CN-118590846-B - WSN energy consumption optimization data acquisition method based on unmanned aerial vehicle in urban environment

CN118590846BCN 118590846 BCN118590846 BCN 118590846BCN-118590846-B

Abstract

The invention belongs to the field of wireless sensor network data acquisition, and particularly relates to a WSN energy consumption optimization data acquisition method based on an unmanned aerial vehicle in an urban environment, which comprises the steps of constructing a wireless sensor network data acquisition system based on the unmanned aerial vehicle in the urban environment, dividing all wireless sensor nodes into a plurality of clusters, selecting one cluster head for each cluster, transmitting all data in the cluster to the cluster head, and uploading the data in the cluster to the unmanned aerial vehicle through the cluster head; and after the cluster heads are selected, an optimization target is constructed by minimizing the flight path of the unmanned aerial vehicle and the total energy consumption of data transmission of the cluster head nodes, and the sequence of the access cluster head nodes of the unmanned aerial vehicle, the hovering point of the data collected by the unmanned aerial vehicle and the unmanned flight path are solved. The invention can effectively reduce the total energy consumption of the wireless sensor network data acquisition system based on the unmanned aerial vehicle in urban environment, improve the network stability and prolong the network life cycle.

Inventors

ZHU JIANG
LIU HAO
ZHANG HAIBO
WU YUNSHUANG
Gong xiaonan
MA YUCHAO
Yang Mayang

Assignees

重庆邮电大学

Dates

Publication Date: 20260505
Application Date: 20240617

Claims (7)

1. The WSN energy consumption optimization data acquisition method based on the unmanned aerial vehicle in the urban environment is characterized by comprising the following steps of: constructing a wireless sensor network data acquisition system based on an unmanned aerial vehicle in an urban environment, dividing all wireless sensor nodes into a plurality of clusters, selecting one cluster head for each cluster, transmitting all data in the cluster to the cluster head, and uploading the data in the cluster to the unmanned aerial vehicle through the cluster head; According to the influence of buildings in the coverage area of a wireless sensor network data acquisition system on the communication distance, carrying out cluster management and cluster head election on wireless sensor nodes in the coverage area based on a KPP-LEACH clustering algorithm, wherein the method comprises the following steps: 101. Initializing the number of node clusters The range is as follows: M is the number of wireless sensor nodes in the wireless sensor network data acquisition system; 102. According to the number of the node clusters The unmanned aerial vehicle clusters the nodes by using a KPP-LEACH algorithm; 103. Calculating cluster head scores of all nodes in each cluster, selecting a node with the highest cluster head score as a cluster head, and broadcasting the node to other member nodes in the cluster; 104. Judging whether the distance between the wireless sensor node in the cluster and the cluster head node meets the communication requirement, and if not, making And returning to the step 102, otherwise outputting the current clustering result; clustering nodes using a KPP-LEACH algorithm includes the steps of: 201. According to the number of the node clusters Randomly initializing K wireless sensor nodes as initial centroids; 202. calculating the distance between all wireless sensor nodes and each centroid in the wireless sensor network data acquisition system, and dividing the wireless sensor nodes into clusters closest to each centroid; 203. after the cluster division is completed, updating the mass center in the cluster, and taking the wireless sensor node with the smallest sum of the distances between the wireless sensor node and other wireless sensor nodes in the cluster as a new mass center; 204. judging whether the mass center is the same wireless sensor node before and after updating, if so, outputting a current clustering result, otherwise, returning to the step 202; After the cluster head is selected, an optimization target is constructed by minimizing the flight path of the unmanned aerial vehicle and the total energy consumption of data transmission of the cluster head nodes, the sequence of the unmanned aerial vehicle access to the cluster head nodes, the hovering point of data collected by the unmanned aerial vehicle and the unmanned flight path are solved, the optimization target is constructed by minimizing the flight path of the unmanned aerial vehicle and the total energy consumption of data transmission of the cluster head nodes, and the optimization target is expressed as: Constraint conditions: Wherein, the As a parameter of the weight-bearing element, Indicating the total energy consumption of the cluster head node for completing one round of data acquisition; Indicating the total energy consumption of the unmanned aerial vehicle for completing one round of data acquisition; Indicating the position of the unmanned plane in the ith time slot; Indicating the starting position of the unmanned aerial vehicle, Indicating the end position of the unmanned aerial vehicle; representing the slot length; the maximum flight speed of the unmanned aerial vehicle is set; Residual energy for the sensing node; Maximum energy for the sensing node; Representing the position coordinates of the ith transmission slot cluster head k, N is the number of transmission time slots; The minimum flight coordinates of the unmanned aerial vehicle; is the maximum flight coordinate of the unmanned aerial vehicle; the data transmission rate of the data transmission between the t time slot unmanned plane and the kth cluster head is represented; Indicating whether the cluster head is collected by an unmanned aerial vehicle or not, wherein K is the number of the cluster heads; representing the position coordinates of the kth cluster head; the method is the maximum acquisition range of the unmanned aerial vehicle.
2. The method for acquiring WSN energy consumption optimization data based on unmanned aerial vehicle in urban environment according to claim 1, wherein the calculation of the actual communication distance of the wireless sensor node comprises the following steps: wherein d represents the coordinate as Wireless sensor node and coordinates of (a) are Is the actual communication distance between the wireless sensor nodes; Is the obstacle communication distance between two nodes; is the barrier-free communication distance between two nodes.
3. The unmanned aerial vehicle-based WSN energy consumption optimization data acquisition method in the urban environment according to claim 1, wherein cluster head scores of nodes are expressed as follows: Wherein, the A cluster head score representing an nth wireless sensor node within a kth cluster; the residual capacity of the current wireless sensor node is calculated; Representing the normalized average communication distance between the r wireless sensor node in the kth cluster and other wireless sensor nodes in the kth cluster.
4. The WSN energy consumption optimization data acquisition method based on the unmanned aerial vehicle in the urban environment according to claim 1, wherein the method is characterized in that a currently optimal unmanned aerial vehicle hovering point and a path to the hovering point of the unmanned aerial vehicle under an optimization target are solved based on a QA reinforcement learning algorithm, and specifically comprises the following steps: Judging whether the current selectable cluster head set is empty or not, if so, ending the selection, if not, generating a random number, and judging the relation between the random number and the greedy factor; If the random number is larger than the greedy factor, selecting an optimal cluster head node from the selectable cluster head set according to the current state of the unmanned aerial vehicle based on a Q-Learning algorithm; otherwise, randomly selecting a cluster head node from the selectable cluster head set according to the current state of the unmanned aerial vehicle based on the Q-Learning algorithm; Planning a hovering point corresponding to a cluster head node selected by the unmanned aerial vehicle from the current position based on an A-algorithm; And storing the current state of the unmanned aerial vehicle, the action executed according to the current state, the rewards obtained by executing the action and the state of the unmanned aerial vehicle after executing the action as a group of experiences in an experience pool.
5. The method for optimizing data collection of WSN energy consumption based on unmanned aerial vehicle in urban environment according to claim 4, wherein the maximum flight range of unmanned aerial vehicle is divided into The method comprises the steps of dividing the unmanned aerial vehicle into multiple grids, taking the center of each grid as the position coordinates of grids where a current unmanned aerial vehicle and a target cluster head are located, wherein the state of the unmanned aerial vehicle is composed of the position coordinates of the unmanned aerial vehicle and the position coordinates of the target cluster head at the current moment t, and the method comprises the steps of 。
6. The method for acquiring WSN energy consumption optimization data based on unmanned aerial vehicle in urban environment according to claim 4, wherein the action space corresponding to the action selected by the Q-Learning algorithm is expressed as The action space corresponding to the action selected based on the algorithm A comprises east, south, west, north, southeast, southwest, northwest, northeast and keeps the current position motionless.
7. The method for optimizing data collection of WSN energy consumption based on unmanned aerial vehicle in urban environment according to claim 4, wherein the rewarding for performing the action comprises: Wherein, the Is a reward function; flight energy consumption for performing actions for the unmanned aerial vehicle; The energy consumption is transmitted after the unmanned aerial vehicle performs the action to reach the target position; The number of the cluster heads is rewarded for the unmanned aerial vehicle to collect, Representing the number of accessed cluster heads; representing crash punishment of the unmanned aerial vehicle; to complete the bonus coefficients for a single cluster head data transmission, Is a punishment coefficient of the unmanned aerial vehicle due to crashing the building, A punishment coefficient for the crash of the unmanned aerial vehicle due to the electric quantity exhaustion, 、 And Are all constant.

Description

WSN energy consumption optimization data acquisition method based on unmanned aerial vehicle in urban environment Technical Field The invention belongs to the technical field of WSN data acquisition, and particularly relates to a WSN energy consumption optimization data acquisition method based on an unmanned aerial vehicle in an urban environment. Background With the continuous development of the internet of things technology, a wireless sensor network (Wireless Sensor Network, WSN) is widely focused and researched as an important component of the internet of things, and the WSN is an ad hoc network consisting of a plurality of small, economical and efficient sensor nodes and has the capability of collecting, processing and transmitting various environmental data in real time. Therefore, the wireless sensor network is widely applied in the fields of smart cities, smart transportation, smart agriculture and the like. However, since the energy resources generally carried by the sensing nodes are limited, and manual battery replacement is difficult to realize, effective reduction of energy consumption has become an important issue in wireless sensing network research. In recent years, in order to effectively reduce energy consumption of a wireless sensor network, a scheme of an Unmanned plane (un-managed AERIAL VEHICLE, UAV) assisted wireless sensor network has attracted a lot of attention. The unmanned aerial vehicle provides a flexible air platform for the wireless sensing network, and can assist in data acquisition, node communication and network management. Because unmanned aerial vehicle has high mobility and deployment flexibility, become the ideal instrument of optimizing wireless sensor network energy consumption. The unmanned aerial vehicle is assisted data acquisition of the wireless sensor network and is fused with the wireless sensor network, the advantages of the unmanned aerial vehicle are fully utilized, and efficient and real-time data acquisition service is provided. The unmanned aerial vehicle is used as an air mobile node, can realize rapid deployment and adjustment through flight, and overcomes the limitations of deployment and communication distance of the sensing node. Through the collaborative operation with the wireless sensor network, the unmanned aerial vehicle can provide wider coverage, real-time data acquisition and high-efficiency communication capability, thereby obviously improving the performance and the energy utilization efficiency of the wireless sensor network. The research of the unmanned aerial vehicle auxiliary wireless sensing network has important significance in theory and practice. Disclosure of Invention Aiming at the problem that the wireless sensor network energy consumption is overlarge because a cluster head node and a sink node in the traditional wireless sensor network are too far away from each other to establish a high-quality link and only multi-hop transmission can be used, the invention provides an unmanned aerial vehicle-based WSN energy consumption optimization data acquisition method in urban environment, which specifically comprises the following steps: Constructing a wireless sensor network data acquisition system based on an unmanned aerial vehicle in an urban environment, dividing all wireless sensor nodes into a plurality of clusters, selecting one cluster head for each cluster, transmitting all data in the cluster to the cluster head, and uploading the data in the cluster to the unmanned aerial vehicle through the cluster head; According to the influence of buildings in the coverage area of the wireless sensor network data acquisition system on the communication distance, carrying out cluster management and cluster head election on wireless sensor nodes in the coverage area based on a KPP-LEACH clustering algorithm; After the cluster heads are selected, an optimization target is constructed by minimizing the flight path of the unmanned aerial vehicle and minimizing the total energy consumption of data transmission of the cluster head nodes, and the sequence of the unmanned aerial vehicle accessing the cluster head nodes, the hovering point of data collected by the unmanned aerial vehicle and the unmanned flight path are solved. Further, the process of performing cluster management and cluster head election on the wireless sensor nodes in the coverage area based on the KPP-LEACH clustering algorithm comprises the following steps: 101. initializing the number K of node clusters, wherein the range of the number K is more than or equal to 1 and less than or equal to M, and M is the number of wireless sensor nodes in a wireless sensor network data acquisition system; 102. according to the number K of the node clusters, the unmanned aerial vehicle clusters the nodes by using a KPP-LEACH algorithm; 103. Calculating cluster head scores of all nodes in each cluster, selecting a node with the highest cluster head score as a cluster head, and broadcasting