CN112737842B - Task safety unloading method based on minimized time delay in air-ground integrated Internet of vehicles - Google Patents
Task safety unloading method based on minimized time delay in air-ground integrated Internet of vehicles Download PDFInfo
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Abstract
The invention discloses a task safety unloading method based on minimum time delay in an air-ground integrated vehicle networking, which comprises the steps of firstly constructing an air-ground integrated vehicle networking model supporting mobile edge calculation, secondly analyzing and modeling a safe transmission mode from a vehicle to an unmanned aerial vehicle edge server and a calculation mode from a local vehicle to the unmanned aerial vehicle edge server, formalizing an air-ground integrated vehicle networking task unloading problem into a multi-target optimization problem related to edge server selection, transmission rate, resource distribution and task unloading and aiming at the minimum time delay, and solving by combining a condition relaxation-numerical value reduction rule and a Lagrange dual decomposition method.
Description
Technical Field
The invention belongs to the technical field of vehicle networks, and particularly relates to a task safety unloading method in a vehicle network.
Background
With the arrival of the 5G internet of everything age, the number of mobile terminals in the internet of vehicles is increased dramatically, and the equipment heterogeneous degree is increased remarkably. Meanwhile, with heavy computing tasks such as intelligent driving or the rise of services with high real-time requirements, wireless resources and mobile terminals face huge challenges. Mobile terminals have limited computing power and memory compared to network core devices. Thus, in the internet of vehicles, it would be a challenge to rely solely on vehicle end devices to accomplish the computationally intensive tasks. The cloud computing unloads the computing task of the mobile terminal to the cloud server, so that the pressure of the terminal equipment is greatly reduced. However, for a large number of device access scenarios such as the internet of vehicles, the flooding of mass data into the cloud computing center will cause core network congestion, and reduce service experience.
Therefore, as another calculation offloading scheme, moving edge calculation has been receiving wide attention in recent years. The mobile edge computing pulls down computing resources from a far-end cloud to a wireless access network side, and computing services are provided for mobile terminals such as vehicles nearby by allocating computing, processing and storing capabilities to network edge nodes such as roadside units. Compare in high in the clouds, edge nodes such as roadside unit are closer to the vehicle, can accomplish the processing to the car networking data locally more high-efficiently.
But on a large spatial scale, obstacles, complex terrain, geographical areas that are difficult to access, bad weather, etc. all may cause the communication link between the vehicle-roadside unit to be broken or the quality of service to be degraded. In particular, in some extreme environments, infrastructure such as roadside units and the like are lacked to assist vehicle communication, and the requirement of users for unloading mass tasks is difficult to meet by simply depending on the ground vehicle networking.
In order to solve the problems, in the first prior art, an air-ground vehicle-mounted cooperative communication system is designed and realized, an air subnet is formed by a plurality of unmanned aerial vehicles, obstacle detection and navigation are performed on the ground by using cameras and GPS information on the unmanned aerial vehicles, ground vehicles can be assisted to transmit road surface information, and the system can be applied to emergency scenes such as road rescue, first-site communication of accidents and the like.
In the method, joint optimization between migration time of a vehicle-mounted computing task and load balancing of edge equipment is realized by constructing a resource model and an execution time model of a vehicle-mounted computing system, an effective computing migration strategy is found for the vehicle-mounted computing task, the vehicle-mounted computing task is completed within expected time, the load balancing of the edge computing equipment is ensured, and requirements of service migration time optimization and resource load optimization of the edge equipment for the vehicle-mounted task in the vehicle-mounted networking can be effectively met.
But the problems of the prior art are as follows:
(1) The air-ground vehicle-mounted cooperative communication system designed in the first prior art fails to unload and timely process the task of the information acquired by the unmanned aerial vehicle camera and the GPS information, and meanwhile, the acquisition of mass data can lead to the rapid increase of the task response delay.
(2) In the prior art, the migration time of two pairs of vehicle-mounted computing tasks and the load balance of edge equipment are jointly optimized, but the method does not consider the task unloading proportion, all tasks are unloaded to the edge nodes of the network, the working strength of the edge nodes is increased, the task is not considered to be locally computed simultaneously with the edge nodes, and the task processing time delay and the working strength of the edge nodes are reduced.
(3) One of the major problems faced by the internet of vehicles is the problem of privacy disclosure, which needs to guarantee the privacy of user data while providing computing services. Due to the openness of radio waves, data transmitted by the mobile terminals (vehicles) of the first and second prior art to the edge node through the wireless channel will be directly exposed to the eavesdropper, and the computation results returned by the edge node to the terminal are also exposed to the eavesdropper.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a task safety unloading method based on minimum time delay in an air-ground integrated vehicle networking, which comprises the steps of firstly constructing an air-ground integrated vehicle networking model supporting mobile edge calculation, secondly analyzing and modeling a safe transmission mode from a vehicle to an unmanned aerial vehicle edge server and a calculation mode of a local vehicle and the unmanned aerial vehicle edge server, formalizing an air-ground integrated vehicle networking task unloading problem into a multi-target optimization problem which is related to edge server selection, transmission rate, resource distribution and task unloading and aims at the minimum time delay, and solving by combining a condition relaxation-numerical value reduction rule and a Lagrange dual decomposition method.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1: constructing an air-ground integrated Internet of vehicles model supporting mobile edge calculation;
step 1-1: one-way roads covered by M drones, each drone deploying an edge server, representing a set of M drones with M = { 1., M }; dividing a road into M sections, wherein the length of each section of road is L = { L = } 1 ,L 2 ,...,L M }; assuming that N vehicles arrive at the starting point of the road, the arrival of the vehicles obeys Poisson distribution, each vehicle has a task to be processed, and the task is expressed as
i ∈ N = {1,2,.., N }, j ∈ Μ = {1,2,.., M }, where d ∈ N = {1,2,..,. N }, where j ∈ Μ = {1,2,. M }, where d ∈ N = i Representing computational tasks xi i Size of (c) i Representing tasks xi i Computing resources required for execution, T th Representing tasks xi i Need to be at T th Internal completion of lambda i,j For the task unload ratio, representing the task xi i The ratio of the task amount unloaded to the edge server deployed by the jth unmanned aerial vehicle to the total task amount;
task xi i Split into two parts that execute in parallel on the local and edge servers; the method specifically comprises the following steps: vehicle i will task λ i,j d i Unloaded to the jth edge server for processing, and vehicle i processes the remaining tasks (1-lambda) i,j )d i Carrying out local processing; suppose that each vehicle can only select one edge server for computational offloading, x i,j Represents a selection decision for vehicle i, x if vehicle i is task off-loaded i,j =1, x if vehicle i is not tasked off-load i,j =0, i.e. x i,j ∈{0,1}；
Step 1-2: task XI i The processing delay of (2) is composed of three parts:
part 1: time required for vehicle i to move from start point to drone j
Namely that
L k ∈L，L k Indicates the length of the k-th road, v i Represents the moving speed of the vehicle i, and k represents the road number;
part 2: vehicle i transmits task xi through wireless channel i Transfer time to offload to drone j
Part 3: unmanned aerial vehicle j processing task xi i Time required
Step 2: constructing a safe transmission model from a vehicle to an unmanned aerial vehicle edge server;
step 2-1: the vehicle and the unmanned aerial vehicle communicate based on an IEEE 802.11p protocol, and the protocol adopts a carrier sense multiple access/collision avoidance mechanism to transmit data;
step 2-2: each vehicle is provided with K antennas, task unloading is carried out on an edge server installed on the unmanned aerial vehicle, meanwhile, a plurality of eavesdroppers exist in the network, and the eavesdropping mode of the eavesdroppers is passive eavesdropping; assuming that the positions and the number of a plurality of eavesdroppers are unknown, and independently receiving and decoding the secret information among the eavesdroppers;
step 2-3: the vehicle uses the Wyner coding scheme for secret information transmission, and the following relations exist:
C i,j -R s ＝R e ≤R th (1)
wherein, C i,j Indicates vehicle i-noneChannel capacity, R, of man-machine j s Representing the safe transmission rate, R, of the vehicle i-UAV j e Indicating the eavesdropping rate, R th Representing a maximum allowable eavesdropping rate; when eavesdropping rate R e Less than the maximum allowable eavesdropping rate R th When the unmanned aerial vehicle is used, the information received by the edge server installed on the unmanned aerial vehicle cannot be intercepted by other equipment, namely, the safe information transmission is realized;
and step 3: building calculation models of a local vehicle and an unmanned aerial vehicle edge server;
step 3-1: the vehicle adopts a transmission/permission transmission mode to reserve a channel; vehicle i is sending task xi i Firstly, sending an RTS control frame to apply for occupying a channel; if the channel is idle, the unmanned aerial vehicle j receives the RTS frame and sends a CTS response frame after the time interval SIFS, and the RTS frame and the CTS frame comprise a task xi i A duration required for the transmission; the vehicle i receives the CTS frame and starts to transmit the task xi after the time interval SIFS i Receiving task xi by unmanned plane j i Feeding back an ACK frame to the vehicle i; meanwhile, other vehicles update own network allocation vectors according to the transmission duration information in the RTS/CTS frame, and postpone the time of accessing the channel;
step 3-2: successful transmission of calculation task xi between vehicle i and unmanned aerial vehicle j i Time of
Expressed as:
wherein the content of the first and second substances,
representing tasks xi i The overhead of the header time of (a),
representing propagation delay, T SIFS 、T ACK 、T DIFS 、T RTS And T CTS Respectively representing the durations of the SIFS frame, the ACK frame, the DIFS frame, the RTS frame and the CTS frame;
representing tasks xi i Is expressed as:
wherein, B j Representing the channel bandwidth of drone j,
indicating i-transmission task xi of vehicle i Transmission power of time, G i,j Representing the channel gain between vehicle i and drone j;
time of channel collision
Expressed as:
thus, the system normalizes the throughput H i,j Expressed as:
wherein the content of the first and second substances,
the probability of the idle channel of the time slot is represented, and the idle channel means that all vehicles in a certain time slot do not send calculation tasks to the unmanned aerial vehicle j;
representing tasks xi i Probability of being successfully transmitted, successful transmission means that only vehicle i sends calculation task xi to UAV j in a certain time slot i ；
According to formulae (3) to (6), the task xi i Unloading task λ from vehicle i i,j d i Time of flight to drone j
Expressed as:
local calculation task (1-lambda) of vehicle i i,j )d i Calculated time of
Expressed as:
wherein f is i Representing the computing resources of vehicle i;
step 3-3: let F j Total computational resources for drone j, f i,j Represents the computational resources allocated by drone j to vehicle i, i.e., f = { f = { (f) 1,j ,f 2,j ,...,f N,j }; the sum of the computing resources allocated by the edge server to all vehicles cannot exceed the computing resources owned by the server itself, i.e. the edge server has
Thus, the computation time of the edge server
Comprises the following steps:
due to the task xi i Can be executed on the vehicle i and the unmanned plane j in parallel, so the task xi i Processing delay of
Expressed as:
wherein, the first and the second end of the pipe are connected with each other,
representing tasks xi i The processing delay at drone j,
and 4, step 4: constructing an objective function and an optimization condition of task safety unloading based on a safety transmission model and a calculation model;
the objective function and the optimization condition of task safety unloading for minimizing time delay are formalized into an optimization problem under a multi-constraint condition:
wherein x represents x i,j In (b), λ represents λ i,j A set of (a);
the purpose of equation (11) is to minimize the processing delay
C1.1 ensuring the task xi i Local processing latency of
And edge server processing time
Not exceeding the maximum allowable delay T th (ii) a C1.2 and C1.3 indicate that each vehicle selects only one edge service for task offloading; c1.4 and C1.5 ensure that the sum of the computing resources allocated to all tasks by an edge server does not exceed the computation of the edge serverThe total amount of resources; c1.6 and C1.7 indicate that the unloaded calculated task rate of each vehicle cannot exceed 1;
and 5: based on an objective function and an optimization condition of task safety unloading, solving through a condition relaxation-numerical value reduction rule and a Lagrange dual decomposition method to construct a task safety unloading method based on minimum time delay;
step 5-1: order to
And given f and λ, the edge server selection problem is expressed as:
in P2, x i,j E {0,1} is an integer program of 0-1, the objective function
Is about x i,j A non-linear function of (d); therefore, the constraint C3.5 is relaxed and x is i,j The epsilon {0,1} relaxation is 0 ≦ x i,j Less than or equal to 1; p2 is represented as P3:
let x * ＝[x 1 ,x 2 ,...,x n ] T Represents the optimal solution, x, of the relaxation problem P3 * ∈[0,1]Wherein n is the number of solutions; using a numerical rule pair x * Rounding off and rounding up;
step 5-2: the optimal edge server is obtained according to the formula (13), and after x is given, the original problem is split into two sub-problems: namely a resource allocation problem P4 given x and λ and a computational offload proportion decision problem P5 given f and x, respectively, expressed as:
wherein the content of the first and second substances,
and kappa i,j Respectively as follows:
wherein the content of the first and second substances,
is composed of
The upper bound of (c);
the problems P4 and P5 are convex problems, and a Lagrange dual decomposition method is adopted to solve f i,j And λ i,j Until convergence, obtain the optimum f i,j And λ i,j 。
The invention has the following beneficial effects:
aiming at the problems in the prior art, the invention combines the mobile edge calculation with the local calculation, can unload and process the tasks of the acquired mass data in time, effectively reduces the task processing time delay, and improves the successful processing proportion of the tasks.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is an air-ground integrated Internet of vehicles supporting mobile edge computing according to the method of the invention.
FIG. 3 is a diagram of a vehicle/edge server equipartition calculation (the task is processed on the edge server half and processed locally, i.e. λ) with ground-based vehicle networking (the task off-loading method adopts the invention) and vehicle/edge server equipartition calculation provided by an embodiment of the invention i,j = 0.5), vehicle local computation (tasks are all computed locally, not uploaded to the edge server for processing, i.e. λ i,j = 0) performance comparison graph for comparing the successful processing ratios of tasks.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
As shown in fig. 1, a task safety unloading method based on minimized time delay in an air-ground integrated vehicle networking includes the following steps:
step 1: as shown in fig. 2, constructing an air-ground integrated internet of vehicles model supporting mobile edge calculation;
step 1-1: a one-way road covered by M drones, each drone deploying an edge server, the set of M drones being represented by M = { 1.,. M }; dividing a road into M sections, wherein the length of each section of road is L = { L = } 1 ,L 2 ,...,L M }; assuming that N vehicles arrive at the starting point of the road, the arrival of the vehicles obeys Poisson distribution, each vehicle has a task to be processed, and the task is expressed as
i ∈ N = {1,2,.., N }, j ∈ M = {1,2,.., M }, where d ∈ N = {1,2,.., N }, where j ∈ M = i Representing a computational task xi i Size of (c) i Representing tasks xi i Computing resources required for execution, T th Representing tasks xi i Need to be at T th Internal completion of lambda i,j Representing the task xi for a task unload ratio i Workload to offload to edge server deployed by jth droneThe ratio of the total amount of tasks;
task xi i Split into two parts that execute in parallel on the local and edge servers; the method specifically comprises the following steps: vehicle i will task lambda i,j d i Off-load to jth edge server for processing, vehicle i pair the remaining tasks (1- λ) i,j )d i Carrying out local processing; let us assume that each vehicle can only select one edge server for computational offloading, x i,j Represents a selection decision for vehicle i, x if vehicle i is task off-loaded i,j =1, x if vehicle i is not tasked off-load i,j =0, i.e. x i,j ∈{0,1}；
Step 1-2: task xi i The processing delay of (2) is composed of three parts:
part 1: time required for vehicle i to move from origin to drone j
Namely, it is
L k ∈L，L k Indicates the length of the k-th road, v i Represents the moving speed of the vehicle i, and k represents the road number;
part 2: vehicle i will task xi through wireless channel i Transfer time to unload to drone j
Part 3: unmanned aerial vehicle j processing task xi i Time required
Step 2: constructing a safe transmission model from a vehicle to an unmanned aerial vehicle edge server;
step 2-1: the vehicle and the unmanned aerial vehicle communicate based on an IEEE 802.11p protocol, and the protocol adopts a carrier sense multiple access/collision avoidance mechanism to transmit data;
step 2-2: each vehicle is provided with K antennas, task unloading is carried out on an edge server installed on the unmanned aerial vehicle, meanwhile, a plurality of eavesdroppers exist in a network, and the eavesdropping mode of the eavesdroppers is passive eavesdropping; the positions and the number of a plurality of eavesdroppers are unknown, and the eavesdroppers independently receive and decode the secret information;
step 2-3: the vehicle uses the Wyner coding scheme for secret information transmission, and the following relations exist:
C i,j -R s ＝R e ≤R th (1)
wherein, C i,j Represents the channel capacity, R, of vehicle i-drone j s Representing the safe transmission rate, R, of vehicle i-drone j e Indicating the eavesdropping rate, R th Representing a maximum allowed interception rate; when eavesdropping rate R e Less than the maximum allowable eavesdropping rate R th When the unmanned aerial vehicle is used, the information received by the edge server installed on the unmanned aerial vehicle cannot be eavesdropped by other equipment, namely, the safe information transmission is realized;
and step 3: building calculation models of a local vehicle and an unmanned aerial vehicle edge server;
step 3-1: the vehicle adopts a transmission/transmission permission mode to reserve a channel; vehicle i is sending a task xi i Firstly, sending an RTS control frame to apply for occupying a channel; if the channel is idle, the unmanned aerial vehicle j receives the RTS frame and sends a CTS response frame after the time interval SIFS, and the RTS frame and the CTS frame comprise a task xi i The duration of time required for transmission; the vehicle i receives the CTS frame and starts to send the task xi after a time interval SIFS i The unmanned aerial vehicle j receives the task xi i Feeding back an ACK frame to the vehicle i; meanwhile, other vehicles update own network allocation vectors according to the transmission duration information in the RTS/CTS frame, and postpone the time of accessing the channel;
step 3-2: successful transmission of calculation task xi between vehicle i and unmanned aerial vehicle j i Time of
Expressed as:
wherein the content of the first and second substances,
representing tasks xi i The overhead of the header time of (a),
representing propagation delay, T SIFS 、T ACK 、T DIFS 、T RTS And T CTS Respectively representing the durations of the SIFS frame, the ACK frame, the DIFS frame, the RTS frame and the CTS frame;
representing tasks xi i Is expressed as:
wherein, B j Indicating the channel bandwidth for drone j,
indicating i-transmission task xi of vehicle i Transmission power of time, G i,j Representing the channel gain between vehicle i and drone j;
time of channel collision
Expressed as:
thus, the system normalizes the throughput H i,j Expressed as:
wherein the content of the first and second substances,
the probability of the idle channel of the time slot is represented, and the idle channel means that all vehicles in a certain time slot do not send calculation tasks to the unmanned aerial vehicle j;
representing tasks xi i Probability of being successfully transmitted, successful transmission means that only vehicle i sends calculation task xi to UAV j in a certain time slot i ；
According to formulae (3) to (6), the task xi i Unloading task λ from vehicle i i,j d i Time of flight to drone j
Expressed as:
local calculation task (1-lambda) of vehicle i i,j )d i Calculated time of
Expressed as:
wherein f is i Representing the computing resources of vehicle i;
step 3-3: the computational resources of the unmanned plane j are limited, so that the limited computational resources need to be distributed to N vehicles as uniformly as possible to ensure that the tasks can be completed on time; let F j Total computational resources for drone j, f i,j Representing the computing resources allocated to vehicle i by drone j, i.e., f = { f 1,j ,f 2,j ,...,f N,j }; the sum of computing resources allocated by the edge server to all vehicles cannotBeyond the computing resources owned by the server itself, i.e.
Thus, the computation time of the edge server
Comprises the following steps:
due to the task xi i Can be executed on the vehicle i and the unmanned plane j in parallel, so the task xi i Processing delay of
Expressed as:
wherein, the first and the second end of the pipe are connected with each other,
representing tasks xi i The processing delay at drone j,
and 4, step 4: constructing an objective function and an optimization condition of task safety unloading based on a safety transmission model and a calculation model;
the objective function and the optimization condition of task safety unloading with minimized time delay are formalized into an optimization problem under a multi-constraint condition:
wherein x represents x i,j In which λ represents λ i,j A set of (a);
the purpose of equation (11) is to minimize processing delay
C1.1 ensuring the task xi i Local processing latency of
And edge server processing time
Not exceeding a maximum allowable delay T th (ii) a C1.2 and C1.3 indicate that each vehicle selects only one edge service for task offloading; c1.4 and C1.5 ensure that the sum of the computing resources distributed to all tasks by the edge server does not exceed the total computing resources of the edge server; c1.6 and C1.7 indicate that the unloaded calculation task rate of each vehicle cannot exceed 1;
and 5: based on an objective function and an optimization condition of task safety unloading, solving through a condition relaxation-numerical value reduction rule and a Lagrange dual decomposition method to construct a task safety unloading method based on minimum time delay;
step 5-1: order to
And given f and λ, the edge server selection problem is expressed as:
in P2, x i,j E {0,1} is an integer program of 0-1, the objective function
Is about x i,j A non-linear function of (d); therefore, the constraint C3.5 is relaxed, and x is i,j The epsilon {0,1} relaxation is 0 ≦ x i,j Less than or equal to 1; p2 is represented by P3:
let x * ＝[x 1 ,x 2 ,...,x n ] T Represents the optimal solution, x, of the relaxation problem P3 * ∈[0,1]Wherein n is the number of solutions; using numerical value reduction rule to x * Rounding off and rounding up;
step 5-2: the optimal edge server is obtained according to the formula (13), and after x is given, the original problem is split into two sub-problems: namely a resource allocation problem P4 given x and λ and a computational offload proportion decision problem P5 given f and x, respectively, expressed as:
wherein the content of the first and second substances,
and kappa i,j Respectively as follows:
wherein the content of the first and second substances,
is composed of
The upper bound of (c);
the problems P4 and P5 are convex problems, and a Lagrangian dual decomposition method is adopted to solve f i,j And λ i,j Until convergence, obtain the optimum f i,j And λ i,j 。
In summary, the task security unloading method for minimizing the time delay specifically includes: decomposing the optimization problem P1, and solving x by using a conditional relaxation-numerical reduction rule under the condition of given f and lambda i,j (ii) a Then, based on the selected edge server x i,j Solving for f by adopting a Lagrange dual decomposition method i,j And λ i,j Until convergence, optimal f is obtained i,j And λ i,j 。
The specific embodiment is as follows:
the embodiment simulates the task safety unloading method based on the minimum time delay in the air-ground integrated Internet of vehicles and the existing mechanism based on the same network parameters, and verifies the superiority of the method. The method comprises the following specific steps: the vehicle transmitting power is 0.25 watt, the unmanned aerial vehicle transmitting power is 1 watt, the number of edge servers installed on the unmanned aerial vehicle is 5, the number of vehicles is 0-60, the unmanned aerial vehicle computing resource is 20GHz, the size of tasks is 10MB, the task generating frequency is 30 s/piece, the maximum time delay allowed for task processing is 10s, and the result is an average value after 10000 times of simulation.
The invention is connected with a ground vehicle network (the task unloading method adopts the invention), and the vehicle/edge server is equally divided and calculated (half of the task is processed on the edge server, and half is processed locally, namely lambda is i,j = 0.5), vehicle local computation (tasks are all computed locally, not uploaded to the edge server for processing, i.e. λ i,j = 0) was compared with the ratio of successful processing of the tasks, and the result is shown in fig. 3.
As can be seen from FIG. 3, the present invention outperforms existing mechanisms in terms of the proportion of successful processing of tasks. In particular, the present invention enables the processing of tasks with minimal vehicle local computing resources as compared to existing mechanisms. The invention can fully utilize the resources of the edge server in the whole air-ground integrated vehicle networking to complete the distribution and processing of tasks. At the same time, the channel between vehicle-drone has better quality of service than the channel between vehicle-RSU, installing edge servers on the drone can offload tasks faster. The air-to-ground integrated internet of vehicles supporting mobile edge computing may therefore provide lower latency and more reliable computing services for users.
To sum up, the task safety unloading method based on the minimum time delay in the air-ground integrated vehicle networking provided by the embodiment of the invention is used for solving the problems in the prior art, firstly, an air-ground integrated vehicle networking model supporting mobile edge calculation is constructed, secondly, the safe transmission mode from a vehicle to an unmanned aerial vehicle edge server and the calculation mode of a local vehicle and the unmanned aerial vehicle edge server are analyzed and modeled, the air-ground integrated vehicle networking task unloading problem is formalized into a multi-target optimization problem which is related to edge server selection, transmission rate, resource distribution and task unloading and takes the minimum time delay as a target, and a condition relaxation-numerical value reduction rule and a Lagrange dual decomposition method are combined.
Claims (1)
1. A task safety unloading method based on minimized time delay in an air-ground integrated vehicle networking is characterized by comprising the following steps:
step 1: constructing an air-ground integrated Internet of vehicles model supporting mobile edge calculation;
step 1-1: a one-way road covered by M drones, each drone deploying an edge server, the set of M drones being represented by M = { 1.,. M }; dividing a road into M sections, wherein the length of each section of road is L = { L = } 1 ,L 2 ,...,L M }; assuming that N vehicles arrive at the starting point of the road, the arrival of the vehicles obeys Poisson distribution, each vehicle has a task to be processed, and the task is expressed as
j ∈ M = {1, 2.., M }, where d i Representing computational tasks xi i Size of (c) i Representing tasks xi i Computing resources required for execution, T th Representing tasks xi i Need to be at T th Internal completion, λ i,j For the task unload ratio, representing the task xi i The ratio of the task amount unloaded to the edge server deployed by the jth unmanned aerial vehicle to the total task amount;
task xi i Split into two parts that execute in parallel on the local and edge servers; the method comprises the following specific steps: vehicle i will task λ i,j d i Off-load to jth edge server for processing, vehicle i pair the remaining tasks (1- λ) i,j )d i Carrying out local processing; let us assume that each vehicle can only select one edge server for computational offloading, x i,j Represents a selection decision for vehicle i, x if vehicle i is task off-loaded i,j =1, x if vehicle i is not tasked off-load i,j =0, i.e. x i,j ∈{0,1}；
Step 1-2: task XI i The processing delay of (2) is composed of three parts:
part 1: time required for vehicle i to move from start point to drone j
Namely that
L k Indicates the length of the k-th road, v i Represents the moving speed of the vehicle i, and k represents the road number;
part 2: vehicle i transmits task xi through wireless channel i Transfer time to offload to drone j
Part 3: unmanned aerial vehicle j processing task xi i Time required
And 2, step: constructing a safe transmission model from a vehicle to an unmanned aerial vehicle edge server;
step 2-1: the vehicle and the unmanned aerial vehicle communicate based on an IEEE 802.11p protocol, and the protocol adopts a carrier sense multiple access/collision avoidance mechanism to transmit data;
step 2-2: each vehicle is provided with K antennas, task unloading is carried out on an edge server installed on the unmanned aerial vehicle, meanwhile, a plurality of eavesdroppers exist in a network, and the eavesdropping mode of the eavesdroppers is passive eavesdropping; the positions and the number of a plurality of eavesdroppers are unknown, and the eavesdroppers independently receive and decode the secret information;
step 2-3: the vehicle uses the Wyner coding scheme for secret information transmission, and the following relations exist:
C i,j -R s ＝R e ≤R th (1)
wherein, C i,j Represents the channel capacity, R, of vehicle i-drone j s Representing the safe transmission rate, R, of vehicle i-drone j e Indicating the eavesdropping rate, R th Representing a maximum allowable eavesdropping rate; when eavesdropping rate R e Less than the maximum allowable eavesdropping rate R th When the unmanned aerial vehicle is used, the information received by the edge server installed on the unmanned aerial vehicle cannot be intercepted by other equipment, namely, the safe information transmission is realized;
and step 3: building calculation models of a local vehicle and an unmanned aerial vehicle edge server;
step 3-1: the vehicle adopts a transmission/permission transmission mode to reserve a channel; vehicle i is sending a task xi i Firstly, sending an RTS control frame to apply for occupying a channel; if the channel is idle, the unmanned aerial vehicle j receives the RTS frame and sends a CTS response frame after the time interval SIFS, and the RTS frame and the CTS frame comprise a task xi i A duration required for the transmission; the vehicle i receives the CTS frame and starts to send the task xi after a time interval SIFS i Receiving task xi by unmanned plane j i Feeding back an ACK frame to the vehicle i; at the same time, other vehicles will rely on the duration of the transmission in the RTS/CTS frameInformation, updating own network allocation vector and postponing the time of accessing the channel;
step 3-2: successful transmission of calculation task xi between vehicle i and unmanned aerial vehicle j i Time of
Expressed as:
wherein the content of the first and second substances,
representing tasks xi i The overhead of the header time of (a),
representing propagation delay, T SIFS 、T ACK 、T DIFS 、T RTS And T CTS Respectively representing the durations of the SIFS frame, the ACK frame, the DIFS frame, the RTS frame and the CTS frame;
representing tasks xi i Is expressed as:
wherein, B j Indicating the channel bandwidth for drone j,
indicating i-transmission task xi of vehicle i Transmission power of time, G i,j Representing the channel gain between vehicle i and drone j;
time of channel collision
Expressed as:
thus, the system normalizes the throughput H i,j Expressed as:
wherein the content of the first and second substances,
the probability of the idle time slot channel is represented, and the idle time slot channel means that all vehicles in a certain time slot do not send calculation tasks to the unmanned aerial vehicle j;
representing tasks xi i Probability of being successfully transmitted, successful transmission means that only vehicle i sends calculation task xi to UAV j in a certain time slot i ；
According to formulae (3) to (6), the task xi i Offloading task λ from vehicle i i,j d i Time of flight to drone j
Expressed as:
local calculation task (1-lambda) of vehicle i i,j )d i Calculated time of
Expressed as:
wherein, f i Representing the computing resources of vehicle i;
step 3-3: let F j Total computational resources for drone j, f i,j Represents the computational resources allocated by drone j to vehicle i, i.e., f = { f = { (f) 1,j ,f 2,j ,...,f N,j }; the sum of the computing resources allocated by the edge server to all vehicles cannot exceed the computing resources owned by the server itself, i.e. the edge server has
Thus, the computation time of the edge server
Comprises the following steps:
due to the task xi i Can be executed on the vehicle i and the unmanned plane j in parallel, so the task xi i Processing delay of
Expressed as:
wherein, the first and the second end of the pipe are connected with each other,
representing tasks xi i The processing delay at drone j,
and 4, step 4: constructing an objective function and an optimization condition of task safety unloading based on a safety transmission model and a calculation model;
the objective function and the optimization condition of task safety unloading for minimizing time delay are formalized into an optimization problem under a multi-constraint condition:
wherein x represents x i,j In which λ represents λ i,j A set of (a);
the purpose of equation (11) is to minimize processing delay
C1.1 ensuring task xi i Local processing latency of
And edge server processing time
Not exceeding a maximum allowable delay T th (ii) a C1.2 and C1.3 indicate that each vehicle selects only one edge service for task offloading; c1.4 and C1.5 ensure that the sum of the computing resources distributed to all tasks by the edge server does not exceed the total computing resources of the edge server; c1.6 and C1.7 indicate that the unloaded calculation task rate of each vehicle cannot exceed 1;
and 5: based on an objective function and an optimization condition of task safety unloading, solving through a condition relaxation-numerical value reduction rule and a Lagrange dual decomposition method to construct a task safety unloading method based on minimum time delay;
step 5-1: order to
And given f and λ, the edge server selection problem is expressed as:
in P2, x i,j E {0,1} is an integer program of 0-1, the objective function
Is about x i,j A non-linear function of (d); therefore, the constraint C2.5 is relaxed and x is i,j The epsilon {0,1} relaxation is 0 ≦ x i,j Less than or equal to 1; p2 is represented by P3:
let x be * ＝[x 1 ,x 2 ,...,x n ] T Represents the optimal solution, x, of the relaxation problem P3 * ∈[0,1]Wherein n is the number of solutions; using a numerical rule pair x * Rounding off and rounding up;
step 5-2: the optimal edge server is obtained according to the formula (13), and after x is given, the original problem is split into two sub-problems: namely a resource allocation problem P4 given x and λ and a computational offload fraction decision problem P5 given f and x, respectively, are represented as:
wherein the content of the first and second substances,
and kappa i,j Respectively as follows:
wherein the content of the first and second substances,
is composed of
The upper bound of (c);
the problems P4 and P5 are convex problems, and a Lagrangian dual decomposition method is adopted to solve f i,j And λ i,j Until convergence, to obtain the optimum f i,j And λ i,j 。
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