CN111314889B - Task unloading and resource allocation method based on mobile edge calculation in Internet of vehicles - Google Patents

Task unloading and resource allocation method based on mobile edge calculation in Internet of vehicles Download PDF

Info

Publication number
CN111314889B
CN111314889B CN202010118884.6A CN202010118884A CN111314889B CN 111314889 B CN111314889 B CN 111314889B CN 202010118884 A CN202010118884 A CN 202010118884A CN 111314889 B CN111314889 B CN 111314889B
Authority
CN
China
Prior art keywords
node
vehicle
task
nodes
user
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010118884.6A
Other languages
Chinese (zh)
Other versions
CN111314889A (en
Inventor
胡斌杰
孙亦婵
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
South China University of Technology SCUT
Original Assignee
South China University of Technology SCUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by South China University of Technology SCUT filed Critical South China University of Technology SCUT
Priority to CN202010118884.6A priority Critical patent/CN111314889B/en
Publication of CN111314889A publication Critical patent/CN111314889A/en
Application granted granted Critical
Publication of CN111314889B publication Critical patent/CN111314889B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0453—Resources in frequency domain, e.g. a carrier in FDMA
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/54—Allocation or scheduling criteria for wireless resources based on quality criteria
    • 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
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
    • 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 discloses a task unloading and resource allocation method based on mobile edge calculation in the Internet of vehicles, which comprises the following specific steps: establishing a vehicle networking communication scene comprising vehicle-to-vehicle V2V and vehicle-to-infrastructure V2I communication; clustering vehicle nodes in a scene, and dividing the vehicle nodes into a V2I user cluster and a V2V user cluster; aiming at a V2V user cluster in a scene, dividing, pairing and optimizing a V2V request node and a service node in the V2V user cluster; calculating the total delay of task processing of all nodes in a scene; and establishing an optimization problem model by taking the minimization of the total delay of vehicle task processing in the vehicle networking system as a target and combining constraint conditions, and solving the optimization problem model by using a quantum particle group algorithm to obtain a channel and calculation resource allocation and each vehicle node power allocation strategy of the vehicle networking system. The invention solves the problem of task unloading and resource allocation based on MEC in the car networking environment with lower complexity.

Description

Task unloading and resource allocation method based on mobile edge calculation in Internet of vehicles
Technical Field
The invention relates to the technical field of task unloading and resource allocation problems of mobile edge computing of an internet of vehicles, in particular to a task unloading and resource allocation method based on mobile edge computing in the internet of vehicles.
Background
With the continuous development of mobile network technology, people are continuously and deeply researching vehicle-mounted communication. By utilizing the wireless communication service with high reliability and low delay provided by the vehicle networking (V2X), the road safety condition can be improved, and the driving experience is improved. A common car networking system includes information exchange paths such as a car-facility (V2I) and a car-car (V2V). Vehicle request tasks can be divided into a business entertainment category and a security category. The merchant entertainment request involves a large data exchange; security class requests have high latency requirements. Vehicle-mounted units have limited computing resources and computing power, and multi-access edge computing (MEC) is used as a new computing paradigm to provide computing services at the edge of a network, closer to end users, and helpful to reduce equipment cost and improve service quality. By unloading the tasks to other resource nodes for processing, the sharing of the surrounding idle computing resources is realized, so that the resources in the network can be fully utilized, and the expenditure of the vehicle nodes can be reduced. In addition, because a large number of devices perform migration and offloading on the computation-intensive tasks, a large number of connections are urgently needed, V2V users in the cellular network can directly communicate with each other by multiplexing uplink channels of V2I users without passing through a base station, and the spectrum efficiency of the whole network is improved, so that the network is allowed to accept more users, and the problem of limited spectrum resources is solved to a certain extent.
The method has the advantages that the vehicle networking system is reasonably planned, tasks are unloaded to other resource nodes to be processed, frequency band resources and computing resources in the vehicle networking system are reasonably distributed, and the total delay of task processing of the vehicle networking system can be minimized on the premise of ensuring the service quality of the tasks.
In the prior art, the computing capability of a cellular network is enhanced by combining the D2D communication of the MEC, transmission delay and computing delay in various communication modes are computed through node parameters, and then a joint optimization problem of task offloading and power allocation is established according to resource limitations to obtain an optimal resource allocation scheme. In the research process of the technical scheme of the invention, at least, it is found that the prior art has the defect that the key problem of how the D2D user establishes the pairing in the D2D communication is not considered, and the default D2D pairing is determined.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a task unloading and resource allocation method based on mobile edge calculation in the internet of vehicles. Specifically, according to the time required by the vehicle node for processing the task and the communication channel capacity between the vehicle node and the base station, clustering the vehicle nodes in the scene by using Gaussian clustering; aiming at the V2V user, the V2V request user and the service user are paired by using a bipartite graph method; on the basis, an optimization problem is established to carry out joint optimization distribution on the user transmission power, the frequency spectrum and the computing resources, so that the total delay of the system is minimum.
The purpose of the invention can be achieved by adopting the following technical scheme:
a task unloading and resource allocation method based on mobile edge computing in Internet of vehicles comprises the following steps:
s1, constructing an application scene of a vehicle networking system with a base station, an edge computing server and vehicles, wherein the vehicles have V2V and V2I communication capacity, V2V represents vehicle-to-vehicle, V2I represents vehicle-to-infrastructure, a request node can unload tasks to nearby service nodes for processing through unloading, a part of the vehicles and the edge computing server are provided with limited channels and computing resources and can provide computing services for other vehicles, the vehicles are called service nodes, and vehicles providing task computing requests are called request nodes;
s2, according to the time required by the vehicle nodes to process self tasks in the application scene of the vehicle networking system and the communication channel capacity of the vehicle nodes and the base station, clustering the vehicle nodes in the application scene of the vehicle networking system by using a Gaussian mixture model, and dividing the vehicle nodes into V2V user clusters and V2I user clusters;
s3, aiming at the V2V user cluster in the scene, dividing, pairing and optimizing the V2V request node and the service node;
s4, calculating the total delay D of the task processing of the whole Internet of vehicles system aiming at the paired V2V pairs and V2I nodes total ；
S5, minimizing total task processing delay D of the Internet of vehicles system total And establishing an optimization problem model by combining constraint conditions, and solving by adopting a Quantum Particle Swarm Optimization (QPSO) algorithm to obtain an optimal solution of the frequency band, the calculation resource allocation and the power allocation of each vehicle node of the vehicle networking system so as to ensure that the total task processing delay of the vehicle networking system is minimum.
Further, the step S2 includes:
s2.1, according to the communication channel capacity of the vehicle nodes and the base station in the application scene of the vehicle networking system, quantifying the delay and the capacity of the vehicle nodes, wherein the process is as follows:
calculating the time required by the vehicle node to process the task of the vehicle node to form a set T = { T = } m L M belongs to M, M represents a set of vehicle nodes, and the task data volume of the mth vehicle node is V m Representing, unit task calculation amount by C m Indicating self-computing power by f m Indicates the time t required for the mth vehicle node to process its own task m The expression is:
Figure BDA0002392325920000031
calculating the channel capacity of the communication between the vehicle node and the base station to form a set gamma = { eta = [ (. Eta) } m L M belongs to M, and the communication channel capacity eta of the mth vehicle node and the base station m The expression is:
Figure BDA0002392325920000032
wherein the content of the first and second substances,
Figure BDA0002392325920000033
the signal to interference and noise ratio is the signal to interference and noise ratio when the mth vehicle node is communicated with the base station, and the expression is as follows:
Figure BDA0002392325920000034
in the above-mentioned formula, the compound has the following structure,
Figure BDA0002392325920000035
a transmit power for the mth vehicle node; LP (d) is the path loss of the channel between the mth vehicle node and the base station, in dB, relative to the distance d between the mth vehicle node and the base station, based on->
Figure BDA0002392325920000036
Represents the average power of the noise;
s2.2, clustering vehicle nodes in the application scene of the Internet of vehicles system by using a Gaussian mixture model, and dividing the vehicle nodes into a V2I user cluster and a V2V user cluster.
Further, the step S2.2 includes:
s2.2.1, establishing a new matrix B of M multiplied by 2 according to the calculation result in the step S2.1, wherein the first column element of the matrix B consists of a time set T required by M vehicles to process self tasks, the second column element of the matrix B consists of a channel capacity set gamma of M vehicles and base stations for communication, and a set X taking the row vector of the matrix B as an element is newly established;
s2.2.2, regarding all data elements in the set X as Gaussian mixture distribution of two models:
Figure BDA0002392325920000041
wherein the content of the first and second substances,
Figure BDA0002392325920000042
representing the probability density of the kth gaussian distribution,
Figure BDA0002392325920000043
x m representing the mth element in the set X, and the parameter to be solved is as follows: mean and variance of two Gaussian functions->
Figure BDA0002392325920000044
And pi 1 ，π 2 ；
S2.2.3, solving parameter values by adopting an Expectation-Maximization algorithm (EM algorithm): defining the number of components k =2, setting a parameter pi for each component k k ，
Figure BDA0002392325920000045
∑ k For data elements in set X, averaging and covariance to->
Figure BDA0002392325920000046
Beginning of sumAn initial value;
s2.2.4, calculating the posterior probability of the kth mixture according to the current parameter value:
Figure BDA0002392325920000047
in the above formula, t represents the current iteration number;
s2.2.5, using the value maximization likelihood function in step s 2.2.4:
posterior probability p (z) according to the kth mixture mk ) Finding the model parameters of a new iteration
Figure BDA0002392325920000048
Figure BDA0002392325920000051
Figure BDA0002392325920000052
Figure BDA0002392325920000053
The log-likelihood function of the gaussian mixture model is obtained as:
Figure BDA0002392325920000054
s2.2.6, judging whether the likelihood function is converged: if convergence occurs, the parameters are output
Figure BDA0002392325920000055
And pi 1 、π 2 (ii) a If not, returning to the step S2.2.3 to continue to execute until the convergence condition is met;
s2.2.7, obtaining the category of each node by using a Bayes discrimination criterion based on the minimum error probability, wherein according to a Bayes formula, the posterior probability formula of the mth node relative to the kth Gaussian mixture distribution model is as follows:
Figure BDA0002392325920000056
wherein, the Bayesian discriminant criterion based on the minimum error rate is as follows:
a) If p (mu) 1 ,∑ 1 |x m )>p(μ 2 ,∑ 2 |x m ) Then, node x is determined m A first offloading scheme should be selected;
b) If p (mu) 1 ,∑ 1 |x m )≤p(μ 2 ,∑ 2 |x m ) Then, node x is determined m A second offloading scheme should be selected.
Further, the step S3 process is as follows:
establishing a set of the V2V users obtained by clustering in the step S2, and recording the set as
Figure BDA0002392325920000057
Figure BDA0002392325920000058
Total ^ in the scoring set>
Figure BDA0002392325920000059
An element; user y's task data volume V y Representing, unit task calculation amount by C y Representing self-computing power by f y And (4) showing. Sequencing the computing power of the V2V users, and calculating the time required by tasks of the vehicles by using vehicle nodes
Figure BDA00023923259200000510
Selecting t as a measure of the vehicle's own computing power y Maximum value->
Figure BDA0002392325920000061
Individual node is drawn intoV2V requests node set J, the remaining @inthe set>
Figure BDA0002392325920000062
Each node is divided into a V2V service node set K; is established as a weighted bipartite graph>
Figure BDA0002392325920000063
V2V request nodes as a set of vertices, based on a bipartite graph>
Figure BDA0002392325920000064
The V2V service node is used as the other vertex set of the bipartite graph, the weight of an edge between the two vertex sets represents the income obtained by establishing connection between the V2V request node and the service node, and the weight of the edge and the task data volume V of the V2V request node j Computing power f of V2V service node k And link quality between communicating nodes, the link quality taking into account the distance d between the nodes jk Thus define ξ jk Represents the weight of the edge in the weighted bipartite graph:
Figure BDA0002392325920000065
solving a maximum matching scheme of the weighted bipartite graph by using a Kuhn-Munkres algorithm to serve as a pairing scheme of the nodes of the V2V communication vehicle and the service node, and recording the scheme as mu = { mu = jk }，j∈J,k∈K。
Further, in step S4, a set of V2I request nodes is defined as I = {1, \8230, I, ·, I }, and a set of V2V request users is defined as J = {1, \8230, J }, and a set of V2V service users is defined as K = {1, \8230, K }, provided that the base station can obtain all channel state information, the step includes:
s4.1, calculating the signal-to-interference-and-noise ratio of the communication between the V2I node and the base station
Figure BDA0002392325920000066
Figure BDA0002392325920000067
Wherein beta is ij ∈{0,1}，β ij =1 denotes that V2V requesting node j multiplexes uplink channel of V2I node I, β ij =0 denotes that V2V requesting node j does not multiplex the upstream channels of V2I node I, each V2V requesting node multiplexes at most one channel, i.e. one channel is multiplexed
Figure BDA0002392325920000068
p i And p j Respectively representing the transmission power of a V2I request node I and a V2V request node j, and meeting the power peak value limit of a vehicle node, namely p i ，p j <p max ，/>
Figure BDA0002392325920000069
Represents the channel gain, Σ, of V2I requesting node I on subchannel n j∈J β ij p j |h j | 2 Represents the interference of V2V communication users to V2I users I, wherein | h j I denotes the channel gain on the sub-channel when V2V user j communicates, N 0 W is the power of additive white Gaussian noise, W is the bandwidth of the sub-channel; />
S4.2, calculating the signal-to-interference-and-noise ratio when the V2V request user j communicates with the service user K
Figure BDA0002392325920000071
Figure BDA0002392325920000072
Wherein the content of the first and second substances,
Figure BDA0002392325920000073
represents the interference of V2I users to V2V receiving end users during V2V communication, sigma j′∈J,j′≠j β ij′ p j′ |h j′ | 2 Representing the interference of other V2V users to V2V receiving end users when V2V communication is carried out;
s4.3, calculating the whole Internet of vehicles systemTotal delay of task processing D total The method consists of unloading delay of the V2I user and processing delay of the V2V user, and the calculation formula is as follows:
Figure BDA0002392325920000074
in the above formula, the first and second carbon atoms are,
Figure BDA0002392325920000075
representing the total delay of task processing of all V2I users, including transmission delay and computation delay, where V i Representing the amount of task data, C, of a V2I user I i Denotes the number of CPU cycles required to calculate 1bit data, f i e Representing the computing resources allocated by the edge server to each V2I user;
Figure BDA0002392325920000076
the task processing total delay of all the V2V paired users is represented, and the task processing total delay comprises the transmission delay of a V2V request user and the calculation delay of a V2V service user on the task and the request task; />
Figure BDA0002392325920000077
Latency of local task computation for V2V users representing unsuccessful pairing, where V j Represents the task data volume, C, of the V2V requesting user j j 、C k Respectively representing the number of CPU cycles required to calculate 1bit tasks from a V2V requesting user and a serving user, f j 、f k Respectively representing the computing power, mu, of a V2V requesting user and a service user jk Indicating a V2V pairing strategy.
Further, in the step S5, the total delay D of task processing of the car networking system is minimized total To target, the optimization problem model is built as follows:
Figure BDA0002392325920000081
C1:β ij ∈{0,1}，
Figure BDA0002392325920000082
C2:
Figure BDA0002392325920000083
C3:
Figure BDA0002392325920000084
C4:
Figure BDA0002392325920000085
C5:γ i c ≥γ i th ，
Figure BDA0002392325920000086
C6:
Figure BDA0002392325920000087
in the above constraint, C1 represents β ij ，μ jk Can only take on values of 0 or 1,
Figure BDA0002392325920000088
indicating the multiplexing situation of the sub-channels, mu jk Representing the pairing condition of the V2V request node and the service node, C2 representing that each V2V request node multiplexes at most one channel, C3 representing the power limit of each V2I node and the V2V pairing user sending node, C4 representing that the sum of the computing resources distributed to all V2I vehicles by the edge server is smaller than the total computing resource of the edge server, and C5 representing that the signal-to-interference-and-noise ratio of each V2I node and the base station for communication is larger than a threshold value gamma i th And C6 indicates that the signal to interference and noise ratio of each V2V user for communication should be greater than the threshold value->
Figure BDA0002392325920000089
Compared with the prior art, the invention has the following advantages and effects:
1) In the task unloading and resource allocation method based on mobile edge calculation in the Internet of vehicles, the V2V user reuses an uplink channel of the V2I user, so that the frequency spectrum utilization rate of the Internet of vehicles system is improved;
2) The invention discloses a task unloading and resource allocation method based on mobile edge computing in the Internet of vehicles, which considers the key problem of how to establish pairing for V2V users in V2V communication;
3) The invention discloses a task unloading and resource allocation method based on mobile edge computing in an internet of vehicles, which is characterized in that the task unloading and resource allocation method is divided into three steps, namely clustering, pairing and solving, and suboptimal solution of an internet of vehicles system is obtained under lower computing difficulty and complexity.
Drawings
FIG. 1 is a flowchart of a method for offloading and allocating resources for a vehicle networking task disclosed in an embodiment of the invention;
FIG. 2 is a schematic diagram of a scenario disclosed in an embodiment of the present invention;
fig. 3 is a flowchart of a pairing method for a V2V communication vehicle node and a service node in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Examples
As shown in fig. 1, the basic embodiment of the present invention discloses a method for task offloading and resource allocation based on mobile edge computing in an internet of vehicles, and the method for task offloading and resource allocation in an internet of vehicles based on mobile edge computing is divided into three main steps of clustering, pairing and solving. The method comprises the steps of pairing and clustering, and determines the task unloading mode of each vehicle, which enables the total unloading delay to be minimum, by comprehensively considering factors such as the task data volume and the calculating capacity of each vehicle in the vehicle networking system based on the mobile edge calculation, the distance between the vehicle-infrastructure (V2I) and the vehicle-vehicle (V2V) and the like. The method comprises the following specific steps:
s1, constructing an application scene of a vehicle networking system with a base station, an edge computing server and vehicles, wherein the vehicles have V2V and V2I communication capabilities, V2V represents vehicles to vehicles, V2I represents vehicles to infrastructure, a request node can unload tasks to nearby service nodes for processing through unloading, a part of vehicles and the edge computing server are provided with limited channels and computing resources and can provide computing services for other vehicles, the service nodes are called, and vehicles for providing task computing requests are called as request nodes;
considering a V2X network scenario with a base station and vehicle nodes, the MEC server is deployed on the base station side, and the car networking system model is shown in fig. 2. In the scene, 1 base station and M densely deployed vehicle nodes are arranged, each vehicle node is provided with a calculation task, and the attribute of the calculation task of the mth node is V m ，C m ，T m Is shown in which V m (unit: bits) represents the size of the calculation task, C m (the unit is: cycles/bit) represents the number of CPU cycles required to calculate 1-bit data, T m Representing the maximum processing delay of the computing task of the mth vehicle node. All vehicles are equipped with a single antenna, so the vehicle can choose to offload computing tasks to the MEC server through V2I communication, or to idle vehicles through V2V communication. Therefore, the vehicle nodes in the scene are divided into three categories: wherein the node that offloads the computing task to the MEC server is a V2I request node; the node for unloading the calculation task to other vehicle nodes is a V2V request node; what is accepted and processed from the other nodes is an idle V2V service node.
S2, according to the time required by the vehicle nodes to process self tasks in the application scene of the vehicle networking system and the communication channel capacity of the vehicle nodes and the base station, clustering the vehicle nodes in the application scene of the vehicle networking system by using a Gaussian mixture model, and dividing the vehicle nodes into a V2V user cluster and a V2I user cluster, wherein the method comprises the following specific steps:
s2.1, according to the channel capacity of communication between the vehicle node and the base station in the scene, the delay and the capacity of the vehicle node are quantized.
Calculating the time required by the vehicle node to process the task per se to form a set T = { T = { (T) } m And | M ∈ M }, wherein M represents the set of vehicle nodes. Suppose that the mth vehicle node has its own computing power f m (in cycle/s), the time t required for the mth vehicle node to process its own task m The expression is:
Figure BDA0002392325920000111
calculating the channel capacity of the communication between the vehicle node and the base station to form a set gamma = { eta = [ (. Eta) } m And | M belongs to M }. Channel capacity η for communication of mth vehicle node with base station m The expression is:
Figure BDA0002392325920000112
wherein the content of the first and second substances,
Figure BDA0002392325920000113
the signal-to-interference-and-noise ratio when the mth vehicle node communicates with the base station is represented by the following expression:
Figure BDA0002392325920000114
in the above formula, the first and second carbon atoms are,
Figure BDA0002392325920000115
a transmit power for the mth vehicle node; LP (d) (in dB) is the path loss of the channel between the mth vehicle node and the base station, related to the distance d between the mth vehicle node and the base station; />
Figure BDA0002392325920000116
Representing the average power of the noise.
S2.2, clustering vehicle nodes in the application scene of the Internet of vehicles system by using a Gaussian mixture model, and dividing the vehicle nodes into a V2I user cluster and a V2V user cluster. The method comprises the following specific steps:
s2.2.1, establishing a new matrix B of M multiplied by 2 according to the calculation result in the step S2.1, wherein the first row element of the matrix B consists of a time set T required by M vehicles to process tasks of the vehicles, the second row element of the matrix B consists of a channel capacity set gamma of the M vehicles and the base station, and a set X taking row vectors of the matrix B as elements is newly established;
s2.2.2, regarding all data elements in the set X as Gaussian mixture distribution of two models:
Figure BDA0002392325920000117
wherein the content of the first and second substances,
Figure BDA0002392325920000118
Figure BDA0002392325920000119
x m representing the mth element in the set X. The parameters to be solved are: mean and variance of two Gaussian functions
Figure BDA00023923259200001110
And pi 1 ，π 2 ；
S2.2.3, solving parameter values by adopting an Expectation-Maximization algorithm (EM algorithm): defining the number of components k =2, setting a parameter pi for each component k k ，
Figure BDA0002392325920000121
∑ k For data elements in set X, averaging and covariance to->
Figure BDA0002392325920000122
And an initial value of Σ;
s2.2.4, calculating the posterior probability of the kth mixture according to the current parameter value:
Figure BDA0002392325920000123
in the above formula, t represents the current iteration number;
s2.2.5, maximizing the likelihood function using the values in step s 2.2.4:
posterior probability p (z) according to k-th mixture mk ) Finding the model parameters of a new iteration
Figure BDA0002392325920000124
Figure BDA0002392325920000125
/>
Figure BDA0002392325920000126
Figure BDA0002392325920000127
The log-likelihood function of the gaussian mixture model is obtained as:
Figure BDA0002392325920000128
s2.2.6, judging whether a likelihood function is converged: if converging, then the parameters are output
Figure BDA0002392325920000129
Figure BDA00023923259200001210
And pi 1 、π 2 (ii) a If not, returning to the step S2.2.3 to continue to execute until the convergence condition is met;
and S2.2.7, obtaining the category of each node by using a Bayesian discriminant criterion based on the minimum error probability. According to the Bayes formula, the posterior probability formula is as follows:
Figure BDA00023923259200001211
wherein, the Bayesian discriminant criterion based on the minimum error rate is as follows:
a) If p (mu) 1 ,∑ 1 |x m )>p(μ 2 ,∑ 2 |x m ) Then determine node x m A first offloading scheme should be selected;
b) If p (. Mu.) 1 ,∑ 1 |x m )≤p(μ 2 ,∑ 2 |x m ) Then determine node x m A second offloading scheme should be selected.
S3, aiming at the V2V user cluster in the scene, dividing, pairing and optimizing the V2V request node and the service node;
as shown in fig. 3, the specific steps are as follows:
establishing a set of the V2V users obtained by clustering in the step S2, and recording the set as
Figure BDA0002392325920000131
Figure BDA0002392325920000132
I.e. in total->
Figure BDA0002392325920000133
An element; sorting the calculation capability of the V2V user, and calculating the time required by the task by using the vehicle node>
Figure BDA0002392325920000134
As to vehicleMeasure of self-computing power, select t y Maximum value->
Figure BDA0002392325920000135
Individual nodes are classified into a V2V request node set J, with the remainder of the set ≧>
Figure BDA0002392325920000136
Each node is divided into a V2V service node set K; is established as a weighted bipartite graph>
Figure BDA0002392325920000137
A V2V request node as a set of vertices in a bipartite graph>
Figure BDA0002392325920000138
The V2V service node is used as the other vertex set of the bipartite graph, the weight of an edge between the two vertex sets represents the income obtained by establishing connection between the V2V request node and the service node, and the weight of the edge and the task data volume V of the V2V request node j Computing power f of V2V service node k And link quality between communicating nodes, the link quality being primarily a function of the distance d between the nodes jk Thus defining ξ jk To represent the weight of the edge in the weighted bipartite graph:
Figure BDA0002392325920000139
solving a maximum matching scheme of weighted bipartite graphs by using a bipartite graph maximum weight matching Algorithm-KM Algorithm (Kuhn-Munkres Algorithm) as a pairing scheme of nodes of a V2V communication vehicle and service nodes, and recording the scheme as mu = { mu = } jk }，j∈J,k∈K。
S4, calculating the total delay D of the task processing of the whole Internet of vehicles system aiming at the paired V2V pairs and V2I nodes total 。
The set of V2I request nodes is I = {1, \8230;, I, ·, I }; the set of V2V requesting users is J = {1, \8230;, J }; the set of V2V service users is K = {1, \8230;, K }. The base station can obtain all channel state information.
The method comprises the following specific steps:
s4.1, calculating the signal-to-interference-and-noise ratio of the communication between the V2I node and the base station
Figure BDA0002392325920000141
Figure BDA0002392325920000142
Wherein beta is ij ∈{0,1}，β ij =1 denotes that V2V requesting node j multiplexes uplink channel of V2I node I, β ij =0 denotes that V2V requesting node j does not multiplex the upstream channels of V2I node I, each V2V requesting node multiplexes at most one channel, i.e. one channel is multiplexed
Figure BDA0002392325920000143
p i And p j Representing the transmit power of the V2I requesting node I and the V2V requesting node j, respectively, which should both meet the power peak limit of the vehicle node, i.e., p i ，p j <p max ，/>
Figure BDA0002392325920000144
Represents the channel gain, Σ, of V2I requesting node I on subchannel n j∈J β ij p j |h j | 2 Represents the interference of V2V communication users to V2I users I, wherein | h j I denotes the channel gain on the sub-channel when V2V user j communicates, N 0 W is the power of additive white Gaussian noise, where W is the bandwidth of the subchannel;
s4.2, calculating the signal-to-interference-and-noise ratio when the V2V request user j communicates with the service user K
Figure BDA0002392325920000145
Figure BDA0002392325920000146
Wherein，
Figure BDA0002392325920000147
Represents the interference of V2I users to V2V receiving end users when in V2V communication, sigma j′∈J,j′≠j β ij′ p j′ |h j′ | 2 Representing the interference of other V2V users to V2V receiving end users when V2V communication is carried out;
s4.3, calculating the total delay D of the task processing of the whole Internet of vehicles system total The calculation formula is composed of the unloading delay of the V2I user and the processing delay of the V2V user, and is as follows:
Figure BDA0002392325920000151
in the above-mentioned formula, the compound has the following structure,
Figure BDA0002392325920000152
representing the total delay of task processing for all V2I users, including transmission delay and computation delay, where V i Representing the amount of task data, C, of a V2I user I i Denotes the number of CPU cycles required to calculate 1bit data, f i e Representing the computing resources allocated by the edge server to each V2I user;
Figure BDA0002392325920000153
the task processing total delay of all the V2V paired users is represented, and the task processing total delay comprises the transmission delay of a V2V request user and the calculation delay of a V2V service user on the task and the request task; />
Figure BDA0002392325920000154
Latency of local task computation for V2V users representing unsuccessful pairing, where V j Represents the task data volume, C, of the V2V requesting user j j 、C k Respectively representing the number of CPU cycles required to compute 1bit tasks from V2V requesting and serving users, f j 、f k Respectively representing the computing power, mu, of a V2V requesting subscriber and a service subscriber jk Indicating a V2V pairing strategy. Specially for treating diabetesOtherwise, the V2V service user can process the task from the V2V requesting user only after the task itself is processed.
S5, minimizing total delay D of task processing of the Internet of vehicles system total Aiming at the aim, an optimization problem model is established by combining constraint conditions, and a Quantum Particle Swarm Optimization (QPSO) algorithm is adopted for solving to obtain the optimal solution of the frequency band of the vehicle networking system, the calculation resource distribution and the power distribution of each vehicle node so as to ensure that the total delay of task processing of the vehicle networking system is minimum.
In the previous step, vehicle nodes are classified and paired to obtain a V2I user set I, a V2V request user set J and a V2V service user set K, and a V2V pairing strategy mu jk On the basis, through the channel, the calculation resource allocation and the power control, the total delay D of the task processing of the Internet of vehicles system is minimized total To target, the optimization problem model is built as follows:
Figure BDA0002392325920000161
C1:β ij ∈{0,1}，
Figure BDA0002392325920000162
C2:
Figure BDA0002392325920000163
C3:
Figure BDA0002392325920000164
C4:
Figure BDA0002392325920000165
C5:γ i c ≥γ i th ，
Figure BDA0002392325920000166
C6:
Figure BDA0002392325920000167
in the above constraint, C1 represents β ij ，μ jk Can only take the value of 0 or 1,
Figure BDA0002392325920000168
indicating the multiplexing of sub-channels, mu jk Representing the pairing situation of the V2V request node and the service node, C2 representing that each V2V request node multiplexes at most one channel, C3 representing the power limit of each V2I node and the V2V pairing user sending node, C4 representing that the sum of the computing resources distributed to all V2I vehicles by the edge server is smaller than the total computing resource of the edge server, and C5 representing that the signal-to-interference-and-noise ratio of each V2I node and the base station for communication is larger than a threshold value gamma i th And C6 indicates that the signal to interference and noise ratio of each V2V user for communication should be greater than the threshold value->
Figure BDA0002392325920000169
The optimization problem is a mixed integer programming problem, and the Quantum Particle Swarm Optimization (QPSO) algorithm is adopted to solve the optimization problem, so that a global suboptimal solution is obtained with low complexity. The specific solving process is as follows:
first, the optimization problem is to allocate N subchannels to all users, assuming that there are Q particles, and the position vector of each particle is composed of four parts, and the position vector of the qth particle is expressed as:
X q ＝(β 11 ,…,β IJ ，f 1 e ,…,f I e ,p 1 ,…,p I ，p 1 ,…,p J ) (16)
wherein the first part beta 11 ,…,β IJ Is an indication of the channel resource reuse status of a V2V communication user, a second part f 1 e ,…,f I e Is the computing resource that the edge server allocates to each V2I user, the third part p 1 ,…,p I Is sent by each V2I communication userA transmission power; fourth part p 1 ,…,p J Is the transmit power of the V2V communication sender. Introducing a penalty function, converting the original optimization problem with the constraint into an unconstrained optimization problem, wherein the converted new objective function is as follows:
Figure BDA0002392325920000171
wherein, f (. Beta.) is ij ,f i e ,p i ,p j ) Represents the objective function in the original question,
Figure BDA0002392325920000172
is a penalty factor, P f (β ij ,f i e ,p i ,p j ) Is a penalty function, which contains all the constraints in the original problem:
Figure BDA0002392325920000173
the QPSO algorithm is used to solve the above equation (15). First, the average best position C(s) of the particles is calculated, which is equal to the average of the best positions of all particles:
Figure BDA0002392325920000174
wherein s represents the number of iterations; q is the number of particles;
Figure BDA0002392325920000181
indicating the optimal position of the qth particle in the current iteration.
Then, the position of the particle is updated as follows:
Figure BDA0002392325920000182
wherein, P q (s) bits for the qth particleUpdating;
Figure BDA0002392325920000183
can be obtained by the following formula: />
Figure BDA0002392325920000184
G best (s) represents the global optimal positions of all particles in the current iteration by comparing the optimal positions of all particles
Figure BDA0002392325920000185
The fitness function of (a) obtains:
Figure BDA0002392325920000186
Figure BDA0002392325920000187
the update formula of the particle position is:
Figure BDA0002392325920000188
where s is the number of iterations, X q (s) denotes the position of the qth particle in the s-th iteration, ε represents the coefficient of contraction expansion, and is generally no greater than 1, u and r are random numbers uniformly distributed over (0, 1).
The QPSO algorithm has the following specific steps:
initializing the position X of each particle q (0) Maximum number of iterations S, at this time
Figure BDA0002392325920000189
Figure BDA00023923259200001810
According to the above equation (20), the global optimum G (0) is taken +>
Figure BDA00023923259200001811
Is preferred.
The number of iterations S starts from 0 and is performed for each particle from 1 to Q, as follows, until S = S-1, i.e. the number of iterations reaches the maximum number of iterations S;
c(s) and P are calculated according to the equations (19) and (20) q (s)；
Updating the position X of the particle according to equation (24) q (s)；
Updating according to the fitness function in equation (17)
Figure BDA0002392325920000191
Judgment of F [ X ] q (s)]And &>
Figure BDA0002392325920000192
If->
Figure BDA0002392325920000193
Then->
Figure BDA0002392325920000194
Figure BDA0002392325920000195
Otherwise
Figure BDA0002392325920000196
Updating G(s) according to equation (17): if it is not
Figure BDA0002392325920000197
Then G(s) = G (s-1), otherwise = R>
Figure BDA0002392325920000198
According to the formula (17), the corresponding value at the global optimal position is calculated, and the obtained result is the global suboptimal solution.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (3)

1. A task unloading and resource allocation method based on mobile edge computing in the Internet of vehicles is characterized by comprising the following steps:
s1, constructing an application scene of a vehicle networking system with a base station, an edge computing server and vehicles, wherein the vehicles have V2V and V2I communication capacity, V2V represents vehicle-to-vehicle, V2I represents vehicle-to-infrastructure, a request node can unload tasks to nearby service nodes for processing through unloading, a part of the vehicles and the edge computing server are provided with limited channels and computing resources and can provide computing services for other vehicles, the vehicles are called service nodes, and vehicles providing task computing requests are called request nodes;
s2, according to the time required by the vehicle nodes to process self tasks in the application scene of the vehicle networking system and the communication channel capacity of the vehicle nodes and the base station, clustering the vehicle nodes in the application scene of the vehicle networking system by using a Gaussian mixture model, and dividing the vehicle nodes into V2V user clusters and V2I user clusters;
s3, aiming at the V2V user cluster in the scene, dividing, matching and optimizing the V2V request node and the service node;
s4, calculating the total delay D of the task processing of the whole Internet of vehicles system aiming at the paired V2V pairs and V2I nodes total ；
S5, minimizing total task processing delay D of the Internet of vehicles system total For the target, an optimization problem model is established by combining constraint conditions, and a quantum particle swarm algorithm is adopted for solving to obtain the optimal solution of the frequency band of the Internet of vehicles system, the allocation of computing resources and the power allocation of each vehicle node so as to ensure that the total task processing delay of the Internet of vehicles system is minimum;
wherein, the step S2 comprises:
s2.1, according to the communication channel capacity of the vehicle nodes and the base station in the application scene of the vehicle networking system, quantifying the delay and the capacity of the vehicle nodes, wherein the process is as follows:
calculating the time required by the vehicle node to process the task per se to form a set T = { T = { (T) } m L M belongs to M, M represents a set of vehicle nodes, and the task data volume of the mth vehicle node is V m Representing, unit task calculation amount by C m Indicating self-computing power by f m Indicates the time t required by the mth vehicle node to process its own task m The expression is:
Figure FDA0004036200090000021
calculating the channel capacity of the communication between the vehicle node and the base station to form a set of Gamma = { eta = [ (+) m L M belongs to M, and the communication channel capacity eta of the mth vehicle node and the base station m The expression is:
Figure FDA0004036200090000022
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0004036200090000023
the signal-to-interference-and-noise ratio when the mth vehicle node communicates with the base station is represented by the following expression:
Figure FDA0004036200090000024
in the above-mentioned formula, the compound has the following structure,
Figure FDA0004036200090000025
a transmit power for the mth vehicle node; LP (d) is the path loss of the channel between the mth vehicle node and the base station, with LP (d) being expressed in units of dB, in relation to the distance d between the mth vehicle node and the base station, is/are>
Figure FDA0004036200090000026
Represents the average power of the noise;
s2.2, clustering vehicle nodes in an application scene of the Internet of vehicles system by using a Gaussian mixture model, and dividing the vehicle nodes into a V2I user cluster and a V2V user cluster;
wherein, the step S2.2 comprises:
s2.2.1, establishing a new matrix B of M multiplied by 2 according to the calculation result in the step S2.1, wherein the first row element of the matrix B consists of a time set T required by M vehicles to process tasks of the vehicles, the second row element of the matrix B consists of a channel capacity set gamma of the M vehicles and the base station, and a set X taking row vectors of the matrix B as elements is newly established;
s2.2.2, regarding all data elements in the set X as Gaussian mixture distribution of two models:
Figure FDA0004036200090000027
wherein the content of the first and second substances,
Figure FDA0004036200090000028
representing the probability density of the kth gaussian distribution,
Figure FDA0004036200090000031
x m representing the mth element in the set X, and the parameter to be solved is as follows: mean and variance of two Gaussian functions +>
Figure FDA0004036200090000032
And pi 1 ，π 2 ；
S2.2.3, solving parameter values by adopting a maximum expectation algorithm: defining the number of components k =2, setting a parameter pi for each component k k ，
Figure FDA0004036200090000033
∑ k Is calculated by averaging and covariance of the data elements in set X to ≥ v>
Figure FDA0004036200090000034
And an initial value of Σ;
s2.2.4, calculating the posterior probability of the kth mixture according to the current parameter value:
Figure FDA0004036200090000035
in the above formula, t represents the current iteration number;
s2.2.5, using the value maximization likelihood function in step s 2.2.4:
posterior probability p (z) according to the kth mixture mk ) Finding the model parameters of a new iteration
Figure FDA0004036200090000036
Figure FDA0004036200090000037
Figure FDA0004036200090000038
Figure FDA0004036200090000039
The log-likelihood function of the gaussian mixture model is obtained as follows:
Figure FDA00040362000900000310
s2.2.6, judgment is similar toHowever, whether the function converges: if converging, then the parameters are output
Figure FDA00040362000900000311
Figure FDA00040362000900000312
And pi 1 、π 2 (ii) a If not, returning to the step S2.2.3 to continue to execute until the convergence condition is met;
s2.2.7, obtaining the category of each node by using a Bayes judgment criterion based on the minimum error probability, wherein according to a Bayes formula, the posterior probability formula of the mth node about the kth Gaussian mixture distribution model is as follows:
Figure FDA0004036200090000041
wherein, the Bayesian discriminant criterion based on the minimum error probability is as follows:
a) If p (mu) 1 ，∑ 1 |x m )＞p(μ 2 ，∑ 2 |x m ) Then, node x is determined m A first offloading scheme should be selected;
b) If p (. Mu.) 1 ，∑ 1 |x m )≤p(μ 2 ，∑ 2 |x m ) Then determine node x m A second offloading scheme should be selected;
wherein, the step S3 process is as follows:
establishing a set of the V2V users obtained by clustering in the step S2, and recording the set as
Figure FDA0004036200090000042
Figure FDA0004036200090000043
Total ^ in the scoring set>
Figure FDA0004036200090000044
An element; user y's task data volume V y Representing, unit task calculation amount by C y Indicating self-computing power by f y Showing that the calculation capacity of the V2V user is sequenced, and the time required by the vehicle node for calculating the task is greater than or equal to>
Figure FDA0004036200090000045
Selecting t as a measure of the vehicle's own computing power y Maximum value->
Figure FDA0004036200090000046
Individual nodes are classified into a V2V request node set J, with the remainder of the set ≧>
Figure FDA0004036200090000047
Each node is divided into a V2V service node set K; is established as a weighted bipartite graph>
Figure FDA0004036200090000048
V2V request nodes as a set of vertices, based on a bipartite graph>
Figure FDA0004036200090000049
The V2V service node is used as another vertex set of the bipartite graph, the weight of an edge between the two vertex sets represents the profit obtained by establishing a connection between the V2V request node and the service node, and the weight of the edge and the task data volume V of the V2V request node j Computing power f of V2V service node k And link quality between communicating nodes, the link quality taking into account the distance d between the nodes jk Thus define ξ jk Represents the weight of the edge in the weighted bipartite graph:
Figure FDA0004036200090000051
solving the maximum matching scheme of weighted bipartite graph by using Kuhn-Munkres algorithm to serve as V2V communication vehicle node and servicePairing scheme of service nodes, denoted as μ = { μ = { [ mu ] jk }，j∈J，k∈K。
2. The method of claim 1, wherein in step S4, a set of V2I request nodes is defined as I = { 1.,. I }, a set of V2V request users is defined as J = { 1.,. J }, a set of V2V service users is defined as K = { 1.,. K }, and the step of assuming that all channel state information is available to the base station comprises:
s4.1, calculating the signal-to-interference-and-noise ratio of the communication between the V2I node and the base station
Figure FDA0004036200090000052
Figure FDA0004036200090000053
Wherein, beta ij ∈{0，1}，β ij =1 denotes that V2V requesting node j multiplexes uplink channel of V2I node I, β ij =0 means that V2V requesting node j does not multiplex uplink channels for V2I node I, each V2V requesting node multiplexes at most one channel, i.e.
Figure FDA0004036200090000054
p i And p j Respectively representing the transmission power of a V2I request node I and a V2V request node j, and meeting the power peak value limit of a vehicle node, namely p i ，p j ＜p max ，/>
Figure FDA0004036200090000055
Represents the channel gain, Σ, of the V2I requesting node I on subchannel n j∈J β ij p j |h j | 2 Represents the interference of V2V communication users to V2I users I, wherein | h j I denotes the channel gain on the sub-channel when V2V user j communicates, N 0 W is the power of additive white gaussian noise,w is the bandwidth of the subchannel;
s4.2, calculating the signal-to-interference-and-noise ratio when the V2V request user j communicates with the service user K
Figure FDA0004036200090000056
Figure FDA0004036200090000057
/>
Wherein the content of the first and second substances,
Figure FDA0004036200090000058
represents the interference of V2I users to V2V receiving end users when in V2V communication, sigma j，∈J，j′≠j β ij′ p j′ |h j′ | 2 Representing the interference of other V2V users to V2V receiving end users when V2V communication is carried out;
s4.3, calculating the total delay D of the task processing of the whole Internet of vehicles system total The method consists of unloading delay of the V2I user and processing delay of the V2V user, and the calculation formula is as follows:
Figure FDA0004036200090000061
in the above formula, the first and second carbon atoms are,
Figure FDA0004036200090000062
representing the total delay of task processing for all V2I users, including transmission delay and computation delay, where V i Representing the amount of task data, C, of V2I user I i Denotes the number of CPU cycles required to calculate 1bit data, f i e Representing the computing resources allocated by the edge server to each V2I user; />
Figure FDA0004036200090000063
Representing the total delay of task processing of all V2V paired users, including the transmission delay of a V2V requesting user and the pairing of a V2V service userDelay of calculation of the self task and the request task; />
Figure FDA0004036200090000064
Latency of local task computation for V2V users representing unsuccessful pairing, where V j Represents the task data volume, C, of the V2V requesting user j j 、C k Respectively representing the number of CPU cycles required to calculate 1bit tasks from a V2V requesting user and a serving user, f j 、f k Respectively representing the computing power, mu, of a V2V requesting user and a service user jk Indicating a V2V pairing strategy.
3. The method for task offloading and resource allocation based on mobile edge computing in internet of vehicles according to claim 1, wherein in step S5, the total delay D of task processing of the internet of vehicles system is minimized total To achieve this, an optimization problem model is built as follows:
Figure FDA0004036200090000071
C1：
Figure FDA0004036200090000072
C2：
Figure FDA0004036200090000073
/>
C3：
Figure FDA0004036200090000074
C4：
Figure FDA0004036200090000075
C5：
Figure FDA0004036200090000076
C6：
Figure FDA0004036200090000077
in the above constraint, C1 represents β ij ，μ jk Can only take the value of 0 or 1,
Figure FDA0004036200090000078
indicating the multiplexing of sub-channels, mu jk Representing the pairing condition of the V2V request node and the service node, C2 representing that each V2V request node multiplexes at most one channel, C3 representing the power limit of each V2I node and the V2V paired user sending node, C4 representing that the sum of the computing resources distributed to all V2I vehicles by the edge server is smaller than the total computing resource of the edge server, and C5 representing that the signal-to-interference-and-noise ratio of each V2I node in communication with the base station is greater than a threshold value->
Figure FDA0004036200090000079
C6 indicates that the signal to interference and noise ratio of each V2V user for communication should be greater than the threshold value->
Figure FDA00040362000900000710
/>
CN202010118884.6A 2020-02-26 2020-02-26 Task unloading and resource allocation method based on mobile edge calculation in Internet of vehicles Active CN111314889B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010118884.6A CN111314889B (en) 2020-02-26 2020-02-26 Task unloading and resource allocation method based on mobile edge calculation in Internet of vehicles

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010118884.6A CN111314889B (en) 2020-02-26 2020-02-26 Task unloading and resource allocation method based on mobile edge calculation in Internet of vehicles

Publications (2)

Publication Number Publication Date
CN111314889A CN111314889A (en) 2020-06-19
CN111314889B true CN111314889B (en) 2023-03-31

Family

ID=71147820

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010118884.6A Active CN111314889B (en) 2020-02-26 2020-02-26 Task unloading and resource allocation method based on mobile edge calculation in Internet of vehicles

Country Status (1)

Country Link
CN (1) CN111314889B (en)

Families Citing this family (38)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111741438B (en) * 2020-06-28 2021-10-08 湖南大学 Edge computing side-end cooperative task unloading method and system considering vehicle movement
CN114258724A (en) * 2020-07-23 2022-03-29 北京小米移动软件有限公司 Logical channel multiplexing method and apparatus, communication device, and storage medium
CN112512018B (en) * 2020-07-24 2022-03-04 北京航空航天大学 Method for dynamically unloading tasks among cooperative vehicles based on mobile edge calculation
CN112055329B (en) * 2020-08-03 2022-06-14 广东工业大学 Edge Internet of vehicles task unloading method suitable for RSU coverage switching
CN111918311B (en) * 2020-08-12 2022-04-12 重庆邮电大学 Vehicle networking task unloading and resource allocation method based on 5G mobile edge computing
CN112101728A (en) * 2020-08-18 2020-12-18 华南理工大学 Energy optimization distribution method for mobile edge computing system
CN112084026A (en) * 2020-09-02 2020-12-15 国网河北省电力有限公司石家庄供电分公司 Low-energy-consumption edge computing resource deployment system and method based on particle swarm
CN112118312B (en) * 2020-09-17 2021-08-17 浙江大学 Network burst load evacuation method facing edge server
CN112671830B (en) * 2020-12-02 2023-05-30 武汉联影医疗科技有限公司 Resource scheduling method, system, device, computer equipment and storage medium
CN112383949B (en) * 2020-11-16 2023-06-20 深圳供电局有限公司 Edge computing and communication resource allocation method and system
CN112654058A (en) * 2020-12-17 2021-04-13 中国刑事警察学院 Mobile edge computing offload and resource allocation algorithm in D2D multicast network
CN112882805A (en) * 2021-01-26 2021-06-01 上海应用技术大学 Profit optimization scheduling method based on task resource constraint
CN112995950B (en) * 2021-02-07 2022-03-29 华南理工大学 Resource joint allocation method based on deep reinforcement learning in Internet of vehicles
CN112911016B (en) * 2021-02-25 2022-04-08 北京邮电大学 Edge-side collaborative computing unloading method and system, electronic equipment and storage medium
CN113014663B (en) * 2021-03-12 2022-03-18 中南大学 Task and resource matching method supporting cross-node computing task survivability and succession
CN113114738B (en) * 2021-03-25 2022-03-25 华南理工大学 SDN-based optimization method for internet of vehicles task unloading
CN113068150B (en) * 2021-04-06 2022-08-02 北京邮电大学 Training method and device, transmission method, equipment and medium of strategy estimation network
CN113132943B (en) * 2021-04-18 2022-04-19 中南林业科技大学 Task unloading scheduling and resource allocation method for vehicle-side cooperation in Internet of vehicles
CN113271627A (en) * 2021-05-14 2021-08-17 天津理工大学 Mobile edge computing unloading method based on chaotic quantum particle swarm optimization strategy
CN113467851B (en) * 2021-05-24 2024-01-23 南京邮电大学 Dynamic vehicle computing task unloading method and device based on vehicle clustering
CN113423091B (en) * 2021-05-24 2022-07-29 西安电子科技大学 Multidimensional resource intelligent joint optimization method and system of vehicle-mounted computing power network
CN113240189B (en) * 2021-06-01 2022-10-14 青岛科技大学 Reputation value-based dynamic vehicle task and calculation force matching method
CN113364859B (en) * 2021-06-03 2022-04-29 吉林大学 MEC-oriented joint computing resource allocation and unloading decision optimization method in Internet of vehicles
CN113504949B (en) * 2021-06-22 2024-01-30 山东师范大学 Task unloading and parameter optimization method and system for MAR client in edge calculation
CN113535261B (en) * 2021-07-05 2022-09-06 云南大学 Internet of vehicles vehicle cluster task unloading method based on edge calculation
CN113490275B (en) * 2021-07-07 2024-03-08 东南大学 NOMA-based Internet of vehicles broadcast communication resource allocation method
CN113727358B (en) * 2021-08-31 2023-09-15 河北工程大学 Edge server deployment and content caching method based on KM and greedy algorithm
CN114153515B (en) * 2021-09-18 2023-09-12 南京邮电大学 Highway internet of vehicles task unloading algorithm based on 5G millimeter wave communication
CN114710497B (en) * 2022-03-11 2023-06-02 厦门理工学院 Internet of vehicles multitasking unloading minimum response time acquisition method
CN114710785B (en) * 2022-04-08 2022-11-29 浙江金乙昌科技股份有限公司 Internet of vehicles cooperative computing resource scheduling design method based on particle swarm algorithm
CN115002108B (en) * 2022-05-16 2023-04-14 电子科技大学 Networking and task unloading method for smart phone serving as computing service node
CN114980029A (en) * 2022-05-20 2022-08-30 重庆邮电大学 Unloading method based on task relevance in Internet of vehicles
CN115022893A (en) * 2022-05-31 2022-09-06 福州大学 Resource allocation method for minimizing total computation time in multi-task edge computing system
CN115103369B (en) * 2022-06-15 2023-05-02 唐尚禹 Access method and device of edge distributed MEC for lightweight industrial application
CN115209373A (en) * 2022-07-11 2022-10-18 天津理工大学 Internet of vehicles task unloading method based on bipartite graph matching strategy
CN115421930B (en) * 2022-11-07 2023-03-24 山东海量信息技术研究院 Task processing method, system, device, equipment and computer readable storage medium
CN115884126B (en) * 2022-12-29 2023-09-15 上海洛轲智能科技有限公司 Method and device for constructing fleet communication network, electronic equipment and storage medium
CN116545853B (en) * 2023-07-04 2023-10-13 南京理工大学 Integrated network multi-objective optimized resource management method based on quantum particle swarm

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109302709A (en) * 2018-09-14 2019-02-01 重庆邮电大学 The unloading of car networking task and resource allocation policy towards mobile edge calculations
CN110035410A (en) * 2019-03-07 2019-07-19 中南大学 Federated resource distribution and the method and system of unloading are calculated in a kind of vehicle-mounted edge network of software definition

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109302709A (en) * 2018-09-14 2019-02-01 重庆邮电大学 The unloading of car networking task and resource allocation policy towards mobile edge calculations
CN110035410A (en) * 2019-03-07 2019-07-19 中南大学 Federated resource distribution and the method and system of unloading are calculated in a kind of vehicle-mounted edge network of software definition

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
超密集网络中基于移动边缘计算的任务卸载和资源优化;张海波等;《电子与信息学报》;20190514(第05期);全文 *

Also Published As

Publication number Publication date
CN111314889A (en) 2020-06-19

Similar Documents

Publication Publication Date Title
CN111314889B (en) Task unloading and resource allocation method based on mobile edge calculation in Internet of vehicles
CN111132077B (en) Multi-access edge computing task unloading method based on D2D in Internet of vehicles environment
CN109413724B (en) MEC-based task unloading and resource allocation scheme
CN110996393B (en) Single-edge computing server and multi-user cooperative computing unloading and resource allocation method
CN109951869B (en) Internet of vehicles resource allocation method based on cloud and mist mixed calculation
CN111372314A (en) Task unloading method and task unloading device based on mobile edge computing scene
CN108924796B (en) Resource allocation and unloading proportion joint decision method
CN114389678B (en) Multi-beam satellite resource allocation method based on decision performance evaluation
CN112084025A (en) Improved particle swarm algorithm-based fog calculation task unloading time delay optimization method
CN107343268B (en) Non-orthogonal multicast and unicast transmission beamforming method and system
CN111629352B (en) V2X resource allocation method based on Underlay mode in 5G cellular network
CN111031547A (en) Multi-user D2D communication resource allocation method based on spectrum allocation and power control
CN111200831A (en) Cellular network computing unloading method fusing mobile edge computing
CN108601036B (en) Internet of vehicles resource optimal scheduling method and device based on successive convex approximation
CN113891477A (en) Resource allocation method based on MEC calculation task unloading in Internet of vehicles
Zhu et al. Joint optimal allocation of wireless resource and MEC computation capability in vehicular network
CN107302801B (en) QoE-oriented double-layer matching game method in 5G mixed scene
CN114153515B (en) Highway internet of vehicles task unloading algorithm based on 5G millimeter wave communication
CN114189521A (en) Method for cooperative computing offload in F-RAN architecture
CN112367523B (en) Resource management method in SVC multicast based on NOMA in heterogeneous wireless network
CN113055860A (en) D2D many-to-many resource allocation method in cellular network
CN108540246B (en) Resource allocation method based on cognitive radio
CN115866787A (en) Network resource allocation method integrating terminal direct transmission communication and multi-access edge calculation
CN116056151A (en) Task unloading and resource allocation combined optimization method in Internet of vehicles
CN111343722B (en) Cognitive radio-based energy efficiency optimization method in edge calculation

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant