CN114337787B - Unmanned aerial vehicle-assisted mobile edge computing system content caching method - Google Patents
Unmanned aerial vehicle-assisted mobile edge computing system content caching method Download PDFInfo
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Abstract
The invention relates to a content caching method of an unmanned aerial vehicle-assisted mobile edge computing system. Comprising the following steps: s1, acquiring a ground node coordinate; s2, dividing the caching process into a file caching stage and a file retrieval stage; s3, establishing a cost minimization optimization model by combining the channel capacity, the unmanned aerial vehicle flight rate and the storage capacity constraint of the unmanned aerial vehicle and the ground node; s4, optimizing the unmanned aerial vehicle waypoints by using a clustering algorithm, so that each ground node is at least in the service range of one unmanned aerial vehicle waypoint; s5, using a simulated annealing algorithm to obtain the flight track of the unmanned aerial vehicle with minimum time consumption; s6, solving the cost minimization optimization model by using a simulated annealing algorithm based on the unmanned aerial vehicle flight trajectory to obtain an optimal caching strategy. Under the conditions of unmanned aerial vehicle endurance, ground node capacity, D2D transmission link channel state and the like, balance between file caching cost and retrieval cost is realized by optimizing the flight track of the unmanned aerial vehicle and the file caching position, and time delay of a system is reduced.
Description
Technical Field
The invention belongs to the technical field of information and communication engineering, and particularly relates to a content caching method of an unmanned aerial vehicle-assisted mobile edge computing system.
Background
Existing Mobile Edge Computing (MEC) network architectures are typically made up of users (terminals), fixed location small base stations (mobile edge computing servers), but in many real life scenarios, such as war or disaster sites, deployment of fixed location small base stations becomes very difficult. In this case, the unmanned aerial vehicle is flexible, and the advantage of rapid deployment and scheduling is highlighted. Therefore, in some mobile edge computing networks, the unmanned aerial vehicle replaces a small base station with a fixed position, is deployed at the edge of a mobile user, and is connected to a mobile edge server through a wireless backhaul to provide functions of data distribution, storage, auxiliary computation, and the like. In order to prolong the communication time of the unmanned aerial vehicle or maximize the communication throughput, an algorithm for optimizing the total overhead of the unmanned aerial vehicle auxiliary mobile edge computing network from the whole system is proposed, but parameters needed in the prior art are not easily available, and the algorithm is difficult to realize.
Therefore, a new optimization scheme is needed.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides the unmanned aerial vehicle-assisted mobile edge computing system content caching method, which realizes the balance between file caching cost and retrieval cost and reduces the time delay of a system by optimizing the flight track and the file caching position of the unmanned aerial vehicle under the condition of comprehensively considering the unmanned aerial vehicle endurance capacity, ground node storage capacity, D2D transmission link channel state and the like.
The invention adopts the following technical scheme:
an unmanned aerial vehicle assisted mobile edge computing system content caching method, comprising the steps of:
s1, acquiring a ground node coordinate;
s2, dividing the caching process into a file caching stage and a file retrieval stage;
s3, comprehensively considering two stages, and establishing a cost minimization optimization model by combining channel capacity constraint, unmanned aerial vehicle flight rate constraint and unmanned aerial vehicle and ground node storage capacity constraint;
s4, optimizing the unmanned aerial vehicle waypoints by using a clustering algorithm, so that each ground node is at least in the service range of one unmanned aerial vehicle waypoint;
s5, obtaining the unmanned aerial vehicle flight track with minimum time consumption by using a simulated annealing algorithm;
s6, solving the cost minimization optimization model by using a simulated annealing algorithm based on the unmanned aerial vehicle flight track with the minimum time consumption so as to obtain an optimal caching strategy and the minimum system time delay.
Preferably, the cost includes file cache cost and average file retrieval cost.
Preferably, the file cache cost C U The calculation formula of (2) is as follows:
wherein T is U Representing the file caching period time, M representing the discretization time slot number of the file caching period time, delta representing the time slot time, N representing the number of files of interest to the user, Y n Indicating the number of packages into which the nth file is divided, N e 1,2, N,indicating the time required for the file buffering phase to complete a packet transfer,/->Representing the total flight distance of the unmanned aerial vehicle in one period, V U Representing the unmanned aerial vehicle flight speed.
As a preferred embodiment of the present invention,average document retrieval cost C G The calculation formula of (2) is as follows:
wherein,representing the time required for the file retrieval phase to complete one packet transmission, K represents the number of ground nodes, K e 1, a.k, p (a) (n) represents the request probability of the nth file, ">Indicating the expected number of files that can be successfully transferred over the D2D link.
As a preferred solution, the optimization objective of the cost minimization optimization model is to minimize the weighted sum of the file caching cost and the average file retrieval cost.
Preferably, the weighted sum C of the file caching cost and the average file retrieval cost θ The calculation formula of (2) is as follows:
wherein θ represents the average file retrieval cost C G And file cache cost C U A weighting factor balanced between.
As a preferred solution, the cost minimization optimization model is specifically:
s.t.
I kn ∈{0,1},k＝1,...,K；n＝1,...,N (14-3)
||q[m]-q[m-1]|| 2 ≤d d ,m＝1,...,M (14-6)
wherein I is kn Representing file caching strategy, if file n is cached on ground node k, it is equal to 0, otherwise it is equal to 1, q [ m ]]Represents the horizontal position of the unmanned plane in time slot m, J mn Indicating the number of packets the slot m drone transmits the nth file,represents an integer>Represent real numbers, M kn Representing a time slot set of a ground node k cache file n; equation (14-4) indicates that the total size of the file cached by each ground node does not exceed its storage capacity Q; equation (14-5) represents that each file is cached by at least one ground node; equation (14-6) shows that the horizontal displacement of the unmanned aerial vehicle in one time slot is smaller than the maximum flight distance d in one time slot δ The method comprises the steps of carrying out a first treatment on the surface of the Equation (14-8) indicates that the number of packets transmitted by the drone per slot is smaller than the maximum transmittable packet number L.
As a preferred scheme, in step S4, specifically: and optimizing the unmanned aerial vehicle route points according to the transmission distance between the unmanned aerial vehicle and the ground nodes and the constraint condition of the flight speed of the unmanned aerial vehicle by using a clustering algorithm, so that each ground node is at least in the service range of one unmanned aerial vehicle route point.
Preferably, step S5 includes the steps of:
s5.1, giving an initial temperature t 0 Randomly selecting one route point as a starting point at the temperature, and generating a flight path connecting all route points according to a certain access sequence 0 Calculate the total path length S 0 ；
S5.2, the current temperature t is set to be a coefficient alpha 1 Decaying cooling to the next temperature t i ；
S5.3, at the current route i On the basis of which the access sequence of two waypoints is randomly changed to generate a new route j Calculating way j Is the total path length S of (2) j ；
S5.4, for the current total path length S i And a newly generated total path length S j If S j <S i Then accept route j The method comprises the steps of carrying out a first treatment on the surface of the If S j ＞S i Then Δs=s is calculated j -S i And calculate p s ＝e-△S/t i Then randomly generating a segment [0,1 ]]The random number r1 obeys uniform distribution, if r1<p s Then accept route j ；
S5.5 at temperature t i Next, step S5.3 and step S5.4 are repeated L 1 s i Secondary, L 1 Representing a first custom value;
s5.6, judging whether an exit condition is met: s is arranged in the continuous first preset iteration times j ＝S i And if the optimal solution is satisfied, the iteration is exited, otherwise, the method returns to the step S5.2 to continue the iteration, so that the optimal solution of the flight trajectory of the unmanned aerial vehicle is obtained, and the shortest flight path S of the unmanned aerial vehicle is obtained.
Preferably, step S6 includes the steps of:
s6.1, giving an initial temperature T, and randomly generating a group of caching strategies I meeting constraint conditions at the temperature 0 According to the formula and the constraint conditionAn initial system delay (C θ ) 0 ，
S6.2, the current temperature T is a coefficient alpha 2 Decaying cooling to the next temperature T i ；
S6.3 at the present solution I i On the basis of which a new solution I is generated by randomly changing the cache nodes of a group of files n j Calculate a new system delay (C θ ) j ；
S6.4, time delay (C) θ ) i And newly generated system delay (C θ ) j If (C) θ ) j <(C θ ) i Then accept the new solution (C θ ) j The method comprises the steps of carrying out a first treatment on the surface of the If (C) θ ) j ＞(C θ ) i Then calculate Δc= (C θ ) j -(C θ ) i And calculate p c ＝e-△C/t i Then randomly generating a segment [0,1 ]]The random number r2 obeys uniform distribution, if r2<p c Then accept the new solution (C θ ) j ；
S6.5 at temperature T i Next, step S6.3 and step S6.4 are repeated L 2 c i Secondary, L 2 Representing a second custom value;
s6.6, judging whether an exit condition is met: the number of consecutive second preset iterations is all (C θ ) j ＝(C θ ) i If yes, the iteration is exited, otherwise, the step S6.2 is returned to for iteration, and the optimal caching strategy I is obtained kn * And system minimum delay C θ *。
The beneficial effects of the invention are as follows:
under the condition of comprehensively considering unmanned aerial vehicle endurance, ground node energy storage capability, D2D transmission link channel state and the like, balance between file caching cost and retrieval cost is realized by optimizing unmanned aerial vehicle flight tracks and file caching positions, and time delay of a system is reduced.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for caching content of a mobile edge computing system assisted by an unmanned aerial vehicle according to the present invention;
FIG. 2 is a schematic diagram of a network topology;
FIG. 3 is a simulation of a flight trajectory of an unmanned aerial vehicle;
fig. 4 is a convergence simulation diagram of the total delay of the system.
Detailed Description
The following specific examples are presented to illustrate the present invention, and those skilled in the art will readily appreciate the additional advantages and capabilities of the present invention as disclosed herein. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
Embodiment one:
referring to fig. 1, the embodiment provides a content caching method of a mobile edge computing system assisted by an unmanned aerial vehicle, which includes the steps of:
s1, acquiring a ground node coordinate;
s2, dividing the caching process into a file caching stage and a file retrieval stage;
s3, comprehensively considering two stages, and establishing a cost minimization optimization model by combining channel capacity constraint, unmanned aerial vehicle flight rate constraint and unmanned aerial vehicle and ground node storage capacity constraint;
s4, optimizing the unmanned aerial vehicle waypoints by using a clustering algorithm, so that each ground node is at least in the service range of one unmanned aerial vehicle waypoint;
s5, obtaining the unmanned aerial vehicle flight track with minimum time consumption by using a simulated annealing algorithm;
s6, solving the cost minimization optimization model by using a simulated annealing algorithm based on the unmanned aerial vehicle flight track with the minimum time consumption so as to obtain an optimal caching strategy and the minimum system time delay.
Therefore, the invention realizes the balance between the file caching cost and the retrieval cost and reduces the time delay of the system by optimizing the flight track of the unmanned aerial vehicle and the file caching position under the condition of comprehensively considering the endurance capacity of the unmanned aerial vehicle, the energy storage capacity of the ground node, the channel state of the D2D transmission link and the like.
Specifically:
the present invention contemplates a network topology as shown in fig. 2. The network comprises an unmanned plane node U, K ground nodes with K positions generated by poisson distribution, wherein K is represented by K epsilon 1, K is represented by N files of interest to users, N is represented by N epsilon 1,2, N is represented by N, and each file can be divided intoIndividual bags, wherein X n Representing the size of file n, S represents the packet size (in bits), and file popularity may be represented as f= { F 1 ,f 2 ,...,f N And of formula f i I e 1, 2..n represents the popularity of file i. The system works periodically, and each period is divided into two phases: a file caching stage and a file retrieval stage. In the file caching stage, the unmanned aerial vehicle plans a flight path and actively transmits each file to a selected ground node, and the ground nodes cooperatively cache all the files. In the file retrieval stage, the ground node selects to retrieve the file from the nearest neighbor of the ground node or from the nearest neighbor in a mode of transmitting the file from the ground node to the ground node through the device-to-device (D2D) according to the requirement of the ground node. This process is described in two parts:
(1) File caching stage
The file request probability is
In a E[0.5,1.5]The skewness of the file distribution is 0.ltoreq.p (a) (n)≤1。
The file caching strategy is as follows
The total size of the files cached by each ground node does not exceed the storage capacity Q:
each file is cached by at least one ground node, so there is
Defining the file cache phase time as T U And further discretizes it into M small equal time slots delta, i.e., T U =mδ, time slot is denoted M e 1, 2. It is assumed that the distance between the drone and GN is approximately constant within each time slot. Maximum flight distance d of unmanned aerial vehicle in one time slot δ ＝δV max Wherein V is max Representing the maximum speed of flight of the drone.
The signal to noise ratio when the unmanned plane transmits the file to the ground node k is
P in the formula U Representing the emission power sigma of unmanned plane U 2 Is the additive Gaussian white noise power, h Uk Is the channel gain from the unmanned plane U to the ground node k, and is the reference position d 0 Signal to noise ratio at =1 meterh U0 Is the unmanned plane U to the reference position d 0 Channel gain, q [ ], ofm]Represents the horizontal position of the unmanned aerial vehicle in time slot m, q [ m-1 ]]Representing the horizontal position of the unmanned plane in m-1 time slot, w k The horizontal position of the ground node k is represented, H is the flight altitude of the unmanned aerial vehicle, I is a norm symbol, and I q [ m ]]-w k The || represents the difference in horizontal distance between the drone and the ground node k, and ||q [ m ]]-q[m-1]|| 2 ≤d δ I.e. the horizontal displacement of the drone in a time slot is less than the maximum flight distance in a time slot.
Order theRepresenting the required signal-to-noise threshold for proper signal reception, R U For the transmission rate of the drone in the file buffering phase (fixed), B is the channel bandwidth of the drone to the ground node, Γ represents the SNR interval between the actual modulation and coding scheme and the theoretical gaussian signaling. If and only if->The ground node can successfully receive the file transmitted by the unmanned aerial vehicle in the time slot m.
The time required to complete a packet transmission isThe maximum number of packets that can be transmitted by the unmanned plane in each time slot is +.>L is more than or equal to 1 and is an integer, and the number of packets sent by the unmanned aerial vehicle in each time slot is less than L, so the following constraint holds:
in J mn And sending the packet number of the nth file for the time slot m unmanned aerial vehicle.
By M kn Representing the set of time slots in which node k caches file n, if node k is selected to cache file n, it needs to receive a total ofY n The encoded packets are used to recover file n. Thus file transfer schedule J mn Should satisfy constraints
The file buffering cost (the total time required for the unmanned aerial vehicle to transmit the file to the selected ground node) is
Wherein T is U Representing the file caching period time, M representing the discretization time slot number of the file caching period time, delta representing the time slot time, N representing the number of files of interest to the user, Y n Indicating the number of packages into which the nth file is divided, N e 1,2, N,indicating the time required for the file buffering phase to complete a packet transfer,/->Representing the total flight distance of the unmanned aerial vehicle in one period, V U Representing the unmanned aerial vehicle flight speed.
(2) Document retrieval stage
By K n ＝{k:I kn =1 } represents the set of ground nodes that cached file n, and when ground node k requests file n, there are two possible cases: file n has been cached by ground node K itself, i.e., K ε K n The file can be directly retrieved from the local cache, and the file retrieval cost is zero; when K is E K n When this occurs, the ground node k will retrieve file n over the D2D link from its nearest ground node that cached file n, adding additional delay due to D2D transmission.
Assuming that the node j has a cached file n, d kj ＝||w k -w j || 2 Represents the distance, w, between ground node k and ground node j k Represents the horizontal position, w, of the ground node k j Representing the horizontal position of the ground node j. The average channel gain between nodes k and j is Representing reference point d 0 Channel gain at=1, α+.2 is the path loss index of the D2D channel between GNs.
By R G The (fixed unit b/s) represents the transmission rate of each GN in the D2D file sharing stage. Thus, the time required to complete a packet transmission isUnlike UAV-to-GT channels where linear propagation link control exists, the ground channel between different GNs is typically subject to additional fading that varies randomly. We assume a quasi-static fading channel in which the instantaneous channel coefficient between GNs is +/for each packet duration>Remain unchanged and vary between different packets. Thus GN i From GN j The instantaneous channel gain of the i-th packet (file) of the search file n can be expressed as +.> Representing the fading component of the terrestrial channel, a>Representing the complementary cumulative distribution function of the fading channel power by F (x), i.e. +.>Where Pr (·) represents the probability sign.
Node k downloads the ith packet with instantaneous signal-to-noise ratio of the link
Wherein P is G D2D link transmit power, sigma, representing file retrieval phase 2 For the additive white gaussian noise power,is the reference point d 0 Signal to noise ratio at=1.
The instantaneous signal-to-noise ratio is not less than a thresholdThe file can be successfully received, i.e. +.>B G Representing the channel bandwidth of D2D communication, Γ represents the signal-to-noise ratio difference between the actual modulation and coding scheme and the theoretical value.
Probability of successful reception of file n by ground node kObeying Rayleigh distribution
Definition of the definitionIndicating the expected number of files n that can be successfully transferred over the D2D link
Based on the above definition, the average file retrieval cost (the average time required for a file request)
Wherein,representing the time required for the file retrieval phase to complete one packet transmission, K represents the number of ground nodes, K e 1, a.k, p (a) (n) represents the request probability of the nth file, ">Indicating the expected number of files that can be successfully transferred over the D2D link.
Weighting defining file cache and file retrieval costs
Wherein θ is 0.ltoreq.θ.ltoreq.1 is the average file retrieval cost C G And file cache cost C U A weighting factor balanced between.
The optimization problem may be defined as a weighted sum minimization problem of file caching and file retrieval costs as follows:
s.t.
I kn ∈{0,1},k＝1,...,K；n＝1,...,N (14-3)
||q[m]-q[m-1]|| 2 ≤d δ , m＝1,...,M (14-6)
wherein I is kn Representing file caching strategy, if file n is cached on ground node k, it is equal to 0, otherwise it is equal to 1, q [ m ]]Represents the horizontal position of the unmanned plane in time slot m, J mn Indicating the number of packets the slot m drone transmits the nth file,represents an integer>Represent real numbers, M kn Representing a time slot set of a ground node k cache file n; equation (14-4) indicates that the total size of the file cached by each ground node does not exceed its storage capacity Q; equation (14-5) represents that each file is cached by at least one ground node; equation (14-6) shows that the horizontal displacement of the unmanned aerial vehicle in one time slot is smaller than the maximum flight distance d in one time slot δ The method comprises the steps of carrying out a first treatment on the surface of the Equation (14-8) indicates that the number of packets transmitted by the drone per slot is smaller than the maximum transmittable packet number L.
According to the description, the problem is a linear mixed integer programming problem, the problem is NP difficult, therefore, heuristic algorithm is needed to solve, unmanned aerial vehicle waypoints are determined through a clustering algorithm, shortest distance and flight path of the unmanned aerial vehicle are obtained through a simulated annealing algorithm according to the unmanned aerial vehicle waypoints, the shortest distance is brought into a system model, and the optimal solution is obtained through the simulated annealing algorithm again.
The detailed steps are as follows:
and (3) a step of: initializing, namely generating k randomly distributed ground nodes, and obtaining coordinates of the ground nodes;
and II: determining the flight trajectory of the unmanned aerial vehicle:
based on the concept of virtual base station layout, solving the coordinates of unmanned aerial vehicle waypoints q [ m ] (hovering points when the unmanned aerial vehicle transmits files), optimizing the unmanned aerial vehicle waypoints q [ m ] in SPSS software by utilizing a clustering algorithm according to the transmission distance between the unmanned aerial vehicle and ground nodes and the constraint condition of the flight speed of the unmanned aerial vehicle, so that each ground node is at least in the service range of one unmanned aerial vehicle waypoint;
and B, calculating the optimal access sequence and the shortest total flight distance of all the waypoints q [ m ] by using a simulated annealing algorithm, wherein the specific process is as follows:
s5.1, giving an initial temperature t 0 Randomly selecting one route point as a starting point at the temperature, and generating a flight path connecting all route points according to a certain access sequence 0 Calculate the total path length S 0 ；
S5.2, the current temperature t is set to be a coefficient alpha 1 (the temperature decay coefficient of the simulated annealing algorithm is generally a constant between 0.5 and 0.99) decays and cools to the next temperature t i (t=t at first iteration) 0 )；
S5.3, at the current route i On the basis of which the access sequence of two waypoints is randomly changed to generate a new route j Calculating way j Is the total path length S of (2) j (way at first iteration) i ＝way 0 )；
S5.4, for the current total path length S i And a newly generated total path length S j If S j <S i Then accept route j The method comprises the steps of carrying out a first treatment on the surface of the If S j ＞S i Then Δs=s is calculated j -S i And calculate p s ＝e-△S/t i Then randomly generating a segment [0,1 ]]The random number r1 obeys uniform distribution, if r1<p s Then accept route j ；
S5.5 at temperature t i Next, steps S5.3 and S5.4 are repeated L 1 s i Secondary, L 1 The user-defined numerical value is represented and can be set according to the requirement;
s5.6, judging whether an exit condition is met: with S for all Y iterations in succession j ＝S i If yes, the iteration is exited, otherwise, the iteration is continued by returning to the step S5.2. And obtaining the optimal solution of the flight path of the unmanned aerial vehicle and the shortest flight path S of the unmanned aerial vehicle.
Thirdly,: on the basis of knowing the flight path S of the shortest flight path of the unmanned aerial vehicle, obtaining an optimal caching strategy I by using a simulated annealing algorithm kn Then calculate the minimum delay C of the system θ The specific process is as follows:
s6.1, giving an initial temperature T, and randomly generating a group of caching strategies I meeting constraint conditions at the temperature 0 Obtaining an initial system delay (C) according to the formula and constraint conditions θ ) 0 ；
S6.2, the current temperature T is a coefficient alpha 2 (the temperature decay factor of the simulated annealing algorithm is generally a constant between 0.5 and 0.99) decays and cools to the next temperature T i (t=t at first iteration) i )；
S6.3 at the present solution I i On the basis of which a new solution I is generated by randomly changing the cache nodes of a group of files n j Calculate a new system delay (C θ ) j (first iteration I) i ＝I 0 )；
S6.4, time delay (C) θ ) i And newly generated system delay (C θ ) j If (C) θ ) j <(C θ ) i Then accept the new solution (C θ ) j The method comprises the steps of carrying out a first treatment on the surface of the If (C) θ ) j ＞(C θ ) i Then calculate Δc= (C θ ) j -(C θ ) i And calculate p c ＝e-△C/t i Then randomly generating a segment [0,1 ]]The random number r2 obeys uniform distribution, if r2<p c Then accept the new solution (C θ ) j ；
S6.5 at temperature T i Next, step S6.3 and step S6.4 are repeated L 2 c i Secondary, L 2 The user-defined numerical values are also represented and can be set according to requirements;
s6.6, judging whether an exit condition is met: all of the successive Y iterations have (C θ ) j ＝(C θ ) i If yes, exiting iteration, otherwise returning to the step B to continue iteration, thereby obtaining the optimal caching strategy I kn And system minimum delay C θ 。
Fig. 3 shows the optimized shortest path unmanned trajectory, which is 18097m in length.
FIG. 4 is an average file retrieval cost C G And file cache cost C U The convergence of the weighted sum of file cache cost and file retrieval cost at a weighting factor θ=0.6, the algorithm terminates after 198 cycles, and the total system cost (weighted sum of file cache cost and file retrieval cost) required for the returned cache policy is 285.7s. It can be observed that as the number of iterations increases, the average file retrieval cost decreases, while the unmanned aerial vehicle cache cost generally increases, and the overall system delay continues to decrease, consistent with the expected results.
The above examples are merely illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the protection scope of the present invention without departing from the design spirit of the present invention.
Claims (1)
1. The unmanned aerial vehicle assisted mobile edge computing system content caching method is characterized by comprising the following steps of:
s1, acquiring a ground node coordinate;
s2, dividing the caching process into a file caching stage and a file retrieval stage;
s3, comprehensively considering two stages, and establishing a cost minimization optimization model by combining channel capacity constraint, unmanned aerial vehicle flight rate constraint and unmanned aerial vehicle and ground node storage capacity constraint;
s4, optimizing the unmanned aerial vehicle waypoints by using a clustering algorithm, so that each ground node is at least in the service range of one unmanned aerial vehicle waypoint;
s5, obtaining the unmanned aerial vehicle flight track with minimum time consumption by using a simulated annealing algorithm;
s6, solving a cost minimization optimization model by using a simulated annealing algorithm based on the unmanned aerial vehicle flight trajectory with the minimum time consumption so as to obtain an optimal caching strategy and the minimum system time delay;
costs include file caching costs, average file retrieval costs;
file cache cost C U The calculation formula of (2) is as follows:
wherein T is U Representing the file caching period time, M representing the discretization time slot number of the file caching period time, delta representing the time slot time, N representing the number of files of interest to the user, Y n Indicating the number of packages into which the nth file is divided, N e 1,2, N,indicating the time required for the file buffering phase to complete a packet transfer,/->Representing the total flight distance of the unmanned aerial vehicle in one period, V U Representing the flight speed of the unmanned aerial vehicle;
average document retrieval cost C G The calculation formula of (2) is as follows:
wherein,representing the time required for the file retrieval phase to complete one packet transmission, K represents the number of ground nodes, K e 1, a.k, p (a) (n) represents the request probability of the nth file, ">Indicating an expected number of nth files that can be successfully transferred over the D2D link;
the optimization objective of the cost minimization optimization model is to minimize the weighted sum of the file caching cost and the average file retrieval cost;
weighted sum of file caching cost and average file retrieval cost C θ The calculation formula of (2) is as follows:
wherein θ represents the average file retrieval cost C G And file cache cost C U A weighting factor balanced between;
the cost minimization optimization model specifically comprises the following steps:
s.t.
I kn ∈{0,1},k＝1,...,K；n＝1,...,N (14-3)
||q[m]-q[m-1]|| 2 ≤d δ ,m＝1,...,M (14-6)
wherein I is kn Representing file caching strategy, if file n is cached on ground node k, it is equal to 0, otherwise it is equal to 1, q [ m ]]Represents the horizontal position of the unmanned plane in time slot m, J mn Indicating the number of packets the slot m drone transmits the nth file,represents an integer>Represent real numbers, M kn Representing a time slot set of a ground node k cache file n; equation (14-4) indicates that the total size of the file cached by each ground node does not exceed its storage capacity Q; equation (14-5) represents that each file is cached by at least one ground node; equation (14-6) shows that the horizontal displacement of the unmanned aerial vehicle in one time slot is smaller than the maximum flight distance d in one time slot δ The method comprises the steps of carrying out a first treatment on the surface of the Equation (14-8) indicates that the number of packets transmitted by the unmanned aerial vehicle per time slot is smaller than the maximum transmittable packet number L;
the step S4 specifically includes: optimizing the unmanned aerial vehicle route points according to the transmission distance between the unmanned aerial vehicle and the ground nodes and the constraint condition of the flight speed of the unmanned aerial vehicle by using a clustering algorithm, so that each ground node is at least in the service range of one unmanned aerial vehicle route point;
in step S5, the following steps are included:
s5.1, giving an initial temperature t 0 And randomly selecting a waypoint at the temperatureGenerating a flight path connecting all the waypoints in a certain access sequence as a starting point 0 Calculate the total path length S 0 ；
S5.2, the current temperature t is set to be a coefficient alpha 1 Decaying cooling to the next temperature t i ；
S5.3, at the current route i On the basis of which the access sequence of two waypoints is randomly changed to generate a new route j Calculating way j Is the total path length S of (2) j ；
S5.4, for the current total path length S i And a newly generated total path length S j If S j ＜S i Then accept route j The method comprises the steps of carrying out a first treatment on the surface of the If S j ＞S i Then Δs=s is calculated j -S i And calculateThen randomly generating an in-interval [0,1 ]]The random number r1 obeys uniform distribution, if r1 < p s Then accept route j ；
S5.5 at temperature t i Next, step S5.3 and step S5.4 are repeated L 1 s i Secondary, L 1 Representing a first custom value;
s5.6, judging whether an exit condition is met: s is arranged in the continuous first preset iteration times j ＝S i If yes, exiting iteration, otherwise returning to the step S5.2 to continue iteration, so as to obtain the optimal solution way of the flight track of the unmanned aerial vehicle and obtain the shortest flight path S of the unmanned aerial vehicle;
in step S6, the method includes the steps of:
s6.1, giving an initial temperature T, and randomly generating a group of caching strategies I meeting constraint conditions at the temperature 0 Obtaining an initial system delay (C) according to the formula and constraint conditions θ ) 0 ，
S6.2, the current temperature T is a coefficient alpha 2 Decaying cooling to the next temperature T i ；
S6.3 at the present solution I i Is based on (1)The machine changes the cache node of a group of files n to generate a new solution I j Calculate a new system delay (C θ ) j ；
S6.4, time delay (C) θ ) i And newly generated system delay (C θ ) j If (C) θ ) j ＜(C θ ) i Then accept the new solution (C θ ) j The method comprises the steps of carrying out a first treatment on the surface of the If (C) θ ) j ＞(C θ ) i Then Δc= (C) θ ) j -(C θ ) i And calculateThen randomly generating an in-interval [0,1 ]]The random number r2 obeys uniform distribution, if r2 < p c Then accept the new solution (C θ ) j ；
S6.5 at temperature T i Next, step S6.3 and step S6.4 are repeated L 2 c i Secondary, L 2 Representing a second custom value;
s6.6, judging whether an exit condition is met: the number of consecutive second preset iterations is all (C θ ) j ＝(C θ ) i If yes, the iteration is exited, otherwise, the step S6.2 is returned to for iteration, and the optimal caching strategy I is obtained kn * And system minimum delay C θ *。
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