A task offloading method, device and electronic equipment suitable for Internet of Vehicles

Abstract

The application relates to a task offloading method and device suitable for Internet of Vehicles and electronic equipment, which comprises the following steps: calculating the local computing efficiency of each vehicle-mounted task in a vehicle-mounted terminal, calculating the signal-to-noise ratio of an MEC offloading link, and calculating the transmission rate of the MEC offloading link; calculating the total offloading time of the vehicle-mounted task according to the computing capacity parameter of an MEC edge computing server, determining the MEC computing efficiency of a roadside unit link based on the total offloading time; screening the vehicle-mounted tasks, and selecting roadside unit execution tasks for offloading from the screened vehicle-mounted tasks. The local computing time and the local computing efficiency can be calculated according to the complexity of the task; the offloading link quality state, the MEC offloading time and the MEC computing efficiency can be calculated according to the distance between the vehicle and the RSU; the lightly loaded roadside unit with good signal-to-noise ratio can be screened as an offloading channel, and the vehicle can be ensured to be always in a good offloading quality environment, thereby providing real-time guarantee for improving the stability of task offloading.

Description

Technical Field

[0001] This application relates to the field of vehicle networking, and in particular to a task offloading method, apparatus and electronic device applicable to vehicle networking. Background Technology

[0002] As an emerging technology, Mobile Edge Computation (MEC) is mainly used to migrate cloud platforms from the mobile core network to the edge of the access network, enabling the rational utilization of computing and storage resources. In the Internet of Vehicles (IoV), the randomness of vehicle movement direction and speed makes dynamic resource scheduling extremely complex. Therefore, the formulation of offloading and caching decisions, as well as the optimized allocation of computing and caching resources, are crucial in IoV networks.

[0003] Current research has focused on task offloading strategies in vehicular networks. For example, Mao Y et al. proposed a complete offloading optimization algorithm, while Lyu X et al. proposed a method considering joint task offloading and resource optimization in multi-user systems. Both of these methods are typical task offloading algorithms in vehicular networks. However, Mao Y's algorithm focuses too much on task offloading itself and doesn't address energy consumption optimization, while Lyu X's algorithm, due to its operational complexity, can only be applied to a single MEC server. Therefore, how to stably offload different tasks to MEC servers on different links while comprehensively considering offloading methods and energy consumption has become an urgent problem to be solved. Summary of the Invention

[0004] This application provides a task offloading method, apparatus, and electronic device suitable for vehicle networking, so as to at least solve the problem of reducing network power consumption while maintaining stable execution of vehicle networking tasks in related technologies.

[0005] In a first aspect, embodiments of this application provide a task offloading method suitable for vehicle networks, including:

[0006] Calculate the local computing efficiency of each vehicle task in the vehicle terminal;

[0007] Collect the link parameters of the MEC offloading link, calculate the signal-to-noise ratio of the MEC offloading link, and calculate the transmission rate of the MEC offloading link in combination with the carrier bandwidth parameters of the roadside unit.

[0008] The total offloading time of the vehicle-mounted task is calculated based on the computing capability parameters of the MEC edge computing server and the transmission rate. The MEC computing efficiency of the roadside unit link used to carry the vehicle-mounted task is determined based on the total offloading time.

[0009] The vehicle-mounted tasks are filtered based on the local computing efficiency, the MEC computing efficiency, and the preset task threshold. From the filtered vehicle-mounted tasks, the roadside unit with the smallest load is selected to perform task unloading.

[0010] In one embodiment, the step of collecting link parameters of the MEC offloading link, calculating the signal-to-noise ratio of the MEC offloading link, and calculating the transmission rate of the MEC offloading link in conjunction with the carrier bandwidth parameters of the roadside unit includes:

[0011] The link parameters of the MEC offloading link, including path loss constant, path loss coefficient, transmit power of the vehicle terminal, and number of subcarriers occupied by a single resource block, are collected. The link path loss between the vehicle and the roadside unit is calculated based on the path loss constant and the path loss coefficient.

[0012] The link channel gain of the roadside unit is calculated based on the transmit power of the vehicle terminal, the Gaussian white noise power spectral density is calculated based on the link parameters of the number of subcarriers occupied by the single resource block, and the link signal-to-noise ratio of the roadside unit is calculated based on the link channel gain of the roadside unit and the Gaussian white noise power spectral density.

[0013] The transmission rate of the MEC offloading link is calculated based on the link signal-to-noise ratio of the roadside unit and the carrier bandwidth of the roadside unit.

[0014] In one embodiment, the step of calculating the total offloading time of the vehicle-mounted task based on the computing capability parameters of the MEC edge computing server and the transmission rate, and determining the MEC computing efficiency of the roadside unit link used to carry the vehicle-mounted task based on the total offloading time, includes:

[0015] Based on the transmission rate, the transmission time of the vehicle-mounted task being unloaded to the roadside unit link is determined. Based on the preset computing power of the MEC edge computing server, the computing time required by the MEC edge computing server is calculated. The transmission time is then compared with the total unloading time of the vehicle-mounted task.

[0016] The MEC computation efficiency of the roadside unit link of the vehicle task is calculated based on the total unloading time and the task size of the vehicle task.

[0017] In one embodiment, the step of filtering the vehicle-mounted tasks based on the local computing efficiency, the MEC computing efficiency, and a preset task threshold, and selecting the roadside unit with the least load from the filtered vehicle-mounted tasks to perform task offloading, includes:

[0018] The expected value of the MEC computation efficiency of the roadside unit link is determined based on the MEC computation efficiency, and the qualified task set is obtained by combining the local computation efficiency with the on-board task selection.

[0019] The qualified task set is split based on the preset task threshold to obtain a qualified small task set and a qualified large task set.

[0020] The roadside unit with the smallest load is selected from the qualified small task set and the qualified large task set to perform task unloading.

[0021] In one embodiment, the step of determining the expected value of the roadside unit link MEC computation efficiency based on the MEC computation efficiency, and then filtering the on-board tasks to obtain a qualified task set based on the local computation efficiency, includes:

[0022] The mathematical expectation value of the MEC computation efficiency of the roadside unit link is determined based on the MEC computation efficiency.

[0023] The qualified task set is obtained by summing up the on-board tasks whose product of the local computing efficiency and the preset efficiency coefficient is lower than the mathematical expectation value.

[0024] In one embodiment, before determining the expected value of the roadside unit link MEC computation efficiency based on the MEC computation efficiency, and combining it with the local computation efficiency to filter the on-board tasks to obtain a qualified task set, the method further includes:

[0025] The roadside unit links are filtered based on a low signal-to-noise ratio (SNR) threshold preset value and a high SNR threshold preset value;

[0026] The roadside unit links with a signal-to-noise ratio higher than the preset low threshold value are organized into a first set of qualified roadside units;

[0027] Links of roadside units with a signal-to-noise ratio higher than the preset high threshold value are organized into a second set of qualified roadside units.

[0028] In one embodiment, selecting the roadside unit with the smallest load from the qualified small task set and the qualified large task set to perform task offloading includes:

[0029] For the vehicle-mounted tasks belonging to the qualified sub-task set, the roadside unit with the smallest load is selected from the vehicle-mounted tasks in the first set of qualified roadside units to perform task unloading.

[0030] For the vehicle-mounted tasks belonging to the qualified large task set, the roadside unit with the smallest load is selected from the vehicle-mounted tasks in the second set of qualified roadside units to perform task unloading.

[0031] Secondly, embodiments of this application provide a task offloading device suitable for vehicle networking, comprising:

[0032] The first computing efficiency calculation module is used to calculate the local computing efficiency of each vehicle task in the vehicle terminal.

[0033] The transmission rate calculation module is used to collect the link parameters of the MEC offloading link, calculate the signal-to-noise ratio of the MEC offloading link, and calculate the transmission rate of the MEC offloading link in combination with the carrier bandwidth parameters of the roadside unit.

[0034] The second computational efficiency calculation module calculates the total offloading time of the vehicle-mounted task based on the computational capability parameters of the MEC edge computing server and the transmission rate, and determines the MEC computational efficiency of the roadside unit link used to carry the vehicle-mounted task based on the total offloading time.

[0035] The task unloading module is used to filter the vehicle-mounted tasks based on the local computing efficiency, the MEC computing efficiency, and a preset task threshold, and select the roadside unit with the least load from the filtered vehicle-mounted tasks to perform task unloading.

[0036] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a task offloading method suitable for the Internet of Vehicles as described in the first aspect above.

[0037] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a task offloading method applicable to the Internet of Vehicles as described in the first aspect above.

[0038] The task offloading method, apparatus, and electronic device applicable to the Internet of Vehicles provided in this application embodiment have at least the following technical effects.

[0039] Through a technical solution comprised of local computation efficiency calculation, MEC offloading link scanning, MEC computation efficiency calculation, qualified set screening, and task offloading, the system can calculate local computation time and efficiency based on task complexity; it can calculate offloading link quality status, MEC offloading time, and MEC computation efficiency based on the distance between the vehicle and the RSU; it can select tasks with high MEC computation efficiency for offloading; it can select lightly loaded roadside units with good signal-to-noise ratio as offloading channels; and it can ensure that the vehicle is always in a good offloading quality environment, providing real-time assurance for improving the stability of task offloading.

[0040] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0041] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0042] Figure 1 This is a flowchart illustrating a task offloading method applicable to the Internet of Vehicles, based on relevant technologies.

[0043] Figure 2 This is a flowchart of step S20 provided by relevant technologies;

[0044] Figure 3 It is a flowchart of step S30 provided by relevant technology;

[0045] Figure 4 It is a flowchart of step S40 provided by relevant technology;

[0046] Figure 5 This is a graph showing the relationship between computational efficiency at different RSU distances and tasks of different sizes, based on relevant technologies.

[0047] Figure 6 This is a comparison chart of MEC task unloading success rates under the same vehicle condition, provided by relevant technologies;

[0048] Figure 7 This is a structural block diagram of a task offloading device suitable for the Internet of Vehicles, provided based on relevant technologies;

[0049] Figure 8 It is a structural diagram of an electronic device provided based on relevant technologies. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0051] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0052] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0053] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0054] Based on the above, this application provides a task offloading method suitable for the Internet of Vehicles (IoV), officially titled SG-MOM (Stability-Guarantee based 5GMEC task Offloading Method in the Internet of Vehicles). It starts by analyzing the local computation latency of the vehicle terminal to calculate the local computation efficiency of different tasks. By utilizing the relative position of the vehicle and Road Side Units (RSUs), it calculates the channel loss on the offloading link. Based on this, it calculates the signal-to-noise ratio and transmission rate of different RSUs, thereby calculating differentiated offloading times and MEC computation efficiency. To ensure stability, only tasks with high MEC computation efficiency are offloaded. For tasks exceeding a specified size, RSUs with good channel conditions are selected as offloading channels. This approach maximizes the efficiency and benefits of IoV task offloading while reducing network energy consumption.

[0055] For ease of description, the parameters used in the following embodiments are listed first, as follows:

[0056] A certain vehicle ;

[0057] Accompanying vehicle task list ;

[0058] Corresponding task size ;

[0059] Computational complexity (CPU consumption) ;

[0060] Roadside units are installed within the vehicle's operating area. Its load ;

[0061] vehicle Distance from roadside unit is .

[0062] Firstly, embodiments of this application provide a task offloading method suitable for the Internet of Vehicles (IoV). Figure 1 This is a flowchart of a task unloading method applicable to vehicle networking, such as... Figure 1 As shown, the method includes:

[0063] Step S10: Calculate the local computing efficiency of each vehicle task in the vehicle terminal.

[0064] The calculation of local computing efficiency consists of two steps:

[0065] First, the computing power of the in-vehicle terminal is preset. Each task of the vehicle terminal is obtained by executing Formula 1. The time required for local computation at the vehicle terminal :

[0066] Formula 1;

[0067] In the formula, This represents a power function.

[0068] Secondly, formula two is executed to obtain each task. Local computing efficiency :

[0069] Formula 2;

[0070] Reference Figure 1 After step S10, step S20 is executed, as follows:

[0071] Step S20: Collect the link parameters of the MEC offloading link, calculate the signal-to-noise ratio of the MEC offloading link, and calculate the transmission rate of the MEC offloading link in combination with the carrier bandwidth parameters of the roadside unit.

[0072] Figure 2 This is a flowchart illustrating step S20 according to an exemplary embodiment, such as... Figure 2 As shown, step S20 specifically includes:

[0073] Step S201: Collect link parameters of the MEC offloading link, including path loss constant, path loss coefficient, transmit power of the vehicle terminal, and number of subcarriers occupied by a single resource block. Calculate the link path loss between the vehicle and the roadside unit based on the path loss constant and path loss coefficient.

[0074] Specifically, the path loss constant of the preset MEC offload link is... and road loss coefficient Formula 3 is used to obtain the vehicle. With each roadside unit Link path loss :

[0075] Formula 3;

[0076] In the formula, This represents a commonly used logarithmic function.

[0077] Step S202: Calculate the link channel gain of the roadside unit based on the transmit power of the vehicle terminal, calculate the Gaussian white noise power spectral density based on the link parameters of the number of subcarriers occupied by a single resource block, and calculate the link signal-to-noise ratio of the roadside unit based on the link channel gain and the Gaussian white noise power spectral density.

[0078] Specifically, the preset transmission power of the vehicle-mounted terminal Formula 4 is used to obtain the vehicle. MEC offloading of channel gain for each RSU link :

[0079] Formula 4;

[0080] Preset Boltzmann constant Kelvin temperature Number of subcarriers occupied by a single resource block Subcarrier bandwidth Calculate the power spectral density of Gaussian white noise. ; Calculate the signal-to-noise ratio of the RSU links for each MEC offloading. .

[0081] Step S203: Calculate the transmission rate of the MEC offloading link based on the link signal-to-noise ratio of the roadside unit and the carrier bandwidth of the roadside unit.

[0082] Specifically, the RSU carrier bandwidth is set. Calculate the transmission rate of each MEC offload link. ,in, This represents the logarithmic function with base 2.

[0083] Reference Figure 1 After step S10, step S30 is executed, as follows:

[0084] Step S30: Calculate the total offloading time of the vehicle-mounted task based on the computing capability parameters of the MEC edge computing server and the transmission rate, and determine the MEC computing efficiency of the roadside unit link used to carry the vehicle-mounted task based on the total offloading time.

[0085] Figure 3 This is a flowchart illustrating step S30 according to an exemplary embodiment, such as... Figure 3 As shown, step S20 specifically includes:

[0086] S301, based on the transmission rate, determine the transmission time of the vehicle-mounted task unloading to the roadside unit link, calculate the required computing time of the MEC edge computing server based on the preset computing power of the MEC edge computing server, and then calculate the total unloading time of the vehicle-mounted task according to the transmission time and the total unloading time of the vehicle-mounted task.

[0087] Specifically, configure the computing power of the MEC edge computing server. Calculate each task The transmission time of each RSU link during offloading to MEC is shown in Formula 5:

[0088] Formula 5;

[0089] The time required for computation on the edge server Each task Total uninstallation time .

[0090] S302, calculate the MEC calculation efficiency of the roadside unit link of the vehicle task based on the total unloading time and the task size of the vehicle task.

[0091] Specifically, measure each task MEC computation efficiency of each RSU link when offloading to the edge server .

[0092] Reference Figure 1 After step S10, step S40 is executed, as follows:

[0093] Step S40: Based on local computing efficiency, MEC computing efficiency and preset task thresholds, the vehicle-mounted tasks are filtered, and the roadside unit with the smallest load is selected from the filtered vehicle-mounted tasks to perform task unloading.

[0094] Figure 4 This is a flowchart illustrating step S40 according to an exemplary embodiment, such as... Figure 4 As shown, step S40 specifically includes:

[0095] S401, determine the mathematical expectation value of the MEC computation efficiency of the roadside unit link based on the MEC computation efficiency, and combine it with the local computation efficiency to screen the vehicle-mounted tasks to obtain a qualified task set.

[0096] This step consists of two parts. The first part involves obtaining a set of qualified roadside units, including:

[0097] 1) The roadside unit links are filtered based on the preset values ​​of low signal-to-noise ratio and high signal-to-noise ratio.

[0098] 2) Organize the roadside unit links with a signal-to-noise ratio higher than the preset value of the low threshold of signal-to-noise ratio into the first set of qualified roadside units, and organize the roadside unit links with a signal-to-noise ratio higher than the preset value of the high threshold of signal-to-noise ratio into the second set of qualified roadside units.

[0099] Set a low signal-to-noise ratio threshold High signal-to-noise ratio threshold For each RSU link offloaded by MEC, filter those that meet the following conditions: RSUs are included in the first set of eligible RSUs. ; Filter out those that meet the criteria: RSUs are included in the second set of eligible RSUs. .

[0100] The second step is to obtain a set of qualified tasks, specifically:

[0101] 1) Determine the mathematical expectation of the MEC computation efficiency of the roadside unit link based on the MEC computation efficiency.

[0102] Set efficiency coefficient For any task Calculate the mathematical expectation of its MEC computational efficiency on each RSU link. .

[0103] 2) Select on-board tasks whose local computing efficiency and the product of the preset efficiency coefficient are lower than the mathematical expectation value to obtain a qualified task set.

[0104] Filter out those that meet the criteria: Tasks included in the qualified task set .

[0105] S402, based on the preset task threshold, split the qualified task set to obtain a qualified small task set and a qualified large task set.

[0106] Specifically, set a task size threshold. From the qualified task set Filter out those that meet the criteria: Tasks included in the qualified mini-task set From the qualified task set Filter out those that meet the criteria: The task is included in the qualified large task set .

[0107] S403: Select the roadside unit with the smallest load from the qualified small task set and the qualified large task set to perform task unloading.

[0108] Specifically, for vehicle-mounted tasks belonging to the qualified small task set, the roadside unit with the smallest load is selected from the vehicle-mounted tasks in the first set of qualified roadside units to perform task unloading; for vehicle-mounted tasks belonging to the qualified large task set, the roadside unit with the smallest load is selected from the vehicle-mounted tasks in the second set of qualified roadside units to perform task unloading.

[0109] For qualified small task sets Any task in the first set of qualified RSUs Select the RSU with the least load for edge offloading; for qualified large task sets Any task in the second set of qualified RSUs The RSU with the lowest load is selected for edge offloading.

[0110] In addition to completing the aforementioned task unloading, it is also necessary to calculate the remaining task set. For the remaining task set All tasks are processed locally, and the MEC uninstallation operation is no longer performed.

[0111] In summary, the technical solution provided in this application, which is applicable to tasks consisting of local computational efficiency calculation, MEC offloading link scanning, MEC computational efficiency calculation, qualified set screening, and task offloading, calculates local computation time and local computational efficiency based on task complexity, and calculates offloading link quality status, MEC offloading time, and MEC computational efficiency based on the distance between the vehicle and the RSU. It can select tasks with high MEC computational efficiency for offloading and select lightly loaded roadside units with good signal-to-noise ratio as offloading channels, thereby ensuring that the vehicle is always in a good offloading quality environment and providing real-time assurance for improving the stability of task offloading.

[0112] Example:

[0113] The invention will now be described in detail using m=3 and n=3 as an example. The task status of the vehicle operation is shown in Table 1:

[0114]

[0115] Table 1. Distribution of Vehicle Operation Tasks

[0116] The details of the roadside units are shown in Table 2:

[0117]

[0118] Table 2 Distribution of Roadside Units

[0119] The basic data is shown in Table 3:

[0120]

[0121] Table 3 Basic Data

[0122] This example describes a 5G MEC task offloading method for ensuring stability in vehicle-to-everything (V2X) networks, including the following steps: local computing efficiency calculation, MEC offloading link scanning, MEC computing efficiency calculation, qualified set screening, and offloading link optimization.

[0123] Step 1: Local computing efficiency calculation

[0124] Step 1-1: Calculate each task The time required for local computation at the vehicle terminal ;

[0125] Steps 1-2: Measure each task Local computing efficiency ;

[0126] Step 2: MEC offload link scan

[0127] Step 2-1: Calculate the vehicle With each roadside unit Link path loss ;

[0128] Step 2-2: Calculate the vehicle MEC offloading of channel gain for each RSU link ;

[0129] Steps 2-3: Calculate the power spectral density of Gaussian white noise ; Calculate the signal-to-noise ratio of the RSU links for each MEC offloading. ;

[0130] Steps 2-4: Calculate the transmission rate of each MEC offload link. ;

[0131] Step 3: MEC computational efficiency calculation

[0132] Step 3-1: Calculate each task Transmission time of each RSU link during offloading to MEC

[0133] ,

[0134] The time required for computation on the edge server ;

[0135] Each task Total uninstallation time

[0136] ;

[0137] Step 3-2: Measure each task MEC computation efficiency of each RSU link when offloading to the edge server

[0138] ;

[0139] Step 4: Qualified set filtering and task unloading

[0140] Step 4-1: For each RSU link offloaded by MEC, filter those that meet the following conditions: RSUs are included in the first set of eligible RSUs. ; Filter out those that meet the criteria: RSUs are included in the second set of eligible RSUs. ;

[0141] Step 4-2: For any task Calculate the mathematical expectation of its MEC computational efficiency on each RSU link. ; Filter out those that meet the criteria: Tasks included in the qualified task set ;

[0142] Step 4-3: From the qualified task set Filter out those that meet the criteria: Tasks included in the qualified mini-task set From the qualified task set Filter out those that meet the criteria: The task is included in the qualified large task set ;

[0143] Step 4-4: For qualified small task sets For any task in the list, the first set of qualified RSUs The corresponding loads are respectively Select the one with the smallest load. Perform edge unloading; for qualified large task sets Any task in the second set of qualified RSUs The corresponding load is From selecting the one with the smallest load Perform edge unloading.

[0144] Steps 4-5: Calculate the remaining task set ;The task Perform local processing instead of executing the MEC uninstallation operation.

[0145] Simulation experiment:

[0146] The performance of the SG-MOM task offloading method of this invention was compared and simulated on the MATLAB platform, and compared with the typical Mao Y complete offloading optimization algorithm and the joint task offloading algorithm in the Lyn X multi-user system. The basic data information is shown in Table 3 above, and the results are shown in the appendix. Figures 5 to 6 As shown.

[0147] Figure 5The graph shows the relationship between computational efficiency and task size at different RSU distances. The horizontal axis represents the size of the onboard task (kB), and the vertical axis represents the MEC computational efficiency (Mbps). Generally, for this algorithm, the closer the vehicle is to the RSU, the higher the MEC computational efficiency; the larger the task size, the higher the MEC computational efficiency. However, when the task size exceeds a certain threshold, the MEC computational efficiency tends to plateau, and improving computational efficiency becomes more costly.

[0148] Figure 6 This chart compares the success rates of MEC task offloading under the same vehicle condition. The horizontal axis represents the number of onboard tasks, and the vertical axis represents the task success rate (%). The SG-MOM algorithm proposed in this application is compared with the task offloading success rates of Mao Y and Lyu X algorithms. Mao Y adopts a complete offloading approach, disregarding the resource status of the local and MEC servers and offloading all tasks to edge servers. This directly leads to task offloading conflicts, potentially causing offloading failures. Lyu X considers joint offloading by multiple users and tasks, which can significantly improve the task offloading success rate, but its operational complexity is high. SG-MOM, on the other hand, performs path optimization based on different offloading link states and varying computational efficiencies, favoring offloading to lightly loaded roadside units, thus achieving a higher offloading success rate.

[0149] Secondly, embodiments of this application provide a task offloading device 500 suitable for vehicle networking. Figure 5 This is a block diagram of a task offloading device suitable for vehicle networking. For example... Figure 7 As shown, the task offloading device for vehicle networking includes:

[0150] The first computing efficiency calculation module 510 is used to calculate the local computing efficiency of each vehicle task in the vehicle terminal.

[0151] The calculation of local computing efficiency consists of two steps:

[0152] First, the computing power of the in-vehicle terminal is preset. Each task of the vehicle terminal is obtained by executing Formula 1. The time required for local computation at the vehicle terminal :

[0153] Formula 1

[0154] In the formula, This represents a power function.

[0155] Secondly, formula two is executed to obtain each task. Local computing efficiency :

[0156] Formula 2

[0157] The transmission rate calculation module 520 is used to collect the link parameters of the MEC offloading link, calculate the signal-to-noise ratio of the MEC offloading link, and calculate the transmission rate of the MEC offloading link in combination with the carrier bandwidth parameters of the roadside unit.

[0158] Specifically, the path loss constant of the preset MEC offload link is... and road loss coefficient Formula 3 is used to obtain the vehicle. With each roadside unit Link path loss :

[0159] Formula 3

[0160] In the formula, This represents a commonly used logarithmic function.

[0161] Step S202: Calculate the link channel gain of the roadside unit based on the transmit power of the vehicle terminal, calculate the Gaussian white noise power spectral density based on the link parameters of the number of subcarriers occupied by a single resource block, and calculate the link signal-to-noise ratio of the roadside unit based on the link channel gain and the Gaussian white noise power spectral density.

[0162] Specifically, the preset transmission power of the vehicle-mounted terminal Formula 4 is used to obtain the vehicle. MEC offloading of channel gain for each RSU link :

[0163] Formula 4

[0164] Preset Boltzmann constant Kelvin temperature Number of subcarriers occupied by a single resource block Subcarrier bandwidth Calculate the power spectral density of Gaussian white noise. ; Calculate the signal-to-noise ratio of the RSU links for each MEC offloading. .

[0165] The transmission rate of the MEC offloading link is calculated based on the link signal-to-noise ratio of the roadside unit and the carrier bandwidth of the roadside unit.

[0166] Specifically, the RSU carrier bandwidth is set. Calculate the transmission rate of each MEC offload link. ,in, This represents the logarithmic function with base 2.

[0167] The second computational efficiency calculation module 530 calculates the total unloading time of the vehicle-mounted task based on the computational capability parameters of the MEC edge computing server and the transmission rate, and determines the MEC computational efficiency of the roadside unit link used to carry the vehicle-mounted task based on the total unloading time.

[0168] The transmission time of the vehicle-mounted task to the roadside unit link is determined based on the transmission rate. The computing time required by the MEC edge computing server is calculated based on the preset computing power of the MEC edge computing server. The transmission time is then combined with the total unloading time of the vehicle-mounted task.

[0169] Specifically, configure the computing power of the MEC edge computing server. ;Calculate each task The transmission time of each RSU link during offloading to MEC is shown in Formula 5:

[0170] Formula 5;

[0171] The time required for computation on the edge server Each task Total uninstallation time .

[0172] The MEC computation efficiency of the roadside unit link of the vehicle task is calculated based on the total unloading time and the task size of the vehicle task.

[0173] Specifically, measure each task MEC computation efficiency of each RSU link when offloading to the edge server .

[0174] The task unloading module 540 is used to filter vehicle-mounted tasks based on local computing efficiency, MEC computing efficiency, and preset task thresholds, and select the roadside unit with the least load from the filtered vehicle-mounted tasks to perform task unloading.

[0175] The expected value of the MEC computation efficiency of the roadside unit link is determined based on the MEC computation efficiency, and the qualified task set is obtained by combining the local computation efficiency.

[0176] This step consists of two parts. The first part involves obtaining a set of qualified roadside units, including:

[0177] 1) The roadside unit links are filtered based on the preset values ​​of low signal-to-noise ratio and high signal-to-noise ratio.

[0178] 2) Organize the roadside unit links with a signal-to-noise ratio higher than the preset value of the low threshold of signal-to-noise ratio into the first set of qualified roadside units; organize the roadside unit links with a signal-to-noise ratio higher than the preset value of the high threshold of signal-to-noise ratio into the second set of qualified roadside units.

[0179] Set a low signal-to-noise ratio threshold High signal-to-noise ratio threshold For each RSU link offloaded by MEC, filter those that meet the following conditions: RSUs are included in the first set of eligible RSUs. ; Filter out those that meet the criteria: RSUs are included in the second set of eligible RSUs. .

[0180] The second step is to obtain a set of qualified tasks, specifically:

[0181] 1) Determine the mathematical expectation of the MEC computation efficiency of the roadside unit link based on the MEC computation efficiency.

[0182] Set efficiency coefficient For any task Calculate the mathematical expectation of its MEC computational efficiency on each RSU link. .

[0183] 2) Select on-board tasks whose product of local computing efficiency and preset efficiency coefficient is lower than the mathematical expectation value to obtain a qualified task set.

[0184] Filter out those that meet the criteria: Tasks included in the qualified task set .

[0185] S402, based on the preset task threshold, split the qualified task set to obtain a qualified small task set and a qualified large task set.

[0186] Specifically, set a task size threshold. From the qualified task set Filter out those that meet the criteria: Tasks included in the qualified mini-task set From the qualified task set Filter out those that meet the criteria: The task is included in the qualified large task set .

[0187] Select the roadside unit with the smallest load from the qualified small task set and the qualified large task set to perform task unloading.

[0188] Specifically, for vehicle-mounted tasks belonging to the qualified small task set, the roadside unit with the smallest load is selected from the vehicle-mounted tasks in the first set of qualified roadside units to perform task unloading; for vehicle-mounted tasks belonging to the qualified large task set, the roadside unit with the smallest load is selected from the vehicle-mounted tasks in the second set of qualified roadside units to perform task unloading.

[0189] For qualified small task sets Any task in the first set of qualified RSUs Select the RSU with the least load for edge offloading; for qualified large task sets Any task in the second set of qualified RSUs The RSU with the lowest load is selected for edge offloading.

[0190] In addition to completing the aforementioned task unloading, it is also necessary to calculate the remaining task set. For the remaining task set All tasks are processed locally, and the MEC uninstallation operation is no longer performed.

[0191] In summary, the task unloading device for vehicle-to-everything (V2X) networks provided in this application, through a technical solution consisting of local computational efficiency calculation, MEC unloading link scanning, MEC computational efficiency calculation, qualified set screening, and task unloading, can calculate local computation time and efficiency based on task complexity; can calculate unloading link quality status, MEC unloading time, and MEC computational efficiency based on the distance between the vehicle and the RSU; can screen tasks with high MEC computational efficiency for unloading; can screen lightly loaded roadside units with good signal-to-noise ratio as unloading channels; and can ensure that the vehicle is always in a good unloading quality environment, providing real-time assurance for improving the stability of task unloading.

[0192] It should be noted that the task offloading device for vehicle networking provided in this embodiment is used to implement the above-described embodiments, and details already described will not be repeated. As used above, terms such as "module," "unit," and "subunit" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the above embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0193] Thirdly, embodiments of this application provide an electronic device, Figure 8 This is a block diagram illustrating an electronic device according to an exemplary embodiment. (e.g.) Figure 8 As shown, the electronic device may include a processor 61 and a memory 62 storing computer program instructions.

[0194] Specifically, the processor 61 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0195] The memory 62 may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory 62 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 62 may include removable or non-removable (or fixed) media. Where appropriate, the memory 62 may be internal or external to a data processing device. In a particular embodiment, the memory 62 is non-volatile memory. In a particular embodiment, the memory 62 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), Extended Data Out Dynamic Random-Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0196] The memory 62 can be used to store or cache various data files that need to be processed and / or communicated, as well as possible computer program instructions executed by the processor 61.

[0197] The processor 61 reads and executes computer program instructions stored in the memory 62 to implement any of the task offloading methods applicable to the Internet of Vehicles in the above embodiments.

[0198] In one embodiment, the electronic device may further include a communication interface 63 and a bus 60. Wherein, as... Figure 8 As shown, the processor 61, memory 62, and communication interface 63 are connected through bus 60 and complete communication with each other.

[0199] The communication interface 63 is used to enable communication between the various modules, devices, units, and / or equipment in the embodiments of this application. The communication port 63 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0200] Bus 60 includes hardware, software, or both, that couples the components of the electronic device together. Bus 60 includes, but is not limited to, at least one of the following: data bus, address bus, control bus, expansion bus, and local bus. For example, and not as a limitation, bus 60 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 60 may include one or more buses. Although specific buses are described and illustrated in the embodiments of this application, this application considers any suitable bus or interconnection.

[0201] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program thereon, which, when executed by a processor, implements a task offloading method for the Internet of Vehicles provided in the first aspect.

[0202] The readable storage medium may be more specifically adopted, including but not limited to: portable disk, hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0203] In a possible implementation, the present invention can also be implemented as a program product comprising program code, which, when the program product is run on a terminal device, causes the terminal device to perform steps implementing the task offloading method for the Internet of Vehicles provided in the first aspect.

[0204] The program code for executing the present invention can be written in any combination of one or more programming languages. The program code can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0205] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0206] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A task offloading method suitable for vehicle networking, characterized in that, include: Calculate the local computing efficiency of each vehicle task in the vehicle terminal; Collect the link parameters of the MEC offloading link, calculate the signal-to-noise ratio of the MEC offloading link, and calculate the transmission rate of the MEC offloading link in combination with the carrier bandwidth parameters of the roadside unit. The total offloading time of the vehicle-mounted task is calculated based on the computing capability parameters of the MEC edge computing server and the transmission rate. The MEC computing efficiency of the roadside unit link used to carry the vehicle-mounted task is determined based on the total offloading time. The vehicle-mounted tasks are filtered based on the local computing efficiency, the MEC computing efficiency, and the preset task threshold. From the filtered vehicle-mounted tasks, the roadside unit with the smallest load is selected to perform task unloading. The calculation of local computing efficiency is divided into two steps: First, the computing power of the in-vehicle terminal is preset. Each task of the vehicle terminal is obtained by executing Formula 1. The time required for local computation at the vehicle terminal : Formula 1; In the formula, Represents a power function. For the first i The computational complexity of each task m This represents the maximum possible value for the number of tasks. Secondly, formula two is executed to obtain each task. Local computing efficiency : Formula 2; In the formula, For the first i The task size of each task; The step of calculating the total offloading time of the vehicle-mounted task based on the computing capability parameters of the MEC edge computing server and the transmission rate, and determining the MEC computing efficiency of the roadside unit link used to carry the vehicle-mounted task based on the total offloading time, includes: Based on the transmission rate, the transmission time of the vehicle-mounted task being unloaded to the roadside unit link is determined. Based on the preset computing power of the MEC edge computing server, the computing time required by the MEC edge computing server is calculated. The transmission time is then compared with the total unloading time of the vehicle-mounted task. The MEC computation efficiency of the roadside unit link of the vehicle task is calculated based on the total unloading time and the task size of the vehicle task. The step of filtering the vehicle-mounted tasks based on the local computing efficiency, the MEC computing efficiency, and a preset task threshold, and selecting the roadside unit with the least load from the filtered vehicle-mounted tasks to perform task offloading, includes: The expected value of the MEC computation efficiency of the roadside unit link is determined based on the MEC computation efficiency, and the qualified task set is obtained by combining the local computation efficiency with the on-board task. The qualified task set is split based on the preset task threshold to obtain a qualified small task set and a qualified large task set. The roadside unit with the smallest load is selected from the qualified small task set and the qualified large task set to perform task unloading.

2. The task offloading method for the Internet of Vehicles according to claim 1, characterized in that, The process of collecting link parameters of the MEC offloading link, calculating the signal-to-noise ratio of the MEC offloading link, and calculating the transmission rate of the MEC offloading link in conjunction with the carrier bandwidth parameters of the roadside unit includes: The link parameters of the MEC offloading link, including path loss constant, path loss coefficient, transmit power of the vehicle terminal, and number of subcarriers occupied by a single resource block, are collected. The link path loss between the vehicle and the roadside unit is calculated based on the path loss constant and the path loss coefficient. The link channel gain of the roadside unit is calculated based on the transmit power of the vehicle terminal, the Gaussian white noise power spectral density is calculated based on the link parameters of the number of subcarriers occupied by the single resource block, and the link signal-to-noise ratio of the roadside unit is calculated based on the link channel gain of the roadside unit and the Gaussian white noise power spectral density. The transmission rate of the MEC offloading link is calculated based on the link signal-to-noise ratio of the roadside unit and the carrier bandwidth of the roadside unit.

3. The task offloading method applicable to the Internet of Vehicles according to claim 1, characterized in that, The step of determining the expected value of the roadside unit link MEC computation efficiency based on the MEC computation efficiency, and then filtering the on-board tasks to obtain a qualified task set based on the local computation efficiency, includes: The mathematical expectation value of the MEC computation efficiency of the roadside unit link is determined based on the MEC computation efficiency. The qualified task set is obtained by summing up the on-board tasks whose product of the local computing efficiency and the preset efficiency coefficient is lower than the mathematical expectation value.

4. The task offloading method applicable to the Internet of Vehicles according to claim 1, characterized in that, Before determining the expected value of the roadside unit link MEC computation efficiency based on the MEC computation efficiency, and before filtering the on-board tasks to obtain a qualified task set in combination with the local computation efficiency, the process further includes: The roadside unit links are filtered based on a low signal-to-noise ratio (SNR) threshold preset value and a high SNR threshold preset value; The roadside unit links with a signal-to-noise ratio higher than the preset low threshold value are organized into a first set of qualified roadside units; Links of roadside units with a signal-to-noise ratio higher than the preset high threshold value are organized into a second set of qualified roadside units.

5. The task offloading method for the Internet of Vehicles according to claim 4, characterized in that, The step of selecting the roadside unit with the smallest load from the qualified small task set and the qualified large task set to perform task offloading includes: For the vehicle-mounted tasks belonging to the qualified sub-task set, the roadside unit with the smallest load is selected from the vehicle-mounted tasks in the first set of qualified roadside units to perform task unloading. For the vehicle-mounted tasks belonging to the qualified large task set, the roadside unit with the smallest load is selected from the vehicle-mounted tasks in the second set of qualified roadside units to perform task unloading.

6. A task offloading device suitable for vehicle networking, characterized in that, include: The first computing efficiency calculation module is used to calculate the local computing efficiency of each vehicle task in the vehicle terminal. The transmission rate calculation module is used to collect the link parameters of the MEC offloading link, calculate the signal-to-noise ratio of the MEC offloading link, and calculate the transmission rate of the MEC offloading link in combination with the carrier bandwidth parameters of the roadside unit. The second computational efficiency calculation module calculates the total offloading time of the vehicle-mounted task based on the computational capability parameters of the MEC edge computing server and the transmission rate, and determines the MEC computational efficiency of the roadside unit link used to carry the vehicle-mounted task based on the total offloading time. The task offloading module is used to filter the vehicle-mounted tasks based on the local computing efficiency, the MEC computing efficiency, and the preset task threshold, and select the roadside unit with the least load from the filtered vehicle-mounted tasks to perform task offloading. The calculation of local computing efficiency consists of two steps: First, the computing power of the in-vehicle terminal is preset. Each task of the vehicle terminal is obtained by executing Formula 1. The time required for local computation at the vehicle terminal : Formula 1; In the formula, Represents a power function. For the first i The computational complexity of each task m This represents the maximum possible value for the number of tasks. Secondly, formula two is executed to obtain each task. Local computing efficiency : Formula 2; In the formula, For the first i The task size of each task; The second computational efficiency calculation module is specifically used for: Based on the transmission rate, the transmission time of the vehicle-mounted task being unloaded to the roadside unit link is determined. Based on the preset computing power of the MEC edge computing server, the computing time required by the MEC edge computing server is calculated. The transmission time is then compared with the total unloading time of the vehicle-mounted task. The MEC computation efficiency of the roadside unit link of the vehicle task is calculated based on the total unloading time and the task size of the vehicle task. Specifically, the task unloading module is used for: The expected value of the MEC computation efficiency of the roadside unit link is determined based on the MEC computation efficiency, and the qualified task set is obtained by combining the local computation efficiency with the on-board task. The qualified task set is split based on the preset task threshold to obtain a qualified small task set and a qualified large task set. The roadside unit with the smallest load is selected from the qualified small task set and the qualified large task set to perform task unloading.

7. An electronic device, characterized in that, include memory, processor, and A computer program stored on the memory and executable on the processor, wherein the processor, when executing the computer program, implements the task offloading method for vehicle networking as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the task unloading method applicable to the Internet of Vehicles as described in any one of claims 1 to 5.

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