WO2021051859A1 - 基于自适应遗传算法的无线传感器网络分簇路由方法 - Google Patents

基于自适应遗传算法的无线传感器网络分簇路由方法 Download PDF

Info

Publication number
WO2021051859A1
WO2021051859A1 PCT/CN2020/092484 CN2020092484W WO2021051859A1 WO 2021051859 A1 WO2021051859 A1 WO 2021051859A1 CN 2020092484 W CN2020092484 W CN 2020092484W WO 2021051859 A1 WO2021051859 A1 WO 2021051859A1
Authority
WO
WIPO (PCT)
Prior art keywords
node
population
individual
nodes
energy
Prior art date
Application number
PCT/CN2020/092484
Other languages
English (en)
French (fr)
Inventor
张颖
陈磊
张斌
王新珩
吴杰
Original Assignee
上海海事大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 上海海事大学 filed Critical 上海海事大学
Priority to US17/762,023 priority Critical patent/US12047860B2/en
Publication of WO2021051859A1 publication Critical patent/WO2021051859A1/zh

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10—Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/17—Shortcut routing, e.g. using next hop resolution protocol [NHRP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/46—Cluster building
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W40/00—Communication routing or communication path finding
    • H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
    • H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W36/00—Hand-off or reselection arrangements
    • H04W36/0005—Control or signalling for completing the hand-off
    • H04W36/0009—Control or signalling for completing the hand-off for a plurality of users or terminals, e.g. group communication or moving wireless networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/06—Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]; Services to user groups; One-way selective calling services
    • H04W4/08—User group management
    • 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

Definitions

  • the invention relates to the technical field of a wireless sensor network cluster routing method, in particular to a wireless sensor network cluster routing method based on an adaptive genetic algorithm.
  • the wireless sensor network consists of many battery-powered sensor devices. These devices can perceive some physical characteristics of the surrounding environment to obtain various data, such as sound, pressure, humidity, temperature, or chemical concentration.
  • each sensor node has the ability to perform simple calculations or directly communicate with other nodes and base stations. The node will transmit the collected data to the base station for further analysis and processing.
  • wireless sensor networks are widely used in air quality monitoring, health monitoring, water quality monitoring, home safety and other fields.
  • the clustered network structure has good scalability, is convenient for energy management, load balance, resource allocation and other advantages, many people have conducted in-depth research on the wireless sensor network clustering routing protocol.
  • the energy of the nodes in the wireless sensor network is provided by their own miniature batteries. Generally, the batteries cannot be replaced, so the use time of the wireless sensor network is limited by energy.
  • the cluster head sends the fused data to the sink node after fusing the data of the cluster, which reduces the amount of data sent and also reduces energy consumption.
  • the cluster head is responsible for the establishment of the cluster, the communication control within the cluster, and the communication between the cluster and the sink node.
  • the purpose of the present invention is to provide a wireless sensor network cluster routing method based on an adaptive genetic algorithm to solve the problems of poor real-time performance, short network life, and low data transmission efficiency of current cluster routing protocols using complex intelligent algorithms.
  • the technical solution of the present invention is to provide a wireless sensor network cluster routing method based on an adaptive genetic algorithm, which includes the following steps:
  • Step 1 In the monitoring area, randomly and evenly deploy sensor nodes to establish a network energy consumption model. Each node has a unique ID and the initial energy is the same. The nodes can judge the relative distance according to the strength of the received signal;
  • Step 2 Use the initial position and energy information of the sensor node to initialize the population
  • Step 3 Individuals in the population calculate fitness values based on the sensor node position and energy information, and perform elite retention and adaptive cross-mutation operations based on the fitness values to generate a new population. Each point on the individual has a chance of crossover and mutation, crossover And the probability of mutation will be adjusted non-linearly with the size of individual fitness;
  • Step 4 Determine whether the fitness value reaches the iteration stop condition, if not, go to step 3, if the iteration is completed, cluster the nodes according to the clustering scheme in the optimal individual;
  • Step 5 Determine whether the cluster head is within the communication range of the base station. If it is, the cluster head communicates directly with the base station; if not, continue to select the relay node according to the distance between the cluster heads, and route the data packet to the relay node until The data packet is transmitted to the base station.
  • the energy consumption of the node is: among them Represents the distance threshold, E elec represents the energy consumed to send or receive 1-bit data, and ⁇ fs and ⁇ mp represent the power amplification coefficients under different communication distances.
  • the iteration stop condition is that the iteration stops after reaching any value between 30 and 50 times.
  • the population size is m
  • the population set is:
  • C(x) represents the xth individual in the population
  • the length of each individual is the number of sensor nodes n
  • B i represents the values of individual genes
  • the value B i T (s) determined by the node S i energy, average energy and a network node survival threshold
  • T (s) as follows Shown:
  • p is the ratio of the number of cluster heads to the total number of nodes
  • r is the current round number
  • G is the set of nodes that have not become cluster heads in the past 1/p round.
  • p is 0.05.
  • the fitness value function of the xth individual in the population is: Among them E avg , sumD CH ⁇ BS (x), sumD S ⁇ CH (x) respectively represent the average energy of the nodes, the sum of the distances from each cluster head node to the base station and the sum of the distances between the member nodes and the corresponding cluster head, minpopD CH ⁇ BS Represents the minimum sum of the distances from each cluster head node of all individuals in the population to the base station, minpopD S ⁇ CH represents the minimum sum of the distances from the member nodes of all individuals in the population to the corresponding cluster head, avgpopD CH ⁇ BS represents the population in the population The average value of the sum of the distances from the cluster head nodes of all individuals to the base station, avgpopD S ⁇ CH represents the average value of the sum of the distances from the member nodes of all individuals in the population to the corresponding cluster head.
  • the invention provides a wireless sensor network cluster routing method based on an adaptive genetic algorithm, which improves the selection, crossover and mutation mechanism of the genetic algorithm, improves the algorithm's global search capability and convergence speed, and solves the problem of using existing application intelligent algorithms
  • the clustering routing protocol has the problem of poor real-time performance while ensuring the validity of the protocol; in the clustering process, the distance between each node and the cluster head node, the distance between the cluster head node and the base station, and the remaining energy of each node are considered.
  • the cluster heads are selected according to the factors.
  • the cluster head nodes communicate with the base station in a multi-hop manner according to the distance between the cluster heads and the neighboring cluster heads, which improves the network life and data transmission efficiency.
  • FIG. 1 is a schematic diagram of a network structure of a wireless sensor network cluster routing based on an adaptive genetic algorithm according to an embodiment of the present invention
  • FIG. 2 is a schematic flowchart of steps of a wireless sensor network cluster routing method based on an adaptive genetic algorithm according to an embodiment of the present invention
  • Embodiment 3 is a schematic structural diagram of an adaptive crossover operation module provided by Embodiment 1 of the present invention.
  • Embodiment 4 is a schematic structural diagram of an adaptive mutation operation module provided by Embodiment 1 of the present invention.
  • Embodiment 1 of the present invention is a schematic diagram of the structure of an adaptive crossover operator curve provided by Embodiment 1 of the present invention.
  • FIG. 6 is a schematic diagram of the curve structure of an adaptive mutation operator provided by Embodiment 1 of the present invention.
  • FIG. 7 is an actual simulation diagram of the adaptive crossover probability according to the adaptation value provided by Embodiment 1 of the present invention.
  • FIG. 8 is an actual simulation diagram of the adaptive mutation probability according to the adaptation value according to the first embodiment of the present invention.
  • the core idea of the present invention is that the present invention provides a wireless sensor network cluster routing method based on adaptive genetic algorithm, which improves the selection, crossover and mutation mechanism of genetic algorithm, improves the algorithm’s global search capability and convergence speed, and solves The problem of poor real-time performance while ensuring the validity of the protocol while ensuring the validity of the clustering routing protocol using the existing application of intelligent algorithms; in the clustering process, the distance between each node and the cluster head node, and the distance between the cluster head node and the base station is considered As well as the remaining energy of each node to select the cluster head, the cluster head node communicates with the base station in a multi-hop manner according to the distance between the cluster head and the neighboring cluster head, which improves the network life and data transmission efficiency.
  • FIG. 1 is a schematic diagram of a network structure of a wireless sensor network cluster routing based on an adaptive genetic algorithm provided by an embodiment of the present invention.
  • the network structure of the cluster routing of the wireless sensor network based on the adaptive genetic algorithm includes several cluster structures 11 and base stations 12.
  • Each cluster structure 11 includes a cluster head node 111 and a number of ordinary nodes 112.
  • the sensor node collects data information of the surrounding environment, and transmits the collected data to the base station 12 through multiple hops for further analysis and processing.
  • FIG. 2 is a schematic flowchart of steps of a wireless sensor network cluster routing method based on an adaptive genetic algorithm provided by an embodiment of the present invention.
  • a cluster routing method for wireless sensor networks based on adaptive genetic algorithm includes the following steps:
  • each node has a unique ID and the initial energy is the same.
  • the nodes can judge the relative distance according to the strength of the received signal;
  • S25 Determine whether the cluster head is within the communication range of the base station. If so, the cluster head communicates directly with the base station; if not, continue to select a relay node according to the distance between the cluster heads, and route the data packet to the relay node until the data The packet is transmitted to the base station.
  • the established network energy consumption model if the node needs to send 1 bit of data to the node whose distance is d, the energy consumption of the node is: among them Represents the distance threshold, E elec represents the energy consumed to send or receive 1-bit data, and ⁇ fs and ⁇ mp represent the power amplification coefficients under different communication distances.
  • the population size is m
  • the population set is:
  • C(x) represents the xth individual in the population, and the length of each individual is the number of sensor nodes n
  • B i represents the values of individual genes
  • the value B i T (s) determined by the node S i energy, average energy and a network node survival threshold
  • T (s) as follows Shown:
  • p is the ratio of the number of cluster heads to the total number of nodes
  • r is the current round number
  • G is the set of nodes that have not become cluster heads in the past 1/p round.
  • p is 0.05.
  • m 20.
  • the fitness value function of the xth individual in the population is: Among them E avg , sumD CH ⁇ BS (x), sumD S ⁇ CH (x) respectively represent the average energy of the nodes, the sum of the distances from each cluster head node to the base station and the sum of the distances between the member nodes and the corresponding cluster head, minpopD CH ⁇ BS Represents the minimum sum of the distances from each cluster head node of all individuals in the population to the base station, minpopD S ⁇ CH represents the minimum sum of the distances from the member nodes of all individuals in the population to the corresponding cluster head, avgpopD CH ⁇ BS represents the population in the population The average value of the sum of the distances from the cluster head nodes of all individuals to the base station, avgpopD S ⁇ CH represents the average value of the sum of the distances from the member nodes of all individuals in the population to the corresponding cluster head.
  • the elite retention in the embodiment of the present invention saves the individual H with the highest fitness value in the current population, and allows the individual to H continues to participate in crossover and mutation operations. If the L fitness value of the worst-performing individual in the next generation is less than that of the previous generation individual H, then the H individual will directly replace the L individual.
  • the elite retention strategy can prevent the optimal individual from being destroyed due to crossover and mutation operations.
  • FIG. 3 is a schematic structural diagram of an adaptive crossover operation module provided by Embodiment 1 of the present invention.
  • the crossover operation is to exchange and recombine the partial structures of the two chromosomes to generate new individuals. Through crossover recombination, the search capability of the genetic algorithm is greatly improved.
  • the embodiment of the present invention adopts a two-point crossover method. First, a random number C rand (x) in the range of 0 to 1 is randomly generated for the individual x in the population.
  • C rand (x) ⁇ P c (x) If C rand (x) ⁇ P c (x), then randomly select two gene cut points CP 1 and CP 2 on the x individual, and compare the genes between CP 1 to CP 2 on the x individual with the x+2 Interchange of genes in corresponding positions of individuals.
  • FIG. 4 is a schematic structural diagram of an adaptive mutation operation module provided by Embodiment 1 of the present invention.
  • the mutation operation is based on this phenomenon.
  • M rand (B i ) In the process of evolution, some errors may occur that may lead to gene mutations.
  • the mutation operation is based on this phenomenon.
  • M rand (B i ) In the process of evolution, some gene values on the chromosome are mutated, the purpose is to increase the diversity of the population and avoid falling into the local optimum.
  • the crossover probability P c and the mutation probability P m are usually fixed values. If P c or P m is too large, the individual structure with a higher fitness value will be easily destroyed, and the algorithm will fall into random search and lose The meaning of genetic algorithm iterative optimization; if P c or P m is too small, the generation of new individuals in the population will also become difficult, leading to stagnation in the evolutionary process and falling into a local optimum. Therefore, when using the classical genetic algorithm to solve different problems, it is necessary to continuously adjust until the appropriate crossover probability and mutation probability are selected, which increases the workload. In order to solve this problem, some researchers have proposed a new adaptive genetic algorithm (New Adaptive Genetic Algorithm, NAGA), in order to avoid falling into the local optimum, NAGA improved the crossover and mutation operators on the basis of AGA, its definition for:
  • NAGA New Adaptive Genetic Algorithm
  • f max is the maximum fitness value
  • f avg is the average fitness value
  • f min is the minimum fitness value
  • f′ is the larger fitness value of the two individuals crossing over
  • f is the fitness value of the current individual
  • P m1 >P m2 >P m3 is the fitness value of the current individual
  • NAGA prevents the algorithm from falling into the local optimal situation to a certain extent, it ignores the distribution of the number of individuals. If the method in NAGA is used to adjust the probability of crossover and mutation linearly with the size of individual fitness, some good individual structures in the population may still be destroyed.
  • P c and P m should change slowly when the individual fitness value is low and maintain at a higher level; P c and P m also change slowly when the individual fitness value is high, and maintain a higher level. While maintaining the diversity of the population, this operation preserves more of the structure of good individuals. In other words, the probability of crossover and mutation should be adjusted nonlinearly with the size of individual fitness.
  • the first embodiment of the present invention uses the Sigmoid function to quantify the adaptive adjustment of the crossover and mutation probability. Non-linear change requirements.
  • FIG. 5 is a schematic diagram of the structure of an adaptive crossover operator curve provided by Embodiment 1 of the present invention. Referring to Figure 5, the maximum value of individual fitness in the population is given a smaller crossover probability to avoid falling into a local optimal state. The minimum value of individual fitness in the population is given a larger crossover probability in order to obtain a satisfactory Fitness value.
  • FIG. 6 is a schematic diagram of the structure of an adaptive mutation operator curve provided by Embodiment 1 of the present invention. Referring to Figure 6, the maximum individual fitness value in the population gives a smaller mutation probability to avoid falling into a local optimal state, and the minimum individual fitness value in the population gives a larger mutation probability in order to obtain a satisfactory Fitness value.
  • FIG. 7 is an actual simulation diagram of the adaptive crossover probability according to the adaptation value according to the first embodiment of the present invention
  • FIG. 8 is an actual simulation diagram of the adaptive mutation probability according to the adaptation value according to the first embodiment of the present invention. Referring to Figure 7 and Figure 8, when most fitness values are at a relatively low level, their cross mutation probability is set to be relatively high.
  • the iteration stop condition is that the iteration stops after reaching any value between 30 and 50 times.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

本发明提出基于自适应遗传算法的无线传感器网络分簇路由方法包括以下步骤：在监测区域内，随机均匀部署传感器节点，节点能根据接收到的信号强弱判断相对距离；利用传感器节点的初始位置和能量信息进行种群初始化操作；计算适应值，根据适应值进行精英保留以及自适应交叉变异操作；判断所述适应值是否达到迭代停止条件，若否，则转至上一步骤，若迭代完成，则根据最优个体中的分簇方案对节点进行分簇；判断簇头是否在基站的通信范围内，如果是，则簇头直接与基站通信；如果不是，继续根据簇头间的距离选择中继节点，将数据包路由至中继节点，直到数据包被传送至基站。改进了遗传算法的选择、交叉和变异机制，提升了算法的全局搜索能力和收敛速度。

Description

基于自适应遗传算法的无线传感器网络分簇路由方法 技术领域
本发明涉及一种无线传感器网络分簇路由方法技术领域，特别涉及一种基于自适应遗传算法的无线传感器网络分簇路由方法。
背景技术
无线传感网络由很多电池驱动的传感器设备所组成。这些设备能感知到周围环境的一些物理特性从而获得各种数据，例如声音、压力、湿度、温度或化学浓度等。同时，每个传感器节点都具有执行简单计算或与其他节点、基站直接通信的能力。节点会将采集到的数据传输到基站进行进一步的分析和处理。如今，无线传感网络被广泛应用于空气质量监测、健康监测、水质监测、家庭安全等领域。
由于分簇网络结构具有良好的扩展性，便于能量管理，均衡负载，资源分配等优势，很多人对无线传感网络分簇路由协议进行了深入的研究。无线传感器网络中节点的能量是由自身携带的微型电池提供的，一般情况下，电池无法更换，所以无线传感器网络的使用时间受到能量的限制。分簇路由方法中簇头在融合了本簇的数据之后再将融合之后的数据发给汇聚节点，减少了数据的发送量，也减少了能量消耗。簇头负责簇的建立、簇内通信控制以及簇与汇聚节点之间的通信。
在设计和部署无线传感网络的过程中，由于部署环境多变，节点能量有限，极易造成各节点传输数据时的负载不均衡，使得部分节点过早能量耗尽，降低了网络的使用寿命；另外，现有的大多数应用复杂智能算法的分簇路由协议存在运算复杂度高、路由实时性差的问题。因此，如何充分利用有限的网络节点能量来延长网络的寿命，提高网络路由的实时性进而提高数据的传输效率，成为了亟需解决的问题。
发明内容
本发明的目的在于提供一种基于自适应遗传算法的无线传感器网络分簇路由方法，以解决目前采用复杂智能算法的分簇路由协议实时性较差、网络寿命短、数据传输效率低下的问题。
为了解决上述技术问题，本发明的技术方案是：提供一种基于自适应遗传算法的无线传感器网络分簇路由方法，包括以下步骤：
步骤1：在监测区域内，随机均匀部署传感器节点，建立网络能耗模型，每个节点都有唯一的ID标识且初始能量相同，节点能根据接收到的信号强弱判断相对距离；
步骤2：利用传感器节点的初始位置和能量信息进行种群初始化操作；
步骤3：种群中个体根据传感节点位置和能量信息计算适应值，并根据适应值进行精英保留以及自适应交叉变异操作，产生新种群，个体上每个点都有几率发生交叉和变异，交叉和变异的概率随着个体适应值的大小进行非线性调整；
步骤4：判断所述适应值是否达到迭代停止条件，若否，则转至步骤3，若迭代完成，则根据最优个体中的分簇方案对节点进行分簇；
步骤5：判断簇头是否在基站的通信范围内，如果是，则簇头直接与基站通信；如果不是，继续根据簇头间的距离选择中继节点，将数据包路由至中继节点，直到数据包被传送至基站。
进一步地，建立的网络能耗模型，若节点需要发送l比特的数据至距离节点为d的节点，则节点的能耗为:
Figure PCTCN2020092484-appb-000001
其中
Figure PCTCN2020092484-appb-000002
表示距离阈值，E elec表示发送或者接收1比特数据所消耗的能量，ε fs和ε mp表示不同通信距离下的功率放大系数。
进一步地，所述迭代停止条件为迭代达到30至50次之间任意值之后停止。
进一步地，利用传感器节点的初始位置和能量信息进行种群初始化操作：种群大小为m，则种群集合为：
Figure PCTCN2020092484-appb-000003
其中C(x)表示种群中的第x条个体，每个个体的长度为传感器节点数n，则个体集合为：C(x)＝{B 1,B 2,B 3,...B i...,B n}，其中B i表示个体上基因的值，B i由节点S i的能量，网络中存活节点的平均能量以及阈 值T(s)决定，T(s)的表达式如下所示：
Figure PCTCN2020092484-appb-000004
其中，p为簇头数占总节点数的比例，r为当前轮数，G为过去1/p轮中未成为簇头的节点的集合。p取0.05。
进一步地，每个节点随机产生一个0-1的随机函数，并将其与阈值T(s)比较，若其小于等于T(s)并且节点S i的能量大于等于当前网络中存活节点的平均能量时，B i＝1,节点S i被选为簇头；当节点能量大于0但小于平均能量时，B i＝0；当节点能量小于0时，B i＝-1，B i判别公式为：
Figure PCTCN2020092484-appb-000005
其中，S i.E表示节点S i的剩余能量,E avgAlive表示网络中存活节点的平均能量。
进一步地，计算适应值：种群中第x条个体适应值函数为：
Figure PCTCN2020092484-appb-000006
其中E avg，sumD CH→BS(x)，sumD S→CH(x)分别表示节点平均能量，各簇头节点至基站的距离之和以及成员节点至相应簇头距离之和，minpopD CH→BS表示种群中所有个体的各簇头节点至基站的距离之和的最小值，minpopD S→CH表示种群中所有个体的成员节点至相应簇头距离之和的最小值，avgpopD CH→BS表示种群中所有个体的各簇头节点至基站的距离之和的平均值，avgpopD S→CH表示种群中所有个体的成员节点至相应簇头距离之和的平均值。
进一步地，自适应交叉概率公式为：
Figure PCTCN2020092484-appb-000007
其中，P c1和P c2为交叉概率调整的上下限，P c1＝0.9，P c2＝0.6，f max为种群中个体适应值的最大值，f avg为种群中所有个体适应值的平均值。
进一步地，自适应变异概率公式为：
Figure PCTCN2020092484-appb-000008
其中，P m1和P m2为变异概率调整的上下限，P m1＝0.03，P m2＝0.01，f max为种群中个体适应值的最大值，f avg为种群中所有个体适应值的平均值。
进一步地，所述精英保留：在保存当前种群中适应值最高的个体H的同时，让个体H继续参与交叉和变异操作，若下一代中表现最差的个体L适应值小于上一代的个体H，则用H个体直接替换掉L个体，设集合
Figure PCTCN2020092484-appb-000009
其中C max(k)和C min(k)分别表示第k代种群Pop(k)中的最优个体和最劣个体，f[C max(k)]和f[C min(k)]为相应个体的适应值，若f[C max(k)]＞f[C min(k+1)]，则：C min(k+1)＝C max(k)，f[C min(k+1)]＝f[C max(k)]。
本发明提供一种基于自适应遗传算法的无线传感器网络分簇路由方法，改进了遗传算法的选择、交叉和变异机制，提升了算法的全局搜索能力和收敛速度，解决了采用现有应用智能算法的分簇路由协议在保证协议有效性的同时实时性较差的问题；在分簇过程中，考虑了各节点与簇头节点间、簇头节点与基站间的距离以及各节点剩余能量三方面因素来进行簇头的选择，簇头节点根据和相邻簇头之间的距离通过多跳的方式与基站通信，提高了网络寿命及数据传输效率。
附图说明
下面结合附图对发明作进一步说明：
图1为本发明实施例提供的基于自适应遗传算法的无线传感器网络分簇路由的网络结构示意图；
图2为本发明实施例提供的基于自适应遗传算法的无线传感器网络分簇路由方法的步骤流程示意图；
图3为本发明实施例一提供的自适应交叉操作模块结构示意图；
图4为本发明实施例一提供的自适应变异操作模块结构示意图；
图5为本发明实施例一提供的自适应交叉算子曲线结构示意图；
图6为本发明实施例一提供的自适应变异算子曲线结构示意图；
图7为本发明实施例一提供的自适应交叉概率随着适应值变化的实际仿真图；
图8为本发明实施例一提供的自适应变异概率随着适应值变化的实际仿真图。
具体实施方式
以下结合附图和具体实施例对本发明提出的基于自适应遗传算法的无线传 感器网络分簇路由方法作进一步详细说明。根据下面说明和权利要求书，本发明的优点和特征将更清楚。需说明的是，附图均采用非常简化的形式且均使用非精准的比率，仅用以方便、明晰地辅助说明本发明实施例的目的。
本发明的核心思想在于，本发明提供一种基于自适应遗传算法的无线传感器网络分簇路由方法，改进了遗传算法的选择、交叉和变异机制，提升了算法的全局搜索能力和收敛速度，解决了采用现有应用智能算法的分簇路由协议在保证协议有效性的同时实时性较差的问题；在分簇过程中，考虑了各节点与簇头节点间、簇头节点与基站间的距离以及各节点剩余能量三方面因素来进行簇头的选择，簇头节点根据和相邻簇头之间的距离通过多跳的方式与基站通信，提高了网络寿命及数据传输效率。
图1为本发明实施例提供的基于自适应遗传算法的无线传感器网络分簇路由的网络结构示意图。参照图1，基于自适应遗传算法的无线传感器网络分簇路由的网络结构包括若干簇型结构11和基站12。每个簇型结构11都包含一个簇头节点111和若干个普通节点112，传感节点采集周围环境的数据信息，并将采集到的数据通过多跳传输到基站12进行进一步的分析和处理。
图2为本发明实施例提供的基于自适应遗传算法的无线传感器网络分簇路由方法的步骤流程示意图。参照图2，一种基于自适应遗传算法的无线传感器网络分簇路由方法，包括以下步骤：
S21、在监测区域内，随机均匀部署传感器节点，建立网络能耗模型，每个节点都有唯一的ID标识且初始能量相同，节点能根据接收到的信号强弱判断相对距离；
S22、利用传感器节点的初始位置和能量信息进行种群初始化操作；
S23、种群中个体根据传感节点位置和能量信息计算适应值，并根据适应值进行精英保留以及自适应交叉变异操作，产生新种群，个体上每个点都有几率发生交叉和变异，交叉和变异的概率随着个体适应值的大小进行非线性调整；
S24、判断所述适应值是否达到迭代停止条件，若否，则转至S23，若迭代完成，则根据最优个体中的分簇方案对节点进行分簇；
S25、判断簇头是否在基站的通信范围内，如果是，则簇头直接与基站通信；如果不是，继续根据簇头间的距离选择中继节点，将数据包路由至中继节点， 直到数据包被传送至基站。
在S21中，建立的网络能耗模型，若节点需要发送l比特的数据至距离节点为d的节点，则节点的能耗为:
Figure PCTCN2020092484-appb-000010
其中
Figure PCTCN2020092484-appb-000011
表示距离阈值，E elec表示发送或者接收1比特数据所消耗的能量，ε fs和ε mp表示不同通信距离下的功率放大系数。
在S22中，利用传感器节点的初始位置和能量信息进行种群初始化操作：种群大小为m，则种群集合为：
Figure PCTCN2020092484-appb-000012
其中C(x)表示种群中的第x条个体，每个个体的长度为传感器节点数n，则个体集合为：C(x)＝{B 1,B 2,B 3,...B i...,B n}，其中B i表示个体上基因的值，B i由节点S i的能量，网络中存活节点的平均能量以及阈值T(s)决定，T(s)的表达式如下所示：
Figure PCTCN2020092484-appb-000013
其中，p为簇头数占总节点数的比例，r为当前轮数，G为过去1/p轮中未成为簇头的节点的集合。p取0.05。在本发明实施例中，m＝20。
进一步地，每个节点随机产生一个0-1的随机函数，并将其与阈值T(s)比较，若其小于等于T(s)并且节点S i的能量大于等于当前网络中存活节点的平均能量时，B i＝1,节点S i被选为簇头；当节点能量大于0但小于平均能量时，B i＝0；当节点能量小于0时，B i＝-1，B i判别公式为：
Figure PCTCN2020092484-appb-000014
其中，S i.E表示节点S i的剩余能量,E avgAlive表示网络中存活节点的平均能量。
在S23中，计算适应值：种群中第x条个体适应值函数为：
Figure PCTCN2020092484-appb-000015
其中E avg，sumD CH→BS(x)，sumD S→CH(x)分别表示节点平均能量，各簇头节点至基站的距离之和以及成员节点至相应簇头距离之和，minpopD CH→BS表示种群中所有个体的各簇头节点至基站的距离之和的最小值，minpopD S→CH表示种群中所有个体的成员节点至相应簇头距离之和的最小值，avgpopD CH→BS表示种群中所有个体的各簇头节点至基站的距离之和的平均值，avgpopD S→CH表示种群中所有个体的成员节点至相应簇头距离之和的平均值。
精英保留：在保存当前种群中适应值最高的个体H的同时，让个体H继续 参与交叉和变异操作，若下一代中表现最差的个体L适应值小于上一代的个体H，则用H个体直接替换掉L个体，设集合
Figure PCTCN2020092484-appb-000016
其中C max(k)和C min(k)分别表示第k代种群Pop(k)中的最优个体和最劣个体，f[C max(k)]和f[C min(k)]为相应个体的适应值，若f[C max(k)]＞f[C min(k+1)]，则：C min(k+1)＝C max(k)，f[C min(k+1)]＝f[C max(k)]。
在传统遗传算法中，个体之间如果只是简单地进行交叉变异，很可能把较好的个体给破坏了。这样就没有达到累积较好基因的目的，反而把原本很好的基因给破坏了。为了防止当前种群的最优个体在下一代发生丢失，导致遗传算法不能收敛到全局最优解，本发明实施例中的精英保留，在保存当前种群中适应值最高的个体H的同时，让个体H继续参与交叉和变异操作，若下一代中表现最差的个体L适应值小于上一代的个体H，则用H个体直接替换掉L个体。精英保留策略可以避免最优个体不会因为交叉和变异操作而被破坏。
实施例一
图3为本发明实施例一提供的自适应交叉操作模块结构示意图。参照图3，交叉操作是将两条染色体的部分结构进行互换重组从而产生新的个体，通过交叉重组，遗传算法的搜索能力大大提升。本发明实施例采用两点交叉的方式，首先，为种群中个体x随机生成一个在0～1范围内的随机数C rand(x)。若C rand(x)≤P c(x),则在x个体上随机选择两个基因切点CP 1和CP 2，将第x个体上CP 1到CP 2之间的基因与第x+2个体相应位置的基因互换。
图4为本发明实施例一提供的自适应变异操作模块结构示意图。参照图4，在进化过程中，可能会出现一些差错而导致基因突变。变异操作便是依据这种现象，通过设置变异概率，使得染色体上的某些基因值发生突变，目的是为了增加种群的多样性，避免陷入局部最优。首先，为个体x上的每个基因随机生成一个在0～1范围内的随机数M rand(B i)。若M rand(B i)≤P c(x),则对发生变异的基因值B i进行替换。对于节点S i，若相应的B i＝0，则变为1，反之亦然。
在传统的遗传算法中，交叉概率P c以及变异概率P m通常为一定值，若P c或P m过大，则适应值较高的个体结构便容易遭到破坏，算法陷入随机搜索，失去了遗传算法迭代寻优的意义；若P c或P m过小，则种群中新个体的产生也会变得困 难，从而导致进化过程停滞，陷入局部最优。因此，在利用经典遗传算法解决不同问题时，需要不断调整直到选出适当的交叉概率以及变异概率，增加了工作量。为了解决这个问题,部分研究者提出了一种新的自适应遗传算法(New Adaptive Genetic Algorithm,NAGA)，为了避免陷入局部最优，NAGA在AGA的基础上改进了交叉和变异算子，其定义为：
Figure PCTCN2020092484-appb-000017
Figure PCTCN2020092484-appb-000018
其中，f max为最大适应值，f avg为平均适应值，f min为最小适应值，f′为交叉的两个个体中较大的适应值，f为当前个体的适应值，P c1＞P c2＞P c3,P m1＞P m2＞P m3。
NAGA虽然在一定程度上避免了算法陷入局部最优的情况，但是却忽略了个体的数量分布情况。若采用NAGA中的方法，让交叉和变异概率随着个体适应值的大小线性调整，种群中的部分优良个体结构依然有可能会被破坏。
因此，P c和P m在个体适应值较低时应变化缓慢，维持在较高的水平；P c和P m在个体适应值较高时同样变化缓慢，维持在较高的水平。此操作在维持种群多样性的同时，更多地保留了优良个体的结构。也就是说，交叉和变异概率应该随着个体适应值大小进行非线性调整。为了实现交叉和变异概率的这种自适应非线性变化，本发明实施例一采用Sigmoid函数来量化交叉和变异概率的自适应调整，Sigmoid函数顶部和底部较为平滑，能够满足本文对交叉和变异概率非线性变化的要求。
本发明实施例一提供的基于Sigmoid函数的交叉和变异公式如下：
自适应交叉概率公式为：
Figure PCTCN2020092484-appb-000019
其中，P c1和P c2为交叉概率调整的上下限，P c1＝0.9，P c2＝0.6，f max为种群中个体适应值的最大值，f avg为种群中所有个体适应值的平均值。
自适应变异概率公式为：
Figure PCTCN2020092484-appb-000020
其中，P m1和P m2为变异概率调整的上下限，P m1＝0.03，P m2＝0.01，f max为种群中个体适应值的最大值，f avg为种群中所有个体适应值的平均值。
图5为本发明实施例一提供的自适应交叉算子曲线结构示意图。参照图5，种群中个体适应值的最大值，给较小的交叉概率，以避免陷入局部最优的状态，种群中个体适应值的最小值，给较大的交叉概率，以期获得一个满意的适应值。
图6为本发明实施例一提供的自适应变异算子曲线结构示意图。参照图6，种群中个体适应值的最大值，给较小的变异概率，以避免陷入局部最优的状态，种群中个体适应值的最小值，给较大的变异概率，以期获得一个满意的适应值。
种群中小于平均值的大多数个体持续以较高的概率进行交叉和变异，大于平均值的部分优良个体则持续以较低的概率进行交叉变异。
图7为本发明实施例一提供的自适应交叉概率随着适应值变化的实际仿真图；图8为本发明实施例一提供的自适应变异概率随着适应值变化的实际仿真图。参照图7以及图8，大部分适应值处在比较低的水平情况下，将它们的交叉变异概率设置得比较高。
在本发明实施例一中，关于S24，判断所述适应值是否达到迭代停止条件，迭代停止条件为迭代达到30至50次之间任意值之后停止。
显然，本领域的技术人员可以对本发明进行各种改动和变形而不脱离本发明的精神和范围。这样，倘若本发明的这些修改和变型属于本发明权利要求及其等同技术的范围之内，则本发明也意图包含这些改动和变型在内。

Claims (10)

  1. 一种基于自适应遗传算法的无线传感器网络分簇路由方法，其特征在于，包括以下步骤：
    步骤1：在监测区域内，随机均匀部署传感器节点，建立网络能耗模型，每个节点都有唯一的ID标识且初始能量相同，节点能根据接收到的信号强弱判断相对距离；
    步骤2：利用传感器节点的初始位置和能量信息进行种群初始化操作；
    步骤3：种群中个体根据传感节点位置和能量信息计算适应值，并根据适应值进行精英保留以及自适应交叉变异操作，产生新种群，个体上每个点都有几率发生交叉和变异，交叉和变异的概率随着个体适应值的大小进行非线性调整；
    步骤4：判断所述适应值是否达到迭代停止条件，若否，则转至步骤3，若迭代完成，则根据最优个体中的分簇方案对节点进行分簇；
    步骤5：判断簇头是否在基站的通信范围内，如果是，则簇头直接与基站通信；如果不是，继续根据簇头间的距离选择中继节点，将数据包路由至中继节点，直到数据包被传送至基站。
  2. 如权利要求1所述的基于自适应遗传算法的无线传感器网络分簇路由方法，其特征在于，建立的网络能耗模型，若节点需要发送l比特的数据至距离节点为d的节点，则节点的能耗为:
    Figure PCTCN2020092484-appb-100001
    其中
    Figure PCTCN2020092484-appb-100002
    表示距离阈值，E elec表示发送或者接收1比特数据所消耗的能量，ε fs和ε mp表示不同通信距离下的功率放大系数。
  3. 如权利要求1所述的基于自适应遗传算法的无线传感器网络分簇路由方法，其特征在于，所述迭代停止条件为迭代达到30至50次之间任意值之后停止。
  4. 如权利要求1所述的基于自适应遗传算法的无线传感器网络分簇路由方法，其特征在于，利用传感器节点的初始位置和能量信息进行种群初始化操作：种群大小为m，则种群集合为：
    Figure PCTCN2020092484-appb-100003
    其中C(x)表示种群中的第x条个体，每个个体的长度为传感器节点数n，则个体集合为：C(x)＝{B 1,B 2,B 3,…B i…,B n}，其中B i表示个体上基因的值，B i由节点S i的能量，网络中存活节点的平均能量以及阈 值T(s)决定，T(s)的表达式如下所示：
    Figure PCTCN2020092484-appb-100004
    其中，p为簇头数占总节点数的比例，r为当前轮数，G为过去1/p轮中未成为簇头的节点的集合。
  5. 如权利要求4所述的基于自适应遗传算法的无线传感器网络分簇路由方法，其特征在于，p取0.05。
  6. 如权利要求4所述的基于自适应遗传算法的无线传感器网络分簇路由方法，其特征在于，每个节点随机产生一个0-1的随机函数，并将其与阈值T(s)比较，若其小于等于T(s)并且节点S i的能量大于等于当前网络中存活节点的平均能量时，B i＝1,节点S i被选为簇头；当节点能量大于0但小于平均能量时，B i＝0；当节点能量小于0时，B i＝－1，B i判别公式为：
    Figure PCTCN2020092484-appb-100005
    其中，S i.E表示节点S i的剩余能量,E avgAlive表示网络中存活节点的平均能量。
  7. 如权利要求1所述的基于自适应遗传算法的无线传感器网络分簇路由方法，其特征在于，计算适应值：种群中第x条个体适应值函数为：
    Figure PCTCN2020092484-appb-100006
    其中E avg，sumD CH→BS(x)，sumD S→CH(x)分别表示节点平均能量，各簇头节点至基站的距离之和以及成员节点至相应簇头距离之和，minpopD CH→BS表示种群中所有个体的各簇头节点至基站的距离之和的最小值，minpopD S→CH表示种群中所有个体的成员节点至相应簇头距离之和的最小值，avgpopD CH→BS表示种群中所有个体的各簇头节点至基站的距离之和的平均值，avgpopD S→CH表示种群中所有个体的成员节点至相应簇头距离之和的平均值。
  8. 如权利要求1所述的基于自适应遗传算法的无线传感器网络分簇路由方法，其特征在于，自适应交叉概率公式为：
    Figure PCTCN2020092484-appb-100007
    其中，P c1和P c2为交叉概率调整的上下限，P c1＝0.9，P c2＝0.6，f max为种群中个体适应值的最大值，f avg为种群中所有个体适应值的平均值。
  9. 如权利要求1所述的基于自适应遗传算法的无线传感器网络分簇路由方 法，其特征在于，自适应变异概率公式为：
    Figure PCTCN2020092484-appb-100008
    其中，P m1和P m2为变异概率调整的上下限，P m1＝0.03，P m2＝0.01，f max为种群中个体适应值的最大值，f avg为种群中所有个体适应值的平均值。
  10. 如权利要求1所述的基于自适应遗传算法的无线传感器网络分簇路由方法，其特征在于，所述精英保留：在保存当前种群中适应值最高的个体H的同时，让个体H继续参与交叉和变异操作，若下一代中表现最差的个体L适应值小于上一代的个体H，则用H个体直接替换掉L个体，设集合C max＝{C max(k):f[C max(k)]＝max[f(C(k))]|C max(k)∈Pop(k),k＝1,2,…}
    C min＝{C min(k):f[C min(k)]＝min[f(C(k))]|C min(k)∈Pop(k),k＝1,2,…}，其中C max(k)和C min(k)分别表示第k代种群Pop(k)中的最优个体和最劣个体，f[C max(k)]和f[C min(k)]为相应个体的适应值，若f[C max(k)]＞f[C min(k+1)]，则：C min(k+1)＝C max(k)，f[C min(k+1)]＝f[C max(k)]。
PCT/CN2020/092484 2019-09-18 2020-05-27 基于自适应遗传算法的无线传感器网络分簇路由方法 WO2021051859A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US17/762,023 US12047860B2 (en) 2019-09-18 2020-05-27 Clustering routing optimization method based on adaptive evolutionary algorithm for wireless sensor network

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201910880208.X 2019-09-18
CN201910880208.XA CN110602757B (zh) 2019-09-18 2019-09-18 基于自适应遗传算法的无线传感器网络分簇路由方法

Publications (1)

Publication Number Publication Date
WO2021051859A1 true WO2021051859A1 (zh) 2021-03-25

Family

ID=68860474

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/092484 WO2021051859A1 (zh) 2019-09-18 2020-05-27 基于自适应遗传算法的无线传感器网络分簇路由方法

Country Status (3)

Country Link
US (1) US12047860B2 (zh)
CN (1) CN110602757B (zh)
WO (1) WO2021051859A1 (zh)

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113747535A (zh) * 2021-08-05 2021-12-03 南京林业大学 一种基于遗传算法的能量异构无线传感器路由系统
CN113849901A (zh) * 2021-07-28 2021-12-28 上海机电工程研究所 针对接触换热系数辨识的改进自适应优化方法及系统
CN114221350A (zh) * 2021-12-08 2022-03-22 国网山东省电力公司电力科学研究院 基于bas-iga算法的分布式光伏集群划分方法和系统
CN115150918A (zh) * 2022-06-06 2022-10-04 西北农林科技大学 一种分簇网络架构下的可靠联邦学习方法、系统、设备及终端
CN115334541A (zh) * 2022-07-12 2022-11-11 中国地质大学（武汉） 一种参数迭代式LoRa网络能效优化方法及系统
CN115426664A (zh) * 2022-08-10 2022-12-02 温州理工学院 一种异构无线传感网络覆盖优化方法
CN115499915A (zh) * 2022-09-26 2022-12-20 国网湖北省电力有限公司荆州供电公司 一种基于虎鲸算法优化的无线传感器网络节点定位方法
CN115767566A (zh) * 2022-11-22 2023-03-07 三峡大学 无线传感器覆盖与路由联合优化的多目标差分进化方法
CN117010132A (zh) * 2023-09-27 2023-11-07 中国船舶集团有限公司第七一九研究所 一种水下多基地声纳系统的空间阵位优化方法及系统
CN117519054A (zh) * 2023-12-11 2024-02-06 广州智业节能科技有限公司 一种高效冷站控制系统

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110602757B (zh) * 2019-09-18 2023-05-12 上海海事大学 基于自适应遗传算法的无线传感器网络分簇路由方法
CN111246416B (zh) * 2020-01-13 2022-03-29 山东交通学院 一种基于遗传算法的水下无线传感器网络拓扑控制方法
CN111683378B (zh) * 2020-06-05 2023-05-30 国网河南省电力公司经济技术研究院 一种面向配电网的可重构无线传感器网络中继部署方法
CN112996079B (zh) * 2021-02-26 2022-12-13 南京工程学院 基于自适应遗传算法的下行noma用户动态分簇方法
CN112822747B (zh) * 2021-03-02 2022-09-30 吉林大学 一种基于遗传算法和蚁群算法的路由选择策略的方法
CN117277434A (zh) * 2023-09-15 2023-12-22 深圳市新枝科技有限公司 发电机组优化控制方法
CN117956540B (zh) * 2024-03-27 2024-06-14 广州市赛科自动化控制设备有限公司 一种室内环境监测方法、装置、设备及存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106934459A (zh) * 2017-02-03 2017-07-07 西北工业大学 一种基于种群进化过程的自适应遗传算法
CN107607972A (zh) * 2017-08-21 2018-01-19 暨南大学 一种基于实数编码自适应遗传算法的整周模糊度快速获取方法
WO2019118453A1 (en) * 2017-12-11 2019-06-20 The Texas A&M University System Agricultural sensor placement and fault detection in wireless sensor networks
CN110049526A (zh) * 2019-04-12 2019-07-23 山东师范大学 Wsn中基于改进分簇算法的数据收集方法及系统
CN110602757A (zh) * 2019-09-18 2019-12-20 上海海事大学 基于自适应遗传算法的无线传感器网络分簇路由方法

Family Cites Families (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI398182B (zh) * 2009-09-01 2013-06-01 Univ Nat Taiwan 應用於無線感測器網路之多跳路由演算法
CN106413031A (zh) * 2016-09-13 2017-02-15 中国人民解放军后勤工程学院 基于节点等级的异构网络自适应分簇算法
CN107889116B (zh) * 2016-09-30 2022-05-10 英国电讯有限公司 多级小区或小区簇的配置方法、装置以及通信系统
WO2018098737A1 (zh) * 2016-11-30 2018-06-07 深圳天珑无线科技有限公司 分布式网络的簇首选取方法、节点及系统
CN107295597B (zh) * 2017-07-28 2019-07-19 北京邮电大学 一种自适应分簇路由方法、装置及电子设备
CN108055685B (zh) * 2017-12-29 2020-06-09 北京农业信息技术研究中心 无线传感器网络中的簇头节点和传感器节点
CN108566663B (zh) * 2018-01-10 2021-06-15 重庆邮电大学 基于扰动粒子群优化的sdwsn能耗均衡路由方法
CN110958659B (zh) 2019-12-12 2022-02-08 南华大学 改进遗传禁忌搜索的深井巷道wsn分簇路由方法及装置
CN111698706B (zh) 2020-06-10 2022-05-13 长春师范大学 基于混沌遗传的无线传感器网络改进leach路由方法
CN113423130B (zh) 2020-09-22 2022-06-21 长春师范大学 一种基于改进遗传算法的环扇无线传感器网络分簇路由方法
AU2021102882A4 (en) 2021-05-27 2021-07-15 A P, Jyothi DR Hybrid swarm optimization algorithm for clustering and routing in iot networks
CN113596951B (zh) 2021-09-02 2023-05-30 哈尔滨工程大学 基于量子礁鲨机制的无线传感器网络分簇路由方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106934459A (zh) * 2017-02-03 2017-07-07 西北工业大学 一种基于种群进化过程的自适应遗传算法
CN107607972A (zh) * 2017-08-21 2018-01-19 暨南大学 一种基于实数编码自适应遗传算法的整周模糊度快速获取方法
WO2019118453A1 (en) * 2017-12-11 2019-06-20 The Texas A&M University System Agricultural sensor placement and fault detection in wireless sensor networks
CN110049526A (zh) * 2019-04-12 2019-07-23 山东师范大学 Wsn中基于改进分簇算法的数据收集方法及系统
CN110602757A (zh) * 2019-09-18 2019-12-20 上海海事大学 基于自适应遗传算法的无线传感器网络分簇路由方法

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
NAYAK PADMALAYA; VATHASAVAI BHAVANI: "Genetic algorithm based clustering approach for wireless sensor network to optimize routing techniques", 2017 7TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING - CONFLUENCE, IEEE, 12 January 2017 (2017-01-12), pages 373 - 380, XP033103252, DOI: 10.1109/CONFLUENCE.2017.7943178 *
WANG TIANSHU, GONGXUAN ZHANG, XICHEN YANG, AHMADREZA VAJDI: "Genetic algorithm for energy-efficient clustering and routing in wireless sensor networks", THE JOURNAL OF SYSTEMS AND SOFTWARE, vol. 146, pages 196 - 214, XP055793051, DOI: 10.1016/j.jss.2018.09.067 *

Cited By (16)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113849901A (zh) * 2021-07-28 2021-12-28 上海机电工程研究所 针对接触换热系数辨识的改进自适应优化方法及系统
CN113849901B (zh) * 2021-07-28 2024-05-03 上海机电工程研究所 针对接触换热系数辨识的改进自适应优化方法及系统
CN113747535A (zh) * 2021-08-05 2021-12-03 南京林业大学 一种基于遗传算法的能量异构无线传感器路由系统
CN113747535B (zh) * 2021-08-05 2024-02-23 南京林业大学 一种基于遗传算法的能量异构无线传感器路由系统
CN114221350B (zh) * 2021-12-08 2023-08-22 国网山东省电力公司电力科学研究院 基于bas-iga算法的分布式光伏集群划分方法和系统
CN114221350A (zh) * 2021-12-08 2022-03-22 国网山东省电力公司电力科学研究院 基于bas-iga算法的分布式光伏集群划分方法和系统
CN115150918A (zh) * 2022-06-06 2022-10-04 西北农林科技大学 一种分簇网络架构下的可靠联邦学习方法、系统、设备及终端
CN115334541A (zh) * 2022-07-12 2022-11-11 中国地质大学（武汉） 一种参数迭代式LoRa网络能效优化方法及系统
CN115334541B (zh) * 2022-07-12 2024-04-16 中国地质大学（武汉） 一种参数迭代式LoRa网络能效优化方法及系统
CN115426664A (zh) * 2022-08-10 2022-12-02 温州理工学院 一种异构无线传感网络覆盖优化方法
CN115499915A (zh) * 2022-09-26 2022-12-20 国网湖北省电力有限公司荆州供电公司 一种基于虎鲸算法优化的无线传感器网络节点定位方法
CN115767566A (zh) * 2022-11-22 2023-03-07 三峡大学 无线传感器覆盖与路由联合优化的多目标差分进化方法
CN115767566B (zh) * 2022-11-22 2024-06-11 三峡大学 无线传感器覆盖与路由联合优化的多目标差分进化方法
CN117010132A (zh) * 2023-09-27 2023-11-07 中国船舶集团有限公司第七一九研究所 一种水下多基地声纳系统的空间阵位优化方法及系统
CN117519054A (zh) * 2023-12-11 2024-02-06 广州智业节能科技有限公司 一种高效冷站控制系统
CN117519054B (zh) * 2023-12-11 2024-06-11 广州智业节能科技有限公司 一种高效冷站控制系统

Also Published As

Publication number Publication date
US20220353783A1 (en) 2022-11-03
CN110602757A (zh) 2019-12-20
US12047860B2 (en) 2024-07-23
CN110602757B (zh) 2023-05-12

Similar Documents

Publication Publication Date Title
WO2021051859A1 (zh) 基于自适应遗传算法的无线传感器网络分簇路由方法
CN112469100B (zh) 基于可充电多基站式无线异构传感器网络的分层路由算法
CN106993320B (zh) 基于多中继多跳的无线传感器网络协作传输路由方法
CN108712767B (zh) 一种无线传感器网络中能耗均衡的簇间多跳路由控制方法
CN108737191B (zh) 面向超密集无线传感器网基于无监督学习的拓扑控制方法
CN105246117B (zh) 一种适用于移动无线传感网的节能路由协议的实现方法
CN105898764B (zh) 多级能量异构的无线传感器网络部署方法
CN112822747B (zh) 一种基于遗传算法和蚁群算法的路由选择策略的方法
WO2022262127A1 (zh) 智慧物联网综合感知系统及方法
Nazi et al. Efficient communications in wireless sensor networks based on biological robustness
Kashyap et al. Genetic-fuzzy based load balanced protocol for WSNs
Sun et al. Energy entropy multipath routing optimization algorithm in MANET based on GA
CN108566658B (zh) 一种无线传感器网络中能耗均衡的分簇算法
CN111107602A (zh) 一种无线体域网能耗和时延加权最小的安全路由选择方法
CN110049465B (zh) 一种基于wsn的水域监测通信方法
CN111447658B (zh) 一种基于sdwsn的分簇路由方法
Farahani Energy Consumption Reduction in Wireless Sensor Network Based on Clustering
Babu et al. CS‐MAODV: Cuckoo search and M‐tree‐based multiconstraint optimal Multicast Ad hoc On‐demand Distance Vector Routing Protocol for MANETs
Peng et al. Real-time transmission optimization for edge computing in industrial cyber-physical systems
Mishra et al. Gecsa: A game theory based energy efficient cluster-head selection approach in wireless sensor networks
CN103260206A (zh) 一种基于影响度因子的混合动态无线路由有效搜索收敛方法
CN103067510B (zh) 海上搜救无线传感网及其能量动态分配方法
Wazed et al. Genetic algorithm based approach for extending the lifetime of two-tiered sensor networks
Sujithra et al. Genetic algorithm based energy efficient data gathering in wireless sensor networks
CN113923802A (zh) 软件定义无线传感器网络中能量高效的分层拓扑控制方法

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20864681

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20864681

Country of ref document: EP

Kind code of ref document: A1

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 10/03/2023)

122 Ep: pct application non-entry in european phase

Ref document number: 20864681

Country of ref document: EP

Kind code of ref document: A1