CN115134370A - Multi-unmanned-aerial-vehicle-assisted mobile edge calculation unloading method - Google Patents
Multi-unmanned-aerial-vehicle-assisted mobile edge calculation unloading method Download PDFInfo
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- H—ELECTRICITY
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
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- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
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- H—ELECTRICITY
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- H04B7/00—Radio transmission systems, i.e. using radiation field
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- H—ELECTRICITY
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- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
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- H04L67/1023—Server selection for load balancing based on a hash applied to IP addresses or costs
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Abstract
The invention discloses a multi-unmanned aerial vehicle assisted mobile edge calculation unloading method, which comprises the following steps: collecting position information of each user on the ground through a software defined network; segmenting different user groups through a clustering algorithm; each unmanned aerial vehicle is set to be in charge of one group; data are transmitted between the users in each area and the corresponding unmanned aerial vehicles in a time division multiple access communication mode, and the data are processed in a partial unloading mode; constructing a mobile edge calculation unloading model; optimizing the constructed mobile edge calculation unloading model to obtain an optimal unmanned aerial vehicle flight track, an optimal calculation task amount of each time slot and an optimal partial unloading variable of each time slot; and carrying out multi-unmanned aerial vehicle-assisted mobile edge calculation unloading according to the optimal unmanned aerial vehicle flight trajectory, the optimal calculation task amount of each time slot and the optimal partial unloading variable of each time slot. The invention can solve the problems of multiple unmanned aerial vehicles and multiple users in a centralized manner, and reduce the energy consumption in the data processing process.
Description
Technical Field
The invention relates to the technical field of edge calculation, in particular to a multi-unmanned-aerial-vehicle-assisted mobile edge calculation unloading method.
Background
A large number of scientific and technological products are present in people's lives. However, most scientific and technical products are computation-sensitive, and have high requirements on not only the processing capability of the CPU, but also user experiences such as time delay, and the like, such as smart cities, unmanned vehicles, virtual reality (AR), and the like. Most local users do not have enough computing power to complete the task with the required delay, and may need to consume a large amount of energy to complete the computing task, which is very bad for the user experience. Although cloud computing is a solution, if a large number of users choose to upload to cloud computing, this may cause serious network congestion, and user experience (Qos) of the users may be very poor. Mobile edge computing (Mobile edge computing) is a good potential solution to the above problem, and compared to the traditional communication network architecture, the network edge can place one or more edge servers, and has both computing resources and storage resources. The generation of the mobile edge computing does not need to replace cloud computing, but plays a complementary role with the cloud computing, and effectively overcomes the defects caused by the cloud computing.
Software Defined Networking (SDN) can be well integrated with edge computing. SDN is a centralized network architecture, and organically separates a control layer and a data layer of network management. The SDN can dynamically collect global network information and network data, and the deployment and scheduling conditions of the global control network are controlled through a control layer, so that the network state is optimized, and the management efficiency of the network is effectively improved.
Unmanned aerial vehicles are used in rich application scenarios in edge computing. The unmanned aerial vehicle can serve as an edge server, in places with a large number of calculation sensitivity tasks and places with intensive personnel, the unmanned aerial vehicle is convenient and flexible in deployment mode, and can fly to a target position quickly, so that a user can unload the calculation tasks to the unmanned aerial vehicle in time for calculation, network throughput is improved, and user experience can be effectively improved. The unmanned aerial vehicle can serve as a relay node, has good Line Of Sight (Link Of Sight) characteristics due to the fact that the unmanned aerial vehicle flies in the air, and can serve as a bridge between a user and an edge server or a base station to recover the network state timely. Based on a Software Defined Network (SDN), an unmanned aerial vehicle and a ground user can serve as data layers, and the SDN can collect network information, environmental positions and other contents of the unmanned aerial vehicle and the user, and then optimize variables such as a trajectory of the unmanned aerial vehicle, resource allocation and an unloading decision of the user through a controller. The network configuration can be flexibly performed even along with the change of the network environment.
Currently, the unloading strategy in a single unmanned aerial vehicle is sufficiently researched, however, for the case of multiple unmanned aerial vehicles, the judgment of unloading decision according to the position of a user is lacked, and a partial unloading scheme is not considered, so that the user needs to consume more energy. And the trajectory scheduling of many unmanned aerial vehicles also can make that unmanned aerial vehicle is better is close to the user and provides better computational service, reduces the energy that unmanned aerial vehicle itself consumed simultaneously. In an application scenario of a large-scale user, a centralized controller is lacked to collect information of the user and the unmanned aerial vehicle to make a better scheduling strategy, so that the user experience is better.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a multi-unmanned-aerial-vehicle-assisted mobile edge calculation unloading method.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
a multi-unmanned aerial vehicle assisted mobile edge computing offloading method comprises the following steps:
s1, collecting the position information of each user on the ground through a software defined network;
s2, based on the position information of each user, segmenting different user groups through a clustering algorithm, wherein one user group is an area;
s3, arranging a plurality of unmanned aerial vehicles, wherein each unmanned aerial vehicle is in charge of one area;
s4, data are transmitted between the users in each area and the corresponding unmanned aerial vehicles in a time division multiple access communication mode, and data are processed in a partial unloading mode, namely, part of data are processed locally by the users, and the other part of data are processed at the unmanned aerial vehicles;
s5, constructing a mobile edge calculation unloading model;
s6, optimizing the moving edge calculation unloading model constructed in the step S5 to obtain the optimal flight path of the unmanned aerial vehicle, the optimal calculation task amount of each time slot and the optimal unloading variable of each time slot part;
and S7, carrying out multi-unmanned aerial vehicle assisted mobile edge calculation unloading according to the optimal unmanned aerial vehicle flight path, the optimal calculation task amount of each time slot and the optimal unloading variable of each time slot part.
Further, in step S5, the objective function of the moving edge computation offload model is the sum of the energy required by all users to transmit data and the energy required by the user to compute locally, which results in the following optimization problem P:
P:
s.t.
C3:l k [i]＞Γ，k∈K
C8:0＜＝ρ k,u [i]＜＝1,k∈K,u∈U,i∈I
C9:C k ρ k,u [i]l k [i]/f k ＜＝δ,k∈K,i∈I
in the objective function, the target function is,
the energy lost for user k to use for local computation in the ith slot,
energy consumed by user k to transmit data to unmanned aerial vehicle u in ith time slot;
the constraint C1 type is used for ensuring that the energy consumption of unloading calculation and flight energy consumption of the unmanned aerial vehicle u are smaller than the capacity of a battery of the unmanned aerial vehicle u in the working time; wherein U is a set of unmanned aerial vehicles; k is a set of users; i represents the number of time slots obtained after the service duration T is divided averagely; alpha (alpha) ("alpha") k,u Taking the value of 0 or 1 for the unloading decision variable;
the energy required by the unmanned aerial vehicle u to complete the unloading task for the user k in the ith time slot;
flight energy required to be consumed by the unmanned aerial vehicle u in the ith time slot; τ is the battery capacity;
constraint C2 is used to ensure that the data amount required by the user can be processed within the service duration T; l k [i]Calculating the amount of calculation required to be carried out in the ith time slot for the user k; l is a radical of an alcohol k Calculating the amount of calculation required to be completed by a user k within a service time length T;
constraint C3 is used to constrain the amount of computation per time slot of the user to be greater than a set value; gamma is a set value;
constraint C4-C5 for specifyingThe initial position and the final time slot stopping position of the unmanned aerial vehicle u;
is the initial position of the unmanned plane u;
the position of the unmanned plane u for the final time slot stop;
constraint C6 is used to ensure that the maximum speed of drone u cannot exceed the maximum speed that drone u can allow to reach; delta is the length of time of one time slot,
the highest speed that the unmanned plane u can allow to reach;
constraint C7 is used to ensure that user k can only select one drone at most as an edge server to choose to offload during the entire time T;
constraint C8 is used to ensure that the amount of partial offloading performed by user k cannot exceed the amount of tasks that user k needs to process at the ith slot; ρ is a unit of a gradient k,u [i]Partial factors of the unloading capacity when the user k transmits to the unmanned plane u in the ith time slot;
constraint C9 is used to ensure that the time for user k to process partial local data at the ith slot cannot exceed the slot time; c k The computer period required for calculating 1bit when the user k calculates locally.
Further, in step S5, the process of calculating the energy consumed by the user k for local calculation in the ith time slot includes:
the computation time that the user k needs to spend according to the ith time slot is as follows:
in the formula (1), C k Calculating a computer period required by 1bit during local calculation for a user k; f. of k Calculating a frequency for user k; alpha is alpha k,u To offload decision variables, values are takenIs 0 or 1; k is a set of users; u is the set of unmanned aerial vehicles; l is a radical of an alcohol k [i]The calculated amount needed to be calculated for the user k in the ith time slot; rho k,u [i]Partial factors of the unloading capacity when the user k in the ith time slot transmits to the unmanned plane u;
according to the energy consumption formula, the energy consumed by the user k for local calculation in the ith time slot is obtained as follows:
in the formula (2), gamma k,u The capacitance dependent energy dissipation factor of the CPU processor transistor for user k.
Further, in step S5, the calculation process of the energy consumed by the user k to transmit data to the drone u in the ith time slot includes:
after a user k selects an unloading task to an unmanned aerial vehicle u in an initial time slot, taking the unmanned aerial vehicle as an unloading object in the whole T time, transmitting data to the unmanned aerial vehicle u by a plurality of users in one time slot, and adopting a time division multiple access communication mode; suppose W k,u [i]For selecting the uploaded task amount in the ith time slot, according to a shannon formula, the size of the transmission data is as follows:
in formula (3), B is the transmission bandwidth, N 0 Is white gaussian noise when transmitting signals, delta is the time length of one time slot; k is u Number of users, P, responsible for offloading for UAV u k,u For the transmission power of user k to drone u in ith slot, g k,u The gain for the corresponding transmission power, in relation to the distance, is defined as
In the formula (4), g 0 Is a distance of 1mTime-channel gain, λ is the path fading index, d k,u The Euclidean distance from the user k to the unmanned plane u is as follows:
in the formula (5), x k And y k Is the coordinate of user k, x u And y u Is the coordinate of the unmanned plane u, and H is the height of the unmanned plane u from the ground;
as can be seen from the above definition, ignoring the data size required to carry the communication protocol for transmitting the data packet, there are:
W k,u ＝α k,u (1-ρ k,u [i])l k [i],k∈K,u∈U (6)
carrying out variable substitution to obtain transmission power, and then obtaining the transmission energy required by a user for sending a data packet according to the fact that the energy is equal to the power multiplied by time:
K v a number of users served to each drone;
and if the data volume required to be returned to the user after the data is processed by the unmanned aerial vehicle is small after the user unloads the task to the unmanned aerial vehicle, the energy consumption required by the user to receive the data is ignored.
Further, in step S5, the calculation process of the flight energy required to be consumed by the drone u in the ith time slot includes:
in order for an unmanned aerial vehicle to complete data transmitted in the last time slot by a user within the time of one time slot, the unmanned aerial vehicle needs to satisfy at least f u [i]The calculation frequency is higher than the preset value, so that calculation unloading can be completed within a defined time; in order to satisfy multiple users and ensure the normal operation of tasks, the unloaded object tasks need to be summed:
in the formula (8), C u Calculating the CPU period required by 1bit for the unmanned aerial vehicle processor;
in the ith time slot, the energy required by the unmanned plane u to complete the task is as follows:
considering that flight energy consumption of the unmanned aerial vehicle is related to the flight speed of the unmanned aerial vehicle, assuming that the speed of the unmanned aerial vehicle in a time slot is unchanged, that is, the speed in a time slot is only related to the positions of the unmanned aerial vehicles at the two ends of the time slot, the speed is:
in the formula (10), q u [i]The position of the unmanned plane u in the ith time slot is determined;
therefore, the flight energy that the unmanned plane u needs to consume in the ith time slot is:
in the formula (11), g is a flight constant related to the stress area of the unmanned aerial vehicle, and m is the mass of the unmanned aerial vehicle u.
Further, the step S6 includes:
s6-1, setting the maximum iteration number N, wherein the initialization iteration number N is 0;
s6-2, calculating the flight track of the unmanned aerial vehicle;
s6-3, calculating the calculation task amount of each time slot;
s6-4, calculating each time slot partial unloading variable;
and S6-5, judging whether N reaches the maximum iteration number N, if so, outputting the latest flight path of the unmanned aerial vehicle, the calculation task amount of each time slot and the partial unloading variable of each time slot, and outputting the output values which are respectively used as the optimal flight path of the unmanned aerial vehicle, the optimal calculation task amount of each time slot and the optimal partial unloading variable of each time slot, if not, N is N +1, and returning to the step S6-2.
Further, the step S6-2 includes:
the fixed partial unloading variables and the unloading data size variable of each time slot user result in a P1 problem, which is a convex problem, and the trajectory of the drone is solved by a CVX toolbox:
further, a P2 problem is obtained by fixing a track variable and a partial unloading variable, and the task quantity required to be calculated in each time slot is obtained by solving through a CVX tool box:
further, the step S6-4 includes:
and (3) fixing the track variable and the calculated amount of each time slot, and solving a partial unloading variable:
compared with the prior art, the principle and the advantages of the scheme are as follows:
1. centralized solution to multiple drones and multiple users using Software Defined Network (SDN) architecture to collect data and make algorithmic decisions
2. The users are classified and preprocessed through the global Kmeans algorithm, so that the unloading decision problem of the users is solved, and the time complexity of the follow-up algorithm is reduced.
3. The optimization problem of multivariable strong coupling is solved by using a block coordinate descending method, so that the energy consumption of a user is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the services required for the embodiments or the technical solutions in the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a multi-UAV assisted mobile edge computing offload method of the present invention;
fig. 2 is a data transmission and processing system for multiple drones and multiple users;
fig. 3 is a schematic diagram of user group division.
Detailed Description
The invention will be further illustrated with reference to specific examples:
due to insufficient computing power, a ground user needs to partially offload a computing task to a drone carrying an edge server in a wireless communication manner, that is, the drone helps the user to perform computing. A Software Defined Network (SDN) is responsible for collecting geographic location information of ground users and information that requires computing services. In the scene, a decision needs to be made on the unloading strategy of the ground user, namely, the user needs to unload to which unmanned aerial vehicle, the quantity of tasks needing unloading is what, and meanwhile, the track of the unmanned aerial vehicle needs to be scheduled, so that the energy consumption and the calculation energy of the transmission task of the user are minimum.
As shown in fig. 1, the method for offloading computing a moving edge assisted by multiple drones according to this embodiment includes the following steps:
s1, collecting the position information of each user on the ground through a software defined network; the system shown in fig. 2 is used, the UAVs is an unmanned plane, and the SDN controller is a controller;
s2, based on the position information of each user, segmenting different user groups through a clustering algorithm, wherein one user group is an area, as shown in figure 3;
s3, arranging a plurality of unmanned aerial vehicles, wherein each unmanned aerial vehicle is in charge of one area;
s4, data are transmitted between the users in each area and the corresponding unmanned aerial vehicles in a time division multiple access communication mode, and data are processed in a partial unloading mode, namely, part of data are processed locally by the users, and the other part of data are processed at the unmanned aerial vehicles;
s5, constructing a mobile edge calculation unloading model;
the objective function of the mobile edge calculation unloading model is the sum of the energy required by all users for transmitting data and the energy required by the local calculation energy of the users, and the following optimization problem P is obtained:
P:
s.t.
C3:l k [i]＞Γ，k∈K
C8:0＜＝ρ k,u [i]＜＝1,k∈K,u∈U,i∈I
C9:C k ρ k,u [i]l k [i]/f k ＜＝δ,k∈K,i∈I
in the objective function, the target function is,
the energy lost for user k to use for local computation in the ith slot,
energy consumed by user k to transmit data to unmanned aerial vehicle u in ith time slot;
the constraint C1 type is used for ensuring that the energy consumption of unloading calculation and flight energy consumption of the unmanned aerial vehicle u are smaller than the capacity of a battery of the unmanned aerial vehicle u in the working time; wherein U is a set of unmanned aerial vehicles; k is a set of users; i represents the number of time slots obtained after the service duration T is divided averagely; alpha is alpha k,u Taking the value of 0 or 1 for the unloading decision variable;
the energy required by the unmanned aerial vehicle u to complete the unloading task for the user k in the ith time slot;
flight energy required to be consumed by the unmanned plane u in the ith time slot; τ is the battery capacity;
constraint C2 is used to ensure that the data amount required by the user can be processed within the service duration T; l k [i]The calculated amount of the user k needing to be calculated in the ith time slot is calculated; l is k Calculating the amount of calculation required to be completed by a user k within a service time length T;
constraint C3 is used to constrain the amount of computation per time slot of the user to be greater than a set value; gamma is a set value;
constraint C4-C5 is used to specify the initial position of drone u and the position of the final slotted stop;
is the initial position of the unmanned plane u;
the position of the unmanned plane u for the final time slot stop;
constraint C6 is used to ensure that the maximum speed of drone u cannot exceed the maximum speed that drone u can allow to reach; delta is the length of time of one time slot,
the highest speed that the unmanned plane u can allow to reach;
constraint C7 is used to ensure that user k can only select one drone at most as an edge server to choose to offload during the entire time T;
constraint C8 is used to ensure that the amount of partial offloading performed by user k cannot exceed the amount of tasks that user k needs to process at the i-th time slot; ρ is a unit of a gradient k,u [i]The partial factors of the unloading amount when the user k transmits to the unmanned plane u in the ith time slot are shown;
constraint C9 is used to ensure that the time for user k to process partial local data at the ith slot cannot exceed the slot time; c k The computer period required for calculating 1bit when the user k calculates locally.
In the above, the calculation process of the energy consumed by the user k for local calculation in the ith time slot includes:
the computation time that the user k needs to spend according to the ith time slot is as follows:
in the formula (1), C k To users k booksCalculating the computer period required by 1bit during ground calculation; f. of k Calculating a frequency for user k; alpha is alpha k,u Taking the value of 0 or 1 for the unloading decision variable; k is a set of users; u is the set of unmanned aerial vehicles; l is k [i]The calculated amount needed to be calculated for the user k in the ith time slot; ρ is a unit of a gradient k,u [i]The partial factors of the unloading amount when the user k transmits to the unmanned aerial vehicle u in the ith time slot are calculated;
according to the energy consumption formula, the energy consumed by the user k for local calculation in the ith time slot is obtained as follows:
in the formula (2), gamma k,u The capacitance dependent energy dissipation factor of the CPU processor transistor for user k.
In the above, the calculation process of the energy consumed by the user k to transmit data to the drone u in the ith time slot includes:
after a user k selects an unloading task to an unmanned aerial vehicle u in an initial time slot, taking the unmanned aerial vehicle as an unloading object in the whole T time, transmitting data to the unmanned aerial vehicle u by a plurality of users in one time slot, and adopting a time division multiple access communication mode; suppose W k,u [i]Selecting the uploaded task amount in the ith time slot, and according to a shannon formula, the size of the transmitted data is as follows:
in formula (3), B is the transmission bandwidth, N 0 Delta is white gaussian noise when transmitting signals, and delta is the time length of one time slot; k u Number of users, P, responsible for offloading for UAV u k,u For the transmission power of user k to drone u in ith slot, g k,u The gain for the corresponding transmission power, in relation to the distance, is defined as
In the formula (4), g 0 Is the channel gain at a distance of 1m, λ is the path fading index, d k,u The Euclidean distance from the user k to the unmanned plane u is as follows:
in the formula (5), x k And y k As the coordinates of user k, x u And y u Is the coordinate of the unmanned plane u, and H is the height of the unmanned plane u from the ground;
as can be seen from the above definition, ignoring the data size required to carry the communication protocol in order to transmit a data packet, there are:
W k,u ＝α k,u (1-ρ k,u [i])l k [i],k∈K,u∈U (6)
carrying out variable substitution to obtain transmission power, and then obtaining the transmission energy required by a user for sending a data packet according to the fact that the energy is equal to the power multiplied by time:
K v a number of users served to each drone;
and if the data volume required to be returned to the user after the data is processed by the unmanned aerial vehicle is small after the user unloads the task to the unmanned aerial vehicle, the energy consumption required by the user to receive the data is ignored.
In the above, the calculation process of the flight energy required to be consumed by the unmanned aerial vehicle u in the ith time slot includes:
in order for an unmanned aerial vehicle to complete data transmitted in the last time slot by a user within the time of one time slot, the unmanned aerial vehicle needs to satisfy at least f u [i]The calculation frequency is above, the calculation unloading can be completed within the defined time; in order to satisfy multiple users and ensure the normal operation of tasks, the unloaded object tasks need to be summed:
in the formula (8), C u Calculating the CPU period required by 1bit for the unmanned aerial vehicle processor;
in the ith time slot, the energy required by the unmanned aerial vehicle u to complete the task is as follows:
considering that flight energy consumption of the unmanned aerial vehicle is related to the flight speed of the unmanned aerial vehicle, assuming that the speed of the unmanned aerial vehicle in a time slot is unchanged, that is, the speed in a time slot is only related to the positions of the unmanned aerial vehicles at the two ends of the time slot, the speed is:
in the formula (10), q u [i]The position of the unmanned plane u in the ith time slot is determined;
therefore, the flight energy that the unmanned plane u needs to consume in the ith time slot is:
in the formula (11), g is a flight constant related to the stress area of the unmanned aerial vehicle, and m is the mass of the unmanned aerial vehicle u.
S6, optimizing the moving edge calculation unloading model constructed in the step S5 to obtain an optimal unmanned aerial vehicle flight path, an optimal calculation task amount of each time slot and an optimal partial unloading variable of each time slot;
the method specifically comprises the following steps:
s6-1, setting the maximum iteration number N, wherein the initialization iteration number N is 0;
s6-2, calculating the flight path of the unmanned aerial vehicle:
the fixed partial offload variable and the offload data size variable for each time slot user result in a P1 problem, which is a convex problem that is solved by the CVX toolbox to arrive at the trajectory of the drone:
s6-3, calculating the calculation task amount of each time slot:
and (3) obtaining a P2 problem by fixing the track variables and the partial unloading variables, and solving by a CVX tool box to obtain the task quantity required to be calculated in each time slot:
s6-4, calculating each time slot partial unloading variable:
and (3) fixing the track variable and the calculated amount of each time slot, and solving a partial unloading variable:
and S6-5, judging whether N reaches the maximum iteration number N, if so, outputting the latest flight path of the unmanned aerial vehicle, the calculation task amount of each time slot and each time slot partial unloading variable, and outputting the output to be used as the optimal flight path of the unmanned aerial vehicle, the optimal calculation task amount of each time slot and the optimal each time slot partial unloading variable respectively, if not, N is N +1, and returning to the step S6-2.
And S7, carrying out multi-unmanned aerial vehicle assisted mobile edge calculation unloading according to the optimal unmanned aerial vehicle flight path, the optimal calculation task amount of each time slot and the optimal unloading variable of each time slot part.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that variations based on the shape and principle of the present invention should be covered within the scope of the present invention.
Claims (9)
1. A multi-unmanned aerial vehicle assisted mobile edge computing unloading method is characterized by comprising the following steps:
s1, collecting the position information of each user on the ground through a software defined network;
s2, based on the position information of each user, segmenting different user groups through a clustering algorithm, wherein one user group is an area;
s3, arranging a plurality of unmanned aerial vehicles, wherein each unmanned aerial vehicle is in charge of an area;
s4, data are transmitted between the users in each area and the corresponding unmanned aerial vehicles in a time division multiple access communication mode, and data are processed in a partial unloading mode, namely, part of data are processed locally by the users, and the other part of data are processed by the unmanned aerial vehicles;
s5, constructing a mobile edge calculation unloading model;
s6, optimizing the moving edge calculation unloading model constructed in the step S5 to obtain an optimal unmanned aerial vehicle flight path, an optimal calculation task amount of each time slot and an optimal partial unloading variable of each time slot;
and S7, carrying out multi-unmanned aerial vehicle assisted mobile edge calculation unloading according to the optimal unmanned aerial vehicle flight path, the optimal calculation task amount of each time slot and the optimal partial unloading variable of each time slot.
2. The method for offloading computation of mobile edge assisted by multiple drones according to claim 1, wherein in step S5, the objective function of the model for offloading computation of mobile edge is the sum of energy required for data transmission of all users and energy required for local computation of energy of users, which is to say, the following optimization problem P is obtained:
P:
s.t.
C1:
C2:
C3:l k [i]＞Γ，k∈K
C4:
C5:
C6:
C7:
C8:0＜＝ρ k,u [i]＜＝1,k∈K,u∈U,i∈I
C9:C k ρ k,u [i]l k [i]/f k ＜＝δ,k∈K,i∈I
in the objective function, the target function is,
the energy lost for user k to use for local computation in the ith slot,
energy consumed by user k to transmit data to unmanned aerial vehicle u in ith time slot;
the constraint C1 type is used for ensuring that the energy consumption of unloading calculation and flight energy consumption of the unmanned aerial vehicle u are smaller than the capacity of a battery of the unmanned aerial vehicle u in the working time; wherein U is a set of unmanned aerial vehicles; k is a set of users; i denotes to average the service duration TThe number of the time slots obtained after the averaging; alpha is alpha k,u Taking the value of 0 or 1 for the unloading decision variable;
the energy required by the unmanned aerial vehicle u to complete the unloading task for the user k in the ith time slot;
flight energy required to be consumed by the unmanned aerial vehicle u in the ith time slot; τ is the battery capacity;
constraint C2 is used to ensure that the data amount required by the user can be processed within the service duration T; l k [i]Calculating the amount of calculation required to be carried out in the ith time slot for the user k; l is a radical of an alcohol k Calculating the amount of calculation required to be completed by a user k within a service time length T;
constraint C3 is used to constrain the amount of computation per time slot of the user to be greater than a set value; gamma is a set value;
constraints C4-C5 are used to specify the initial position of drone u and the position of the final slotted landing;
is the initial position of drone u;
the position of the unmanned plane u for the final time slot stop;
constraint C6 is used to ensure that the maximum speed of drone u cannot exceed the maximum speed that drone u can allow to reach; delta is the length of time of one time slot,
the highest speed that the unmanned plane u can allow to reach;
constraint C7 is used to ensure that user k can only select one drone at most as an edge server to choose to offload during the entire time T;
constraint C8 is used to ensure that user k partially offloadsThe amount of the user k cannot exceed the task amount required to be processed by the user k in the ith time slot; rho k,u [i]The partial factors of the unloading amount when the user k transmits to the unmanned plane u in the ith time slot are shown;
constraint C9 is used to ensure that the time for user k to process partial local data at the ith slot cannot exceed the slot time; c k The computer period required for calculating 1bit when the user k calculates locally.
3. The method of claim 2, wherein in step S5, the step of calculating the energy consumed by user k for local calculation in the ith time slot includes:
the computation time that the user k needs to spend according to the ith time slot is as follows:
in the formula (1), C k Calculating a computer period required by 1bit during local calculation for a user k; f. of k Calculating a frequency for user k; alpha is alpha k,u Taking the value of 0 or 1 for the unloading decision variable; k is a set of users; u is the set of unmanned aerial vehicles; l is k [i]The calculated amount needed to be calculated for the user k in the ith time slot; rho k,u [i]Partial factors of the unloading capacity when the user k in the ith time slot transmits to the unmanned plane u;
according to the energy consumption formula, the energy consumed by the user k for local calculation in the ith time slot is obtained as follows:
in the formula (2), gamma k,u The capacitance dependent energy dissipation factor of the CPU processor transistor for user k.
4. The method of claim 2, wherein the step S5 of calculating the energy consumed by the user k to deliver data to the drone u in the ith time slot includes:
after a user k selects an unloading task to an unmanned aerial vehicle u in an initial time slot, taking the unmanned aerial vehicle as an unloading object in the whole T time, transmitting data to the unmanned aerial vehicle u by a plurality of users in one time slot, and adopting a time division multiple access communication mode; suppose W k,u [i]Selecting the uploaded task amount in the ith time slot, and according to a shannon formula, the size of the transmitted data is as follows:
in formula (3), B is the transmission bandwidth, N 0 Is white gaussian noise when transmitting signals, delta is the time length of one time slot; k u Number of users, P, responsible for offloading for UAV u k,u For the transmission power of user k to drone u in ith slot, g k,u The gain for the corresponding transmission power, which is distance-dependent, is defined as
In the formula (4), g 0 Is the channel gain at a distance of 1m, λ is the path fading index, d k,u The Euclidean distance from the user k to the unmanned plane u is as follows:
in the formula (5), x k And y k As the coordinates of user k, x u And y u Is the coordinate of the unmanned plane u, and H is the height of the unmanned plane u from the ground;
ignoring the data size needed to carry the communication protocol in order to send a packet, there are:
W k,u ＝α k,u (1-ρ k,u [i])l k [i],k∈K,u∈U (6)
carrying out variable substitution to obtain transmission power, and then obtaining the transmission energy required by a user for sending a data packet according to the fact that the energy is equal to the power multiplied by time:
K v the number of users served for each drone;
if the data amount required to be returned to the user after the unmanned aerial vehicle processes the data is small after the user unloads the task to the unmanned aerial vehicle, the energy consumption required by the user to receive the data is ignored.
5. The method of claim 2, wherein in step S5, the calculation process of the flight energy required to be consumed by the drone u in the ith time slot includes:
in order for an unmanned aerial vehicle to complete data transmitted in the last time slot by a user within the time of one time slot, the unmanned aerial vehicle needs to satisfy at least f u [i]The calculation frequency is above, the calculation unloading can be completed within the defined time; in order to satisfy multiple users and ensure the normal operation of tasks, the unloaded object tasks need to be summed:
in formula (8), C u Calculating the CPU period required by 1bit for the unmanned aerial vehicle processor;
in the ith time slot, the energy required by the unmanned plane u to complete the task is as follows:
considering that flight energy consumption of the unmanned aerial vehicle is related to the flight speed of the unmanned aerial vehicle, assuming that the speed of the unmanned aerial vehicle in a time slot is unchanged, that is, the speed in a time slot is only related to the positions of the unmanned aerial vehicles at the two ends of the time slot, the speed is:
in the formula (10), q u [i]The position of the unmanned plane u in the ith time slot is determined;
therefore, the flight energy that the unmanned plane u needs to consume in the ith time slot is:
in the formula (11), g is a flight constant related to the stress area of the unmanned aerial vehicle, and m is the mass of the unmanned aerial vehicle u.
6. The method of claim 2, wherein the step S6 includes:
s6-1, setting the number of initialization iterations N as 0, and setting the maximum number of iterations N;
s6-2, calculating the flight track of the unmanned aerial vehicle;
s6-3, calculating the calculation task amount of each time slot;
s6-4, calculating each time slot partial unloading variable;
and S6-5, judging whether N reaches the maximum iteration number N, if so, outputting the latest flight path of the unmanned aerial vehicle, the calculation task amount of each time slot and each time slot partial unloading variable, and outputting the output to be used as the optimal flight path of the unmanned aerial vehicle, the optimal calculation task amount of each time slot and the optimal each time slot partial unloading variable respectively, if not, N is N +1, and returning to the step S6-2.
7. The method of claim 6, wherein the step S6-2 includes:
the fixed partial offload variable and the offload data size variable for each time slot user result in a P1 problem, which is a convex problem that is solved by the CVX toolbox to arrive at the trajectory of the drone:
8. the method of claim 6, wherein the step S6-3 includes:
and (3) obtaining a P2 problem by fixing the track variables and the partial unloading variables, and solving by a CVX tool box to obtain the task quantity required to be calculated in each time slot:
9. the method of claim 6, wherein the step S6-4 comprises:
and (3) fixing the track variable and the calculated amount of each time slot, and solving a partial unloading variable:
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