CN-115545106-B - AoI sensitive data collection method and system in multi-unmanned aerial vehicle

CN115545106BCN 115545106 BCN115545106 BCN 115545106BCN-115545106-B

Abstract

The invention provides a AoI-sensitive data collection method and a AoI-sensitive data collection system in a multi-unmanned aerial vehicle, which relate to the technical field of mobile communication and comprise the steps of collecting the number and position information of ground sensors, initializing the number and the positions of the ground sensors, determining the number of data collection points and the position coordinates of each data collection point, establishing SN-CP (serial-parallel processor) association, clustering the data collection points according to the position coordinates of the data collection points and the SN-CP association, establishing CP-UAV association again, optimizing the unmanned aerial vehicle track under each data collection point according to the coordinates of the data collection points and the CP-UAV association to minimize the average AoI and the maximum AoI of each sensor data, completing data collection, and completing data collection tasks while guaranteeing the freshness of the sensor information.

Inventors

ZHAI LINBO
Gao Xingxia
DENG XIAOKE
JIA YUJUAN
YANG FENG
ZHAO JINGMEI

Assignees

山东师范大学

Dates

Publication Date: 20260512
Application Date: 20221008

Claims (7)

1. A method for AoI-sensitive data collection in a multiple unmanned aerial vehicle, comprising: Collecting the number and position information of the ground sensors, and initializing the number and the positions of the ground sensors; Determining the number of data acquisition points and the position coordinates of each data acquisition point, and establishing the association between the sensors and the data acquisition points, wherein the process is that the positions of the data acquisition points are determined by adopting a SCADC algorithm of density clustering and the association between the sensors and the data acquisition points is established by considering the time dimension, and each sensor corresponds to only one data acquisition point; The steps of SCADC algorithm adopting density clustering are as follows: calculating the space-time distance between the sensors The number of sensors in the space-time neighborhood is considered as The number of sensors in the area centered on epsilon as radius, the number of sensors in the area centered on epsilon If the number of the sensors is greater than or equal to MinPts in epsilon neighborhood of (2), the sensors As a core object, serving as a candidate cluster head for subsequent iteration; in the density clustering algorithm, iterative updating between adjacent sensors conveys two kinds of information (1) By the following constitution Neighboring nodes to which it is sent Representing How large the capacity of the cluster head is to be it, (2) By the following constitution Neighboring nodes to which it is sent Indicating how much capacity itself is Is a cluster head of (a); When convergence is reached, if Greater than 0, the sensor The position of the sensor is the position of a data acquisition point, and the position association relation between each sensor and the corresponding cluster head is established; Wherein, the Represents the degree of self-responsibility, which measures the sensor To the extent that it is suitable for its own cluster head, Represents the self-availability, which measures the sensor As the external support rate of the cluster head, For determining whether a sensor can become a cluster head when Greater than 0, indicating a sensor The self-responsibility and the self-availability of the sensor reach the condition of becoming a cluster head Is selected as a cluster head; Clustering the data acquisition points according to the position coordinates of the data acquisition points and the association between the sensors and the data acquisition points, and establishing the association between the data acquisition points and the unmanned aerial vehicle; And optimizing the unmanned aerial vehicle track under each data acquisition point according to the coordinates of the data acquisition point and the association between the data acquisition point and the unmanned aerial vehicle so as to minimize the average AoI and the maximum AoI of each sensor data, and completing data collection.
2. The method for collecting data sensitive to AoI in multiple unmanned aerial vehicles according to claim 1, wherein the specific step of initializing the number and positions of the ground sensors is to establish a three-dimensional coordinate system, collect position coordinates of the ground sensors with an origin as a center, initialize the number and positions of the ground sensors, and change the positions of the ground sensors with time, the data center is located at the origin, and the number of unmanned aerial vehicles is 1 at the beginning.
3. The method for collecting AoI-sensitive data in a multi-unmanned aerial vehicle according to claim 1, wherein the specific step of establishing the association between the data acquisition points and the unmanned aerial vehicle is to cluster the data acquisition points by adopting a CUKK-means algorithm combined with K-means to form a data acquisition point cluster comprising a plurality of data acquisition points, and establishing the association between the data acquisition points and the unmanned aerial vehicle, wherein each data acquisition point is associated with only one unmanned aerial vehicle, and each unmanned aerial vehicle accesses only the data acquisition points in one data acquisition point cluster; the CUKK-means algorithm specifically comprises the steps of mapping data acquisition points in a space to a high-dimensional kernel space through nonlinear mapping, and then clustering in the kernel space; When clustering is performed in the kernel space, a new kernel function is specifically provided, and the kernel function is: Wherein, the Representing the center of the kth cluster, Is the CP point And cluster center The distance between the two plates is set to be equal, Is a data center And cluster center The distance between the two plates is set to be equal, Representing the association state between the unmanned aerial vehicle and the data acquisition point cp; Indicating the number of sensor clusters and, Representing a nonlinear function, transforming the original coordinates into a high-dimensional space, L representing the number of data acquisition points, Representing data acquisition points Is provided with a coordinate of the position of (c), Representing a data center CP point refers to the best point for data collection by the drone.
4. The AoI-sensitive data collection method of claim 1, wherein optimizing the trajectory of the drone with minimum average AoI and maximum AoI of each sensor data based on the coordinates of the data collection points and the association between the data collection points and the drone is performed by first initializing the endurance and speed of the drone, initializing the pheromone concentration on each sub-path of the drone using analytic hierarchy process, determining an fitness function, and optimizing the trajectory of the drone.
5. A AoI-sensitive data collection system in a multiple unmanned aerial vehicle, comprising: The data acquisition module is used for acquiring the number and position information of the ground sensors and initializing the number and the positions of the ground sensors; the data association module is used for determining the number of the data acquisition points and the position coordinates of each data acquisition point, and establishing association between the sensor and the data acquisition points; the optimization module is used for optimizing the unmanned aerial vehicle track under each data acquisition point according to the coordinates of the data acquisition point and the association between the data acquisition point and the unmanned aerial vehicle so as to minimize the average AoI and the maximum AoI of each sensor data and finish data collection; The method comprises the steps of determining the number of data acquisition points and the position coordinates of each data acquisition point, wherein the process of establishing the association between the sensor and the data acquisition points is to consider the time dimension, determine the positions of the data acquisition points by adopting a SCADC algorithm of density clustering and establish the association between the sensor and the data acquisition points, and each sensor corresponds to only one data acquisition point; The steps of SCADC algorithm adopting density clustering are as follows: calculating the space-time distance between the sensors The number of sensors in the space-time neighborhood is considered as The number of sensors in the area centered on epsilon as radius, the number of sensors in the area centered on epsilon If the number of the sensors is greater than or equal to MinPts in epsilon neighborhood of (2), the sensors As a core object, serving as a candidate cluster head for subsequent iteration; in the density clustering algorithm, iterative updating between adjacent sensors conveys two kinds of information (1) By the following constitution Neighboring nodes to which it is sent Representing How large the capacity of the cluster head is to be it, (2) By the following constitution Neighboring nodes to which it is sent Indicating how much capacity itself is Is a cluster head of (a); When convergence is reached, if Greater than 0, the sensor The position of the sensor is the position of a data acquisition point, the position association relation between each sensor and the corresponding cluster head is established, Represents the degree of self-responsibility, which measures the sensor To the extent that it is suitable for its own cluster head, Represents the self-availability, which measures the sensor As the external support rate of the cluster head, For determining whether a sensor can become a cluster head when Greater than 0, indicating a sensor The self-responsibility and the self-availability of the sensor reach the condition of becoming a cluster head Is selected as the cluster head.
6. A computer readable storage medium, characterized in that a plurality of instructions are stored, which instructions are adapted to be loaded by a processor of a terminal device and to perform the AoI-sensitive data collection method in a drone according to any one of claims 1-4.
7. A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions, the computer readable storage medium being configured to store instructions adapted to be loaded by the processor and to perform a AoI-sensitive data collection method in a drone according to any one of claims 1 to 4.

Description

AoI sensitive data collection method and system in multi-unmanned aerial vehicle Technical Field The invention relates to the technical field of mobile communication, in particular to a AoI sensitive data collection method and system in a multi-unmanned aerial vehicle. Background The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. With the development of artificial intelligence, big data, cloud computing, and mobile edge computing, today's mobile applications become more time-delay sensitive. Unmanned aerial vehicles play an increasingly important role in the fields of military, disaster relief, medical treatment and the like. In recent years, with the increase of the coverage requirement of a global wireless communication network, an Unmanned Aerial Vehicle (UAV) is combined with a mobile network, so that the unmanned aerial vehicle communication can be supported in a low-cost and high-mobility mode, the possibility is provided for establishing a new special air-to-ground communication link, and when natural disasters such as epidemic diseases, floods and earthquakes occur, the unmanned aerial vehicle can be deployed in a data collection scene, so that the freshness of information is improved, and disaster losses are reduced. Unmanned aerial vehicle is deployed in wireless communication network as mobile communication base station, because advantages such as low cost, flexibility degree are high, deployment flexibility, etc. are widely used in military affairs, relief of disaster, medical treatment and other fields. For example, ground base stations in disaster areas are often destroyed and fail to provide timely communication services, which is detrimental to rescue operation. Because urban traffic hotspots such as evening, concerts and the like are dense, the coverage of the traditional ground base station often cannot meet the requirements, and user equipment often has no signal. In view of the above, unmanned aerial vehicle auxiliary data acquisition in wireless sensor networks has attracted considerable attention. The unmanned aerial vehicle is more flexible, mobile, small-sized and energy-limited than the ground base station, provides reliable communication, and provides more timely data collection for ground Sensor Nodes (SNs) to ensure the freshness of data. Unmanned aerial vehicles have wide application and many advantages in mobile communications. However, due to the small size, carrying capacity and energy storage capacity of the unmanned aerial vehicle, the unmanned aerial vehicle cannot collect data for a long time, the uploading sequence of sensor data and the path of the unmanned aerial vehicle between data Collection Points (CPs) are complicated, so that the freshness of the data is not high, and in certain scenes with high information timeliness requirements, however, due to the limited cruising capacity of the unmanned aerial vehicle, in scenes with large area and dense sensors, the unmanned aerial vehicle can hardly complete the data collection task while guaranteeing the freshness of the sensor information. Disclosure of Invention In order to solve the above problems, the invention provides a AoI sensitive data collection method and system in a multi-unmanned plane, which uses AoI to measure the freshness of data, minimizes the maximum AoI and average AoI of the data in the sensor, introduces a correlation and planning strategy from beginning to end, and optimizes two AoI of the data in the sensor through an iterative three-step process. According to some embodiments, the present invention employs the following technical solutions: A AoI-sensitive data collection method in a multi-drone, comprising: Collecting the number and position information of the ground sensors, and initializing the number and the positions of the ground sensors; Determining the number of data acquisition points and the position coordinates of each data acquisition point, and establishing the association between the sensor and the data acquisition points; Clustering the data acquisition points according to the position coordinates of the data acquisition points and the association between the sensors and the data acquisition points, and establishing the association between the data acquisition points and the unmanned aerial vehicle; And optimizing the unmanned aerial vehicle track under each data acquisition point according to the coordinates of the data acquisition point and the association between the data acquisition point and the unmanned aerial vehicle so as to minimize the average AoI and the maximum AoI of each sensor data, and completing data collection. According to some embodiments, the present invention employs the following technical solutions: a AoI-sensitive data collection system in a multi-drone, comprising: The data acquisition module is used for acquiring the number and position information of the gro