CN115037638B

CN115037638B - Unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness

Info

Publication number: CN115037638B
Application number: CN202210674878.8A
Authority: CN
Inventors: 王莉; 张宇明; 费爱国; 印思源; 徐连明
Original assignee: Beijing University of Posts and Telecommunications
Current assignee: Beijing University of Posts and Telecommunications
Priority date: 2022-06-14
Filing date: 2022-06-14
Publication date: 2023-10-20
Anticipated expiration: 2042-06-14
Also published as: CN115037638A

Abstract

The invention provides a low-energy-consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method. The method comprises the following steps: determining the energy consumption of all unmanned aerial vehicle nodes in each time slot; based on AoI of all unmanned aerial vehicle nodes in each time slot, determining network average AoI corresponding to all unmanned aerial vehicle nodes in the total time; based on the Lyapunov optimization theory, converting the energy consumption of all unmanned aerial vehicle nodes in each time slot and the target constraint condition corresponding to the network average AoI to obtain the minimum average energy consumption; based on the network average AoI and the minimum average energy consumption, a sampling strategy of the unmanned aerial vehicle sampling nodes in time slots and a transmission scheduling strategy and a routing strategy of all unmanned aerial vehicle nodes in each time slot are determined. The unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness can limit the energy consumption to the greatest extent and obtain the minimum average energy consumption.

Description

Unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness

Technical Field

The invention relates to the technical field of unmanned aerial vehicles, in particular to a low-energy-consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method.

Background

The method has the advantages that the operators in China are wide, natural disasters are frequent, and after the disasters occur, the infrastructure is damaged due to lack of infrastructure in the mountain close forest areas or huge disasters, so that rescue workers in disaster areas cannot know the specific disaster situation of the disaster areas. With the development of mobile ad hoc network technology, unmanned aerial vehicles are utilized to collect and transmit disaster area information, so that a solution is provided for the problems. However, the collection and transmission of data information in a rough emergency site using an unmanned aerial vehicle still faces some technical challenges. Firstly, because the distance between nodes is too long or the terrain is blocked, the nodes cannot be fully connected, so that information sharing is realized through multi-hop transmission of other nodes in the network, and the ground node can acquire a data packet through any unmanned aerial vehicle node; secondly, the conflict exists between the high requirement of the emergency rescue scene on the timeliness of the acquired information and the limited energy consumption of the unmanned aerial vehicle, so that the acquisition and communication networking cannot be operated efficiently; thirdly, in order to ensure timeliness of disaster information shared in networking, the sampling unmanned aerial vehicle tends to maximize data acquisition frequency and data packet update frequency of all nodes in networking, however, unrestricted data acquisition and policy-free information sharing in networking can lead to congestion of wireless links, increase of transmission delay and weakening of timeliness of information, and meanwhile, limited energy resource consumption of the unmanned aerial vehicle is too fast, and networking survival time is reduced. In order to ensure that the information stored at the unmanned aerial vehicle node has certain timeliness under the condition of reducing the energy consumption as much as possible, the routing scheduling strategy for setting the optimal acquisition frequency and designing the data packet of the unmanned aerial vehicle is very critical.

In order to measure the timeliness of the information, the information age (Age of Information, aoI) is introduced as a quantization index. Currently, many AoI related research works are focused on single-hop transmission networks, aiming at differences under different queue models, unmanned aerial vehicle path planning problems, wireless sensor network scenes and the like, and few research is performed in multi-hop transmission networks, so that the emergency unmanned aerial vehicle networking scenes and the energy consumption problems in the emergency unmanned aerial vehicle networking scenes are not considered. Therefore, the invention aims at the problems of timeliness and limited energy consumption of the content in the multi-hop transmission scene of the emergency unmanned aerial vehicle and performs cooperative optimization on the sampling and scheduling strategy.

Disclosure of Invention

The invention provides a low-energy-consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method, which is used for solving the technical problem that unmanned aerial vehicle energy consumption is too fast in the prior art.

The invention provides a low-energy consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method, which comprises the following steps:

determining the energy consumption of all unmanned aerial vehicle nodes in each time slot based on the energy consumed by unmanned aerial vehicle sampling nodes in each time slot, the energy consumed by all unmanned aerial vehicle nodes in each time slot for receiving data packets and the energy consumed by all unmanned aerial vehicle nodes in each time slot for transmitting data packets;

Based on AoI of all unmanned aerial vehicle nodes in each time slot, determining network average AoI corresponding to all unmanned aerial vehicle nodes in total time;

based on a Lyapunov optimization theory, converting the energy consumption of all unmanned aerial vehicle nodes in each time slot and a target constraint condition corresponding to the network average AoI, and obtaining the minimum average energy consumption under the condition that the network average AoI is determined to be smaller than a AoI threshold;

determining a sampling strategy of the unmanned aerial vehicle sampling node in the time slot and a transmission scheduling strategy and a routing strategy of all unmanned aerial vehicle nodes in each time slot based on the network average AoI and the minimum average energy consumption;

the unmanned aerial vehicle node comprises the unmanned aerial vehicle sampling node and an unmanned aerial vehicle transmission node, and the total time is determined based on the total time slot number and the time length corresponding to each time slot.

In some embodiments, before determining the energy consumption of all the drone nodes in each time slot, the method further includes:

determining energy and data packets AoI consumed by the unmanned sampling node in each time slot based on the data packets generated by the unmanned sampling node in each time slot;

Determining the average AoI, the energy consumed by all the unmanned aerial vehicle nodes in each time slot to receive the data packet, and the energy consumed by all the unmanned aerial vehicle nodes in each time slot to transmit the data packet, respectively, based on the transmission scheduling policy, the routing policy, the data packet AoI, and the unmanned aerial vehicle node AoI;

wherein the data packet AoI is AoI of each data packet in each unmanned node, and the unmanned node AoI is AoI of the data packet received last in each unmanned node.

In some embodiments, the converting, based on the lyapunov optimization theory, the energy consumption of all the unmanned aerial vehicle nodes in each time slot and the constraint condition corresponding to the network average AoI, and obtaining the minimum average energy consumption if it is determined that the network average AoI is less than the AoI threshold value includes:

constructing an objective function based on the energy consumption of all unmanned aerial vehicle nodes in each time slot;

converting the target constraint condition into a sampling queue and a AoI queue based on the Lyapunov optimization theory;

based on the dynamic change conditions of the sampling queue and the AoI queue, obtaining the minimum total energy consumption under the condition that the network average AoI is determined to be smaller than a AoI threshold value;

The target constraint condition is determined based on the average number of times the unmanned aerial vehicle sampling node samples the data packet corresponding to each service in each time slot, the average AoI of the data packet corresponding to each service in each unmanned aerial vehicle node in each time slot, the data packet transmitted by the unmanned aerial vehicle node in each time slot and the next hop node selected by the unmanned aerial vehicle node in each time slot.

In some embodiments, the objective function is represented by:

wherein T is the total time slot number;

e (t) is the energy consumption of all unmanned aerial vehicle nodes in the t time slot;

sampling energy consumed by a node in the t time slot for the unmanned aerial vehicle;

receiving the energy consumed by the data packet in the t time slot for all unmanned aerial vehicle nodes;

the energy consumed by the data packet is sent in the t time slot for all unmanned aerial vehicle nodes;

is the sampling power consumption coefficient of service b, +.>Is the reception power consumption coefficient of service b, +.>Is the transmission power consumption coefficient of service b, +.>Data packet for indicating whether the unmanned aerial vehicle sampling node s generates a service b, +.>For indicating whether the drone node has chosen to transmit the packet of traffic b generated by the drone sampling node s within the t time slot, y _ji (t) means for indicating whether the drone node j transmits a data packet to the drone node i, y in said t time slot _ij (t) is used to indicate whether the drone node i transmits a data packet to the drone node j within the t time slot.

In some embodiments, the target constraint is:

wherein ,a kth data packet for indicating whether said drone node has chosen to transmit traffic b generated by drone sampling node s within said t time slot,/-for>Average AoI, y corresponding to service b in ith unmanned plane node _ni (t) means for indicating whether or not the unmanned node n transmits a data packet to the unmanned node i, at time slot t>Representing the total number of unmanned aerial vehicle nodes, < + >>Representing the total number of sampling nodes of the unmanned aerial vehicle, < >>Representing the total number of categories, K, of service data _b The maximum number of data packets of the service b which can be acquired by the unmanned aerial vehicle sampling node in T time slots is represented.

The invention also provides a low-energy consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control device, which comprises:

the first determining module is used for determining the energy consumption of all the unmanned aerial vehicle nodes in each time slot based on the energy consumed by the unmanned aerial vehicle sampling nodes in each time slot, the energy consumed by all the unmanned aerial vehicle nodes in each time slot for receiving the data packet and the energy consumed by all the unmanned aerial vehicle nodes in each time slot for transmitting the data packet;

A second determining module, configured to determine a network average AoI corresponding to all the unmanned aerial vehicle nodes in the total time based on AoI of all the unmanned aerial vehicle nodes in each time slot;

the third determining module is configured to convert, based on a lyapunov optimization theory, energy consumption of all unmanned aerial vehicle nodes in each time slot and a target constraint condition corresponding to the network average AoI, and obtain a minimum average energy consumption when it is determined that the network average AoI is smaller than a AoI threshold;

a fourth determining module, configured to determine, based on the network average AoI and the minimum average energy consumption, a sampling policy of the unmanned aerial vehicle sampling node in the time slot and a transmission scheduling policy and a routing policy of all unmanned aerial vehicle nodes in each time slot;

In some embodiments, the apparatus further comprises:

a fifth determining module, configured to determine, based on the data packet generated by the unmanned aerial vehicle sampling node in each time slot, energy consumed by the unmanned aerial vehicle sampling node in each time slot and the data packet AoI;

A sixth determining module, configured to determine, based on the transmission scheduling policy, the routing policy, the data packet AoI, and the drone node AoI, the average AoI, the energy consumed by all the drone nodes receiving the data packet in each time slot, and the energy consumed by all the drone nodes transmitting the data packet in each time slot, respectively;

The invention also provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness when executing the program.

The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the unmanned aerial vehicle network data acquisition and transmission control method of low energy consumption and high timeliness as described in any one of the above.

The invention also provides a computer program product comprising a computer program which when executed by a processor implements the unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness as described in any one of the above.

The unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness can limit the energy consumption to the greatest extent, so that the minimum average energy consumption is obtained.

Drawings

In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic flow chart of a low-energy consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method provided by the invention;

fig. 2 is a schematic diagram of data acquisition and transmission of a control method for data acquisition and transmission of an unmanned aerial vehicle network with low energy consumption and high timeliness, which is provided by the invention;

fig. 3 is a schematic diagram of a transmission scheduling and routing strategy of an unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness provided by the invention;

fig. 4 is a schematic diagram of a sampling flow of a low-energy consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method provided by the invention;

Fig. 5 is a schematic diagram of a transmission scheduling and routing flow of an unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness provided by the invention;

fig. 6 is a node distribution schematic diagram of an unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness, which is provided by the invention;

fig. 7 is a schematic diagram of AoI of an unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness provided by the invention;

fig. 8 is an average AoI variation schematic diagram of an unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness provided by the invention;

fig. 9 is a schematic diagram of total energy consumption change of the unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness;

fig. 10 is a second schematic diagram AoI of the unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness provided by the invention;

fig. 11 is a third schematic diagram AoI of the unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness provided by the invention;

fig. 12 is a schematic diagram of algorithm time complexity of an unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness provided by the invention;

Fig. 13 is a schematic structural diagram of an unmanned aerial vehicle network data acquisition and transmission control device with low energy consumption and high timeliness;

fig. 14 is a schematic diagram of the physical structure of the electronic device provided by the present invention.

Detailed Description

For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The invention is based on an unmanned aerial vehicle emergency networking scene, and considers information acquisition of unmanned aerial vehicle networking and information sharing among nodes, wherein unmanned aerial vehicle sampling nodes acquire disaster information and share the information to all unmanned aerial vehicle transmission nodes, and for nodes exceeding communication distance or with signals blocked, information sharing is realized through multi-hop transmission of data. The method considers the influence of the acquisition frequency on the energy consumption and the timeliness of the acquired information, and the energy consumption is too fast due to the too high frequency, and the timeliness of the information is poor due to the too low frequency; furthermore, the invention also considers the influence of transmission scheduling of data types and multi-hop routing among nodes on the timeliness of network information, and for the transmission nodes, the timeliness of received data is determined by the selection of source nodes and routing paths. In summary, in order to ensure timeliness of information acquisition and energy consumption minimization, the invention provides a low-energy-consumption unmanned networking sampling and transmission combined optimization method facing information age, aiming at information sampling nodes, the sampling frequency of the information sampling nodes is controlled, information updating is ensured, and meanwhile, the energy consumption condition is limited; aiming at sampling nodes and transmission nodes, a data packet transmission method is controlled, a sharing path is designed, and the information timeliness of the network is maintained. In order to meet the requirements of energy consumption and timeliness simultaneously, the problem of converting the timeliness of information into the stability of a virtual queue is solved, the balance between the two is described by utilizing the Lyapunov optimization technology, the problem of sampling and transmission route scheduling is decoupled into two independent problems, and the solution algorithm is respectively designed, so that the total energy consumption is minimized under the condition that the timeliness of the information is kept within a certain range.

Fig. 1 is a schematic flow chart of a low-energy consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method provided by the invention. Referring to fig. 1, the present invention provides a low-energy consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method, which may include: step 110, step 120, step 130 and step 140.

Step 110, determining the energy consumption of all the unmanned aerial vehicle nodes in each time slot based on the energy consumed by the unmanned aerial vehicle sampling nodes in each time slot, the energy consumed by all the unmanned aerial vehicle nodes in each time slot for receiving the data packet, and the energy consumed by all the unmanned aerial vehicle nodes in each time slot for transmitting the data packet;

step 120, determining a network average AoI corresponding to all unmanned aerial vehicle nodes in the total time based on AoI of all unmanned aerial vehicle nodes in each time slot;

130, converting the energy consumption of all unmanned aerial vehicle nodes in each time slot and a target constraint condition corresponding to network average AoI based on a Lyapunov optimization theory, and obtaining the minimum average energy consumption under the condition that the network average AoI is determined to be smaller than a AoI threshold;

step 140, determining a sampling strategy of unmanned aerial vehicle sampling nodes in time slots and a transmission scheduling strategy and a routing strategy of all unmanned aerial vehicle nodes in each time slot based on network average AoI and minimum average energy consumption;

The unmanned aerial vehicle nodes comprise unmanned aerial vehicle sampling nodes and unmanned aerial vehicle transmission nodes, and the total time is determined based on the total time slot number and the time length corresponding to each time slot.

In practical implementation, a time slot system can be considered, and the observation period is divided intoA set of time slots, denoted as

As shown in fig. 2, the space is divided into two planes with the size of x y in the air part and the ground part, the unmanned plane nodes are distributed on the air plane, and the rescue worker nodes are distributed on the ground plane. Assuming that there are N unmanned aerial vehicle nodes in the scene, the index of the unmanned aerial vehicle nodes is defined as

Wherein S sampling unmanned aerial vehicles are provided with information acquisition equipment for collecting disaster information and broadcasting data to the transmission unmanned aerial vehicle nodes, and the indexes of the unmanned aerial vehicle sampling nodes are defined as

The remaining R unmanned aerial vehicle nodes are unmanned aerial vehicle transmission nodes and are used for sharing information among unmanned aerial vehicles and transmitting local information to ground nodes, and indexes of the transmission unmanned aerial vehicle nodes are defined as follows

Due to the variety of disaster area information types, the sampling unmanned aerial vehicle is provided with various types of acquisition equipment (such as temperature transmissionSensor, humidity sensor, camera, etc.) to collect different information, assuming a total of B different collection devices, dividing the data collected by the sampling unmanned aerial vehicle from the different collection devices into B different service data, defining the index of the service data as By K _b And the number of the maximum service b data packets which can be acquired by the unmanned aerial vehicle sampling node in T time slots is represented.

Record d ₀ For the maximum communication distance of any unmanned aerial vehicle node, the mutual distance is smaller than d ₀ A communication link exists between the two nodes. Assuming that the distance between the air plane and the ground plane is less than d ₀ That is, when any unmanned aerial vehicle node flies to any ground node, data transmission can be performed. Considering that the transmission unmanned plane cannot obtain the accurate position of the rescue node, only an opportunity transmission strategy can be adopted, namely data transmission is carried out when the transmission unmanned plane and the transmission unmanned plane happen to be in a transmission range.

In step 110, the energy consumption of all the drone nodes in each slot includes:

1. the unmanned aerial vehicle samples the energy consumed by the node in each time slot;

2. all unmanned aerial vehicle nodes receive the energy consumed by the data packet in each time slot;

3. all unmanned nodes send the energy consumed by the data packets in each time slot.

In practical implementation, the unmanned aerial vehicle application scenario can be constructed into a sampling model, a routing and transmission scheduling model, a queue model and a AoI model, as follows:

transmission scheduling and routing model

In each time slot, each unmanned node needs to perform transmission scheduling, that is, determines the source of a data packet to be transmitted by the unmanned node and the corresponding service type in the time slot. Using binary variables Representing packet transmission conditions at time tIf so, when a certain unmanned plane node selects to transmit the kth data packet of the service b generated by the unmanned plane sampling node s in the t time slot, the corresponding state value is 1, otherwise, the corresponding state value is 0. Considering that all unmanned aerial vehicle nodes transmit on the same frequency band, in order to avoid interference, a single unmanned aerial vehicle node can only transmit one data packet of one service at most in the same time slot, namely, the following constraint is satisfied:

each unmanned plane node also needs to perform routing selection, namely, in each time slot, the unmanned plane node needs to select the next hop to which neighbor node, and binary variable y is used _ij (t) indicates whether or not the unmanned node i transmits a data packet to the unmanned node j, y at the time slot t _ni (t) indicates whether or not the drone node n transmits a data packet to the drone node i at time t, and if so, its value is 1, otherwise it is 0. Assuming that the unmanned aerial vehicle node works in a half duplex mode, the unmanned aerial vehicle node cannot transmit and receive simultaneously, so the following constraint exists, whereinRepresenting a set of nodes within i communication range.

(II) sampling model

Because each unmanned aerial vehicle node can only send a data packet in each time slot, in order to guarantee timeliness of the data packet, each unmanned aerial vehicle node only generates a data packet in each time slot.

Thus, with binary variablesRepresentation t time slot unmanned aerial vehicle sampling node +.>Whether or not to generate businessIf t time slots generate the data packet, the value is 1, otherwise, the value is 0.

Sampling node for unmanned aerial vehicleWhether or not to generate business +.>Binary variable for kth packet of (b)It means that the value of this packet is 1 if it is generated, and 0 otherwise. />And->The relationship of (2) is as follows:

in actual implementation, based on the data packet generated by the unmanned aerial vehicle sampling node in each time slot and the sampling energy consumption coefficient of a certain service corresponding to the data packet, the energy consumed by the unmanned aerial vehicle sampling node in each time slot can be determined. The sampling energy consumption coefficient is determined according to the unmanned aerial vehicle data acquisition requirement, and is not particularly limited herein.

The generation time of the data packet is in time slot unitsRepresenting the time when the unmanned sampling node s generated the kth packet of traffic b, +.>The calculation method of (2) is as follows:

in order to ensure that unmanned aerial vehicle nodes participating in rescue acquire comprehensive information of disaster areas, the transmission quantity of each service needs to be ensured, so that the average sampling times of each service of each unmanned aerial vehicle sampling node are subjected to minimum value constraint, and are ensured to be larger than a threshold valueUse->The time average number of service b data packets sampled by the unmanned aerial vehicle sampling node s is represented, and the constraint inequality is as follows:

(III) queue model

Each unmanned plane node maintains B service queues to store data packets of different services, the maximum length of each queue is Q, the queues follow the principle of first come first serve (First Come First Service, FCFS), and if the unmanned plane node is new to a data packet and the queues are full, the data packet which arrives first in the queues is discarded. By vectorsRepresenting the state of unmanned plane node i in t time slot service b queue by +.>Indicating that the xth data packet in the queue is unmanned aerial vehicle sampling node s _x Sampled kth traffic b data packet.

(IV) AoI model

In some embodiments, before determining the energy consumption of all the drone nodes in each time slot, further includes:

determining energy consumed by the unmanned aerial vehicle sampling node in each time slot and a data packet AoI based on the data packet generated by the unmanned aerial vehicle sampling node in each time slot;

based on the transmission scheduling policy, the routing policy, the data packet AoI and the unmanned aerial vehicle node AoI, an average AoI, energy consumed by all unmanned aerial vehicle nodes in each time slot for receiving the data packet, and energy consumed by all unmanned aerial vehicle nodes in each time slot for transmitting the data packet are determined respectively;

wherein data packet AoI is AoI for each data packet in each unmanned node, and unmanned node AoI is AoI for the most recently received data packet in each unmanned node.

The present invention meters AoI in units of time slots. By usingAnd (3) whether the unmanned aerial vehicle node i transmits the data packet of the service b sampled by the unmanned aerial vehicle sampling node s to the unmanned aerial vehicle node j or not in t time slots, if so, the data packet is 1, and if not, the data packet is 0.And (3) indicating whether the t-time slot unmanned plane node j transmits the kth service b data packet sampled by the unmanned plane sampling node s to the unmanned plane node i, if so, the value is 1, otherwise, the value is 0. The relationship between the two is as follows:

in practical implementation, for packet transmission, based on a receiving energy consumption coefficient of a service corresponding to a packet, where the next hop node is selected by the unmanned plane node in each time slot, the unmanned plane node can determine energy consumed by the packet received in each time slot. The receiving energy consumption coefficient is determined according to the data transmission requirement of the unmanned aerial vehicle, and is not particularly limited herein.

For data packet reception, the energy consumed by all the unmanned aerial vehicle nodes for transmitting the data packet in each time slot can be determined based on the transmission and reception energy consumption coefficient of a service corresponding to the data packet by the next hop node selected by the unmanned aerial vehicle nodes in each time slot. The transmission energy consumption coefficient is determined according to the data transmission requirement of the unmanned aerial vehicle, and is not particularly limited herein.

AoI can be divided into AoI and AoI data packets according to the body. The AoI of the packet is the time that the packet has elapsed since generation; node AoI is the age of the most recent information at the node, aoI of the most recently received packet.

Because unmanned aerial vehicle sampling node only outwards transmits the data package, does not receive the data package, consequently need not AoI constraint to unmanned aerial vehicle sampling node.

The specific update rules for AoI are as follows:

1. for AoI of the data packet, aoI of the data packet at t+1 time slot is the current time slot minus the generated time slot of the data packet, i.e

2. For the unmanned plane transmission node AoI, the AoI change of a certain service at the node in the t+1 time slot is judged according to whether the node in the current time slot has received the data packet of the service or not, if the node does not receive the data packet, aoI of the data packet at the node in the t+1 time slot is the value of t time slot AoI plus one time slot, and if the data packet is received, t+1 time slot AoI is changed into the data packet AoI. The AoI of the b traffic at the t+1 slot inode is thus as follows:

the first partData representing that t-slot unmanned node i did not receive traffic b sampled by unmanned sampling node sBag (S)>AoI, which represents traffic b at unmanned node i, increases by 1 compared to the last slot;

Second partIndicating that the t time slot i node receives the kth service b data packet sampled by the unmanned aerial vehicle sampling node s,/I>AoI, which represents traffic b at drone node i, becomes AoI of this packet.

3. For the unmanned aerial vehicle sampling node, since the unmanned aerial vehicle sampling node does not directly transmit data to the ground node, aoI has no influence on the information aging performance, and therefore all AoI of the unmanned aerial vehicle sampling nodes are set to be constant 0.

In order to ensure that the information of each service at the unmanned aerial vehicle transmission node has certain timeliness, the average information age of each service of each unmanned aerial vehicle transmission node is ensuredLess than threshold A (A>0)。

For a sampling node of the drone,epsilon is any number greater than 0). This constraint can therefore be deduced for all unmanned nodes as follows:

in some embodiments, based on the lyapunov optimization theory, converting the energy consumption of all the unmanned aerial vehicle nodes in each time slot and the target constraint condition corresponding to the network average AoI, and obtaining the minimum average energy consumption under the condition that the network average AoI is determined to be smaller than the AoI threshold value comprises:

based on Lyapunov optimization theory, converting the target constraint condition into a sampling queue and a AoI queue;

Based on the dynamic change conditions of the sampling queue and the AoI queue, obtaining the minimum total energy consumption under the condition that the network average AoI is determined to be smaller than the AoI threshold value;

the target constraint condition is determined based on an average number of times the unmanned aerial vehicle sampling node samples the data packet corresponding to each service in each time slot, an average AoI of the data packet corresponding to each service in each unmanned aerial vehicle node in each time slot, the data packet transmitted by the unmanned aerial vehicle node in each time slot, and a next hop node selected by the unmanned aerial vehicle node in each time slot.

Because unmanned aerial vehicle energy is limited, in order to make unmanned aerial vehicle network survival time as long as possible, the invention takes minimum average energy consumption of all unmanned aerial vehicle nodes in total time as an optimization target.

In actual implementation, the energy consumption of all the unmanned nodes in each time slot can be represented by the following formula:

wherein e (t) is the energy consumption of all unmanned aerial vehicle nodes in the t-th time slot;

first itemThe energy consumed by the sampling node of the unmanned aerial vehicle in the t time slot is sampled; second itemReceiving the energy consumed by the data packet in t time slots for all unmanned aerial vehicle nodes; third itemThe energy consumed by the data packet is sent in the t time slot for all unmanned aerial vehicle nodes;

Is the sampling power consumption coefficient of service b, +.>Is the reception power consumption coefficient of service b, +.>Is the transmission power consumption coefficient of service b, +.>Data packet for indicating whether a service b is generated by a sampling node s of the unmanned aerial vehicle,/for example>Data packet for indicating whether a drone node chooses to transmit traffic b generated by a drone sampling node s within a t time slot, y _ji (t) means for indicating whether the drone node j transmits a data packet to the drone nodes i, y in the t time slot _ij (t) is used to indicate whether the drone node i transmits a data packet to the drone node j in the t slot.

Data packet for representing whether or not t-slot unmanned plane node j transmits traffic b sampled by unmanned plane sampling node s to unmanned plane node i, a>And the data packet is used for indicating whether the unmanned aerial vehicle node i transmits the service b sampled by the unmanned aerial vehicle sampling node s to the unmanned aerial vehicle node j in the t time slot.

In actual implementation, in order to ensure that the unmanned aerial vehicle node obtains the comprehensive information of the disaster area, the transmission quantity of each service needs to be ensured, so that the minimum value constraint is carried out on the average sampling times of each service of each unmanned aerial vehicle sampling node according to the times of data packets generated in each time slot by the unmanned aerial vehicle sampling node, and the minimum value constraint is ensured to be larger than a sampling times threshold value.

Considering that all unmanned aerial vehicle nodes transmit on the same frequency band, in order to avoid interference, a single unmanned aerial vehicle node can only transmit one data packet of one service at most in the same time slot, and then the number of the data packets transmitted by the unmanned aerial vehicle node in each time slot is more than or equal to 0 and less than or equal to 1.

In order to ensure that the information of each service at the unmanned aerial vehicle node has certain timeliness, the average AoI of each service in each unmanned aerial vehicle node is made to be smaller than an information age threshold value based on the information age corresponding to each data packet in the unmanned aerial vehicle node.

Each unmanned aerial vehicle node still needs to carry out routing, namely in each time slot this unmanned aerial vehicle node need select next hop to send to which neighbor node, consequently, based on the next hop node that unmanned aerial vehicle node selected in each time slot, it is assumed that unmanned aerial vehicle node work under half duplex mode, unmanned aerial vehicle node can't receive and dispatch simultaneously, consequently retrain whether to send the data packet to next hop unmanned aerial vehicle node to unmanned aerial vehicle node in each time slot.

And constructing an objective function based on the energy consumption of all unmanned aerial vehicle nodes in each time slot. The objective function is the function corresponding to the minimum average consumption, and is as follows:

wherein T is the total time slot number;

In one embodiment, the target constraints are:

/>

wherein ,a kth data packet for indicating whether the drone node has chosen to transmit traffic b generated by the drone sampling node s within the t time slot,/v>For representing the average information age, y corresponding to service b in the ith unmanned aerial vehicle node _ni (t) means for indicating whether or not the unmanned node n transmits a data packet to the unmanned node i, at time slot t>Representing the total number of unmanned aerial vehicle nodes, < >>Representing the total number of sampling nodes of the unmanned aerial vehicle, +.>Representing the total number of categories, K, of service data _b Service representing that unmanned aerial vehicle sampling node can collect in T time slotsb, the maximum number of data packets.

In the actual implementation of this process,for total time average energy consumption +.>The optimization problem is modeled as P1:

the above (10 a) to (10 g) are all target constraints.

In problem (10), the optimization objective is to obtain the minimum average energy consumptionThe method comprises the steps that a target constraint condition (10 a) represents the sampling condition of an unmanned aerial vehicle node and only comprises sampling and non-sampling conditions, a target constraint condition (10 b) represents the average sampling number of the unmanned aerial vehicle node to be larger than a threshold value, a target constraint condition (10 c) represents the average AoI of the unmanned aerial vehicle node to be smaller than the threshold value, a target constraint condition (10 d) represents the data packet transmission condition and only comprises transmission and non-transmission conditions, a target constraint condition (10 e) represents the condition that the same unmanned aerial vehicle node can only acquire one data packet of one service in one time slot at most, a target constraint condition (10 f) represents the condition that route selection among unmanned aerial vehicle nodes only comprises transmission and non-transmission, and a target constraint condition (10 g) represents that the unmanned aerial vehicle node works in a half duplex mode.

To satisfy the constraints of equations (5) and (8), namely target constraints (10 b) and (10 c), the sampling and AoI constraints are translated into virtual queue stability problems. Specifically, two virtual queues are definedRepresenting the sample queue and AoI queue, respectively. The dynamic change condition of the two virtual queues at each time slot is:

the stability of the system is measured by the lyapunov function L (Θ (t)) as shown in equation (13).

Balance by using Lyapunov drift theory shown in (14)Delta L is the change of system queue state between successive time slots ₁ The smaller the value of (c) is, the more stable the system is.

ΔL ₁ ＝L(Θ(t+T))-L(Θ(t)) (14)

By DeltaL ₁ A change in queue status is represented by equation (15):

in order to minimize the total node energy consumption while maintaining network stability, the goal is to minimize the upper boundary of the drift-energy consumption function for each slot, as shown in equation (16), where φ ₁ (pi) represents the energy consumption based on the policy pi, and V is a trade-off parameter for adjusting the network stability and energy consumption performance.

ΔL ₁ +Vφ ₁ (π) (16)

Based on inequality (max { a-b,0} +c) ² ≤a ² +b ² +c ² 2a (b-c) bringing in a queue model pattern (11),c=0, obtainable:

substituting formula (17) into formula (15), the first term to the right of the equal sign of formula (15) is:

for the first two terms to the right of the inequality:

Wherein C is a constant, and thus formula (18) can be converted into:

the second term to the right of the inequality sign in equation (15) is the same:

wherein C, D in the formulas (19) and (20) are constants independent of variables.

Taking AoI update rule (7) into equation (20), one can obtain:

wherein ,bringing formulae (19), (20) into formula (16),. DELTA.L ₁ +Vφ ₁ (pi) becomes:

wherein E is a constant. The equation (22) has five terms in total, the first term being a constant, the third and fifth terms being system state variables, so it is easy to see that the decision variables are sampledIn relation to the second item only, the transmission scheduling and routing decision variables +.>Only the third term, therefore, the problem of minimizing the lower bound of (P) can be decoupled into two sub-problems of sampling and routing, namely (P2) and (P3).

In the case of the problem (23),representing the sampling situation at t slots, +.>Representing a sample virtual queue, +.>Is constant, representing the power consumption of one sample, +.>Is constant and represents the minimum sampling threshold. (23 a) constraint indicates that there can be only two sampling cases for a node: sample 1 and not sample 0.

In the case of the problem (24),representing the transmission scheduling and routing strategy of unmanned node i in t time slots, as shown in fig. 3,/->Representing a transmission scheduling policy, i.e. whether the kth service b data packet, y, collected by the unmanned sampling node s is selected _ji (t) represents a routing policy, i.e. whether the drone node i receives a data packet from a neighbor node j. />Represent AoI virtual queue, +.>AoI, < +.>Indicating the time of generation of the data packet +.>Representing the energy consumption generated by the node transmitting and receiving data. Constraint (24 a) indicates that there are only two possible packet transmission scheduling actions, transmission is 1 and no transmission is 0. Constraint (24 b) indicates that the routing between nodes is only transmission or not, constraint (24 c) indicates that the unmanned node operates in half duplex mode, and constraint (24 d) indicates that the generation time of the data packet is within the system operation time range.

According to the unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness, provided by the invention, under the condition that the energy consumption and timeliness requirements are simultaneously met, the information timeliness is converted into the problem of virtual queue stability, the balance between the two is described by utilizing the Lyapunov optimization technology, the problem of sampling and transmission route scheduling is decoupled into two independent problems, the solution algorithms are respectively designed, and the total energy consumption is minimized under the condition that the timeliness of the acquired information is kept within a certain range.

The above problems P1, P2 and (P3) are now solved. Firstly solving the problem (P1), observing the formula (23) to find that each unmanned aerial vehicle sampling node can be according to The value determines whether or not to sample traffic b in the current slot. If at time t the node does not sample new data, i.e +.>The value of (P1) is 0 and therefore the minimum value of (P1) should be 0 or less. For any unmanned sampling node s, at time t, according to +.>To decide whether to collect service b data, the algorithm is as follows:

as shown in fig. 4, the specific algorithm on time slot t resolves as follows:

(1) Sample number look-up: checking the number of sampled service data packets of the unmanned plane node, if the number reaches the upper limit K _b And if the service is not sampled, checking the next service.

(2) Sampling decision: calculation ofIf the value is greater than 0, the time slot does not sample the service data packet; otherwise, if the value is less than 0, sampling is performed.

(3) And (5) updating the state: updatingAnd the number of sampled traffic packets.

Next solve (P2) the problem, proposing an iterative greedy strategy under which each node decides when to receive which traffic packet from which neighboring node by evaluating:

1. influence of adjacent node transmitting different service data packets to itself on itself AoI

2. Energy consumption by transceiving data packetsConsider one node at a time +. >And tries to activate a link for data transmission. The selection of link and packet traffic type is based on the weight parameter +.>The right half of the product of the formula (24) is as follows: />

wherein And the generation time of the kth b service data collected by the s node in the j node queue is represented, and if no data packet exists in the node queue, the value is 0.

The algorithm performs link activation and service selection under the condition that the scheduling constraint and the FCFS constraint are satisfied, and the transmission scheduling and routing algorithm at the t time slot is as follows:

/>

as shown in fig. 5, the specific algorithm on time slot t resolves as follows:

(1) Traversing the link weights:

optionally, a node first checks its communication status, and since each node uses half duplex mode, if the node is already in the transmit/receive state, the next node is skipped and the algorithm is transferred to the next node.

If the node is in idle state, traversing B service data queues of all nodes in the communication range according to FCFS constraint, and takingThe first packet in the queue and calculate its weight +.>The ownership values are stored and sorted according to size.

(2) Link activation:

the (i, j) link satisfying all three of the following conditions may be activated for transmission in this time slot:

1、Is the smallest of the ownership values.

2、The value is less than 0.

3. The j node is in an idle state.

(3) And (5) updating the state:

if there is an (i, j) link activated, the state of i, j is changed to receive/transmit. And updating the AoI queue of the receiving node and the service data queues of the two nodes at the same time.

In order to verify the unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness, a Queue length-Based Back-Pressure (Q-BP) algorithm and a Delay-Based Back-Pressure (D-BP) algorithm are respectively adopted to compare with LEA-SR performance.

The Q-BP algorithm takes the length difference of the queues among the nodes as a weight, and the D-BP algorithm takes the time delay difference of the first data packet in the queues among the nodes as a weight, and selects the data packet and the next hop node based on the principle of the maximum weight.

Comparing the Q-BP and the D-BP with the LEA-SR, the performance differences of the three schemes on the total node energy consumption and the average AoI under the conditions of different time slot lengths, maximum queue lengths, network parameters, the number of sampling nodes and the like are compared. The specific simulation parameters of the present invention are set as shown in table 1.

The simulation parameters are shown in table 1, in a default scene, 20 unmanned aerial vehicle nodes are uniformly and randomly distributed in a circular area with 80m as a radius, 5 unmanned aerial vehicle nodes are randomly selected as sensing nodes, the furthest communication distance between the nodes is 30m, and a node distribution schematic diagram is shown in fig. 6.

The system operates within 100 time slots, the service type is set to 3, the maximum average AoI threshold is set to 12, the Lyapunov optimization network parameter is set to 50, and the maximum data queue length of each node is set to 3.

Table 1 simulation parameter table

FIG. 7 is an average AoI thresholdSet to 6, the node averages AoI for three services per slot. The first few slots are the start-up times of the sampling nodes, which just start sampling, and do not transmit data packets to the transmitting node, so AoI at the transmitting node is 0. After the start time, the transmission node gradually receives the sampled data packets of three services, and the average AoI of the nodes of the three services rises rapidly first and then fluctuates in the interval of 1 to 4 respectively. Detailed data are shown in table 2, traffic 1, 2The average AoI of 3 is 2.12, 2.22 and 2.43 respectively, the maximum value floats within the range of 4+/-0.3, the standard deviation floats within the range of 0.9+/-0.08, and the proposed strategy can be seen to meet the requirement that the average AoI of all services is smaller than 6, the maximum AoI value can also be met, and the floating range is small, and the performance is excellent and stable.

Table 2 AoI analysis table of different service data

Service type	Average AoI	Maximum AoI	Standard deviation of
				Service 1	2.12	4.20	0.98
Service 2	2.22	3.73	0.83
				Service 3	2.43	4.27	0.91

Fig. 8 and 9 show the variation of the node average AoI and the node total energy consumption with the variation of the total slot length, respectively. The dot lines represent the case of using the proposed solution of the present invention, the asterisk lines represent the case of using the D-BP algorithm, and the plus line represents the case of using the Q-BP algorithm. The dashed line and the solid line represent cases where the maximum queue length is 3 and 6, respectively. The ordinate of fig. 8 takes the logarithmic value of AoI because the gap AoI between the different algorithms is too large. As can be seen from fig. 8, as the total slot length increases, the value of average AoI gradually increases and tends to converge, and the longer the maximum queue length, the greater the value of average AoI tends to be. From the comparison of the performance of three algorithms: the Q-BP algorithm performs worst in AoI performance because only queue length differences are considered; D-BP considers the delay difference of data packets in the queue, so that the performance is greatly improved compared with Q-BP in AoI, and under the condition that the maximum queue length is 6 and the total time slot length is less than 400, the AoI performance is slightly better than LEA-SR, but the LEA-SR performance is better in total energy consumption as seen in figure 8, so that the LEA-SR is better in energy consumption performance under the condition that AoI performance is met, and the comprehensive performance is best in combination with the energy consumption performance of figure 9. Specifically, taking the total time slot length of 800 as an example, when the maximum queue length is 3, the log (AoI) value of LEA-SR is reduced by 77.5% compared with Q-BP algorithm, and is reduced by 55.3% compared with D-BP algorithm; at a maximum queue length of 6, LEA-SR is reduced by 67.6% compared to Q-BP and 38.7% compared to D-BP algorithm.

From the convergence time of AoI performance, the LEA-SR algorithm converges around 300 slots, the Q-BP algorithm converges around 600 slots, and the D-BP algorithm does not converge yet at 800 slots. In summary, the LEA-SR has the fastest convergence speed, the smallest convergence value and the best performance.

Fig. 10 shows the average AoI as a function of maximum queue length and number of sampling nodes using the sampling and scheduling routing strategy provided by the present invention. As can be seen from the figure, when the queue length is equal to or greater than 2, the longer the maximum queue length, the larger the value of average AoI, and the larger the number of sampling nodes, the smaller the value of average AoI. Specifically, taking the maximum queue length of 5 as an example, s=7 is reduced by 10.7% from AoI in the case of s=5, s=9 is reduced by 10.6% from AoI in the case of s=7, s=11 is reduced by 5.6% from AoI in the case of s=9, s=15 is reduced by 7.2% from AoI in the case of s=11, and the influence of the increase of the sampling node on AoI gradually decreases as the specific gravity of the sampling node and the transmission node increases. The effect of the maximum queue length on AoI is that when the queue length is equal to 1, because of the lack of data packet reserves at the nodes, it is difficult to meet the multi-hop transmission requirement in time, so when the queue length is increased to 2, the pressure of multi-hop transmission is relieved, resulting in a slight decrease in AoI; when the queue length is greater than or equal to 2, the more packets stored in the queue, the fewer the number of discarded old packets, the more old packets stored, resulting in an increase in the overall AoI of the data in the network. The influence of the number of the sampling nodes on AoI is that the more the sampling nodes are, the more the source of fresh data is represented, on the one hand, the more the transmission opportunity between the transmission nodes and the sampling nodes is increased, and the timeliness of the data packet caused by the long transmission path is reduced; when the number of sampling nodes exceeds the number of transmission nodes, the change of AoI is saturated, so that the effect of the continuous increase of the number of sampling nodes on the reduction of AoI is greatly reduced.

Fig. 11 shows the change of AoI and energy consumption with the network parameter V under the sampling and scheduling routing strategy provided by the present invention. In the figure, the left ordinate represents AoI value, the right ordinate represents energy consumption value, the dot line represents AoI change curve, and the plus sign curve represents energy consumption change curve. The parameter V is used as a tuning parameter to adjust AoI the trade-off between performance and network power consumption, as shown in equation (16). The larger the V value, the more the system favors low power consumption performance. From the change in the energy consumption curve, the energy consumption value of the LEA-SR decreases with an increase in the V value, as can be seen from equations (23), (24), because the increase in the V value prevents the number of samples and the number of transmissions, thereby reducing the energy consumption. As can be seen from the AoI curve change, the value AoI of the LEA-SR decreases and then increases with increasing V value, because when V value increases, frequent sampling of the sampling node is suppressed, the queuing time of the sampled data in the sampling node queue is reduced, and thus the data AoI is reduced, but as V value continues to increase, the suppression effect on sampling and transmission is further enhanced, the number of sampled data packets is insufficient and the number of transmission times is reduced, resulting in a slow data update rate, and thus AoI is increased.

Fig. 12 shows the time complexity curves of three algorithms in each time slot in a time slot system with total duration of 50, and the detailed data are shown in table 3. It can be seen from the graph that the time complexity of the three algorithms increases rapidly first, and converges after a certain time slot, because the time complexity increases rapidly after the algorithm starts, and the algorithm stabilizes after a certain time, i.e. the time complexity fluctuates within a certain range. For example, the time complexity of the algorithm LEA-SR provided in this embodiment reaches a maximum of 9.06X10 in 6 slots ^-5 And then drops rapidly, substantially 7 x 10 after 7 time slots ^-5 Internally fluctuating, overall mean of 6.658×10 ^-5 . As can be seen from the average, the LEA-SR algorithm has the lowest average time complexity, 18.8% less than the Q-BP algorithm and 12% less than the D-BP algorithm. As can be seen from the standard deviation, the fluctuation range of the LEA-SR algorithm is minimum, the stability is strongest, and the fluctuation range is reduced by 31% compared with the Q-BP algorithm and reduced by 16.3% compared with the D-BP algorithm. From the convergence time point of view, LEA-SR is similar to that of the D-BP algorithm, and is reduced by 36.4% compared with that of the Q-BP algorithm. In summary, the time complexity of the LEA-SR algorithm has the best overall performance in terms of size, stability and convergence time, because Q-BP needs to traverse all queues of multiple nodes to obtain the queue length when calculating the scheduling weight, increasing the algorithm complexity, and the complexity is related to the number of data packets in the queues, and the fluctuation is large; the D-BP needs to check the time delay of the first data packet in each data queue of the node and the peripheral node one by one, and compare the time delay with the time delay of the first data packet one by one; LEA-SR discards queue traversal and compares one by one, directly calculates weights from neighbor node information, thus reducing the time complexity of the algorithm.

Table 3 time complexity analysis table of different algorithms

Algorithm	Maximum value	Average value of	Standard deviation of	Convergence time slots
					Q-BP	9.95×10 ^-5	8.20×10 ^-5	1.64×10 ^-5	11
D-BP	8.65×10 ^-5	7.56×10 ^-5	1.35×10 ^-5	6
					LEA-SR	9.06×10 ^-5	6.66×10 ^-5	1.13×10 ^-5	7

The low-energy-consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control device provided by the invention is described below, and the low-energy-consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control device described below and the low-energy-consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method described above can be correspondingly referred to each other.

Fig. 13 is a schematic structural diagram of an unmanned aerial vehicle network data acquisition and transmission control device with low energy consumption and high timeliness. Referring to fig. 13, the present invention provides a low-energy consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control device, which may include: the first determination module 1310, the second determination module 1320, the third determination module 1330, and the fourth determination module 1340.

A first determining module 1310, configured to determine energy consumption of all the unmanned aerial vehicle nodes in each time slot based on energy consumed by the unmanned aerial vehicle sampling node in each time slot, energy consumed by all the unmanned aerial vehicle nodes in each time slot for receiving the data packet, and energy consumed by all the unmanned aerial vehicle nodes in each time slot for transmitting the data packet;

A second determining module 1320, configured to determine a network average AoI corresponding to all the unmanned aerial vehicle nodes in the total time based on AoI of all the unmanned aerial vehicle nodes in each time slot;

a third determining module 1330, configured to convert, based on a lyapunov optimization theory, energy consumption of all unmanned aerial vehicle nodes in each time slot and a target constraint condition corresponding to the network average AoI, and obtain a minimum average energy consumption when it is determined that the network average AoI is less than a AoI threshold;

a fourth determining module 1340 configured to determine a sampling policy of the unmanned aerial vehicle sampling node in the time slot and a transmission scheduling policy and a routing policy of all unmanned aerial vehicle nodes in each time slot based on the network average AoI and the minimum average energy consumption;

In some embodiments, the apparatus further comprises:

In some embodiments, the third determination module is further to:

In some embodiments, the objective function is represented by:

wherein T is the total time slot number;

is the sampling power consumption coefficient of service b, +.>Is the reception power consumption coefficient of service b, +.>Is the transmission power consumption coefficient of service b, +.>Data packet for indicating whether the unmanned aerial vehicle sampling node s generates a service b, +.>Data packet for indicating whether the drone node chooses to transmit traffic b generated by the drone sampling node s within the t time slot, y _ji (t) means for indicating whether the drone node j transmits a data packet to the drone node i, y in said t time slot _ij (t) is used to indicate whether the drone node i transmits a data packet to the drone node j within the t time slot.

In some embodiments, the target constraint is:

wherein ,a kth data packet for indicating whether said drone node has chosen to transmit traffic b generated by drone sampling node s within said t time slot,/-for >Average AoI, y corresponding to service b in ith unmanned plane node _ni (t) means for indicating whether or not the unmanned node n transmits a data packet to the unmanned node i, at time slot t>Representing the total number of unmanned aerial vehicle nodes, < + >>Representing the total number of sampling nodes of the unmanned aerial vehicle, < >>Representing the total number of categories, K, of service data _b The maximum number of data packets of the service b which can be acquired by the unmanned aerial vehicle sampling node in T time slots is represented.

Fig. 14 illustrates a physical structure diagram of an electronic device, as shown in fig. 14, which may include: processor 1410, communication interface (Communications Interface) 1420, memory 1430 and communication bus 1440, wherein processor 1410, communication interface 1420 and memory 1430 communicate with each other via communication bus 1440. The processor 1410 may invoke logic instructions in the memory 1430 to perform a low power and high timeliness unmanned network data acquisition and transmission control method comprising:

In addition, the logic instructions in the memory 1430 described above may be implemented in the form of software functional units and may be stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

In another aspect, the present invention also provides a computer program product, where the computer program product includes a computer program, where the computer program can be stored on a non-transitory computer readable storage medium, where the computer program, when executed by a processor, can perform the low-energy and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method provided by the above methods, and the method includes:

In still another aspect, the present invention further provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the low-power and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method provided by the above methods, the method comprising:

The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. The unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness is characterized by comprising the following steps:

the unmanned aerial vehicle node comprises the unmanned aerial vehicle sampling node and an unmanned aerial vehicle transmission node, and the total time is determined based on the total time slot number and the time length corresponding to each time slot;

based on the lyapunov optimization theory, the converting the energy consumption of all the unmanned aerial vehicle nodes in each time slot and the target constraint condition corresponding to the network average AoI, and obtaining the minimum average energy consumption under the condition that the network average AoI is determined to be smaller than the AoI threshold value, includes:

based on the dynamic change conditions of the sampling queue and the AoI queue, obtaining the minimum average energy consumption under the condition that the network average AoI is determined to be smaller than a AoI threshold value;

the target constraint condition is determined based on the average number of times that the unmanned aerial vehicle sampling node samples the data packet corresponding to each service in each time slot, the average AoI of the data packet corresponding to each service in each unmanned aerial vehicle node in each time slot, the data packet transmitted by the unmanned aerial vehicle node in each time slot and the next hop node selected by the unmanned aerial vehicle node in each time slot;

The objective function is represented by the following formula:

wherein T is the total time slot number;

2. The low-energy-consumption and high-timeliness unmanned aerial vehicle network data acquisition and transmission control method of claim 1, wherein prior to determining the energy consumption of all unmanned aerial vehicle nodes in each time slot, further comprising:

3. The unmanned aerial vehicle network data acquisition and transmission control method with low energy consumption and high timeliness according to claim 1, wherein the target constraint condition is:

wherein ,a kth data packet for indicating whether said drone node has chosen to transmit traffic b generated by drone sampling node s within said t time slot,/-for>Average AoI, y corresponding to service b in ith unmanned plane node _ni (t) means for indicating whether or not the unmanned node n transmits a data packet to the unmanned node i, at time slot t >Representing the total number of unmanned aerial vehicle nodes, < + >>Representing the total number of sampling nodes of the unmanned aerial vehicle, < >>Representing the total number of categories, K, of service data _b The maximum number of data packets of the service b which can be acquired by the unmanned aerial vehicle sampling node in T time slots is represented.

4. The utility model provides a unmanned aerial vehicle network data acquisition and transmission controlling means of low energy consumption and high timeliness which characterized in that includes:

the third determining module is specifically configured to:

The objective function is represented by the following formula:

wherein T is the total time slot number;

5. The low energy and high timeliness unmanned aerial vehicle network data acquisition and transmission control apparatus of claim 4, wherein the apparatus further comprises:

6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the low energy and high timeliness unmanned aerial vehicle network data acquisition and transmission control method of any one of claims 1 to 3 when the program is executed by the processor.

7. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the low energy and high timeliness unmanned aerial vehicle network data acquisition and transmission control method of any one of claims 1 to 3.