CN110543185B - Unmanned aerial vehicle data collection method based on minimum information age - Google Patents

Unmanned aerial vehicle data collection method based on minimum information age Download PDF

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CN110543185B
CN110543185B CN201910658753.4A CN201910658753A CN110543185B CN 110543185 B CN110543185 B CN 110543185B CN 201910658753 A CN201910658753 A CN 201910658753A CN 110543185 B CN110543185 B CN 110543185B
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CN110543185A (en
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刘娟
童鹏
王玺钧
陈波
谢玲富
屈龙
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Xi'an Sireida Intelligent Technology Co.,Ltd.
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Abstract

The invention discloses an unmanned aerial vehicle data collection method based on minimized information age, which comprises the following steps: (1) the data center u _0 acquires the positions of M sensing nodes in the wireless network; (2) the data center u _0 uploads time according to the sensing node
Figure DDA0002136894930000011
And a clustering weight γ; (3) according to the obtained data uploading time
Figure DDA0002136894930000012
And the flight time eta _ (k, k ^') of the unmanned aerial vehicle, and executing a path planning algorithm; (4) storing the unmanned aerial vehicle data collection scheme corresponding to the current clustering weight gamma; (5) the unmanned aerial vehicle starts from the data center, and the optimal unmanned aerial vehicle flight track u ^ obtained according to the above. The invention provides a clustering method for pre-adjusting the position of a data collection point of an unmanned aerial vehicle and the distance between the data collection point and a sensing node, which effectively balances the data transmission time of all the sensing nodes and the flight time of the unmanned aerial vehicle, so that the information age of the sensing node is not sharply increased along with the increase of the network scale, and the clustering method is suitable for different wireless sensing network scales.

Description

Unmanned aerial vehicle data collection method based on minimum information age
Technical Field
The invention relates to the field of wireless sensor network data acquisition, in particular to a novel unmanned aerial vehicle data collection method based on minimum information age.
Background
The essence of the wireless sensor network is that effective data required by application is acquired from a monitoring scene through a flexibly deployed sensor, and the effective data is a link between a sensing layer and an application layer, so that a reasonable wireless data collection scheme is designed to be very important; the existing widely used wireless data collection methods include multi-hop wireless route forwarding, internet of things, satellite communication and the like, and due to the defects of high power consumption, low reliability, high system maintenance cost, high construction cost and the like, the wireless data collection methods cannot be effectively applied to some special scenes, especially scenes sensitive to data timeliness, such as natural disaster scenes of fire, earthquake and the like, and therefore, the data collection method for ensuring the freshness of the perceived data becomes the key for solving the problems.
The unmanned aerial vehicle has the characteristics of high maneuverability, flexible deployment, low operation cost and the like, and is widely applied to various scenes such as cruising, logistics, surveying and mapping recently; as a mobile data collector with extremely high controllability, the unmanned aerial vehicle is particularly suitable for application occasions with rare human smoke or unsuitability for human activities, and improves the data collection performance through flight and communication control, on one hand, a sensing node does not need to keep a working state all the time, can wake up from a dormant state when the unmanned aerial vehicle is in the near range, collects data according to the command of the unmanned aerial vehicle, realizes when the sensing node needs to collect, and saves the energy of the sensing node; on the other hand, the unmanned aerial vehicle can establish a line-of-sight communication link with the sensing node when approaching the sensing node, and then transmits sensing data, so that the transmitting power of the sensing node is effectively reduced, and the loss rate of data packets can also be reduced; therefore, unmanned aerial vehicle data acquisition can improve sensing node's energy efficiency, prolongs wireless sensor network's life.
In order to improve the data acquisition performance, the existing unmanned aerial vehicle data acquisition technology is mainly improved and optimized in two aspects of unmanned aerial vehicle communication and flight control; abdulla et al propose An efficient data collection method for unmanned aerial vehicles based on adaptive modulation and time division multiplexing in the article of "adaptive data collection technique for improved availability in UAS-aided networks", sensing nodes are divided into different clusters, and a cluster head of each cluster obtains a fair time division access channel opportunity to directly communicate with the unmanned aerial vehicle, but they do not consider the timeliness of the collected data. Tripathi et al propose a data collection and propagation method based on minimum average and peak information ages of a network graph, wherein an unmanned aerial vehicle is used as a mobile data relay node, flies along an optimal random path in the network graph, and accesses each sensing node in a traversing manner, when the number of sensing nodes in the network increases, the Age of acquired data information increases, and the timeliness of data cannot be guaranteed.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle data collection method based on minimized information age aiming at the defects of the prior art, and the information age of the data sampled by the sensing node is minimized by balancing the data transmission time and the flight time of the unmanned aerial vehicle, so that the timeliness of the sampled data is ensured.
The technical method adopted by the invention for solving the technical problems comprises the following steps:
(1) based on a data center, M ground sensing nodes and a wireless sensing network of an unmanned aerial vehicle, the data center acquires the positions of the M sensing nodes, sets the flight altitude and the speed of the unmanned aerial vehicle, sets the position of any one sensing node as the horizontal coordinate of a data collection point of the unmanned aerial vehicle, and calculates the data uploading time from all the M sensing nodes to a candidate data collection point of the unmanned aerial vehicle;
(2) according to the data uploading time of the sensing nodes, executing a clustering algorithm, and determining a group of unmanned aerial vehicle data collection points and coordinates thereof, sensing nodes associated with each collection point and an uploading sequence thereof:
(2a) executing a clustering algorithm, dividing all M sensing nodes into K clusters, and associating each cluster of sensing nodes to an unmanned aerial vehicle data collection point;
(2b) adjusting the coordinates of corresponding data collection points of the unmanned aerial vehicle according to the position of each cluster of sensing nodes, and determining the association relationship between each data collection point and which sensing nodes of the unmanned aerial vehicle;
(2c) determining the uploading sequence of the sensing nodes associated with each data collection point, and updating the data uploading time from the sensing nodes to the data collection points;
(3) Executing a path planning algorithm, and determining the optimal flight track of the unmanned aerial vehicle along the data collection point, so that the utility function value of the information age is minimum;
(4) changing the size of the cluster, repeating the steps (2) and (3) until the clustering result is not changed any more, and finding out the coordinates of the data collection points of the unmanned aerial vehicle with the optimal information age, the sensing nodes associated with the data collection points, the uploading sequence and the flight trajectory;
(5) the unmanned aerial vehicle starts from the data center according to the data collection scheme determined in the step (4), accesses the data collection points one by one, stops at each data collection point, establishes a communication link with the associated sensing node, receives and stores data uploaded by the sensing node, and flies back to the data center for data unloading and processing:
(5a) starting from the current position, the unmanned aerial vehicle flies to the next station according to the flight track planned in the step (3);
(5b) if the current position is a data collection point, the unmanned aerial vehicle establishes communication links with the associated sensing nodes one by one according to the uploading sequence of the sensing nodes determined in the step (2), receives sampling data uploaded by the sensing nodes, stores the sampling data in a cache area, and executes the step (5 a); otherwise, the drone offloads the stored sampled data to the data center.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the method, the unmanned aerial vehicle is adopted for relaying data forwarding, and joint optimization is carried out on the data collection point position and the flight path of the unmanned aerial vehicle and the sensing data uploading sequence of the sensing nodes, so that the information age of the sensing nodes is reduced, and the timeliness of the acquired sensing data is ensured.
2. The method of the invention adopts a clustering method to adjust the position of the data collection point of the unmanned aerial vehicle and the distance between the data collection point and the sensing node in advance, effectively balances the data transmission time of all the sensing nodes and the flight time of the unmanned aerial vehicle, and ensures that the information age of the sensing node is not sharply increased along with the increase of the network scale, thereby being suitable for different wireless sensing network scales.
Drawings
Fig. 1 is a schematic view of a scene to which the unmanned aerial vehicle data collection method based on minimized information age is applicable.
Fig. 2 is a flow chart of a method for collecting data of an unmanned aerial vehicle based on minimizing information age according to the present invention.
Fig. 3 is a sub-flow block diagram of the unmanned aerial vehicle data collection point and the association relationship between the unmanned aerial vehicle data collection point and the sensing node, which are established by the unmanned aerial vehicle data collection method based on the minimized information age.
Fig. 4 is a simulation comparison diagram of maximum information ages of the unmanned aerial vehicle data collection method based on the minimum information age in different path planning methods according to this embodiment.
Fig. 5 is a simulation comparison diagram of average information ages of the unmanned aerial vehicle data collection method based on the minimum information age in different path planning methods according to this embodiment.
Detailed Description
The invention is described in further detail below with reference to the accompanying examples.
Referring to fig. 1, the network scenario used in this embodiment includes a data center u0And M sensing nodes distributed in the two-dimensional plane area and having position coordinates of w0＝(x0,y00) and wS,i＝(xi,yi0) (i ∈ {1,2,3, …, M }). Each sensing node is assigned an ID serial number, and the position coordinates of the sensing node can be acquired by GPS or other positioning technologies and then transmitted to a data center u in a wireless mode0The maximum communication range of the sensing node is Dmax. The sensing node m samples the monitored object, and encapsulates the sampling data and the time stamp with the length of LmIn a packet of bits. The unmanned aerial vehicle can fly at a constant speed at the height h and the speed v and can also hover in the air to collect ground node sensing data. According to the position coordinates of each node, the data center u 0Calculating any sensing node m and unmanned aerial vehicle air collection pointA linear distance between k is
Figure BDA0002136894910000031
Here (x)k,yk) The ground coordinates corresponding to the unmanned aerial vehicle collecting point k in the air. When the sensing nodes m upload data to the unmanned aerial vehicle, the ground-air wireless communication links established by the sensing nodes m are easily influenced by factors such as weather and environment, line-of-sight or non-line-of-sight links may occur, so that the wireless signal propagation path loss is different, and the probability of the line-of-sight links is PLoS(dm,k) The calculation can be as follows:
Figure BDA0002136894910000032
where a and b are constants that depend on the environment, the average path loss is calculated:
Figure BDA0002136894910000041
wherein f iscIs the carrier frequency, c is the speed of light, βLoSAnd betaNLoSRepresenting extra path loss, 1-P, in line-of-sight and non-line-of-sight links, respectivelyLoS(dm,k) Is a non line-of-sight link probability. When the sensing node m is powered by PmData rate R when uploading data packets to the dronem,kCalculated by the following formula:
Figure BDA0002136894910000042
where B is the communication bandwidth, β is the channel gain at a reference point (e.g., 1 meter from the node), σ2Is the receiver noise power.
Referring to fig. 2, the working steps of this embodiment are as follows:
step 1, initializing data collection parameters of the unmanned aerial vehicle.
Data center u0Setting values of flying height h and speed v of unmanned aerial vehicle, wherein the flying height h of the unmanned aerial vehicle canTo be set at a maximum value h maxAnd a minimum value hminAt any value in between, the flying speed v may be set at 0 to the maximum value vmaxAny value in between. Obtaining the positions of M sensing nodes, setting the position of each sensing node as the horizontal coordinate of a candidate collection point of the unmanned aerial vehicle, and calculating the distance d from the sensing node M to the candidate collection point k of the unmanned aerial vehiclem,k(M, k e {1,2,3, …, M }), and calculating the average data transmission rate R from the sensing node M to the data collection point k according to the formulam,kAnd upload time
Figure BDA0002136894910000043
Setting the clustering weight gamma as zero and the maximum clustering weight gamma as gammamaxAnd the iteration step size is Δ γ.
Step 2, according to the data transmission time of the sensing node obtained in the step 1
Figure BDA0002136894910000044
And a clustering weight γ, performing a clustering algorithm based on Affinity Propagation (AP) method.
The specific flow is shown in fig. 3:
(2a) setting the number of initialization iterations t to 0 and setting the maximum number of iterations tmaxIs a larger constant, the correlation coefficient between the sensing nodes m and j
Figure BDA0002136894910000045
As follows:
Figure BDA0002136894910000046
first message variable alpham,jIs the message transmitted from the sensing node m to the node j, and the second message variable rhom,jIs a message passed by a sensing node j to a node m, a first message variable alpham,jAnd a second message variable ρm,jAre all initialized to zero, counter tcInitialized to 1, maximum value t of counterc,maxSetting the cluster head set gamma to be a constant, and initializing the cluster head set gamma to be an empty set;
(2b) First elimination in the t-th iterationThe univariate alpham,j(t) and a second message variable ρm,j(t) updates as follows:
Figure BDA0002136894910000051
Figure BDA0002136894910000052
wherein N (j) is a neighboring node of the sensing node j, namely the distance between the sensing node j and the sensing node j is less than or equal to the communication range DmaxNode of, N+(j) The method comprises the steps of (1) including a sensing node j and adjacent nodes N (j);
(2c) current new cluster head set ΓnewSetting the message to be an empty set, and if the sum of the first message variable and the second message variable transmitted to the sensing node m by the sensing node m is more than 0, meeting alpham,m(t)+ρm,m(t)>0, the node m is a cluster head, and m is added into the current new cluster head set gammanewExecuting the step (2 d); otherwise, the node m is not a cluster head, and step (2d) is executed.
(2d) If f isnewIf t, the counter is incremented by 1, tc＝tc+1, performing step (2 e); otherwise, the counter is cleared, tcAnd (0) updating the cluster head set gamma (gamma)newExecuting the step (2 e);
(2e) if t isc<tc,maxAnd t<tmaxUpdating the iteration time t to t +1, and executing the step (2 b); otherwise, the iteration process is terminated, and the association relations between the node m and all the K cluster head nodes j are compared
Figure BDA0002136894910000053
Find out
Figure BDA0002136894910000054
Smallest cluster
Figure BDA0002136894910000055
Dividing the sensing nodes M into the kth cluster, thus dividing the M sensing nodes intoForming a K ═ f | cluster, and recording the nodes in the kth cluster as JkAssociating them to a data collection point k, performing step (2 f);
(2f) A sensing node set J related to the data collection point k obtained in the step (2e)k(K-1, …, K) according to node position wS,i(i∈Jk) Solving the 1-center problem to obtain
Figure BDA0002136894910000056
Updating optimal position w of data collection point k of unmanned aerial vehiclekSo that data collection points k to JkThe maximum distance between each node in the node is minimum;
(2g) obtaining K unmanned aerial vehicle data collection point coordinates w according to the step (2f)k(K is 1, …, K), recalculating the data uploading time from the sensing node m to the unmanned aerial vehicle collection point K
Figure BDA0002136894910000057
Set of sensing nodes J associated with data collection point kkNode upload time in
Figure BDA0002136894910000058
Sorting from big to small as | JkUploading sequence V of | sensing nodesk；
(2h) Calculating the flight time of the unmanned aerial vehicle between any two data collection points k and k' and the flight time from the data collection point k to the data center to obtain etak,k′＝v-1dk,k′＝v-1||wk-wk′||(k,k′＝0,1,...,K)；
Step 3, uploading time of the data obtained according to the step (2g)
Figure BDA0002136894910000059
And the flight time eta of the unmanned aerial vehicle obtained in the step (2h)k,k′Executing a dynamic planning algorithm, and determining the unmanned aerial vehicle flight track with the maximum node information age utility function:
(3a) calculating the total uploading time of the sensing nodes associated with the data collection point kWorkshop
Figure BDA0002136894910000061
Calculating the time for the unmanned aerial vehicle to return to the data center for unloading data
Figure BDA0002136894910000062
The initial data collection point is k equal to 1;
(3b) the set of data collection points other than the starting data collection point K is U ═ 1, …, K \ { K }, listing all subsets of U
Figure BDA0002136894910000063
The minimum path cost corresponding to the flight paths of the nodes in the S accessed by the unmanned aerial vehicle one by one from the data collection point k is f (k, S), and when the maximum information age of the nodes is taken as a utility function, f (k, S) is iteratively calculated as follows:
Figure BDA0002136894910000064
wherein f ismax(k, S) is the minimum value of the maximum information age of the flight paths of the nodes in S visited one by the unmanned aerial vehicle from the data collection point k; when the average information age of all nodes is taken as a utility function, f (k, S) is iteratively calculated as follows:
Figure BDA0002136894910000065
wherein | S | - [ ∑ S | - ]i∈S|JiI is the total number of sensing nodes in S, χiIs the total uploading time f of the sensing nodes associated with the data collection point k obtained in the step (3a)ave(k, S) is the minimum value of the average information age of the flight paths of the nodes in S visited one by the drone from the data collection point k.
(3c) If K is equal to K, all possible flight paths and expenses of the unmanned aerial vehicle are calculated, and step (3d) is executed; otherwise, updating the starting data collection point k to k +1, and executing the step (3 b);
(3d) first stageDetermining the first accessed data collection point U by using the initialized backtracking data collection point set U as {1, …, K } 1＝argmink∈Uf (k, U \ k }), and updating the backtracking data collection point set U ═ U \ U }1Determine a second accessed data collection point u2＝argmink∈Uf (K, U \ K }), and iterating until U becomes a null set to obtain the K-th accessed data collection point UKFinding out the optimal flight path u ═ u [ u ] of the unmanned plane with the optimal information age0,u1,…,uK,u0]；
(3e) Calculating utility function value A of sensing node information ageuIncluding the maximum information age A of the sensing nodemaxAnd mean information age Aave：
Figure BDA0002136894910000066
Figure BDA0002136894910000067
Step 4, storing the unmanned aerial vehicle data collection scheme corresponding to the current clustering weight gamma, wherein the scheme comprises unmanned aerial vehicle data collection points K and coordinates w thereofkAnd each data collection point k is associated with a sensing node set JkAnd its optimal upload order VkUnmanned aerial vehicle is along the flight path u of data collection point, utility function value A of sensing node information ageu. Updating the clustering weight gamma-gamma + delta gamma if gamma is less than or equal to gammamaxExecuting the step 2; otherwise, comparing the utility function values of the information ages of the multiple groups of sensing nodes, and selecting the clustering weight gamma which minimizes the utility function value of the information age*Determining an optimal unmanned aerial vehicle data collection scheme
Figure BDA0002136894910000071
(k＝1,…,K*) And step 5 is executed.
And 5, starting the unmanned aerial vehicle from the data center, and obtaining the optimal unmanned aerial vehicle flight track u according to the steps 2-4 *To get it toThe data collection point is accessed, the sensing data of the related sensing nodes are collected and stored, and the sensing data fly back to the data center for data unloading and processing:
(5a) unmanned plane slave uk(k＝0,1,…,K*) Starting from a straight line at a speed v to the next station uk+1；
(5b) If u isk+1Is a data collection point, and step (5c) is executed; otherwise, executing the step (5 g);
(5c) unmanned aerial vehicle collects point u to this datak+1Associated sensing node
Figure BDA0002136894910000072
Broadcasting a handshake message, transmitting a confirmation message back after the sensing node receives the handshake message, and establishing the unmanned aerial vehicle and the sensing node
Figure BDA0002136894910000073
The communication link between the nodes, the order of the initialization uploading node i is 1;
(5d) according to
Figure BDA0002136894910000074
Specified uploading sequence, unmanned aerial vehicle to sensing node
Figure BDA0002136894910000075
Broadcasting an uploading instruction message which comprises the ith uploading sensing node
Figure BDA0002136894910000076
The ID of (1);
(5e) sensing node
Figure BDA0002136894910000077
After receiving the uploading instruction message, the mobile terminal will
Figure BDA0002136894910000078
Comparing the node ID with the self ID, if the node ID is the same as the self ID, performing sensing data sampling, packaging the data and the time stamp together in a data packet, and controlling the power PmUpload data packet to unmanned aerial vehicle, if notThe message is ignored;
(5f) the unmanned aerial vehicle receives and stores a data packet from the sensing node m, and updates an uploading node sequence i to i + 1; if node order is uploaded
Figure BDA0002136894910000079
Performing step (5 d); otherwise, updating the data collection point sequence number k to k +1, and executing the step (5 a);
(5g) The unmanned aerial vehicle sends handshake information to the data center, after receiving the confirmation information, a communication link is established with the data center, the stored sensing data is transmitted by power P, and the data center receives sensing data packets of all nodes.
The effect of the invention can be further illustrated by simulation:
(1) simulation conditions
The scene used in this embodiment includes a data center with coordinates w0The number of sensing nodes is 19, and the sensing nodes are randomly distributed in a square area with the side length of 2 km. Unmanned aerial vehicle airspeed is 20 meters per second for v, and the fly height is 100 meters for h, and unmanned aerial vehicle and sensing node communication radius are Dmax1000 m, system bandwidth B10 MHz, sensing node transmission power Pm0.1watt, noise power σ2Carrier frequency f of-110 dBmc2GHz, light speed c 3 × 108The channel gain beta of the reference distance point is-60 dB, betaLoS0dB and betaNLoS3dB, environment constant a 9.61 and b 0.16, and packet length Lm1Mbits, maximum number of iterations tmax＝105And the maximum value of the counter is tc,maxMaximum clustering weight γ of 100max100 and an iteration step Δ γ of 5.
(2) Emulated content and results
Simulation 1, verifying that the unmanned aerial vehicle data collection scheme generated by the method can enable the sensing node to obtain the minimum maximum information age through data collection point optimization and path planning in a scene of 19 nodes.
FIG. 4 shows that: by adjusting the clustering weight, the number of data collection points of the unmanned aerial vehicle and the coordinates of the data collection points can be changed, on the basis, the maximum information age of the sensing nodes can be effectively reduced by adopting a proper path planning method, the freshness of sensing node perception data is ensured, and the maximum information age index is irrelevant to the node uploading sequence.
Simulation 2, verifying that the unmanned aerial vehicle data collection scheme generated by the method can enable the sensing nodes to obtain the minimum average information age through data collection point adjustment, sensing node uploading sequence optimization and path planning in a scene of 19 nodes.
FIG. 5 shows that: similarly, through adjusting the clustering weight, the number of data collection points and the coordinates of the unmanned aerial vehicle can be changed, on the basis, the average information age of the sensing nodes can be effectively reduced through optimizing the flight track of the unmanned aerial vehicle and the uploading sequence of the sensing nodes, and the freshness of the sensing data of the sensing nodes is ensured.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (9)

1. An unmanned aerial vehicle data collection method based on minimized information age comprises the following steps:
(1) data center u0Obtaining positions w of M sensing nodes in wireless networkS，m(M is 1, … and M), setting the flight height h and the speed v of the unmanned aerial vehicle, setting the position of any sensing node as the horizontal coordinate of the data collection point of the unmanned aerial vehicle, and setting the data center u as the horizontal coordinate of the data collection point of the unmanned aerial vehicle0Calculating uploading time from the sensing node m to the candidate unmanned aerial vehicle data collection point k
Figure FDA0003607289690000011
Initializing clustering weight gamma and maximum clustering weight gammamaxAnd the iteration step size is Δ γ;
(2) data center u0According to the above-mentioned sensing nodeTime of flight
Figure FDA0003607289690000012
And a clustering weight γ, performing a clustering algorithm:
(2a) dividing M sensing nodes into K clusters, associating each cluster of nodes to an unmanned aerial vehicle data collection point, and determining a sensing node set J associated with the data collection point Kk(K1, …, K) based on the sensing node location w within each clusterS，i(i∈Jk) Updating position w of data collection point k of unmanned aerial vehiclek；
(2b) Collecting point position w according to the datak(K-1, …, K) and sensing node position wS，i(i∈Jk) Recalculating data uploading time from the sensing node m to the unmanned aerial vehicle collection point k
Figure FDA0003607289690000013
Set of sensing nodes J associated with data collection point kkNode upload time in
Figure FDA0003607289690000014
Sorting from big to small as | JkUploading sequence V of | sensing nodesk；
(2c) Collecting point position w according to the datakCalculating the flight time eta of the unmanned plane between any two data collection points k and kk，k′And time of flight η of data collection point k to data centerk，0；
(3) According to the obtained data uploading time
Figure FDA0003607289690000015
And time of flight η of the dronek，k′Executing a path planning algorithm, and determining the unmanned aerial vehicle flight track with the maximum node information age utility function:
(3a) according to the obtained data uploading time
Figure FDA0003607289690000016
Calculating the total uploading time of the sensing nodes related to the data collection point k
Figure FDA0003607289690000017
Calculating data unloading time of unmanned aerial vehicle in data center
Figure FDA0003607289690000018
(3b) Executing an information age-optimal unmanned aerial vehicle flight path planning algorithm, and determining the flight path u of the unmanned aerial vehicle along the data collection point as [ u [ [ u ]0，u1，…，uK，u0]Wherein u iskIs the data collection point of the kth visit, and the flight time eta of the unmanned plane along each small section of path in u is calculatedk，k+1(k＝0，1，…，K)；
(3c) According to the obtained flight path u of the unmanned aerial vehicle, the data uploading time
Figure FDA0003607289690000021
Total upload time χkAnd time of flight ηk，k+1Calculating utility function value A of sensing node information ageu；
(4) Storing the data collection scheme of the unmanned aerial vehicle corresponding to the current clustering weight gamma, wherein the scheme comprises the number K of data collection points of the unmanned aerial vehicle and the coordinates w of the data collection points kAnd each data collection point k is associated with a sensing node set JkAnd its optimal upload order VkUnmanned aerial vehicle along flight path u of data collection point, utility function value A of sensing node information ageuUpdating the clustering weight gamma-gamma + delta-gamma, if gamma is less than or equal to gammamaxExecuting the step (2); otherwise, comparing the utility function values of the information ages of the multiple groups of sensing nodes, and selecting the clustering weight gamma which minimizes the utility function value of the information age*Determining an optimal unmanned aerial vehicle data collection scheme
Figure FDA0003607289690000022
Executing the step (5);
(5) unmanned aerial vehicle slave dataStarting at the heart, obtaining the optimal flight path u of the unmanned aerial vehicle according to the above*And visiting the data collection points one by one, collecting and storing the perception data of the associated sensing nodes, and flying back to the data center for data unloading and processing:
(5a) unmanned aerial vehicle slave uk(k＝0，1，…，K*) Starting from a straight line at a speed v to the next station uk+1；
(5b) If u isk+1Is a data collection point, and step (5c) is executed; otherwise, executing the step (5 g);
(5c) unmanned aerial vehicle collects point u to this datak+1Associated sensing node
Figure FDA0003607289690000023
Broadcasting a handshake message, transmitting a confirmation message back after the sensing node receives the handshake message, and establishing the unmanned aerial vehicle and the sensing node
Figure FDA0003607289690000024
The communication link between the nodes, the order of the initialization uploading node i is 1;
(5d) According to the following
Figure FDA0003607289690000025
A specified uploading sequence of unmanned aerial vehicles to the sensing node
Figure FDA0003607289690000026
Broadcasting an uploading instruction message which comprises the ith uploading sensing node
Figure FDA0003607289690000027
The ID of (2);
(5e) sensing node
Figure FDA0003607289690000028
After receiving the uploading instruction message, the mobile terminal will
Figure FDA0003607289690000029
Node pointID is compared with self ID, if the ID is the same, sensing data sampling is carried out, data and time stamp are packaged in a data packet together, and power P is usedmUploading the data packet to the unmanned aerial vehicle, otherwise ignoring the message;
(5f) the unmanned aerial vehicle receives and stores a data packet from the sensing node m, and updates an uploading node sequence i to i + 1; if node order is uploaded
Figure FDA0003607289690000031
Performing step (5 d); otherwise, updating the data collection point sequence number k to k +1, and executing the step (5 a);
(5g) the unmanned aerial vehicle sends a handshake message to the data center, establishes a communication link with the data center after receiving the confirmation message, and transmits the stored sensing data with power P; and the data center receives the sensing data packets of all the nodes.
2. The method for collecting data of unmanned aerial vehicle based on minimized information age as claimed in claim 1, wherein the setting of the flying height h and velocity v of unmanned aerial vehicle in step (1) means that the flying height h of unmanned aerial vehicle can be set at a maximum value hmaxAnd a minimum value hminOf any value in between, the speed v may be set at 0 to a maximum value v maxAny value in between.
3. The unmanned aerial vehicle data collection method based on minimized information age as claimed in claim 1, wherein the uploading time of the sensing node m to the candidate unmanned aerial vehicle data collection point k in step (1)
Figure FDA0003607289690000032
That is, the unmanned aerial vehicle hovers above a sensing node k, and each sensing node m is provided with power PmUpload length to unmanned aerial vehicle is LmThe probability of line-of-sight link being PLoS(dm，k) The calculation can be as follows:
Figure FDA0003607289690000033
where a and b are constants that depend on the environment, the average path loss is calculated:
Figure FDA0003607289690000034
wherein f iscIs the carrier frequency, c is the speed of light, βLoSAnd betaNLoSRepresenting extra path loss, 1-P, in line-of-sight and non-line-of-sight links, respectivelyLoS(dm，k) Non line-of-sight link probability when sensing node m is powered by power PmData rate R when uploading data packets to the dronem，kCalculated by the following formula:
Figure FDA0003607289690000041
where B is the communication bandwidth, β is the channel gain at the reference point, σ2For receiver noise power, the upload time is equal to
Figure FDA0003607289690000042
4. The method for unmanned aerial vehicle data collection based on minimized information age according to claim 1, wherein step (2a) can use clustering algorithm based on Affinity Propagation (AP) method, and comprises the following steps:
(2a1) setting the number of initialization iterations t to 0 and setting the maximum number of iterations t maxIs a larger constant, the correlation coefficient between the sensing nodes m and j
Figure FDA0003607289690000043
As follows:
Figure FDA0003607289690000044
first message variable alpham，jAnd a second message variable ρm，jAre all initialized to zero, counter tcInitialized to 1, maximum value t of counterc，maxSetting the cluster head set gamma to be a constant, and initializing the cluster head set gamma to be an empty set;
(2a2) first message variable a in the t-th iterationm，j(t) and a second message variable ρm，j(t) updated as follows:
Figure FDA0003607289690000045
Figure FDA0003607289690000046
wherein N (j) is a neighboring node of the sensing node j, namely the distance between the sensing node j and the sensing node j is less than or equal to the communication range DmaxNode of, N+(j) The method comprises the steps of (1) including a sensing node j and adjacent nodes N (j);
(2a3) current new cluster head set ΓnewSetting the message to be an empty set, and if the sum of the first message variable and the second message variable transmitted to the sensing node m by the sensing node m is more than 0, meeting alpham，m(t)+ρm，m(t) > 0, the node m is a cluster head, and m is added into the current new cluster head set gammanewExecuting the step (2a 4); otherwise, the node m is not a cluster head, and the step (2a4) is executed;
(2a4) if f isnewIf t, the counter is incremented by 1, tc＝tc+1, go to step (2a 5); otherwise, the counter is reset, tcAnd (0) updating the cluster head set gamma (gamma)newExecuting the step (2a 5);
(2a5) if t isc＜tc，maxAnd t < tmaxUpdating the iteration time t to t +1, and executing the step (2a 2); otherwise, the iteration process is terminated, and the association relations between the node m and all K cluster head nodes j are compared
Figure FDA0003607289690000051
Find out
Figure FDA0003607289690000052
Smallest cluster
Figure FDA0003607289690000053
Dividing the sensing nodes M into the kth cluster, so as to divide the M sensing nodes into K ═ Γ | clusters, and recording the nodes in the kth cluster as J ═ Γ |kAssociating them to a data collection point k, performing step (2a 6);
(2a6) according to the position w of each cluster nodeS，i(i∈Jk) Solving the 1-center problem to obtain
Figure FDA0003607289690000054
Updating optimal position w of data collection point k of unmanned aerial vehiclek。
5. The unmanned aerial vehicle data collection method based on minimized information age as claimed in claim 1, wherein step (2a) can also use K-means based clustering algorithm, and the following steps are performed:
(2a 1') iteration number is t ═ 0, and K ═ gamma unmanned aerial vehicle data collection point positions are initialized
Figure FDA0003607289690000055
Figure FDA0003607289690000056
(2a 2') calculating the distance between each sensing node m and K unmanned aerial vehicle data collection points
Figure FDA0003607289690000057
Associating node m to unmanned aerial vehicle data collection point
Figure FDA0003607289690000058
Thus, M sensing nodes are divided into K clusters according to the distance, and the first sensing node isThe nodes in the k cluster are denoted as Jk；
(2a 3') based on the node position w of each clusterS，i(i∈Jk) Solving the 1-center problem to obtain
Figure FDA0003607289690000061
Updating optimal position of data collection point k of unmanned aerial vehicle
Figure FDA0003607289690000062
(2a 4') if the drone data collection point has not changed, namely:
Figure FDA0003607289690000063
the iterative process is terminated; otherwise, the number of update iterations t ═ t +1, step (2a 2') is performed.
6. The method for collecting data of unmanned aerial vehicle based on minimized information age as claimed in claim 1, wherein the step (3a) is to calculate the data unloading time of unmanned aerial vehicle in data center
Figure FDA0003607289690000064
When the unmanned plane is powered by power P0Data rate R when transmitting data packets of all sensing nodes to data center0Calculated by the following formula:
Figure FDA0003607289690000065
where β' is the channel gain of the line-of-sight path reference point, the unload time is equal to
Figure FDA0003607289690000066
7. The method of claim 1, wherein the step (3b) is performed according to the minimum information age-based drone data collection methodAn optimal-age unmanned aerial vehicle flight path planning algorithm is used for determining the flight path u of the unmanned aerial vehicle along a data collection point as u0，u1，…，uK，u0]The dynamic programming algorithm can be adopted and carried out according to the following steps:
(3b1) setting a starting data collection point k of first access of the unmanned aerial vehicle as 1;
(3b2) the set of data collection points other than the starting data collection point K is U ═ 1, …, K } \ { K }, listing all subsets of U
Figure FDA0003607289690000067
The minimum path cost corresponding to the flight paths of the nodes in the S accessed by the unmanned aerial vehicle one by one from the data collection point k is f (k, S), and when the maximum information age of the nodes is taken as a utility function, f (k, S) is iteratively calculated as follows:
Figure FDA0003607289690000068
When the average information age of all nodes is taken as a utility function, f (k, S) is iteratively calculated as follows:
Figure FDA0003607289690000069
wherein | S | ═ Σi∈S|JiI is the total number of sensing nodes in S, χiThe total uploading time of the sensing nodes associated with the data collection point k obtained in the step (3 a);
(3b3) if K is equal to K, all possible flight paths and costs of the unmanned aerial vehicle are calculated, executing step (364); otherwise, updating the starting data collection point k-k +1, and executing the step (3b 2);
(3b4) initializing a set of backtracking data collection points U' {1, …, K }, and determining a first accessed data collection point U1＝argmink∈Uf (k, U ' \ { k }), updating the backtracking data collection point set U ' ═ U ' \ { U }, and updating the backtracking data collection point set U ' ═ U ' \\\ { U }, and updating the backtracking data collection point set U ' \ U } and the backtracking data collection point set U and U ' \\ U and U } are updated according to the updated backtracking data collection point set1Determine second accessData collection point u2＝argmink∈Uf (K, U '\ { K }), and so on until U' becomes an empty set, resulting in the K-th accessed data collection point UKFinding out the optimal flight path u ═ u [ u ] of the unmanned plane with the optimal information age0，u1，…，uK，u0]。
8. The method of claim 1, wherein the step (3b) of executing an information age-optimized unmanned aerial vehicle flight trajectory planning algorithm determines a flight path u ═ u of the unmanned aerial vehicle along the data collection point0，u1，…，uK，u0]A genetic algorithm can also be adopted, and the method comprises the following steps:
(3b 1') initializing population size NcGenetic algebra NgProbability of variation δmThe current genetic algebra n is 0;
(3b 2') production Scale NcEach individual representing a feasible path u of the drone along the data collection point;
(3b 3') estimating the fitness of each individual u as
Figure FDA0003607289690000071
l (u) is the cost of the path u, and when the maximum information age of the node is taken as the utility function, the cost of the path can be expressed as:
Figure FDA0003607289690000072
wherein eta is(k)，(k+1)The flight time from the kth data collection point to the (k + 1) th data collection point of the unmanned aerial vehicle along the path u, and when the average information age of the node is taken as a utility function, the path cost can be expressed as:
Figure FDA0003607289690000073
wherein the content of the first and second substances,
Figure FDA0003607289690000074
representing the total number of the associated sensing nodes from the 1 st data collection point to the k data collection point of the unmanned aerial vehicle along the path u;
(3b 4') pair size NcPerforms a proportional selection operation, each individual selected probability being proportional to its fitness, thus yielding N'cIndividual parent chromosomes;
(3b5 ') is generated N'cThe parent chromosomes are combined in pairs in sequence
Figure FDA0003607289690000081
Performing sequential crossing (OX) operation on the parent chromosomes to generate new offspring chromosomes;
(3b 6') with probability of delta for each individual chromosome mCarrying out local slope climbing mutation (2-opt) operation to generate new chromosomes when mutation occurs;
(3b 7') estimating the fitness of each individual u in the population as
Figure FDA0003607289690000082
Selection of NcThe chromosomes with the maximum fitness form a new population, the current genetic algebra is increased by 1, namely N is equal to N +1, and if the genetic algebra N is equal to NgPerforming step (3b 8'); otherwise, performing step (3b 4');
(3b 7') determining that the chromosome with the maximum fitness corresponds to the optimal unmanned aerial vehicle flight path u, and calculating the node information age l (u) correspondingly.
9. The unmanned aerial vehicle data collection method for minimizing information age according to claim 1, wherein the utility function value A of the information age of the sensor nodes is calculated in the step (3b)uWhen the maximum information age of the sensing node is taken as a utility function, AuAs follows:
Figure FDA0003607289690000083
wherein (n) represents the nth data collection point of the drone along the path u,
Figure FDA0003607289690000084
is the uploading time from the sensing node m to the data collection point (n) and takes the average information age of the sensing node as a utility function AuAs follows:
Figure FDA0003607289690000085
wherein the content of the first and second substances,
Figure FDA0003607289690000086
and the transmission time of the j-th sensing node corresponding to the nth data collection point of the path u of the unmanned aerial vehicle is represented.
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