CN113626107A - Mobile computing unloading method, system and storage medium - Google Patents
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Abstract
The embodiment of the disclosure provides a mobile computing unloading method, a system and a storage medium, which belong to the technical field of data processing, and specifically comprise the following steps: when a target time slice starts, collecting real-time information of an edge server and each piece of health-care Internet of things equipment; constructing a local calculation model, an edge calculation model, an energy collection model and a privacy entropy model according to the real-time information of the edge server and the real-time information of each piece of Internet of things equipment; constructing a target function according to the local calculation model, the edge calculation model, the energy collection model and the privacy entropy model; and maximizing an objective function and solving a task unloading strategy in an objective time slice. According to the scheme, the real-time information of the Internet of things equipment and the edge server is collected, the calculation model, the energy collection model and the privacy entropy model are established, the target optimization function is obtained, the unloading decision vector can be finally obtained through solving, and the adaptability of wireless energy supply and the privacy protection strength are improved.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of data processing, in particular to a mobile computing unloading method, a mobile computing unloading system and a storage medium.
Background
At present, with the acceleration of an information process, the state information of personnel needs to be monitored and tracked in real time in certain specific scenes, and in order to better meet the requirements of mobile computing, mobile edge computing is effectively supplemented and perfected for a mobile system supported by mobile cloud computing. However, the conventional mobile cloud computing method depends on power supply of hardware, and the offloading policy of the internet of things device is highly related to the wireless channel power gain, so long as the wireless channel condition between the internet of things device and the edge server is good, the device offloads as many computing tasks as possible to the server, thereby causing easy disclosure of privacy of the user.
Therefore, a mobile computing offloading method with high adaptability and security is needed.
Disclosure of Invention
In view of the above, embodiments of the present disclosure provide a mobile computing offloading method, system and storage medium, which at least partially solve the problem in the prior art that the adaptability and security are poor.
In a first aspect, an embodiment of the present disclosure provides a mobile computing offloading method, including:
the method comprises the steps that when a target time slice begins, real-time information of an edge server and each health-care Internet of things device is collected, wherein the real-time information of the edge server comprises wireless channel power gain between each Internet of things device and the edge server, and the real-time information of the Internet of things devices comprises calculation task size generated by the Internet of things devices and wireless energy received by the Internet of things devices from a radio frequency energy source;
constructing a local calculation model, an edge calculation model, an energy collection model and a privacy entropy model according to the real-time information of the edge server and the real-time information of each piece of Internet of things equipment;
constructing an objective function according to the local computation model, the edge computation model, the energy collection model and the privacy entropy model;
and maximizing the objective function and solving a task unloading strategy in the objective time slice.
According to a particular implementation of the embodiments of the present disclosure, the wireless channel power gain follows a markov model with a set of states and transition probabilities.
In accordance with the present disclosureIn one embodiment, the local computation model is
Wherein,
representing the local computing power of the Internet of things device i, k is an effective switched capacitor dependent on a chip architecture, Di(t) is wireless energy received by the Internet of things device from a radio frequency energy source, ai(t) represents the task offloading policy of the internet of things device i at the target time slice t, ciRepresenting the computational resources required to compute the 1-bit task.
According to a specific implementation manner of the embodiment of the present disclosure, the edge calculation model is
Wherein,
represents the time delay of local processing of the task generated by the internet of things device i in the time slice t,
which is indicative of the time delay of the transmission,
indicating that the edge server is computing the time delay,
the corresponding transmission energy consumption of the equipment of the Internet of things is represented,
and representing the corresponding idle energy consumption of the equipment i of the Internet of things.
According to a specific implementation manner of the embodiment of the disclosure, the energy collection model is
Wherein v is the energy conversion efficiency,
in order to be able to transmit the intensity of the energy,
road stiffness damage index, G represents the combined gain of the radio frequency energy transmitter antenna and the Internet of things equipment antenna, diAnd (t) is the distance between the Internet of things equipment i and the radio frequency energy emitter.
According to a specific implementation manner of the embodiment of the disclosure, the formula of the entropy value in the privacy entropy model is
Wherein,
according to a specific implementation manner of the embodiment of the present disclosure, the objective function is
Wherein,
representing each of the Internet of things devicesThe battery capacity at each moment does not exceed their maximum value
Indicating that the sum of the computing resources allocated to the IOT equipment by the edge server does not exceed the computing resource F of the edge serveredg，
And representing the unloading strategy of the equipment of the Internet of things.
According to a specific implementation manner of the embodiment of the present disclosure, the step of maximizing the objective function and solving the task offloading policy in the objective time slice includes:
solving the target function by using a deep reinforcement learning method to obtain an unloading decision vector in the target time slice;
and generating the task unloading strategy according to the unloading decision vector.
In a second aspect, an embodiment of the present disclosure further provides a mobile computing offloading system, which is applied to the mobile computing offloading method according to the foregoing disclosed embodiment, and the mobile computing offloading system includes:
an edge server;
a plurality of the internet of things equipment, all the internet of things equipment is in communication connection with the edge server.
In a third aspect, the disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the mobile computing offloading method of the first aspect or any implementation manner of the first aspect.
In a fourth aspect, the present disclosure also provides a computer program product including a computer program stored on a non-transitory computer readable storage medium, the computer program including program instructions that, when executed by a computer, cause the computer to perform the mobile computing offloading method of the first aspect or any implementation manner of the first aspect.
The mobile computing offloading scheme in an embodiment of the present disclosure includes: the method comprises the steps that when a target time slice begins, real-time information of an edge server and each health-care Internet of things device is collected, wherein the real-time information of the edge server comprises wireless channel power gain between each Internet of things device and the edge server, and the real-time information of the Internet of things devices comprises calculation task size generated by the Internet of things devices and wireless energy received by the Internet of things devices from a radio frequency energy source; constructing a local calculation model, an edge calculation model, an energy collection model and a privacy entropy model according to the real-time information of the edge server and the real-time information of each piece of Internet of things equipment; constructing an objective function according to the local computation model, the edge computation model, the energy collection model and the privacy entropy model; and maximizing the objective function and solving a task unloading strategy in the objective time slice.
The beneficial effects of the embodiment of the disclosure are: according to the scheme, the real-time information of the Internet of things equipment and the edge server is collected, the calculation model, the energy collection model and the privacy entropy model are established, the target optimization function is obtained, the unloading decision vector can be finally obtained through solving, and the adaptability of wireless energy supply and the privacy protection strength are improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings needed to be used in the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a mobile computing offloading method according to an embodiment of the present disclosure;
fig. 2 is a partial flowchart of a mobile computing offloading method according to an embodiment of the disclosure;
fig. 3 is a schematic structural diagram of a mobile computing offload system according to an embodiment of the present disclosure.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and size of the components in actual implementation, and the type, amount and ratio of the components in actual implementation may be changed arbitrarily, and the layout of the components may be more complicated.
In addition, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, it will be understood by those skilled in the art that the aspects may be practiced without these specific details.
At present, with the acceleration of an information process, the state information of personnel needs to be monitored and tracked in real time in certain specific scenes, and in order to better meet the requirements of mobile computing, mobile edge computing is effectively supplemented and perfected for a mobile system supported by mobile cloud computing. However, traditional mobile cloud computing approaches rely on the power supply of hardware, and these internet of things devices will be implanted within or worn on the body surface of the user, making it impractical to charge them by wired charging. Therefore, green energy harvesting technology is considered as a promising technology, which can extend the battery life of the internet of things device and provide a higher quality of service experience for the user. However, the green energy extracted from the natural environment depends to a large extent on the stability of the respective energy source. The quality and reliability of energy supply are difficult to guarantee by green energy collection technology, which has a great influence on a data management system which needs to monitor and analyze data in real time.
And the unloading strategy of the equipment of the Internet of things is highly related to the power gain of the wireless channel, as long as the condition of the wireless channel between the equipment of the Internet of things and the edge server is good, the equipment can unload as many calculation tasks as possible to the server, and the power gain of the wireless channel is highly related to the distance between the equipment of the Internet of things and the edge server. Thus, an attacker can infer wireless channel information by monitoring the size of offload tasks received by an honest but curious edge server to reveal the user's location privacy, resulting in easy disclosure of the user's privacy.
The embodiment of the disclosure provides a mobile computing unloading method, which can be applied to a user health state monitoring process in a medical system scene.
Referring to fig. 1, a flow chart of a mobile computing offloading method according to an embodiment of the present disclosure is schematically shown. As shown in fig. 1, the method mainly comprises the following steps:
s101, collecting real-time information of an edge server and each health-care Internet of things device when a target time slice starts, wherein the real-time information of the edge server comprises wireless channel power gain between each Internet of things device and the edge server, and the real-time information of the Internet of things devices comprises calculation task size generated by the Internet of things devices and wireless energy received by the Internet of things devices from a radio frequency energy source;
in specific implementation, when each program runs, a corresponding running time period is allocated to the program, the running time period is used as the target time slice, then at the beginning of the target time slice, the running states of the edge server and each health-care internet-of-things device are collected, for example, the power gain of a wireless channel between each internet-of-things device and the edge server is analyzed to form real-time information of the edge server, and the size of a calculation task generated by the internet-of-things device and the wireless energy received by the internet-of-things device from a radio frequency energy source are analyzed to form the real-time information of the internet-of-things device.
Optionally, the radio channel power gain follows a markov model with a set of states and transition probabilities.
For example, at the beginning of the target time slice t, obtaining a wireless channel gain h between the edge server and the internet of things device ii(t), we assume that the wireless channel gain h between the internet of things device and the edge service follows a set of states
And transition probability
The markov model of (1), wherein,
at the same time, starting at said target time slice tThen, a calculation task Z generated by the Internet of things equipment i is obtainedi(t)＝(Di(t)，ci) Wherein D isi(t) represents the size of the data volume of the calculation task reaching the Internet of things device i in the target time slice t, ciRepresenting the computational resources required to compute the 1-bit task. And when the target time slice t begins, acquiring wireless energy b received by the Internet of things equipment i from a radio frequency energy sourcei(t)。
S102, constructing a local calculation model, an edge calculation model, an energy collection model and a privacy entropy model according to the real-time information of the edge server and the real-time information of each piece of Internet of things equipment;
specifically, after the real-time information of the edge server and the real-time information of each piece of internet-of-things equipment are acquired, a local computation model, an edge computation model, an energy collection model and a privacy entropy model can be constructed according to the real-time information of the edge server and the real-time information of each piece of internet-of-things equipment, wherein the local computation model can be used for computing the corresponding energy consumption of the piece of internet-of-things equipment in the local computation task, the edge computation model can be used for computing the total execution delay and the total energy consumption of the edge server and all pieces of internet-of-things equipment, the energy collection model can be used for computing the wireless energy acquired by each piece of internet-of-things equipment in the time slot, so that the amount of energy converted into the electric energy used by the piece of internet-of-things equipment can be obtained, and the privacy entropy model can be used for computing the chaos of the computation task on each piece of internet-of-things equipment, the privacy entropy model can thus be defined and adapted to achieve encryption of user information.
S103, constructing an objective function according to the local calculation model, the edge calculation model, the energy collection model and the privacy entropy model;
in specific implementation, there are expressions for the local computation model, the edge computation model, the energy collection model, and the privacy entropy model, and the expressions of the local computation model, the edge computation model, the energy collection model, and the privacy entropy model may be combined to construct the objective function.
Specifically, the objective function is
Wherein,
the battery capacity of each Internet of things device at each moment is not more than the maximum value
Indicating that the sum of the computing resources allocated to the IOT equipment by the edge server does not exceed the computing resource F of the edge serveredg，
And representing the unloading strategy of the equipment of the Internet of things.
And S104, maximizing the objective function and solving a task unloading strategy in the objective time slice.
In specific implementation, when the objective function is obtained, the objective function can be solved in a maximized manner, so that a task unloading strategy in the objective time slice is obtained, and each piece of internet of things equipment is calculated and unloaded according to the task unloading strategy, so that the service life of the equipment is prolonged, and the encryption protection of customer information is improved.
According to the mobile computing unloading method provided by the embodiment, a computing model, an energy collection model and a privacy entropy model are established through real-time information of the Internet of things equipment and the edge server, a target optimization function is obtained, and finally an unloading decision vector can be obtained through solving.
On the basis of the above embodiment, the local computation model is
During specific implementation, the internet of things equipment has certain computing capacity, and computing tasks can be processed locally on the internet of things equipment. The processing time of the task in the local calculation process only considers the calculation time and does not consider the transmission time. Therefore, the time delay of local processing of the task generated by the internet of things device i at the target time slice t is:
wherein,
representing the local computing power of an Internet of things device i, ai(t) represents the task offload policy for device i at the target time slice t.
Then, the corresponding energy consumption of the internet of things device i in the local computing task, that is, the local computing model, may be calculated as:
wherein the energy consumption per calculation cycle is defined as ∈ ═ kf2And k is the effective switched capacitance depending on the chip architecture.
Optionally, the edge calculation model is
In specific implementation, in consideration of the shortage of computing resources of the internet of things equipment, all computing tasks cannot be processed on the local computing model, and some tasks need to be unloaded to the edge computing model for processing. The processing time of the task unloaded to the edge server comprises the uploading transmission time of the local task and the calculation time of the edge server, and the downloading transmission time of the task result is not considered because the data volume of the task return result is small. The time delay for the task generated by the internet of things device i in the time slice t to be offloaded to the edge server for processing is defined as:
wherein, the transmission delay can be calculated as:
the corresponding transmission energy consumption of the internet of things equipment can be calculated as follows:
wherein p isiThe transmission power of the internet of things device i.
Meanwhile, the edge server may calculate the time delay as:
wherein,
computing resources allocated to the Internet of things device i for the edge server within the target time slice t, and
Fedgis the total computing resources of the edge server.
The corresponding idle energy consumption of the internet of things device i can be calculated as follows:
wherein,
and the idle power of the Internet of things equipment i.
Then, combining the local computation and the offload computation, the total execution delay and the total energy consumption of the system, i.e. the edge computation model, can be expressed by the following formula:
further, the energy collection model is
Where v is the energy conversion efficiency, ρ is the transmitted energy intensity,
road stiffness damage index, G represents the combined gain of the radio frequency energy transmitter antenna and the Internet of things equipment antenna, diAnd (t) is the distance between the Internet of things equipment i and the radio frequency energy emitter.
During specific implementation, the internet of things equipment can collect radio frequency signals from a special radio frequency energy source and convert the radio frequency signals into electric energy to be stored in the battery by being provided with the radio frequency energy collector, and the charging and discharging processes of each energy collecting module can be carried out simultaneously. The rf energy harvesting process is modeled as a continuous energy packet arrival, and the energy arrivals at different time slices obey independent equal distributions. The electrical energy that can be collected within the target time slice may be calculated by the energy collection model.
Optionally, the formula of entropy value in the privacy entropy model is
Wherein,
in specific implementation, considering that the possibility that the privacy of the consumer location is revealed by the task offloading decision of the internet of things device needs to be fully considered, a privacy metric, namely privacy entropy, related to the offloading policy scheduling of the internet of things device needs to be defined first. The magnitude of the chaos degree of an event is generally described by using the information entropy, and it is not difficult to find that in the privacy entropy model, the larger the value of the privacy entropy is, the larger the chaos degree of a calculation task is, the safer a user is, and the better the position privacy of the user is protected.
In particular, the method comprises the following steps of,
the set channel quality judgment threshold value between the Internet of things equipment and the edge server is shown when
The channel conditions between them are considered good, at which time the internet of things devices tend to offload all tasks to the edge server.
The judgment threshold value of the energy collected from the radio frequency energy emitter by the equipment of the Internet of things is shown when
In time, the energy obtained by the internet of things equipment is considered to be sufficient, and the channel condition between the internet of things equipment and the radio frequency energy emitter is good. In order to protect location privacy, the internet of things device needs to deliberately reduce the offloading rate under good channel conditions and increase the offloading rate under poor channel conditions. The greater the deviation of the unloading frequency from the initial unloading frequency, the greater the privacy entropy and the lower the risk of the target user location privacy being locked.
On the basis of the foregoing embodiment, as shown in fig. 2, step S104 maximizes the objective function, and solves the task offloading policy in the objective time slice, including:
s201, solving the objective function by using a deep reinforcement learning method to obtain an unloading decision vector in the objective time slice;
in specific implementation, after the objective function is obtained, the deep reinforcement learning method can be used to perform deep learning on the data in the target time slice and then solve the objective function, so as to obtain the unloading decision vector in the target time slice
The robustness of the decision making process is improved.
And S202, generating the task unloading strategy according to the unloading decision vector.
After the unloading decision vector is obtained, the task unloading strategy can be generated according to the unloading decision vector, and meanwhile, after the task unloading strategy is generated, the equipment or the system can automatically execute the task unloading strategy, or the task unloading strategy is sent to an operator and is selected to be executed by the operator.
Corresponding to the above method embodiment, referring to fig. 3, the embodiment of the present disclosure further provides a mobile computing offload system 30 corresponding to the above embodiment, the system includes:
an edge server 301;
all the internet of things devices 302 are in communication connection with the edge server 301.
During specific implementation, the internet of things equipment is used for monitoring state information of a user, the edge server is used for unloading a calculation task of the internet of things equipment, real-time information of the edge server and real-time information of each piece of internet of things equipment can be collected according to connection relations between all pieces of internet of things equipment 302 and the edge server 301 in a target time slice, a local calculation model, an edge calculation model, an energy collection model and a privacy entropy model are built according to the real-time information, and then an objective function is built and a task unloading strategy is obtained through solving according to the local calculation model, the edge calculation model, the energy collection model and the privacy entropy model.
The disclosed embodiments also provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the mobile computing offloading method in the aforementioned method embodiments.
The disclosed embodiments also provide a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions that, when executed by a computer, cause the computer to perform the mobile computing offloading method of the aforementioned method embodiments.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform the steps associated with the method embodiments.
Alternatively, the computer readable medium carries one or more programs which, when executed by the electronic device, enable the electronic device to perform the steps associated with the method embodiments.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware.
It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.
The above description is only for the specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present disclosure should be covered within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (10)
1. A mobile computing offload method, comprising:
the method comprises the steps that when a target time slice begins, real-time information of an edge server and each health-care Internet of things device is collected, wherein the real-time information of the edge server comprises wireless channel power gain between each Internet of things device and the edge server, and the real-time information of the Internet of things devices comprises calculation task size generated by the Internet of things devices and wireless energy received by the Internet of things devices from a radio frequency energy source;
constructing a local calculation model, an edge calculation model, an energy collection model and a privacy entropy model according to the real-time information of the edge server and the real-time information of each piece of Internet of things equipment;
constructing an objective function according to the local computation model, the edge computation model, the energy collection model and the privacy entropy model;
and maximizing the objective function and solving a task unloading strategy in the objective time slice.
2. The method of claim 1, wherein the wireless channel power gain follows a markov model with a set of states and transition probabilities.
3. The method of claim 1, wherein the local computational model is
Wherein,
representing the local computing power of the Internet of things device i, k is an effective switched capacitor dependent on a chip architecture, Di(t) is wireless energy received by the Internet of things device from a radio frequency energy source, ai(t) represents the task offloading policy of the internet of things device i at the target time slice t, ciRepresenting the computational resources required to compute the 1-bit task.
4. The method of claim 3, wherein the edge calculation model is
Wherein, Ti loc(T) represents the time delay of local processing of the task generated by the Internet of things device i in the time slice T, Ti tra(T) denotes the transmission delay, Ti edg(t) represents the edge server computation latency,
the corresponding transmission energy consumption of the equipment of the Internet of things is represented,
and representing the corresponding idle energy consumption of the equipment i of the Internet of things.
5. The method of claim 4, wherein the energy harvesting model is
Where v is the energy conversion efficiency, ρ is the transmitted energy intensity,
road stiffness damage index, G represents the combined gain of the radio frequency energy transmitter antenna and the Internet of things equipment antenna, diAnd (t) is the distance between the Internet of things equipment i and the radio frequency energy emitter.
6. The method of claim 5, wherein the entropy value in the privacy entropy model is formulated as
Wherein,
7. the method of claim 1, wherein the objective function is
Wherein,
representing each of the Internet of thingsThe battery capacity of the device at each moment does not exceed their maximum value
Indicating that the sum of the computing resources allocated to the IOT equipment by the edge server does not exceed the computing resource F of the edge serveredg，
And representing the unloading strategy of the equipment of the Internet of things.
8. The method of claim 1, wherein the step of maximizing the objective function and solving a task offload policy within the target timeslice comprises:
solving the target function by using a deep reinforcement learning method to obtain an unloading decision vector in the target time slice;
and generating the task unloading strategy according to the unloading decision vector.
9. A mobile computing offloading system, applied to the mobile computing offloading method of any one of claims 1 to 8, comprising:
an edge server;
a plurality of the internet of things equipment, all the internet of things equipment is in communication connection with the edge server.
10. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the mobile computing offload method of any of preceding claims 1-8.
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