CN113626107A - Mobile computing unloading method, system and storage medium - Google Patents

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

Mobile computing offloading method, system and storage medium

technical field

The embodiments of the present disclosure relate to the technical field of data processing, and in particular, to a mobile computing offloading method, system, and storage medium.

Background technique

At present, with the acceleration of the informatization process, it is necessary to monitor and track the status information of personnel in real time in some specific scenarios. In order to better meet the needs of mobile computing, mobile edge computing has become a mobile system supported by mobile cloud computing. effective supplement and improvement. However, the traditional mobile cloud computing method relies on the power supply of hardware, and the offloading strategy of IoT devices is highly related to the wireless channel power gain. As long as the wireless channel conditions between IoT devices and edge servers are good, the devices will be exhausted It is possible that many computing tasks are offloaded to the server, resulting in easy leakage of user privacy.

It can be seen that there is an urgent need for a mobile computing offloading method with strong adaptability and security.

SUMMARY OF THE INVENTION

In view of this, embodiments of the present disclosure provide a mobile computing offloading method, system, and storage medium, which at least partially solve the problems of poor adaptability and security in the prior art.

In a first aspect, an embodiment of the present disclosure provides a method for uninstalling mobile computing, including:

At the beginning of the target time slice, real-time information of the edge server and each healthcare IoT device is collected, wherein the real-time information of the edge server includes the wireless channel power gain between each of the IoT devices and the edge server , the real-time information of the IoT device includes the size of the computing task generated by the IoT device and the wireless energy received by the IoT device from the radio frequency energy source;

Build a local computing model, an edge computing model, an energy harvesting model and a privacy entropy model according to the real-time information of the edge server and the real-time information of each of the IoT devices;

constructing an objective function according to the local computing model, the edge computing model, the energy harvesting model and the privacy entropy model;

Maximize the objective function, and solve the task offloading policy in the target time slice.

According to a specific implementation of the embodiment of the present disclosure, the wireless channel power gain follows a Markov model with a state set and transition probability.

其中，

表示物联网设备i的本地计算能力，k为取决于芯片架构的有效开关电容，D_i(t)为所述物联网设备从射频能量源接收到的无线能量，a_i(t)表示所述物联网设备i在所述目标时间片t的任务卸载策略，c_i表示计算1bit任务所需要的计算资源。According to a specific implementation of the embodiment of the present disclosure, the local computing model is

in,

represents the local computing capability of the IoT device i, k is the effective switched capacitor depending on the chip architecture, D _i (t) is the wireless energy received by the IoT device from the RF energy source, and a _i (t) represents the The task offloading strategy of the IoT device i in the target time slice t, and c _i represents the computing resources required for computing a 1-bit task.

其中，

表示物联网设备i在t时间片生成的任务在本地处理的时间延迟，

表示传输时延，

表示边缘服务器计算时延，

表示物联网设备相应的传输能耗，

表示物联网设备i相应的空闲能耗。According to a specific implementation manner of the embodiment of the present disclosure, the edge computing model is

in,

represents the time delay of local processing of tasks generated by IoT device i in time slice t,

represents the transmission delay,

represents the computing delay of the edge server,

Represents the corresponding transmission energy consumption of IoT devices,

Indicates the corresponding idle energy consumption of IoT device i.

其中，v为能量转换效率，

为传输的能量强度，

路劲损坏指数，G表示射频能量发射器天线和物联网设备天线的组合增益，d_i(t)为物联网设备i与射频能量发射器的距离。According to a specific implementation of the embodiment of the present disclosure, the energy collection model is

where v is the energy conversion efficiency,

is the transmitted energy intensity,

Road strength damage index, G represents the combined gain of the RF energy transmitter antenna and the IoT device antenna, and d _i (t) is the distance between the IoT device i and the RF energy transmitter.

According to a specific implementation manner of the embodiment of the present disclosure, the entropy value formula in the privacy entropy model is:

in,

According to a specific implementation manner of the embodiment of the present disclosure, the objective function is

表示每个所述物联网设备在每个时刻的电池容量都不超过它们的最大值

表示边缘服务器分配给所述物联网设备的计算资源的总和不超过所述边缘服务器的计算资源F^edg，

表示所述物联网设备的卸载策略。in,

Indicates that the battery capacity of each of said IoT devices does not exceed their maximum value at any time

indicates that the sum of the computing resources allocated by the edge server to the IoT device does not exceed the computing resource F ^edg of the edge server,

Represents an uninstall policy for the IoT device.

According to a specific implementation manner of the embodiment of the present disclosure, the step of maximizing the objective function to solve the task offloading strategy in the target time slice includes:

Use the deep reinforcement learning method to solve the objective function, and obtain the unloading decision vector in the target time slice;

The task offloading policy is generated according to the offloading decision vector.

In a second aspect, an embodiment of the present disclosure further provides a mobile computing uninstallation system, which is applied to the mobile computing uninstallation method according to the above disclosed embodiments. The mobile computing uninstallation system includes:

edge server;

A plurality of the IoT devices, all of which are connected in communication with the edge server.

In a third aspect, embodiments of the present disclosure further provide a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the foregoing first aspect or the first The mobile computing offloading method of any one of the implementations of an aspect.

In a fourth aspect, an embodiment of the present disclosure further provides a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer When executed, the computer is caused to execute the mobile computing offloading method in the foregoing first aspect or any implementation manner of the first aspect.

The mobile computing offloading solution in the embodiment of the present disclosure includes: at the beginning of a target time slice, collecting real-time information of an edge server and each healthcare IoT device, wherein the real-time information of the edge server includes each IoT device The wireless channel power gain between the device and the edge server, the real-time information of the IoT device includes the computing task size generated by the IoT device and the wireless energy received by the IoT device from the radio frequency energy source; according to The real-time information of the edge server and the real-time information of each of the IoT devices construct a local computing model, an edge computing model, an energy harvesting model and a privacy entropy model; according to the local computing model, the edge computing model, the The energy harvesting model and the privacy entropy model are used to construct an objective function; the objective function is maximized to solve the task offloading policy in the target time slice.

The beneficial effects of the embodiments of the present disclosure are: through the solution of the present disclosure, real-time information of IoT devices and edge servers is collected, a calculation model, an energy collection model and a privacy entropy model are established, an objective optimization function is obtained, and finally an unloading decision vector can be obtained by solving , which improves the adaptability of wireless energy supply and the strength of privacy protection.

Description of drawings

In order to explain the technical solutions of the embodiments of the present disclosure more clearly, the following briefly introduces the accompanying drawings that need to be used in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

FIG. 1 is a schematic flowchart of a method for uninstalling mobile computing according to an embodiment of the present disclosure;

FIG. 2 is a schematic partial flowchart of a method for offloading mobile computing according to an embodiment of the present disclosure;

FIG. 3 is a schematic structural diagram of a mobile computing offloading system according to an embodiment of the present disclosure.

Detailed ways

The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

The embodiments of the present disclosure are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are only some, but not all, embodiments of the present disclosure. The present disclosure can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present disclosure. It should be noted that the following embodiments and features in the embodiments may be combined with each other under the condition of no conflict. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present disclosure.

It is noted that various aspects of embodiments within the scope of the appended claims are described below. 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 illustrative only. Based on this disclosure, those skilled in the art should appreciate that an 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 may be 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 also be noted that the drawings provided in the following embodiments are only illustrative of the basic concept of the present disclosure, and the drawings only show the components related to the present disclosure rather than the number, shape and the number of components in actual implementation. For dimension drawing, the type, quantity and proportion of each component can be changed at will in actual implementation, and the component layout may also be more complicated.

Additionally, in the following description, specific details are provided to facilitate a thorough understanding of the examples. However, one skilled in the art will understand that the described aspects may be practiced without these specific details.

At present, with the acceleration of the informatization process, it is necessary to monitor and track the status information of personnel in real time in some specific scenarios. In order to better meet the needs of mobile computing, mobile edge computing has become a mobile system supported by mobile cloud computing. effective supplement and improvement. However, traditional mobile cloud computing methods rely on the power supply of hardware, and these IoT devices will be implanted in the user's body or worn on the user's body surface, which makes it impractical to charge them through wired charging. Therefore, green energy harvesting technology is considered as a promising technology that can extend the battery life of IoT devices and provide users with a higher quality of service experience. However, the green energy obtained from the natural environment largely depends on the stability of the corresponding energy source. It is difficult for green energy harvesting technologies to guarantee the quality and reliability of energy supply, which has a great impact on data management systems that require real-time monitoring and analysis of data.

And the offloading strategy of IoT devices is highly related to the wireless channel power gain. As long as the wireless channel conditions between the IoT device and the edge server are good, the device will offload as many computing tasks as possible to the server, and the wireless channel power gain is related to the IoT device. The distance between networked devices and edge servers is highly correlated. Therefore, attackers can infer wireless channel information by monitoring the size of offloading tasks received by honest but curious edge servers to reveal users' location privacy, which leads to easy leakage of users' privacy.

Embodiments of the present disclosure provide a mobile computing offloading method, which can be applied to a process of monitoring a user's health state in a medical system scenario.

Referring to FIG. 1 , it is a schematic flowchart of a method for offloading mobile computing according to an embodiment of the present disclosure. As shown in Figure 1, the method mainly includes the following steps:

S101, at the beginning of a target time slice, collect real-time information of an edge server and each healthcare IoT device, wherein the real-time information of the edge server includes a wireless channel between each IoT device and the edge server Power gain, the real-time information of the IoT device includes the size of the computing task generated by the IoT device and the wireless energy received by the IoT device from the radio frequency energy source;

In specific implementation, when each program runs, a corresponding running period will be allocated to the program, and the running period will be used as the target time slice, and then at the beginning of the target time slice, the edge server and each The operating status of the health care IoT devices, for example, analyzing the wireless channel power gain between each of the IoT devices and the edge server to form real-time information of the edge server, analyzing the The size of the computing task and the wireless energy received by the IoT device from the radio frequency energy source to form the real-time information of the IoT device.

Optionally, the wireless channel power gain follows a Markov model with state sets and transition probabilities.

和转移概率

的马尔可夫模型，其中，

同时，在所述目标时间片t开始时，获得物联网设备i产生的计算任务Z_i(t)＝(D_i(t)，c_i)，其中，D_i(t)表示在所述目标时间片t到达物联网设备i的计算任务数据量大小，c_i表示计算1bit任务所需要的计算资源。以及，在所述目标时间片t开始时，获得物联网设备i从射频能量源中接收到的无线能量b_i(t)。For example, at the beginning of the target time slice t, obtain the wireless channel gain h _i (t) between the edge server and the IoT device i, we assume that the wireless channel gain between the IoT device and the edge service h follows a state set with

and transition probability

The Markov model of , where,

At the same time, at the beginning of the target time slice t, the computing task Z _i (t)=(D _i (t), c _i ) generated by the IoT device i is obtained, where D _i (t) represents the Time slice t is the size of the computing task data that reaches the IoT device _i , and ci represents the computing resources required to calculate a 1-bit task. And, at the beginning of the target time slice t, the wireless energy b _i (t) received by the IoT device i from the radio frequency energy source is obtained.

S102, constructing a local computing model, an edge computing model, an energy harvesting model and a privacy entropy model according to the real-time information of the edge server and the real-time information of each of the IoT devices;

Specifically, after acquiring the real-time information of the edge server and the real-time information of each IoT device, a local computing model can be constructed according to the real-time information of the edge server and the real-time information of each IoT device , edge computing model, energy harvesting model and privacy entropy model, wherein, the local computing model can be used to calculate the corresponding energy consumption of the local computing task of the IoT device, and the edge computing model can calculate the edge server and The total execution delay and total energy consumption of all the IoT devices, the energy harvesting model can be used to calculate the wireless energy acquired by each IoT device in the time slot, so that it can be converted for the IoT device The amount of electrical energy used, and the privacy entropy model can be used to calculate the degree of confusion of computing tasks on each of the Internet of Things devices, so that the privacy entropy model can be defined and adjusted to achieve user information. encryption.

S103, constructing an objective function according to the local computing model, the edge computing model, the energy harvesting model and the privacy entropy model;

During specific implementation, the local computing model, the edge computing model, the energy collection model and the privacy entropy model have expressions corresponding to each other, and the local computing model, the edge computing model, the energy The expression of the collection model and the privacy entropy model is combined to construct the objective function.

Specifically, the objective function is

表示每个所述物联网设备在每个时刻的电池容量都不超过它们的最大值

表示边缘服务器分配给所述物联网设备的计算资源的总和不超过所述边缘服务器的计算资源F^edg，

表示所述物联网设备的卸载策略。in,

Indicates that the battery capacity of each of said IoT devices does not exceed their maximum value at any time

indicates that the sum of the computing resources allocated by the edge server to the IoT device does not exceed the computing resource F ^edg of the edge server,

Represents an uninstall policy for the IoT device.

S104: Maximize the objective function, and solve the task unloading strategy in the target time slice.

During specific implementation, when the objective function is obtained, the objective function can be maximized and solved, so as to obtain a task offloading strategy in the target time slice, and each IoT device is subjected to the task offloading strategy according to the task offloading strategy. Computational offload to improve device lifespan and encryption protection of customer information.

In the mobile computing offloading method provided in this embodiment, a computing model, an energy harvesting model and a privacy entropy model are established by using the real-time information of the Internet of Things devices and edge servers to obtain an objective optimization function, and finally an offloading decision vector can be obtained by solving.

On the basis of the above embodiment, the local computing model is

其中，

表示物联网设备i的本地计算能力，k为取决于芯片架构的有效开关电容，D_i(t)为所述物联网设备从射频能量源接收到的无线能量，a_i(t)表示所述物联网设备i在所述目标时间片t的任务卸载策略，c_i表示计算1bit任务所需要的计算资源。

in,

represents the local computing capability of the IoT device i, k is the effective switched capacitor depending on the chip architecture, D _i (t) is the wireless energy received by the IoT device from the RF energy source, and a _i (t) represents the The task offloading strategy of the IoT device i in the target time slice t, and c _i represents the computing resources required for computing a 1-bit task.

During specific implementation, the IoT device itself has a certain computing capability, and computing tasks can be processed locally on the IoT device. The processing time of the task in the local calculation process only considers the calculation time, not the transmission time. Therefore, the local processing time delay of the task generated by the IoT device i in the target time slice t is:

表示物联网设备i的本地计算能力，a_i(t)表示设备i在所述目标时间片t的任务卸载策略。in,

represents the local computing capability of the IoT device i, and a _i (t) represents the task offloading strategy of the device i in the target time slice t.

Then, the corresponding energy consumption of the local computing task of the IoT device i, that is, the local computing model can be calculated as:

where the energy consumption per computing cycle is defined as ε=kf ² , where k is the effective switched capacitance depending on the chip architecture.

Optionally, the edge computing model is

其中，

表示物联网设备i在t时间片生成的任务在本地处理的时间延迟，

表示传输时延，

表示边缘服务器计算时延，

表示物联网设备相应的传输能耗，

表示物联网设备i相应的空闲能耗。

in,

represents the time delay of local processing of tasks generated by IoT device i in time slice t,

represents the transmission delay,

represents the computing delay of the edge server,

Represents the corresponding transmission energy consumption of IoT devices,

Indicates the corresponding idle energy consumption of IoT device i.

其中，传输时延可计算为：

物联网设备相应的传输能耗可计算为：

其中，p_i为物联网设备i的传输功率。During specific implementation, considering the lack of computing resources of IoT devices, all computing tasks cannot be processed on the local computing model, and some tasks need to be offloaded to the edge computing model for processing. The processing time of the task offloading to the edge server includes the upload and transmission time of the local task and the computing time of the edge server. Since the amount of data returned by the task is small, the download and transmission time of the task result is not considered. The time delay of offloading the tasks generated by the IoT device i in the time slice t to the edge server for processing is defined as:

Among them, the transmission delay can be calculated as:

The corresponding transmission energy consumption of IoT devices can be calculated as:

Among them, pi is the transmission power of the IoT device _i .

其中，

为所述目标时间片t内所述边缘服务器分配给所述物联网设备i的计算资源，并且

F^edg为所述边缘服务器总的计算资源。Meanwhile, the computing delay of the edge server can be calculated as:

in,

the computing resources allocated to the IoT device i by the edge server within the target time slice t, and

F ^edg is the total computing resources of the edge server.

其中，

为所述物联网设备i的空闲功率。The corresponding idle energy consumption of the IoT device i can be calculated as:

in,

is the idle power of the IoT device i.

Then, by combining local computing and offloading computing, the total execution delay and total energy consumption of the system, that is, the edge computing model, can be expressed by the following formula:

其中，v为能量转换效率，ρ为传输的能量强度，

路劲损坏指数，G表示射频能量发射器天线和物联网设备天线的组合增益，d_i(t)为物联网设备i与射频能量发射器的距离。Further, the energy harvesting model is

where v is the energy conversion efficiency, ρ is the transmitted energy intensity,

Road strength damage index, G represents the combined gain of the RF energy transmitter antenna and the IoT device antenna, and d _i (t) is the distance between the IoT device i and the RF energy transmitter.

In specific implementation, by being equipped with a radio frequency energy harvester, the IoT device can collect radio frequency signals from a dedicated radio frequency energy source, convert it into electrical energy, and store it in the battery. The charging and discharging process of each energy harvesting module is can be done simultaneously. The RF energy harvesting process is modeled as successive energy packet arrivals, and the energy arrivals in different time slices obey the IID. The electric energy that can be harvested in the target time slice can be calculated by the energy harvesting model.

Optionally, the entropy value formula in the privacy entropy model is:

in,

During specific implementation, considering the need to fully consider the possibility of consumer location privacy leakage due to the task offloading decision of the IoT device, it is necessary to first define a privacy metric for scheduling the IoT device offloading strategy, that is, privacy entropy. Information entropy is usually used to describe the degree of chaos of an event. It is not difficult to find that in the privacy entropy model, the larger the value of privacy entropy is, the more chaotic the computing task will be, the safer the user will be, and the user's location privacy will be affected. better protected.

表示设定的所述物联网设备和所述边缘服务器间信道好坏判断阈值，当

时，便认为它们之间的信道条件良好，此时，所述物联网设备倾向于将所有的任务卸载到所述边缘服务器。

表示设定的所述物联网设备从射频能量发射器中收集到的能量大小判断阈值，当

时，便认为所述物联网设备获得的能量充足，所述物联网设备和射频能量发射器间的信道条件良好。为了保护位置隐私，所述物联网设备需要在良好的信道条件下故意降低卸载速率，在不良的信道条件下提高卸载速率。卸载频率偏离初始卸载频率越大，隐私熵就越大，目标用户位置隐私被锁定的风险就越低。specific,

Indicates the set threshold for judging the quality of the channel between the IoT device and the edge server. When

When the channel conditions between them are considered to be good, the IoT device tends to offload all tasks to the edge server.

Indicates the set judgment threshold of the amount of energy collected by the IoT device from the radio frequency energy transmitter.

is considered that the energy obtained by the IoT device is sufficient, and the channel condition between the IoT device and the RF energy transmitter is good. To protect location privacy, the IoT device needs to deliberately reduce the offload rate under good channel conditions and increase the offload rate under poor channel conditions. The larger the offloading frequency deviates from the initial offloading frequency, the greater the privacy entropy, and the lower the risk of target user location privacy being locked.

On the basis of the above embodiment, as shown in FIG. 2 , as described in step S104, maximizing the objective function to solve the task unloading strategy in the target time slice, including:

S201, using a deep reinforcement learning method to solve the objective function to obtain an unloading decision vector in the target time slice;

提高了决策过程的鲁棒性。During 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 target time slice. Unloading Decision Vectors

Improves the robustness of the decision-making process.

S202. Generate the task offloading policy according to the offloading decision vector.

After obtaining the offloading decision vector, the task offloading strategy can be generated according to the offloading decision vector. Meanwhile, after the task offloading strategy is generated, the device or system can automatically execute the task offloading strategy, or describe the task offloading strategy. The uninstall policy is sent to the operator, who chooses to execute it.

Corresponding to the above method embodiments, referring to FIG. 3 , an embodiment of the present disclosure further provides a mobile computing offloading system 30 corresponding to the above embodiments, and the system includes:

edge server 301;

A plurality of the Internet of Things devices 302, all of the Internet of Things devices 302 are connected to the edge server 301 in communication.

During specific implementation, the IoT device is used to monitor the status information of the user, the edge server is used to offload the computing task of the IoT device, and within the target time slice, all the IoT devices 302 and the The connection relationship of the edge server 301 collects the real-time information of the edge server and the real-time information of each of the Internet of Things devices, and builds a local computing model, an edge computing model, an energy collection model and a privacy entropy model accordingly, and then according to the The local computing model, the edge computing model, the energy harvesting model and the privacy entropy model are used to construct an objective function and solve to obtain a task offloading strategy.

Embodiments of the present disclosure further provide a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the mobile computing offloading method in the foregoing method embodiments .

Embodiments of the present disclosure also provide a computer program product, the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program includes program instructions, when the program instructions are executed by a computer, make The computer executes the mobile computing offloading method in the foregoing method embodiments.

It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above. More specific examples of computer readable storage media may include, but are not limited to, electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable Programmable read only memory (EPROM or flash memory), fiber optics, portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present disclosure, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device . Program code embodied on a computer readable medium may be transmitted using any suitable medium including, but not limited to, electrical wire, optical fiber cable, RF (radio frequency), etc., or any suitable combination of the foregoing.

The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or may exist alone without being assembled into the electronic device.

The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device can execute the relevant steps of the above-mentioned method embodiments.

Alternatively, the above computer-readable medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device can execute the relevant steps of the above method embodiments.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including object-oriented programming languages—such as Java, Smalltalk, C++, but also conventional Procedural programming language - such as the "C" language or similar programming language. 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 kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider through Internet connection).

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 that contains one or more logical functions for implementing the specified functions executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks 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 is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or operations , or can be implemented in a combination of dedicated hardware and computer instructions.

The units involved in the embodiments of the present disclosure may be implemented in a software manner, and may also be implemented in a hardware manner.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof.

The above are only specific embodiments of the present disclosure, but the protection scope of the present disclosure is not limited to this. Any person skilled in the art who is familiar with the technical scope of the present disclosure can easily think of changes or substitutions. All should be included within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

Claims (10)

1. a mobile computing unloading method, is characterized in that, comprises: At the beginning of the target time slice, real-time information of the edge server and each healthcare IoT device is collected, wherein the real-time information of the edge server includes the wireless channel power gain between each of the IoT devices and the edge server , the real-time information of the IoT device includes the size of the computing task generated by the IoT device and the wireless energy received by the IoT device from the radio frequency energy source; Build a local computing model, an edge computing model, an energy harvesting model and a privacy entropy model according to the real-time information of the edge server and the real-time information of each of the IoT devices; constructing an objective function according to the local computing model, the edge computing model, the energy harvesting model and the privacy entropy model; Maximize the objective function, and solve the task offloading policy in the target time slice. 2. The method of claim 1, wherein the wireless channel power gain follows a Markov model with state sets and transition probabilities. 3. The method according to claim 1, wherein the local computing model is

in,

represents the local computing capability of the IoT device i, k is the effective switched capacitor depending on the chip architecture, D _i (t) is the wireless energy received by the IoT device from the RF energy source, and a _i (t) represents the The task offloading strategy of the IoT device i in the target time slice t, and c _i represents the computing resources required for computing a 1-bit task. 4. The method according to claim 3, wherein the edge computing model is

Among them, T _i ^loc (t) represents the time delay of local processing of tasks generated by IoT device i in time slice t, T _i ^tra (t) represents the transmission delay, and T _i ^edg (t) represents the computing delay of the edge server ,

Represents the corresponding transmission energy consumption of IoT devices,

Indicates the corresponding idle energy consumption of IoT device i. 5. The method according to claim 4, wherein the energy harvesting model is

where v is the energy conversion efficiency, ρ is the transmitted energy intensity,

Road strength damage index, G represents the combined gain of the RF energy transmitter antenna and the IoT device antenna, and d _i (t) is the distance between the IoT device i and the RF energy transmitter. 6. The method according to claim 5, wherein the entropy value formula in the privacy entropy model is:

in,

7. method according to claim 1, is characterized in that, described objective function is

in,

Indicates that the battery capacity of each of said IoT devices does not exceed their maximum value at any time

indicates that the sum of the computing resources allocated by the edge server to the IoT device does not exceed the computing resource F ^edg of the edge server,

Represents an uninstall policy for the IoT device. 8. method according to claim 1, is characterized in that, described maximizing described objective function, the step of solving the task unloading strategy in described target time slice, comprises: Use the deep reinforcement learning method to solve the objective function, and obtain the unloading decision vector in the target time slice; The task offloading policy is generated according to the offloading decision vector. 9. A mobile computing unloading system, wherein the system is applied to the mobile computing unloading method according to any one of claims 1 to 8, comprising: edge server; A plurality of the IoT devices, all of which are connected in communication with the edge server. 10. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the mobile computing of any of the preceding claims 1-8 Uninstall method.

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