A multi-source multi-target fusion mobile edge computing method and system

Abstract

The application relates to a multi-source multi-target fusion mobile edge computing method and system, which comprises the following steps: acquiring residual energy of each MEC site, setting an opening and closing energy threshold, determining the MEC sites participating in calculation according to the size relationship between the opening and closing energy threshold and the residual energy of each MEC site; grouping all the MEC sites participating in calculation, after all the MEC sites participating in calculation receive the calculation tasks transmitted by surrounding vehicles, for each group of MEC sites, allocating tasks to each MEC site according to the energy state of each MEC site and the transmission time between each MEC site; each MEC site respectively executes the allocated task, and returns to the vehicle after execution. Compared with the prior art, the application has the advantages of low time delay and applicability to various scenes.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology for vehicle networking, and in particular to a multi-source, multi-target fusion mobile edge computing method and system. Background Technology

[0002] In real-world road scenarios, massive amounts of data are generated during vehicle operation. This data is often subject to various interferences from the real environment. Therefore, it is necessary to use methods such as compressed sensing to preprocess multi-source data with errors and redundancy, and to fuse and process massive multi-source information with high robustness in real time. These processing methods place new demands on the computing power of hardware devices, and the high computing power of hardware devices requires sufficient power to ensure it.

[0003] On remote highways and other roads without cables, the energy for roadside MEC (Multi-access Edge Computing) devices is often provided by wind or solar power. This power supply method will cause differences in energy reserves among different sites. In extreme cases, a site may be unable to work properly due to a failure of the power generation equipment. Therefore, it is worthwhile to study how to coordinate and allocate computing tasks according to the energy reserves of each site and reduce dependence on a single site.

[0004] Existing technologies have made many efforts to solve the above-mentioned technical problems. For example, the patent CN108880893A discloses "A method for joint energy harvesting and task offloading of mobile edge computing servers". By modeling the power status of MEC servers and the energy consumption required for user task execution, the optimal offloading strategy is determined and centralized task allocation is carried out, thereby minimizing the joint execution overhead of latency and energy consumption.

[0005] For example, the patent CN109413615A, entitled "Energy Delay Trade-off Based on MEC Energy Sensing Offloading in Vehicle Networking," considers the energy of the MEC server and the processing latency of tasks, and allocates specific tasks by setting user preferences (preferring low latency or low energy consumption).

[0006] Although the aforementioned existing technologies have all made some contributions to coordinating and allocating computing tasks, they all have the following drawbacks:

[0007] (1) Existing technologies only consider the case where the MEC server has sufficient power, and lack discussion of scenarios where remote areas need to be powered by wind and solar energy, which has certain limitations;

[0008] (2) The centralized task allocation method used in the existing technology will cause excessive latency in the vehicle-road cooperative scenario of highway. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a multi-source, multi-target fusion mobile edge computing method and system. This invention has the advantages of low latency and applicability to various scenarios.

[0010] The objective of this invention can be achieved through the following technical solutions:

[0011] A multi-source, multi-target fusion mobile edge computing method includes the following steps:

[0012] Obtain the remaining energy of each MEC site, set the on / off energy threshold, and determine the MEC sites to participate in the calculation based on the relationship between the on / off energy threshold and the remaining energy of each MEC site.

[0013] All participating MEC sites are grouped. After all participating MEC sites receive the computing tasks transmitted from surrounding vehicles, tasks are assigned to each MEC site in each group based on the energy status of each MEC site and the transmission time between each MEC site.

[0014] Each MEC site executes its assigned task and sends the completed task back to the vehicle.

[0015] Furthermore, the strategy for grouping all participating MEC sites is as follows:

[0016] Based on the location information of the MEC sites involved in the calculation, they are grouped using a clustering method.

[0017] Furthermore, the strategy for grouping all participating MEC sites is as follows:

[0018] Based on the remaining power status of the MEC sites participating in the calculation, MEC sites with low remaining power are assigned to the same group as MEC sites with high remaining power.

[0019] Furthermore, the specific process for determining the MEC sites involved in the calculation is as follows:

[0020] MEC sites with remaining energy below the on / off energy threshold are shut down, while MEC sites with remaining energy above the on / off energy threshold are included in the calculation.

[0021] The present invention also provides a mobile edge computing system applying the multi-source, multi-objective fusion mobile edge computing method described above, comprising:

[0022] Multiple MEC sites: used to receive computing tasks transmitted from surrounding vehicles and execute those tasks;

[0023] MEC Management Components: Used to determine the MEC sites and MEC groups that participate in the computation;

[0024] Multiple group allocator: Used to assign computing tasks to MEC sites within the same group.

[0025] Furthermore, the MEC management component receives the remaining energy status of all MEC sites, sets the on / off energy threshold, determines the MEC sites to participate in the calculation based on the relationship between the on / off energy threshold and the remaining energy of each MEC site, and groups the MEC sites to participate in the calculation.

[0026] Furthermore, the MEC management component sets an on / off energy threshold and sends the on / off energy threshold to each MEC site. Each MEC site determines whether to participate in the calculation based on the difference between its remaining energy and the on / off energy threshold, and performs self-organizing grouping.

[0027] Furthermore, MEC sites with remaining energy below the switching energy threshold are shut down, while MEC sites with remaining energy above the switching energy threshold are used as MEC sites for calculation.

[0028] Furthermore, each group's group allocator is a MEC site selected by each group after grouping is completed. It receives and allocates tasks to each MEC site in the group based on the energy status of each MEC site and the transmission time between each MEC site.

[0029] Furthermore, the MEC site communicates with the vehicle via RSU and / or base station.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] (1) The present invention first determines the MEC sites to be included in the calculation by the remaining power of each MEC site, and shuts down some sites, which can adapt to situations such as low traffic volume and low energy supply. At the same time, the MEC sites to be included in the calculation are grouped and tasked, which reduces the allocation delay compared with the centralized task allocation of the prior art.

[0032] (2) This invention addresses the issue of MEC sites being unable to perform computing and communication tasks normally when their energy is low. It proposes shutting down the sites and, considering both site location and shutdown conditions, proposes grouping tasks for allocation. Compared to existing technologies, this approach is more suitable for scenarios involving varying energy reserves or abnormal operation of MEC sites on highways in remote areas. Attached Figure Description

[0033] Figure 1 This is a flowchart illustrating a multi-source, multi-target fusion mobile edge computing method provided by the present invention.

[0034] Figure 2This is a schematic diagram of the structure of a mobile edge computing system that utilizes a multi-source, multi-target fusion mobile edge computing method, as provided by the present invention.

[0035] Figure 3 This diagram illustrates how the MEC management component centrally handles the opening and closing of various MEC sites.

[0036] Figure 4 This diagram illustrates how the MEC management component assigns tasks to groups of MEC sites participating in the computation.

[0037] Figure 5 This is a schematic diagram of the self-organizing opening and closing of each MEC site.

[0038] Figure 6 This is a schematic diagram of the self-organizing grouping of each MEC site. Detailed Implementation

[0039] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0040] According to a first aspect of the invention, reference is made to Figure 1 As shown, this embodiment provides a multi-source, multi-target fusion mobile edge computing method, including the following steps:

[0041] S1: Obtain the remaining energy of each MEC site, set the on / off energy threshold, and determine the MEC sites to participate in the calculation based on the relationship between the on / off energy threshold and the remaining energy of each MEC site.

[0042] Considering that MEC sites cannot perform computing and communication tasks normally when their energy is low, MEC sites with remaining energy below the on / off energy threshold are shut down, while MEC sites with remaining energy above the on / off energy threshold are used as MEC sites to participate in computing.

[0043] S2: Group all MEC sites participating in the calculation. For each group of MEC sites, select one MEC site as the group allocator for task allocation in this group. Each group allocator obtains the energy status of each MEC site in its group and the transmission time between each MEC site.

[0044] As an optional implementation, the grouping strategy for grouping all MEC sites participating in the calculation is as follows: based on the location information of the MEC sites participating in the calculation, they are grouped using a clustering method.

[0045] As an optional implementation, the strategy of grouping all MEC sites participating in the calculation is as follows: based on the remaining power of the MEC sites participating in the calculation, MEC sites with low remaining power are assigned to the same group as MEC sites with high remaining power.

[0046] S3: Each MEC site receives computing tasks transmitted from surrounding vehicles and uploads them to the corresponding group allocator. Each group allocator assigns tasks to each MEC site based on the energy status of each MEC site and the transmission time between each MEC site.

[0047] S4: Each MEC station executes its assigned task, and after the task is completed, it is transmitted back to a specific location and finally back to the vehicle.

[0048] According to a second aspect of the invention, reference is made to Figure 2 As shown, this embodiment provides a system applying the multi-source, multi-objective fusion mobile edge computing method described above. It is applicable to all scenarios, especially in remote areas where roads or power facilities are inadequate, and electricity needs to be provided by wind or solar power. In this scenario, how to coordinate MEC resources to efficiently complete computing tasks is a problem worthy of study. The system includes:

[0049] Multiple MEC sites: used to receive computing tasks transmitted from surrounding vehicles and execute those tasks.

[0050] MEC Management Components: Used to determine the MEC sites and MEC groups that participate in the computation;

[0051] Multiple group allocator: Used to receive and allocate tasks to each MEC site in the group based on the energy status of each MEC site and the transmission time between each MEC site.

[0052] In this embodiment, the system also includes service areas located at both ends of the highway. The service areas are able to provide sufficient power and are equipped with MEC sites for participating in calculations and base stations for communication.

[0053] As an optional implementation method, refer to Figure 3 and Figure 4 As shown, the MEC management component centrally processes the data. Specifically, the MEC management component receives the remaining energy status of all MEC sites, sets the on / off energy threshold, determines the MEC sites to participate in the calculation based on the relationship between the on / off energy threshold and the remaining energy of each MEC site, and groups the MEC sites to participate in the calculation.

[0054] As an optional implementation method, refer to Figure 5 and Figure 6As shown, each MEC site self-organizes its opening and closing and grouping. The MEC management component sets the opening and closing energy threshold and sends the opening and closing energy threshold to each MEC site. Each MEC site determines whether to participate in the calculation based on the difference between its remaining energy and the opening and closing energy threshold, and then performs self-organized grouping.

[0055] Specifically, MEC sites with remaining energy below the switching energy threshold are shut down, while MEC sites with remaining energy above the switching energy threshold are used as MEC sites for calculation.

[0056] The preferred embodiments of the present invention have been described in detail above. It should be understood that those skilled in the art can make numerous modifications and variations based on the concept of the present invention without creative effort. Therefore, all technical solutions that can be obtained by those skilled in the art based on the concept of the present invention through logical analysis, reasoning, or limited experimentation on the basis of existing technology should be within the scope of protection defined by the claims.

Claims

1. A multi-source, multi-target fusion mobile edge computing method, characterized in that, Includes the following steps: Obtain the remaining energy of each MEC site, set the on / off energy threshold, and determine the MEC sites to participate in the calculation based on the relationship between the on / off energy threshold and the remaining energy of each MEC site. All participating MEC sites are grouped. After all participating MEC sites receive the computing tasks transmitted from surrounding vehicles, tasks are assigned to each MEC site in each group based on the energy status of each MEC site and the transmission time between each MEC site. Each MEC site executes its assigned task and sends a data back to the vehicle upon completion. The specific strategy for grouping all participating MEC sites is as follows: Based on the remaining power status of the MEC sites participating in the calculation, MEC sites with low remaining power and MEC sites with high remaining power are assigned to the same group. The specific process for determining the MEC sites to participate in the calculation is as follows: MEC sites with remaining energy below the on / off energy threshold are shut down, while MEC sites with remaining energy above the on / off energy threshold are included in the calculation. Multiple group allocators are set up to allocate computing tasks to the MEC sites in the group. Each group allocator is a MEC site selected in each group after the grouping is completed. It receives and allocates tasks to each MEC site in the group according to the energy status of each MEC site and the transmission time between each MEC site.

2. The multi-source, multi-target fusion mobile edge computing method according to claim 1, characterized in that, Another strategy for grouping all participating MEC sites is: Based on the location information of the MEC sites involved in the calculation, they are grouped using a clustering method.

3. A mobile edge computing system applying the multi-source, multi-target fusion mobile edge computing method as described in any one of claims 1-2, characterized in that, include: Multiple MEC sites: used to receive computing tasks transmitted from surrounding vehicles and execute those tasks; MEC Management Components: Used to determine the MEC sites and MEC groups that participate in the computation; Multiple group allocator: Used to assign computing tasks to MEC sites within the same group.

4. The mobile edge computing system according to claim 3, characterized in that, The MEC management component receives the remaining energy status of all MEC sites, sets the on / off energy threshold, determines the MEC sites to participate in the calculation based on the relationship between the on / off energy threshold and the remaining energy of each MEC site, and groups the MEC sites to participate in the calculation.

5. The mobile edge computing system according to claim 3, characterized in that, The MEC management component sets an on / off energy threshold and sends the on / off energy threshold to each MEC site. Each MEC site determines whether to participate in the calculation based on the difference between its remaining energy and the on / off energy threshold, and then performs self-organizing grouping.

6. The mobile edge computing system according to any one of claims 4 or 5, characterized in that, MEC sites with remaining energy below the switching energy threshold are shut down, while MEC sites with remaining energy above the switching energy threshold are included in the calculation.

7. The mobile edge computing system according to claim 3, characterized in that, The MEC site communicates with the vehicle via RSU and / or base station.

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