CN115037751B - Unmanned aerial vehicle-assisted heterogeneous Internet of vehicles task migration and resource allocation method - Google Patents

Unmanned aerial vehicle-assisted heterogeneous Internet of vehicles task migration and resource allocation method Download PDF

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CN115037751B
CN115037751B CN202210744842.2A CN202210744842A CN115037751B CN 115037751 B CN115037751 B CN 115037751B CN 202210744842 A CN202210744842 A CN 202210744842A CN 115037751 B CN115037751 B CN 115037751B
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vehicle
unloading
task
vehicles
decision
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CN115037751A (en
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宋晓勤
王书墨
宋铁成
彭昱捷
杨雨露
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Shenzhen Institute Of Southeast University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H04L67/104—Peer-to-peer [P2P] networks
    • H04L67/1074—Peer-to-peer [P2P] networks for supporting data block transmission mechanisms
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention provides an unmanned aerial vehicle-assisted heterogeneous Internet of vehicles task migration and resource allocation method, which aims at a scene of cooperatively calculating and unloading a mobile edge server and an unmanned aerial vehicle, and firstly obtains a decision of whether a vehicle is unloaded or not through potential game, namely whether the vehicle decides to calculate locally or to unload to a MEC server or unmanned aerial vehicle. For the vehicles for determining task unloading, a distributed resource allocation method is adopted, the base station is not required to intensively schedule channel state information, the vehicles for determining task unloading are regarded as intelligent bodies, and each vehicle for determining task unloading selects unloading nodes and transmitting power based on local observation state information through DDQN training deep reinforcement learning. The algorithm can minimize the system time delay under the limit of the maximum transmitting power, and achieves good balance between complexity and performance.

Description

Unmanned aerial vehicle-assisted heterogeneous Internet of vehicles task migration and resource allocation method
Technical Field
The invention relates to a vehicle networking technology, in particular to a task migration and resource allocation method of a vehicle networking assisted by an unmanned aerial vehicle, and more particularly relates to a task migration and resource allocation method of a heterogeneous vehicle networking assisted by an unmanned aerial vehicle.
Background
With the development of the internet of vehicles, various vehicle applications such as route planning, autopilot and infotainment applications are emerging. The applications can ensure travel safety and also can provide entertainment interconnection in travel. However, most of these applications are delay sensitive, resource intensive, characterized by complex computation and high energy requirements. Many vehicles currently have limited memory capacity and insufficient computing resources to meet these application-critical delay constraints. Mobile edge computing (mobile edge computation, MEC) can provide low-latency computing services for vehicles by deploying computing and storage resources at the network edge, with MEC servers deployed on roadside units providing computing services for vehicles in a typical scenario of the internet of vehicles. In intelligent road construction, unmanned aerial vehicles are used for road patrol, bridge inspection and road damage inspection. When the unmanned aerial vehicle patrols in a certain area, the powerful computing power of the unmanned aerial vehicle can also be used as an MEC server.
At present, unmanned aerial vehicle-assisted movement edge calculation is still in a starting stage, and only a few researches have been conducted on the field in detail. And the existing research mainly optimizes the calculation unloading strategy, but does not fully consider the problem of cooperation and the problem of communication resource allocation among heterogeneous MEC servers under a time-varying channel.
Therefore, the invention provides an unmanned aerial vehicle-assisted heterogeneous Internet of vehicles task migration and resource allocation method, which aims at a scene of cooperative calculation and unloading of a mobile edge server and an unmanned aerial vehicle, takes system delay minimization as an optimization target of task migration and resource allocation, and achieves good balance between complexity and performance.
Disclosure of Invention
The invention aims to: aiming at the problems in the prior art, the unmanned aerial vehicle assisted heterogeneous Internet of vehicles task migration and resource allocation method is provided, and the unmanned aerial vehicle can provide computing resources for vehicles. The method is to adopt a hybrid spectrum access technology for transmission, so as to realize the minimization of system time delay.
The technical scheme is as follows: aiming at the scene of cooperative computing and unloading of the mobile edge server and the unmanned aerial vehicle, the aim of minimizing the system time delay is achieved by reasonably and efficiently computing and unloading decisions and resource allocation. In order to reduce system time delay and improve spectrum utilization rate, a hybrid spectrum access technology is adopted for transmission, a vehicle unloads tasks to MEC server calculation on a road side unit through a vehicle-to-road facility (vehicle to infrastructure, V2I) link, the tasks are unloaded to an unmanned aerial vehicle for calculation through a vehicle-to-vehicle (vehicle to vehicle, V2V) link, and the V2I and V2V links are accessed to different slices through a 5G slicing technology without mutual interference. Firstly, a decision of whether the vehicle is unloaded or not is obtained through potential game, namely, the vehicle decides local calculation or is unloaded to an MEC server or unmanned aerial vehicle calculation. For the vehicles for determining task unloading, a distributed resource allocation method is adopted, the base station is not required to intensively schedule channel state information, each vehicle for determining task unloading is regarded as an intelligent agent, and the transmitting power is selected based on the local observation state information. By establishing a Deep reinforcement Learning model, the Deep reinforcement Learning model is optimized by utilizing a Double Deep Q-Learning Network (DDQN). And obtaining the unloading node and the transmitting power of each vehicle for deciding the task unloading according to the optimized DDQN model. The invention is realized by the following technical scheme: an unmanned aerial vehicle assisted heterogeneous Internet of vehicles task migration and resource allocation method comprises the following steps:
(1) The method comprises the steps that a Mobile Edge Computing (MEC) server is deployed in a Road Side Unit (RSU), a system is provided with a unmanned aerial vehicle to provide computing service for a vehicle, and a computing task of the vehicle can be processed locally and unloaded to the unmanned aerial vehicle or the MEC server;
(2) Establishing a communication model and a calculation model comprising N vehicles and M unmanned aerial vehicles, and further establishing a joint calculation migration and resource allocation model;
(3) Each vehicle acquires positions of the unmanned plane and the MEC, calculates the occupation condition of resources and task information;
(4) Based on potential game, obtaining a decision of whether each vehicle is unloaded, and establishing a deep reinforcement learning model for the vehicle unloaded by the determined task with the aim of reducing the system time delay according to the obtained vehicle unloading decision;
(5) Training a deep reinforcement learning model based on the DDQN;
(6) In the execution stage, the vehicle n with the calculation task judges whether the task is unloaded or not through potential game, and decides the unloaded vehicle n 0 Obtaining current state from local observations
Figure BSA0000276505790000021
Obtaining unloading nodes and transmitting power of the vehicle by using the trained deep reinforcement learning model;
further, the step (2) includes the following specific steps:
(2a) The method comprises the steps of establishing a communication model for calculating and unloading of the Internet of vehicles, wherein the system comprises N vehicles, M unmanned aerial vehicles and a road side unit for deploying an MEC server, and the vehicles are assembled
Figure BSA0000276505790000022
Representation, set->
Figure BSA0000276505790000023
Representing an unmanned aerial vehicle, the mission of the vehicle n may be represented as +.>
Figure BSA0000276505790000024
c n Representing the number of CPU cycles, s, required for the vehicle n to complete a task n Task data size indicating unloading of vehicle n, < +.>
Figure BSA0000276505790000025
Representing the maximum tolerable delay for the task execution by vehicle n. Regarding each time slot, the vehicle generates a task for unloading decision of the vehicle task +.>
Figure BSA0000276505790000026
Representation, a n =0 means that vehicle n performs the calculation task locally, a n =1 denotes the task off-load of vehicle n to MEC server calculation over V2I link, a n =2 means that vehicle n is unloading the task via V2V link from the drone calculation. The V2V communication and the V2I communication do not interfere with each other using a 5G slicing technique,
by collection
Figure BSA0000276505790000027
Representing task calculation locations, where loc, uav [ m]And MEC respectively represents locally executing the calculation tasks, unloading the tasks to the mth unmanned aerial vehicle for calculation, and unloading the tasks to the MEC server for calculation. Task off-load location->
Figure BSA0000276505790000028
Sign (I) of>
Figure BSA0000276505790000029
The calculation task representing vehicle n is performed at position z,/->
Figure BSA00002765057900000210
The calculation task representing vehicle n is not performed at location z.
(2b) The vehicle n offloads the task to the drone m as signal to interference plus noise ratio (SINR):
Figure BSA00002765057900000211
the transmission rate at which the vehicle n offloads the mission to the drone m is expressed as:
Figure BSA00002765057900000212
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BSA00002765057900000213
representing the transmission bandwidth of a vehicle offloading tasks to a drone, P n Representing the emission power, sigma, of the vehicle n 2 Represents noise power, h n，uav[m] Represents the channel gain of vehicle n to drone m, < >>
Figure BSA0000276505790000031
Indicating the disturbance to vehicle n caused by a vehicle other than vehicle n offloading a task to drone m
Figure BSA0000276505790000032
Wherein the method comprises the steps of
Figure BSA0000276505790000033
a n′ When=2, J (a n′ =2) =1, otherwise J (a n′ ＝2)＝0，P n′ Indicating the emission power of the vehicle n', h n′，uav[m] Representing the channel gain of vehicle n' to drone m;
(2c) Likewise, vehicle n expresses the signal-to-interference-and-noise ratio (SINR) of the task offloading to the MEC server as:
Figure BSA0000276505790000034
the transmission rate at which vehicle n offloads tasks to the MEC server is expressed as:
Figure BSA0000276505790000035
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BSA0000276505790000036
transmission bandwidth, P, representing the offloading of tasks by a vehicle to an MEC server n Representing the emission power, sigma, of the vehicle n 2 Represents noise power, h n，mec Indicating the channel gain of vehicle n to MEC server, < >>
Figure BSA0000276505790000037
Indicating the disturbance to vehicle n by a vehicle other than vehicle n offloading tasks to the MEC server
Figure BSA0000276505790000038
Wherein the method comprises the steps of
Figure BSA0000276505790000039
a n′ When=1, J (a n′ =1) =1, otherwise J (a n′ ＝1)＝0，P n′ Indicating the emission power of the vehicle n', h n′，mec Representing the channel gain of vehicle n' to the MEC server;
(2d) Establishing a calculation model, a n =0 means that vehicle n performs the calculation task locally,
Figure BSA00002765057900000310
representing the local computing power of vehicle n, the local computation delay is:
Figure BSA00002765057900000311
a n =1 denotes the calculation of the vehicle n to unload the task to the MEC server through the V2R link, and the uploading delay of the vehicle n to upload the task to the MEC server is:
Figure BSA00002765057900000312
the calculation time delay of the vehicle n uploading the task to the MEC server is as follows:
Figure BSA00002765057900000313
Figure BSA00002765057900000314
the computing power allocated to the tasks of vehicle n for the MEC server.
a n =2 denotes the task being offloaded to the drone calculation by vehicle n over the V2R link, the upload latency of the task being uploaded to drone m by vehicle n being
Figure BSA0000276505790000041
The calculation time delay for uploading the task to the unmanned plane m by the vehicle n is as follows
Figure BSA0000276505790000042
Figure BSA0000276505790000043
The computing power allocated to the task of vehicle n for unmanned aircraft m ignores the issuing latency, so that the latency of the vehicle n's offloading of the task to the MEC server is
Figure BSA0000276505790000044
The delay of the vehicle n unloading the task to the unmanned aerial vehicle m is:
Figure BSA0000276505790000045
the latency of local computation, task offloading to the MEC server, and task offloading to the drone can be expressed as:
Figure BSA0000276505790000046
(2e) In summary, the following objective functions and constraints can be established:
Figure BSA0000276505790000047
wherein the constraint conditions C1 and C2 indicate that the tasks can only be executed locally, offloaded to MEC server calculation or offloaded to unmanned aerial vehicle calculation, each calculation task can only select one calculation mode, the constraint condition C3 indicates the local calculation capability range of the vehicle n,
Figure BSA0000276505790000051
is the local maximum computing power of the vehicle n, the constraints C4 and C5 mean that the computing power of the MEC server and the unmanned aerial vehicle allocated to the vehicle is not negative, the constraints C6 and C7 mean that the computing power of the MEC server and the unmanned aerial vehicle allocated to the vehicle cannot exceed the maximum computing power thereof, F mec Is the maximum computing power of MEC server, F uav[m] Is the maximum computing power of the unmanned aerial vehicle m; constraint conditions C8 and C9 indicate that the task of the vehicle n is unloaded to the MEC server or the unmanned aerial vehicle to execute calculation so as to meet the maximum time delay constraint; the C10 constraint indicates that the vehicle n transmit power is non-negative and meets its maximum transmit power constraint;
further, the step (4) comprises the following specific steps:
(4a) The decision of whether each vehicle is unloaded or not is obtained based on potential game, and the unloading decision of the task vehicle is modeled as potential game and expressed as
Figure BSA0000276505790000052
Wherein->
Figure BSA0000276505790000053
A is a collection of vehicles n For the unloading decision of vehicle n, u n Is the cost function of vehicle n.
In the gaming model, each vehicle is a resource competitor, so there are N vehicles competing for limited resources within the network, each vehicle can choose to offload calculations or perform task calculations locally, where a) n E {0,1} is the unloading decision for vehicle n,
Figure BSA0000276505790000054
representing a set of unloading decisions for all vehicles, a n =0 means that vehicle n performs the calculation task locally, a n =1 means that vehicle n offloads the task to the MEC server or drone for calculation. When the unloading decision of the vehicle n is a n When the cost function is expressed as u n (a n ，a -n ) Wherein a is -n Representing a set of unloading decisions for all vehicles except vehicle n. Each vehicle may wish to minimize its own cost by finding the optimal unloading decision, i.e +.>
Figure BSA0000276505790000055
Potential gaming converges to Nash equilibrium, i.e. an offloading decision is found by optimal response iteration
Figure BSA0000276505790000056
The absence of changing the current offloading decision for all vehicles may minimize its own costs.
(4b) Based on offloading decisions
Figure BSA0000276505790000057
By means of the collection->
Figure BSA0000276505790000058
Decision to offload in a vehicle
Figure BSA0000276505790000059
Vehicle, N 0 Representation->
Figure BSA00002765057900000510
The number of vehicles, defining the state s as the observed information and low-dimensional fingerprint information related to the transmitting power and unloading node, including vehicle n 0 To unmanned plane->
Figure BSA00002765057900000511
Channel state information>
Figure BSA00002765057900000512
Vehicle n 0 Channel state information to MEC +.>
Figure BSA00002765057900000513
Vehicle n 0 To unmanned plane->
Figure BSA00002765057900000514
Is received->
Figure BSA00002765057900000515
Vehicle n 0 Interference received to MEC +.>
Figure BSA00002765057900000516
Vehicle n 0 Task information of->
Figure BSA00002765057900000517
The training round number e and the random exploration variable epsilon in the epsilon-greedy algorithm, namely
Figure BSA00002765057900000518
Will be
Figure BSA00002765057900000519
The vehicle is regarded as an agent, each time the vehicle is based on the current state +.>
Figure BSA00002765057900000520
Selecting an unloading node and a transmitting power;
(4c) Defining each vehicle n deciding to unload 0 Acts as selected offload nodes and transmit power, denoted as
Figure BSA00002765057900000521
Figure BSA0000276505790000061
For vehicle n 0 Selected tasksUnloading node->
Figure BSA0000276505790000062
For vehicle n 0 Discrete transmit power levels;
(4d) Defining a reward function r, the goal of offloading is to offload decisions
Figure BSA0000276505790000063
Is to minimize all offloading decisions +.>
Figure BSA0000276505790000064
The task processing delay of the vehicle, therefore the reward function can be expressed as:
Figure BSA0000276505790000065
where b is a fixed value used to adjust the value of the bonus function,
(4e) According to the established state, action and rewarding function, establishing a deep reinforcement learning model based on Q learning, and each vehicle n deciding unloading 0 Establishing a corresponding evaluation function
Figure BSA0000276505790000066
Vehicle n indicating decision to unload 0 From state->
Figure BSA0000276505790000067
Execution of action->
Figure BSA0000276505790000068
The discount rewards generated later, the Q value update function is:
Figure BSA0000276505790000069
wherein r is t For the instant prize function, gamma is the discount factor,
Figure BSA00002765057900000610
for determining unloading vehicle n 0 Acquisition of observation information and low-dimensional fingerprint information concerning the transmit power and the offload node at time t,/>
Figure BSA00002765057900000611
Vehicle n indicating decision to unload 0 Execute +.>
Figure BSA00002765057900000612
Status of the back->
Figure BSA00002765057900000613
For action->
Figure BSA00002765057900000614
An action space is formed.
Further, the step (5) comprises the following specific steps:
(5a) Starting up an environment simulator, initializing predicted network parameters of each agent
Figure BSA00002765057900000615
And target network parameters->
Figure BSA00002765057900000616
(5b) Initializing a training round number P;
(5c) Updating the vehicle position and the unmanned plane position, acquiring the occupation condition of the unmanned plane and MEC computing resources, task information and the like, and initializing a time step t in the P round;
(5d) Running the predictive network asynchronously for each agent based on input state
Figure BSA00002765057900000617
Output action->
Figure BSA00002765057900000618
And obtain instant rewards r t At the same time go to the next state +.>
Figure BSA00002765057900000619
Thereby obtaining training data->
Figure BSA00002765057900000620
/>
(5e) Training data
Figure BSA00002765057900000621
Storing the experience playback pools;
(5f) Each agent randomly samples N from the experience playback pool k Training data
Figure BSA00002765057900000622
Composing data set D, inputting predictive network +.>
Figure BSA00002765057900000623
(5g) Each agent calculates a Loss value Loss (n 0 ) Updating an agent predictive network by back propagation of neural networks using a small batch gradient descent strategy
Figure BSA00002765057900000624
Parameters of (2);
(5h) The training times reach the target network updating interval, and according to the predicted network parameters
Figure BSA0000276505790000071
Updating target network parameters
Figure BSA0000276505790000072
(5i) Judging whether t < K is satisfied, wherein K is the total time step in the p rounds, if so, t=t+1, entering the step (5 c), otherwise, entering the step (5 j);
(5j) Judging whether p < I is met, wherein I is a training round number set threshold value, if so, p=p+1, entering a step (5 c), otherwise, finishing optimization, and obtaining an optimized deep reinforcement learning model;
further, the step (6) comprises the following specific steps:
(6a) Acquiring unloading decisions of vehicles through potential games according to unmanned plane positions, MEC (mean time between computing) calculation resource occupation conditions and task information, and calculating a vehicle n which is not locally calculated for each unloading decision 0 Acquiring state information at the time
Figure BSA0000276505790000073
(6b) Each unloading decision is not a locally calculated vehicle n 0 Inputting state information by using a trained deep reinforcement learning model
Figure BSA0000276505790000074
(6c) Outputting an optimal action strategy, i.e. each unloading decision is not a locally calculated vehicle n 0 Unloading node for optimal vehicle selection
Figure BSA0000276505790000075
And transmit power->
Figure BSA0000276505790000076
The beneficial effects are that: the invention provides an unmanned aerial vehicle assisted heterogeneous Internet of vehicles task migration and resource allocation method, which aims at a scene of cooperatively calculating and unloading a mobile edge server and an unmanned aerial vehicle, adopts a hybrid spectrum access technology to transmit, a V2V link and a V2I access different slices based on a 5G slice technology, do not interfere with each other, obtain a decision of whether a vehicle is locally calculated or not through potential game, optimize unloading nodes and transmitting power of the unloaded vehicle by adopting deep double Q learning, minimize system time delay to realize task calculation, and the algorithm combining the potential game and the deep double Q learning can effectively solve the joint optimization problem of the unloading decision and the transmitting power of the vehicle, thereby achieving good balance between complexity and performance. .
In summary, in the scenario of collaborative computing and unloading of the mobile edge server and the unmanned aerial vehicle, the unmanned aerial vehicle-assisted heterogeneous internet of vehicles task migration and resource allocation method provided by the invention is superior in minimizing system time delay.
Drawings
Fig. 1 is a flowchart of an unmanned aerial vehicle assisted heterogeneous internet of vehicles task migration and resource allocation method provided by an embodiment of the present invention;
fig. 2 is a schematic diagram of a system for unmanned aerial vehicle assisted task migration and resource allocation of heterogeneous internet of vehicles according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a DDQN algorithm framework provided by an embodiment of the present invention;
Detailed Description
The core idea of the invention is that: aiming at a scene of cooperatively calculating and unloading by a mobile edge server and an unmanned aerial vehicle, a hybrid spectrum access technology is adopted for transmission, a V2V link and a V2I are accessed into different slices based on a 5G slice technology, the mutual interference is avoided, a decision of whether a vehicle is unloaded or not is obtained through potential game, the decision is taken as an intelligent body, a deep reinforcement learning model is established, and a deep double-Q learning optimization deep reinforcement learning model is adopted. And obtaining optimal unloading nodes and transmitting power for deciding to unload the vehicle according to the optimized deep reinforcement learning model, so as to achieve the aim of minimizing the system time delay.
The present invention is described in further detail below.
The method comprises the steps that (1), a Mobile Edge Computing (MEC) server is deployed in a Road Side Unit (RSU), a system is deployed to enable an unmanned aerial vehicle to provide computing service for a vehicle, computing tasks of the vehicle can be processed locally, and the computing tasks are unloaded to the unmanned aerial vehicle or the MEC server;
step (2), establishing a communication model and a calculation model comprising N vehicles and M unmanned aerial vehicles, and further establishing a joint calculation migration and resource allocation model, wherein the method specifically comprises the following steps:
(2a) The method comprises the steps of establishing a communication model for calculating and unloading of the Internet of vehicles, wherein the system comprises N vehicles, M unmanned aerial vehicles and a road side unit for deploying an MEC server, and the vehiclesBy collection
Figure BSA0000276505790000081
Representation, set->
Figure BSA0000276505790000082
Representing an unmanned aerial vehicle, the mission of the vehicle n may be represented as +.>
Figure BSA0000276505790000083
c n Representing the number of CPU cycles, s, required for the vehicle n to complete a task n Task data size indicating unloading of vehicle n, < +.>
Figure BSA0000276505790000084
Representing the maximum tolerable delay for the task execution by vehicle n. Regarding each time slot, the vehicle generates a task for unloading decision of the vehicle task +.>
Figure BSA0000276505790000085
Representation, a n =0 means that vehicle n performs the calculation task locally, a n =1 denotes the task off-load of vehicle n to MEC server calculation over V2I link, a n =2 means that vehicle n is unloading the task via V2V link from the drone calculation. The V2V communication and the V2I communication do not interfere with each other using a 5G slicing technique,
by collection
Figure BSA0000276505790000086
Representing task calculation locations, where loc, uav [ m]And MEC respectively represents locally executing the calculation tasks, unloading the tasks to the mth unmanned aerial vehicle for calculation, and unloading the tasks to the MEC server for calculation. Task off-load location->
Figure BSA0000276505790000087
Sign (I) of>
Figure BSA0000276505790000088
The calculation task representing vehicle n is performed at position z,/->
Figure BSA0000276505790000089
The calculation task representing vehicle n is not performed at location z.
(2b) The vehicle n offloads the task to the drone m as signal to interference plus noise ratio (SINR):
Figure BSA00002765057900000810
the transmission rate at which the vehicle n offloads the mission to the drone m is expressed as:
Figure BSA00002765057900000811
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BSA00002765057900000812
representing the transmission bandwidth of a vehicle offloading tasks to a drone, P n Representing the emission power, sigma, of the vehicle n 2 Represents noise power, h n，uav[m] Represents the channel gain of vehicle n to drone m, < >>
Figure BSA00002765057900000813
Indicating the disturbance to vehicle n caused by a vehicle other than vehicle n offloading a task to drone m
Figure BSA00002765057900000814
Wherein the method comprises the steps of
Figure BSA00002765057900000815
a n′ When=2, J (a n′ =2) =1, otherwise J (a n′ ＝2)＝0，P n′ Indicating the emission power of the vehicle n', h n′，uav[m] Representing the channel gain of vehicle n' to drone m;
(2c) Likewise, vehicle n expresses the signal-to-interference-and-noise ratio (SINR) of the task offloading to the MEC server as:
Figure BSA0000276505790000091
the transmission rate at which vehicle n offloads tasks to the MEC server is expressed as:
Figure BSA0000276505790000092
wherein, the liquid crystal display device comprises a liquid crystal display device,
Figure BSA0000276505790000093
transmission bandwidth, P, representing the offloading of tasks by a vehicle to an MEC server n Representing the emission power, sigma, of the vehicle n 2 Represents noise power, h n，mec Indicating the channel gain of vehicle n to MEC server, < >>
Figure BSA0000276505790000094
Indicating the disturbance to vehicle n by a vehicle other than vehicle n offloading tasks to the MEC server
Figure BSA0000276505790000095
Wherein the method comprises the steps of
Figure BSA0000276505790000096
a n′ When=1, J (a n′ =1) =1, otherwise J (a n′ ＝1)＝0，P n′ Indicating the emission power of the vehicle n', h n′，mec Representing the channel gain of vehicle n' to the MEC server;
(2d) Establishing a calculation model, a n =0 means that vehicle n performs the calculation task locally,
Figure BSA0000276505790000097
representing the local computing power of vehicle n, the local computation delay is:
Figure BSA0000276505790000098
a n =1 denotes the calculation of the vehicle n to unload the task to the MEC server through the V2R link, and the uploading delay of the vehicle n to upload the task to the MEC server is:
Figure BSA0000276505790000099
the calculation time delay of the vehicle n uploading the task to the MEC server is as follows:
Figure BSA00002765057900000910
Figure BSA00002765057900000911
the computing power allocated to the tasks of vehicle n for the MEC server.
a n =2 denotes the calculation of the vehicle n by the vehicle-mounted cloud server for unloading the task via the V2R link, and the uploading delay of the vehicle n to the unmanned aerial vehicle m is
Figure BSA00002765057900000912
The calculation time delay for uploading the task to the unmanned plane m by the vehicle n is as follows
Figure BSA00002765057900000913
Figure BSA00002765057900000914
The computing power allocated to the task of vehicle n for unmanned aircraft m ignores the issuing latency, so that the latency of the vehicle n's offloading of the task to the MEC server is
Figure BSA0000276505790000101
The delay of the vehicle n unloading the task to the unmanned aerial vehicle m is:
Figure BSA0000276505790000102
the latency of local computation, task offloading to the MEC server, and task offloading to the drone can be expressed as:
Figure BSA0000276505790000103
(2e) In summary, the following objective functions and constraints can be established:
Figure BSA0000276505790000104
wherein the constraint conditions C1 and C2 indicate that the tasks can only be executed locally, offloaded to MEC server calculation or offloaded to unmanned aerial vehicle calculation, each calculation task can only select one calculation mode, the constraint condition C3 indicates the local calculation capability range of the vehicle n,
Figure BSA0000276505790000105
is the local maximum computing power of the vehicle n, the constraints C4 and C5 mean that the computing power of the MEC server and the unmanned aerial vehicle allocated to the vehicle is not negative, the constraints C6 and C7 mean that the computing power of the MEC server and the unmanned aerial vehicle allocated to the vehicle cannot exceed the maximum computing power thereof, F mec Is the maximum computing power of MEC server, F uav[m] Is the maximum computing power of the unmanned aerial vehicle m; constraint conditions C8 and C9 indicate that the task of the vehicle n is unloaded to the MEC server or the unmanned aerial vehicle to execute calculation so as to meet the maximum time delay constraint; the C10 constraint indicates that the vehicle n transmit power is non-negative and meets its maximum transmit power constraint;
step (3), each vehicle acquires positions of the unmanned plane and the MEC, calculates resource occupation conditions and task information;
step (4), obtaining a decision of whether each vehicle is unloaded or not based on potential game, and establishing a deep reinforcement learning model for the vehicle unloaded by the determined task with the aim of reducing the system time delay according to the obtained vehicle unloading decision, wherein the method comprises the following specific steps of:
(4a) The decision of whether each vehicle is unloaded or not is obtained based on potential game, and the unloading decision of the task vehicle is modeled as potential game and expressed as
Figure BSA0000276505790000111
Wherein->
Figure BSA0000276505790000112
A is a collection of vehicles n For the unloading decision of vehicle n, u n Cost function n for vehicle n
In the gaming model, each vehicle is a resource competitor, so there are N vehicles competing for limited resources within the network, each vehicle can choose to offload calculations or perform task calculations locally, where a) n E {0,1} is the unloading decision for vehicle n,
Figure BSA0000276505790000113
representing a set of unloading decisions for all vehicles, a n =0 means that vehicle n performs the calculation task locally, a n =1 means that vehicle n offloads the task to the MEC server or drone for calculation. When the unloading decision of the vehicle n is a n When the cost function is expressed as u n (a n ，a -n ) Wherein a is -n Representing a set of unloading decisions for all vehicles except vehicle n. Each vehicle may wish to minimize its own cost by finding the optimal offloading decision, i.e
Figure BSA0000276505790000114
Potential gaming converges to Nash equilibrium, i.e. an offloading decision is found by optimal response iteration
Figure BSA0000276505790000115
The absence of changing the current offloading decision for all vehicles may minimize its own costs.
(4b) Based on offloading decisions
Figure BSA0000276505790000116
By means of the collection->
Figure BSA0000276505790000117
Unloading decision in a vehicle->
Figure BSA0000276505790000118
Vehicle, N 0 Representation->
Figure BSA0000276505790000119
The number of vehicles, defining the state s as the observed information and low-dimensional fingerprint information related to the transmitting power and unloading node, including vehicle n 0 To unmanned plane->
Figure BSA00002765057900001110
Channel state information>
Figure BSA00002765057900001111
Vehicle n 0 Channel state information to MEC +.>
Figure BSA00002765057900001112
Vehicle n 0 To unmanned plane->
Figure BSA00002765057900001113
Is received->
Figure BSA00002765057900001114
Vehicle n 0 Interference received to MEC +.>
Figure BSA00002765057900001115
Vehicle n 0 Task information of->
Figure BSA00002765057900001116
The training round number e and the random exploration variable epsilon in the epsilon-greedy algorithm, namely
Figure BSA00002765057900001117
Will be
Figure BSA00002765057900001118
Vehicle is regarded as an agent, each time the vehicle is based on the current state +.>
Figure BSA00002765057900001119
Selecting an unloading node and a transmitting power;
(4c) Defining each vehicle n deciding to unload 0 Acts as selected offload nodes and transmit power, denoted as
Figure BSA00002765057900001120
Figure BSA00002765057900001121
For vehicle n 0 Selected task offload node, ">
Figure BSA00002765057900001122
For vehicle n 0 Discrete transmit power levels;
(4d) Defining a reward function r, the goal of offloading is to offload decisions
Figure BSA00002765057900001123
Is to minimize all offloading decisions +.>
Figure BSA00002765057900001124
The task processing delay of the vehicle, therefore the reward function can be expressed as:
Figure BSA00002765057900001125
where b is a fixed value used to adjust the value of the bonus function,
(4e) According to the established state, action and rewarding function, establishing a deep reinforcement learning model based on Q learning, and each vehicle n deciding unloading 0 Establishing a corresponding evaluation function
Figure BSA0000276505790000121
Vehicle n indicating decision to unload 0 From state->
Figure BSA0000276505790000122
Execution of action->
Figure BSA0000276505790000123
The discount rewards generated later, the Q value update function is:
Figure BSA0000276505790000124
wherein r is t For the instant prize function, gamma is the discount factor,
Figure BSA0000276505790000125
for determining unloading vehicle n 0 Acquisition of observation information and low-dimensional fingerprint information concerning the transmit power and the offload node at time t,/>
Figure BSA0000276505790000126
Vehicle n indicating decision to unload 0 Execute +.>
Figure BSA0000276505790000127
Status of the back->
Figure BSA0000276505790000128
For action->
Figure BSA0000276505790000129
An action space is formed.
And (5) training a deep reinforcement learning model based on the DDQN, which specifically comprises the following steps:
(5a) Starting up an environment simulator, initializing predicted network parameters of each agent
Figure BSA00002765057900001210
And target network parameters->
Figure BSA00002765057900001211
(5b) Initializing a training round number P;
(5c) Updating the vehicle position and the unmanned plane position, acquiring the occupation condition of the unmanned plane and MEC computing resources, task information and the like, and initializing a time step t in the P round;
(5d) Running the predictive network asynchronously for each agent based on input state
Figure BSA00002765057900001212
Output action->
Figure BSA00002765057900001213
And acquires the instant prize rt while going to the next state +.>
Figure BSA00002765057900001214
Thereby obtaining training data->
Figure BSA00002765057900001215
(5e) Training data
Figure BSA00002765057900001216
Storing the experience playback pools;
(5f) Each agent randomly samples N from the experience playback pool k Training data
Figure BSA00002765057900001217
Composing the numberAccording to set D, input prediction network->
Figure BSA00002765057900001218
(5g) Each agent calculates a Loss value Loss (n 0 ) Updating an agent predictive network by back propagation of neural networks using a small batch gradient descent strategy
Figure BSA00002765057900001219
Parameters of (2);
(5h) The training times reach the target network updating interval, and according to the predicted network parameters
Figure BSA00002765057900001220
Updating target network parameters +.>
Figure BSA00002765057900001221
(5i) Judging whether t < K is satisfied, wherein K is the total time step in the p rounds, if so, t=t+1, entering the step (5 c), otherwise, entering the step (5 j);
(5j) Judging whether p < I is met, wherein I is a training round number set threshold value, if so, p=p+1, entering a step (5 c), otherwise, finishing optimization, and obtaining an optimized deep reinforcement learning model;
step (6), in the execution stage, the vehicle n with the calculation task judges whether the task is unloaded or not through potential game, and determines the unloaded vehicle n 0 Obtaining current state from local observations
Figure BSA0000276505790000131
Obtaining unloading nodes and transmitting power of the vehicle by using the trained deep reinforcement learning model, and specifically comprising the following steps:
(6a) Acquiring unloading decisions of vehicles through potential games according to unmanned plane positions, MEC (mean time between computing) calculation resource occupation conditions and task information, and calculating a vehicle n which is not locally calculated for each unloading decision 0 Acquiring state information at the time
Figure BSA0000276505790000132
(6b) Each unloading decision is not a locally calculated vehicle n 0 Inputting state information by using a trained deep reinforcement learning model
Figure BSA0000276505790000133
(6c) Outputting an optimal action strategy, i.e. each unloading decision is not a locally calculated vehicle n 0 Unloading node for optimal vehicle selection
Figure BSA0000276505790000134
And transmit power->
Figure BSA0000276505790000135
In fig. 1, a flowchart of an unmanned aerial vehicle assisted heterogeneous internet of vehicles task migration and resource allocation method is described, firstly, a vehicle judges whether a task is unloaded through potential game, and the unloaded vehicle selects an unloading node and transmitting power based on a deep reinforcement learning model trained by DDQN.
In fig. 2, a system model of unmanned aerial vehicle-assisted heterogeneous internet of vehicles task migration and resource allocation is depicted, it being seen that MEC servers and unmanned aerial vehicles can provide computing services for vehicles.
In fig. 3, an algorithmic framework of DDQN is depicted, which contains two networks, a predicted network and a target network, respectively.
According to the description of the invention, it should be apparent to those skilled in the art that the invention provides the unmanned aerial vehicle-assisted heterogeneous Internet of vehicles task migration and resource allocation method, which can effectively reduce system time delay and achieve good balance between complexity and performance.
What is not described in detail in the present application belongs to the prior art known to those skilled in the art.

Claims (1)

1. The unmanned aerial vehicle assisted heterogeneous Internet of vehicles task migration and resource allocation method is characterized by comprising the following steps of:
(1) The method comprises the steps that a Mobile Edge Computing (MEC) server is deployed in a Road Side Unit (RSU), a system is provided with a unmanned aerial vehicle to provide computing service for a vehicle, and a computing task of the vehicle can be processed locally and unloaded to the unmanned aerial vehicle or the MEC server;
(2) Establishing a communication model and a calculation model comprising N vehicles and M unmanned aerial vehicles, and further establishing a joint calculation migration and resource allocation model;
(3) Each vehicle acquires positions of the unmanned plane and the MEC, calculates the occupation condition of resources and task information;
(4) Based on potential game, obtaining a decision of whether each vehicle is unloaded, and establishing a deep reinforcement learning model for the vehicle unloaded by the determined task with the aim of reducing the system time delay according to the obtained vehicle unloading decision;
(5) Training a deep reinforcement learning model based on the DDQN;
(6) In the execution stage, the vehicle n with the calculation task judges whether the task is unloaded or not through potential game, and decides the unloaded vehicle n 0 Obtaining current state from local observations
Figure FSA0000276505780000011
Obtaining unloading nodes and transmitting power of the vehicle by using the trained deep reinforcement learning model;
further, the step (4) comprises the following specific steps:
(4a) The decision of whether each vehicle is unloaded or not is obtained based on potential game, and the unloading decision of the task vehicle is modeled as potential game and expressed as
Figure FSA0000276505780000012
Wherein->
Figure FSA0000276505780000013
A is a collection of vehicles n For the unloading decision of vehicle n, u n For the cost function of vehicle N, each vehicle is a resource competitor in the game model, so that N vehicles raceContending for limited resources within the network, each vehicle may choose to offload calculations or perform task calculations locally, where a) n E {0,1} is the unload decision for vehicle n, +.>
Figure FSA0000276505780000014
Representing a set of unloading decisions for all vehicles, a n =0 means that vehicle n performs the calculation task locally, a n =1 means that vehicle n is unloading the task to MEC server or drone for calculation, when the unloading decision of vehicle n is a n When the cost function is expressed as u n (a n ，a -n ) Wherein a is -n Representing a set of unloading decisions for all vehicles except vehicle n, each vehicle may wish to minimize its own cost by finding the optimal unloading decision, i.e
Figure FSA0000276505780000015
Wherein the method comprises the steps of
Figure FSA0000276505780000016
Calculating the time delay of the task for vehicle n locally, < >>
Figure FSA0000276505780000017
Delay in offloading tasks to MEC server for vehicle n, +.>
Figure FSA0000276505780000018
For the time delay calculated by the task unloading to the unmanned plane for the vehicle n, the potential game converges to Nash equilibrium, i.e. the unloading decision is found by optimal response iteration +.>
Figure FSA0000276505780000019
The absence of changing the current offloading decision for all vehicles can minimize its own costs;
(4b) Based on offloading decisions
Figure FSA00002765057800000110
By means of the collection->
Figure FSA00002765057800000111
Unloading decision in a vehicle->
Figure FSA00002765057800000112
Vehicle, N 0 Representation->
Figure FSA00002765057800000113
Number of vehicles, unloading decision in vehicle +.>
Figure FSA00002765057800000114
The vehicle is regarded as an intelligent agent, and the states s are defined as observed information and low-dimensional fingerprint information related to the transmitting power and the unloading nodes, including the vehicle n 0 To unmanned plane->
Figure FSA00002765057800000115
Channel state information>
Figure FSA00002765057800000116
Vehicle n 0 Channel state information to MEC +.>
Figure FSA00002765057800000117
Vehicle n 0 To unmanned plane->
Figure FSA00002765057800000118
Is received->
Figure FSA00002765057800000119
Vehicle n 0 Interference received to MEC +.>
Figure FSA0000276505780000021
Vehicle n 0 Task information of->
Figure FSA0000276505780000022
The training round number e and the random exploration variable epsilon in the epsilon-greedy algorithm, namely
Figure FSA0000276505780000023
Will be
Figure FSA0000276505780000024
The vehicles are regarded as intelligent bodies, and each time n is a vehicle 0 Based on the current state->
Figure FSA0000276505780000025
Selecting an unloading node and a transmitting power;
(4c) Defining each vehicle n for which unloading is decided 0 Acts as selected offload nodes and transmit power, denoted as
Figure FSA0000276505780000026
Figure FSA0000276505780000027
For vehicle n 0 Selected task offload node, ">
Figure FSA0000276505780000028
For vehicle n 0 A set of selectable task offloading nodes, +.>
Figure FSA0000276505780000029
For vehicle n 0 Discrete transmit power levels;
(4d) Defining a reward function r, the goal of offloading is to offload decisions
Figure FSA00002765057800000210
The vehicle selects an unloading node and a transmitting power, in the range satisfying the maximum transmitting powerUnder beam, minimize all offloading decisions +.>
Figure FSA00002765057800000211
The task processing delay of the vehicle, therefore the reward function can be expressed as:
Figure FSA00002765057800000212
where b is a fixed value used to adjust the value of the bonus function,
Figure FSA00002765057800000213
representing vehicle n 0 Is at position z 0 Execution (S)>
Figure FSA00002765057800000214
Representing vehicle n 0 Is not at position z 0 Execution (S)>
Figure FSA00002765057800000215
Representing vehicle n 0 Is at position z 0 Time delay of execution;
(4e) According to the established state, action and rewarding function, establishing a deep reinforcement learning model based on Q learning, and each vehicle n deciding unloading 0 Establishing a corresponding evaluation function
Figure FSA00002765057800000216
Vehicle n indicating decision to unload 0 From state->
Figure FSA00002765057800000217
Execution of action->
Figure FSA00002765057800000218
The discount rewards generated later, the Q value update function is:
Figure FSA00002765057800000219
wherein r is t For the instant prize function, gamma is the discount factor,
Figure FSA00002765057800000220
for determining unloading vehicle n 0 Acquisition of observation information and low-dimensional fingerprint information concerning the transmit power and the offload node at time t,/>
Figure FSA00002765057800000221
Vehicle n indicating decision to unload 0 At time t execute
Figure FSA00002765057800000222
Status of the back->
Figure FSA00002765057800000223
For action->
Figure FSA00002765057800000224
An action space is formed. />
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