CN118102325A - Three-dimensional directed sensor network coverage control method - Google Patents
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Abstract
The invention discloses a three-dimensional directional sensor network coverage control method, which comprises the steps of randomly initializing the positions and the perception directions of nodes of a directional sensor network, and calculating the resultant force of virtual forces born by all nodes in the directional sensor network in a three-dimensional monitoring area; dividing the sensing direction of each node into a horizontal deflection angle and a vertical pitch angle, and obtaining an optimal solution of the node sensing direction by adopting VPESOA algorithm; and adjusting the perception direction of the nodes according to the optimal solution, and constructing a node dormancy set by utilizing the hop count from the cluster head node to other nodes in each cluster and the perception contribution rate of each node, so as to schedule the working states of each node. According to the invention, through an improved aigrette algorithm, virtual force among nodes and redundant node dormancy are comprehensively considered, the sensing direction of the nodes and the node dormancy set are optimized, the monitoring capability of the directed sensor network can be effectively improved, and the service life of the network is prolonged.
Description
Technical Field
The invention belongs to the technical field of wireless sensor networks, and particularly relates to a three-dimensional directional sensor network coverage control method.
Background
With the increasing complexity of monitoring environments, people have generated higher pursuit of more comprehensive, diversified and accurate data information, and data acquisition and transmission tasks are generally performed by using sensor nodes with directional sensing functions. Because the nodes in the directed sensor network have rotatability, people can flexibly design strategies according to different actual task demands and adjust the perception direction of the nodes, thereby realizing high-efficiency real-time monitoring of a monitoring area. This flexibility makes the directed sensor network an ideal choice for adapting to complex monitoring requirements.
In the directed sensor network, the coverage control method plays a vital role in indexes such as coverage rate, service life, stability and the like of the network. Through a proper coverage control method, the performance of the network can be effectively improved, and the monitoring area is ensured to be monitored comprehensively and accurately. Therefore, research on a proper coverage control method has important significance for improving the monitoring effect of the directional sensor network.
The prior art provides a directional sensor network coverage control scheme in a two-dimensional environment, wherein a cosine search function is introduced in a coverage enhancement stage to improve a gravity search algorithm so as to obtain an optimal node perception direction in the two-dimensional environment, and a node sleep strategy based on a security set is introduced in a node sleep stage so as to reduce network redundancy rate. However, the research of the method is based on a two-dimensional environment, the node perception model has the limitation, and the influence of cluster head nodes on the efficiency and energy consumption of the directed sensor network is not considered.
The prior art also provides a directional sensor network coverage control scheme in a three-dimensional environment, and the scheme can improve the perception range and coverage rate of nodes in the three-dimensional environment. But this approach does not take into account the impact of redundant node dormancy after node coverage enhancement on network efficiency and lifetime.
Therefore, a scheme is needed that can comprehensively consider the node perception direction of the directional sensor network node coverage enhancement stage and the work scheduling of the redundant nodes and cluster head nodes in the node dormancy stage, and improve the monitoring capability and service life of the directional sensor network.
The information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention aims to provide a three-dimensional directional sensor network coverage control method, which adds a parallel stage to an aigrette algorithm (ESOA) by utilizing Virtual Force (VFA) and Particle Swarm (PSO), searches an optimal pitch angle and an optimal horizontal angle of a node perception direction of a directional sensor network by using an improved aigrette algorithm (VPESOA), and designs a working schedule of the node based on the perception contribution rate of each node, thereby overcoming the defects in the prior art.
In order to achieve the above purpose, the present invention provides a three-dimensional directional sensor network coverage control method, which comprises the following steps:
S01, initializing each node of the directed sensor network, and randomly deploying nodes of N directed sensor networks and node perception directions of M directed sensor networks on a three-dimensional monitoring area, wherein the position sets of the nodes of the N directed sensor networks are as follows The node perception direction set of M directed sensor networks is thatTaking node perception direction sets of the M directional sensor networks as a population;
S02, after the nodes are initialized in the step S01, calculating resultant force of virtual forces born by each node in the three-dimensional monitoring area, and obtaining an optimal solution of the node perception direction by utilizing VPESOA algorithm;
s03, adjusting the sensing direction of the nodes by utilizing the optimal solution of the node sensing direction obtained in the step S02, and calculating the sensing contribution rate of each node;
s04, clustering the nodes by adopting a k-means algorithm, and constructing a node dormancy set by using the perceived contribution rate of the nodes and the sum of hops from the cluster head node to other nodes in each cluster, so as to execute a node dormancy strategy.
Preferably, in the technical solution, in step S02, the step of obtaining an optimal solution of the sensing direction of the directional sensor network node is: 2.1, dividing the perception direction of each node in the directional sensor network into a horizontal deflection angle alpha and a vertical pitch angle beta, namelyCalculating the coverage rate of the population by using a perception model of the directional sensor, and taking the coverage rate of the population as an adaptability value of the population;
the sensing model of the directed sensor network node j is as follows:
，
Wherein, As a function of the distance of node j from centroid point q,As a function of the horizontal deflection angle between node j and centroid point q,As a function of the vertical pitch angle between node j and centroid point q,An obstacle perception function between a node j and a centroid point q;
，
，
，
，
Wherein, For the Euclidean distance of node j from centroid point q, R j is the perceived radius of node j,Is the angle at which the principal perceived direction of node j is projected on the same horizontal plane as the line between node j and centroid point q,Half of the horizontal view constraint for node j,Is the angle at which the principal perceived direction of node j is projected on the same vertical plane as the line between node j and centroid point q,For node j, the pitch perspective constraint is half (the horizontal perspective constraint and pitch perspective constraint of the node depend on their physical properties, e.g., assuming a radar sensor is mounted in a fixed location, its horizontal perspective constraint depends on the width of the radar beam; if the width of the radar beam is 30 degrees, then the radar can perceive targets within 30 degrees in the horizontal direction, and targets outside this range cannot be detected by the radar);
coverage rate is expressed by the ratio of the total number of centroid points in the whole three-dimensional monitoring area to the total number of centroid points covered by the centroid points in the directed sensor network:
，
，
Wherein, For the centroid point q in the three-dimensional monitoring area to be covered by the sensing area of node j,The method comprises the steps that the number of centroid points in a directional sensor network is covered, N is the number of nodes, and Q is the total number of centroid points in a three-dimensional monitoring area;
2.2, calculating resultant force of virtual forces born by each node in the three-dimensional monitoring area, keeping the horizontal deflection angle of each node unchanged, and updating the vertical pitch angle of each node by adopting a vertical pitch angle optimization stage of VPESOA algorithm; keeping the vertical pitch angle of each node unchanged, and updating the horizontal deflection angle of each node by adopting a horizontal deflection angle optimization stage of VPESOA algorithm;
the virtual force of the node L received by the node j is:
，
Wherein, For the euclidean distance between node j and node L,The perceived radius of node j;
vertical pitch optimization stage of VPESOA algorithm:
，
，
Wherein, The vertical pitch angle of the jth node in the ith population of individuals after the t-th iteration is set; The angle adjustment quantity of the jth node in the ith population of individuals after the t-th iteration is set; c 1、c2 is an acceleration factor, c 3 is an acceleration factor that adjusts the virtual force effect; For the best vertical pitch angle experienced by the jth node in the ith population of individuals, Is the global optimal vertical pitch angle; r 1、r2、r3 is a random number between [0,1 ]; the adjustment quantity of the jth node in the ith population of individuals under the action of vertical virtual force is obtained;
，
，
，
Wherein, Is that，For the resultant of the virtual forces experienced by node j,The included angle formed between the virtual force applied by the node j and the component force in the horizontal direction is m, and the number of the nodes in the 2R j near the node j is m; As the weight coefficient of the light-emitting diode, Is the minimum coefficient of inertia of 0.4,For a maximum inertia coefficient of 0.9, t is the number of iterations at this time,The maximum iteration number; is the virtual force of node L to which node j is subjected, The sensing radius of the node j;
horizontal deflection angle optimization stage in VPESOA algorithm:
each iteration updates three horizontal deflection angles for each node 、、：
，
，
，
Wherein,Is the horizontal deflection angle of the jth node in the ith population of individuals after the t-th iteration,For the maximum side length of the three-dimensional monitoring area,Horizontal deflection angle of jth node in ith population of individuals under horizontal virtual forceIs used for adjusting the quantity factor of the (a),Is thatIs a random number of (a) and (b),Is a random number between 0,1,、Is a random number between 0, 0.5),For the optimal horizontal deflection angle experienced by the jth node in the ith population of individuals,For a global optimum horizontal deflection angle,The adjustment quantity of the jth node in the ith population of individuals under the action of horizontal virtual force is obtained;
，
，
，
，
Wherein, For the adjustment of the jth node in the ith population of individuals towards the optimal horizontal deflection angle direction experienced by the jth node,Towards the jth node in the ith population of individualsThe adjustment amount of the horizontal deflection angle direction,For the adjustment of the jth node in the ith population of individuals toward the global optimum horizontal bias angle direction,Towards the jth node in the ith population of individualsThe adjustment amount of the horizontal deflection angle direction,、、Respectively horizontal deflection angles、、A corresponding fitness value;
2.3, calculating the fitness value of the population by using the vertical pitch angle after each update of the nodes, comparing the updated fitness value with the fitness value before the update, and selecting the vertical pitch angle corresponding to the larger fitness value as the vertical pitch angle of the current node; calculating three fitness values corresponding to the population by using three horizontal deflection angles after each updating of the nodes, comparing the three fitness values after updating with the fitness values before updating respectively, and selecting the horizontal deflection angle corresponding to the largest fitness value in the three fitness values as the horizontal deflection angle of the current node only when the three fitness values after updating are all larger than the fitness value before updating;
And 2.4, repeating the steps 2.2-2.3 for iterative updating, and outputting an optimal vertical pitch angle and an optimal horizontal deflection angle set after the iteration is completed.
Preferably, in the technical solution, in step S03, the perceived contribution rate of the node in the directional sensor network is:
，
Wherein, For the number of all centroid points perceived by node j in the directed sensor network,Representing the number of centroid points perceived by node j and not perceived by other nodes in the directed sensor network.
Preferably, in the technical solution, in step S04, the step of constructing the dormancy set of the directed sensor network node includes: 4.1, randomly selecting K nodes from the nodes of the N directional sensor networks as initial cluster head nodes;
4.2, for the remaining N-K nodes, calculating the distances between each node and K initial cluster head nodes, and respectively classifying the remaining N-K nodes into the cluster where the initial cluster head node with the smallest distance is located;
4.3, for each cluster, updating the cluster head node of each cluster; all nodes in each cluster are enabled to serve as cluster head nodes in the cluster where the nodes are located in turn, the sum of the numbers of hops from the nodes to other nodes in the cluster is calculated, and the node with the smallest sum of the numbers of hops is selected as the final cluster head node of the cluster;
4.4, calculating the priority score of each node according to the perceived contribution rate of each node and the sum of hops from each node to other nodes in the cluster where each node is located Setting a sleep thresholdNodes with priority scores exceeding the dormancy threshold are classified as active nodes, nodes with priority scores below the dormancy threshold are regarded as redundant nodes, and the redundant nodes are classified as a dormancy node setIn (a) and (b); the priority score is:
，
Wherein, As the sum of the hops of node j to the other individual nodes in the cluster where it is located,To adjust the parameters, setThe purpose of (1) is to better identify redundant nodes in order to balance the perceived contribution rate and hop count of the nodes and the influence on node priority scores;
Specifically: some nodes have larger perceived contribution rate than the dormancy threshold, but have larger hops to reach the cluster head nodes in the cluster, and if the nodes continue to work without dormancy, the network operation and the service life are negatively influenced; while some nodes have a perceived contribution rate slightly lower than the sleep threshold, the number of hops reaching the cluster head node is small, so that the nodes can continue to work to play roles of backup and fault tolerance in the data transmission process, and the stability and reliability of the network can be positively influenced.
Compared with the prior art, the invention has the following beneficial effects:
According to the invention, the sensing direction of the nodes of the directed sensor network is decomposed into a horizontal deflection angle and a vertical pitch angle through an improved aigrette algorithm (VPESOA), and the horizontal deflection angle and the vertical pitch angle are optimized; in the optimization stage of the horizontal deflection angle and the vertical pitch angle of the node, the relation between the virtual force born by the node and the node adjustment angle is introduced, the defect that the traditional algorithm is easy to fall into local optimization is overcome, and the optimization time of the node angle is effectively shortened; according to the invention, after the node perception direction is optimized, the influence of the redundant node and the cluster head node on the directed sensor network is considered, the redundant node dormancy strategy is designed, and the network operation efficiency and the network service life are improved.
Drawings
FIG. 1 is a flow chart of a three-dimensional directional sensor network coverage control method of the present invention;
FIG. 2 is a schematic diagram of a directed sensor network node perception model according to the present invention;
Fig. 3 is a schematic diagram of a directed sensor network node according to the present invention subjected to a virtual force.
Detailed Description
The following detailed description of specific embodiments of the invention is, but it should be understood that the invention is not limited to specific embodiments.
Throughout the specification and claims, unless explicitly stated otherwise, the term "comprise" or variations thereof such as "comprises" or "comprising", etc. will be understood to include the stated element or component without excluding other elements or components.
As shown in fig. 1, the coverage control method of the three-dimensional directional sensor network comprises the following steps:
S01, initializing each node of the directed sensor network, and randomly deploying node positions of N directed sensor networks and node perception directions of M directed sensor networks on a three-dimensional monitoring area, wherein node positions of the N directed sensor networks are collected as follows The node perception direction set of M directed sensor networks is thatTaking node perception direction sets of the M directional sensor networks as a population;
for example, in a three-dimensional monitoring region 500X 500, the number of initialization populations is 50, the number of nodes is 100, the node perception radius is 60, the maximum iteration number is 100, dispersing the three-dimensional monitoring area into cubes with the same size and side length of 1, wherein the mass center of each cube is a target mass center point q;
S02, after the nodes are initialized in the step S01, calculating resultant force of virtual forces born by each node in the three-dimensional monitoring area, and obtaining an optimal solution of the node perception direction by utilizing VPESOA algorithm;
2.1, dividing the perception direction of each node in the directional sensor network into a horizontal deflection angle alpha and a vertical pitch angle beta, namely Calculating the coverage rate of the population by using a perception model of the directional sensor, and taking the coverage rate of the population as an adaptability value of the population;
as shown in FIG. 2, wherein the horizontal deflection angle of the node j is Vertical pitch angle ofThe perception model of the directed sensor node j is:
，
Wherein, As a function of the distance of node j from centroid point q,As a function of the horizontal deflection angle between node j and centroid point q,As a function of the vertical pitch angle between node j and centroid point q,An obstacle perception function between a node j and a centroid point q;
，
，
，
，
Wherein, For the Euclidean distance of node j from centroid point q, R j is the perceived radius of node j,Is the angle at which the principal perceived direction of node j is projected on the same horizontal plane as the line between node j and centroid point q,Half of the horizontal view constraint for node j,Is the angle at which the principal perceived direction of node j is projected on the same vertical plane as the line between node j and centroid point q,For node j, the pitch perspective constraint is half (the horizontal perspective constraint and pitch perspective constraint of the node depend on their physical properties, e.g., assuming a radar sensor is mounted in a fixed location, its horizontal perspective constraint depends on the width of the radar beam; if the width of the radar beam is 30 degrees, then the radar can perceive targets within 30 degrees in the horizontal direction, and targets outside this range cannot be detected by the radar);
coverage rate is expressed by the ratio of the total number of centroid points in the whole three-dimensional monitoring area to the total number of centroid points covered by the centroid points in the directed sensor network:
，
，
Wherein, For the centroid point q in the three-dimensional monitoring area to be covered by the sensing area of node j,The method comprises the steps that the number of centroid points in a directional sensor network is covered, N is the number of nodes, and Q is the total number of centroid points in a three-dimensional monitoring area;
2.2, calculating resultant force of virtual forces born by each node in the three-dimensional monitoring area, keeping the horizontal deflection angle of each node unchanged, and updating the vertical pitch angle of each node by adopting a vertical pitch angle optimization stage of VPESOA algorithm; keeping the vertical pitch angle of each node unchanged, and updating the horizontal deflection angle of each node by adopting a horizontal deflection angle optimization stage of VPESOA algorithm;
the virtual force of the node L received by the node j is:
，
Wherein, For the euclidean distance between node j and node L,The perceived radius of node j; as shown in fig. 3, node j receives a virtual force F jL of node L, the direction of which is directed from the centroid of the sensing region of node L to the centroid of the sensing region of node j, virtual force F jL is decomposed by triangle rule into a horizontal virtual force F jLxy and a vertical virtual force F jLz,,Θ can be calculated; for example, when the two centroid points are known as a (1, 2, 3) and B (4, 5, 6), the vector ab= (3, 3) is calculated first, then the projection ab_h= (3, 0) of the vector AB on the horizontal plane is calculated, and the angle θ is calculated:
，
，
，
；
vertical pitch optimization stage of VPESOA algorithm:
，
，
Wherein, The vertical pitch angle of the jth node in the ith population of individuals after the t-th iteration is set; The angle adjustment quantity of the jth node in the ith population of individuals after the t-th iteration is set; c 1、c2 is an acceleration factor, c 3 is an acceleration factor that adjusts the virtual force effect; For the best vertical pitch angle experienced by the jth node in the ith population of individuals, Is the global optimal vertical pitch angle; r 1、r2、r3 is a random number between [0,1 ]; the adjustment quantity of the jth node in the ith population of individuals under the action of vertical virtual force is obtained;
，
，
，
Wherein, Is that，For the resultant of the virtual forces experienced by node j,The included angle formed between the virtual force applied by the node j and the component force in the horizontal direction is m, and the number of the nodes in the 2R j near the node j is m; As the weight coefficient of the light-emitting diode, Is the minimum coefficient of inertia of 0.4,For a maximum inertia coefficient of 0.9, t is the number of iterations at this time,The maximum iteration number; is the virtual force of node L to which node j is subjected, The sensing radius of the node j;
horizontal deflection angle optimization stage in VPESOA algorithm:
each iteration updates three horizontal deflection angles for each node 、、：
，
，
，
Wherein,Is the horizontal deflection angle of the jth node in the ith population of individuals after the t-th iteration,For the maximum side length of the three-dimensional monitoring area,Horizontal deflection angle of jth node in ith population of individuals under horizontal virtual forceIs used for adjusting the quantity factor of the (a),Is thatIs a random number of (a) and (b),Is a random number between 0,1,、Is a random number between 0, 0.5),For the optimal horizontal deflection angle experienced by the jth node in the ith population of individuals,For a global optimum horizontal deflection angle,The adjustment quantity of the jth node in the ith population of individuals under the action of horizontal virtual force is obtained;
，
，
，
，
Wherein, For the adjustment of the jth node in the ith population of individuals towards the optimal horizontal deflection angle direction experienced by the jth node,Towards the jth node in the ith population of individualsThe adjustment amount of the horizontal deflection angle direction,For the adjustment of the jth node in the ith population of individuals toward the global optimum horizontal bias angle direction,Towards the jth node in the ith population of individualsThe adjustment amount of the horizontal deflection angle direction,、、Respectively horizontal deflection angles、、A corresponding fitness value;
2.3, calculating the fitness value of the population by using the vertical pitch angle after each update of the nodes, comparing the updated fitness value with the fitness value before the update, and selecting the vertical pitch angle corresponding to the larger fitness value as the vertical pitch angle of the current node; calculating three fitness values corresponding to the population by using three horizontal deflection angles after each updating of the nodes, comparing the three fitness values after updating with the fitness values before updating respectively, and selecting the horizontal deflection angle corresponding to the largest fitness value in the three fitness values as the horizontal deflection angle of the current node only when the three fitness values after updating are all larger than the fitness value before updating;
2.4, repeating the steps 2.2-2.3 for iterative updating, and outputting an optimal vertical pitch angle and an optimal horizontal deflection angle set after the iteration is completed;
s03, adjusting the sensing direction of the nodes by utilizing the optimal solution of the node sensing direction obtained in the step S02, and calculating the sensing contribution rate of each node; the perceived contribution rate of the nodes in the directed sensor network is as follows:
，
Wherein, For the number of all centroid points perceived by node j in the directed sensor network,Representing a number of centroid points perceived by node j and not perceived by other nodes in the directed sensor network;
S04, clustering the nodes by adopting a k-means algorithm, and constructing a node dormancy set by using the perceived contribution rate of the nodes and the sum of hops from the cluster head node to other nodes in each cluster so as to execute a node dormancy strategy;
4.1, randomly selecting K nodes from the nodes of the N directional sensor networks as initial cluster head nodes;
4.2, for the remaining N-K nodes, calculating the distances between each node and K initial cluster head nodes, and respectively classifying the remaining N-K nodes into the cluster where the initial cluster head node with the smallest distance is located;
4.3, for each cluster, updating the cluster head node of each cluster; all nodes in each cluster are enabled to serve as cluster head nodes in the cluster where the nodes are located in turn, the sum of the numbers of hops from the nodes to other nodes in the cluster is calculated, and the node with the smallest sum of the numbers of hops is selected as the final cluster head node of the cluster;
For example, assuming that there are 4 nodes within 1 cluster, the direct communication distance of node 1 to node 2 is 1 hop, node 1 to node 3 is 2 hop, and node 1 to node 4 is 3 hop, then when node 1 serves as a cluster head node, the sum of the hop numbers of node 1 is 6 (1+2+3);
4.4, calculating the priority score of each node according to the perceived contribution rate of each node and the sum of hops from each node to other nodes in the cluster where each node is located Setting a sleep thresholdNodes with priority scores exceeding the dormancy threshold are classified as active nodes, nodes with priority scores below the dormancy threshold are regarded as redundant nodes, and the redundant nodes are classified as a dormancy node setIn (a) and (b); the priority score is:
，
Wherein, As the sum of the hops of node j to the other individual nodes in the cluster where it is located,To adjust the parameters, setThe purpose of (1) is to better identify redundant nodes in order to balance the perceived contribution rate and hop count of the nodes and the influence on node priority scores;
Specifically: some nodes have larger perceived contribution rate than the dormancy threshold, but have larger hops to reach the cluster head nodes in the cluster, and if the nodes continue to work without dormancy, the network operation and the service life are negatively influenced; while some nodes have a perceived contribution rate slightly lower than the sleep threshold, the number of hops reaching the cluster head node is small, so that the nodes can continue to work to play roles of backup and fault tolerance in the data transmission process, and the stability and reliability of the network can be positively influenced.
The foregoing descriptions of specific exemplary embodiments of the present invention are presented for purposes of illustration and description. It is not intended to limit the invention to the precise form disclosed, and obviously many modifications and variations are possible in light of the above teaching. The exemplary embodiments were chosen and described in order to explain the specific principles of the invention and its practical application to thereby enable one skilled in the art to make and utilize the invention in various exemplary embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the claims and their equivalents.
Claims (5)
1. The three-dimensional directional sensor network coverage control method comprises the following steps:
S01, initializing each node of the directed sensor network, and randomly deploying nodes of N directed sensor networks and node perception directions of M directed sensor networks on a three-dimensional monitoring area, wherein the position sets of the nodes of the N directed sensor networks are as follows The node perception direction set of M directed sensor networks is thatTaking node perception direction sets of the M directional sensor networks as a population;
S02, after the nodes are initialized in the step S01, calculating resultant force of virtual forces born by each node in the three-dimensional monitoring area, and obtaining an optimal solution of the node perception direction by utilizing VPESOA algorithm;
s03, adjusting the sensing direction of the nodes by utilizing the optimal solution of the node sensing direction obtained in the step S02, and calculating the sensing contribution rate of each node;
s04, clustering the nodes by adopting a k-means algorithm, and constructing a node dormancy set by using the perceived contribution rate of the nodes and the sum of hops from the cluster head node to other nodes in each cluster, so as to execute a node dormancy strategy.
2. The three-dimensional directional sensor network coverage control method according to claim 1, characterized by comprising the steps of: in step S02, the step of obtaining the optimal solution of the node perception direction includes: 2.1, dividing the perception direction of each node in the directional sensor network into a horizontal deflection angle alpha and a vertical pitch angle beta, namelyCalculating the coverage rate of the population by using a perception model of the directional sensor, and taking the coverage rate of the population as an adaptability value of the population;
2.2, calculating resultant force of virtual forces born by each node in the three-dimensional monitoring area, keeping the horizontal deflection angle of each node unchanged, and updating the vertical pitch angle of each node by adopting a vertical pitch angle optimization stage of VPESOA algorithm; keeping the vertical pitch angle of each node unchanged, and updating the horizontal deflection angle of each node by adopting a horizontal deflection angle optimization stage of VPESOA algorithm;
2.3, calculating the fitness value of the population by using the vertical pitch angle after each update of the nodes, comparing the updated fitness value with the fitness value before the update, and selecting the vertical pitch angle corresponding to the larger fitness value as the vertical pitch angle of the current node; calculating three fitness values corresponding to the population by using three horizontal deflection angles after each updating of the nodes, comparing the three fitness values after updating with the fitness values before updating respectively, and selecting the horizontal deflection angle corresponding to the largest fitness value in the three fitness values as the horizontal deflection angle of the current node only when the three fitness values after updating are all larger than the fitness value before updating;
And 2.4, repeating the steps 2.2-2.3 for iterative updating, and outputting an optimal vertical pitch angle and an optimal horizontal deflection angle set after the iteration is completed.
3. The three-dimensional directional sensor network coverage control method according to claim 2, characterized by comprising the steps of: vertical pitch angle optimization stage based on VPESOA algorithm:
，
，
Wherein, The vertical pitch angle of the jth node in the ith population of individuals after the t-th iteration is set; /(I)The angle adjustment quantity of the jth node in the ith population of individuals after the t-th iteration is set; c 1、c2 is an acceleration factor, c 3 is an acceleration factor that adjusts the virtual force effect; /(I)For the optimal vertical pitch angle experienced by the jth node in the ith population of individuals,/>Is the global optimal vertical pitch angle; r 1、r2、r3 is a random number between [0,1 ]; /(I)The adjustment quantity of the jth node in the ith population of individuals under the action of vertical virtual force is obtained;
，
，
，
Wherein, For/>，/>For the resultant of virtual forces experienced by node j,/>The included angle formed between the virtual force applied by the node j and the component force in the horizontal direction is m, and the number of the nodes in the 2R j near the node j is m; /(I)Is a weight coefficient,/>Is 0.4 of minimum inertia coefficient,/>Is 0.9 as the maximum inertia coefficient, t is the iteration number at the moment,/>The maximum iteration number; is the virtual force of node L to which node j is subjected,/> The sensing radius of the node j;
horizontal deflection angle optimization stage in VPESOA algorithm:
each iteration updates three horizontal deflection angles for each node 、/>、/>：
，
，
，
Wherein,For the horizontal deflection angle of the jth node in the ith population of individuals after the t iteration,/>Is the maximum side length of the three-dimensional monitoring area,/>For the j-th node in the i-th population of individuals under the action of horizontal virtual forceAdjustment quantity factor of/>For/>Random number of/>Is a random number between [0,1 ]/>、/>Is a random number between [0, 0.5)/>For the optimal horizontal deflection angle experienced by the jth node in the ith population of individuals,/>For global optimal horizontal deflection angle,/>The adjustment quantity of the jth node in the ith population of individuals under the action of horizontal virtual force is obtained;
，
，
，
，
Wherein, For the adjustment quantity of the jth node in the ith population of individuals towards the self-experienced optimal horizontal deflection angle direction,/>For the j-th node orientation/>, in the i-th population of individualsAdjustment of the horizontal deflection angle direction,/>For the adjustment of the jth node in the ith population of individuals towards the global optimal horizontal bias angle direction,/>For the j-th node orientation/>, in the i-th population of individualsAdjustment of the horizontal deflection angle direction,/>、/>、/>Respectively horizontal deflection angles、/>、/>Corresponding fitness value.
4. The three-dimensional directional sensor network coverage control method according to claim 1, characterized by comprising the steps of: in step S03, the perceived contribution of the node is:
，
Wherein, For the number of all centroid points perceived by node j in the directed sensor network,/>Representing the number of centroid points perceived by node j and not perceived by other nodes in the directed sensor network.
5. The three-dimensional directional sensor network coverage control method according to claim 1, characterized by comprising the steps of: in step S04, the construction steps of the dormancy set of the nodes of the directed sensor network are as follows: 4.1, randomly selecting K nodes from the nodes of the N directional sensor networks as initial cluster head nodes;
4.2, for the remaining N-K nodes, calculating the distances between each node and K initial cluster head nodes, and respectively classifying the remaining N-K nodes into the cluster where the initial cluster head node with the smallest distance is located;
4.3, for each cluster, updating the cluster head node of each cluster; all nodes in each cluster are enabled to serve as cluster head nodes in the cluster where the nodes are located in turn, the sum of the numbers of hops from the nodes to other nodes in the cluster is calculated, and the node with the smallest sum of the numbers of hops is selected as the final cluster head node of the cluster;
4.4, calculating the priority score of each node according to the perceived contribution rate of each node and the sum of hops from each node to other nodes in the cluster where each node is located Setting dormancy threshold/>Nodes with priority scores exceeding the dormancy threshold are classified as active nodes, nodes with priority scores below the dormancy threshold are regarded as redundant nodes, and the redundant nodes are classified as dormancy node sets/>In (a) and (b); the priority score is:
，
Wherein, Is the sum of the hops of node j to the other nodes in the cluster where it is located,/>To adjust the parameters.
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