CN114710785B - Internet of vehicles cooperative computing resource scheduling design method based on particle swarm algorithm - Google Patents
Internet of vehicles cooperative computing resource scheduling design method based on particle swarm algorithm Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
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- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
- H04W16/10—Dynamic resource partitioning
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- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/16—Central resource management; Negotiation of resources or communication parameters, e.g. negotiating bandwidth or QoS [Quality of Service]
- H04W28/18—Negotiating wireless communication parameters
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- 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]
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
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Abstract
The invention provides a particle swarm algorithm-based vehicle networking cooperative computing resource scheduling design method, which reduces the overall delay by introducing vehicle cooperative computing auxiliary vehicle-mounted application. The method converts an actual problem into an optimization function solving problem, applies and designs a heuristic algorithm, and equivalently converts a complex mixed integer nonlinear programming problem into a variable in a particle swarm evolution mechanism; by utilizing the method, the dependence of designers on mathematical theory knowledge can be reduced, the method is more easily applied to other resource scheduling systems, and a resource scheduling method with a more complex structure and more excellent performance can be designed; meanwhile, compared with the traditional traversal method, the method greatly reduces the search complexity, reduces the average transmission power and saves the energy consumption.
Description
Technical Field
The invention belongs to the field of wireless communication network application in a new generation of information technology, and particularly relates to a particle swarm algorithm-based resource scheduling design method for Internet of vehicles cooperation calculation.
Background
With the advent of computing-intensive and delay-sensitive vehicle applications, such as Virtual Reality (VR), augmented Vehicle Reality (AVR), and image recognition, have presented significant challenges for autonomous vehicles with limited computing resources. These applications require a large amount of computing resources to analyze large amounts of raw sensing data and require real-time processing. The vehicle auxiliary computing can reduce the problem of high delay caused by insufficient vehicle-mounted computing resources, and by unloading the vehicle-mounted application to an idle auxiliary vehicle, the application tasks of the vehicle with insufficient computing resources can be quickly processed, so that the overall delay performance is improved. However, similar offloading and resource scheduling problems are usually very complex, and different application scenarios may generate different optimization problems and corresponding solutions, and the traditional mathematical solution method for such problems is complex and not beneficial to simplify the design and large-scale application. Therefore, the resource scheduling design is realized by using an intelligent algorithm and an intelligent design mode, and the method has strong requirements.
As early as the 17 th century, european mathematician Fermat proposed the problem of solving the maximum or minimum values according to the derivative relation, but the system theory is not formed, and then in the following time, the optimization method is slowly developed until the second world war of 20 decades and forty, and some optimization algorithms such as linear programming and the like are generated due to the urgent need of military operational research. With the expansion of the fields of economy, military and science and technology after the second war, the optimization algorithm is developed rapidly.
Particle swarm optimization is a heuristic computational technique, originally proposed by Eberhar and Kennedy in 1995. Through continuous development, the method has the characteristics of fast convergence, low complexity and high precision, and is a practical method for solving the problem of complex optimization.
Shahin Gheitanchi et al use a particle swarm algorithm to solve the problem of resource allocation in wireless communication systems. (S.Gheitanchi, F.Ali and E.Stipidis, "Particle Swarm Optimization for Resource Allocation in OFDMA,"2007 15th International Conference on Digital Signal processing,2007, pp.383-386, doi. The article applies the method to subcarrier allocation in OFDMA, and the result shows that compared with the traditional technology, the method greatly reduces the computational complexity and increases the flexibility.
In addition, sandra Scott-Hayward et al develop a multi-user wireless network resource scheduling method using a particle swarm algorithm. (S.Scott-Hayward and E.Garcia-Palacios, "A PSO ap reach to resource allocation in wireless networks,"2012 35th International Conference on Telecommunications and Signal Processing (TSP), 2012, pp.151-155, doi. The capability of the particle swarm optimization to reach the optimal solution shows that the method has the potential of resource allocation in a heterogeneous application program network which is not suitable for the convex optimization method.
Hanan Al-Tous et Al studied the problem of resource scheduling in multiuser cooperative communication by particle swarm optimization in 2016. (H.Al-Tous and I.Barhumi, "Distributed resource allocation for multi-user multi-relay AF cooperative Communication,"2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS), 2014, pp.1-6, doi. A method for distributing distributed resources based on fitness function is provided.
The method of resource allocation using particle swarm optimization was published in CIS by Qiang Wang et al in 2017. (Q.Wang, T.Chen and H. -L.Liu, "Resource Allocation for D2D underdevelopment Communication Systems Using PSO,"2017 13th International Conference on Computational Integration and Security (CIS), 2017, pp.202-206, doi. The method adopts a particle swarm algorithm to design a frequency spectrum sharing method, aims at optimizing the system speed, and improves the frequency spectrum utilization rate.
The process of solving the problem of complex communication resource allocation by the traditional optimization algorithm is complex, and sometimes even a corresponding solution cannot be obtained. In practical application, due to a complex space-time scene, the final effect of the method often cannot meet the index requirement. In addition, when a communication scene changes, due to the change of a system model, a designed optimization algorithm generally cannot be applied to a new system, and a modeling extraction optimization problem needs to be re-established and then solved, so that the method is not beneficial to large-scale application. Therefore, the method for resource scheduling design based on particle swarm can greatly save labor cost and reduce design time. By using the resource scheduling design method, a designer only needs to input the acquired vehicle and road channel information to realize the automatic generation of the unloading target and the resource scheduling. With the continuous development of the intelligent optimization algorithm, a resource scheduling scheme meeting the requirements can be more conveniently designed by means of the mutual combination of the optimization algorithm and the simulation software, and the method also becomes an effective way for designing a resource scheduling system.
Disclosure of Invention
In order to solve the problems in the vehicle auxiliary computing unloading and resource scheduling design, the invention provides a vehicle networking cooperative computing resource scheduling design method based on a particle swarm algorithm, in order to achieve the aim, the invention designs vehicle auxiliary computing unloading and resource scheduling design steps based on the particle swarm algorithm, and the unloading and resource scheduling design steps are as follows:
the method comprises the following steps: dividing an actual road into even sub-regions according to distance, adopting frequency reuse among the spaced regions, and enabling each sub-region to have a vehicle auxiliary calculation; and selecting the idle vehicle with the highest computing resource as an auxiliary vehicle, and taking the rest vehicles as potential unloading requesting vehicles.
Step two: and establishing a system mathematical model according to the specific vehicle network environment and the task type, and calculating to generate an optimization target. The optimization target is the total time delay for all vehicles to complete the task, including the local computation time delay and the auxiliary computation time delay (transmission time delay and auxiliary computation time delay). The optimization variables include the unloaded vehicle selection, and the transmit power allocation of the unloaded vehicle. The constraint condition is determined according to the environmental parameters.
Step three: and encoding the unloading vehicles with the optional optimized variables and the corresponding transmitting power by aiming at a particle swarm algorithm, setting particle variable boundaries according to constraint conditions, and selecting proper particle population quantity, iteration times and internal parameters.
Step four: according to the channel condition, the unloading vehicle number of the initial particle is selected by adopting a roulette wheel method, the corresponding transmission power is selected by adopting a given function, and the step corresponds to the initial population establishment in the particle swarm optimization.
Step five: and importing the calculation result into a particle swarm algorithm learning mechanism, and realizing the optimization of data through a generation mechanism and a movement mechanism of the particle swarm algorithm. And according to a multi-objective optimization strategy, each particle is moved to the optimal solution direction in iteration through a series of optimization mechanisms. By means of the random movement of parts in each particle, a new solution space can be explored in the optimization process, and the joint action of a plurality of factors is considered.
Step six: and judging whether the newly generated particles exceed the constraint condition, and correcting the exceeded part to enable the newly generated particles to meet the constraint condition.
Step seven: and according to the convergence condition of the calculation result, performing iterative calculation judgment: and judging whether the convergence condition is met or not according to a preset maximum iteration step number or a preset convergence condition. If the convergence condition is met, stopping calculation, and deriving an unloading target and a corresponding transmission power distribution parameter result; otherwise, returning to the step five to continue the calculation.
Step eight: and importing the calculated unloading target and the transmission power parameter result into a total time delay calculation model to generate the unloading target of the vehicle, and obtaining the cooperative calculation resource scheduling result of the Internet of vehicles.
More specifically, in step one of the present disclosure, a road is divided into M sub-regions according to distance, and each N sub-regions use the same frequency for auxiliary computation transmission. Suppose there are K vehicles in total, denoted VU, on a road k K is K, K = {1,2, …, K }. The idle vehicle with the largest computing resource in each subarea is taken as an auxiliary vehicle and is marked as AU i I e {1,2, …, M }, other vehicles in each sub-area are taken as optional unloading requesting vehicles, and the number set is recorded as RU according to the sub-area where the vehicles are located m ,m∈{1,2,…,M}。
For step two of the disclosure, there may be one vehicle unloaded to the auxiliary vehicle calculation for each sub-area, and the unloading decision of all K vehicles may be described as: a = { a = 1 ,a 2 ,…,a K },
k∈K，
When a is k Indicating selection of vehicle VU when =1 k Offloading tasks to AUs as off-load requested vehicles m Performing auxiliary calculation, otherwise
Indicating VU k Local calculations are performed. Further obtain VU k Task delay of (2):
wherein
In order to calculate the time delay locally,
time delays calculated for offloading to an auxiliary vehicle, including vehicle VU k To auxiliary vehicles AU i Is delayed
And assisting the calculated time delay of the vehicle
Transmitting of individual vehicles power P = { P = 1 ,P 2 ,…,P K H, there is a constraint of 0 ≦ P k ≤P k max ，P k max Is the maximum transmit power. And further obtaining the task minimization time delay optimization target of all vehicles:
aiming at the third step of the invention, the vehicle number is used for representing the unloading request vehicle in each subarea, and corresponding transmission power is applied to generate the particle code with the dimension of 2M: x = [ r ] 1 ,r 2 ,…,r M ,P 1 ,P 2 ,…,P M ]＝[x r ,x P ]Wherein r is m = k stands for selecting vehicle VU k As a request to unload the vehicle, P m Is its corresponding transmit power.
Aiming at the fourth step of the invention, the initial population is selected by adopting a roulette wheel method according to the initial channel condition of each vehicle, the better the initial channel condition between the vehicles is, the higher the probability of selecting the vehicle as the initial population is:
wherein p is k,i For selecting vehiclesVehicle VU k As probability of requesting to unload the vehicle, g k,i Is VU k And auxiliary vehicles AU m Channel information between.
The initial transmission power is selected to be any value of the maximum power and half of the maximum power:
P k,i ＝rand(P max )
where rand is a random function between 0.5 and 1.
Aiming at the fifth step of the invention content, according to a particle swarm evolution mechanism, aiming at the particle swarm coding, in the optimization process, the weight value position x of the l-dimension particle is adjusted l And velocity v l To implement the change of each particle parameter to obtain a new solution.
In particular, by pbest l And the optimal position of the particle for obtaining the optimal time delay at the current iteration time is represented, and the global optimal position obtained from all the particles until the current iteration time is represented by the gbest. By combining the velocity vector and the position vector, the particle swarm optimization algorithm approaches to the optimal position, and a new solution space is searched step by step. The following equation describes the update plan details for each particle as follows:
v l [t+1]＝w[t]v l [t]+c cog ·rand 1 (pbest l [t]-x l [t])+c soc ·rand 2 (gbest[t]-x l [t])
x l [t+1]＝x l [t]+v l [t+1]；
where t is the number of iterations, c cog And c soc Respectively a predetermined cognitive parameter and a predetermined social parameter, rand 1 And rand 2 Is a random function for generating parameters between 0 and 1 to provide a new exploration direction. Using a weighting coefficient w t]To calculate the step size controlling the particle velocity, w t when the number of iterations t increases]And decreases. Normally the weighting factors may be as follows: w [ t ]]＝w max -(w max -w min )·t/T pso ；
Wherein T is pso Is the maximum number of iteration steps, w, of the particle swarm max And w min Given a weighting factor.
Aiming at the sixth step of the invention content, because the number of the vehicle requesting to unload is a real number, the front M dimension of the obtained new particle is rounded, the exceeding part is corrected according to the constraint condition, the number of the vehicle in the exceeding sub-area is the nearest number of the vehicle in the atomic area,
given boundary values for the transmit power outside the power range,
and as described in the seventh step, when the calculation result reaches the maximum iteration step number, ending the particle swarm calculation. At this point, the gbest result of the last step is derived as the final optimization solution.
And step eight, importing the calculated unloading target and the transmission power parameter result into a total time delay calculation model to obtain the task time delays of all vehicles. And the vehicle carrying out the auxiliary unloading transmits the task to the auxiliary vehicle for calculation by adopting the corresponding transmitting power, the transmitting power of the vehicle not carrying out the auxiliary unloading is 0, the local calculation is carried out, and finally the scheduling result of the cooperative calculation resources of the Internet of vehicles is obtained.
Drawings
FIG. 1 is a flow chart of resource allocation design based on particle swarm optimization;
FIG. 2 is a schematic diagram of road vehicle networking cooperative computing resource scheduling;
FIG. 3 is a diagram illustrating sub-region division and frequency reuse;
FIG. 4 is a schematic diagram of an initial population selection roulette wheel of a particle swarm algorithm;
FIG. 5 is a time delay comparison diagram of methods such as resource allocation and exhaustion based on particle swarm optimization;
fig. 6 is a comparison graph of average transmission power of methods such as resource allocation and exhaustion based on particle swarm optimization.
Detailed Description
In order to further illustrate the technical means and effects of the present invention, the following description is given with reference to the embodiments and the performances of the present invention.
The embodiment discloses a design method for scheduling cooperative computing resources of the Internet of vehicles based on a particle swarm algorithm. Illustrative embodiments of the invention are shown in the drawings, and various aspects of the invention are achieved by the present invention with reference to the drawings. The following concepts and embodiments may be implemented in any of a variety of ways and are not limited to the embodiments described below.
The sub-regions are set according to the road environment, in this example, a single-lane road with a length of 200 meters is used, and the sub-regions are equally divided into 4 sub-regions (M =4, sub-area1-sub-area 4), and the sub-regions with different frequencies use the same frequency band (N =2, freq1-freq 2), as shown in fig. 2, wherein the arrows respectively represent the local calculation, the unloading auxiliary calculation and the generated transmission interference of the vehicle. The number of vehicles in each subarea is 3 to 8, and the positions are randomly distributed. Task size of each vehicle is [5-20 ]]Mbit, randomly generated. The idle vehicle generation probability is 0.3. Channel bandwidth B v Is 2MHz, maximum transmission power P max 30dBm, noise power σ 2 Is-114 dBm. The transmission rate R of auxiliary unloading in frequency reuse can be obtained k,i For calculating transmission time delay
Where k ∈ RU m J is the number of the vehicles requested to be unloaded in the interval subarea, and j belongs to RU m+2 . Channel information adoption model | g k,i | 2 ＝α k,i |h k,i | 2 In which α is k,i ＝63.3+17.7log 10 (d k,i ) Is largeScale fading, d k,i Is the distance between the two vehicles,
is a small scale fading.
The corresponding initial particle population is generated in the top M dimension using the roulette wheel algorithm, and fig. 4 shows the probability of a selected vehicle number for unload assist calculation. In FIG. 4 r1-r6 are represented as the first M-dimensional vector x in the population of particles r And correspondingly carrying out unloading auxiliary calculation on the vehicle number. Last M-dimensional transmit power vector x P Generated by the rand function in step four.
Performing iterative computation by adopting a particle swarm algorithm, wherein the specific weight coefficient is respectively set as 256 times of population and 128 times of iteration cog And c soc Respectively taking out 2.03,w max And w min Take 0.9 and 0.5, respectively.
And combining the obtained result with the original optimization problem, calculating the overall time delay performance, randomly generating vehicle environment information according to the second step, performing calculation and solving for 1000 times, and averaging to obtain an average performance result of the vehicle networking cooperative computing resource scheduling design based on the particle swarm optimization of the method, and recording the average performance result as V2V-PSO.
An exhaustive method is adopted to obtain an optimal solution, wherein the number of the selectable transmission power combinations of the vehicles is set to be 5, and a comparison with the method is made in figure 5 and is marked as V2V-optimal-exceeded. In addition, the method of randomly selecting the vehicles requiring unloading by adopting the maximum sending power and the method of optimally requesting unloading by adopting the maximum sending power are compared with the method only by adopting a Local calculation method and are respectively marked as V2V-random-fixed, V2V-optimal-fixed and Local-fixed. As can be seen from fig. 5, as the number of vehicles in each sub-area increases, the total time delay increases, and the method can achieve performance close to the optimal solution and reduce the task time delay better than local computation and other unloading methods. In fig. 6, the average transmit power of the method and the method of exhaustive optimization and fixed maximum transmit power are denoted by P-PSO, P-optimal and P-fixed, respectively. It can be found that the method can reduce the average transmission power and save energy while achieving the approximate optimal solution.
Through analysis, when the number of vehicles existing in each cell is 8, the search complexity of the method is 256 multiplied by 128, and the search complexity of the optimal solution obtained by the exhaustion method is 8 4 ×5 4 . Therefore, the method can greatly reduce the algorithm complexity and improve the application efficiency. Particularly, as the number of vehicles increases, in order to better improve the auxiliary calculation efficiency, the number M of sub-regions should also increase, and the search complexity of the exhaustive method increases exponentially and is not suitable for solving the optimization problem any more.
Claims (7)
1. A design method for scheduling cooperative computing resources in Internet of vehicles based on a particle swarm algorithm is characterized in that the automatic design step of cooperative computing resource scheduling is as follows:
the method comprises the following steps: dividing an actual road into a plurality of sub-areas according to distance, selecting an idle vehicle with the highest computing resource in each sub-area as an auxiliary computing vehicle, performing networking auxiliary computing in the plurality of sub-areas simultaneously, and performing local computing on the other vehicles;
step two: establishing a system mathematical model according to a specific vehicle network environment and a task type, and setting the total calculation time delay of all vehicles as an optimization target; setting parameters of the algorithm according to actual conditions, wherein the parameters comprise the number of variables, the variation range of the variables and the size of a population;
step three: encoding the optimized parameter variable selectable unloading vehicles and the corresponding transmitting power aiming at the particle swarm algorithm: establishing an initial population in a particle swarm algorithm, selecting the unloading vehicle number of an initial particle by adopting a roulette plate method, selecting corresponding transmitting power by adopting a given function, and setting a particle variable boundary according to a constraint condition;
step four: introducing the calculated particle variable boundary result into a particle swarm algorithm learning mechanism, and realizing the optimization of data through a generation mechanism and a movement mechanism of the particle swarm algorithm; according to a multi-objective optimization strategy, each particle is moved to the optimal solution direction in iteration through a series of optimization mechanisms; by means of random movement of parts of particles, a new solution space can be explored in the optimization process, and new variables are adjusted according to constraints;
step five: and importing the calculated unloading targets and the transmission power parameter results into a total time delay calculation model to generate the unloading targets of all vehicles, so as to obtain the cooperative calculation resource scheduling result of the Internet of vehicles.
2. The Internet of vehicles cooperative computing resource scheduling design method based on particle swarm optimization according to claim 1, wherein in the first step, the road is equally divided into even sub-areas, a frequency reuse technology is adopted, and non-adjacent sub-areas reuse the same frequency spectrum.
3. The Internet of vehicles cooperative computing resource scheduling design method based on particle swarm optimization according to claim 1, wherein in step two, parameters to be optimized in a system mathematical model are converted into parameter variables in the mathematical model, specifically including selection of unloading vehicles and auxiliary computing vehicles and corresponding transmitting power; in the actual engineering optimization process, the total calculation time delay of all vehicles is set as an optimization target, and a corresponding relation is established between the transmission time delay obtained through calculation and the optimized parameters.
4. The Internet of vehicles cooperative computing resource scheduling design method based on the particle swarm optimization algorithm as claimed in claim 3, wherein in the third step, each factor affecting the optimization objective can be represented as a variable parameter, the number of the variable parameters and the type of the variable parameters are determined by the factors affecting the optimization objective in the practical problem, and the set x = [ r ] is used 1 ,r 2 ,···,r M ,P 1 ,P 2 ,···,P M ]＝[x r ,x P ]Encoding a population, wherein x r Is an integer variable representing the number of the selected unloaded vehicle, r 1 ,r 2 ,···,r M Indicating a selected request to unload the vehicle, x P Is a continuous variable, representing the corresponding transmit power, P 1 ,P 2 ,···,P M M represents the number of sub-regions for the corresponding transmit power; and (5) generating an initial population according to the population parameters set in the step two by adopting parameter variables in the mathematical model.
5. The Internet of vehicles cooperative computing resource scheduling design method based on particle swarm optimization according to claim 4, wherein in the fourth step, the computing result is imported into a learning mechanism of particle swarm optimization, and the optimization of data is realized through a generation mechanism and a movement mechanism of the particle swarm optimization; adjusting the particle variables exceeding the constraint condition: for new variable x r Rounding and ensuring to be in the same sub-region for x exceeding the transmission power boundary P Taking boundary values.
6. The method for designing scheduling of resources for cooperative computing in internet of vehicles based on the particle swarm optimization according to claim 5, wherein the vehicles subjected to auxiliary unloading and non-auxiliary unloading are subjected to auxiliary computing and local computing respectively according to the time delay condition of each node in a system mathematical model, so that an optimized scheduling result of resources for cooperative computing in internet of vehicles is realized.
7. The Internet of vehicles cooperative computing resource scheduling design method based on the particle swarm optimization is characterized in that in the fifth step, the optimized population in each step is introduced into a total time delay calculation model to generate unloading targets of all vehicles, and values of all optimized parameter variables are obtained according to the final optimized calculation result to obtain the final Internet of vehicles cooperative computing resource scheduling method.
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