CN115243273A - A wireless sensor network coverage optimization method and device, equipment, medium - Google Patents

A wireless sensor network coverage optimization method and device, equipment, medium Download PDF

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CN115243273A
CN115243273A CN202211165906.XA CN202211165906A CN115243273A CN 115243273 A CN115243273 A CN 115243273A CN 202211165906 A CN202211165906 A CN 202211165906A CN 115243273 A CN115243273 A CN 115243273A
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CN115243273B (en
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周心博
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Kunming University of Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention discloses a method, a device, equipment and a medium for optimizing the coverage of a wireless sensor network, belonging to the field of network optimization, wherein the optimization method comprises the following steps: s1, obtaining a probability perception optimization model of a sensor node network based on a Boolean perception model; s2, constructing a wireless sensor network coverage multi-objective optimization model with sensor network coverage and node energy power consumption as optimization parameters; s3, aiming at the established model, the mixed mayflies-frog jump algorithm based on Tent chaotic map and Levy flight strategy is solved. Aiming at the effectiveness of coverage of a wireless sensor network, the method comprehensively considers the sensing attenuation and the coverage hole of the wireless sensor, and establishes a probability sensing optimization model of the sensor network; node energy consumption is further introduced, and a multi-objective optimization model of comprehensive energy and coverage rate is constructed; the traditional mayflies algorithm and the mixed frog-leap algorithm are fused, and Tent chaotic mapping and Levy flight mechanism are introduced to improve the convergence precision and the global optimization capability of the algorithm.

Description

一种无线传感器网络覆盖优化方法及装置、设备、介质A wireless sensor network coverage optimization method and device, equipment and medium

技术领域technical field

本发明属于无线传感器布局优化领域，更具体的说涉及一种无线传感器网络覆盖优化方法及装置、设备、介质。The invention belongs to the field of wireless sensor layout optimization, and more particularly relates to a wireless sensor network coverage optimization method and device, equipment and medium.

背景技术Background technique

随着物联网、自动化及人工智能领域技术的高速发展，传感器技术已从传统的单源获取信息转变为集成化、多源化、异构化的具有感知、计算和通信能力的无线传感器网络（WSN）。其将传感器技术、分布式信息处理技术、嵌入式技术及通信技术等先进技术高度融合，已成为现代军工、医疗、国家安全等领域的热门研究方向之一。With the rapid development of technology in the fields of Internet of Things, automation and artificial intelligence, sensor technology has changed from the traditional single-source acquisition of information to an integrated, multi-source, and heterogeneous wireless sensor network (WSN) with sensing, computing and communication capabilities. ). It highly integrates advanced technologies such as sensor technology, distributed information processing technology, embedded technology and communication technology, and has become one of the hot research directions in the fields of modern military industry, medical care, and national security.

无线传感器网络覆盖优化即通过优化各传感器的部署位置，以解决由于随机部署产生的覆盖率低、感知能力较差、节点分布不均匀、覆盖空洞较多等问题，可直接影响到无线网络数据传输的安全性和准确性。Wireless sensor network coverage optimization is to optimize the deployment location of each sensor to solve the problems of low coverage, poor sensing ability, uneven node distribution, and many coverage holes due to random deployment, which can directly affect wireless network data transmission. security and accuracy.

当下对于传感器节点覆盖模型的研究主要分为0-1感知模型和概率感知模型，其中0-1感知模型较为理想化，未能全面考虑到现实情况下无线传感网络的复杂应用环境，以及传感器节点的感知能力随距离衰弱等问题。The current research on sensor node coverage model is mainly divided into 0-1 perception model and probabilistic perception model. Among them, the 0-1 perception model is more ideal and fails to fully consider the complex application environment of wireless sensor networks in reality, and the sensor The perception ability of nodes weakens with distance.

目前的文章仅有少量设计到无线传感器网络覆盖优化，但是没有任何文章设计传感器网络覆盖率和节点能量功耗的联合优化。At present, only a few papers are designed to optimize wireless sensor network coverage, but none of them design the joint optimization of sensor network coverage and node energy consumption.

目前还没有专利将基于Tent混沌映射和Levy飞行策略的混合蜉蝣-蛙跳算法应用于无线传感器网络覆盖优化方法中。There is no patent to apply the hybrid mayfly-leapfrog algorithm based on Tent chaotic map and Levy flight strategy to the coverage optimization method of wireless sensor network.

因此，亟需一种无线传感器网络覆盖优化方法。Therefore, there is an urgent need for a wireless sensor network coverage optimization method.

发明内容SUMMARY OF THE INVENTION

本发明的目的是提供一种无线传感器网络覆盖优化方法，以解决上述现有技术中的问题，能够降低生产过程中的资源浪费、提高仓储空间利用率及订单响应效率，得到更优的资源规划及货位分配结果。The purpose of the present invention is to provide a wireless sensor network coverage optimization method to solve the above-mentioned problems in the prior art, reduce resource waste in the production process, improve storage space utilization and order response efficiency, and obtain better resource planning and the result of the slot allocation.

为了实现上述目的，本发明是采用以下技术方案实现的：步骤一、根据布尔感知模型设计无线传感器节点，基于监测概率和覆盖空洞建立得到针对传感器网络覆盖率的目标函数，从而构建出传感器节点网络的概率感知模型；In order to achieve the above object, the present invention is realized by adopting the following technical solutions: Step 1: Design a wireless sensor node according to a Boolean perception model, and establish an objective function for sensor network coverage based on monitoring probability and coverage hole, thereby constructing a sensor node network. The probabilistic perception model of ;

步骤二、基于步骤一所建概率感知优化模型，构建以传感器网络覆盖率和节点能量功耗为优化参数的无线传感器网络覆盖多目标优化模型；Step 2: Based on the probabilistic perception optimization model built in Step 1, construct a wireless sensor network coverage multi-objective optimization model with sensor network coverage and node energy consumption as optimization parameters;

步骤三、针对所构建的优化模型，将蜉蝣算法（MA）的收敛速度和局部搜索能力与混合蛙跳算法的鲁棒性与全局寻优能力相结合，设计基于Tent混沌映射和Levy飞行策略的混合蜉蝣-蛙跳算法进行求解。Step 3. For the optimization model constructed, combine the convergence speed and local search ability of the mayfly algorithm (MA) with the robustness and global optimization ability of the hybrid frog leaping algorithm, and design an algorithm based on Tent chaotic map and Levy flight strategy. The hybrid mayfly-leapfrog algorithm is used to solve the problem.

进一步地，所述的步骤一根据布尔感知模型设计无线传感器节点，基于监测概率和覆盖空洞建立得到针对传感器网络覆盖率的目标函数，从而构建出传感器节点网络的概率感知模型采用以下步骤实现的：Further, the first step is to design the wireless sensor node according to the Boolean perception model, and establish an objective function for the coverage of the sensor network based on the monitoring probability and the coverage hole, so that the probability perception model of the sensor node network is constructed using the following steps to achieve:

步骤1.1、假设将二维区域S划分为

Figure 646546DEST_PATH_IMAGE001
个网格，网格点集合
Figure 658365DEST_PATH_IMAGE002
，在其中布置N个静态无线传感器节点，设传感器集合
Figure 747543DEST_PATH_IMAGE003
，其中基于布尔感知模型，每个节点的监测范围均为以R为半径 的圆形区域； Step 1.1. Suppose the two-dimensional area S is divided into
Figure 646546DEST_PATH_IMAGE001
grid, set of grid points
Figure 658365DEST_PATH_IMAGE002
, in which N static wireless sensor nodes are arranged, and the sensor set is set
Figure 747543DEST_PATH_IMAGE003
, where based on the Boolean perception model, the monitoring range of each node is a circular area with R as the radius;

步骤1.2、对于区域S中的某一网格点

Figure 666958DEST_PATH_IMAGE004
，其与无线传感器节点
Figure 157982DEST_PATH_IMAGE005
间的欧式距离为： Step 1.2, for a grid point in the area S
Figure 666958DEST_PATH_IMAGE004
, which is related to wireless sensor nodes
Figure 157982DEST_PATH_IMAGE005
The Euclidean distance between is:

Figure 340702DEST_PATH_IMAGE006
（1）
Figure 340702DEST_PATH_IMAGE006
(1)

步骤1.3、考虑到实际情况下传感器节点的最优监测距离和传感误差距离，则节点

Figure 651597DEST_PATH_IMAGE007
对于网格点
Figure 312386DEST_PATH_IMAGE008
的监测感知概率可表述如下： Step 1.3. Considering the optimal monitoring distance and sensing error distance of the sensor node in the actual situation, the node
Figure 651597DEST_PATH_IMAGE007
for grid points
Figure 312386DEST_PATH_IMAGE008
The monitoring perception probability of can be expressed as follows:

Figure 660846DEST_PATH_IMAGE009
（2）
Figure 660846DEST_PATH_IMAGE009
(2)

Figure 14467DEST_PATH_IMAGE010
(3)
Figure 14467DEST_PATH_IMAGE010
(3)

其中，

Figure 609397DEST_PATH_IMAGE011
为传感器节点
Figure 339455DEST_PATH_IMAGE012
的最优监测距离，
Figure 539492DEST_PATH_IMAGE013
为其传感误差距离，
Figure 64015DEST_PATH_IMAGE014
为感知能 力随距离的衰减系数； in,
Figure 609397DEST_PATH_IMAGE011
for sensor nodes
Figure 339455DEST_PATH_IMAGE012
The optimal monitoring distance of ,
Figure 539492DEST_PATH_IMAGE013
For its sensing error distance,
Figure 64015DEST_PATH_IMAGE014
is the attenuation coefficient of perception ability with distance;

步骤1.4、则区域内所有传感器节点对于

Figure 349502DEST_PATH_IMAGE015
的联合监测概率可表述为： Step 1.4, all sensor nodes in the area are
Figure 349502DEST_PATH_IMAGE015
The joint monitoring probability of can be expressed as:

Figure 617673DEST_PATH_IMAGE016
（4）
Figure 617673DEST_PATH_IMAGE016
(4)

步骤1.5、假设

Figure 468954DEST_PATH_IMAGE017
为该节点能被有效监测到的最小概率阈值，假设能被有效监 测到的网格点集合为
Figure 898798DEST_PATH_IMAGE018
，则对于其中任意点应满足条件： Step 1.5. Assumptions
Figure 468954DEST_PATH_IMAGE017
is the minimum probability threshold that the node can be effectively monitored, assuming that the set of grid points that can be effectively monitored is
Figure 898798DEST_PATH_IMAGE018
, then for any point, the condition should be satisfied:

Figure 671582DEST_PATH_IMAGE019
（5）
Figure 671582DEST_PATH_IMAGE019
(5)

对于不满足上式的网格点，即在区域S中且在集合

Figure 743443DEST_PATH_IMAGE020
以外的网格点则为覆盖空 洞； For grid points that do not satisfy the above formula, that is, in the area S and in the set
Figure 743443DEST_PATH_IMAGE020
The grid points outside are covered holes;

步骤1.6、基于以上，为了最大化满足式（5）的点的个数，建立以无线传感器网络覆盖率为优化目标的目标函数：Step 1.6. Based on the above, in order to maximize the number of points satisfying Equation (5), establish an objective function with the wireless sensor network coverage as the optimization objective:

Figure 652494DEST_PATH_IMAGE021
（6）
Figure 652494DEST_PATH_IMAGE021
(6)

由此构建了传感器节点网络的概率感知优化模型。Therefore, a probabilistic perception optimization model of sensor node network is constructed.

进一步地，所述的步骤二为降低能力损耗并优化网络资源，基于步骤一所建概率感知优化模型，构建以传感器网络覆盖率和节点能量功耗为优化参数的无线传感器网络覆盖多目标优化模型；Further, the second step is to reduce the capacity loss and optimize the network resources. Based on the probability perception optimization model built in the first step, a wireless sensor network coverage multi-objective optimization model with sensor network coverage and node energy consumption as optimization parameters is constructed. ;

步骤2.1、由于传感器节点的能量储备有限，引入传感器节点的能耗作为优化目标之一，由于信号传输时，节点部分能力需在传输过程中进行信号放大，则节点在链路上传输kbit数据的能耗可表示为:Step 2.1. Due to the limited energy reserve of the sensor node, the energy consumption of the sensor node is introduced as one of the optimization goals. Since part of the capacity of the node needs to amplify the signal during the transmission process during signal transmission, the node transmits kbit data on the link. Energy consumption can be expressed as:

Figure 784398DEST_PATH_IMAGE022
（7）
Figure 784398DEST_PATH_IMAGE022
(7)

Figure 572707DEST_PATH_IMAGE023
（8）
Figure 572707DEST_PATH_IMAGE023
(8)

其中

Figure 448259DEST_PATH_IMAGE024
为输出节点和接收节点间的距离，
Figure 211815DEST_PATH_IMAGE025
是信号放大倍数，
Figure 514621DEST_PATH_IMAGE026
是多径衰 减模型信号放大倍数，
Figure 996418DEST_PATH_IMAGE027
为单个传感器节点发送bit数据的能量损耗； in
Figure 448259DEST_PATH_IMAGE024
is the distance between the output node and the receiving node,
Figure 211815DEST_PATH_IMAGE025
is the signal amplification factor,
Figure 514621DEST_PATH_IMAGE026
is the multipath fading model signal amplification factor,
Figure 996418DEST_PATH_IMAGE027
The energy consumption of sending bit data for a single sensor node;

步骤2.2、由此，优化模型的总目标函数应表述为最大化传感器网络覆盖率及最小化节点能量功耗：Step 2.2. Thus, the overall objective function of the optimization model should be expressed as maximizing sensor network coverage and minimizing node energy consumption:

Figure 410081DEST_PATH_IMAGE028
（9）
Figure 410081DEST_PATH_IMAGE028
(9)

其中

Figure 746254DEST_PATH_IMAGE029
表述每个节点间的传输能耗。 in
Figure 746254DEST_PATH_IMAGE029
Express the transmission energy consumption between each node.

进一步地，所述的步骤三针对所构建的无线传感器网络覆盖多目标优化模型，将蜉蝣算法（MA）的收敛速度和局部搜索能力与混合蛙跳算法的鲁棒性与全局寻优能力相结合，设计一种基于Tent混沌映射和Levy飞行策略的混合蜉蝣-蛙跳算法进行求解；Further, in the third step, for the constructed wireless sensor network coverage multi-objective optimization model, the convergence speed and local search ability of the mayfly algorithm (MA) are combined with the robustness and global optimization ability of the hybrid frog leaping algorithm. , design a hybrid mayfly-frog leaping algorithm based on Tent chaotic map and Levy flight strategy to solve;

通过Levy飞行策略扩大算法搜索范围，并使算法能够快速跳出局部最优；算法总体流程表述如下：The search range of the algorithm is expanded through the Levy flight strategy, and the algorithm can quickly jump out of the local optimum; the overall process of the algorithm is described as follows:

步骤3.1、初始化参数，基于Tent混沌映射机制得到初始种群，并初始化个体速度；Step 3.1, initialize parameters, obtain the initial population based on the Tent chaotic mapping mechanism, and initialize the individual speed;

Tent 映射是一种分段线性映射函数，具有参数少、操作简单、映射呈现的结果分布密度比较均匀，具有很好的遍历性等优势，其初始化种群如下式所示Tent mapping is a piecewise linear mapping function, which has the advantages of less parameters, simple operation, relatively uniform distribution density of the results presented by the mapping, and good ergodicity. Its initialization population is shown in the following formula

Figure 219960DEST_PATH_IMAGE030
（10）
Figure 219960DEST_PATH_IMAGE030
(10)

其中个体

Figure 191983DEST_PATH_IMAGE031
由上一个个体和参数
Figure 409338DEST_PATH_IMAGE032
决定，参数
Figure 147487DEST_PATH_IMAGE033
； of which individual
Figure 191983DEST_PATH_IMAGE031
by the previous individual and parameters
Figure 409338DEST_PATH_IMAGE032
decision, parameter
Figure 147487DEST_PATH_IMAGE033
;

步骤3.2、将初始种群随机划分为雄性蜉蝣

Figure 792095DEST_PATH_IMAGE034
和雌性蜉蝣种群
Figure 45221DEST_PATH_IMAGE035
； Step 3.2. Randomly divide the initial population into male mayflies
Figure 792095DEST_PATH_IMAGE034
and female mayfly populations
Figure 45221DEST_PATH_IMAGE035
;

步骤3.3、计算每个个体的适应度并从高到低排序，基于混合蛙跳算法分别将雄性种群和雌性种群划分为m个子种群，其中：Step 3.3: Calculate the fitness of each individual and sort them from high to low. Based on the hybrid frog leaping algorithm, the male population and the female population are divided into m sub-populations, where:

Figure 800688DEST_PATH_IMAGE036
（11）
Figure 800688DEST_PATH_IMAGE036
(11)

Figure 658922DEST_PATH_IMAGE037
（12）
Figure 658922DEST_PATH_IMAGE037
(12)

步骤3.4、对于雄性种群和雌性种群中的每个子种群，将其中的个体按适应度大小 排序，将每个子种群中适应度最优和最劣的个体分别记

Figure 208852DEST_PATH_IMAGE038
和
Figure 886958DEST_PATH_IMAGE039
； Step 3.4. For each subpopulation in the male population and the female population, sort the individuals according to their fitness, and record the individuals with the best and worst fitness in each subpopulation respectively.
Figure 208852DEST_PATH_IMAGE038
and
Figure 886958DEST_PATH_IMAGE039
;

步骤3.5、按照式（13）（14）对每个子种群的

Figure 446116DEST_PATH_IMAGE040
进行蛙跳更新； Step 3.5, according to formula (13) (14), for each subpopulation
Figure 446116DEST_PATH_IMAGE040
Perform leapfrog updates;

Figure 158857DEST_PATH_IMAGE041
（13）
Figure 158857DEST_PATH_IMAGE041
(13)

Figure 145267DEST_PATH_IMAGE042
（14）
Figure 145267DEST_PATH_IMAGE042
(14)

其中

Figure 107407DEST_PATH_IMAGE043
，若
Figure 204676DEST_PATH_IMAGE044
适应度优于
Figure 870511DEST_PATH_IMAGE045
，则保留
Figure 762243DEST_PATH_IMAGE046
，否则将 当前全局最优
Figure 414942DEST_PATH_IMAGE047
替换
Figure 315902DEST_PATH_IMAGE048
，再按照式（13）（14）进行更新，若仍没得到进化，则产生一随机 个体替换
Figure 3235DEST_PATH_IMAGE049
； in
Figure 107407DEST_PATH_IMAGE043
,like
Figure 204676DEST_PATH_IMAGE044
fitness is better than
Figure 870511DEST_PATH_IMAGE045
, then keep
Figure 762243DEST_PATH_IMAGE046
, otherwise the current global optimal
Figure 414942DEST_PATH_IMAGE047
replace
Figure 315902DEST_PATH_IMAGE048
, and then update according to formulas (13) and (14), if the evolution has not been obtained, a random individual replacement will be generated.
Figure 3235DEST_PATH_IMAGE049
;

步骤3.6、反复迭代步骤3.5，直至达到局部最大迭代次数

Figure 128186DEST_PATH_IMAGE050
； Step 3.6, repeat step 3.5 until the local maximum number of iterations is reached
Figure 128186DEST_PATH_IMAGE050
;

步骤3.7、将蛙跳更新后的子种群重新合并为雄性、雌性蜉蝣种群，计算个体适应度大小，并进行蜉蝣算法更新；Step 3.7, re-merge the updated subpopulations of leapfrog into male and female mayfly populations, calculate the individual fitness, and update the mayfly algorithm;

Figure 268180DEST_PATH_IMAGE051
（15）
Figure 268180DEST_PATH_IMAGE051
(15)

Figure 707252DEST_PATH_IMAGE052
（16）
Figure 707252DEST_PATH_IMAGE052
(16)

其中每个雄性蜉蝣个体i根据式（15）进行更新：where each male mayfly individual i is updated according to formula (15):

其中

Figure 249091DEST_PATH_IMAGE053
为个体i的第j维速度，
Figure 748206DEST_PATH_IMAGE054
为该蜉蝣历史最优位置，
Figure 109917DEST_PATH_IMAGE055
为全局最 优个体位置，
Figure 352679DEST_PATH_IMAGE056
为正吸引系数，
Figure 749026DEST_PATH_IMAGE057
为舞蹈系数，随机数
Figure 419042DEST_PATH_IMAGE058
in
Figure 249091DEST_PATH_IMAGE053
is the jth dimension velocity of individual i,
Figure 748206DEST_PATH_IMAGE054
is the historical optimal position of the mayfly,
Figure 109917DEST_PATH_IMAGE055
is the global optimal individual position,
Figure 352679DEST_PATH_IMAGE056
is the positive coefficient of attraction,
Figure 749026DEST_PATH_IMAGE057
is the dance coefficient, a random number
Figure 419042DEST_PATH_IMAGE058

每个雌性蜉蝣个体i根据式（17）进行更新：Each female mayfly individual i is updated according to equation (17):

Figure 533628DEST_PATH_IMAGE059
（17）
Figure 533628DEST_PATH_IMAGE059
(17)

Figure 583011DEST_PATH_IMAGE060
（18）
Figure 583011DEST_PATH_IMAGE060
(18)

其中

Figure 630602DEST_PATH_IMAGE061
为雌雄个体
Figure 471519DEST_PATH_IMAGE062
之间的笛卡尔距离，
Figure 73401DEST_PATH_IMAGE063
为随机飞行系数， in
Figure 630602DEST_PATH_IMAGE061
male and female
Figure 471519DEST_PATH_IMAGE062
the Cartesian distance between,
Figure 73401DEST_PATH_IMAGE063
is the random flight coefficient,

步骤3.8、根据式（19）（20）进行雌雄交配操作，得到新一代种群；Step 3.8. According to formula (19) (20), the male and female mating operation is carried out to obtain a new generation of population;

Figure 657966DEST_PATH_IMAGE064
（19）
Figure 657966DEST_PATH_IMAGE064
(19)

Figure 763326DEST_PATH_IMAGE065
（20）
Figure 763326DEST_PATH_IMAGE065
(20)

其中

Figure 775144DEST_PATH_IMAGE066
； in
Figure 775144DEST_PATH_IMAGE066
;

步骤3.9、基于Levy飞行策略再对新一代种群位置进行更新，避免种群陷入局部最优；Step 3.9, based on the Levy flight strategy, update the position of the new generation population to avoid the population falling into local optimum;

对于新种群中的每一个个体，根据式（21）进行Levy飞行更新，并通过适应度确定是否保留更新后的个体；For each individual in the new population, perform Levy flight update according to formula (21), and determine whether to retain the updated individual through fitness;

Figure 864323DEST_PATH_IMAGE067
（21）
Figure 864323DEST_PATH_IMAGE067
(twenty one)

其中

Figure 987000DEST_PATH_IMAGE068
为（0，2）间随机数，且： in
Figure 987000DEST_PATH_IMAGE068
is a random number between (0, 2), and:

Figure 9182DEST_PATH_IMAGE069
（22）
Figure 9182DEST_PATH_IMAGE069
(twenty two)

Figure 457481DEST_PATH_IMAGE070
（23）
Figure 457481DEST_PATH_IMAGE070
(twenty three)

Figure 768377DEST_PATH_IMAGE071
（24）
Figure 768377DEST_PATH_IMAGE071
(twenty four)

步骤3.10、对每个个体位置和速度进行边界处理；Step 3.10, perform boundary processing on each individual position and velocity;

步骤3.11、算法总迭代次数加一，判断是否达到最大迭代次数

Figure 694744DEST_PATH_IMAGE072
，若是则输出最优 解，算法结束；反之则返回步骤3.2。 Step 3.11. Add one to the total number of iterations of the algorithm to determine whether the maximum number of iterations is reached
Figure 694744DEST_PATH_IMAGE072
, if so, output the optimal solution and the algorithm ends; otherwise, return to step 3.2.

根据本发明第一方面可得，第二方面，一种无线传感器网络覆盖优化方法的装置包括：According to the first aspect of the present invention, in the second aspect, an apparatus for a wireless sensor network coverage optimization method includes:

数据获取模块，用于获取传感器分布的数据集；The data acquisition module is used to acquire the data set distributed by the sensor;

数据处理模块，用于对获取的无线网络传感器数据进行处理，得到无线网络传感器模型的数据；The data processing module is used to process the acquired wireless network sensor data to obtain the data of the wireless network sensor model;

数据计算模块，用于依据处理后的数据对无线网络传感器模型进行量化计算。The data calculation module is used to perform quantitative calculation on the wireless network sensor model according to the processed data.

另一方面，一种电子设备，包括：处理器和存储器，所述存储器上存储有计算机可读指令，所述计算机可读指令被所述处理器执行时实现一种无线传感器网络覆盖优化方法。In another aspect, an electronic device includes: a processor and a memory, wherein computer-readable instructions are stored on the memory, and when the computer-readable instructions are executed by the processor, a method for optimizing wireless sensor network coverage is implemented.

又一方面，一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现一种无线传感器网络覆盖优化方法。In yet another aspect, a computer-readable storage medium has a computer program stored thereon, the computer program implements a wireless sensor network coverage optimization method when executed by a processor.

本发明有益效果：Beneficial effects of the present invention:

本发明的基于混合蜉蝣-蛙跳算法的无线传感器网络覆盖优化方法，针对无线传感器网络覆盖的有效性，综合考虑无线传感器感知衰减和覆盖空洞，建立传感器网络概率感知优化模型；进一步引入节点能量消耗，构建综合能量和覆盖率的多目标优化模型；将传统蜉蝣算法和混合蛙跳算法融合，并引入Tent混沌映射和Levy飞行机制以提高算法收敛精度和全局寻优能力。The wireless sensor network coverage optimization method based on the hybrid mayfly-frog leap algorithm of the present invention, aiming at the effectiveness of the wireless sensor network coverage, comprehensively considers the wireless sensor perception attenuation and coverage holes, and establishes a sensor network probability perception optimization model; further introduces node energy consumption , build a multi-objective optimization model with comprehensive energy and coverage; integrate traditional mayfly algorithm and hybrid frog leaping algorithm, and introduce Tent chaotic map and Levy flight mechanism to improve algorithm convergence accuracy and global optimization ability.

附图说明Description of drawings

为使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作进一步描述，其中：In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be further described below in conjunction with the accompanying drawings, wherein:

图1为本发明提供的基于混合蜉蝣-蛙跳算法的无线传感器网络覆盖优化方法的无线传感器的有效监测区域和覆盖空洞示意图；1 is a schematic diagram of an effective monitoring area and a coverage hole of a wireless sensor of the wireless sensor network coverage optimization method based on the hybrid mayfly-frog leaping algorithm provided by the present invention;

图2为本发明提供的基于混合蜉蝣-蛙跳算法的无线传感器网络覆盖优化方法的基于Tent混沌映射和Levy飞行策略的混合蜉蝣-蛙跳算法流程图。2 is a flowchart of the hybrid mayfly-leapfrog algorithm based on Tent chaotic map and Levy flight strategy of the wireless sensor network coverage optimization method based on the hybrid mayfly-leapfrog algorithm provided by the present invention.

具体实施方式Detailed ways

下面将结合附图1-图2对本发明进行详细说明，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。The present invention will be described in detail below with reference to accompanying drawings 1 to 2, and the technical solutions in the embodiments of the present invention will be described clearly and completely. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the implementations. example. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

步骤一、根据布尔感知模型设计无线传感器节点，基于监测概率和覆盖空洞建立得到针对传感器网络覆盖率的目标函数，从而构建出传感器节点网络的概率感知模型。Step 1: Design the wireless sensor nodes according to the Boolean perception model, and establish an objective function for the coverage of the sensor network based on the monitoring probability and the coverage hole, so as to construct a probabilistic perception model of the sensor node network.

所述的步骤一根据布尔感知模型设计无线传感器节点，基于监测概率和覆盖空洞建立得到针对传感器网络覆盖率的目标函数，从而构建出传感器节点网络的概率感知模型采用以下步骤实现的：The first step is to design the wireless sensor node according to the Boolean perception model, and establish an objective function for the sensor network coverage based on the monitoring probability and the coverage hole, so as to construct the probability perception model of the sensor node network. The following steps are used to achieve:

步骤1.1、假设将二维区域S划分为

Figure 40275DEST_PATH_IMAGE073
个网格，网格点集合
Figure 125387DEST_PATH_IMAGE002
，在其中布置N个静态无线传感器节点，设传感器集合
Figure 923579DEST_PATH_IMAGE003
，其中基于布尔感知模型，每个节点的监测范围均为以R为半径 的圆形区域； Step 1.1. Suppose the two-dimensional area S is divided into
Figure 40275DEST_PATH_IMAGE073
grid, set of grid points
Figure 125387DEST_PATH_IMAGE002
, in which N static wireless sensor nodes are arranged, and the sensor set is set
Figure 923579DEST_PATH_IMAGE003
, where based on the Boolean perception model, the monitoring range of each node is a circular area with R as the radius;

步骤1.2、对于区域S中的某一网格点

Figure 653637DEST_PATH_IMAGE004
，其与无线传感器节点
Figure 853675DEST_PATH_IMAGE005
间的欧式距离为： Step 1.2, for a grid point in the area S
Figure 653637DEST_PATH_IMAGE004
, which is related to wireless sensor nodes
Figure 853675DEST_PATH_IMAGE005
The Euclidean distance between is:

Figure 378197DEST_PATH_IMAGE006
（1）
Figure 378197DEST_PATH_IMAGE006
(1)

步骤1.3、考虑到实际情况下传感器节点的最优监测距离和传感误差距离，则节点

Figure 460422DEST_PATH_IMAGE007
对于网格点
Figure 728593DEST_PATH_IMAGE008
的监测感知概率可表述如下： Step 1.3. Considering the optimal monitoring distance and sensing error distance of the sensor node in the actual situation, the node
Figure 460422DEST_PATH_IMAGE007
for grid points
Figure 728593DEST_PATH_IMAGE008
The monitoring perception probability of can be expressed as follows:

Figure 783136DEST_PATH_IMAGE009
（2）
Figure 783136DEST_PATH_IMAGE009
(2)

Figure 744139DEST_PATH_IMAGE010
(3)
Figure 744139DEST_PATH_IMAGE010
(3)

其中，

Figure 251344DEST_PATH_IMAGE011
为传感器节点
Figure 57626DEST_PATH_IMAGE012
的最优监测距离，
Figure 232255DEST_PATH_IMAGE013
为其传感误差距离，
Figure 98580DEST_PATH_IMAGE014
为感知能 力随距离的衰减系数。 in,
Figure 251344DEST_PATH_IMAGE011
for sensor nodes
Figure 57626DEST_PATH_IMAGE012
The optimal monitoring distance of ,
Figure 232255DEST_PATH_IMAGE013
For its sensing error distance,
Figure 98580DEST_PATH_IMAGE014
is the attenuation coefficient of perception ability with distance.

步骤1.4、则区域内所有传感器节点对于

Figure 358660DEST_PATH_IMAGE015
的联合监测概率可表述为： Step 1.4, all sensor nodes in the area are
Figure 358660DEST_PATH_IMAGE015
The joint monitoring probability of can be expressed as:

Figure 968633DEST_PATH_IMAGE016
(4)
Figure 968633DEST_PATH_IMAGE016
(4)

步骤1.5、假设

Figure 797436DEST_PATH_IMAGE017
为该节点能被有效监测到的最小概率阈值，假设能被有效监 测到的网格点集合为
Figure 100242DEST_PATH_IMAGE018
，则对于其中任意点应满足条件： Step 1.5. Assumptions
Figure 797436DEST_PATH_IMAGE017
is the minimum probability threshold that the node can be effectively monitored, assuming that the set of grid points that can be effectively monitored is
Figure 100242DEST_PATH_IMAGE018
, then for any point, the condition should be satisfied:

Figure 582038DEST_PATH_IMAGE019
（5）
Figure 582038DEST_PATH_IMAGE019
(5)

对于不满足上式的网格点，即在区域S中且在集合

Figure 995702DEST_PATH_IMAGE074
以外的网格点则为覆盖空洞； For grid points that do not satisfy the above formula, that is, in the area S and in the set
Figure 995702DEST_PATH_IMAGE074
The grid points outside are covered holes;

步骤1.6、基于以上，为了最大化满足式（5）的点的个数，建立以无线传感器网络覆盖率为优化目标的目标函数：Step 1.6. Based on the above, in order to maximize the number of points satisfying Equation (5), establish an objective function with the wireless sensor network coverage as the optimization objective:

Figure 879345DEST_PATH_IMAGE021
（6）
Figure 879345DEST_PATH_IMAGE021
(6)

由此构建了传感器节点网络的概率感知优化模型。Therefore, a probabilistic perception optimization model of sensor node network is constructed.

步骤二、进一步考虑到每个传感器的能耗问题，为降低能力损耗并优化网络资源，基于步骤一所建概率感知优化模型，构建以传感器网络覆盖率和节点能量功耗为优化参数的无线传感器网络覆盖多目标优化模型。In step 2, further considering the energy consumption of each sensor, in order to reduce capacity loss and optimize network resources, based on the probability perception optimization model built in step 1, construct a wireless sensor with sensor network coverage and node energy consumption as optimization parameters. Network coverage multi-objective optimization model.

所述的步骤二为降低能力损耗并优化网络资源，基于步骤一所建概率感知优化模型，构建以传感器网络覆盖率和节点能量功耗为优化参数的无线传感器网络覆盖多目标优化模型；The second step is to reduce the capacity loss and optimize the network resources, based on the probability perception optimization model built in the first step, construct a wireless sensor network coverage multi-objective optimization model with sensor network coverage and node energy consumption as optimization parameters;

步骤2.1、由于传感器节点的能量储备有限，引入传感器节点的能耗作为优化目标之一，由于信号传输时，节点部分能力需在传输过程中进行信号放大，则节点在链路上传输kbit数据的能耗可表示为:Step 2.1. Due to the limited energy reserve of the sensor node, the energy consumption of the sensor node is introduced as one of the optimization goals. Since part of the capacity of the node needs to amplify the signal during the transmission process during signal transmission, the node transmits kbit data on the link. Energy consumption can be expressed as:

Figure 87472DEST_PATH_IMAGE022
（7）
Figure 87472DEST_PATH_IMAGE022
(7)

Figure 56565DEST_PATH_IMAGE023
（8）
Figure 56565DEST_PATH_IMAGE023
(8)

其中

Figure 8341DEST_PATH_IMAGE075
为输出节点和接收节点间的距离，
Figure 808806DEST_PATH_IMAGE025
是信号放大倍数，
Figure 453414DEST_PATH_IMAGE026
是多径衰减 模型信号放大倍数，
Figure 909803DEST_PATH_IMAGE027
为单个传感器节点发送bit数据的能量损耗； in
Figure 8341DEST_PATH_IMAGE075
is the distance between the output node and the receiving node,
Figure 808806DEST_PATH_IMAGE025
is the signal amplification factor,
Figure 453414DEST_PATH_IMAGE026
is the multipath fading model signal amplification factor,
Figure 909803DEST_PATH_IMAGE027
The energy consumption of sending bit data for a single sensor node;

步骤2.2、由此，优化模型的总目标函数应表述为最大化传感器网络覆盖率及最小化节点能量功耗：Step 2.2. Thus, the overall objective function of the optimization model should be expressed as maximizing sensor network coverage and minimizing node energy consumption:

Figure 399691DEST_PATH_IMAGE028
（9）
Figure 399691DEST_PATH_IMAGE028
(9)

其中

Figure 992346DEST_PATH_IMAGE029
表述每个节点间的传输能耗。 in
Figure 992346DEST_PATH_IMAGE029
Express the transmission energy consumption between each node.

步骤三、针对所构建的优化模型，将蜉蝣算法MA的收敛速度和局部搜索能力与混合蛙跳算法的鲁棒性与全局寻优能力相结合，设计基于Tent混沌映射和Levy飞行策略的混合蜉蝣-蛙跳算法进行求解。Step 3. According to the constructed optimization model, combine the convergence speed and local search ability of the mayfly algorithm MA with the robustness and global optimization ability of the hybrid frog leaping algorithm, and design a hybrid mayfly based on the Tent chaotic map and the Levy flight strategy. - Leapfrog algorithm to solve.

传统蜉蝣算法虽具有较好寻优能力，但仍存在收敛速度慢、稳定性不强易陷入局部最优等问题，将其与全局寻优能力较强的混合蛙跳算法结合，以平衡算法的全局搜索能力。同时引入Tent混沌映射机制和Levy飞行策略，利用Tent混沌映射产生高质量初始解，提高初始解的多样性，从而提升算法收敛速度和求解精度；通过Levy飞行策略扩大算法搜索范围，并使算法能够快速跳出局部最优。算法总体流程表述如下。Although the traditional mayfly algorithm has good optimization ability, it still has problems such as slow convergence speed, weak stability and easy to fall into local optimum. It is combined with the hybrid frog leaping algorithm with strong global optimization ability to balance the global optimization of the algorithm. search capability. At the same time, the Tent chaotic mapping mechanism and the Levy flight strategy are introduced, and the Tent chaotic map is used to generate high-quality initial solutions and improve the diversity of the initial solutions, thereby improving the algorithm convergence speed and solution accuracy. The Levy flight strategy expands the search range of the algorithm and enables the algorithm to Quickly jump out of the local optimum. The overall flow of the algorithm is described as follows.

所述的步骤三针对所构建的无线传感器网络覆盖多目标优化模型，将蜉蝣算法（MA）的收敛速度和局部搜索能力与混合蛙跳算法的鲁棒性与全局寻优能力相结合，设计一种基于Tent混沌映射和Levy飞行策略的混合蜉蝣-蛙跳算法进行求解；In the third step, for the constructed wireless sensor network coverage multi-objective optimization model, the convergence speed and local search ability of the mayfly algorithm (MA) are combined with the robustness and global optimization ability of the hybrid frog leap algorithm. A hybrid mayfly-leapfrog algorithm based on Tent chaotic map and Levy flight strategy is used to solve the problem;

通过Levy飞行策略扩大算法搜索范围，并使算法能够快速跳出局部最优；算法总体流程表述如下：The search range of the algorithm is expanded through the Levy flight strategy, and the algorithm can quickly jump out of the local optimum; the overall process of the algorithm is described as follows:

步骤3.1、初始化参数，基于Tent混沌映射机制得到初始种群，并初始化个体速度；Step 3.1, initialize parameters, obtain the initial population based on the Tent chaotic mapping mechanism, and initialize the individual speed;

Tent 映射是一种分段线性映射函数，具有参数少、操作简单、映射呈现的结果分布密度比较均匀，具有很好的遍历性等优势，其初始化种群如下式所示Tent mapping is a piecewise linear mapping function, which has the advantages of less parameters, simple operation, relatively uniform distribution density of the results presented by the mapping, and good ergodicity. Its initialization population is shown in the following formula

Figure 807855DEST_PATH_IMAGE030
（10）
Figure 807855DEST_PATH_IMAGE030
(10)

其中个体

Figure 748611DEST_PATH_IMAGE031
由上一个个体和参数
Figure 104506DEST_PATH_IMAGE032
决定，参数
Figure 817247DEST_PATH_IMAGE033
； of which individual
Figure 748611DEST_PATH_IMAGE031
by the previous individual and parameters
Figure 104506DEST_PATH_IMAGE032
decision, parameter
Figure 817247DEST_PATH_IMAGE033
;

步骤3.2、将初始种群随机划分为雄性蜉蝣

Figure 538078DEST_PATH_IMAGE034
和雌性蜉蝣种群
Figure 969060DEST_PATH_IMAGE035
； Step 3.2. Randomly divide the initial population into male mayflies
Figure 538078DEST_PATH_IMAGE034
and female mayfly populations
Figure 969060DEST_PATH_IMAGE035
;

步骤3.3、计算每个个体的适应度并从高到低排序，基于混合蛙跳算法分别将雄性种群和雌性种群划分为m个子种群，其中：Step 3.3: Calculate the fitness of each individual and sort them from high to low. Based on the hybrid frog leaping algorithm, the male population and the female population are divided into m sub-populations, where:

Figure 800749DEST_PATH_IMAGE036
（11）
Figure 800749DEST_PATH_IMAGE036
(11)

Figure 633576DEST_PATH_IMAGE037
（12）
Figure 633576DEST_PATH_IMAGE037
(12)

步骤3.4、对于雄性种群和雌性种群中的每个子种群，将其中的个体按适应度大小 排序，将每个子种群中适应度最优和最劣的个体分别记

Figure 525309DEST_PATH_IMAGE038
和
Figure 443586DEST_PATH_IMAGE039
； Step 3.4. For each subpopulation in the male population and the female population, sort the individuals according to their fitness, and record the individuals with the best and worst fitness in each subpopulation respectively.
Figure 525309DEST_PATH_IMAGE038
and
Figure 443586DEST_PATH_IMAGE039
;

步骤3.5、按照式（13）（14）对每个子种群的

Figure 141284DEST_PATH_IMAGE040
进行蛙跳更新； Step 3.5, according to formula (13) (14), for each subpopulation
Figure 141284DEST_PATH_IMAGE040
Perform leapfrog updates;

Figure 563038DEST_PATH_IMAGE041
（13）
Figure 563038DEST_PATH_IMAGE041
(13)

Figure 891251DEST_PATH_IMAGE042
（14）
Figure 891251DEST_PATH_IMAGE042
(14)

其中

Figure 765666DEST_PATH_IMAGE043
，若
Figure 470317DEST_PATH_IMAGE044
适应度优于
Figure 15086DEST_PATH_IMAGE045
，则保留
Figure 514201DEST_PATH_IMAGE046
，否则 将当前全局最优
Figure 938229DEST_PATH_IMAGE047
替换
Figure 180991DEST_PATH_IMAGE048
，再按照式（13）（14）进行更新，若仍没得到进化，则产生一随 机个体替换
Figure 577338DEST_PATH_IMAGE049
； in
Figure 765666DEST_PATH_IMAGE043
,like
Figure 470317DEST_PATH_IMAGE044
fitness is better than
Figure 15086DEST_PATH_IMAGE045
, then keep
Figure 514201DEST_PATH_IMAGE046
, otherwise the current global optimal
Figure 938229DEST_PATH_IMAGE047
replace
Figure 180991DEST_PATH_IMAGE048
, and then update according to formulas (13) and (14), if the evolution has not been obtained, a random individual replacement will be generated.
Figure 577338DEST_PATH_IMAGE049
;

步骤3.6、反复迭代步骤3.5，直至达到局部最大迭代次数

Figure 247353DEST_PATH_IMAGE050
； Step 3.6, repeat step 3.5 until the local maximum number of iterations is reached
Figure 247353DEST_PATH_IMAGE050
;

步骤3.7、将蛙跳更新后的子种群重新合并为雄性、雌性蜉蝣种群，计算个体适应度大小，并进行蜉蝣算法更新；Step 3.7, re-merge the updated subpopulations of leapfrog into male and female mayfly populations, calculate the individual fitness, and update the mayfly algorithm;

Figure 96361DEST_PATH_IMAGE051
（15）
Figure 96361DEST_PATH_IMAGE051
(15)

Figure 877235DEST_PATH_IMAGE076
（16）
Figure 877235DEST_PATH_IMAGE076
(16)

其中每个雄性蜉蝣个体i根据式（15）进行更新：where each male mayfly individual i is updated according to formula (15):

其中

Figure 393667DEST_PATH_IMAGE053
为个体i的第j维速度，
Figure 31322DEST_PATH_IMAGE054
为该蜉蝣历史最优位置，
Figure 633204DEST_PATH_IMAGE055
为全局最 优个体位置，
Figure 217769DEST_PATH_IMAGE056
为正吸引系数，
Figure 588708DEST_PATH_IMAGE077
为舞蹈系数，随机数
Figure 334947DEST_PATH_IMAGE058
in
Figure 393667DEST_PATH_IMAGE053
is the jth dimension velocity of individual i,
Figure 31322DEST_PATH_IMAGE054
is the historical optimal position of the mayfly,
Figure 633204DEST_PATH_IMAGE055
is the global optimal individual position,
Figure 217769DEST_PATH_IMAGE056
is the positive coefficient of attraction,
Figure 588708DEST_PATH_IMAGE077
is the dance coefficient, a random number
Figure 334947DEST_PATH_IMAGE058

每个雌性蜉蝣个体i根据式（17）进行更新：Each female mayfly individual i is updated according to equation (17):

Figure 155617DEST_PATH_IMAGE059
（17）
Figure 155617DEST_PATH_IMAGE059
(17)

Figure 543873DEST_PATH_IMAGE078
（18）
Figure 543873DEST_PATH_IMAGE078
(18)

其中

Figure 769318DEST_PATH_IMAGE061
为雌雄个体i之间的笛卡尔距离，
Figure 952037DEST_PATH_IMAGE063
为随机飞行系数， in
Figure 769318DEST_PATH_IMAGE061
is the Cartesian distance between male and female individuals i,
Figure 952037DEST_PATH_IMAGE063
is the random flight coefficient,

步骤3.8、根据式（19）（20）进行雌雄交配操作，得到新一代种群；Step 3.8. According to formula (19) (20), the male and female mating operation is carried out to obtain a new generation of population;

Figure 325250DEST_PATH_IMAGE064
（19）
Figure 325250DEST_PATH_IMAGE064
(19)

Figure 251618DEST_PATH_IMAGE065
（20）
Figure 251618DEST_PATH_IMAGE065
(20)

其中

Figure 331569DEST_PATH_IMAGE066
； in
Figure 331569DEST_PATH_IMAGE066
;

步骤3.9、基于Levy飞行策略再对新一代种群位置进行更新，避免种群陷入局部最优；Step 3.9, based on the Levy flight strategy, update the position of the new generation population to avoid the population falling into local optimum;

对于新种群中的每一个个体，根据式（21）进行Levy飞行更新，并通过适应度确定是否保留更新后的个体；For each individual in the new population, perform Levy flight update according to formula (21), and determine whether to retain the updated individual through fitness;

Figure 75403DEST_PATH_IMAGE067
（21）
Figure 75403DEST_PATH_IMAGE067
(twenty one)

其中

Figure 873595DEST_PATH_IMAGE068
为（0，2）间随机数，且： in
Figure 873595DEST_PATH_IMAGE068
is a random number between (0, 2), and:

Figure 603653DEST_PATH_IMAGE069
（22）
Figure 603653DEST_PATH_IMAGE069
(twenty two)

Figure 538111DEST_PATH_IMAGE070
（23）
Figure 538111DEST_PATH_IMAGE070
(twenty three)

Figure 65563DEST_PATH_IMAGE071
（24）
Figure 65563DEST_PATH_IMAGE071
(twenty four)

步骤3.10、对每个个体位置和速度进行边界处理；Step 3.10, perform boundary processing on each individual position and velocity;

步骤3.11、算法总迭代次数加一，判断是否达到最大迭代次数

Figure 351051DEST_PATH_IMAGE072
，若是则输出最优 解，算法结束；反之则返回步骤3.2。 Step 3.11. Add one to the total number of iterations of the algorithm to determine whether the maximum number of iterations is reached
Figure 351051DEST_PATH_IMAGE072
, if so, output the optimal solution and the algorithm ends; otherwise, return to step 3.2.

根据本发明第一方面可得，第二方面，一种无线传感器网络覆盖优化方法的装置包括：According to the first aspect of the present invention, in the second aspect, an apparatus for a wireless sensor network coverage optimization method includes:

数据获取模块，用于获取传感器分布的数据集；The data acquisition module is used to acquire the data set distributed by the sensor;

数据处理模块，用于对获取的无线网络传感器数据进行处理，得到无线网络传感器模型的数据；The data processing module is used to process the acquired wireless network sensor data to obtain the data of the wireless network sensor model;

数据计算模块，用于依据处理后的数据对无线网络传感器模型进行量化计算。The data calculation module is used to perform quantitative calculation on the wireless network sensor model according to the processed data.

另一方面，一种电子设备，包括：处理器和存储器，所述存储器上存储有计算机可读指令，所述计算机可读指令被所述处理器执行时实现一种无线传感器网络覆盖优化方法。In another aspect, an electronic device includes: a processor and a memory, wherein computer-readable instructions are stored on the memory, and when the computer-readable instructions are executed by the processor, a method for optimizing wireless sensor network coverage is implemented.

又一方面，一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现一种无线传感器网络覆盖优化方法。In yet another aspect, a computer-readable storage medium has a computer program stored thereon, the computer program implements a wireless sensor network coverage optimization method when executed by a processor.

本领域普通技术人员可以理解上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储介质中，存储介质可以包括：闪存盘、只读存储器(read-only memory，ROM)、随机存取器(randomaccessmemory，RAM)、磁盘或光盘等。应该指出，上述详细说明都是示例性的，旨在对本申请提供进一步的说明。Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium can include: Flash disk, read-only memory (read-only memory, ROM), random access device (random access memory, RAM), magnetic disk or optical disk, etc. It should be noted that the above detailed description is exemplary and intended to provide further explanation for the present application.

除非另有指明，本文使用的所有技术和科学术语均具有与本申请所属技术领域的普通技术人员的通常理解所相同的含义。需要注意的是，这里所使用的术语仅是为了描述具体实施方式，而非意图限制根据本申请所述的示例性实施方式。如在这里所使用的，除非上下文另外明确指出，否则单数形式也意图包括复数形式。此外，还应当理解的是，当在本说明书中使用术语“包含”和/或“包括”时，其指明存在特征、步骤、操作、器件、组件和/或它们的组合。Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It should be noted that the terminology used herein is for the purpose of describing specific embodiments only, and is not intended to limit the exemplary embodiments described in accordance with the present application. As used herein, the singular forms are also intended to include the plural forms unless the context clearly dictates otherwise. In addition, it should also be understood that when the terms "comprising" and/or "comprising" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and/or combinations thereof.

应当理解的是，本发明的上述具体实施方式仅仅用于示例性说明或解释本发明的原理，而不构成对本发明的限制。因此，在不偏离本发明的精神和范围的情况下所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。此外，本发明所附权利要求旨在涵盖落入所附权利要求范围和边界、或者这种范围和边界的等同形式内的全部变化和修改例。It should be understood that the above-mentioned specific embodiments of the present invention are only used to illustrate or explain the principle of the present invention, but not to limit the present invention. Therefore, any modifications, equivalent replacements, improvements, etc. made without departing from the spirit and scope of the present invention should be included within the protection scope of the present invention. Furthermore, the appended claims of this invention are intended to cover all changes and modifications that fall within the scope and boundaries of the appended claims, or the equivalents of such scope and boundaries.

Claims (7)

1. A wireless sensor network coverage optimization method is applied to wireless sensor network coverage optimization in various wireless communication technical fields and other intelligent fields, and is characterized in that: the method comprises the following steps:
designing wireless sensor nodes according to a Boolean sensing model, and establishing a target function aiming at the coverage rate of a sensor network based on monitoring probability and a coverage hole so as to construct a probability sensing model of the sensor node network;
secondly, constructing a wireless sensor network coverage multi-objective optimization model taking the coverage rate of the sensor network and the energy consumption of nodes as optimization parameters based on the probability perception optimization model established in the step one;
and thirdly, aiming at the constructed optimization model, combining the convergence speed and local search capacity of the mayflies algorithm MA with the robustness and global optimization capacity of the mixed-frog jump algorithm, designing the mixed-mayflies-jump algorithm based on Tent chaotic maps and Levy flight strategies for solution.
2. The method of claim 1 for optimizing coverage in a wireless sensor network, wherein: the method comprises the following steps that firstly, wireless sensor nodes are designed according to a Boolean perception model, and a target function for the coverage rate of the sensor network is established and obtained based on the monitoring probability and the coverage hole, so that the probability perception model of the sensor node network is established and realized by the following steps:
step 1.1, suppose that the two-dimensional region S is divided into
Figure 525418DEST_PATH_IMAGE001
Individual grid, set of grid points
Figure 640005DEST_PATH_IMAGE002
In which N static wireless sensor nodes are arranged, a sensor set
Figure 158229DEST_PATH_IMAGE003
Based on a Boolean sensing model, the monitoring range of each node is a circular area with R as the radius;
step 1.2, for a certain grid point in the region S
Figure 674661DEST_PATH_IMAGE004
Of wireless sensor node
Figure 515578DEST_PATH_IMAGE005
The Euclidean distance between them is:
Figure 914199DEST_PATH_IMAGE006
（1）
step 1.3, considering the optimal monitoring distance and sensing error distance of the sensor node under the actual condition, the node
Figure 498764DEST_PATH_IMAGE007
For grid points
Figure 869702DEST_PATH_IMAGE008
The monitoring perception probability of (2) can be expressed as follows:
Figure 881521DEST_PATH_IMAGE009
（2）
Figure 705120DEST_PATH_IMAGE010
(3)
wherein,
Figure 827797DEST_PATH_IMAGE011
as sensor nodes
Figure 53242DEST_PATH_IMAGE012
The optimum monitoring distance of (a) is,
Figure 235962DEST_PATH_IMAGE013
for the purpose of sensing the error distance,
Figure 812436DEST_PATH_IMAGE014
attenuation coefficient of perception ability with distance;
step 1.4, all sensor node pairs in the region
Figure 535542DEST_PATH_IMAGE015
The joint monitoring probability of (a) can be expressed as:
Figure 615493DEST_PATH_IMAGE016
（4）
step 1.5, suppose
Figure 231764DEST_PATH_IMAGE017
Assuming that the set of grid points that can be effectively monitored is the minimum probability threshold at which the node can be effectively monitored
Figure 29955DEST_PATH_IMAGE018
Then for any point therein the condition should be satisfied:
Figure 494435DEST_PATH_IMAGE019
（5）
for grid points not satisfying the above formula, i.e. in the region S and in the set
Figure 428893DEST_PATH_IMAGE020
The other grid points are coverage holes;
step 1.6, based on the above, in order to maximize the number of points satisfying the formula (5), establishing an objective function with the coverage rate of the wireless sensor network as an optimization target:
Figure 218994DEST_PATH_IMAGE021
（6）
therefore, a probability perception optimization model of the sensor node network is constructed.
3. The method of claim 1 for optimizing coverage of a wireless sensor network, wherein the method comprises the following steps: secondly, reducing the capacity loss and optimizing network resources, and constructing a wireless sensor network coverage multi-objective optimization model with the sensor network coverage rate and the node energy power consumption as optimization parameters based on the probability perception optimization model established in the first step;
step 2.1, because the energy reserve of the sensor node is limited, the energy consumption of the sensor node is introduced as one of optimization targets, and because partial capacity of the node needs to be amplified in the transmission process during signal transmission, the energy consumption of the node for transmitting kbit data on the link can be expressed as follows:
Figure 238903DEST_PATH_IMAGE022
（7）
Figure 772652DEST_PATH_IMAGE023
（8）
wherein
Figure 623934DEST_PATH_IMAGE024
Which is the distance between the output node and the receiving node,
Figure 319357DEST_PATH_IMAGE025
is the amplification factor of the signal and is,
Figure 92141DEST_PATH_IMAGE026
is the signal amplification factor of the multi-path attenuation model,
Figure 164002DEST_PATH_IMAGE027
transmitting the energy loss of bit data for a single sensor node;
step 2.2, therefore, the overall objective function of the optimization model should be expressed as maximizing the sensor network coverage and minimizing the node energy power consumption:
Figure 807473DEST_PATH_IMAGE028
（9）
wherein
Figure 939377DEST_PATH_IMAGE029
And expressing the transmission energy consumption between each node.
4. The method of claim 1 for optimizing coverage of a wireless sensor network, wherein the method comprises the following steps: aiming at the constructed wireless sensor network coverage multi-target optimization model, combining the convergence speed and local search capability of the Mayfly Algorithm (MA) with the robustness and global optimization capability of the hybrid frog-jump algorithm, designing a hybrid mayfly-frog-jump algorithm based on Tent chaotic mapping and Levy flight strategy for solving;
the algorithm searching range is expanded through a Levy flight strategy, and the algorithm can quickly jump out of local optimum; the overall flow of the algorithm is expressed as follows:
step 3.1, initializing parameters, obtaining an initial population based on a Tent chaotic mapping mechanism, and initializing individual speed;
tent mapping is a piecewise linear mapping function, has the advantages of few parameters, simple operation, uniform distribution density of results presented by mapping, good ergodicity and the like, and the initialized population of the Tent mapping function is shown as the following formula
Figure 933878DEST_PATH_IMAGE030
（10）
Wherein the individual
Figure 809430DEST_PATH_IMAGE031
From the last individual and parameter
Figure 841496DEST_PATH_IMAGE032
Determination of parameters
Figure 878722DEST_PATH_IMAGE033
；
Step 3.2, randomly dividing the initial population into mayflies
Figure 422836DEST_PATH_IMAGE034
And female dayflies
Figure 836500DEST_PATH_IMAGE035
；
Step 3.3, calculating the fitness of each individual and sequencing the fitness from high to low, and dividing the male population and the female population into m sub-populations respectively based on a mixed frog-leaping algorithm, wherein:
Figure 720142DEST_PATH_IMAGE036
（11）
Figure 928270DEST_PATH_IMAGE037
（12）
and 3.4, sequencing the individuals in each sub-population of the male population and the female population according to the fitness, and respectively recording the individuals with the optimal fitness and the individuals with the worst fitness in each sub-population
Figure 631783DEST_PATH_IMAGE038
And
Figure 849138DEST_PATH_IMAGE039
；
step 3.5, for each sub-population according to equation (13) (14)
Figure 587287DEST_PATH_IMAGE040
Updating the frog leap;
Figure 28633DEST_PATH_IMAGE041
（13）
Figure 485022DEST_PATH_IMAGE042
（14）
wherein
Figure 240488DEST_PATH_IMAGE043
If, if
Figure 98723DEST_PATH_IMAGE044
The adaptability is better than
Figure 914232DEST_PATH_IMAGE045
Then remain
Figure 323829DEST_PATH_IMAGE046
Otherwise, the current global optimum is obtained
Figure 882986DEST_PATH_IMAGE047
Replacement of
Figure 595727DEST_PATH_IMAGE048
Then, updating according to the formulas (13) and (14), if evolution is not obtained, generating a random individual replacement
Figure 582138DEST_PATH_IMAGE049
；
Step 3.6, iterating step 3.5 repeatedly until reaching local maximum iteration times
Figure 13119DEST_PATH_IMAGE050
；
Step 3.7 recombining the updated sub-populations of frog jumps into the male and female mayflies, calculating the individual fitness size and performing the updating of the mayflies;
Figure 907126DEST_PATH_IMAGE051
（15）
Figure 474374DEST_PATH_IMAGE052
（16）
each mayflies is updated according to equation (15):
wherein
Figure 631685DEST_PATH_IMAGE053
Is the j-th dimension velocity of the individual i,
Figure 284384DEST_PATH_IMAGE054
for the optimal position of the mayflies in history,
Figure 185344DEST_PATH_IMAGE055
in order to be a global optimum of the individual positions,
Figure 607098DEST_PATH_IMAGE056
is a positive coefficient of attraction, and,
Figure 935311DEST_PATH_IMAGE057
for dance coefficient, random numbers
Figure 75305DEST_PATH_IMAGE058
Each female mayflies i is updated according to equation (17):
Figure 514377DEST_PATH_IMAGE059
（17）
Figure 855884DEST_PATH_IMAGE060
（18）
wherein
Figure 354998DEST_PATH_IMAGE061
Is a male or female individual
Figure 982289DEST_PATH_IMAGE062
The cartesian distance between the two electrodes is set to be,
Figure 225051DEST_PATH_IMAGE063
in order to have a random coefficient of flight,
step 3.8, carrying out male and female mating operation according to the formulas (19) and (20) to obtain a new generation of population;
Figure 621397DEST_PATH_IMAGE064
（19）
Figure 291413DEST_PATH_IMAGE065
（20）
wherein
Figure 140420DEST_PATH_IMAGE066
；
Step 3.9, updating the position of the new generation of population based on the Levy flight strategy to avoid the population from falling into local optimum;
for each individual in the new population, levy flight updating is carried out according to a formula (21), and whether the updated individual is reserved or not is determined through fitness;
Figure 983612DEST_PATH_IMAGE067
（21）
wherein is an inter (0, 2) random number, and:
Figure 500044DEST_PATH_IMAGE068
（22）
Figure 340961DEST_PATH_IMAGE069
（23）
Figure 677264DEST_PATH_IMAGE070
（24）
step 3.10, carrying out boundary processing on the position and the speed of each individual;
step 3.11, adding one to the total iteration number of the algorithm, and judging whether the maximum iteration number is reached
Figure 261829DEST_PATH_IMAGE071
If yes, outputting an optimal solution, and finishing the algorithm; otherwise, the step 3.2 is returned.
5. A device of a wireless sensor network coverage optimization method is used for the wireless sensor network coverage optimization method based on the hybrid mayfly-frog leap algorithm, characterized in that: the device of the method for optimizing the coverage of the wireless sensor network based on the hybrid mayflies-frog jump algorithm comprises the following steps:
the data acquisition module is used for acquiring a data set distributed by the sensors;
the data processing module is used for processing the acquired wireless network sensor data to obtain data of a wireless network sensor model;
and the data calculation module is used for carrying out quantitative calculation on the wireless network sensor model according to the processed data.
6. An electronic device, comprising: a processor and a memory, the memory having stored thereon computer readable instructions, which when executed by the processor, implement a wireless sensor network coverage optimization method as claimed in any one of claims 1 to 4.
7. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a wireless sensor network coverage optimization method according to any one of claims 1 to 4.
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CN116916475A (en) * 2023-08-10 2023-10-20 华东交通大学 A wireless sensor network deployment method based on multi-strategy improved honey badger algorithm
CN116916475B (en) * 2023-08-10 2024-05-07 华东交通大学 A wireless sensor network deployment method based on multi-strategy improved honey badger algorithm
CN117812614A (en) * 2023-12-29 2024-04-02 杭州芯享科技有限公司 Multi-target dynamic optimization decision method of wireless sensor network
CN117939420A (en) * 2024-01-08 2024-04-26 哈尔滨理工大学 WSN target coverage method and device based on chaotic adaptive moth algorithm
CN119729556A (en) * 2025-02-28 2025-03-28 南京信息工程大学 Improved snake Cone algorithm-based network structure optimization method with unmanned perception

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