CN108600938B - A method for intelligent selection of sensing service nodes in the Internet of Vehicles - Google Patents
A method for intelligent selection of sensing service nodes in the Internet of VehiclesDownload PDFInfo
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Abstract
Translated fromChinese
Description
Translated fromChinese技术领域technical field
本发明涉及车联网移动群智感知的数据获取领域，尤其是一种车联网中感知服务节点智能选择方法。The invention relates to the field of data acquisition of mobile crowd intelligence perception in the Internet of Vehicles, in particular to a method for intelligent selection of sensing service nodes in the Internet of Vehicles.
背景技术Background technique
车联网(IoV)是一个允许车辆以及其周围传感器和人群进行信息共享的复杂动态，开放融合的移动网络系统。车联网(IoV)为了解决泛在异构移动融合网络环境中智能管理和信息服务的可计算性、可扩展性和可持续性问题，通过一系列先进的信息通信技术与信息处理技术，对人、车、固定和移动传感器和道路交通基础设施等包含的大规模复杂静态或动态的信息进行感知、认知、传输和计算，使其最终能够实现人、车、环境的深度融合。移动群智感知的核心思想是通过感知移动个体的信息，从而挖掘出移动群体的信息，实现对个体或群体的泛在化服务，因此移动群智感知采集的数据不再仅仅局限于位置，移动设备附带的各种传感器在个体数据采集时都能够发挥作用。从车联网和移动群智感知的内涵可见，群智感知是车联网服务的核心特征之一。The Internet of Vehicles (IoV) is a complex dynamic, open and converged mobile network system that allows information sharing between vehicles and their surrounding sensors and people. In order to solve the problems of computability, scalability and sustainability of intelligent management and information services in the ubiquitous heterogeneous mobile converged network environment, Internet of Vehicles (IoV), through a series of advanced information communication technology and information processing technology, has , vehicles, fixed and mobile sensors, and road traffic infrastructure and other large-scale complex static or dynamic information for perception, cognition, transmission and calculation, so that it can finally realize the deep integration of people, vehicles and the environment. The core idea of mobile crowd-sensing is to dig out the information of mobile groups by sensing the information of mobile individuals and realize ubiquitous services for individuals or groups. Therefore, the data collected by mobile crowd-sensing is no longer limited to location, mobile The various sensors that come with the device all play a role in the collection of individual data. From the connotation of Internet of Vehicles and mobile crowd-sensing, it can be seen that crowd-sensing is one of the core features of IoV services.
传统的车联网数据获取是基于重要路口和断面的地磁线圈、摄像机等交通监测手段，这种方式感知覆盖范围有限、实时性受限，而基于移动群智感知的数据获取方式移动性强，感知的覆盖范围更广，车辆行驶规律性强，因此基于移动群智感知的车联网数据获取方法是传统车联网数据获取方式的有力补充，但目前对于车联网移动群智感知的数据获取的研究不足，缺乏合理的节点选择机制，所有的车辆都能作为感知节点，但我们可能只需要其中一部分就能获取到想要的信息，如果将所有车辆都定义为服务节点，就会增加大量的不必要的工作，会造成资源的大量浪费，所以设计出一种合理的服务节点选择方法是当前研究的热点。The traditional data acquisition of the Internet of Vehicles is based on traffic monitoring methods such as geomagnetic coils and cameras at important intersections and sections. This method has limited sensing coverage and limited real-time performance, while the data acquisition method based on mobile crowd intelligence has strong mobility and perception. Therefore, the data acquisition method of the Internet of Vehicles based on mobile crowdsensing is a powerful supplement to the traditional data acquisition methods of the Internet of Vehicles, but the current research on the data acquisition of mobile crowdsensing of the Internet of Vehicles is insufficient , lacking a reasonable node selection mechanism, all vehicles can be used as sensing nodes, but we may only need a part of them to obtain the desired information. If all vehicles are defined as service nodes, it will increase a lot of unnecessary It will cause a lot of waste of resources, so designing a reasonable service node selection method is the current research hotspot.
发明内容SUMMARY OF THE INVENTION
发明目的：本发明针对目前车联网移动群智感知的数据获取的研究不足，缺乏合理的节点选择机制问题，提出一种车联网中感知服务节点智能选择方法。Purpose of the invention: The present invention proposes an intelligent selection method for sensing service nodes in the Internet of Vehicles, aiming at the problem of insufficient research on data acquisition and lack of reasonable node selection mechanism in the current Internet of Vehicles mobile crowd-sensing.
技术方案：本发明提供一种车联网中感知服务节点智能选择方法，该方法包括步骤：Technical solution: The present invention provides a method for intelligent selection of sensing service nodes in the Internet of Vehicles, the method comprising the steps of:
(1)选定采集区域和目标区域，获取采集区域中周期时间内从采集区域到目标区域的车辆节点历史轨迹数据，选取出现频率最高的M个车辆节点，根据这M个车辆节点的历史轨迹数据预测各车辆节点从采集区域出发并在预设的时刻tend到达目标区域的概率；(1) Select the collection area and the target area, obtain the historical trajectory data of the vehicle nodes from the collection area to the target area in the periodic time in the collection area, select the M vehicle nodes with the highest frequency, according to the historical trajectories of the M vehicle nodes The data predicts the probability that each vehicle node starts from the collection area and arrives at the target area at the preset time tend ;
(2)以选取的车辆节点构成车联网能够覆盖目标区域为目标问题，从M个车辆节点中随机选出多个车辆节点集合作为目标问题的解；以生成的解作为种群，根据车辆节点到达目标区域的概率和车辆节点在tend时刻距离目标区域的距离构建适应度函数，采用遗传算法搜索出种群中适应度函数值最优的染色体作为目标问题的最优解；(2) Taking the selected vehicle nodes to form the Internet of Vehicles to cover the target area as the target problem, randomly select multiple vehicle node sets from M vehicle nodes as the solution of the target problem; take the generated solutions as the population, according to the vehicle node arrival The probability of the target area and the distance between the vehicle node and the target area at the time tend construct a fitness function, and the genetic algorithm is used to search out the chromosome with the best fitness function value in the population as the optimal solution of the target problem;
(3)将最优解对应的车辆节点作为用于构建车联网的感知服务节点。(3) The vehicle node corresponding to the optimal solution is used as the perception service node for building the Internet of Vehicles.
进一步的，所述预测一个车辆节点到达目标区域的概率的方法为：Further, the method for predicting the probability of a vehicle node reaching the target area is:
(2-1)从对应车辆节点的历史轨迹数据中提取该车辆节点连续时间下的位置转移概率，绘制位置转移概率关于时间的关系曲线，选取关系曲线峰值点对应的时间节点为位置转移时间节点；记第n个位置时间转移节点为tn；(2-1) Extract the position transition probability of the vehicle node in continuous time from the historical trajectory data of the corresponding vehicle node, draw the relationship curve of the position transition probability with respect to time, and select the time node corresponding to the peak point of the relationship curve as the position transition time node ; record the nth position time transfer node as tn ;
(2-2)采用高斯分布函数模型拟合步骤(2-1)中提取的位置转移时间节点，得到车辆节点位置转移概率与转移时间节点的关系式：(2-2) Using the Gaussian distribution function model to fit the position transfer time node extracted in step (2-1), the relationship between the position transfer probability of the vehicle node and the transfer time node is obtained:
P(tn|Lα→Lx)＝∑nπnN(tn|μn,σn)，∑nπn＝1,πn≥0P(tn |Lα →Lx )=∑n πn N(tn |μn ,σn ), ∑n πn =1,πn ≥0
式中，P(tn|Lα→Lx)表示车辆节点在时刻tn在位置Lα处发生位置转移的概率，πn表示高斯分布权值系数，N(tn|μn,σn)表示位置转移时间节点tn服从期望值为μn、标准差为σn的高斯分布；In the formula, P(tn |Lα →Lx ) represents the probability of the position transition of the vehicle node at the position Lα at time tn , πn represents the Gaussian distribution weight coefficient, N(tn |μn ,σn ) means that the position transfer time node tn obeys the Gaussian distribution with the expected value μn and the standard deviation σn ;
(2-3)根据提取出的位置转移时间节点构建车辆节点的位置转移马尔科夫模型；(2-3) Constructing the position transfer Markov model of the vehicle node according to the extracted position transfer time node;
(2-4)在时间区间[tstart,tend]内对马尔科夫模型描述的位置转移过程进行迭代求解，得到车辆节点到达目标区域的概率，tstart为起始时刻。(2-4) Iteratively solve the position transfer process described by the Markov model within the time interval [tstart , tend ], and obtain the probability that the vehicle node reaches the target area, where tstart is the starting time.
进一步的，所述位置转移马尔科夫模型为：Further, the position transfer Markov model is:
P(Lα→Lβ,tn)＝P(X(tn-1)＝Lα)×P(tn|Lα→Lx)P(Lα →Lβ ,tn )=P(X(tn-1 )=Lα )×P(tn |Lα →Lx )
P(X(tn)＝Lβ)＝∑αP(Lα→Lβ,tn)P(X(tn )=Lβ )=∑α P(Lα →Lβ ,tn )
式中，X(t)表示在时刻t车辆节点的位置，P(Lα→Lβ,tn)表示在时刻tn车辆节点从位置Lα转移到位置Lβ的概率，P(X(tn-1)＝Lα)表示在前一时刻tn-1车辆节点处于位置Lα的概率，P(X(tn)＝Lβ)表示在时刻tn车辆节点处于位置Lβ的概率。In the formula, X(t) represents the position of the vehicle node at time t, P(Lα →Lβ ,tn ) represents the probability that the vehicle node transfers from the position Lα to the position Lβ at the time tn , P(X( tn-1 )=Lα ) represents the probability that the vehicle node is at the position Lα at the previous time tn-1 , and P(X(tn )=Lβ ) represents the probability that the vehicle node is at the position Lβ at the time tn probability.
进一步的，所述对马尔科夫模型进行迭代求解的步骤为：Further, the steps of iteratively solving the Markov model are:
(4-1)设置参数：定义Pi为车辆节点i在时刻tend到达目标区域的概率；定义Lcur为车辆当前位置，Lcur为采集区域中的任意一个位置点；定义tnow为当前时间点；定义Pcur为当前概率值；(4-1) Setting parameters: define Pi as the probability that the vehicle node i reaches the target area at time tend ; define Lcur as the current position of the vehicle, Lcur as any position point in the collection area; define tnow as the current Time point; define Pcur as the current probability value;
(4-2)初始化：Pi＝0，Pcur＝0，tnow＝tstart；(4-2) Initialization: Pi =0, Pcur =0, tnow =tstart ;
(4-3)获取tnow的下一个位置时间转移节点tn，判断tn是否大于tend，若是，则终止迭代，计算Pi＝Pi+Pcur；否则，执行步骤(4-4)；(4-3) Obtain the next position and time transfer node tn of tnow , and judge whether tn is greater than tend , if so, terminate the iteration, and calculate Pi =Pi +Pcur ; otherwise, execute step (4-4 );
(4-4)计算：(4-4) Calculation:
P(tn|Lα→Lx)＝∑nπnN(tn|μn,σn)，∑nπn＝1,πn≥0P(tn |Lα →Lx )=∑n πn N(tn |μn ,σn ), ∑n πn =1,πn ≥0
Pcur＝P(X(tnow)＝Lcur)×P(tn|Lcur→Lx)Pcur =P(X(tnow )=Lcur )×P(tn |Lcur →Lx )
更新tnow＝tn，返回步骤(4-3)。Update tnow =tn , and return to step (4-3).
进一步的，所述周期时间为一年或一个月。Further, the cycle time is one year or one month.
进一步的，所述车辆节点的历史轨迹数据包括对应车辆节点在各个采样时间点所在的位置。Further, the historical trajectory data of the vehicle node includes the position of the corresponding vehicle node at each sampling time point.
进一步的，所述适应度函数为:Further, the fitness function is:
f＝ω1P+ω2Df=ω1 P+ω2 D
P＝1-Πi∈G(1-Pi)P=1-Πi∈G (1-Pi )
D＝(∑i∈GDi)/nD=(∑i∈G Di )/n
式中，G为目标问题的一个解，即一个染色体；n为G中车辆节点的数量，Di表示车辆节点i距离目标区域的距离，P为G的联合概率，D为G的联合距离；ω1和ω2均为权值系数，0＜ω1＜1，0＜ω2＜1，0＜ω1+ω2＜1。In the formula, G is a solution of the target problem, that is, a chromosome; n is the number of vehicle nodes in G, Di is the distance between vehicle node i and the target area, P is the joint probability of G, and D is the joint distance of G; Both ω1 and ω2 are weight coefficients, 0<ω1 <1, 0<ω2 <1, and 0<ω1 +ω2 <1.
进一步的，在采用遗传算法搜索最优解之前还包括步骤：Further, before using the genetic algorithm to search for the optimal solution, it also includes steps:
对车辆节点集合进行编码，将编码的结果作为初始种群的染色体，染色体表示为：The vehicle node set is encoded, and the encoded result is used as the chromosome of the initial population, and the chromosome is expressed as:
G＝(g1,g2,…,gM)G=(g1 ,g2 ,...,gM )
式中，gi表示车辆节点i的编码，i＝1,2,…,M，gi的取值为0或1；gi取值为0时，表示G中未选取车辆节点i；gi取值为1时，表示G中选取车辆节点i。In the formula,gi represents the code of the vehicle node i, i=1,2,...,M, the value ofgi is 0 or 1; when the value ofgi is 0, it means that the vehicle node i is not selected in G; g When the value ofi is 1, it means that the vehicle node i is selected in G.
进一步的，所述采用遗传算法搜索目标问题的最优解的具体步骤为：Further, the specific steps of using the genetic algorithm to search for the optimal solution of the target problem are:
(9-1)初始化种群为：G1，G2，…，GK；Gk为种群中的第k个基因，表示目标问题的第k个解，k＝1,2，…，K,K为步骤(2)中生成的解的总数；(9-1) Initialize the population as: G1 , G2 , ..., GK ; Gk is the kth gene in the population, representing the kth solution of the target problem, k=1, 2, ..., K, K is the total number of solutions generated in step (2);
(9-2)计算种群中各染色体适应度函数值；(9-2) Calculate the fitness function value of each chromosome in the population;
(9-3)从种群中选取适应度值最高的k个染色体；(9-3) Select the k chromosomes with the highest fitness value from the population;
(9-4)以选出的a个染色体为父体进行交叉变异，产生新一代的种群，返回步骤(9-2)；(9-4) Take the selected a chromosome as the father to carry out crossover mutation to generate a new generation of population, and return to step (9-2);
(9-5)重复执行步骤(9-2)至(9-4)，直至迭代次数满足预设阈值。(9-5) Repeat steps (9-2) to (9-4) until the number of iterations meets a preset threshold.
进一步的，所述迭代次数为100次。Further, the number of iterations is 100 times.
有益效果：与现有技术相比，本发明采用基于高斯分析的马尔科夫位置预测方法，根据车辆节点的历史轨迹数据预测得到车辆节点到目标区域的概率，然后利用遗传算法进行对车辆节点进行优化选取，解决了车联网移动群智感知缺乏合理的节点选择机制的问题，节省了节点资源。Beneficial effect: Compared with the prior art, the present invention adopts the Markov position prediction method based on Gaussian analysis, predicts the probability of the vehicle node to the target area according to the historical trajectory data of the vehicle node, and then uses the genetic algorithm to carry out the analysis of the vehicle node. The optimized selection solves the problem of lack of a reasonable node selection mechanism for IoV mobile crowd-sensing and saves node resources.
附图说明Description of drawings
图1为本发明的原理流程图；Fig. 1 is the principle flow chart of the present invention;
图2为基于高斯分析的马尔科夫位置预测原理图。Figure 2 is a schematic diagram of Markov position prediction based on Gaussian analysis.
具体实施方式Detailed ways
下面结合附图对本发明做更进一步的解释。The present invention will be further explained below in conjunction with the accompanying drawings.
为了解决车联网移动群智感知的数据获取缺乏合理的节点选择机制的问题，本发明提出了一种车联网中感知服务节点智能选择方法，该方法的原理流程如图1所示：In order to solve the problem of lack of a reasonable node selection mechanism for data acquisition of mobile crowd intelligence in the Internet of Vehicles, the present invention proposes an intelligent selection method for sensing service nodes in the Internet of Vehicles. The principle flow of the method is shown in Figure 1:
首先选定采集区域和目标区域，获取采集区域中周期时间内从采集区域到目标区域的车辆节点历史轨迹数据，选取出现频率最高的M个车辆节点，根据这M个车辆节点的历史轨迹数据预测各车辆节点从采集区域出发并在预设的时刻tend到达目标区域的概率。First select the acquisition area and the target area, obtain the historical trajectory data of vehicle nodes from the acquisition area to the target area in the periodic time in the acquisition area, select the M vehicle nodes with the highest frequency, and predict based on the historical trajectory data of these M vehicle nodes The probability that each vehicle node starts from the collection area and arrives at the target area at the preset time tend .
然后，以选取的车辆节点构成车联网能够覆盖目标区域为目标问题，从M个车辆节点中随机选出多个车辆节点集合作为目标问题的解；以生成的解作为种群，根据车辆节点到达目标区域的概率和车辆节点在tend时刻距离目标区域的距离构建适应度函数，采用遗传算法搜索出种群中适应度函数值最优的染色体作为目标问题的最优解；Then, taking the selected vehicle nodes to form the Internet of Vehicles to cover the target area as the target problem, randomly select multiple vehicle node sets from M vehicle nodes as the solution of the target problem; take the generated solutions as the population, and reach the target according to the vehicle nodes. The probability of the area and the distance between the vehicle node and the target area at the time of tend construct the fitness function, and the genetic algorithm is used to search out the chromosome with the best fitness function value in the population as the optimal solution of the target problem;
最后，将最优解对应的车辆节点作为用于构建车联网的感知服务节点，用来保证车联网可靠运行。Finally, the vehicle node corresponding to the optimal solution is used as the sensing service node for building the Internet of Vehicles to ensure the reliable operation of the Internet of Vehicles.
本发明主要包括两部分：(一)计算车辆节点在目标时间tend到目标区域的概率；(二)利用遗传算法提取最优服务节点集。The invention mainly includes two parts: (1) calculating the probability of the vehicle node reaching the target area at the target time tend ; (2) extracting the optimal service node set by using the genetic algorithm.
下面分别对两个部分进行具体分析：The two parts are analyzed in detail below:
(一)计算车辆节点在目标时间tend到目标区域的概率，具体步骤为：(1) Calculate the probability that the vehicle node reaches the target area at the target time tend , the specific steps are:
(1)设采集到的任意一个车辆节点在时刻tstart的位置为Lstart，要预测在tend时刻该车辆节点的位置Lend，则构建问题模型为：(1) Let the position of any collected vehicle node at time tstart be Lstart , and to predict the position Lend of the vehicle node at time tend , the problem model is constructed as:
式中，P(X(tend)＝Lx)表示车辆节点在时刻tend处于位置Lx的概率。In the formula, P(X(tend )=Lx ) represents the probability that the vehicle node is at the position Lx at the time tend .
(2)为了求解式(1)，本方案建立如图2所示的马尔科夫模型，图中包括两种状态转移节点，其中，
假设车辆节点的运动行为符合马尔科夫过程，则车辆节点在下一时刻tn出现在位置Lβ的概率只与其前一时刻tn-1位于位置Lα的概率和tn时刻在位置Lα处发生转移的概率P(tn|Lα→Lx)有关。故
P(Lα→Lβ,tn)＝P(X(tn-1)＝Lα)×P(tn|Lα→Lx)P(Lα →Lβ ,tn )=P(X(tn-1 )=Lα )×P(tn |Lα →Lx )
关于
P(X(t)＝Lβ)＝∑αP(Lα→Lβ，t)P(X(t)=Lβ )=∑α P(Lα →Lβ , t)
(3)利用马尔科夫模型进行位置预测时，往往都采用等间隔的时间划分作为一个状态的转移点。例如以1分钟为单位进行序列建模，每个状态转移点则相距1分钟，预测过程中则也采用分钟为单位进行预测。然而，车辆节点位于不同地点的行为不同，停留时间也不尽相同，统一的时间划分必然会导致预测结果的不准确。为此，本发明采用高斯混合模型对马尔科夫模型中的状态转移点进行发现，具体步骤为：(3) When using the Markov model for position prediction, the time division at equal intervals is often used as the transition point of a state. For example, the sequence modeling is performed in units of 1 minute, and each state transition point is separated by 1 minute, and the prediction process is also performed in minutes. However, the behavior of vehicle nodes at different locations is different, and the stay time is also different. The unified time division will inevitably lead to inaccurate prediction results. To this end, the present invention uses a Gaussian mixture model to discover the state transition points in the Markov model, and the specific steps are:
1)从对应车辆节点的历史轨迹数据中提取该车辆节点连续时间下的位置转移概率，绘制位置转移概率关于时间的关系曲线，选取关系曲线峰值点对应的时间节点为位置转移时间节点；记第n个位置时间转移节点为tn；1) Extract the position transition probability of the vehicle node in continuous time from the historical trajectory data of the corresponding vehicle node, draw the relationship curve of the position transition probability with respect to time, and select the time node corresponding to the peak point of the relationship curve as the position transition time node; The n position time transfer nodes are tn ;
2)采用高斯分布函数模型拟合提取的位置转移时间节点，得到车辆节点位置转移概率与转移时间节点的关系式：2) Use the Gaussian distribution function model to fit the extracted position transfer time nodes, and obtain the relationship between the position transfer probability of the vehicle node and the transfer time node:
P(tn|Lα→Lx)＝∑nπnN(tn|μn,σn)，∑nπn＝1,πn≥0P(tn |Lα →Lx )=∑n πn N(tn |μn ,σn ), ∑n πn =1,πn ≥0
式中，P(tn|Lα→Lx)表示车辆节点在时刻tn在位置Lα处发生位置转移的概率，πn表示高斯分布权值系数，N(tn|μn,σn)表示位置转移时间节点tn服从期望值为μn、标准差为σn的高斯分布；In the formula, P(tn |Lα →Lx ) represents the probability of the position transition of the vehicle node at the position Lα at time tn , πn represents the Gaussian distribution weight coefficient, N(tn |μn ,σn ) means that the position transfer time node tn obeys the Gaussian distribution with the expected value μn and the standard deviation σn ;
3)将车辆节点位置转移概率与转移时间节点的关系式代入位置转移马尔科夫模型：3) Substitute the relationship between the vehicle node position transition probability and the transition time node into the position transition Markov model:
P(Lα→Lβ,tn)＝P(X(tn-1)＝Lα)×P(tn|Lα→Lx)P(Lα →Lβ ,tn )=P(X(tn-1 )=Lα )×P(tn |Lα →Lx )
P(X(tn)＝Lβ)＝∑αP(Lα→Lβ,tn)P(X(tn )=Lβ )=∑α P(Lα →Lβ ,tn )
4)在时间区间[tstart,tend]内对马尔科夫模型描述的位置转移过程进行迭代求解，得到车辆节点到达目标区域的概率，tstart为起始时刻；求解的步骤为：4) In the time interval [tstart , tend ], iteratively solve the position transfer process described by the Markov model, and obtain the probability of the vehicle node reaching the target area, tstart is the starting time; the steps of solving are:
S1：设置参数：定义Pi为车辆节点i在时刻tend到达目标区域的概率；定义Lcur为车辆当前位置，Lcur为采集区域中的任意一个位置点；定义tnow为当前时间点；定义Pcur为当前概率值；S1: Setting parameters: define Pi as the probability of vehicle node i reaching the target area at time tend ; define Lcur as the current position of the vehicle, Lcur as any position point in the collection area; define tnow as the current time point; Define Pcur as the current probability value;
S2：初始化：Pi＝0，Pcur＝0，tnow＝tstart；S2: Initialization: Pi = 0, Pcur = 0, tnow = tstart ;
S3：获取tnow的下一个位置时间转移节点tn，判断tn是否大于tend，若是，则终止迭代，计算Pi＝Pi+Pcur；否则，执行步骤S4；S3: Obtain the next position and time transfer node tn of tnow , and judge whether tn is greater than tend , and if so, terminate the iteration, and calculate Pi =Pi +Pcur ; otherwise, execute step S4;
S4：计算：S4: Calculate:
P(tn|Lα→Lx)＝∑nπnN(tn|μn,σn)，∑nπn＝1,πn≥0P(tn |Lα →Lx )=∑n πn N(tn |μn ,σn ), ∑n πn =1,πn ≥0
Pcur＝P(X(tnow)＝Lcur)×P(tn|Lcur→Lx)Pcur =P(X(tnow )=Lcur )×P(tn |Lcur →Lx )
更新tnow＝tn，返回步骤S3。Update tnow =tn , and return to step S3.
通过上述步骤，可以得到M个车辆节点在时刻tend到达目标区域的概率。Through the above steps, the probability that M vehicle nodes reach the target area at time tend can be obtained.
(二)利用遗传算法提取最优服务节点集(2) Using genetic algorithm to extract the optimal service node set
(4)对车辆节点集合进行编码，将编码的结果作为初始种群的染色体，染色体表示为：(4) Encode the vehicle node set, and use the encoded result as the chromosome of the initial population. The chromosome is expressed as:
G＝(g1,g2,…,gM)G=(g1 ,g2 ,...,gM )
式中，gi表示车辆节点i的编码，i＝1,2,…,M，gi的取值为0或1；gi取值为0时，表示G中未选取车辆节点i；gi取值为1时，表示G中选取车辆节点i。In the formula,gi represents the code of the vehicle node i, i=1,2,...,M, the value ofgi is 0 or 1; when the value ofgi is 0, it means that the vehicle node i is not selected in G; g When the value ofi is 1, it means that the vehicle node i is selected in G.
(5)初始化种群为：G1，G2，…，GK；Gk为种群中的第k个基因，表示目标问题的第k个解，k＝1,2，…，K,K为步骤(2)中生成的解的总数；(5) Initialize the population as: G1 , G2 , ..., GK ; Gk is the kth gene in the population, representing the kth solution of the target problem, k=1, 2, ..., K, K is the total number of solutions generated in step (2);
(6)计算种群中各染色体适应度函数值；(6) Calculate the fitness function value of each chromosome in the population;
f＝ω1P+ω2Df=ω1 P+ω2 D
P＝1-Πi∈G(1-Pi)P=1-Πi∈G (1-Pi )
D＝(∑i∈GDi)/nD=(∑i∈G Di )/n
式中，G为目标问题的一个解，即一个染色体；n为G中车辆节点的数量，Di表示车辆节点i距离目标区域的距离，P为G的联合概率，D为G的联合距离；ω1和ω2均为权值系数，0＜ω1＜1，0＜ω2＜1，0＜ω1+ω2＜1。In the formula, G is a solution of the target problem, that is, a chromosome; n is the number of vehicle nodes in G, Di is the distance between vehicle node i and the target area, P is the joint probability of G, and D is the joint distance of G; Both ω1 and ω2 are weight coefficients, 0<ω1 <1, 0<ω2 <1, and 0<ω1 +ω2 <1.
(7)从种群中选取适应度值最高的a个染色体；(7) Select a chromosome with the highest fitness value from the population;
(8)以选出的a个染色体为父体进行交叉变异，产生新一代的种群，返回步骤(6)；(8) Carry out crossover mutation with the selected a chromosome as the father to generate a new generation of population, and return to step (6);
(9)重复执行步骤(6)至(8)，直至迭代次数满足预设阈值。(9) Repeat steps (6) to (8) until the number of iterations meets the preset threshold.
最终得到的适应度函数值最优的染色体作为用于构建车联网的感知服务节点。Finally, the chromosome with the optimal fitness function value is used as the sensing service node for building the Internet of Vehicles.
本方案中的迭代次数可以根据需要选取，此处选取迭代次数为100。The number of iterations in this scheme can be selected as required, and the number of iterations is selected as 100 here.
本发明利用遗传算法进行服务节点的选取，在所有进行感知的车辆中得到最优的，将其选为服务节点，提高感知服务质量，且能够避免资源浪费。The present invention uses the genetic algorithm to select service nodes, obtains the best one among all sensing vehicles, selects it as the service node, improves the perceived service quality, and can avoid resource waste.
以上所述仅是本发明的优选实施方式，应当指出：对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。The above is only the preferred embodiment of the present invention, it should be pointed out that: for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can also be made, and these improvements and modifications are also It should be regarded as the protection scope of the present invention.
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