CN111669767B - Sensor network dynamic deployment method - Google Patents

Sensor network dynamic deployment method Download PDF

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CN111669767B
CN111669767B CN202010458814.5A CN202010458814A CN111669767B CN 111669767 B CN111669767 B CN 111669767B CN 202010458814 A CN202010458814 A CN 202010458814A CN 111669767 B CN111669767 B CN 111669767B
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CN111669767A (en
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孙茜
王小艺
许继平
张慧妍
王立
于家斌
申志平
羊峰波
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Beijing Technology and Business University
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    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W16/00Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18Network planning tools
    • H04W16/20Network planning tools for indoor coverage or short range network deployment
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W16/00Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22Traffic simulation tools or models
    • H04W16/225Traffic simulation tools or models for indoor or short range network
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W40/00Communication routing or communication path finding
    • H04W40/02Communication route or path selection, e.g. power-based or shortest path routing
    • H04W40/04Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources
    • H04W40/10Communication route or path selection, e.g. power-based or shortest path routing based on wireless node resources based on available power or energy
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04WWIRELESS COMMUNICATION NETWORKS
    • H04W84/00Network topologies
    • H04W84/18Self-organising networks, e.g. ad-hoc networks or sensor networks
    • YGENERAL 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
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02DCLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00Reducing energy consumption in communication networks
    • Y02D30/70Reducing energy consumption in communication networks in wireless communication networks

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Abstract

本发明提出了一种传感器网络动态部署方法。首先,对非重点监测区域内的传感器进行定位,选其为移动节点。其次,对重点监测区域利用同心圆法找出目标节点。最后,根据弗洛伊德算法计算出将移动节点移动到目标节点的级联移动路径,从而实现了基于区域特征模型的水质传感器网络的动态部署。本发明通过传感器节点的级联移动,可利用更短的时间,在保证网络生命周期的前提下实现对传感器网络重点监测区域的动态部署,使之达到更佳的覆盖性能。

Figure 202010458814

The invention proposes a dynamic deployment method of a sensor network. First, locate the sensors in the non-key monitoring area and select them as mobile nodes. Secondly, the concentric circle method is used to find the target nodes in the key monitoring areas. Finally, the cascade moving path to move the mobile node to the target node is calculated according to the Floyd algorithm, thus realizing the dynamic deployment of the water quality sensor network based on the regional characteristic model. The present invention realizes the dynamic deployment of key monitoring areas of the sensor network in a shorter period of time through the cascading movement of the sensor nodes on the premise of ensuring the network life cycle, so as to achieve better coverage performance.

Figure 202010458814

Description

一种传感器网络动态部署方法A dynamic deployment method for sensor networks

技术领域Technical Field

本发明涉及环境监测和传感器网络领域,尤其涉及一种传感器网络动态部署方法的研究。The present invention relates to the field of environmental monitoring and sensor networks, and in particular to a study of a sensor network dynamic deployment method.

背景技术Background Art

随着电子通信技术的发展,传感器网络越来越多的在环境监测、智能家居、医疗、军事等领域。从资源合理利用和保证监测数据准确性的角度出发,运用传感器可以移动的特点,在有限传感器数目的条件下,把非重点监测区域的传感器移动到重点监测区域进行实时监控。监测区域的完整网络覆盖对于无线传感器的部署尤为关键,所以必须保证重点监测区域更加有效的覆盖。With the development of electronic communication technology, sensor networks are increasingly used in environmental monitoring, smart home, medical, military and other fields. From the perspective of rational use of resources and ensuring the accuracy of monitoring data, the characteristics of sensors that can be moved are used. Under the condition of a limited number of sensors, sensors in non-key monitoring areas are moved to key monitoring areas for real-time monitoring. Complete network coverage of the monitoring area is particularly critical for the deployment of wireless sensors, so more effective coverage of key monitoring areas must be guaranteed.

当监测区域出现覆盖漏洞的时候,那么当前传感器网络的节点通信能力、信息的计算和处理能力、网络的生命周期等都会受到相应的影响,因此需要对传感器节点中覆盖空洞的点进行适当的修复,从而保证网络对信息的感知和监测能力不会受到相应的影响。进一步讲覆盖漏洞的出现,会导致其相邻节点的失效,损害整个网络的可靠性,进而导致网络中大量的节点得不到充分的利用。所以,为了保证监测区域内的覆盖面积、服务质量以及网络资源的充分调配,需要对网络中形成的漏洞进行及时有效的修补。When coverage holes appear in the monitoring area, the node communication capability, information calculation and processing capability, and network life cycle of the current sensor network will be affected accordingly. Therefore, it is necessary to properly repair the coverage holes in the sensor nodes to ensure that the network's perception and monitoring capabilities of information will not be affected accordingly. Furthermore, the appearance of coverage holes will cause the failure of its adjacent nodes, damage the reliability of the entire network, and then cause a large number of nodes in the network to not be fully utilized. Therefore, in order to ensure the coverage area, service quality, and sufficient allocation of network resources in the monitoring area, it is necessary to promptly and effectively repair the holes formed in the network.

因此,通过传感器节点的移动策略可实现对传感器网络的有效部署,可为精确的环境监测提供充实的理论依据。Therefore, the effective deployment of sensor networks can be achieved through the mobility strategy of sensor nodes, which can provide a substantial theoretical basis for accurate environmental monitoring.

发明内容Summary of the invention

本发明的目的在于提出一种传感器网络动态部署方法,可为传感器网络的部署提供理论基础,可广泛应用于环境监测、智能家居等领域。The purpose of the present invention is to propose a sensor network dynamic deployment method, which can provide a theoretical basis for the deployment of sensor networks and can be widely used in environmental monitoring, smart home and other fields.

为达到上述目的,本发明提出一种传感器网络动态部署方法,具体包括建立监测区域模型,确定移动节点坐标,确定目标节点坐标以及传感器节点的级联移动四个基本步骤。To achieve the above object, the present invention proposes a sensor network dynamic deployment method, which specifically includes four basic steps: establishing a monitoring area model, determining mobile node coordinates, determining target node coordinates, and cascading movement of sensor nodes.

步骤一,在本发明的一个实施例中,所述建立监测区域模型进一步包括:Step 1: In one embodiment of the present invention, the step of establishing a monitoring area model further comprises:

监测区域包括重点监测区域和非重点监测区域,对监测区域进行离散化处理,将其离散化为M个网格点,其中任意一个网格点pj的坐标为(xj,yj),在监测区域中均匀部署一组具有相同感知半径r的传感器节点,设s={s1,s2,s3…sn}代表该传感器节点的集合,其中任意一个传感器节点si的坐标为(xi,yi);计算si到点pj的欧氏距离定义为:The monitoring area includes key monitoring areas and non-key monitoring areas. The monitoring area is discretized into M grid points, where the coordinates of any grid point p j are (x j , y j ). A group of sensor nodes with the same sensing radius r are uniformly deployed in the monitoring area. Let s = {s 1 , s 2 , s 3 …s n } represent the set of sensor nodes, where the coordinates of any sensor node si are ( xi , yi ); the Euclidean distance from si to point p j is defined as:

Figure GDA0004156585900000021
Figure GDA0004156585900000021

则监测区域中某个网格点pj被传感器节点覆盖的情况为:Then the situation where a grid point pj in the monitoring area is covered by a sensor node is:

Figure GDA0004156585900000022
Figure GDA0004156585900000022

P(si,pj)=1说明该网格点能被传感器节点覆盖。P(s i ,p j )=1 indicates that the grid point can be covered by the sensor node.

步骤二,在本发明的一个实施例中,所述确定移动节点坐标进一步包括:Step 2: In one embodiment of the present invention, determining the coordinates of the mobile node further includes:

移动节点坐标为非重点监测区域中的传感器节点坐标,由于非重点监测区域范围已知,只需在非重点监测区域中寻找相应传感器的节点坐标即可。The coordinates of the mobile node are the coordinates of the sensor nodes in the non-key monitoring area. Since the range of the non-key monitoring area is known, it is only necessary to find the node coordinates of the corresponding sensor in the non-key monitoring area.

步骤三,在本发明的一个实施例中,所述确定目标节点坐标进一步包括:Step 3, in one embodiment of the present invention, determining the target node coordinates further includes:

目标节点坐标为重点监测区域中未被覆盖的网格点坐标,具体步骤如下:The target node coordinates are the coordinates of the uncovered grid points in the key monitoring area. The specific steps are as follows:

(1)根据上一步骤确定移动节点的个数N;(1) Determine the number of mobile nodes N according to the previous step;

(2)在重点监测区域中找出未被传感器覆盖的区域,遍历所有未被传感器覆盖区域的网格节点,以所有节点为圆心,网格的宽度为初始半径,并以网格宽度大小为递增半径向外做同心圆,同心圆的最大半径为传感器的感知半径;(2) Find the area not covered by the sensor in the key monitoring area, traverse all the grid nodes of the area not covered by the sensor, take all the nodes as the center of the circle, and use the width of the grid as the initial radius. Then, make concentric circles outward with the grid width as the increasing radius. The maximum radius of the concentric circles is the sensing radius of the sensor.

(3)同心圆最大环与原先传感器覆盖区域出现重合时,停止同心圆半径的增加,记录此时的圆心位置和半径大小;(3) When the largest ring of the concentric circle overlaps with the original sensor coverage area, stop increasing the radius of the concentric circle and record the center position and radius size at this time;

(4)设Q={q1,q2,...qm}为所有未被传感器覆盖区域的网格节点的同心圆的集合,在集合中找到半径最大的圆,若出现最大半径相同,则取圆心位置横纵坐标之和较小的圆,记录此时圆的节点和半径的大小,并把此时圆所在的区域归并为传感器覆盖区域;(4) Let Q = {q 1 ,q 2 ,...q m } be the set of concentric circles of all grid nodes in the area not covered by the sensor. Find the circle with the largest radius in the set. If the maximum radius is the same, take the circle with the smaller sum of the horizontal and vertical coordinates of the center position. Record the size of the node and radius of the circle at this time, and merge the area where the circle is located into the sensor coverage area.

依次重复(2),(3),(4)步骤,待确定下来的圆的个数等于N时,结束此过程,此时依次确定下来的圆心位置即为目标节点的位置。Repeat steps (2), (3), and (4) in sequence. When the number of circles determined is equal to N, the process ends. At this time, the center positions of the circles determined in sequence are the positions of the target nodes.

步骤四,在本发明的一个实施例中,所述传感器节点的级联移动进一步包括:Step 4: In one embodiment of the present invention, the cascade movement of the sensor nodes further comprises:

在确定了移动节点和目标节点坐标之后,需要决定如何把移动节点移动到目标节点之上;将弗洛伊德算法引入到传感器的级联移动中,将非重点监测区域的传感器节点和重点监测区域中目标节点作为起始点和终点,将其余传感器作为路径算法节点;After determining the coordinates of the mobile node and the target node, it is necessary to decide how to move the mobile node to the target node; introduce the Floyd algorithm into the cascade movement of sensors, take the sensor nodes in the non-key monitoring area and the target node in the key monitoring area as the starting point and the end point, and take the remaining sensors as the path algorithm nodes;

设置一个带权且有向图G=(V,E,C),其中传感器节点集合V,节点连通线路集合E,以及距离邻接矩阵C;传感器节点V中的元素v0,v1,…,vl表示l个传感器节点;节点连通线路集合E由V中节点连接而成,其中元素ek=[vivj],节点vi与节点vj之间相连;C为图G对应的距离邻接矩阵,其元素cij如下式所示:Set up a weighted directed graph G = (V, E, C), where the sensor node set V, the node connection line set E, and the distance adjacency matrix C; the elements v 0 ,v 1 ,…,v l in the sensor node V represent l sensor nodes; the node connection line set E is formed by connecting the nodes in V, where the element e k = [v i v j ], and the node vi is connected to the node v j ; C is the distance adjacency matrix corresponding to the graph G, and its elements c ij are shown as follows:

Figure GDA0004156585900000031
Figure GDA0004156585900000031

通过构建有向网络图模拟传感器网络,通过弗洛伊德算法对已经确定的移动节点和目标节点之间最短路径进行规划;弗洛伊德算法的基本思想是直接在图的带权邻接矩阵中用插入项点的方法依次构造出v个矩阵D(1)、D(2)、…、D(v),使得最后得到的矩阵D(v)成为图的距离矩阵,同时也求出插入点矩阵以得到两点间的最短路径。其中,D(0)的元素

Figure GDA0004156585900000032
表示传感器网络模型中两个相连节点vi和vj之间的距离,用下式表示,By constructing a directed network graph to simulate the sensor network, the shortest path between the determined mobile node and the target node is planned by the Floyd algorithm. The basic idea of the Floyd algorithm is to construct v matrices D (1) , D (2) , ..., D (v) in sequence by inserting item points in the weighted adjacency matrix of the graph, so that the final matrix D (v) becomes the distance matrix of the graph, and the insertion point matrix is also calculated to obtain the shortest path between two points. Among them, the elements of D (0)
Figure GDA0004156585900000032
It represents the distance between two connected nodes vi and vj in the sensor network model, which is expressed as follows:

Figure GDA0004156585900000033
Figure GDA0004156585900000033

D(k)中的元素用下式计算:The elements in D (k) are calculated using the following formula:

Figure GDA0004156585900000034
Figure GDA0004156585900000034

R(k)中的元素用下式计算:The elements in R (k) are calculated using the following formula:

Figure GDA0004156585900000035
Figure GDA0004156585900000035

利用图G的距离邻接矩阵C,经过弗洛伊德算法,求得最短距离矩阵D(n)和最短路径矩阵R(n),R(n)中只保存了移动节点和目标节点之间最短的路径,在这个基础上,选择出最短的级联路径;最终在保证网络生命周期的前提下实现了对重点监测区域的有效部署,提高了网络的监测能力。Using the distance adjacency matrix C of graph G and the Floyd algorithm, we get the shortest distance matrix D (n) and the shortest path matrix R (n) . R (n) only stores the shortest path between the mobile node and the target node. On this basis, we select the shortest cascade path. Finally, we achieve effective deployment of key monitoring areas while ensuring the network life cycle, thereby improving the network's monitoring capabilities.

图2所示为监测区域模型,传感器节点均匀部署在监测区域内,黑色方框包围区域代表重点监测区域,浅灰色方框包围区域代表非重点监测区域。经过级联移动后的网络部署图如图3所示,可以看出,非重点监测区域的传感器移动到了重点监测区域内,大大增加了重点监测区域的覆盖率。图4为采用直接移动和级联移动传感器节点总的移动距离对比图,直接移动即直接将非重点监测区域的节点移动到重点监测区域,不经过中间节点。由图可以看出,传感器节点达到新的平衡时,级联移动总的移动距离要略高于直接移动,这和级联移动涉及到中间节点的移动有关。但对比网络达到平衡所需的时间,级联移动需要23.963s,直接移动需要35.012s,因此,相比较直接移动,级联移动达到平衡点的时间要远远低于直接移动,利用级联移动可以有效减少网络调节的时间。图5为级联移动和直接移动节点剩余能量对比图,由图5可以看出,通过直接移动虽然能够以较少的节点实现对区域的重点覆盖,但移动过后的节点剩余能量较低,不能继续很好的执行水质的监测任务。而通过级联移动,能够使得更多的节点参与进来,共同承担传感器节点移动过程中能量的消耗,当网络达到新的平衡时,节点的剩余能量相对均衡,能够有效的延长网络寿命。Figure 2 shows the monitoring area model. Sensor nodes are evenly deployed in the monitoring area. The area surrounded by black boxes represents the key monitoring area, and the area surrounded by light gray boxes represents the non-key monitoring area. The network deployment diagram after cascade movement is shown in Figure 3. It can be seen that the sensors in the non-key monitoring area are moved to the key monitoring area, which greatly increases the coverage of the key monitoring area. Figure 4 is a comparison of the total moving distance of sensor nodes using direct movement and cascade movement. Direct movement means directly moving the nodes in the non-key monitoring area to the key monitoring area without passing through the intermediate nodes. It can be seen from the figure that when the sensor nodes reach a new balance, the total moving distance of cascade movement is slightly higher than that of direct movement. This is related to the movement of intermediate nodes involved in cascade movement. However, compared with the time required for the network to reach equilibrium, cascade movement takes 23.963s and direct movement takes 35.012s. Therefore, compared with direct movement, the time for cascade movement to reach the equilibrium point is much lower than that of direct movement. Using cascade movement can effectively reduce the time for network adjustment. Figure 5 is a comparison of the residual energy of cascaded and direct mobile nodes. It can be seen from Figure 5 that although direct mobile can achieve key coverage of the area with fewer nodes, the residual energy of the nodes after the move is low and cannot continue to perform the water quality monitoring task well. Cascade mobile can enable more nodes to participate and share the energy consumption during the movement of sensor nodes. When the network reaches a new balance, the residual energy of the nodes is relatively balanced, which can effectively extend the network life.

本发明提出的一种传感器网络动态部署方法,可实现对监测区域的有效监测,为环境的有效监测和综合治理提供充实的理论依据。The present invention provides a sensor network dynamic deployment method, which can realize effective monitoring of the monitoring area and provide a substantial theoretical basis for effective monitoring and comprehensive management of the environment.

附图说明BRIEF DESCRIPTION OF THE DRAWINGS

图1为本发明实施例的一种传感器网络动态部署方法流程图;FIG1 is a flow chart of a sensor network dynamic deployment method according to an embodiment of the present invention;

图2为本发明实施例的监测区域模型;FIG2 is a monitoring area model according to an embodiment of the present invention;

图3为本发明实施例的经过级联移动后的网络部署图;FIG3 is a diagram of a network deployment after cascade movement according to an embodiment of the present invention;

图4为本发明实施例的移动距离随时间变化曲线;FIG4 is a curve showing movement distance versus time according to an embodiment of the present invention;

图5为本发明实施例的节点剩余能量对比图。FIG5 is a diagram comparing the residual energy of nodes according to an embodiment of the present invention.

具体实施方式DETAILED DESCRIPTION

下面详细描述本发明的实施例,所述实施例的示例在附图中示出,其中自始至终相同或类似的标号表示相同或类似的意义。下面所描述的实施例是示例性的,仅用于解释本发明,而不能解释为对本发明的限制。Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar meanings. The embodiments described below are exemplary and are only used to explain the present invention, and cannot be interpreted as limiting the present invention.

本发明是针对环境监测过程中,针对复杂的监测环境,提出的一种传感器网络动态部署方法。The present invention proposes a sensor network dynamic deployment method for complex monitoring environments during environmental monitoring.

为了能够对本发明有更清楚的理解,在此进行简要描述。本发明包括四个基本步骤:步骤一,建立监测区域模型;步骤二,确定移动节点坐标;步骤三,确定目标节点坐标;步骤四,传感器节点的级联移动。In order to have a clearer understanding of the present invention, a brief description is given here. The present invention includes four basic steps: step one, establishing a monitoring area model; step two, determining the coordinates of the mobile node; step three, determining the coordinates of the target node; step four, cascade movement of the sensor node.

具体的,图1所示为本发明实施例的一种传感器网络动态部署方法的流程图,包括以下步骤:Specifically, FIG1 is a flow chart of a sensor network dynamic deployment method according to an embodiment of the present invention, comprising the following steps:

步骤S101,建立监测区域模型。Step S101, establishing a monitoring area model.

在本发明的一个实施例中,监测区域包括重点监测区域和非重点监测区域,对监测区域进行离散化处理,将其离散化为M个网格点,其中任意一个网格点pj的坐标为(xj,yj),在监测区域中均匀部署一组具有相同感知半径r的传感器节点,设s={s1,s2,s3…sn}代表该传感器节点的集合,其中任意一个传感器节点si的坐标为(xi,yi);计算si到点pj的欧氏距离定义为:In one embodiment of the present invention, the monitoring area includes a key monitoring area and a non-key monitoring area. The monitoring area is discretized into M grid points, wherein the coordinates of any grid point p j are (x j , y j ). A group of sensor nodes with the same sensing radius r are uniformly deployed in the monitoring area. Let s = {s 1 , s 2 , s 3 …s n } represent the set of sensor nodes, wherein the coordinates of any sensor node si are ( xi , yi ). The Euclidean distance from si to point p j is defined as:

Figure GDA0004156585900000051
Figure GDA0004156585900000051

则监测区域中某个网格点pj被传感器节点覆盖的情况为:Then the situation where a grid point pj in the monitoring area is covered by a sensor node is:

Figure GDA0004156585900000052
Figure GDA0004156585900000052

P(si,pj)=1说明该网格点能被传感器节点覆盖;P(s i ,p j )=1 indicates that the grid point can be covered by the sensor node;

步骤S102,确定移动节点坐标。Step S102, determining the coordinates of the mobile node.

在本发明的一个实施例中,移动节点坐标为非重点监测区域中的传感器节点坐标,由于非重点监测区域范围已知,只需在非重点监测区域中寻找相应传感器的节点坐标即可。In one embodiment of the present invention, the mobile node coordinates are the sensor node coordinates in the non-key monitoring area. Since the range of the non-key monitoring area is known, it is only necessary to find the node coordinates of the corresponding sensor in the non-key monitoring area.

步骤S103,确定目标节点坐标。Step S103, determining the target node coordinates.

在本发明的一个实施例中,目标节点坐标为重点监测区域中未被覆盖的网格点坐标,具体步骤如下:In one embodiment of the present invention, the target node coordinates are the coordinates of the uncovered grid points in the key monitoring area, and the specific steps are as follows:

(1)根据上一步骤确定移动节点的个数N;(1) Determine the number of mobile nodes N according to the previous step;

(2)在重点监测区域中找出未被传感器覆盖的区域,遍历所有未被传感器覆盖区域的网格节点,以所有节点为圆心,网格的宽度为初始半径,并以网格宽度大小为递增半径向外做同心圆,同心圆的最大半径为传感器的感知半径;(2) Find the area not covered by the sensor in the key monitoring area, traverse all the grid nodes of the area not covered by the sensor, take all the nodes as the center of the circle, and use the width of the grid as the initial radius. Then, make concentric circles outward with the grid width as the increasing radius. The maximum radius of the concentric circles is the sensing radius of the sensor.

(3)同心圆最大环与原先传感器覆盖区域出现重合时,停止同心圆半径的增加,记录此时的圆心位置和半径大小;(3) When the largest ring of the concentric circle overlaps with the original sensor coverage area, stop increasing the radius of the concentric circle and record the center position and radius size at this time;

(4)设Q={q1,q2,...qm}为所有未被传感器覆盖区域的网格节点的同心圆的集合,在集合中找到半径最大的圆,若出现最大半径相同,则取圆心位置横纵坐标之和较小的圆,记录此时圆的节点和半径的大小,并把此时圆所在的区域归并为传感器覆盖区域;(4) Let Q = {q 1 ,q 2 ,...q m } be the set of concentric circles of all grid nodes in the area not covered by the sensor. Find the circle with the largest radius in the set. If the maximum radius is the same, take the circle with the smaller sum of the horizontal and vertical coordinates of the center position. Record the size of the node and radius of the circle at this time, and merge the area where the circle is located into the sensor coverage area.

依次重复(2),(3),(4)步骤,待确定下来的圆的个数等于N时,结束此过程,此时依次确定下来的圆心位置即为目标节点的位置。Repeat steps (2), (3), and (4) in sequence. When the number of circles determined is equal to N, the process ends. At this time, the center positions of the circles determined in sequence are the positions of the target nodes.

步骤S104,传感器节点的级联移动。Step S104: cascade movement of sensor nodes.

在本发明的一个实施例中,在确定了移动节点和目标节点坐标之后,需要决定如何把移动节点移动到目标节点之上;将弗洛伊德算法引入到传感器的级联移动中,将非重点监测区域的传感器节点和重点监测区域中目标节点作为起始点和终点,将其余传感器作为路径算法节点。In one embodiment of the present invention, after determining the coordinates of the mobile node and the target node, it is necessary to decide how to move the mobile node to the target node; the Floyd algorithm is introduced into the cascade movement of the sensor, the sensor nodes in the non-key monitoring area and the target nodes in the key monitoring area are used as the starting point and the end point, and the remaining sensors are used as the path algorithm nodes.

设置一个带权且有向图G=(V,E,C),其中传感器节点集合V,节点连通线路集合E,以及距离邻接矩阵C;传感器节点V中的元素v0,v1,…,vl表示l个传感器节点;节点连通线路集合E由V中节点连接而成,其中元素ek=[vivj],节点vi与节点vj之间相连;C为图G对应的距离邻接矩阵,其元素cij如式(3)所示:Suppose a weighted directed graph G = (V, E, C), where the sensor node set V, the node connection line set E, and the distance adjacency matrix C; the elements v 0 ,v 1 ,…,v l in the sensor node V represent l sensor nodes; the node connection line set E is formed by connecting the nodes in V, where the element e k = [v i v j ], and the node vi is connected to the node v j ; C is the distance adjacency matrix corresponding to the graph G, and its elements c ij are shown in formula (3):

Figure GDA0004156585900000061
Figure GDA0004156585900000061

通过构建有向网络图模拟传感器网络,通过弗洛伊德算法对已经确定的移动节点和目标节点之间最短路径进行规划;弗洛伊德算法的基本思想是直接在图的带权邻接矩阵中用插入项点的方法依次构造出v个矩阵D(1)、D(2)、…、D(v),使得最后得到的矩阵D(v)成为图的距离矩阵,同时也求出插入点矩阵以得到两点间的最短路径。其中,D(0)的元素

Figure GDA0004156585900000062
表示传感器网络模型中两个相连节点vi和vj之间的距离,用公式(4)表示,D(k)、R(k)的计算为公式(5),公式(6):By constructing a directed network graph to simulate the sensor network, the shortest path between the determined mobile node and the target node is planned by the Floyd algorithm. The basic idea of the Floyd algorithm is to construct v matrices D (1) , D (2) , ..., D (v) in sequence by inserting points in the weighted adjacency matrix of the graph, so that the final matrix D (v) becomes the distance matrix of the graph, and the insertion point matrix is also calculated to obtain the shortest path between two points. Among them, the elements of D (0)
Figure GDA0004156585900000062
The distance between two connected nodes v i and v j in the sensor network model is expressed by formula (4). The calculation of D (k) and R (k) is as follows:

Figure GDA0004156585900000063
Figure GDA0004156585900000063

Figure GDA0004156585900000064
Figure GDA0004156585900000064

Figure GDA0004156585900000065
Figure GDA0004156585900000065

利用图G的距离邻接矩阵C,经过弗洛伊德算法,求得最短距离矩阵D(n)和最短路径矩阵R(n),R(n)中只保存了移动节点和目标节点之间最短的路径,在这个基础上,选择出最短的级联路径;最终在保证网络生命周期的前提下实现了对重点监测区域的有效部署,提高了网络的监测能力。Using the distance adjacency matrix C of graph G and the Floyd algorithm, we get the shortest distance matrix D (n) and the shortest path matrix R (n) . R (n) only stores the shortest path between the mobile node and the target node. On this basis, we select the shortest cascade path. Finally, we achieve effective deployment of key monitoring areas while ensuring the network life cycle, thereby improving the network's monitoring capabilities.

通过本发明提出的一种传感器网络动态部署方法,可实现对传感器网络的优化部署,为环境的有效监测和综合治理提供充实的理论依据。The sensor network dynamic deployment method proposed in the present invention can realize the optimized deployment of the sensor network and provide a substantial theoretical basis for the effective monitoring and comprehensive management of the environment.

最后应说明的是:以上实施例仅用以说明本发明的技术方案,而非对其限制。本领域的普通技术人员应当理解:其依然可以对前述实施例所记载的技术方案进行修改,或对其中部分技术特征进行等同替换,而这些修改或者替换,并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围,本发明的范围由所附权利要求及其等同限定。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them. Those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some of the technical features therein by equivalents, and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. The scope of the present invention is defined by the attached claims and their equivalents.

Claims (1)

1. A dynamic deployment method of a sensor network is characterized in that: the method comprises the following steps of establishing a monitoring area model, determining a mobile node coordinate, determining a target node coordinate and cascading movement of a sensor node;
the establishing the monitoring area model comprises the following steps: the monitoring area comprises a key monitoring area and a non-key monitoring area, discretization processing is carried out on the monitoring area, and the monitoring area is discretized into M grid points, wherein any grid point p j Is (x) j ,y j ) Uniformly deploying a group of sensor nodes with the same sensing radius r in a monitoring area, and setting s= { s 1 ,s 2 ,s 3 …s n And represents the set of sensor nodes, any one of which is s i Is (x) i ,y i ) The method comprises the steps of carrying out a first treatment on the surface of the Calculation s i To point p j The euclidean distance of (2) is defined as:
Figure FDA0004156585890000011
then a certain grid point p in the area is monitored j The cases covered by the sensor nodes are:
Figure FDA0004156585890000012
P(s i ,p j ) =1 indicates that the grid point can be covered by a sensor node;
the determining mobile node coordinates includes: the mobile node coordinates are sensor node coordinates in the non-key monitoring area, and as the range of the non-key monitoring area is known, the node coordinates of the corresponding sensor are only needed to be searched in the non-key monitoring area;
the determining the target node coordinates includes: the target node coordinates are grid point coordinates which are not covered in the key monitoring area; the method specifically comprises the following steps:
(1) Determining the number N of mobile nodes;
(2) Finding out areas which are not covered by the sensor in the key monitoring areas, traversing all grid nodes of the areas which are not covered by the sensor, taking all nodes as circle centers, taking the width of the grid as an initial radius, and taking the width of the grid as an incremental radius to make concentric circles outwards, wherein the maximum radius of the concentric circles is the sensing radius of the sensor;
(3) Stopping the increase of the radius of the concentric circle when the maximum ring of the concentric circle is overlapped with the coverage area of the original sensor, and recording the position of the circle center and the radius at the moment;
(4) Let q= { Q 1 ,q 2 ,...q m The method comprises finding out the circle with the largest radius from the set of concentric circles of all grid nodes not covered by the sensor, if the largest radius is the same, taking the circle with smaller sum of the horizontal and vertical coordinates of the circle center position,
recording the node and radius of the circle at the moment, and merging the area where the circle is located into a sensor coverage area;
(5) Sequentially repeating the steps (2), (3) and (4), ending the process when the number of circles to be determined is equal to N, and sequentially determining the positions of the centers of circles to be determined to be the positions of the target nodes;
the cascading movement of the sensor nodes includes: after determining the coordinates of the mobile node and the target node, a decision is made as to how to move the mobile node over the target node; introducing a Floyd algorithm into cascade movement of the sensors, taking sensor nodes of non-key monitoring areas and target nodes in key monitoring areas as starting points and end points, and taking the rest sensors as path algorithm nodes;
setting a weighted directed graph g= (V, E, C), wherein the set of sensor nodes V, the set of node communication lines E, and the distance adjacency matrix C; element V in sensor node V 0 ,v 1 ,…,v l Representing l sensor nodes; the node communication line set E is formed by connecting nodes in V, wherein the element E k =[v i v j ]Node v i And node v j Are connected with each other; c is the distance adjacency matrix corresponding to the graph G, and the element C thereof ij As shown in formula (3):
Figure FDA0004156585890000021
simulating a sensor network by constructing a directed network diagram, and planning the determined shortest path between the mobile node and the target node by using a Fluedel algorithm; the basic idea of the Floyd algorithm is to construct v matrices D in turn by inserting entry points directly in weighted adjacency matrices of the graph (1) 、D (2) 、…、D (v) So that the resulting matrix D (v) Forming a distance matrix of the graph, and simultaneously, solving an insertion point matrix to obtain the shortest path between two points; wherein D is (0) Elements of (2)
Figure FDA0004156585890000025
Representing two connected nodes v in a sensor network model i And v j The distance between them is expressed by the formula (4), D (k) 、R (k) The calculation of the element in the formula (5), the formula (6):
Figure FDA0004156585890000022
Figure FDA0004156585890000023
Figure FDA0004156585890000024
obtaining a shortest distance matrix D by using a distance adjacent matrix C of the graph G and through a Floride algorithm (n) And a shortest path matrix R (n) ,R (n) Only the shortest path between the mobile node and the target node is saved, and on the basis, the shortest cascade path is selected; finally, on the premise of ensuring the life cycle of the network, the effective deployment of the heavy point monitoring area is realized, and the network is improvedMonitoring the capacity.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102280826A (en) * 2011-07-30 2011-12-14 山东鲁能智能技术有限公司 Intelligent robot inspection system and intelligent robot inspection method for transformer station
WO2016082254A1 (en) * 2014-11-27 2016-06-02 中国科学院沈阳自动化研究所 Robust coverage method for relay nodes in double-layer structure wireless sensor network
CN107613502A (en) * 2017-09-07 2018-01-19 广东工业大学 A sensor network irregular area node positioning method and device
CN108064047A (en) * 2018-01-17 2018-05-22 北京工商大学 A kind of water quality sensor network optimization dispositions method based on population
CN109874160A (en) * 2019-03-06 2019-06-11 安徽建筑大学 Routing method based on wireless sensor network node reputation evaluation
CN109922478A (en) * 2019-01-14 2019-06-21 北京工商大学 A kind of water quality sensor network optimization dispositions method based on improvement cuckoo algorithm

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR3064857B1 (en) * 2017-04-04 2020-07-03 Commissariat A L'energie Atomique Et Aux Energies Alternatives SECURE END-TO-END COMMUNICATION FOR MOBILE SENSOR IN AN IOT NETWORK

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102280826A (en) * 2011-07-30 2011-12-14 山东鲁能智能技术有限公司 Intelligent robot inspection system and intelligent robot inspection method for transformer station
WO2016082254A1 (en) * 2014-11-27 2016-06-02 中国科学院沈阳自动化研究所 Robust coverage method for relay nodes in double-layer structure wireless sensor network
CN107613502A (en) * 2017-09-07 2018-01-19 广东工业大学 A sensor network irregular area node positioning method and device
CN108064047A (en) * 2018-01-17 2018-05-22 北京工商大学 A kind of water quality sensor network optimization dispositions method based on population
CN109922478A (en) * 2019-01-14 2019-06-21 北京工商大学 A kind of water quality sensor network optimization dispositions method based on improvement cuckoo algorithm
CN109874160A (en) * 2019-03-06 2019-06-11 安徽建筑大学 Routing method based on wireless sensor network node reputation evaluation

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Nessrine Chakchouk.A Survey on Opportunistic Routing in Wireless Communication Networks.《IEEE》.2015,全文. *
宋志强 ; 方武 ; 卢爱红 ; .基于虚拟力的混合传感器节点覆盖问题研究.仪表技术与传感器.2017,(09),全文. *
郭新明 ; 张瑾 ; 陈伟 ; 李康 ; .基于Voronoi图的无线传感器网络栅栏覆盖算法设计.计算技术与自动化.2020,(01),全文. *

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