CN113891477A - Resource allocation method based on MEC calculation task unloading in Internet of vehicles - Google Patents

Resource allocation method based on MEC calculation task unloading in Internet of vehicles Download PDF

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CN113891477A
CN113891477A CN202111300905.7A CN202111300905A CN113891477A CN 113891477 A CN113891477 A CN 113891477A CN 202111300905 A CN202111300905 A CN 202111300905A CN 113891477 A CN113891477 A CN 113891477A
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vehicle
task
unloading
tvi
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张海波
刘香渝
刘开健
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/53—Allocation or scheduling criteria for wireless resources based on regulatory allocation policies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • 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

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Abstract

The invention relates to the field of task unloading and resource optimization in the Internet of vehicles, in particular to a resource optimization method based on MEC calculation task unloading in the Internet of vehicles, which is used for researching a partial task unloading and resource allocation strategy of the Internet of vehicles for minimizing system overhead aiming at the problem that the Internet of vehicles needs to process a large number of low-delay tasks under limited resources and dynamic topology with the assistance of a mobile edge calculation technology; considering the difference of the road side unit and the vehicle as service nodes and the task time delay, the communication distance and the calculation resource constraint to establish a mathematical model; decomposing the mixed integer non-convex problem into three subproblems for joint solving; converting the computing resource allocation sub-problem into a convex optimization problem through variable replacement to obtain an optimal unloading ratio; the invention can effectively determine the unloading decision, allocate channel resources and computing resources and reduce the system overhead.

Description

Resource allocation method based on MEC calculation task unloading in Internet of vehicles
Technical Field
The invention relates to the field of task unloading and resource optimization in an Internet of vehicles, in particular to a resource allocation method based on MEC (media independent component) calculation task unloading in an Internet of vehicles.
Background
The continuous progress of new generation wireless technology and industrial technology brings necessary technical support for the development of internet of vehicles. Not only does the number of vehicles increase dramatically, the performance of vehicles is also increasingly being optimized to become more intelligent. Emerging automotive applications, such as coordinated autopilot, intelligent traffic control, coordinated environmental awareness, etc., are emerging, whereby the internet of vehicles will face the challenge of processing large amounts of data under resource constraints, high latency requirements, and topology changes.
Mobile Edge Computing (MEC) distributes Computing platforms from the Mobile core network to the Edge of the Mobile access network, reducing the end-to-end delay of Mobile service delivery and enhancing the user experience. Cellular-vehicle to electric (C-V2X) technology relies on Cellular networks to enable vehicle to surroundings communication. By combining the MEC technology and the C-V2X technology, tasks are unloaded to Road Side Units (RSUs) or other vehicles in a Vehicle to Vehicle (V2V) communication mode by Vehicle to infrastructure (V2I) communication mode, and the burden of nodes can be effectively relieved by utilizing free resources.
Currently, many valuable research results have been obtained in the prior art for the task offloading and resource allocation problem of combining MECs in the internet of vehicles, but few research results are performed on partial offloading under the V2X offloading. In addition, most of the schemes do not consider the difference between the RSU and different vehicles as service nodes, or only consider whether the task is unloaded, do not study specific unloading objects, and some of the schemes do not consider the limitations of mobility and communication distance of the vehicles and resource allocation after unloading.
Therefore, under the constraint of a real scene, a good unloading strategy is customized and reasonable resource allocation is combined, so that the time delay of task calculation can be reduced, and the energy consumption of each node in the system is balanced.
Disclosure of Invention
In order to solve the above prior art problems, the present invention provides a resource allocation method based on MEC calculation task offloading in an internet of vehicles, with the assistance of a mobile edge calculation technology, aiming at the problem that the internet of vehicles needs to process a large number of low-delay tasks under limited resources and dynamic topology, and with the principle of minimizing system overhead, determining a task offloading and resource allocation strategy of the internet of vehicles, so as to complete the task offloading and resource optimization problems in the vehicle edge calculation system, determining the task offloading strategy by calculating the minimum total overhead in the internet of vehicles system, and allocating system resources according to the task offloading strategy, including:
s1: constructing a resource allocation framework based on MEC computing task unloading;
s2: determining an unloading mode of the task vehicle according to a resource distribution framework based on MEC calculation task unloading, wherein the unloading mode comprises that the task vehicle is unloaded to a service vehicle and the task vehicle is unloaded to a road side unit;
s3: determining an unloading decision according to the unloading mode and the initial unloading ratio of the task vehicle;
s4: allocating channel allocation resources for the selected service node according to the unloading decision; calculating the total system overhead according to the unloading decision;
s5: updating the unloading ratio according to the total system overhead;
s6: and repeating the steps S3-S5 to obtain the optimal unloading ratio, and unloading the tasks and distributing the resources on the distributed channel distribution resources according to the optimal unloading ratio.
Preferably, the constructing of the resource allocation framework based on MEC computing task offloading includes:
acquiring vehicle information, and dividing the vehicle into a task vehicle TV and a service vehicle SV according to the vehicle information; the task vehicle TV is a vehicle with a task and needing unloading, and the service vehicle SV is a vehicle with idle resources;
taking a service vehicle SV and a road side unit RSU as a service node SN; when the task vehicle executes task unloading, dividing the task into local task unloading and unloading to a service node;
calculating the time delay and energy consumption of local task unloading;
offloading to a service node includes offloading to a road side unit, RSU, and offloading to a service vehicle, SV; calculating time delay and energy consumption unloaded to a Road Side Unit (RSU); the time delay and energy consumption for offloading to the service vehicle SV are calculated.
Preferably, the formula for calculating the total overhead of the system is as follows:
Figure BDA0003338298050000031
s.t.C1:
Figure BDA0003338298050000032
C2:
Figure BDA0003338298050000033
C3:
Figure BDA0003338298050000034
C4:
Figure BDA0003338298050000035
C5:
Figure BDA0003338298050000036
C6:
Figure BDA0003338298050000037
C7:
Figure BDA0003338298050000038
wherein the content of the first and second substances,
Figure BDA00033382980500000314
representing a collection of vehicles that have a mission to unload,
Figure BDA00033382980500000310
Cirepresenting the cost of a mission vehicle, C representing the total system cost, λtRepresenting a delay factor, λeRepresenting energy consumption factors, which determine the importance of time delay and energy consumption in the system; t isiRepresenting the system delay, EiRepresents the system energy consumption; giRepresents the offload decision factor, when giWhen the number is 1, the selected unloading object is RSU, and when giWhen the value is 0, the unloading object is SV; k is a radical ofi,jIs a vehicle matching factor, which indicates whether TVi selects SVj as a service object, when ki,jWhen the value is 1, TVi selects to be unloaded to SVj, and if TVi and SVj are not matched, k isi,j＝0；fi rsuRepresenting the computing resources allocated by the RSU to TVi, frsu-maxRepresenting maximum computing power, ξ, of the RSUiIndicating the ratio of task unloading, Ti maxRepresenting the maximum tolerant time delay of the TVi task; thetai,kIndicates whether a subchannel k is assigned to a TVi when θi,kWhen 1, subchannel k is assigned to TVi when θi,kWhen 0, subchannel k is not allocated to TVi;
Figure BDA00033382980500000311
represents tiThe distance between the instants TVi and the RSU,
Figure BDA00033382980500000312
represents tiThe distance between the times TVi and SVj;
preferably, the task vehicle makes an unloading decision according to a resource allocation framework for computing task unloading based on the MEC, and determines an unloading mode of the task vehicle, including: defining the task matching degree M equal to the task calculation time delay of the ith task vehicle TVi selecting the jth service vehicle SVj as the unloading object
Figure BDA00033382980500000313
And distance weighting, aiming at maximizing the matching degree, and finding the most suitable service object for the SV:
Figure BDA0003338298050000041
s.t.C2:
Figure BDA0003338298050000042
C7:
Figure BDA0003338298050000043
wherein the content of the first and second substances,
Figure BDA00033382980500000410
denotes the initial unloading ratio, αtAnd alphadA weight value representing a calculation time and a distance; n is a radical ofSSet of vehicles representing service vehicles, Pi、PjIndicating the position of the vehicle, DV2VIndicating the communication distance of the task vehicle to the service vehicle.
Further, the process of finding the most suitable task unloading object for the task vehicle includes: the method for searching for a proper service object for the service vehicle SV by adopting a dynamic taboo length taboo search pairing algorithm based on the task matching degree comprises the following steps:
s1: setting an initial solution and an initial tabu table, wherein the initial tabu table is empty;
s2: calculating the neighborhood of the current solution, setting the calculated neighborhood as a tabu object, and filling the tabu object into an initial tabu table to obtain an optimal tabu solution and a non-optimal tabu solution;
s3: judging whether the task matching degree M jointly determined by the optimal taboo solution and the optimal non-taboo solution is superior to the existing optimal solution M', if so, updating the optimal solution, otherwise, not updating the optimal solution, and returning to S2; when the difference value of M and M' is smaller than the minimum value epsilon or reaches the maximum iteration number, stopping updating;
s4: according to the optimal solution, task vehicles are gathered
Figure BDA0003338298050000044
Is divided into sets
Figure BDA0003338298050000045
And
Figure BDA0003338298050000046
wherein, aggregate
Figure BDA00033382980500000411
Set of vehicles indicating selection of the V2I unload mode
Figure BDA0003338298050000048
Presentation selectionThe set of vehicles in the V2V unloaded mode is shown.
Preferably, allocating the channel allocation resource for the selected serving node according to the offloading decision comprises:
s1: set of vehicles to be tasked for unloading
Figure BDA0003338298050000049
Sorting according to the ascending order of the task maximum tolerance time Timax of the TV;
s2: respectively making the first K sequenced TVs into K colors, and finishing channel allocation of the first K TVs according to each color corresponding to one channel;
s3: adopting a graph coloring algorithm to color the TV without the allocated channel and update a channel allocation matrix;
s4: according to the expected time delay
Figure BDA0003338298050000051
And Ti maxSequentially judging the TV color of the unallocated channel if
Figure BDA0003338298050000052
Less than Ti maxTVi is assigned k colors, i.e., k channels are assigned to the TV; if it is not
Figure BDA0003338298050000053
Greater than Ti maxTVi cannot be assigned to k colors, i.e., k channels are not assigned to TVi.
The calculation formula of the predicted time delay is as follows:
Figure BDA0003338298050000054
wherein r isi rsuTo achieve TVi transmission rate on k channel in the offloading mode of the task vehicle to the roadside unit V2I,
Figure BDA0003338298050000055
for TV in unloading mode with task vehicle unloaded to service vehicle V2Vi the transmission rate of the k-channel,
Figure BDA0003338298050000056
representing the preliminary unloading ratio of vehicle i, ciIndicating the number of CPUs, s, required for vehicle i to calculate the vehicle's missioniIndicating the size of the vehicle task in vehicle i, giRepresents an offload decision factor, fi avRepresenting the local computing power of the vehicle i,
Figure BDA0003338298050000057
is represented by NSSet of vehicles, k, representing service vehiclesi,jWhich is indicative of a vehicle matching factor,
Figure BDA0003338298050000058
representing the computational power of SVj.
Preferably, the process of performing task offloading and resource allocation on the channel allocation resources according to the optimal offloading ratio includes: after determining the offload mode and channel allocation, the mission vehicle TV is divided into two new vehicle sets
Figure BDA0003338298050000059
And
Figure BDA00033382980500000510
divide the optimization objective into CV2IAnd CV2VRespectively solving the optimization targets;
comparing the local computation time with the time offloaded to the SV
Figure BDA00033382980500000511
Divided into two new sets
Figure BDA00033382980500000512
And
Figure BDA00033382980500000513
when T isi local≥Ti off-svThe task vehicle TVi belongs to
Figure BDA00033382980500000514
When T isi local＜Ti off-svTVi belongs to
Figure BDA00033382980500000515
Comparing the locally calculated time with the time of offloading to the road side unit RSU
Figure BDA00033382980500000516
Divided into two new sets
Figure BDA00033382980500000517
And
Figure BDA00033382980500000518
when T isi local≥Ti off-rsuWhen TVi belongs to
Figure BDA00033382980500000519
When T isi local＜Ti off-rsuTVi belongs to
Figure BDA00033382980500000520
Wherein, Ti localRepresents the time delay, T, calculated locally by the TVi of the mission vehiclei off-svIndicating an unloading delay, T, for a task to an service vehiclei off-rsuIndicating the offload delay of the task to the roadside unit.
Further, the process of solving the optimization objective includes: when the unloading mode is determined that the task vehicle is unloaded to the service vehicle V2V, CV2VThe optimization target is as follows:
Figure BDA0003338298050000061
Figure BDA0003338298050000062
Figure BDA0003338298050000063
Figure BDA0003338298050000064
Figure BDA0003338298050000065
Figure BDA0003338298050000066
Figure BDA0003338298050000067
wherein, CV2VRepresenting the system overhead of the offload mode for offloading task vehicles to service vehicles, caRepresentation collection
Figure BDA0003338298050000068
In which the number of CPUs required for the mission of the vehicle is calculated, cbRepresentation collection
Figure BDA0003338298050000069
Calculating the number of CPUs required by the tasks of the vehicle;
Figure BDA00033382980500000610
representation collection
Figure BDA00033382980500000611
The maximum time delay for the task of the medium vehicle to be unloaded,
Figure BDA00033382980500000612
representation collection
Figure BDA00033382980500000613
Maximum time delay for task offloading of the medium vehicle; saRepresentation collection
Figure BDA00033382980500000614
Size of the medium vehicle task, sbRepresentation collection
Figure BDA00033382980500000615
Size of the medium vehicle task, paRepresentation collection
Figure BDA00033382980500000616
Calculated power of medium vehicle, pbRepresentation collection
Figure BDA00033382980500000617
Calculated power of medium vehicle, xiaRepresentation collection
Figure BDA00033382980500000618
Unloading ratio of (xi)bRepresentation collection
Figure BDA00033382980500000619
Unload ratio of (k) ("kappa")vRepresents the calculated coefficient of energy consumption of the vehicle,
Figure BDA00033382980500000620
representation collection
Figure BDA00033382980500000621
Computing power of the mission vehicle itself, λtRepresenting a delay factor, λeWhich represents a factor of the energy consumption,
Figure BDA00033382980500000622
representation collection
Figure BDA00033382980500000623
The transmission rate at which the mission vehicle TVi communicates with the service vehicle SVj,
Figure BDA00033382980500000624
representation collection
Figure BDA00033382980500000625
The transmission rate when the TVi and the SVj communicate is determined;
Figure BDA00033382980500000626
to the computing power of SVj, ki,jK is a vehicle matching factor indicating whether TVi selects SVj as a service objecta,jRepresentation collection
Figure BDA00033382980500000627
Whether the middle TVi selects SVj as a service object, kb,jRepresentation collection
Figure BDA00033382980500000628
Whether the medium TVi selects SVj as a service object or not, when k isi,j、ka,jOr kb,jEquals 1 indicating that the task vehicle selects to be unloaded to the service vehicle, when k isi,j、ka,jOr kb,jAt 0, task vehicle TVi and service vehicle SVj do not match;
Figure BDA0003338298050000071
representation collection
Figure BDA0003338298050000072
The distance between TVi and SVj in (1),
Figure BDA0003338298050000073
representation collection
Figure BDA0003338298050000074
Distance between TVi and SVj, DV2VRepresenting a communication distance of the task vehicle to the service vehicle;
after the unloading mode is determined to be that the task vehicle is unloaded to the roadside unit V2I, the optimization objective CV2IComprises the following steps:
Figure BDA0003338298050000075
Figure BDA0003338298050000076
Figure BDA0003338298050000077
Figure BDA0003338298050000078
Figure BDA0003338298050000079
Figure BDA00033382980500000710
Figure BDA00033382980500000711
Figure BDA00033382980500000712
Figure BDA00033382980500000713
wherein, CV2IRepresenting the total system overhead of the offloading mode for offloading of the task vehicle to the roadside unit, ccRepresentation collection
Figure BDA00033382980500000714
In which the number of CPUs required for the mission of the vehicle is calculated, cdRepresentation collection
Figure BDA00033382980500000715
Calculating the number of CPUs required by the tasks of the vehicle;
Figure BDA00033382980500000716
representation collection
Figure BDA00033382980500000717
The maximum time delay for the task of the medium vehicle to be unloaded,
Figure BDA00033382980500000718
representation collection
Figure BDA00033382980500000719
Maximum time delay for task offloading of the medium vehicle; scRepresentation collection
Figure BDA00033382980500000720
Size of the medium vehicle task, sdRepresentation collection
Figure BDA00033382980500000721
Task size of middle vehicle, pcRepresentation collection
Figure BDA00033382980500000722
Calculated power of medium vehicle, pdRepresentation collection
Figure BDA00033382980500000723
Calculated power of medium vehicle, pcomRepresenting the calculated power of the road side unit RSU,
Figure BDA00033382980500000724
representation collection
Figure BDA00033382980500000725
The unloading ratio of (a) to (b),
Figure BDA00033382980500000726
representation collection
Figure BDA00033382980500000727
Unload ratio of (k) ("kappa")vRepresents the calculated coefficient of energy consumption of the vehicle,
Figure BDA00033382980500000728
representation collection
Figure BDA00033382980500000729
The local computing power of the vehicle in question,
Figure BDA00033382980500000730
representation collection
Figure BDA00033382980500000731
Local computing power of the vehicle, frsu-maxDenotes λtDenotes λeIt is shown that,
Figure BDA0003338298050000081
representing the maximum computing power, f, of the RSUrsu-maxDenotes the maximum computing power, λ, of the RSUtRepresenting a delay factor, λeWhich represents a factor of the energy consumption,
Figure BDA0003338298050000082
representing RSU to set
Figure BDA0003338298050000083
The computing power allocated to the vehicle in (1),
Figure BDA0003338298050000084
representing RSU to set
Figure BDA0003338298050000085
The computing power allocated to the vehicle in (1),
Figure BDA0003338298050000086
representation collection
Figure BDA0003338298050000087
The transmission rate at which the mission vehicle TVi communicates with the RSU,
Figure BDA0003338298050000088
representation collection
Figure BDA0003338298050000089
The transmission rate when the TVi communicates with the RSU;
Figure BDA00033382980500000810
is a set
Figure BDA00033382980500000811
The computing power of the mission vehicle is determined,
Figure BDA00033382980500000812
is a set
Figure BDA00033382980500000813
The computing power of the RSU;
Figure BDA00033382980500000814
representation collection
Figure BDA00033382980500000815
The distance between the medium TVi and the RSU,
Figure BDA00033382980500000816
representation collection
Figure BDA00033382980500000817
Distance between TVi and RSU, DV2IRepresenting the communication distance of TVi to RSU.
Preferably, both the V2V optimization problem and the V2I optimization problem can be solved by using a CVX toolkit.
And further, calculating an optimization target and judging whether the calculation result is converged, if so, outputting an optimal unloading ratio and an optimal resource allocation result obtained according to the optimal unloading ratio, if not, updating the unloading ratio according to the optimization target, performing iterative calculation until the result is converged to an optimal solution, outputting the optimal unloading ratio and the optimal resource allocation result obtained according to the optimal unloading ratio, and then, performing task unloading and resource allocation on the allocated channel allocation resources by the task vehicle according to the optimal unloading ratio.
The invention has the beneficial effects that: aiming at the problem of resource allocation of task unloading based on MEC calculation in a vehicle-mounted network system, the resource constraint is calculated and a mathematical model is established by considering the difference of a road side unit and a vehicle as service nodes, the task time delay and the communication distance, the problem is decomposed into three sub-problems to be solved jointly, the concept of vehicle matching degree is provided, and a reasonable unloading object is determined by utilizing a dynamic taboo length taboo search algorithm. And the image coloring algorithm is adopted to allocate channels, so that the channel interference is reduced. An optimal unloading strategy, a channel resource and computing resource allocation scheme are provided, and the computing resource allocation sub-problem is converted into a convex optimization problem through variable replacement to obtain an optimal unloading ratio. Compared with the prior art, the method and the device have the advantages that the time delay of task calculation is reduced, the energy consumption of each node in the system is balanced, and the performance in the aspects of energy consumption and time delay is improved.
Drawings
FIG. 1 is a system model diagram of the method for partial task offloading and resource allocation based on MEC of the present invention;
fig. 2 is a flowchart of an implementation of the MEC-based task offloading and resource allocation method in the internet of vehicles according to the present invention.
Detailed Description
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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a resource allocation method based on MEC calculation task unloading in an internet of vehicles, as shown in figure 1, the method determines part of task unloading and resource allocation strategies of the internet of vehicles by taking the minimum system overhead as a principle aiming at the problem that the internet of vehicles needs to process a large number of low-delay tasks under limited resources and dynamic topology with the assistance of a mobile edge calculation technology, so as to complete the task unloading and resource optimization problems in an edge calculation system of vehicles, determines the task unloading strategies by calculating the minimum total overhead in the internet of vehicles, and allocates system resources according to the task unloading strategies, as shown in figure 2, the method comprises the following steps:
s1: constructing a resource allocation framework based on MEC computing task unloading;
s2: determining an offloading pattern of the task vehicle according to a resource allocation framework for computing task offloading based on the MEC, the offloading pattern including offloading of the task vehicle to a service vehicle (V2V) and offloading of the task vehicle to a roadside unit (V2I);
s3: determining an unloading decision according to the unloading mode and the initial unloading ratio of the task vehicle;
s4: allocating channel allocation resources for the selected service node according to the unloading decision; calculating the total system overhead according to the unloading decision;
s5: updating the unloading ratio according to the total system overhead;
s6: and repeating the steps S3-S5 to obtain the optimal unloading ratio, and unloading the tasks and distributing the resources on the distributed channel distribution resources according to the optimal unloading ratio.
Further, constructing a resource allocation framework based on MEC computing task offloading includes: acquiring vehicle information, and dividing the vehicle into a task vehicle TV and a service vehicle SV according to the vehicle information; the task vehicle TV is a vehicle with a task and needing unloading, and the service vehicle SV is a vehicle with idle resources; taking a service vehicle SV and a road side unit RSU as a service node SN; when the task vehicle executes task unloading, dividing the task into local task unloading and unloading to a service node; calculating the time delay and energy consumption of local task unloading; offloading to a service node includes offloading to a road side unit, RSU, and offloading to a service vehicle, SV; calculating time delay and energy consumption unloaded to a Road Side Unit (RSU); calculating time delay and energy consumption of unloading to a service vehicle SV; the specific process is as follows:
consider a vehicle communication network consisting of several co-running vehicles, roadside RSUs and one macro base station. The vehicles run in the same direction on the road, and the vehicles in the system are collected into
Figure BDA0003338298050000101
The set of vehicles in which the task needs to be unloaded is
Figure BDA0003338298050000102
The type of Vehicle is called a Task Vehicle (TV), and the ith Task Vehicle in a Task Vehicle set is represented by TVi; the collection of vehicles with spare resources
Figure BDA0003338298050000103
Referred to as the Service Vehicle (SV), the jth Service Vehicle in the set of Service vehicles is denoted by SVj. Wherein each vehicle has only one attribute, i.e.
Figure BDA0003338298050000104
Tasks can be locally calculated by the TV by using own resources, can be unloaded to an MEC server for calculation through an RSU in a V2I mode, or can be unloaded to surrounding SVs for calculation in a V2V mode.
The parameter of the vehicle i is expressed as
Figure BDA0003338298050000105
PiIndicating the position P of the vehiclei＝(xi,yi)，vi、θiAnd fiRespectively representing the speed of the vehicle, the travel angle at a certain reference line and the calculation capacity of the vehicle. In addition, the task information that the current vehicle needs to be unloaded is represented as
Figure BDA0003338298050000106
Wherein s isi,ci,Ti maxRespectively representing the size of the vehicle task, the number of CPUs required for calculation and the maximum time delay. The RSU and SV together become a service node SN (service node) to receive the TV unloading request. Set of available subchannels per RSU as
Figure BDA0003338298050000107
The bandwidth of each subchannel is BHz.
The system adopts OFDM to allocate orthogonal channels for the V2I mode, and the V2V mode multiplexes the uplink transmission signals of the V2I mode. Introduction of NTThe channel connection matrix C of xk describes the channel allocation. Thetai,kIndicating whether a subchannel k is allocated to TVi. When theta isi,kWhen 1, subchannel k is assigned to TVi when θi,kWhen 0, subchannel k is not allocated to TVi. Therefore, the signal to Interference plus Noise ratio sinr (signal to Interference plus Noise ratio) and the transmission rate of TVi when the sub-channel k communicates with the RSU are respectively:
Figure BDA0003338298050000108
Figure BDA0003338298050000109
similarly, the signal to interference plus noise ratio SINR and the transmission rate when TVi and SVj communicate are respectively:
Figure BDA0003338298050000111
Figure BDA0003338298050000112
wherein the content of the first and second substances,
Figure BDA0003338298050000113
and
Figure BDA0003338298050000114
channel gain, p, for TVi in channel k communicating with RSU and SVj, respectivelyiTo transmit power, σ2Is noise interference in the communication process. r isi rsuAnd
Figure BDA0003338298050000115
respectively, the transmission rates of TVi being offloaded to RSU and SV. Due to movement of the vehicleWhile V2X (Vehicle to evaporating) has a limited communication range, the communication distance of the task Vehicle in V2I mode and the communication distance of the task Vehicle in V2V mode are respectively denoted as DV2IAnd DV2VWhether the communication distance between the dynamic nodes satisfies the condition needs to be considered.
TVi and SVj have the positions after t time respectively
Figure BDA0003338298050000116
And
Figure BDA0003338298050000117
among them are:
Figure BDA0003338298050000118
Figure BDA0003338298050000119
the position of the RSU is not changed, so the task calculation completion time T at TViiThe distances between the inner TV and the SN are respectively:
Figure BDA00033382980500001110
Figure BDA00033382980500001111
the TV, as a node with excess computing tasks, needs to request an offload service. In order to balance the resource utilization of the TV and the SN and improve the resource utilization rate of the system, the TV adopts partial unloading, a task is divided into two parts which can be divided, the two parts are respectively unloaded to a certain SN locally and unloaded to a plurality of SNs, and the unloading of the task to the SNs is not considered temporarily. Therefore, the time delay and energy consumption of the local calculation of the task vehicle TVi are respectively:
Figure BDA00033382980500001112
Figure BDA00033382980500001113
wherein f isi tvDenotes the computing power of TVi itself, κvIs the calculated power consumption coefficient of the vehicle, related to chip performance, where κv＝10-28。
Because the computing power of the RSU is stronger than that of the SV, when the RSU is used as the SN, the RSU can simultaneously receive task requests of a plurality of TVs, and the SV selects a computing mode with non-CPU preemption to serve the TVs. Therefore, when the TVi selects to offload to the RSU, the time delay and energy consumption of offloading are respectively:
Figure BDA0003338298050000121
Figure BDA0003338298050000122
wherein T isi tran-rsuAnd Ti com-rsuRespectively the transmission and computation time delay, f, offloaded to the RSUi rsuRepresenting the computing resources allocated by the RSU to the TVi,
Figure BDA0003338298050000123
and
Figure BDA0003338298050000124
respectively, the transmission energy, p, offloaded to the RSUrsuRepresenting the calculated power of the RSU. Here the task backhaul delay is ignored.
When the TVi selects to unload to the SVj, the unloading time delay and the energy consumption are respectively as follows:
Figure BDA0003338298050000125
Figure BDA0003338298050000126
wherein
Figure BDA0003338298050000127
And
Figure BDA0003338298050000128
the transmission and computation delays offloaded to the RSU, respectively, are also disregarded for the backhaul delay.
Figure BDA0003338298050000129
And
Figure BDA00033382980500001210
respectively, the transmission and computational power consumption offloaded to the RSU.
Figure BDA00033382980500001211
Is the computing power of SVj.
To represent the specific SN selected by TVi offloading, an offloading decision factor g is introducedi. When g isiWhen 1, the selected uninstalled object is RSU. When g isiWhen 0, it means that the unloading target is SV, in which case a specific SV needs to be further determined, when k isi,jWhen the value is 1, TVi selects to be unloaded to SVj, and if TVi and SVj are not matched, k isi,j0. Thus, the latency and energy consumption of the task offloading process can be expressed as:
Figure BDA00033382980500001212
Figure BDA00033382980500001213
thus, the system latency and energy consumption can be expressed as:
Figure BDA0003338298050000131
Ti＝max{Ti local,Ti off}
based on the above information, the optimization objective is to minimize the system overhead, which is expressed as:
Figure BDA0003338298050000132
s.t.C1:
Figure BDA0003338298050000133
C2:
Figure BDA0003338298050000134
C3:
Figure BDA0003338298050000135
C4:
Figure BDA0003338298050000136
C5:
Figure BDA0003338298050000137
C6:
Figure BDA0003338298050000138
C7:
Figure BDA0003338298050000139
wherein the content of the first and second substances,
Figure BDA00033382980500001310
representing a collection of vehicles that have a mission to unload,
Figure BDA00033382980500001311
Cirepresents oneOverhead of the mission vehicle, C denotes the total system overhead, λtRepresenting a delay factor, λeRepresents the energy consumption factor, TiRepresenting the system delay, EiRepresents the system energy consumption; giDenotes the offload decision factor, ki,jRepresenting a vehicle matching factor, fi rsuRepresenting the computing resources allocated by the RSU to TVi, frsu-maxRepresenting maximum computing power, ξ, of the RSUiIndicating the ratio of task unloading, Ti maxRepresenting the maximum tolerant time delay of the TVi task; thetai,kIndicates whether a subchannel k is allocated to TVi,
Figure BDA00033382980500001312
represents the distance between the TVi and the RSU,
Figure BDA00033382980500001313
representing the distance between TVi and SVj.
Further, determining an unloading mode of the task vehicle according to a resource allocation framework for computing task unloading based on the MEC, and making an unloading decision according to the unloading mode and an initial unloading ratio of the task vehicle, wherein the unloading decision comprises:
when the SV considers the service TV, the matching degree of the self computing capability and the task receiving is considered as much as possible, wherein the matching degree comprises the fit between the computing capability and the task receiving and the distance between the SV and the TV; defining the matching degree M to be equal to the weight of calculating time delay and distance, and finding the most appropriate service object for the SV by taking the maximum matching degree as a target:
Figure BDA0003338298050000141
s.t.C2:
Figure BDA0003338298050000142
C7:
Figure BDA0003338298050000143
wherein the content of the first and second substances,
Figure BDA0003338298050000144
indicating the initial unloading ratio, the initial unloading ratio of the task vehicle is determined by the TV and is recorded as
Figure BDA0003338298050000145
αtAnd alphadA weight value representing a calculation time and a distance;
Figure BDA0003338298050000146
set of vehicles representing mission-required unloading, NSSet of vehicles, k, representing service vehiclesi,jRepresenting a vehicle matching factor, PiIndicating the position of vehicle i, PjRepresents the vehicle j position;
Figure BDA0003338298050000147
represents tiDistance between times TVi and SVj, DV2VIndicating the communication distance of the task vehicle to the service vehicle.
The problem is a 0-1 integer programming problem that can be solved with heuristic algorithms. The invention adopts a dynamic taboo length taboo search pairing algorithm (DTTS) based on the task Matching degree to solve the problem. The pre-calculation of the dynamic taboo length taboo search pairing algorithm based on the task matching degree is as follows: relax non-convex constraint C7 to
Figure BDA0003338298050000148
A penalty function is used to convert the constrained mathematical model into unconstrained mathematical programming, where L represents a large number, preferably, L is set to 10000, and the maximum degree of matching is expressed as: .
Figure BDA0003338298050000149
Further, when the tabu search algorithm is adopted to solve the problem, the parameter settings are as follows:
(1) initial solution: the initial solution affects the convergence speed and the result of the tabu algorithm, so that the initial solution is different from the traditional TS algorithm which randomly generates an initial value or a greedy algorithm which searches the initial solution, and the closest pairing is set as the initial solution.
(2) Neighborhood: the neighborhood satisfies the condition of
Figure BDA00033382980500001410
Thus sharing (N)T-NS-2)×NS2 neighbourhoods.
(3) Contraindicated subjects: and (4) listing the selection when the optimal value is changed for any SVj single row as a contraindication object.
(4) Taboo length: the method sets a dynamic tabu length, so that when the function value is obviously reduced, the algorithm can search in more unknown fields and pay more attention to local fine search when the function value is reduced; the taboo length is set according to the formula
Figure BDA0003338298050000151
Where ω is a coefficient of variation and Δ M represents the variation of the function value.
(5) Candidate solutions: the method is composed of a neighborhood solution set with an optimal solution with taboo and an optimal solution with non-taboo.
(6) Privilege criteria: based on an evaluation value criterion within a certain number of iterations.
Further, a tabu search algorithm is adopted to find a suitable service object for the service vehicle SV, which includes:
s1: setting an initial solution and an initial tabu table, wherein the initial tabu table is empty;
s2: calculating the neighborhood of the current solution, setting the calculated neighborhood as a tabu object, and filling the tabu object into an initial tabu table to obtain an optimal tabu solution and a non-optimal tabu solution;
s3: judging whether the task matching degree M jointly determined by the optimal taboo solution and the optimal non-taboo solution is superior to the existing optimal solution M', if so, updating the optimal solution, otherwise, not updating the optimal solution, and returning to S2; when the difference value of M and M' is smaller than the minimum value epsilon or reaches the maximum iteration number, stopping updating;
s4: according to the optimal solution, task vehicles are gathered
Figure BDA0003338298050000152
Is divided into sets
Figure BDA0003338298050000153
And
Figure BDA0003338298050000154
it is composed of
In (1), set
Figure BDA0003338298050000158
Set of vehicles indicating selection of the V2I unload mode
Figure BDA0003338298050000156
Indicating a collection of vehicles with the V2V unload mode selected.
Further, after determining the offload objects, the subchannel assignments are converted to a graph coloring model, K channel resources are modeled as K different colors, and all TVs are treated as vertices. When the two nodes are set to be in the same color, namely, the two TVs use the same channel when communicating with respective service nodes, the same frequency interference is generated correspondingly, and the transmission speed and the transmission time are influenced; allocating channel allocation resources for the selected serving node according to the offloading decision comprises:
s1: set of vehicles to be tasked for unloading
Figure BDA0003338298050000157
Task maximum tolerance time T according to TVi maxSorting in ascending order;
s2: respectively making the first K sequenced TVs into K colors, and finishing channel allocation of the first K TVs according to each color corresponding to one channel;
s3: adopting a graph coloring algorithm to color the TV without the allocated channel and update a channel allocation matrix;
s4: according to the expected time delay
Figure BDA0003338298050000161
And Ti maxSequentially judging the TV color of the unallocated channel if
Figure BDA0003338298050000162
Less than Ti maxTVi is assigned k colors, i.e., k channels are assigned to the TV; if it is not
Figure BDA0003338298050000163
Greater than Ti maxTVi cannot be assigned to k colors, i.e., k channels are not assigned to TVi.
The calculation formula of the predicted time delay is as follows:
Figure BDA0003338298050000164
wherein r isi rsuTo achieve TVi transmission rate on k channel in the offloading mode of the task vehicle to the roadside unit V2I,
Figure BDA0003338298050000165
to achieve TVi transmission rate on k-channel in the task vehicle offload to service vehicle V2V offload mode,
Figure BDA0003338298050000166
representing the preliminary unloading ratio of vehicle i, ciIndicating the number of CPUs, s, required for vehicle i to calculate the vehicle's missioniIndicating the size of the vehicle task in vehicle i, giRepresents an offload decision factor, fi avRepresenting the local computing power of the vehicle i,
Figure BDA0003338298050000167
representing the roadside units RSU averaging the computational resources used to compute the offload tasks,N Sset of vehicles, k, representing service vehiclesi,jWhich is indicative of a vehicle matching factor,
Figure BDA0003338298050000168
representing the computational power of SVj.
Further, updating the unloading ratio according to the total overhead of the system, performing iterative computation until the optimal unloading ratio is obtained, and performing task unloading and resource allocation according to the optimal unloading ratio, wherein the iterative computation comprises the following steps:
after SN determination and channel assignment, the TV is divided into two new vehicle sets
Figure BDA0003338298050000169
And
Figure BDA00033382980500001610
the optimization objective is classified as C due to the difference in computing power and service properties when SN is SV or RSU, respectivelyV2IAnd CV2VAnd respectively solving the optimization targets.
After the offload mode determination and channel assignment, the task vehicle TV is divided into two new vehicle sets
Figure BDA00033382980500001611
And
Figure BDA00033382980500001612
divide the optimization objective into CV2IAnd CV2VRespectively optimizing and solving;
comparing the local computation time with the time offloaded to the SV
Figure BDA00033382980500001613
Divided into two new sets
Figure BDA00033382980500001614
And
Figure BDA00033382980500001615
when T isi local≥Ti off-svThe task vehicle TVi belongs to
Figure BDA00033382980500001616
When T isi local＜Ti off-svTVi belongs to
Figure BDA00033382980500001617
Comparing the locally calculated time with the time of offloading to the road side unit RSU
Figure BDA00033382980500001618
Divided into two new sets
Figure BDA00033382980500001619
And
Figure BDA00033382980500001620
when T isi local≥Ti off-rsuWhen TVi belongs to
Figure BDA00033382980500001621
When T isi local＜Ti off-rsuTVi belongs to
Figure BDA00033382980500001622
Wherein, Ti localRepresenting the locally calculated time delay, T, of the task vehicle ii off-svIndicating an unloading delay, T, for a task to an service vehiclei off-rsuIndicating the offload delay of the task to the roadside unit.
The process of solving the optimization objective includes: the optimization objectives of section V2V are:
Figure BDA0003338298050000171
s.t.C4,C5,C7
since there are variable comparison terms, the nature of this sub-problem cannot be directly judged, and therefore will be
Figure BDA0003338298050000172
Is divided intoTwo sets of
Figure BDA0003338298050000173
And
Figure BDA0003338298050000174
when T isi local≥Ti off-svWhen TVi belongs to
Figure BDA0003338298050000175
When T isi local＜Ti off-svTVi belongs to
Figure BDA0003338298050000176
The optimization objective can be written as:
Figure BDA0003338298050000177
Figure BDA0003338298050000178
Figure BDA0003338298050000179
Figure BDA00033382980500001710
Figure BDA00033382980500001711
Figure BDA00033382980500001712
Figure BDA00033382980500001713
wherein, CV2VRepresenting the system overhead of the offload mode for offloading task vehicles to service vehicles, caRepresentation collection
Figure BDA00033382980500001714
In which the number of CPUs required for the mission of the vehicle is calculated, cbRepresentation collection
Figure BDA00033382980500001715
Calculating the number of CPUs required by the tasks of the vehicle;
Figure BDA00033382980500001716
representation collection
Figure BDA00033382980500001717
The maximum time delay for the task of the medium vehicle to be unloaded,
Figure BDA00033382980500001718
representation collection
Figure BDA00033382980500001719
Maximum time delay for task offloading of the medium vehicle; saRepresentation collection
Figure BDA0003338298050000181
Size of the medium vehicle task, sbRepresentation collection
Figure BDA0003338298050000182
Size of the medium vehicle task, paRepresentation collection
Figure BDA0003338298050000183
Calculated power of medium vehicle, pbRepresentation collection
Figure BDA0003338298050000184
Calculated power of medium vehicle, xiaRepresentation collection
Figure BDA0003338298050000185
Unloading ratio of (xi)bRepresentation collection
Figure BDA0003338298050000186
Unload ratio of (k) ("kappa")vRepresents the calculated coefficient of energy consumption of the vehicle,
Figure BDA0003338298050000187
representation collection
Figure BDA0003338298050000188
Computing power of the mission vehicle itself, λtRepresenting a delay factor, λeWhich represents a factor of the energy consumption,
Figure BDA0003338298050000189
representation collection
Figure BDA00033382980500001810
The transmission rate at which the medium duty vehicle TVi communicates with the service vehicle SV j,
Figure BDA00033382980500001811
representation collection
Figure BDA00033382980500001812
The transmission rate when the TVi and the SVj communicate is determined;
Figure BDA00033382980500001813
to the computing power of SVj, ki,jK is a vehicle matching factor indicating whether TVi selects SVj as a service objecta,jRepresentation collection
Figure BDA00033382980500001814
Whether the middle TVi selects SVj as a service object, kb,jRepresentation collection
Figure BDA00033382980500001815
Whether TVi in (1) selects SVj as the service object,
Figure BDA00033382980500001816
representation collection
Figure BDA00033382980500001817
The distance between TVi and SVj in (1),
Figure BDA00033382980500001818
representation collection
Figure BDA00033382980500001819
Distance between TVi and SVj, DV2VIndicating the communication distance of the task vehicle to the service vehicle.
When the communication mode is determined as V2I, the optimization objective CV2IComprises the following steps:
Figure BDA00033382980500001820
s.t.:C3,C4,C5,C7
this problem is partially the same as for V2V, there is a comparison term, so it will be the same
Figure BDA00033382980500001821
Two new sets are divided:
Figure BDA00033382980500001822
and
Figure BDA00033382980500001823
and the resources of the RSUs are allocated such that variables in the variables are coupled, which problem remains an unsolved problem, thus enabling
Figure BDA00033382980500001824
Taken into the above formula, one can obtain:
Figure BDA0003338298050000191
Figure BDA0003338298050000192
Figure BDA0003338298050000193
Figure BDA0003338298050000194
Figure BDA0003338298050000195
Figure BDA0003338298050000196
Figure BDA0003338298050000197
Figure BDA0003338298050000198
Figure BDA0003338298050000199
CV2Irepresenting the total system overhead of the offloading mode for offloading of the task vehicle to the roadside unit, ccRepresentation collection
Figure BDA00033382980500001910
In which the number of CPUs required for the mission of the vehicle is calculated, cdRepresentation collection
Figure BDA00033382980500001911
Calculating the number of CPUs required by the tasks of the vehicle;
Figure BDA00033382980500001912
representation collection
Figure BDA00033382980500001913
The maximum time delay for the task of the medium vehicle to be unloaded,
Figure BDA00033382980500001914
representation collection
Figure BDA00033382980500001915
Maximum time delay for task offloading of the medium vehicle; scRepresentation collection
Figure BDA00033382980500001916
Size of the medium vehicle task, sdRepresentation collection
Figure BDA00033382980500001917
Task size of middle vehicle, pcRepresentation collection
Figure BDA00033382980500001918
Calculated power of medium vehicle, pdRepresentation collection
Figure BDA00033382980500001919
Calculated power of medium vehicle, pcomRepresenting the calculated power of the road side unit RSU,
Figure BDA00033382980500001920
representation collection
Figure BDA00033382980500001921
The unloading ratio of (a) to (b),
Figure BDA00033382980500001922
representation collection
Figure BDA00033382980500001923
Unload ratio of (k) ("kappa")vRepresenting the calculated coefficient of energy consumption of the vehicle, fc avRepresentation collection
Figure BDA00033382980500001924
The local computing power of the vehicle in question,
Figure BDA00033382980500001925
representation collection
Figure BDA00033382980500001926
Local computing power of the vehicle, frsu-maxDenotes the maximum computing power, λ, of the RSUtRepresenting a delay factor, λeWhich represents a factor of the energy consumption,
Figure BDA00033382980500001927
representing RSU to set
Figure BDA00033382980500001928
The computing power allocated to the vehicle in (1),
Figure BDA00033382980500001929
representing RSU to set
Figure BDA00033382980500001930
Computing power assigned to a vehicle, representation set
Figure BDA00033382980500001931
The transmission rate at which the mission vehicle TVi communicates with the RSU,
Figure BDA00033382980500001932
representation collection
Figure BDA00033382980500001933
The transmission rate when the TVi communicates with the RSU;
Figure BDA00033382980500001934
is a set
Figure BDA00033382980500001935
The computing power of the mission vehicle is determined,
Figure BDA00033382980500001936
is a set
Figure BDA00033382980500001937
The computing power of the RSU;
Figure BDA00033382980500001938
representation collection
Figure BDA00033382980500001939
The distance between the medium TVi and the RSU,
Figure BDA00033382980500001940
representation collection
Figure BDA0003338298050000201
Distance between TVi and RSU, DV2IRepresenting the communication distance of TVi to RSU.
Preferably, the V2V optimization problem and the optimization condition are both related to the variable ξaAnd xibThe linear function of (a) is a convex optimization problem, the problem can be solved by using a CVX toolkit, the V2I optimization problem is a sum function of an exponential function and the linear function, the problem is a convex optimization problem, the problem is also solved by using the CVX toolkit, and the unloading ratio updating sum is similar to that of V2V.
And further, calculating an optimization target and judging whether the calculation result is converged, if so, outputting an optimal unloading ratio and an optimal resource allocation result obtained according to the optimal unloading ratio, if not, updating the unloading ratio according to the optimization target, performing iterative calculation until the result is converged to an optimal solution, outputting the optimal unloading ratio and the optimal resource allocation result obtained according to the optimal unloading ratio, and then, performing task unloading and resource allocation on the allocated channel allocation resources by the task vehicle according to the optimal unloading ratio.
Aiming at the problem of resource allocation of task unloading based on MEC calculation in a vehicle-mounted network system, the resource constraint is calculated and a mathematical model is established by considering the difference of a road side unit and a vehicle as service nodes, the task time delay and the communication distance, the problem is decomposed into three sub-problems to be solved jointly, the concept of vehicle matching degree is provided, and a reasonable unloading object is determined by utilizing a dynamic taboo length taboo search algorithm. And the image coloring algorithm is adopted to allocate channels, so that the channel interference is reduced. An optimal unloading strategy, a channel resource and computing resource allocation scheme are provided, and the computing resource allocation sub-problem is converted into a convex optimization problem through variable replacement to obtain an optimal unloading ratio. Compared with the prior art, the method and the device have the advantages that the time delay of task calculation is reduced, the energy consumption of each node in the system is balanced, and the performance in the aspects of energy consumption and time delay is improved.
The above-mentioned embodiments, which further illustrate the objects, technical solutions and advantages of the present invention, should be understood that the above-mentioned embodiments are only preferred embodiments of the present invention, and should not be construed as limiting the present invention, and any modifications, equivalents, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (10)

1. A resource allocation method based on MEC calculation task unloading in the car networking is characterized in that a task unloading strategy is determined by calculating the minimum total overhead in a car networking system, and system resources are allocated according to the task unloading strategy, and the method comprises the following steps:
s1: constructing a resource allocation framework based on MEC computing task unloading;
s2: determining an offloading pattern of the task vehicle according to a resource allocation framework for computing task offloading based on the MEC, the offloading pattern including offloading of the task vehicle to a service vehicle V2V and offloading of the task vehicle to a roadside unit V2I;
s3: determining an unloading decision according to the unloading mode and the initial unloading ratio of the task vehicle;
s4: allocating channel allocation resources for the selected service node according to the unloading decision; calculating the total system overhead according to the unloading decision;
s5: updating the unloading ratio according to the total system overhead;
s6: and repeating the steps S3-S5 to obtain the optimal unloading ratio, and performing task unloading and resource allocation on the channel allocation resources according to the optimal unloading ratio.
2. The method for allocating the resources based on the MEC computing task offloading in the Internet of vehicles according to claim 1, wherein constructing the resource allocation framework based on the MEC computing task offloading comprises:
acquiring vehicle information, and dividing the vehicle into a task vehicle TV and a service vehicle SV according to the vehicle information; taking a service vehicle SV and a road side unit RSU as a service node SN; when the task vehicle executes task unloading, dividing the task into local task unloading and unloading to a service node;
calculating the time delay and energy consumption of local task unloading;
offloading to a service node includes offloading to a road side unit, RSU, and offloading to a service vehicle, SV; calculating time delay and energy consumption unloaded to a Road Side Unit (RSU); the time delay and energy consumption for offloading to the service vehicle SV are calculated.
3. The method for allocating the resources based on the MEC calculation task uninstallation in the Internet of vehicles according to claim 1, wherein the formula for calculating the total overhead of the system is as follows:
Figure FDA0003338298040000021
s.t.
Figure FDA0003338298040000022
C2:
Figure FDA0003338298040000023
C3:
Figure FDA0003338298040000024
C4:
Figure FDA0003338298040000025
C5:
Figure FDA0003338298040000026
C6:
Figure FDA0003338298040000027
C7:
Figure FDA0003338298040000028
wherein the content of the first and second substances,
Figure FDA0003338298040000029
representing a collection of vehicles that have a mission to unload,
Figure FDA00033382980400000210
Cirepresenting the cost of a mission vehicle, C representing the total system cost, λtRepresenting a delay factor, λeRepresents the energy consumption factor, TiRepresenting the system delay, EiRepresents the system energy consumption; giDenotes the offload decision factor, ki,jWhich is indicative of a vehicle matching factor,
Figure FDA00033382980400000211
representing the computing resources allocated by the RSU to TVi, frsu -maxRepresenting maximum computing power, ξ, of the RSUiThe ratio of the task to be unloaded is indicated,
Figure FDA00033382980400000212
representing the maximum tolerant time delay of the TVi task; thetai,kIndicates whether a subchannel k is allocated to TV or noti，
Figure FDA00033382980400000213
Represents the distance between the TVi and the RSU,
Figure FDA00033382980400000214
representing the distance between TVi and SVj.
4. The method for allocating the resource based on the MEC calculation task unloading in the internet of vehicles according to claim 1, wherein the determining the unloading mode of the task vehicle comprises: defining a task matching degree M, wherein the task matching degree is equal to the weighted sum of task calculation time delay and distance of an ith task vehicle TVi selecting a jth service vehicle SVj as an unloading object, and the distance refers to the distance between the task vehicle and a service node; and calculating the maximum task matching degree of the task, and searching the most appropriate task unloading object for the task vehicle according to the maximum task matching degree.
5. The resource allocation method based on MEC calculation task uninstallation in the internet of vehicles according to claim 4, wherein the formula for calculating the maximum task matching degree is as follows:
Figure FDA0003338298040000031
s.t.C2:
Figure FDA0003338298040000032
C7:
Figure FDA0003338298040000033
wherein the content of the first and second substances,
Figure FDA00033382980400000312
indicating the initial unloading ratio, alphatAnd alphadWeight values respectively representing the calculation time and the distance;
Figure FDA0003338298040000034
set of vehicles representing mission-required unloading, NSSet of vehicles, k, representing service vehiclesi,jRepresenting a vehicle matching factor, PiIndicating the position of vehicle i, PjIndicating vehiclesThe j position;
Figure FDA0003338298040000035
represents tiDistance between times TVi and SVj, DV2VIndicating the communication distance of the task vehicle to the service vehicle.
6. The method for allocating the resources for task offloading based on the MEC calculation in the internet of vehicles according to claim 4, wherein the process of finding the most suitable task offloading object for the task vehicle comprises: the method for searching for a proper service object for the service vehicle SV by adopting a dynamic taboo length taboo search pairing algorithm based on the task matching degree comprises the following steps:
s1: setting an initial solution and an initial tabu table, wherein the initial tabu table is empty;
s2: calculating the neighborhood of the current solution, setting the calculated neighborhood as a tabu object, and filling the tabu object into an initial tabu table to obtain an optimal tabu solution and a non-optimal tabu solution;
s3: judging whether the task matching degree M jointly determined by the optimal taboo solution and the optimal non-taboo solution is superior to the existing optimal solution M', if so, updating the optimal solution, otherwise, not updating the optimal solution, and returning to S2; when the difference value of M and M' is smaller than the minimum value epsilon or reaches the maximum iteration number, stopping updating;
s4: according to the optimal solution, task vehicles are gathered
Figure FDA0003338298040000036
Is divided into sets
Figure FDA0003338298040000037
And
Figure FDA0003338298040000038
wherein, aggregate
Figure FDA0003338298040000039
Set of vehicles indicating selection of the V2I unload mode
Figure FDA00033382980400000310
Indicating a collection of vehicles with the V2V unload mode selected.
7. The method for allocating the resources based on the MEC computing task offloading in the internet of vehicles according to claim 1, wherein allocating the resources for the selected service node according to the offloading decision comprises:
s1: set of vehicles to be tasked for unloading
Figure FDA00033382980400000311
Task maximum tolerance time of vehicle TV according to task
Figure FDA0003338298040000041
Sorting in ascending order;
s2: respectively making the first K sequenced TVs into K colors, and finishing channel allocation of the first K TVs according to each color corresponding to one channel;
s3: adopting a graph coloring algorithm to color the TV of the unallocated channel and updating a channel allocation matrix;
s4: according to the expected time delay
Figure FDA0003338298040000042
Maximum tolerant delay with TVi task
Figure FDA0003338298040000043
Sequentially judging the TV color of the unallocated channel if
Figure FDA0003338298040000044
Is less than
Figure FDA0003338298040000045
TVi is assigned k colors, i.e., k channels are assigned to the TV; if it is not
Figure FDA0003338298040000046
Is greater than
Figure FDA0003338298040000047
TVi cannot be assigned to k colors, i.e., k channels are not assigned to TVi.
8. The method for allocating the resources based on the MEC calculation task unloading in the Internet of vehicles according to claim 7, wherein the calculation formula of the predicted time delay is as follows:
Figure FDA0003338298040000048
wherein the content of the first and second substances,
Figure FDA0003338298040000049
to achieve TVi transmission rate on k channel in the offloading mode of the task vehicle to the roadside unit V2I,
Figure FDA00033382980400000410
to achieve TVi transmission rate on k-channel in the task vehicle offload to service vehicle V2V offload mode,
Figure FDA00033382980400000411
representing the preliminary unloading ratio of vehicle i, ciIndicating the number of CPUs, s, required for vehicle i to calculate the vehicle's missioniIndicating the size of the vehicle task in vehicle i, giA decision factor for the offloading is represented, which,
Figure FDA00033382980400000412
representing the local computing power of the vehicle i,
Figure FDA00033382980400000413
representing the average allocation of the RSU to the computing resources of TVi, NS representing the vehicle set of the service vehicle, ki,jWhich is indicative of a vehicle matching factor,
Figure FDA00033382980400000414
representing the computational power of SVj.
9. The method for allocating the resources based on the MEC calculation task offloading in the internet of vehicles according to claim 1, wherein the process of performing the task offloading and the resource allocation on the channel allocation resources according to the optimal offloading ratio comprises:
upon determining the offload mode and channel allocation, the task vehicle TVs are divided into vehicle sets
Figure FDA00033382980400000415
And
Figure FDA00033382980400000416
divide the optimization objective into CV2IAnd CV2VRespectively solving the optimization targets;
comparing the local computation time with the time offloaded to the SV
Figure FDA00033382980400000417
Divided into two new sets
Figure FDA00033382980400000418
And
Figure FDA00033382980400000419
when in use
Figure FDA00033382980400000420
The task vehicle TVi belongs to
Figure FDA00033382980400000421
When in use
Figure FDA00033382980400000422
TVi belongs to
Figure FDA00033382980400000423
Comparing the locally calculated time with the time of offloading to the road side unit RSU
Figure FDA00033382980400000424
Divided into two new sets
Figure FDA0003338298040000051
And
Figure FDA0003338298040000052
when in use
Figure FDA0003338298040000053
When TVi belongs to
Figure FDA0003338298040000054
When in use
Figure FDA0003338298040000055
TVi belongs to
Figure FDA0003338298040000056
Wherein the content of the first and second substances,
Figure FDA0003338298040000057
representing the locally calculated time delay of the mission vehicle TVi,
Figure FDA0003338298040000058
indicating an unloading delay for the task to be unloaded to the service vehicle,
Figure FDA0003338298040000059
indicating the offload delay of the task to the roadside unit.
10. The method of claim 9 for MEC-based task offloading in a vehicle networkingThe resource allocation method is characterized in that the process of solving the optimization objective comprises the following steps: when the unloading mode is determined that the task vehicle is unloaded to the service vehicle V2V, CV2VThe optimization target is as follows:
Figure FDA00033382980400000510
s.t.
Figure FDA00033382980400000511
Figure FDA00033382980400000512
Figure FDA00033382980400000513
Figure FDA00033382980400000514
Figure FDA00033382980400000515
Figure FDA00033382980400000516
wherein, CV2VRepresenting the system overhead of the offload mode for offloading task vehicles to service vehicles, caRepresentation collection
Figure FDA00033382980400000517
In which the number of CPUs required for the mission of the vehicle is calculated, cbRepresentation collection
Figure FDA00033382980400000518
Calculating the number of CPUs required by the tasks of the vehicle;
Figure FDA00033382980400000519
representation collection
Figure FDA00033382980400000520
The maximum time delay for the task of the medium vehicle to be unloaded,
Figure FDA00033382980400000521
representation collection
Figure FDA00033382980400000522
Maximum time delay for task offloading of the medium vehicle; saRepresentation collection
Figure FDA00033382980400000523
Size of the medium vehicle task, sbRepresentation collection
Figure FDA00033382980400000524
Size of the medium vehicle task, paRepresentation collection
Figure FDA00033382980400000525
Calculated power of medium vehicle, pbRepresentation collection
Figure FDA00033382980400000526
Calculated power of medium vehicle, xiaRepresentation collection
Figure FDA00033382980400000527
Unloading ratio of (xi)bRepresentation collection
Figure FDA00033382980400000528
Unload ratio of (k) ("kappa")vRepresents the calculated coefficient of energy consumption of the vehicle,
Figure FDA00033382980400000529
representation collection
Figure FDA00033382980400000530
Computing power of the mission vehicle itself, λtRepresenting a delay factor, λeWhich represents a factor of the energy consumption,
Figure FDA0003338298040000061
representation collection
Figure FDA0003338298040000062
The transmission rate at which the mission vehicle TVi communicates with the service vehicle SVj,
Figure FDA0003338298040000063
representation collection
Figure FDA0003338298040000064
The transmission rate when the TVi and the SVj communicate is determined;
Figure FDA0003338298040000065
to the computing power of SVj, ki,jK is a vehicle matching factor indicating whether TVi selects SVj as a service objecta,jRepresentation collection
Figure FDA0003338298040000066
Whether the middle TVi selects SVj as a service object, kb,jRepresentation collection
Figure FDA0003338298040000067
Whether TVi in (1) selects SVj as the service object,
Figure FDA0003338298040000068
representation collection
Figure FDA0003338298040000069
The distance between TVi and SVj in (1),
Figure FDA00033382980400000610
representation collection
Figure FDA00033382980400000611
Distance between TVi and SVj, DV2VRepresenting a communication distance of the task vehicle to the service vehicle;
after the unloading mode is determined to be that the task vehicle is unloaded to the roadside unit V2I, the optimization objective CV2IComprises the following steps:
Figure FDA00033382980400000612
s.t.
Figure FDA00033382980400000613
Figure FDA00033382980400000614
Figure FDA00033382980400000615
Figure FDA00033382980400000616
Figure FDA00033382980400000617
Figure FDA00033382980400000618
Figure FDA00033382980400000619
Figure FDA00033382980400000620
wherein, CV2IRepresenting the total system overhead of the offloading mode for offloading of the task vehicle to the roadside unit, ccRepresentation collection
Figure FDA00033382980400000621
In which the number of CPUs required for the mission of the vehicle is calculated, cdRepresentation collection
Figure FDA00033382980400000622
Calculating the number of CPUs required by the tasks of the vehicle;
Figure FDA00033382980400000623
representation collection
Figure FDA00033382980400000624
The maximum time delay for the task of the medium vehicle to be unloaded,
Figure FDA00033382980400000625
representation collection
Figure FDA00033382980400000626
Maximum time delay for task offloading of the medium vehicle; scRepresentation collection
Figure FDA00033382980400000627
Size of the medium vehicle task, sdRepresentation collection
Figure FDA00033382980400000628
Task size of middle vehicle, pcRepresentation collection
Figure FDA0003338298040000071
Calculated power of medium vehicle, pdRepresentation collection
Figure FDA0003338298040000072
Calculated power of medium vehicle, pcomRepresenting the calculated power of the road side unit RSU,
Figure FDA0003338298040000073
representation collection
Figure FDA0003338298040000074
The unloading ratio of (a) to (b),
Figure FDA0003338298040000075
representation collection
Figure FDA0003338298040000076
Unload ratio of (k) ("kappa")vRepresents the calculated coefficient of energy consumption of the vehicle,
Figure FDA0003338298040000077
representation collection
Figure FDA0003338298040000078
The local computing power of the vehicle in question,
Figure FDA0003338298040000079
representation collection
Figure FDA00033382980400000710
Local computing power of the vehicle, frsu-maxDenotes the maximum computing power, λ, of the RSUtRepresenting a delay factor, λeWhich represents a factor of the energy consumption,
Figure FDA00033382980400000725
representing RSU to set
Figure FDA00033382980400000711
The computing power allocated to the vehicle in (1),
Figure FDA00033382980400000712
representing RSU to set
Figure FDA00033382980400000713
Computing power assigned to a vehicle, representation set
Figure FDA00033382980400000714
The transmission rate at which the mission vehicle TVi communicates with the RSU,
Figure FDA00033382980400000715
representation collection
Figure FDA00033382980400000716
The transmission rate when the TVi communicates with the RSU;
Figure FDA00033382980400000717
is a set
Figure FDA00033382980400000718
The computing power of the mission vehicle is determined,
Figure FDA00033382980400000719
is a set
Figure FDA00033382980400000720
The computing power of the RSU;
Figure FDA00033382980400000721
representation collection
Figure FDA00033382980400000722
Between TVi and RSUThe distance of (a) to (b),
Figure FDA00033382980400000723
representation collection
Figure FDA00033382980400000724
Distance between TVi and RSU, DV2IRepresenting the communication distance of TVi to RSU.
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