CN114626298A - State updating method for efficient caching and task unloading in unmanned aerial vehicle-assisted Internet of vehicles - Google Patents

State updating method for efficient caching and task unloading in unmanned aerial vehicle-assisted Internet of vehicles Download PDF

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CN114626298A
CN114626298A CN202210246271.XA CN202210246271A CN114626298A CN 114626298 A CN114626298 A CN 114626298A CN 202210246271 A CN202210246271 A CN 202210246271A CN 114626298 A CN114626298 A CN 114626298A
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秦晓琦
胡楠
马楠
张治�
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Abstract

The invention discloses a state updating method for efficient caching and task unloading in an unmanned aerial vehicle-assisted Internet of vehicles, which comprises the steps of firstly, taking the freshness of a cache model used for calculation and the freshness of a calculation unloading process into consideration to serve as the information freshness of state updating of a vehicle in order to achieve the aim of comprehensively ensuring the safety of automatic driving, and secondly, combining three technologies of vehicle edge calculation, caching and unmanned aerial vehicle, designing a strategy for making decisions on cache updating, user association and resource allocation in a dynamic environment to minimize the energy consumption of a system and ensure the timeliness of information. And finally, dividing the experience cache pool by using a deep reinforcement learning algorithm and adopting an algorithm based on a deep certainty strategy gradient, and selecting an experience training neural network from two different cache pools in proportion, so that the convergence speed is increased, the reward oscillation after convergence is reduced, the reward value after convergence is improved, and the algorithm performance is improved.

Description

State updating method for efficient caching and task unloading in unmanned aerial vehicle-assisted Internet of vehicles
Technical Field
The invention relates to the technical field of automatic driving, in particular to a state updating method for efficient caching and task unloading in an unmanned aerial vehicle auxiliary Internet of vehicles.
Background
With the continuous development of the internet of vehicles, various applications and services of the internet of vehicles are generated, and a large amount of data traffic is brought to the internet of vehicles, so that the contradiction between computation overload and limited frequency spectrum and computation resources in the traditional vehicle-base station type internet of vehicles architecture is increasingly prominent. The contradiction brings challenges to the traditional internet of vehicles difficult to construct in supporting time delay sensitive and resource demand diversified applications and services and dealing with sudden traffic situations. As one of the important application scenarios of the internet of vehicles, the problem is further aggravated by the fact that the automatic driving needs to frequently sense the external environment and generates a large data flow. Therefore, a great deal of research shows that mobile edge computing can be introduced into a vehicle networking architecture, a vehicle edge computing technology is used, a task computing process of a vehicle is sunk to the edge of a network, core network pressure is reduced, and the challenges can be effectively met.
Caching, a technique for storing content in a server in advance by prediction, can reduce frequent responses, and is often used to assist edge calculation to help optimize various performance indicators. Caching is also often used as an aid in designing strategies for vehicle edge calculation. By introducing the caching technology, the computing delay can be effectively reduced, and meanwhile, the energy consumption of the system is reduced.
The unmanned aerial vehicle technology can be used for assisting in vehicle edge calculation due to flexibility and mobility of the unmanned aerial vehicle. When sudden traffic conditions or large sudden flow are met, the vehicle edge computing architecture is difficult to support various task requirements. At this time, the unmanned aerial vehicle with the edge calculation server nearby is dispatched to the burst flow road section, and effective handling can be achieved. Therefore, in the prior art, the unmanned aerial vehicle technology is also often used as an auxiliary technology for the vehicle edge calculation.
In various applications and services of the internet of vehicles, vehicles often have certain requirements on freshness of obtained information. In an automatic driving scene, task calculation results obtained by a vehicle need to timely, accurately and efficiently reflect external conditions, and the difference between the task calculation results and the external real conditions is small, namely, fresh calculation results need to be obtained. In recent years, information age is regarded as a method for measuring information freshness, defined as the time elapsed since information generation, and much research is currently conducted on focusing this freshness on the computation uninstallation process to characterize the freshness of the results obtained by the computation uninstallation process.
With regard to vehicle edge calculation research in an autonomous driving scenario, it is a primary objective to ensure safety during autonomous driving. In order to ensure the safety of the vehicle, the vehicle needs to frequently sense the environmental information, and interacts with the environment in a large amount and frequently, and meanwhile, the vehicle can continuously and intensively generate calculation tasks. This is a challenge for conventional car networking architectures. The existing technology ensures the safety and user experience of automatic driving by ensuring the time delay of the calculation unloading process. In actual situations, whether a calculation result obtained by the automatic driving vehicle can timely and accurately reflect the external real situation is represented by using the index of time delay, which is obviously not powerful enough. Therefore, the invention uses the information age to represent the difference between the calculated result obtained by the vehicle and the actual external situation. Meanwhile, the conventional calculation unloading strategy considering the information age usually focuses only on the calculation unloading process, and ignores the information freshness of the used cached calculation model. Aiming at the defect, the information freshness of the cached content is also considered into the freshness of the calculation result, so that the freshness of the information result obtained by the vehicle is comprehensively ensured.
Disclosure of Invention
The invention provides a strategy for solving the problems of minimized system energy consumption and guaranteed information timeliness by making decisions on cache updating, user association and resource allocation in a dynamic environment and combining cache and unmanned aerial vehicle technologies aiming at the problems of high requirements on safety and timeliness, high vehicle mobility, dense tasks, quick network topology change and high system overhead in a cache-assisted unmanned aerial vehicle-vehicle edge computing architecture and an automatic driving scene, and provides a state updating method for efficient cache and task unloading in an unmanned aerial vehicle-assisted internet.
In order to achieve the above purpose, the invention provides the following technical scheme:
a state updating method for efficient caching and task unloading in an unmanned aerial vehicle-assisted Internet of vehicles is characterized in that a system model comprises a macro base station provided with an MEC server, an unmanned aerial vehicle with computing capability and a plurality of vehicles provided with computing capability, and the method comprises the following steps:
s1, calculating the freshness of the data used by the tasks and the freshness of the unloading process, and taking the freshness as the freshness of the state update of the vehicle;
s2, minimizing the total energy consumption of the system in the time slot t, wherein the total energy consumption comprises the energy consumption generated by updating the cache content in the time slot t and the energy consumption generated by processing the vehicle task in the time slot t;
s3, dividing the experience cache pool by an algorithm based on the depth certainty strategy gradient, and selecting an experience training neural network from two different cache pools according to the proportion.
Further, the method for calculating the freshness of the cache model in step S1 is as follows:
definition of
Figure BDA0003545206930000031
And
Figure BDA0003545206930000032
indicating whether vehicle i and UAV j have cache content w in time slot t,
Figure BDA0003545206930000033
and
Figure BDA0003545206930000034
indicating that there is a buffer w, otherwise 0, vehicleAnd the cache capacity of the UAV is expressed as:
Figure BDA0003545206930000035
Figure BDA0003545206930000036
wherein the content of the first and second substances,
Figure BDA0003545206930000037
and
Figure BDA0003545206930000038
representing buffer capacity, l, of vehicle i and UAV jwA data amount indicating the content w;
definition of
Figure BDA0003545206930000039
And
Figure BDA00035452069300000310
two binary variables, representing whether the vehicle i and the UAV j update or replace the buffer w at the time slot t,
Figure BDA00035452069300000311
and
Figure BDA00035452069300000312
indicating that the cache w is updated or replaced at the time slot t, and otherwise, the cache w is 0; for vehicle i:
Figure BDA00035452069300000313
similarly, for UAV j, we can get:
Figure BDA00035452069300000314
Figure BDA00035452069300000315
and
Figure BDA00035452069300000316
respectively representing the timeliness, namely the freshness of the cache, of the cache contents of the vehicle and the UAV; assuming that when the content w is replaced by other kinds of cache data for a certain time, the freshness is set to an infinite number I, we get:
Figure BDA00035452069300000317
Figure BDA00035452069300000318
further, the calculation method of the freshness of the uninstallation process in step S1 is:
the vehicle generates a computing task and sends a resource access request to the MeNB, the request including { e ] of the task wi(t)，sw，zw-direction of travel, -speed of travel, -current position information, where ei(t) indicates the type of task of vehicle i in time slot t, swIndicates the size of the task w, zwRepresenting the CPU period required by the calculation of the secondary task w; after the MeNB collects this information, the MeNB makes decisions on the handling of the vehicle i task, to use
Figure BDA0003545206930000041
Indicating whether the task is processed locally, by
Figure BDA0003545206930000042
Expressed in several ways:
1) local calculation:
Figure BDA0003545206930000043
2) unloading to a UAV:
Figure BDA0003545206930000044
3) offloading to MeNB:
Figure BDA0003545206930000045
4) calculating for the moment:
Figure BDA0003545206930000046
then there are:
Figure BDA0003545206930000047
further, when calculating locally, use
Figure BDA0003545206930000048
The calculation capability of the vehicle i is shown, and the local calculation time delay of the vehicle i is shown as follows:
Figure BDA0003545206930000049
the corresponding energy consumption is expressed as:
Figure BDA00035452069300000410
weight factor muiRepresents the energy consumption required by the CPU of vehicle i per cycle calculation, expressed as:
Figure BDA00035452069300000411
the locally computed delay does not exceed one slot τ, with:
Figure BDA00035452069300000412
further, when unloaded to UAV, the transmission rate between vehicle i and UAV j is expressed as:
Figure BDA00035452069300000413
wherein, bi，j(t) is the bandwidth of the vehicle i when communicating with UAV j, MeNB, p is the vehicle transmit power to mission, βi，j(t) is the channel gain, σ, for vehicle i in communication with UAV j2Is the power spectral density of white noise;
the channel gain between vehicle i and UAV j is expressed as:
Figure BDA00035452069300000414
wherein d isi，j(t) represents the distance between vehicle i and UAV j;
establishing a coordinate axis on the region
Figure BDA00035452069300000415
To represent the x, y coordinates of the position of the vehicle, UAV, and the velocity of the vehicle and UAV as
Figure BDA0003545206930000051
The distance between the directional vehicle i and UAV j, with positive and negative indicating travel, is expressed as:
Figure BDA0003545206930000052
if the mission of vehicle i is offloaded to UAV j at time t, i must be within the coverage of UAV j in one time slot, i.e., the connection distance between vehicle i and UAV j must be within one time slot, i.e., the next time slot starts at a time greater than the communication radius R of UAV, then there are:
Figure BDA0003545206930000053
further, when unloading to MeNB, the transfer rate between vehicle i and MeNB:
Figure BDA0003545206930000054
wherein, bi，M+1(t) is the bandwidth of the communication between vehicle i and MeNB, gi，M+1(t) is the square of the average channel gain between vehicle i and MeNB, the channel gain between vehicle i and MeNB:
the time at which vehicle i transmits the task to the UAVj or MeNB is expressed as:
Figure BDA0003545206930000055
the MEC server in UAV and MeNB calculates the time delay for the task of vehicle i as:
Figure BDA0003545206930000056
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003545206930000057
representing the computing resources allocated by the vehicle i in the MEC server j in the time slot t;
the total time delay for unloading is:
Figure BDA0003545206930000058
the total offload latency specification is limited to one time slot:
Figure BDA0003545206930000059
energy consumption during unloading:
Figure BDA00035452069300000510
definition of
Figure BDA0003545206930000061
Indicating the freshness of the status update of the vehicle i, i.e. the age of the status update, at the time slot t
Figure BDA0003545206930000062
Indicating the time when task w (w is the first to be processed task in the task queue) occurred, so the age of the state update is expressed as:
Figure BDA0003545206930000063
provision not to exceed threshold Ath：
Figure BDA0003545206930000064
Further, the energy consumption ξ (t) resulting from buffering updates in the time slot t is expressed as:
Figure BDA0003545206930000065
wherein θ (J/bit) is a weighting factor for converting the cache update data amount into energy, and represents the energy consumed by caching 1bit of data.
Energy consumption E caused by task processing in time slot ti(t) is expressed as:
Figure BDA0003545206930000066
the energy consumption phi (t) of the system in time slot t due to task transmission is represented as:
Figure BDA0003545206930000067
total energy consumption of system in time slot t
Figure BDA0003545206930000068
Expressed as:
Figure BDA0003545206930000069
further, step S2 minimizes the system energy consumption for T slots, and this optimization problem is expressed as:
Figure BDA00035452069300000610
further, in step S3, the searched experience is stored in different experience buffer pools according to different qualities, and the buffer pool dividing method includes: finding out a threshold value for dividing better experience and worse experience, and dividing the experience cache pool into a better experience pool and a worse experience pool through the threshold value.
Further, in step S3, the minimization problem of step S2 is converted into an MDP problem, where { Sc, Ac, Tc, Rc } is defined by a tuple, Sc is a set of system states, Ac is a set of system actions, T is a set of system actions, andc＝{p(sc′|sc，ac) Is the set of transition probabilities, Rc：Sc×Ac→RcIs the reward function, the strategy pi is the mapping of Sc to Sc, the MDP problem is defined as follows:
state space: in the time slot t, defining a set of system states as coordinates of the unmanned aerial vehicle and the vehicle, a task request type of each vehicle, cache states of the unmanned aerial vehicle and the vehicle, and an information age of a first task to be processed of the vehicle;
an action space: in the time slot t, the action space of the system is the correlation condition of the vehicle and each cache node, and whether the cache is updated or not with the existing cache;
the reward function: the reward function is set as the sum of the energy consumption and penalty functions of the system.
Compared with the prior art, the invention has the beneficial effects that:
the traditional task unloading strategy focuses only on the calculation unloading process, and ignores the freshness of the cached data used by the calculation task. Aiming at the defect, the invention not only considers the freshness of information in the process of calculating unloading, but also considers the freshness of cached data used in the task calculation into the freshness of the vehicle state update so as to comprehensively ensure the freshness of the vehicle state update.
In the aspect of system energy consumption, compared with the conventional automatic driving research, the method considers a green system, simultaneously considers the information freshness of the calculation result obtained by the vehicle and the energy consumption of the system, and designs a strategy for solving the problem of minimized system energy consumption and ensuring the timeliness of vehicle state updating by making decisions on cache updating, user association and resource allocation in a dynamic environment by combining three technologies of vehicle edge calculation, cache and unmanned aerial vehicle aiming at the problems of a cache-assisted edge calculation architecture, high requirements on safety and timeliness in an automatic driving scene, high vehicle mobility, intensive tasks, fast network topology change and high system overhead.
The invention considers the solving need of the problem, needs to use a deep reinforcement learning strategy and adopts a deep certainty strategy gradient (DDPG) algorithm as a basis. In the method, the state space and the action space are large, and the algorithm convergence is slow. Therefore, the traditional DDPG algorithm is improved, the threshold for dividing better experience and poorer experience is found out through a large number of experiments, the experience cache pool is divided into a better experience pool and a poorer experience pool through the threshold, and the experience training neural network is respectively selected from the two experience pools according to the proportion during training. Simulation shows that the improved algorithm has faster convergence speed, higher reward value after convergence and smaller oscillation.
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In order to more clearly illustrate the embodiments of the present application or technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a system model of a state updating method for efficient caching and task unloading in an unmanned aerial vehicle-assisted internet of vehicles according to an embodiment of the present invention.
Detailed Description
The invention designs a state updating method for efficient caching and task unloading in an unmanned aerial vehicle-assisted internet of vehicles, a system model is shown in figure 1, a region in a city is considered, in the region, a road is a two-lane bidirectional driving road section, and a Macro base station (Macro eNodeB, MeNB) provided with an MEC server provides full signal coverage for the whole road section of the region. Unmanned Aerial Vehicles (UAVs) with computing power are arranged above the road, and the number of the UAVs is M. Each vehicle (all equipped with MEC server) can periodically generate different types of calculation tasks, the number of vehicles is N, and the tasks are W types in total. The density of vehicles in the area and the speed of travel are random to simulate the reality of vehicle travel in a city. In this area, the MeNB collects information and makes decisions. The time slot system is considered in the present embodiment.
1. Cache update policy
In the present invention, an MEC server can only be used to process the corresponding task if the corresponding content is already cached on the MEC server. Assume that the MeNB caches all content and keeps updating to ensure that the content is fresh and fresh here. UAV and vehicle have limited cache capacity. The buffer update process may be completed within one time slot. Consider T slots.
Definition of
Figure BDA0003545206930000081
And
Figure BDA0003545206930000082
indicating whether vehicle i and UAV j have cache content w in time slot t,
Figure BDA0003545206930000083
and
Figure BDA0003545206930000084
with a buffer w, otherwise 0, the buffer capacity of the vehicle and UAV is expressed as:
Figure BDA0003545206930000085
Figure BDA0003545206930000086
wherein the content of the first and second substances,
Figure BDA0003545206930000087
and
Figure BDA0003545206930000088
representing buffer capacity, l, of vehicle i and UAV jwIndicating the amount of data of the content w.
Definition of
Figure BDA0003545206930000089
And
Figure BDA00035452069300000810
two binary variables, representing whether the vehicle i and the UAV j update or replace the buffer w at the time slot t,
Figure BDA00035452069300000811
and
Figure BDA00035452069300000812
indicating that the buffer w is updated or replaced at time slot t, otherwise it is 0. Due to the limited buffer capacity of the vehicle and the UAV, the obsolete buffer data will be updated to an updated version or replaced with other content. Obviously, if the vehicle or UAV does not cache w before the time slot t, the cached data w cannot be updated in the time slot t, so we can get:
Figure BDA0003545206930000091
similarly, for UAV j, we can get:
Figure BDA0003545206930000092
in the invention, the information timeliness of the calculation result is related to the timeliness of a cache model used for calculating the vehicle task and the transmission and calculation processes.
Figure BDA0003545206930000093
And
Figure BDA0003545206930000094
the timeliness, i.e. freshness, of the cached content of the vehicle and UAV, respectively. It is assumed that when the content w is replaced with other kinds of cache data for a certain time, the freshness is set to an infinite number I. So we can get:
Figure BDA0003545206930000095
Figure BDA0003545206930000096
2. compute offload policy
Once the vehicle has generated the computing task, it sends a resource access request to the MeNB, the request including { e ] for task wi(t)，sw，zwInformation such as driving direction, driving speed, current position, etc. e.g. of the typei(t) indicates the type of task of vehicle i in time slot t, swRepresents the size of the task w, zwRepresenting the CPU period required by the calculation of the secondary task; after the MeNB collects this information, the MeNB makes decisions on the handling of the vehicle i task, to use
Figure BDA0003545206930000097
Indicating whether the task is processed locally, by
Figure BDA0003545206930000098
Expressed in several ways:
1) local calculation:
Figure BDA0003545206930000099
2) unloading to a UAV:
Figure BDA00035452069300000910
3) offloading to MeNB:
Figure BDA00035452069300000911
4) temporarily, not calculating:
Figure BDA00035452069300000912
then there are:
Figure BDA0003545206930000101
2.1 local calculation:
by using
Figure BDA0003545206930000102
The calculation capability of the vehicle i is shown, and the local calculation time delay of the vehicle i is shown as follows:
Figure BDA0003545206930000103
the corresponding energy consumption is as follows:
Figure BDA0003545206930000104
weight factor muiRepresenting the energy consumption required by the CPU of vehicle i per cycle calculation, there are:
Figure BDA0003545206930000105
the delay of the local computation is limited in this study to not exceed one slot τ. Comprises the following steps:
Figure BDA0003545206930000106
2.2 task offloading
In case of task offloading, the MEC server in the UAV or MeNB performs the computational task.
(1) Unloading to a UAV:
when a vehicle transmits a computational task to a UAV, other UAVs are interfered with by the transmission process. Thus, the transmission rate between vehicle i and UAV j may be expressed as:
Figure BDA0003545206930000107
wherein, bi，j(t) is the bandwidth of the vehicle i in communication with UAV j, MeNB, p is the transmit power the vehicle will be tasked with, βi，j(t) is the channel gain, σ, for vehicle i in communication with UAV j2Is the power spectral density of white noise.
The channel gain between vehicle i and UAV j may be expressed as:
Figure BDA0003545206930000108
wherein d isi，j(t) represents the distance between vehicle i and UAV j. Establishing a coordinate axis on the region
Figure BDA0003545206930000109
To represent the x, y coordinates of the position of the vehicle, UAV, and the velocity of the vehicle and UAV as
Figure BDA00035452069300001010
The distance between the directional vehicle i and UAV j, with positive and negative indicating travel, is expressed as:
Figure BDA0003545206930000111
if the mission of vehicle i is offloaded to UAV j at time t, i must be within the coverage of UAV j in one time slot, i.e., the connection distance between vehicle i and UAV j must be within one time slot, i.e., the next time slot starts at a time greater than the communication radius R of UAV, then there are:
Figure BDA0003545206930000112
(2) offloading to MeNB:
transmission rate between vehicle i and MeNB:
Figure BDA0003545206930000113
wherein, bi，M+1(t) bandwidth when vehicle i communicates with MeNB, p transmission power when vehicle i transmits tasks to MeNB, gi，M+1(t) is the square of the average channel gain between vehicle i and MeNB.
The time when vehicle i transmits task w to UAV j or MeNB may be expressed as:
Figure BDA0003545206930000114
the time delay for computing the mission of vehicle i by the MEC server in UAV and MeNB may be expressed as:
Figure BDA0003545206930000115
wherein the content of the first and second substances,
Figure BDA0003545206930000116
indicating the computing resources allocated in MEC server j in time slot t by vehicle i. Thus, the total time delay to unload is:
Figure BDA0003545206930000117
the offload delay specification is limited to one time slot:
Figure BDA0003545206930000118
energy consumption during unloading:
Figure BDA0003545206930000121
definition of
Figure BDA0003545206930000122
Indicating the freshness of the status update of the vehicle i, i.e., the age of the status update, at the time slot t. By using
Figure BDA0003545206930000123
Represents the time when task w was generated, so the age of the state update can be expressed as:
Figure BDA0003545206930000124
provision not to exceed threshold Ath：
Figure BDA0003545206930000125
3. System object
The energy consumption of the system comes from two aspects: on the one hand, the updating of the cache contents and, on the other hand, the processing of the vehicle tasks. Buffering the energy consumption xi (t) generated by the update in the time slot t:
Figure BDA0003545206930000126
wherein, θ (J/bit) is a weighting factor for converting the amount of the cache update data into energy, and represents the energy required to be consumed by caching 1bit of data.
Energy consumption E generated by task processing in time slot ti(t))：
Figure BDA0003545206930000127
The energy consumption phi (t) of the system in time slot t due to task transmission is represented as:
Figure BDA0003545206930000128
in summary, the system consumes the total energy in the time slot t
Figure BDA0003545206930000129
Figure BDA00035452069300001210
The aim of the invention is to minimize the system energy consumption of T time slots while satisfying various constraints, and this optimization problem can be expressed as:
Figure BDA00035452069300001211
s.t. status update age limit: (3)(4)(5)(6)(22)(23)
And (4) cache limitation: (1)(2)(24)
Task processing constraints: (7) - (21)(25)(26)(27)
4. Improved depth deterministic strategy gradient algorithm
In the system object of the present invention,
Figure BDA0003545206930000131
and
Figure BDA0003545206930000132
is a discrete variable, bandwidth association bi，j(t) is a continuous variable. The problem is complex, belongs to the category of MINLP, and is difficult to deal with. Aiming at the problems of continuous generation of tasks, high maneuverability and fast change of channel conditions in the Internet of vehicles environment, a fast decision algorithm based on deep reinforcement learning is proposed.
Therefore, the present invention converts the problem to be solved by the system target into an MDP problem, which is defined by a tuple { Sc, Ac, Tc, Rc }, where Sc is a set of system states, Ac is a set of system actions, and T is a set of system actionsc＝{p(sc′|sc，ac) Is a set of transition probabilities, and Rc：Sc×Ac→RcIs a reward function. Strategy pi is the Sc to Sc mapping, so the MDP problem is defined as follows:
(1) state space: in the time slot t, a set of system states is defined as coordinates of the unmanned aerial vehicle and the vehicle, a task request type of each vehicle, cache states of the unmanned aerial vehicle and the vehicle, and an information age of a first task to be processed of the vehicle.
(2) An action space: in the time slot t, the action space of the system is the association condition of the vehicle and each cache node, and whether the cache is updated or not with the existing cache.
(3) The reward function: the reward function is set as the sum of the energy consumption and penalty functions of the system. The penalty function is to prevent the size of the buffered data from exceeding the buffer capacity and if it is determined that the vehicle task is offloaded to the drone, the vehicle exceeds a time slot within the communication range of the drone.
Due to the randomness of the system, the state transition probability is difficult to model. Therefore, we use a model-free reinforcement learning algorithm based on a Deep Deterministic Policy Gradient (DDPG) algorithm to learn and update the computational resource allocation strategy. Unlike the conventional DDPG algorithm which randomly samples data during training without considering data quality, in the present invention, the explored experience is stored in different experience buffer pools according to the quality difference. Then, the poor experience and the good experience are randomly selected in proportion within a certain step number, the correlation among data is eliminated, the stability of neural network training is improved, and oscillation is reduced.
In the method, firstly, in order to achieve the aim of comprehensively ensuring the safety of automatic driving, the freshness of a cache model used for calculation and the freshness of a calculation unloading process are simultaneously considered and used as the information freshness of a calculation result obtained by a vehicle. This consideration is more realistic than previous studies.
Second, in an autonomous driving scenario, the vehicle needs to frequently interact with the external environment to sense the external environment. Therefore, the invention mainly aims at the problems of high requirements on safety and timeliness, high vehicle mobility, dense tasks, fast network topology change and high system overhead in a cache-assisted edge computing architecture and an automatic driving scene, and designs a strategy for minimizing system energy consumption and ensuring information timeliness by making decisions on cache updating, user association and resource allocation in a dynamic environment by combining three technologies of vehicle edge computing, caching and unmanned aerial vehicles.
Finally, the problem exists in the form of mixed integer nonlinear programming (MINLP), which is a troublesome problem. Therefore, the method uses a deep reinforcement learning algorithm, adopts an algorithm based on a deep certainty strategy gradient, divides the experience cache pools, and selects the experience training neural network from two different cache pools in proportion, thereby accelerating the convergence speed, reducing the reward oscillation after the convergence, improving the reward value after the convergence and improving the algorithm performance.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: it is to be understood that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof, but such modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A state updating method for efficient caching and task unloading in an unmanned aerial vehicle-assisted Internet of vehicles is characterized in that a system model comprises a macro base station provided with an MEC server, an unmanned aerial vehicle with computing capability and a plurality of vehicles provided with computing capability, and the method comprises the following steps:
s1, calculating the freshness of the cache model and the freshness of the unloading process as the freshness of the vehicle state update;
s2, minimizing the total energy consumption of the system in the time slot t, wherein the total energy consumption comprises the energy consumption generated by updating the cache content in the time slot t and the energy consumption generated by processing the vehicle task in the time slot t;
s3, dividing the experience cache pool by an algorithm based on the depth certainty strategy gradient, and selecting an experience training neural network from two different cache pools according to the proportion.
2. The method for updating the state of efficient caching and task offloading in the network of unmanned aerial vehicles as claimed in claim 1, wherein the step S1 is a method for calculating freshness of the cache model:
definition of
Figure FDA0003545206920000011
And
Figure FDA0003545206920000012
indicating whether vehicle i and UAV j have cache content w in time slot t,
Figure FDA0003545206920000013
and
Figure FDA0003545206920000014
indicating that there is a cachew, otherwise 0, the buffer capacity of the vehicle and UAV is expressed as:
Figure FDA0003545206920000015
Figure FDA0003545206920000016
wherein the content of the first and second substances,
Figure FDA0003545206920000017
and
Figure FDA0003545206920000018
representing buffer capacity, l, of vehicle i and UAV jwA data amount indicating the content w;
definition of
Figure FDA0003545206920000019
And
Figure FDA00035452069200000110
two binary variables, representing whether the vehicle i and the UAV j update or replace the buffer w at the time slot t,
Figure FDA00035452069200000111
and
Figure FDA00035452069200000112
indicating that the cache w is updated or replaced at the time slot t, and otherwise, the cache w is 0; for vehicle i:
Figure FDA00035452069200000113
for UAV j:
Figure FDA00035452069200000114
Figure FDA00035452069200000115
and
Figure FDA00035452069200000116
respectively representing the timeliness, namely the freshness of the cache, of the cache contents of the vehicle and the UAV; assuming that when the content w is replaced by other kinds of cache data for a certain time, the freshness is set to an infinite number I, we get:
Figure FDA0003545206920000021
Figure FDA0003545206920000022
3. the method for updating the status of efficient caching and task offloading in the drone-assisted internet of vehicles according to claim 1, wherein the method for calculating the freshness of the offloading process in step S1 is:
the vehicle generates a calculation task and sends a resource access request to the MeNB, wherein the request comprises { e ] of the task wi(t)，sw，zw-direction of travel, -speed of travel, -current position information, where ei(t) indicates the type of task of vehicle i in time slot t, swIndicates the size of the task w, zwRepresenting the CPU period required by the calculation of the secondary task w; after the MeNB collects this information, the MeNB makes decisions on the handling of the vehicle i task, to use
Figure FDA0003545206920000023
Indicating whether the task is processed locally, by
Figure FDA0003545206920000024
Expressed in several ways:
1) local calculation:
Figure FDA0003545206920000025
2) unloading to a UAV:
Figure FDA0003545206920000026
3) offloading to MeNB:
Figure FDA0003545206920000027
4) temporarily, not calculating:
Figure FDA0003545206920000028
then there are:
Figure FDA0003545206920000029
4. the method for efficiently caching and task-off status updates in the internet of vehicles assisted by drones as claimed in claim 3, wherein the local computation is performed by
Figure FDA00035452069200000210
The calculation capability of the vehicle i is shown, and the local calculation time delay of the vehicle i is shown as follows:
Figure FDA00035452069200000211
the corresponding energy consumption is expressed as:
Figure FDA00035452069200000212
weight factor muiRepresents the energy consumption required by the CPU of vehicle i per cycle calculation, expressed as:
Figure FDA00035452069200000213
the locally computed delay does not exceed one slot τ, with:
Figure FDA0003545206920000031
5. the method for efficiently caching and task off-loading in an unmanned aerial vehicle-assisted internet of vehicles according to claim 3, wherein the transfer rate between vehicle i and UAV j when off-loading to UAV is expressed as:
Figure FDA0003545206920000032
wherein, bi，j(t) is the bandwidth of the vehicle i when communicating with UAV j, MeNB, p is the vehicle transmit power to mission, βi，j(t) is the channel gain, σ, for vehicle i in communication with UAV j2Is the power spectral density of white noise;
the channel gain between vehicle i and UAV j is expressed as:
Figure FDA0003545206920000033
wherein d isi，j(t) represents the distance between vehicle i and UAV j;
establishing a coordinate axis on the region
Figure FDA0003545206920000034
To represent the x, y coordinates of the position of the vehicle, UAV, the speed of the vehicle and UAVIs composed of
Figure FDA0003545206920000035
The distance between the directional vehicle i and UAV j, with positive and negative indicating travel, is expressed as:
Figure FDA0003545206920000036
if the mission of vehicle i is offloaded to UAV j at time t, i must be within the coverage of UAV j in one time slot, i.e., the connection distance between vehicle i and UAV j must be within one time slot, i.e., the next time slot starts at a time greater than the communication radius R of UAV, then there are:
Figure FDA0003545206920000037
r represents the communications coverage radius of the UAV.
6. The method for status update of efficient caching and task offloading in unmanned aerial vehicle-assisted internet of vehicles according to claim 3, wherein when offloading to MeNB, the transfer rate between vehicle i and MeNB is:
Figure FDA0003545206920000038
wherein, bi，M+1(t) is the bandwidth of the communication between vehicle i and MeNB, gi，M+1(t) is the square of the average channel gain between vehicle i and MeNB, gi，M+1(t) is the channel gain between vehicle i and MeNB:
the time at which vehicle i transmits task w to UAV j or MeNB is expressed as:
Figure FDA0003545206920000041
the time delay for computing the mission of vehicle i by MEC server in UAV and MeNB is expressed as:
Figure FDA0003545206920000042
wherein the content of the first and second substances,
Figure FDA0003545206920000043
representing the computing resources allocated by the vehicle i in the MEC server j in the time slot t;
the total time delay for unloading is:
Figure FDA0003545206920000044
the offload delay specification is limited to one time slot:
Figure FDA0003545206920000045
energy consumption during unloading:
Figure FDA0003545206920000046
definition of
Figure FDA0003545206920000047
Indicating the freshness of the status update of the vehicle i, i.e. the age of the status update, at the time slot t
Figure FDA0003545206920000048
Representing the time when task w is generated, the age of the state update is represented as:
Figure FDA0003545206920000049
provision not to exceed threshold Ath：
Figure FDA00035452069200000410
7. The status updating method for efficient caching and task unloading in unmanned aerial vehicle-assisted internet of vehicles according to claim 1, wherein energy consumption ξ (t) generated by caching updating in time slot t is expressed as:
Figure FDA00035452069200000411
wherein, θ (J/bit) is a weight factor for converting the cache updating data amount into energy, and represents the energy consumed by caching 1bit of data;
energy consumption E caused by task processing in time slot ti(t) is expressed as:
Figure FDA0003545206920000051
the energy consumption phi (t) of the system in time slot t due to task transmission is represented as:
Figure FDA0003545206920000052
total energy consumption of system in time slot t
Figure FDA0003545206920000053
Expressed as:
Figure FDA0003545206920000054
8. the method for updating status of efficient caching and task offloading in unmanned aerial vehicle-assisted internet of vehicles according to claim 1, wherein step S2 minimizes system energy consumption for T timeslots, and the optimization problem is expressed as:
Figure FDA0003545206920000055
9. the method for updating the status of efficient caching and task offloading in the unmanned aerial vehicle-assisted internet of vehicles according to claim 1, wherein in step S3, the explored experience is stored in different experience buffer pools according to different qualities, and the buffer pool division method comprises: finding out a threshold value for dividing better experience and worse experience, and dividing the experience cache pool into a better experience pool and a worse experience pool through the threshold value.
10. The UAV-assisted IOV state updating method of claim 1, wherein in step S3, the minimization of step S2 is converted into an MDP problem, where a tuple defines { Sc, Ac, Tc, Rc }, where Sc is a set of system states, Ac system actions, T _ Systeme _ action, and T _ Systeme _ actionc＝{p(sc′|sc，ac) Is the set of transition probabilities, Rc：Sc×Ac→RcIs the reward function, strategy pi is the Sc to Sc mapping, and the MDP problem is defined as follows:
state space: in the time slot t, defining a set of system states as coordinates of the unmanned aerial vehicle and the vehicle, a task request type of each vehicle, cache states of the unmanned aerial vehicle and the vehicle, and an information age of a first task to be processed of the vehicle;
an action space: in the time slot t, the action space of the system is the correlation condition of the vehicle and each cache node, and whether the cache is updated or not with the existing cache;
the reward function: the reward function is set as the sum of the energy consumption and penalty functions of the system.
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CN115037751A (en) * 2022-06-28 2022-09-09 东南大学深圳研究院 Unmanned aerial vehicle-assisted heterogeneous Internet of vehicles task migration and resource allocation method
CN115052262A (en) * 2022-06-22 2022-09-13 东南大学深圳研究院 Potential game-based vehicle networking computing unloading and power optimization method
CN115484568A (en) * 2022-08-12 2022-12-16 北京邮电大学 Cache data transmission method and device, electronic equipment and medium

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CN115052262A (en) * 2022-06-22 2022-09-13 东南大学深圳研究院 Potential game-based vehicle networking computing unloading and power optimization method
CN115037751A (en) * 2022-06-28 2022-09-09 东南大学深圳研究院 Unmanned aerial vehicle-assisted heterogeneous Internet of vehicles task migration and resource allocation method
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