculated based on the vehicle transmission rate and the time it takes for the vehicle to pass through the base station signal coverage area.

[0049]

[0050] Among them, T i is the time it takes for the vehicle on lane i to cross the base station signal coverage area, is the maximum transmission rate between the vehicle in lane i and the base station.

[0051] Consider vehicles in the same lane maintaining the same speed v i , R is the communication distance, T i It can be calculated as

[0052]

[0053] The vehicle in the slow lane passes through the coverage area of ​​the base station, and the speed of the vehicle in the slow lane is v s , the amount of data unloaded to the base station through the V2B link is

[0054]

[0055] Then the vehicle in the fast lane passes through the coverage area of ​​the base station, and the speed of the vehicle in the fast lane v q , the amount of data unloaded to the base station through the V2B link is

[0056]

[0057] This study introduces drones to extend the communication time between vehicles on the expressway and the base station. Within the base station's signal range, express vehicles unload data to the base station via a V2B link. After leaving the base station's signal range, express vehicles continue unloading data to the base station via a V2U2B link. The drone extends communication time by the time it takes for the slow and fast vehicles to cross the base station's signal coverage area. Due to highway speed limits, this study sets the maximum speed difference between fast and slow vehicles at 16 m / s. The drone's signal coverage is sufficient to support relay communication. The maximum data transmission rate of the V2U2B link of the express lane vehicle is 10 ...

[0058]

[0059] The sum of the data unloaded by the express train to the base station and the data unloaded by the express train through the drone relay is

[0060]

[0061] According to the above C q and C s When the formula (7) is satisfied, data transmission fairness can be achieved.

[0062] S3 specifically includes the following steps:

[0063] After a vehicle leaves the base station's signal coverage area, the communication link between the vehicle and the base station is relayed by a drone, transforming the one-hop V2B link into a two-hop V2U2B link. However, the paper "Velocity-Adaptive Access Scheme for MEC-Assisted Platooning Networks: Access Fairness Via Data Freshness" only considers one-hop V2B links, so its vehicle information age model is not applicable to drone-based V2U2B links.

[0064] This paper first models the data load rate of drones. Secondly, for drone relay communication, considering that a drone receives data packets from different vehicles, this paper proposes a system information age model to describe the impact of drone mobile deployment on information age.

[0065] (1) Data load rate model of drones

[0066] According to the literature "Age of information in a cellular internet of uavs: Sensing and communication tradeoff design", the interruption probability of communication between the fast lane vehicle Nk and the UAV is defined as

[0067]

[0068] Among them, δ is the parameter for evaluating the perception performance of the UAV, e is the natural logarithm, is the distance between the UAV and the kth vehicle on the fast lane at time t.

[0069] P success is the probability of successful transmission between the UAV and the vehicle, which can be expressed as

[0070] P success =1-P out,Nk (38)

[0071] The UAV load rate is defined as the probability ρ of successful transmission between the UAV and the vehicle, written as

[0072] ρ=P success (39)

[0073] Substituting formula (14) and (15) into (16), we can get

[0074]

[0075] The drone's data payload, ρ, is the primary factor in minimizing AoI. According to the study "Real-Time Status: How Often Should One Update?", throughput is maximized when ρ approaches 1. AoI is minimized when ρ ≈ 0.625.

[0076] (2) Information age model based on drone relay

[0077] A drone can receive data from multiple vehicles, so packets may queue in the drone's packet buffer. At a given moment, this paper assumes that the maximum number of packets served by a drone is 1. Furthermore, the arrival rate and service rate of packets in the drone's buffer are defined as λ and μ.

[0078] First, the queueing of data packets sent by each vehicle arriving at the drone's buffer is considered a discrete state. Second, the Age of Information (AoI) is defined as Δ(t) = tu(t), where u(t) is the time when the most recent data packet arrives at the buffer, and t is the current time. Therefore, the AoI can be considered a continuous state. Stochastic hybrid systems can handle both continuous and discrete states. Therefore, the present invention uses the discrete states of the SHS to describe the queueing of data packets, while using the continuous states of the SHS to describe the changing AoI.

[0079] Vehicle Nj represents a vehicle other than vehicle Nk in the fast lane. For discrete states, define the drone's buffer service state at time t as q(t) = q, where q∈Q = {0, k, j}. q = 0 indicates that there are no packets in the drone's buffer. q = k indicates that packets sent by vehicle Nk in the fast lane are being served by the drone's buffer. q = j indicates that packets sent by vehicle Nj are being served by the drone's buffer. Figure 3 is a Markov chain with discrete states q(t).

[0080] The age of the data packet information sent by vehicle Nk is expressed as Δ k For continuous states, define the tuple x(t) = [x0(t), x1(t)] to represent the information age of the data packet sent by vehicle Nk at time t in the buffer. Among them, x0(t) is the information age Δ of the data packet when the data packet of vehicle Nk arrives at the drone buffer at time t k (t). x1(t) is the information age Δ' of the data packet when the data packet of vehicle Nk has received the service and been sent k It should be noted that when the data packet of vehicle Nk is completed and sent at time t, the information age x0(t) will be reset to x1(t). In state 0 (i.e., q = 0), x1(t) does not need to be calculated and remains at 0. In state k (i.e., q = k), x1(t) represents the information age of the data packet of vehicle Nk in the buffer. In state j (i.e., q = j), the data packet of vehicle Nj is receiving service. Since the data packet receiving service will not affect the age of the data packet from vehicle Nk, x1(t) does not need to be calculated in state j and remains at 0.

[0081] The average information age of the data packets sent by vehicle Nk can be written as

[0082]

[0083] Let N q is the number of vehicles in the fast lane. According to the literature “The age of information: Real-time status updating by multiple sources”, when the load of vehicles in the fast lane on the drone is ρq Equal to ρ / N q When (i∈N q ), the total age of the UAV’s data packets ∑Δ is minimized. At this time, the average age of the data packets sent by vehicle Nk can be written as

[0084]

[0085] S4 specifically includes the following steps:

[0086] In order to achieve fairness in data transmission and information timeliness, the present invention achieves the following two goals by adjusting the movement of the drone.

[0087] The first objective function f1 is the absolute value of the amount of data unloaded to the base station by vehicles in the two lanes.

[0088] f1=|C q -C s | (43)

[0089] The second objective function f2 is the total information age of the UAV relay link.

[0090]

[0091] Combining the above two objective functions and their constraints, the multi-objective optimization model is:

[0092]

[0093] Minimum speed v'0 and maximum speed v0,X U and Y U Corresponding to the x-axis and y-axis coordinates of the drone, the mathematical constraints are

[0094]

[0095] Among them, the optimization goal minf1 means to minimize the data difference between the fast and slow lanes. The optimization goal minf2 is to minimize the total information age of the drone relay link. The first constraint is to ensure that the vehicle speed v in the network is i Between the minimum allowed speed v'0 and the maximum allowed speed v0. The second and third constraints are to ensure that the UAV's movement range is within the signal coverage range R of the base station.

[0096] Furthermore, the step S5 specifically includes the following steps:

[0097] The present invention adopts the multi-objective Grey Wolf MOGWO algorithm to solve the above multi-objective optimization problem by adjusting the mobile deployment coordinates of the UAV and obtain the optimal position coordinates of the UAV.

[0098] Compared with the prior art, the present invention has the following beneficial effects:

[0099] 1. With the introduction of drone-assisted offloading, the communication link between the vehicle and the base station is converted from a V2B link to a V2U2B link. The vehicle information age model proposed in the document "Velocity-Adaptive Access Scheme for MEC-Assisted Platooning Networks: Access Fairness Via Data Freshness" is no longer applicable. This paper proposes a system information age model for drone-assisted offloading that accurately describes the relationship between information age in the vehicle network and drone mobile deployment.

[0100] 2. This paper proposes a multi-objective optimization problem to jointly optimize the fairness and information age of UAV-assisted offloading. To solve this problem, a multi-objective Grey Wolf (MOGWO) algorithm is used to adjust the mobile deployment coordinates of UAVs to ensure fairness in transmission for vehicles of different speeds and minimize the growth rate of information age.

[0101] 3. Compared with the baseline algorithm, the present invention significantly improves the fairness of the amount of unloaded data in scenarios with high vehicle speed differences, controls the growth of information age, and provides an efficient and fair task unloading solution for vehicles of different speeds. BRIEF DESCRIPTION OF THE DRAWINGS

[0102] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0103] Figure 1 It is the scene graph used in the present invention.

[0104] Figure 2 This is the channel link diagram in the present invention.

[0105] Figure 3 is the discrete state Markov chain in the SHS of the present invention.

[0106] Figure 4 This is a relationship diagram between the lane unloading data volume difference and the average speed when the vehicle speed difference is 8m / s in the present invention.

[0107] Figure 5 This is a relationship diagram between the lane unloading data volume difference and the average speed when the vehicle speed difference is 16m / s in the present invention.

[0108] Figure 6 Schematic diagram of the UAV load rate ρ under different vehicle speed differences in the present invention.

[0109] Figure 7Schematic diagram of the AoI growth rate under high vehicle speed difference in the present invention.

[0110] Figure 8 This is the moving trajectory diagram of the drone under different vehicle speed differences in the present invention. DETAILED DESCRIPTION

[0111] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. Of course, the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0112] Example 1

[0113] The simulation uses Python 3.10 and MATLAB R2023a. Consider a scenario of a two-lane one-way highway. The base station height is set to 30m, and the signal coverage range is set to 200m. The vehicle transmit power is 23dBm, and the vehicle antenna gain is 2dBi. The base station antenna gain is 15dBi. The noise power between the vehicle and the base station is -97dBm. The shadow fading between the vehicle and the base station is 5dB. The UAV transmit power is 20dBm, the UAV antenna gain is 5dBi, and the UAV service rate μ is 1. The remaining simulation parameters are shown in the simulation parameter table. In the simulation, the UAV-assisted offloading (UAV-MOGWO) scheme of the present invention and the minimum contention window adjustment (CW-MOPSO) scheme proposed in the document "Velocity-Adaptive Access Scheme for MEC-Assisted Platooning Networks: Access Fairness Via Data Freshness" are used respectively, and their performance is compared.

[0114] Simulation parameter table 1

[0115]

[0116]

[0117] Figure 4 The performance comparison of the two algorithms for unloading data when the speed difference is 8m / s. Figure 4As can be seen, the fairness performance of the CW-MOPSO scheme is significantly inferior to that of the UAV-MOGWO scheme of the present invention. Specifically, the CW-MOPSO scheme exhibits a significant difference in the amount of data unloaded by vehicles on the two lanes. This is because the MAC layer minimum contention window in the CW-MOPSO scheme ranges from 0 to 1023. As vehicle speed continues to increase, the minimum contention window setting cannot be continuously reduced, resulting in a weakening of the CW-MOPSO scheme's improvement in transmission fairness at high speeds. The UAV-MOGWO scheme, on the other hand, performs better in terms of data unload fairness, maintaining a low difference in data unloaded between the two lanes and remaining essentially constant within the average vehicle speed range of 22.5 to 26 m / s. Its data fairness performance is more stable and robust. The CW-MOPSO scheme achieves fairness between fast and slow vehicles by reducing the amount of data unloaded by slower vehicles. On the one hand, as the overall average vehicle speed increases, the impact of the speed difference on communication time, and therefore on fairness, decreases, resulting in a reduction in the amount of data unloaded by slower vehicles to the base station. On the other hand, the competition window adjustment effect of the CW-MOPSO scheme gradually decreases with the increase of vehicle speed.

[0118] Figure 5 The performance comparison of the two algorithms in terms of unloading data volume when the speed difference is 16m / s. Figure 4 When the speed difference is 8 m / s, the difference in the amount of unloading data between the fast and slow lanes of the two schemes increases, indicating that the increase in the speed difference leads to a decrease in the fairness of unloading for both schemes. This shows that a high speed difference does have a negative impact on the fairness of vehicle unloading data. Specifically, taking the average speed of 22.5 m / s as an example, compared with the case of 8 m / s, the difference in the amount of unloading data of the CW-MOPSO scheme when the speed difference is 16 m / s increases by 2.04×10 9 bit, while the UAV-MOGWO solution only increases 1.23×10 8 This shows that under the condition of high vehicle speed difference, the fairness of UAV-MOGWO is still better than that of CW-MOPSO.

[0119] Example 2

[0120] The parameter settings of Example 2 are the same as those of Example 1.

[0121] Figure 6 The figure shows the UAV load rate ρ under different vehicle speed differences. According to the research in the paper "Real-Time Status: How Often Should One Update?", when ρ is close to 1, the throughput is maximized. When ρ is about 0.625, the AoI is minimized. Figure 6As shown in the figure, when the speed difference between the two lanes is 8 m / s, the UAV load factor ρ is approximately 0.625. When the speed difference between the two lanes increases further to 16 m / s, the transmission efficiency decreases, and the UAV load factor drops slightly, but remains close to 0.625. This shows that the speed difference has little impact on the UAV load factor. Furthermore, since the UAV load factor remains near 0.625, the UAV-MOGWO solution can minimize the AoI.

[0122] Figure 7 is the growth rate of AoI under high vehicle speed difference. Since a drone relay link is added in the network transmission, the information age of the UAV-MOGWO scheme is higher than that of the CW-MOPSO scheme. Therefore, the UAV-MOGWO scheme needs to reduce the negative impact on the information age as much as possible while fairly distributing channel resources. The "AoI growth rate" defined in the present invention is the difference between the information age of the UAV-MOGWO scheme and the information age of the CW-MOPSO scheme, divided by the information age of the CW-MOPSO scheme, to evaluate the growth rate of the information age of the UAV-MOGWO scheme relative to the information age of the CW-MOPSO scheme. In the high vehicle speed difference environment, the UAV-MOGWO scheme can, on the one hand, fairly distribute channel resources, and on the other hand, control the age growth rate within 7%. In the high vehicle speed difference scenario, the scheme of the present invention achieves a balance between communication fairness and information timeliness.

[0123] Figure 8 Figure 2 shows the drone's trajectory under different vehicle speed differences. As can be seen, the drone's trajectory trends under the two speed difference conditions are generally similar. As the speed difference increases, vehicles in the fast lane will move out of the base station's signal coverage more quickly, necessitating the drone to relay transmissions to these vehicles earlier. To this end, the drone's trajectory gradually shifts toward the outside of the base station to ensure timely relay support after vehicles in the fast lane leave the base station's signal coverage, thus maintaining communication link continuity.

[0124] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A UAV-assisted offloading method based on fairness and information age, comprising the following steps: S1: Establish channel link model; S2: Establish a fairness model; S3: Build an information age model; S4: Establish a "fairness-information age" dual-objective optimization model; S5: Solve the above dual-objective optimization problem through the multi-objective grey wolf MOGWO algorithm.

2. The UAV-assisted offloading method based on fairness and information age according to claim 1 is characterized in that: The step S1 comprises the following steps: (1) One-hop V2B communication When a vehicle enters the coverage area of ​​the base station, it sends a data packet to the base station via the V2B link. Based on Shannon’s formula, the maximum data transmission rate of the fast lane V2B link is Where B represents the vehicle communication bandwidth, represents the signal-to-noise ratio between fast lane vehicles and BS; The maximum data transmission rate of the slow lane V2B link is in, represents the signal-to-noise ratio between slow lane vehicles and BS; (2) Two-hop V2U2B communication based on UAV relay After the vehicle leaves the base station's signal coverage area, a drone can be positioned between the vehicle and the base station. The vehicle sends data to the drone via the V2U link, and the drone then forwards the data to the base station via the U2B link. First, the signal-to-noise ratio of the V2U link is calculated. Second, the signal-to-noise ratio of the U2B link is calculated. Third, an amplify-and-forward (AF) relay strategy is used to integrate the V2U and U2B links into a V2U2B link. The signal-to-noise ratio of the V2U2B link is calculated, and the maximum data transmission rate of the V2U2B link is derived based on the Shannon equation. ①V2U link When the vehicle leaves the communication range of the base station, it transmits a signal to the drone. The signal-to-noise ratio of the vehicle-to-UAV link is Among them, PR V2U Indicates the signal power received by the drone, P V2U,noise is the noise power of the V2U link, ②U2B link The signal-to-noise ratio from the drone to the base station is Among them, PR U2B Indicates the signal power received by BS from UAV, P U2B,noise is the noise power of the U2B link; ③Amplify and forward strategy Data is sent from the vehicle to the drone via the V2U link, and then sent from the drone to the base station via U2B. The drone uses a relay protocol to convert the two-hop link into a V2U2B link, selecting the mainstream amplification and forwarding protocol. Based on the amplification and forwarding principle, the signal-to-noise ratio of V2U2B can be calculated using the following formula: in, is a constant representing the relay gain; Based on Shannon's formula, the maximum data transmission rate of the V2U2B link is 3. The UAV-assisted offloading method based on fairness and information age according to claim 2 is characterized in that: The step S2 specifically includes the following steps: Make the absolute value of the difference in the amount of data unloaded to the base station by vehicles in the two lanes as small as possible min|C q -C s |(7) Among them, C q represents the amount of data unloaded to the base station by vehicles in the fast lane, C s It indicates the amount of data unloaded to the base station by vehicles in the slow lane; Calculate the amount of data unloaded by the vehicle to the base station based on the vehicle transmission rate and the time it takes for the vehicle to pass through the base station signal coverage area Among them, T i is the time it takes for the vehicle on lane i to cross the base station signal coverage area, is the maximum transmission rate of communication between the vehicle in lane i and the base station; Consider vehicles in the same lane maintaining the same speed v i , R is the communication distance, T i Calculated as The vehicle in the slow lane passes through the coverage area of ​​the base station, and the speed of the vehicle in the slow lane is v s , the amount of data unloaded to the base station through the V2B link is Then the vehicle in the fast lane passes through the coverage area of ​​the base station, and the speed of the vehicle in the fast lane v q , the amount of data unloaded to the base station through the V2B link is Drones are introduced to extend the communication time between vehicles on the express lane and the base station. The express train unloads data to the base station via the V2B link within the base station signal range. After leaving the base station signal range, the express train switches to the V2U2B link to continue unloading data to the base station. The drone extends the communication time by the time difference between the slow train and the express train crossing the base station signal coverage range. Due to the speed limit on highways, the maximum speed difference between fast and slow trains is set at 16m / s. The drone signal coverage range is sufficient to support relay communication. The maximum data transmission rate of the V2U2B link of the express lane vehicle is 10 ... The sum of the data unloaded by the express train to the base station and the data unloaded by the express train through the drone relay is 4. The UAV-assisted offloading method based on fairness and information age according to claim 3 is characterized in that: The step S3 comprises the following steps: (1) Data load rate model of drones The interruption probability of communication between the fast lane vehicle Nk and the UAV is defined as Among them, δ is the parameter for evaluating the perception performance of the UAV, e is the natural logarithm, is the distance between the UAV and the kth vehicle on the fast lane at time t; P success is the probability of successful transmission between the UAV and the vehicle, which is expressed as P success =1-P out,Nk (15) The UAV load rate is defined as the probability ρ of successful transmission between the UAV and the vehicle, written as p=P success (16) Substituting formula (14) and (15) into (16), we get (2) Information age model based on drone relay The age of the data packet information sent by vehicle Nk is expressed as Δ k For continuous states, define the tuple x(t) = [x0(t), x1(t)] to represent the information age of the data packet sent by vehicle Nk at time t in the buffer, where x0(t) is the information age Δ of the data packet when the data packet of vehicle Nk arrives at the UAV buffer at time t. k (t), x1(t) is the information age Δ' of the data packet when the data packet of vehicle Nk has received the service and is sent k , when the data packet of vehicle Nk is completed and sent at time t, the information age x0(t) will be reset to x1(t), where q is an integer. In state 0, i.e., q=0, x1(t) does not need to be calculated and remains 0. In state k, i.e., q=k, x1(t) represents the information age of the data packet of vehicle Nk in the buffer. In state j, q=j, the data packet of vehicle Nj is receiving service. In state j, x1(t) does not need to be calculated and remains 0. The average information age of the data packets sent by vehicle Nk is written as Among them, the service rate μ of the data packets in the UAV buffer, the load ρ of the fast lane vehicle in the UAV q ; Let N q is the number of vehicles in the fast lane, when the load of the fast lane vehicle in the UAV is ρ q Equal to ρ / N q When i∈N q , the sum of the information age of the UAV's data packets ∑Δ is minimized, and the average information age of the data packets sent by vehicle Nk is written as 5. The UAV-assisted offloading method based on fairness and information age according to claim 4 is characterized in that: The step S4 comprises the following steps: S41. Adjust the drone's motion to achieve the following two goals: The first objective function f1 is the absolute value of the amount of data unloaded from vehicles in the two lanes to the base station: f1=|C q -C s | (20) The second objective function f2 is the total information age of the drone relay link: S42. Combining the above two objective functions and their constraints, the multi-objective optimization model is: Minimum speed v'0 and maximum speed v0,X U and Y U Corresponding to the x-axis and y-axis coordinates of the drone, the mathematical constraints are Among them, the optimization goal minf1 means to make the data difference of the fast and slow lanes as small as possible, and the optimization goal minf2 is to make the total information age of the drone relay link as small as possible. The first constraint is to ensure that the vehicle speed v in the network i Between the minimum allowed speed v'0 and the maximum allowed speed v0, the second and third constraints are to ensure that the UAV's movement range is within the signal coverage range R of the base station.

6. The UAV-assisted offloading method based on fairness and information age according to claim 5, characterized in that: The step S5 comprises the following steps: The multi-objective Grey Wolf MOGWO algorithm is used to solve the above multi-objective optimization problem by adjusting the mobile deployment coordinates of the UAV and obtain the optimal position coordinates of the UAV.

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