CN115134370B - Multi-unmanned aerial vehicle assisted mobile edge computing and unloading method - Google Patents

Multi-unmanned aerial vehicle assisted mobile edge computing and unloading method Download PDF

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CN115134370B
CN115134370B CN202210718376.0A CN202210718376A CN115134370B CN 115134370 B CN115134370 B CN 115134370B CN 202210718376 A CN202210718376 A CN 202210718376A CN 115134370 B CN115134370 B CN 115134370B
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time slot
unmanned aerial
aerial vehicle
user
unloading
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CN115134370A (en
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蓝康伟
刘义
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Guangdong University of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • 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
    • H04L67/1004—Server selection for load balancing
    • H04L67/1021—Server selection for load balancing based on client or server locations
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
    • H04B7/18502—Airborne stations
    • H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • 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
    • H04L67/1004—Server selection for load balancing
    • H04L67/1023—Server selection for load balancing based on a hash applied to IP addresses or costs
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention discloses a multi-unmanned aerial vehicle assisted mobile edge computing and unloading method, which comprises the following steps: collecting the position information of each user on the ground through a software defined network; different user groups are partitioned through a clustering algorithm; setting each unmanned aerial vehicle to be responsible for a group; the users in each area and the corresponding unmanned aerial vehicle transmit data in a time division multiple access communication mode, and the data is processed in a partial unloading mode; constructing a mobile edge computing 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 unloading variable of each time slot part; and carrying out multi-unmanned aerial vehicle assisted mobile edge calculation unloading according to the optimal unmanned aerial vehicle flight track, the optimal calculation task amount of each time slot and the optimal unloading variable of each time slot part. The invention can solve the problems of multiple unmanned planes and multiple users in a centralized way, and reduce the energy consumption in the data processing process.

Description

Multi-unmanned aerial vehicle assisted mobile edge computing and unloading method
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 technological products are in the life of people. However, most scientific products are computation sensitive, and have high requirements on processing capacity of a CPU, and high requirements on user experience such as time delay, for example, smart cities, unmanned automobiles, virtual reality (AR), and the like. Most local users do not have enough computing power to complete the task with the required latency and may require a significant amount of energy to complete the computing task, resulting in a very poor user experience. Although cloud computing is a solution, if a large number of users opt to upload to cloud computing, this can cause severe network congestion and the user's (Qos) user experience can be very poor. Mobile edge computing (Mobile edge computing) is a good potential solution to the above problems, and the network edge may place one or more edge servers with computing resources and storage resources, as opposed to conventional communication network architectures. The mobile edge computing is not used for replacing the cloud computing, but plays a complementary role with the cloud computing, so that the defects caused by the cloud computing are effectively overcome.
Software Defined Networking (SDN) may be well integrated with edge computing. SDN is a centralized network architecture that is organically separated from the control layer and the data layer of network management. SDN can dynamically collect global network information and network data, and the deployment scheduling condition of the global regulation network is controlled through a control layer, so that the network state is optimized, and the management efficiency of the network is effectively improved.
Unmanned aerial vehicle is used in abundant application scene at edge calculation. The unmanned aerial vehicle can serve as an edge server, and in places with a large number of computation sensitivity tasks and places with dense personnel, the unmanned aerial vehicle deployment mode is convenient and flexible, and can fly to a target position quickly, so that a user can timely unload the computation tasks to the unmanned aerial vehicle for computation, network throughput is improved, and user experience can be effectively improved. The unmanned aerial vehicle can serve as a relay node, and the unmanned aerial vehicle flies in the air, so that the unmanned aerial vehicle has the characteristic Of good Line Of Sight communication (Line Of Sight), can serve as a bridge between a user and an edge server or a base station, and can recover the network state in time. Based on a Software Defined Network (SDN), the unmanned aerial vehicle and the ground user can be used as data layers, the SDN can collect network information, environment positions and other contents of the unmanned aerial vehicle and the user, and then variables such as track of the unmanned aerial vehicle, resource allocation, unloading decision of the user and the like are optimized through a controller. The network configuration can be flexibly performed even with the change of the network environment.
The unloading strategy in the single unmanned aerial vehicle is fully researched currently, however, in the case of multiple unmanned aerial vehicles, judgment of unloading decision according to the user position is lacking, and partial unloading scheme is not considered, so that more energy is consumed by the user. And the track scheduling of many unmanned aerial vehicles also can make unmanned aerial vehicle better be close to the user and provide better calculation service, reduces the energy that unmanned aerial vehicle itself consumed simultaneously. In the application scene of large-scale users, a centralized controller is lacking to collect information of the users and unmanned aerial vehicles so as to better make scheduling strategies, and therefore the experience of the users 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 computing and unloading method.
In order to achieve the above purpose, the technical scheme provided by the invention is as follows:
a multi-unmanned aerial vehicle assisted mobile edge computing and unloading method comprises the following steps:
s1, collecting position information of each user on the ground through a software defined network;
s2, based on the position information of each user, different user groups are segmented through a clustering algorithm, and one user group is an area;
s3, arranging a plurality of unmanned aerial vehicles, wherein each unmanned aerial vehicle is responsible for an area;
s4, transmitting data between the users in each area and the corresponding unmanned aerial vehicle in a time division multiple access communication mode, and processing the data in a partial unloading mode, namely processing part of the data locally to the users and processing the other part of the data at the unmanned aerial vehicle;
s5, constructing a mobile edge calculation unloading model;
s6, optimizing the mobile edge calculation unloading model constructed in the step S5 to obtain an optimal unmanned aerial vehicle flight track, an optimal calculation task amount of each time slot and an optimal unloading variable of each time slot part;
s7, carrying out multi-unmanned aerial vehicle assisted mobile edge calculation unloading according to the optimal unmanned aerial vehicle flight track, the optimal calculation task amount of each time slot and the optimal unloading variable of each time slot part.
Further, in the step S5, the objective function of the mobile edge computing and unloading model is the sum of the energy required by all users to transmit data and the energy required by the users to compute the energy locally, so as to obtain the following optimization problem P:
P:
Figure BDA0003710337040000031
s.t.
Figure BDA0003710337040000032
Figure BDA0003710337040000033
C3:l k [i]＞Γ，k∈K
Figure BDA0003710337040000034
Figure BDA0003710337040000035
Figure BDA0003710337040000036
Figure BDA0003710337040000037
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 case of the objective function,
Figure BDA0003710337040000038
energy lost for user k for local calculation in the ith time slot, +.>
Figure BDA0003710337040000039
The energy consumed for user k to transmit data to drone u in the ith time slot;
constraint C1 is used for ensuring that the energy consumption and the flight energy consumption of unloading calculation of the unmanned aerial vehicle u are smaller than the capacity of a battery thereof in working timeAn amount of; wherein U is a collection of unmanned aerial vehicles; k is a set of users; i represents the number of time slots obtained after the average division of the service time length T; alpha k,u For unloading decision variables, the value is 0 or 1;
Figure BDA00037103370400000310
the energy required by the unmanned plane u to finish the task unloading for the user k in the ith time slot; />
Figure BDA0003710337040000041
The flight energy required to be consumed by the unmanned plane u in the ith time slot is obtained; τ is the battery capacity;
constraint C2 is used for guaranteeing that the data quantity required by the user can be processed in service duration T; l (L) k [i]The calculation amount needed to be calculated in the ith time slot for the user k; l (L) k The calculation amount required to be completed for the user k in the service time length T;
constraint C3 is used for constraining the calculated amount of each time slot of a user to be larger than a certain set value; Γ is a set value;
constraint C4-C5 is used for defining the initial position and the final time slot stopping position of the unmanned plane u;
Figure BDA0003710337040000042
is the initial position of the unmanned plane u; />
Figure BDA0003710337040000043
The position for stopping the final time slot of the unmanned plane u;
constraint C6 is used for ensuring that the highest speed of unmanned plane u cannot exceed the highest speed allowed to be achieved by unmanned plane u; delta is the length of time of one slot,
Figure BDA0003710337040000044
the highest speed that can be allowed to be reached for the unmanned aerial vehicle u;
constraint C7 is used for ensuring that user k can only select one unmanned aerial vehicle at most as an edge server for selective unloading in the whole time T;
constraint C8 is used for ensuring that the amount of partial unloading performed by user k cannot exceed the amount of tasks required to be processed by user k in the ith time slot; ρ k,u [i]A partial factor of the unloading capacity of the user k when the user k is transmitted to the unmanned plane u in the ith time slot;
constraint C9 is used for ensuring that the time of local data of a part of processing of the ith time slot of user k cannot exceed the size of the time slot; c (C) k The computer period required for 1bit is calculated for user k to calculate locally.
Further, in the step S5, the calculation process of the energy lost by the user k for local calculation in the ith time slot includes:
the calculation time spent by the user k according to the ith time slot is as follows:
Figure BDA0003710337040000045
in the formula (1), C k Calculating a computer period required by 1bit for the local calculation of the user k; f (f) k Calculating a frequency for user k; alpha k,u For unloading decision variables, the value is 0 or 1; k is a set of users; u is a collection of unmanned aerial vehicles; l (L) k [i]The calculation amount needed to be calculated for the user k in the ith time slot; ρ k,u [i]A partial factor of the unloading capacity when the user k is transmitted to the unmanned plane u in the ith time slot;
according to the energy consumption formula, in the ith time slot, the energy consumed by the user k for local calculation is as follows:
Figure BDA0003710337040000051
in the formula (2), gamma k,u The capacitance dependent power consumption factor for user k's CPU processor transistor.
Further, in the step S5, the calculation process of the energy consumed by the user k to transmit the data to the unmanned plane u in the ith time slot includes:
after the user k selects to unload tasks to the unmanned plane u in the initial time slot, the user k is in the wholeTaking the unmanned aerial vehicle as an unloading object in a 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; let W be k,u [i]In order to select the task amount uploaded in the ith time slot, according to shannon formula, the transmission data size is:
Figure BDA0003710337040000052
/>
in the formula (3), B is the transmission bandwidth, N 0 For Gaussian white noise when transmitting signals, delta is the time length of one time slot; k (K) u The number of users responsible for offloading for unmanned plane u, P k,u For the transmission power of the user from user k to unmanned u in the ith time slot, g k,u For the gain of the corresponding transmission power, the distance is defined as
Figure BDA0003710337040000053
In the formula (4), g 0 Is the channel gain at a distance of 1m, lambda is the path fading index, d k,u The Euclidean distance from user k to drone u:
Figure BDA0003710337040000054
in the formula (5), x k And y k For the coordinates of user k, x u And y u The coordinate of the unmanned aerial vehicle u is the height of the unmanned aerial vehicle 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 send the data packet, there are:
W k,u ＝α k,u (1-ρ k,u [i])l k [i],k∈K,u∈U (6)
variable substitution is carried out to obtain transmission power, and then the transmission energy required by a user for transmitting the data packet is obtained according to the energy equal to the power multiplied by time, and the transmission energy is as follows:
Figure BDA0003710337040000061
K v the number of users served for each drone;
if the data volume to be returned to the user after the unmanned aerial vehicle finishes processing the data is small after the user unloads the task to the unmanned aerial vehicle, the energy consumption required by the user for receiving the data is ignored.
Further, in the step S5, the calculation process of the flight energy required to be consumed by the unmanned plane u in the ith time slot includes:
in order for the drone to complete the data transmitted by the user in one time slot, the drone needs to satisfy at least f u [i]The calculation unloading can be completed within the defined time only by the calculation frequency; to satisfy multiple users, the task is guaranteed to be normally performed, so the offloaded object tasks need to be summed:
Figure BDA0003710337040000062
in the formula (8), C u Calculating a 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:
Figure BDA0003710337040000063
considering that the flight consumption energy 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 one time slot is unchanged, namely the speed is only related to the positions of the unmanned aerial vehicles at two ends of the time slot in one time slot, the speed is:
Figure BDA0003710337040000071
in the formula (10), q u [i]The position of the unmanned plane u in the ith time slot is shown;
the flight energy that the drone u needs to consume in the ith slot is therefore:
Figure BDA0003710337040000072
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, initializing iteration number n=0, and setting the maximum iteration number N;
s6-2, calculating the flight path of the unmanned aerial vehicle;
s6-3, calculating the calculation task quantity of each time slot;
s6-4, calculating partial unloading variables of each time slot;
s6-5, judging whether N reaches the maximum iteration number N, if so, outputting the latest unmanned aerial vehicle flight track, the calculation task quantity of each time slot and the unloading variable of each time slot part, respectively corresponding to the output unmanned aerial vehicle flight track, the calculation task quantity of each time slot and the unloading variable of each time slot part, if not, n=n+1, and returning to the step S6-2.
Further, the step S6-2 includes:
the P1 problem is obtained by fixing part unloading variables and unloading data size variables of users in each time slot, the problem is a convex problem, and the track of the unmanned aerial vehicle is obtained by solving through a CVX tool box:
Figure BDA0003710337040000073
further, the P2 problem is obtained by the fixed track variable and the partial unloading variable, and the task quantity required to be calculated for each time slot is obtained by solving through a CVX tool box:
Figure BDA0003710337040000081
further, the step S6-4 includes:
the fixed track variable and the calculated amount of each time slot are used for solving partial unloading variables:
Figure BDA0003710337040000082
compared with the prior art, the scheme has the following principle and advantages:
1. data collection and algorithm decision making using Software Defined Networking (SDN) architecture, solving the multi-unmanned and multi-user problems in a centralized manner
2. The users are classified by the global Kmeans algorithm and preprocessed, so that the problem of unloading decision of the users is solved, and the time complexity of the subsequent algorithm is reduced.
3. The optimization problem of multi-variable strong coupling is solved by using the method of block coordinate descent, 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 of the prior art, the services required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the figures in the following description are only some embodiments of the present invention, and that other figures can be obtained according to these figures without inventive effort to a person skilled in the art.
FIG. 1 is a schematic flow chart of a multi-unmanned aerial vehicle assisted mobile edge computing and unloading method of the present invention;
FIG. 2 is a diagram of a multi-unmanned multi-user data transmission and processing system;
FIG. 3 is a schematic diagram of user population division.
Detailed Description
The invention is further illustrated by the following examples:
ground users need to partially offload computing tasks to an unmanned aerial vehicle carrying an edge server in a wireless communication manner due to insufficient computing power, namely the unmanned aerial vehicle helps the users to perform computation. A Software Defined Network (SDN) is responsible for collecting geographical location information of ground subscribers and information that requires computing services. In the scene, a decision needs to be made on an unloading strategy of a ground user, namely, the user needs to unload to which unmanned aerial vehicle, the amount of tasks to be unloaded is what, and meanwhile, the track of the unmanned aerial vehicle needs to be scheduled, so that the energy consumption and the calculated energy of the transmission task of the user are minimum.
As shown in fig. 1, the multi-unmanned aerial vehicle assisted mobile edge computing and unloading method according to the embodiment includes the following steps:
s1, collecting position information of each user on the ground through a software defined network; wherein, using the system shown in fig. 2, UAVs is unmanned plane and SDN controller is controller;
s2, based on the position information of each user, different user groups are segmented through a clustering algorithm, and one user group is an area, as shown in FIG. 3;
s3, arranging a plurality of unmanned aerial vehicles, wherein each unmanned aerial vehicle is responsible for an area;
s4, transmitting data between the users in each area and the corresponding unmanned aerial vehicle in a time division multiple access communication mode, and processing the data in a partial unloading mode, namely processing part of the data locally to the users and processing the other part of the data at the unmanned aerial vehicle;
s5, constructing a mobile edge calculation unloading model;
the objective function of the mobile edge computing and unloading model is the sum of the energy required by all users to transmit data and the energy required by the users to locally compute the energy, namely the following optimization problem P is obtained:
P:
Figure BDA0003710337040000101
s.t.
Figure BDA0003710337040000102
Figure BDA0003710337040000103
C3:l k [i]＞Γ，k∈K
Figure BDA0003710337040000104
Figure BDA0003710337040000105
Figure BDA0003710337040000106
Figure BDA0003710337040000107
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 case of the objective function,
Figure BDA0003710337040000108
energy lost for user k for local calculation in the ith time slot, +.>
Figure BDA0003710337040000109
Delivering data to user k in the ith time slotEnergy consumed by unmanned plane u;
constraint C1 is used for ensuring that the energy consumption and flight energy consumption of unloading calculation are smaller than the capacity of a battery of the unmanned aerial vehicle u in working time; wherein U is a collection of unmanned aerial vehicles; k is a set of users; i represents the number of time slots obtained after the average division of the service time length T; alpha k,u For unloading decision variables, the value is 0 or 1;
Figure BDA00037103370400001010
the energy required by the unmanned plane u to finish the task unloading for the user k in the ith time slot; />
Figure BDA00037103370400001011
The flight energy required to be consumed by the unmanned plane u in the ith time slot is obtained; τ is the battery capacity;
constraint C2 is used for guaranteeing that the data quantity required by the user can be processed in service duration T; l (L) k [i]The calculation amount needed to be calculated in the ith time slot for the user k; l (L) k The calculation amount required to be completed for the user k in the service time length T;
constraint C3 is used for constraining the calculated amount of each time slot of a user to be larger than a certain set value; Γ is a set value;
constraint C4-C5 is used for defining the initial position and the final time slot stopping position of the unmanned plane u;
Figure BDA0003710337040000111
is the initial position of the unmanned plane u; />
Figure BDA0003710337040000112
The position for stopping the final time slot of the unmanned plane u;
constraint C6 is used for ensuring that the highest speed of unmanned plane u cannot exceed the highest speed allowed to be achieved by unmanned plane u; delta is the length of time of one slot,
Figure BDA0003710337040000113
the highest speed that can be allowed to be reached for the unmanned aerial vehicle u;
constraint C7 is used for ensuring that user k can only select one unmanned aerial vehicle at most as an edge server for selective unloading in the whole time T;
constraint C8 is used for ensuring that the amount of partial unloading performed by user k cannot exceed the amount of tasks required to be processed by user k in the ith time slot; ρ k,u [i]A partial factor of the unloading capacity of the user k when the user k is transmitted to the unmanned plane u in the ith time slot;
constraint C9 is used for ensuring that the time of local data of a part of processing of the ith time slot of user k cannot exceed the size of the time slot; c (C) k The computer period required for 1bit is calculated for user k to calculate locally.
In the foregoing, the calculation process of the energy lost by the user k for local calculation in the ith time slot includes:
the calculation time spent by the user k according to the ith time slot is as follows:
Figure BDA0003710337040000114
in the formula (1), C k Calculating a computer period required by 1bit for the local calculation of the user k; f (f) k Calculating a frequency for user k; alpha k,u For unloading decision variables, the value is 0 or 1; k is a set of users; u is a collection of unmanned aerial vehicles; l (L) k [i]The calculation amount needed to be calculated for the user k in the ith time slot; ρ k,u [i]A partial factor of the unloading capacity when the user k is transmitted to the unmanned plane u in the ith time slot;
according to the energy consumption formula, in the ith time slot, the energy consumed by the user k for local calculation is as follows:
Figure BDA0003710337040000115
in the formula (2), gamma k,u The capacitance dependent power consumption factor for user k's CPU processor transistor.
In the above, the calculation process of the energy consumed by the user k to transmit the data to the unmanned plane 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; let W be k,u [i]In order to select the task amount uploaded in the ith time slot, according to shannon formula, the transmission data size is:
Figure BDA0003710337040000121
in the formula (3), B is the transmission bandwidth, N 0 For Gaussian white noise when transmitting signals, delta is the time length of one time slot; k (K) u The number of users responsible for offloading for unmanned plane u, P k,u For the transmission power of the user from user k to unmanned u in the ith time slot, g k,u For the gain of the corresponding transmission power, the distance is defined as
Figure BDA0003710337040000122
In the formula (4), g 0 Is the channel gain at a distance of 1m, lambda is the path fading index, d k,u The Euclidean distance from user k to drone u:
Figure BDA0003710337040000123
in the formula (5), x k And y k For the coordinates of user k, x u And y u The coordinate of the unmanned aerial vehicle u is the height of the unmanned aerial vehicle 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 send the data packet, there are:
W k,u ＝α k,u (1-ρ k,u [i])l k [i],k∈K,u∈U (6)
variable substitution is carried out to obtain transmission power, and then the transmission energy required by a user for transmitting the data packet is obtained according to the energy equal to the power multiplied by time, and the transmission energy is as follows:
Figure BDA0003710337040000131
K v the number of users served for each drone;
if the data volume to be returned to the user after the unmanned aerial vehicle finishes processing the data is small after the user unloads the task to the unmanned aerial vehicle, the energy consumption required by the user for receiving the data is ignored.
In the foregoing, the calculation process of the flight energy required to be consumed by the unmanned plane u in the ith time slot includes:
in order for the drone to complete the data transmitted by the user in one time slot, the drone needs to satisfy at least f u [i]The calculation unloading can be completed within the defined time only by the calculation frequency; to satisfy multiple users, the task is guaranteed to be normally performed, so the offloaded object tasks need to be summed:
Figure BDA0003710337040000132
in the formula (8), C u Calculating a 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:
Figure BDA0003710337040000133
considering that the flight consumption energy 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 one time slot is unchanged, namely the speed is only related to the positions of the unmanned aerial vehicles at two ends of the time slot in one time slot, the speed is:
Figure BDA0003710337040000134
in the formula (10), q u [i]The position of the unmanned plane u in the ith time slot is shown;
the flight energy that the drone u needs to consume in the ith slot is therefore:
Figure BDA0003710337040000135
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 mobile edge calculation unloading model constructed in the step S5 to obtain an optimal unmanned aerial vehicle flight track, an optimal calculation task amount of each time slot and an optimal unloading variable of each time slot part;
the method specifically comprises the following steps:
s6-1, initializing iteration number n=0, and setting the maximum iteration number N;
s6-2, calculating the flight path of the unmanned aerial vehicle:
the P1 problem is obtained by fixing part unloading variables and unloading data size variables of users in each time slot, the problem is a convex problem, and the track of the unmanned aerial vehicle is obtained by solving through a CVX tool box:
Figure BDA0003710337040000141
s6-3, calculating the calculation task amount of each time slot:
the P2 problem is obtained by the fixed track variable and the partial unloading variable, and the task quantity required to be calculated for each time slot is obtained by solving through a CVX tool box:
Figure BDA0003710337040000142
s6-4, calculating partial unloading variables of each time slot:
the fixed track variable and the calculated amount of each time slot are used for solving partial unloading variables:
Figure BDA0003710337040000151
s6-5, judging whether N reaches the maximum iteration number N, if so, outputting the latest unmanned aerial vehicle flight track, the calculation task quantity of each time slot and the unloading variable of each time slot part, respectively corresponding to the output unmanned aerial vehicle flight track, the calculation task quantity of each time slot and the unloading variable of each time slot part, if not, n=n+1, and returning to the step S6-2.
S7, carrying out multi-unmanned aerial vehicle assisted mobile edge calculation unloading according to the optimal unmanned aerial vehicle flight track, the optimal calculation task amount of each time slot and the optimal unloading variable of each time slot part.
The above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, so variations in shape and principles of the present invention should be covered.

Claims (8)

1. The multi-unmanned aerial vehicle assisted mobile edge computing and unloading method is characterized by comprising the following steps of:
s1, collecting position information of each user on the ground through a software defined network;
s2, based on the position information of each user, different user groups are segmented through a clustering algorithm, and one user group is an area;
s3, arranging a plurality of unmanned aerial vehicles, wherein each unmanned aerial vehicle is responsible for an area;
s4, transmitting data between the users in each area and the corresponding unmanned aerial vehicle in a time division multiple access communication mode, and processing the data in a partial unloading mode, namely processing part of the data locally to the users and processing the other part of the data at the unmanned aerial vehicle;
s5, constructing a mobile edge calculation unloading model;
s6, optimizing the mobile edge calculation unloading model constructed in the step S5 to obtain an optimal unmanned aerial vehicle flight track, an optimal calculation task amount of each time slot and an optimal unloading variable of each time slot part;
s7, carrying out multi-unmanned aerial vehicle assisted mobile edge calculation unloading according to the optimal unmanned aerial vehicle flight track, the optimal calculation task amount of each time slot and the optimal unloading variable of each time slot part;
in the step S5, the objective function of the mobile edge computing and unloading model is the sum of the energy required by all users to transmit data and the energy required by the users to compute the energy locally, so as to obtain the following optimization problem P:
P:
Figure FDA0004176462110000011
s.t.
C1:
Figure FDA0004176462110000012
C2:
Figure FDA0004176462110000013
C3:l k [i]＞Γ，k∈K
C4:
Figure FDA0004176462110000014
C5:
Figure FDA0004176462110000021
C6:
Figure FDA0004176462110000022
C7:
Figure FDA0004176462110000023
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 case of the objective function,
Figure FDA0004176462110000024
energy lost for user k for local calculation in the ith time slot, +.>
Figure FDA0004176462110000025
The energy consumed for user k to transmit data to drone u in the ith time slot;
constraint C1 is used for ensuring that the energy consumption and flight energy consumption of unloading calculation are smaller than the capacity of a battery of the unmanned aerial vehicle u in working time; wherein U is a collection of unmanned aerial vehicles; k is a set of users; i represents the number of time slots obtained after the average division of the service time length T; alpha k,u For unloading decision variables, the value is 0 or 1;
Figure FDA0004176462110000026
the energy required by the unmanned plane u to finish the task unloading for the user k in the ith time slot; />
Figure FDA0004176462110000027
The flight energy required to be consumed by the unmanned plane u in the ith time slot is obtained; τ is the battery capacity;
constraint C2 is used for guaranteeing that the data quantity required by the user can be processed in service duration T; l (L) k [i]The calculation amount needed to be calculated in the ith time slot for the user k; l (L) k The calculation amount required to be completed for the user k in the service time length T;
constraint C3 is used for constraining the calculated amount of each time slot of a user to be larger than a certain set value; Γ is a set value;
constraint C4-C5 is used for defining the initial position and the final time slot stopping position of the unmanned plane u;
Figure FDA0004176462110000028
is the initial position of the unmanned plane u; />
Figure FDA0004176462110000029
The position for stopping the final time slot of the unmanned plane u;
constraint C6 is used for ensuring that the highest speed of unmanned plane u cannot exceed the highest speed allowed to be achieved by unmanned plane u; delta is the length of time of one slot,
Figure FDA00041764621100000210
the highest speed that can be allowed to be reached for the unmanned aerial vehicle u;
constraint C7 is used for ensuring that user k can only select one unmanned aerial vehicle at most as an edge server for selective unloading in the whole time T;
constraint C8 is used for ensuring that the amount of partial unloading performed by user k cannot exceed the amount of tasks required to be processed by user k in the ith time slot; ρ k,u [i]A partial factor of the unloading capacity of the user k when the user k is transmitted to the unmanned plane u in the ith time slot;
constraint C9 is used for ensuring that the time of local data of a part of processing of the ith time slot of user k cannot exceed the size of the time slot; c (C) k The computer period required for 1bit is calculated for user k to calculate locally.
2. The method according to claim 1, wherein in step S5, the calculation process of the energy lost by the user k for the local calculation in the ith time slot includes:
the calculation time spent by the user k according to the ith time slot is as follows:
Figure FDA0004176462110000031
in the formula (1), C k Local to user kCalculating a computer period required by 1bit in a computing mode; f (f) k Calculating a frequency for user k; alpha k,u For unloading decision variables, the value is 0 or 1; k is a set of users; u is a collection of unmanned aerial vehicles; l (L) k [i]The calculation amount needed to be calculated for the user k in the ith time slot; ρ k,u [i]A partial factor of the unloading capacity when the user k is transmitted to the unmanned plane u in the ith time slot;
according to the energy consumption formula, in the ith time slot, the energy consumed by the user k for local calculation is as follows:
Figure FDA0004176462110000032
in the formula (2), gamma k The capacitance dependent power consumption factor for user k's CPU processor transistor.
3. The method according to claim 1, wherein in step S5, the calculation process of the energy consumed by the user k to transmit data to the unmanned u in the ith time slot comprises:
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; let W be k,u [i]In order to select the task amount uploaded in the ith time slot, according to shannon formula, the transmission data size is:
Figure FDA0004176462110000041
in the formula (3), B is the transmission bandwidth, N 0 For Gaussian white noise when transmitting signals, delta is the time length of one time slot; k (K) u The number of users responsible for offloading for unmanned plane u, P k,u For the transmission power of the user from user k to unmanned u in the ith time slot, g k,u For the corresponding transmission powerIs related to the distance, defined as
Figure FDA0004176462110000042
In the formula (4), g 0 Is the channel gain at a distance of 1m, lambda is the path fading index, d k,u The Euclidean distance from user k to drone u:
Figure FDA0004176462110000043
in the formula (5), x k And y k For the coordinates of user k, x u And y u The coordinate of the unmanned aerial vehicle u is the height of the unmanned aerial vehicle u from the ground;
neglecting the data size required to carry the communication protocol in order to send the data packet, there are:
W k,u ＝α k,u (1-ρ k,u [i])l k [i],k∈K,u∈U (6)
variable substitution is carried out to obtain transmission power, and then the transmission energy required by a user for transmitting the data packet is obtained according to the energy equal to the power multiplied by time, and the transmission energy is as follows:
Figure FDA0004176462110000044
K v the number of users served for each drone;
if the data volume to be returned to the user after the unmanned aerial vehicle finishes processing the data is small after the user unloads the task to the unmanned aerial vehicle, the energy consumption required by the user for receiving the data is ignored.
4. A multi-unmanned aerial vehicle assisted mobile edge computing and offloading method according to claim 3, wherein in step S5, the process of computing the flight energy required to be consumed by unmanned aerial vehicle u in the ith time slot comprises:
in order for the drone to complete the data transmitted by the user in one time slot, the drone needs to satisfy at least f u [i]The calculation unloading can be completed within the defined time only by the calculation frequency; to satisfy multiple users, the task is guaranteed to be normally performed, so the offloaded object tasks need to be summed:
Figure FDA0004176462110000051
in the formula (8), C u Calculating a 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:
Figure FDA0004176462110000052
considering that the flight consumption energy 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 one time slot is unchanged, namely the speed is only related to the positions of the unmanned aerial vehicles at two ends of the time slot in one time slot, the speed is:
Figure FDA0004176462110000053
in the formula (10), q u [i]The position of the unmanned plane u in the ith time slot is shown;
the flight energy that the drone u needs to consume in the ith slot is therefore:
Figure FDA0004176462110000054
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.
5. The multi-unmanned aerial vehicle-assisted mobile edge computing offload method of claim 4, wherein step S6 comprises:
s6-1, initializing iteration number n=0, and setting the maximum iteration number N;
s6-2, calculating the flight path of the unmanned aerial vehicle;
s6-3, calculating the calculation task quantity of each time slot;
s6-4, calculating partial unloading variables of each time slot;
s6-5, judging whether N reaches the maximum iteration number N, if so, outputting the latest unmanned aerial vehicle flight track, the calculation task quantity of each time slot and the unloading variable of each time slot part, respectively corresponding to the output unmanned aerial vehicle flight track, the calculation task quantity of each time slot and the unloading variable of each time slot part, if not, n=n+1, and returning to the step S6-2.
6. The multi-unmanned aerial vehicle assisted mobile edge computing offload method of claim 5, wherein step S6-2 comprises:
the P1 problem is obtained by fixing part unloading variables and unloading data size variables of users in each time slot, the problem is a convex problem, and the track of the unmanned aerial vehicle is obtained by solving through a CVX tool box:
Figure FDA0004176462110000061
7. the multi-unmanned aerial vehicle-assisted mobile edge computing offload method of claim 5, wherein step S6-3 comprises:
the P2 problem is obtained by the fixed track variable and the partial unloading variable, and the task quantity required to be calculated for each time slot is obtained by solving through a CVX tool box:
Figure FDA0004176462110000062
8. the multi-unmanned aerial vehicle assisted mobile edge computing offload method of claim 5, wherein step S6-4 comprises:
the fixed track variable and the calculated amount of each time slot are used for solving partial unloading variables:
Figure FDA0004176462110000071
/>
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