CN112188441A - Task unloading method and system adopting unmanned aerial vehicle in edge network and storage medium - Google Patents
Task unloading method and system adopting unmanned aerial vehicle in edge network and storage medium Download PDFInfo
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- CN112188441A CN112188441A CN202011104801.4A CN202011104801A CN112188441A CN 112188441 A CN112188441 A CN 112188441A CN 202011104801 A CN202011104801 A CN 202011104801A CN 112188441 A CN112188441 A CN 112188441A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
- H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W28/00—Network traffic management; Network resource management
- H04W28/02—Traffic management, e.g. flow control or congestion control
- H04W28/08—Load balancing or load distribution
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention relates to the technical field of edge computing, and discloses a task unloading method, a task unloading system and a storage medium which adopt an unmanned aerial vehicle in an edge network, so as to realize load balancing and energy consumption optimization. The method comprises the following steps: determining at least two anchor points of a first type with data quantity exceeding a set condition; planning a primary flight path of the unmanned aerial vehicle traversing each first type of anchor point; screening the residual nodes with the distance smaller than a set threshold value on the primary flight path of the unmanned aerial vehicle as second-class anchor points; planning a non-anchor point node to select a route path with the minimum cost reaching any one of the first class and the second class of anchor points and send a task to the corresponding anchor point; or the non-anchor node selects the route path with the minimum cost reaching the first class of anchor points to send the task to the corresponding anchor points; correcting the preliminary flight path to obtain a final flight path which is included in the second type of anchor points; and instructing the unmanned aerial vehicle to sequentially collect tasks from the corresponding first class anchor points and second class anchor points according to the final flight path.
Description
Technical Field
The invention relates to the technical field of edge computing, in particular to a task unloading method and system adopting an unmanned aerial vehicle in an edge network and a storage medium.
Background
The edge network has advanced greatly, and the most significant performance of the edge network is that billions of Internet of Things (IoT) devices are deployed in various applications of the edge network for data perception and task processing. Research has shown that the number of devices currently connected to an internet of things has exceeded 200 billion and is growing at a much faster rate. With the deployment of a huge number of internet of things devices on the network edge, the network edge has huge computing and storage capacity. Meanwhile, the data volume generated by the huge number of internet of things devices is up to tens of TB each day. In a traditional cloud computing mode, data sensed by a network edge needs to be uploaded to a cloud end for processing, and processing results need to be returned to a user at the network edge from the cloud end through a long route. On one hand, uploading a large amount of data to the cloud of the network center consumes a large amount of energy, and the calculated result needs to be returned to the user through a long routing path, so that the user experience (QoE) is not good due to large delay and jitter. On the other hand, the cloud is overloaded and the performance is reduced, and meanwhile, the computing and storage capacities of a large number of internet of things devices on the edge of the network are not fully utilized. Edge computing and fog computing form a new computing mode aiming at the current network change, and in the computing mode, computing and data processing are carried out on a network edge server, so that the conditions of energy consumption and bandwidth occupation and poor user experience of uploading data to a cloud are avoided, and the development of an edge network computing mode is promoted.
Currently, more and more applications with high computational requirements, such as machine learning, virtual reality, and the like, are deployed on internet of things devices. Relatively speaking, resource-hungry internet of things devices do not have enough computing power to meet many application computing requirements. In this case, edge calculation, fog calculation, is a better solution. In the computing method, the tasks of the equipment of the internet of things are offloaded to the edge server with more sufficient computing power near the equipment for processing, so that greater efficiency can be achieved. Therefore, task offloading is an effective computing method, which can make full use of computing resources in the edge network to provide timely and effective computing, reduce the defects that the original computing mode needs to pass through a long route to the cloud, and has large energy consumption, bandwidth occupation, large delay and network jitter, improve the service life of the internet of things equipment, and improve user experience. Thus, once the task offloading of the edge network has been proposed, there has been a great deal of research work on task offloading.
There have been some studies using unmanned planes for task offloading. There are two main categories, one is mainly the study of task collection. Data collection can be considered a particular task in such studies. Each internet of things device senses the surrounding environment and needs to send sensed data to the data center, but due to the limitation of cost, many internet of things devices do not have the capability of communicating with the data center. Therefore, the method for collecting data by adopting the unmanned aerial vehicle is an effective, timely and low-cost method.
Ebrahimi et al propose a method of data collection that is commonly used for many data collections. In such a strategy, researchers divide a network into a plurality of clusters (clusters), each cluster selects a node called a Cluster Head (CH), other nodes are called member nodes (member nodes), and all the member nodes send own data to the cluster head nodes through multi-hop or directly. Therefore, the unmanned aerial vehicle can collect data of the whole network as long as the unmanned aerial vehicle flies to the cluster head nodes for data collection. Obviously, the method can greatly reduce the flying distance of the unmanned aerial vehicle, thereby effectively reducing the energy consumption of the unmanned aerial vehicle. However, data collection is not the same as task offloading after all. In task offloading, the amount of data required in some tasks is very large. And some tasks require a very small amount of data. The most energy consumption in the internet of things equipment is data transmission, so that if a node where a task with large data volume is located is not selected as a cluster head, the task needs to be transmitted to the cluster head, and therefore large energy is consumed. Thus, data collection policies cannot be applied directly to task offloading.
Researchers put forward that the unmanned aerial vehicle flies to the area where the Internet of things equipment is located, and then communication connection and task unloading are directly established between the unmanned aerial vehicle and each piece of Internet of things equipment. This method is not necessarily an efficient method. Because there are several disadvantages in such an approach (1) the energy consumption of the drone is very costly. In such a manner, one is that the trajectory that the drone needs to fly needs to cover each internet of things device that needs to be offloaded for a task. Because covering the thing networking equipment region needs very big flight track, therefore long track can seriously consume unmanned aerial vehicle's energy. Secondly, the unmanned aerial vehicle flies to the area where the internet of things equipment is located, each internet of things equipment in the areas directly communicates with the unmanned aerial vehicle, so that communication conflicts and competition are very large, and communication error codes and retransmission can be caused due to unstable flying of the unmanned aerial vehicle, so that the energy consumption and the time for communication are long. Therefore, the unmanned aerial vehicle needs to hover over the internet of things device for a long time, and therefore energy consumption needed by the unmanned aerial vehicle for staying is very large. But the delay in task offloading is also very large. (2) The energy consumption of the internet of things equipment is also very large. In this way, all internet of things devices need to wait for the unmanned aerial vehicle to fly into the communication range of the unmanned aerial vehicle before task unloading can be carried out. And the energy consumption on the equipment of the Internet of things is large, and the delay is large.
The important consideration for task offloading with drones is energy consumption. On the one hand, internet of things devices are often powered by batteries, and thus, their energy is extremely limited. In task unloading, on one hand, if the internet of things equipment unloads the task to the unmanned aerial vehicle, the energy consumption of calculation can be reduced, and the time required by calculation results is reduced. But communication while uploading tasks to the drone is energy consuming. And communication is the most energy consuming part of the internet of things equipment. Generally, a task is associated with a certain amount of data. And different tasks have different amounts of data that need to be uploaded when they are unloaded. Some tasks that are data volume intensive require a large amount of data to be uploaded when offloaded. The task for this type of application is virtual reality, since it requires the uploading of large amounts of data for computation. While some tasks with sparse data volumes require only a small amount of data to be uploaded while being offloaded. Applications such as secure key cracking belong to these, and only few parameters are uploaded, but the calculation amount is extremely large. For the internet of things equipment, the energy consumption is mainly determined by the data volume needing to be uploaded, so that the data volume size of the task needs to be carefully considered during task unloading to maximally save energy, which is not sufficiently researched in the past.
On the other hand, the energy of the drone is also limited although its energy is much larger than the internet of things devices. But the energy that unmanned aerial vehicle need consume when flying is far more than thing networking equipment, and is subject to unmanned aerial vehicle's size and dead weight, and moreover, unmanned aerial vehicle's energy load still is limited. How to effectively reduce the energy consumption of the unmanned aerial vehicle is still an important consideration. And the energy consumption of the unmanned aerial vehicle is mainly divided into the following 2 components: one part is the energy needed to maintain the unmanned aerial vehicle in flight, and the energy consumption is the main energy consumption. The energy consumption required by the unmanned aerial vehicle during flying is generally related to the flying distance, so that the effective reduction of the flying distance of the unmanned aerial vehicle is an effective energy consumption reduction method; in addition, more energy is consumed if the drone is hovering while being unloaded. Another part of the energy consumption is the energy consumption required for task offloading and task computation. The service life of the internet of things equipment can be prolonged, and the energy consumption of the unmanned aerial vehicle is reduced, so that the unmanned aerial vehicle can serve more internet of things equipment and a wider area. Thus, reducing the energy consumption of both internet of things devices and drones has attracted extensive attention from researchers.
Disclosure of Invention
The invention mainly aims to disclose a task unloading method, a task unloading system and a storage medium which adopt an unmanned aerial vehicle in an edge network so as to realize load balancing and energy consumption optimization.
In order to achieve the above object, the present invention discloses a task offloading method using an unmanned aerial vehicle in an edge network, comprising:
determining at least two anchor points of a first type with data quantity exceeding a set condition;
planning a primary flight path of the unmanned aerial vehicle traversing each first type of anchor point;
screening the residual nodes with the distance smaller than a set threshold value on the primary flight path of the unmanned aerial vehicle as second-class anchor points;
planning a non-anchor point node to select a route path with the minimum cost reaching any one of the first class and the second class of anchor points and send a task to the corresponding anchor point; or the non-anchor node selects the route path with the minimum cost reaching the first class of anchor points to send the task to the corresponding anchor points;
correcting the preliminary flight path to obtain a final flight path which is included in the second type of anchor points;
and instructing the unmanned aerial vehicle to sequentially collect tasks from the corresponding first class anchor points and second class anchor points according to the final flight path.
Corresponding to the method, the invention also discloses a task unloading system adopting the unmanned aerial vehicle in the edge network, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the steps of the method are realized when the processor executes the computer program.
Correspondingly, the invention also discloses a computer storage medium, on which a computer program is stored, wherein the program realizes the steps of the method when being executed by a processor.
The invention has the following beneficial effects:
the nodes with large task amount are determined as first class anchor points, and tasks on the first class anchor points can be directly unloaded to the unmanned aerial vehicle without being routed to other nodes, so that the saved energy and the cost are the most; optimizing a flight path, and bringing the flight path into a second type of anchor point with smaller flight cost; then, the task routing of the whole system is redistributed and energy balance is carried out, the non-anchor node sends the task to the corresponding anchor point along the minimum cost routing path, and systematic overall planning also enables the energy consumption between the unmanned aerial vehicle and the edge network task node to be balanced; the energy consumption of the whole system is greatly optimized.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic flow chart of a task offloading method using an unmanned aerial vehicle in an edge network according to a preferred embodiment of the present invention.
Fig. 2 is a comparison diagram of the assumed data volume of the sensor nodes under different methods.
FIG. 3 is a schematic diagram of variance versus node energy consumption.
Fig. 4 is a comparison of network lifetime under different methods.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example 1
The embodiment discloses a task unloading method using an unmanned aerial vehicle in an edge network, as shown in fig. 1, including:
step S1 is to determine at least two or more anchor points of the first type whose data amount exceeds the set condition.
In this step, optionally, the setting condition for determining the first type anchor point may be to select several nodes with the largest data amount according to a set proportion as the first type anchor point; or firstly calculating the average data volume of all nodes of the edge network, and then determining the nodes exceeding the specific threshold of the average data volume as the first class anchor points; or determining whether the node data volume is an outlier by adopting a Grubbs test method, further judging whether the node data volume is a large data volume node, and further taking part or all of the determined large data volume nodes as the first-class anchor points.
And step S2, planning a preliminary flight path of the unmanned aerial vehicle traversing each first-type anchor point.
In this step, the preliminary flight path is preferably designed with an overall shortest path; the flight path of the unmanned aerial vehicle can be specifically designed according to a traveler algorithm.
And S3, screening the residual nodes with the distance smaller than a set threshold value on the primary flight path of the unmanned aerial vehicle as second-class anchor points.
In this step, for example: if part of the nodes are less than the specified distance from the primary flight path of the unmanned aerial vehicle
，
The value is generally 20 meters depending on the actual network; this node also incorporates a second type of anchor point that is a drone, i.e., the drone also flies through this node, adding some anchor points at less flight cost.
And step S4, planning the non-anchor point node to select the route path with the minimum cost reaching any anchor point in the first class and the second class of anchor points, and sending the task to the corresponding anchor point. In the method, the second class anchor point is regarded as the equivalent relation with the first class anchor point, so that more selection space is added for route optimization of the subsequent overall task. As a variation, the non-anchor node may also select a route path with the minimum cost to reach the first type of anchor, and send the task to the corresponding anchor.
And step S5, correcting the preliminary flight path to obtain a final flight path including the second type of anchor points.
And S6, instructing the unmanned aerial vehicle to sequentially collect tasks from the corresponding first class anchor points and second class anchor points according to the final flight path. Namely: and the unmanned plane goes through all anchor points, so that all tasks are collected.
The simulation experiment gives the sensor location in the sensor network and the data volume and the calculation volume of each sensor. According to the method of the embodiment, the nodes with large data volume in the sensor are determined, and then the anchor points are determined. In order to compare the data transmission energy consumption of a small-scale wireless sensor network under the condition that the data measuring values of nodes with large data volume are different, 8 sensors are randomly arranged in a rectangular area with the side length of 10m multiplied by 10m, a network is constructed according to the method of the invention, then networking is carried out according to the traditional mode of the traditional algorithm, the data transmission energy consumption conditions of the sensor network corresponding to each algorithm under the condition of given data volume and calculated amount are compared, and the result is shown in figure 2.
The data amount of a sensor node with a small data amount but a large calculation amount or other cases is set to 0.75 Mb. The data volume of the large data volume sensor node is 0.75(Mb) to 25 (Mb). As can be seen from fig. 2, when the data volumes of the large data volume node and the common node are similar, the difference between the method and the conventional algorithm in the data transmission energy consumption of the sensor network is not large, but as the data volume of the large data volume sensor node gradually increases, the conventional algorithm does not use the node as a data sink node, so that a large amount of data is transmitted between the sensor nodes, so that the data transmission energy consumption of the wireless sensor network increases, and the energy consumption of the sensor increases. It can be seen that the larger the difference between the data volume of the large-data-volume node and the data volume of the surrounding common nodes is, the larger the difference between the method and the conventional algorithm in terms of energy consumption is. With the increase of the data volume of the large-data-volume node, the energy saving rate of the method is higher and higher, and when the value is 25(Mb), the energy saving rate is close to 80%.
The following experiment illustrates the performance comparison of the method of the present embodiment in load balancing optimization. In the experiment, a plurality of sensors are randomly arranged in a 10m multiplied by 10m area, the data volume of the sensors is subjected to normal distribution with the mean value of 0.5 and the variance of 0.1, and the number of large data volume nodes is 2. In order to avoid loss of generality, 30 experiments are respectively carried out on the conditions that the number of the sensors is 8, 9, 10, 11 and 12, the energy consumption value before and after each experiment and the variance of the data received by the large data volume node are recorded, and the experimental result is shown in the load balancing front and back variances in fig. 3.
As can be seen from fig. 3, the load balancing algorithm has a significant effect on reducing the data volume difference of the data sink nodes. With the increase of the number of nodes in the small-scale network, the network becomes complex, and the number of nodes which can be used for adjusting the data transmission direction and further adjusting the variance becomes larger, so that the probability of the nodes which are more suitable for adjusting the variance is increased, the load balancing effect is better, and the variance is reduced along with the increase of the complexity of the network. When the number of sensors is 8, 9, 10, 11, 12, respectively, the percentage of variance reduction is 84.15%, 82.83%, 83.52%, 85.05%, 81.11%, respectively.
Furthermore, 50 sensors are arranged in a 100m × 100m area to simulate a large-scale network, the data volume of the common nodes is subjected to normal distribution with the mean value of 0.5 and the variance of 0.1, and the large-data-volume nodes are randomly deployed according to actual conditions. Experiments compare the differences of the method and the traditional algorithm in system energy consumption, the variance of the data aggregation nodes of the whole wireless sensor network and the service life of the sensor network. Assuming in the experiments that the battery capacity of each sensor is the same and that the energy level is 50J each, the lifetime of the wireless sensor network is defined as: the time required for a power-down condition to occur for the first time at a node in the entire wireless sensor network. Fig. 4 shows the comparison of the lifetime of the sensor network for the two methods in 50 random experiments. As can be seen from fig. 4, since the method of the present embodiment balances the energy consumption of each node in the network, the service life of the wireless sensor network is increased. Compared with the traditional algorithm, in the 50 random experiments, the average network life of the method is 345.5767 rounds, while the average network life of the traditional algorithm is only 184.2939 rounds, so that the method of the embodiment improves 87.51% of the sensor network life.
In conclusion, the method can reduce the energy consumption of the task routing, balance the energy consumption, prolong the service life of the network and improve the network performance.
Example 2
Corresponding to the method, the invention also discloses a task unloading system adopting the unmanned aerial vehicle in the edge network, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the steps of the method are realized when the processor executes the computer program.
Example 3
Correspondingly, the invention also discloses a computer storage medium, on which a computer program is stored, wherein the program realizes the steps of the method when being executed by a processor.
In summary, the task offloading method, system and storage medium using the unmanned aerial vehicle in the edge network disclosed in the embodiments of the present invention have the following beneficial effects:
the nodes with large task amount are determined as first class anchor points, and tasks on the first class anchor points can be directly unloaded to the unmanned aerial vehicle without being routed to other nodes, so that the saved energy and the cost are the most; optimizing a flight path, and bringing the flight path into a second type of anchor point with smaller flight cost; then, the task routing of the whole system is redistributed and energy balance is carried out, the non-anchor node sends the task to the corresponding anchor point along the minimum cost routing path, and systematic overall planning also enables the energy consumption between the unmanned aerial vehicle and the edge network task node to be balanced; the energy consumption of the whole system is greatly optimized.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. A task unloading method adopting an unmanned aerial vehicle in an edge network is characterized by comprising the following steps:
determining at least two anchor points of a first type with data quantity exceeding a set condition;
planning a primary flight path of the unmanned aerial vehicle traversing each first type of anchor point;
screening the residual nodes with the distance smaller than a set threshold value on the primary flight path of the unmanned aerial vehicle as second-class anchor points;
planning a non-anchor point node to select a route path with the minimum cost reaching any one of the first class and the second class of anchor points and send a task to the corresponding anchor point;
correcting the preliminary flight path to obtain a final flight path which is included in the second type of anchor points;
and instructing the unmanned aerial vehicle to sequentially collect tasks from the corresponding first class anchor points and second class anchor points according to the final flight path.
2. A task offloading system employing drones in an edge network, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method of claim 1.
3. A computer storage medium having a computer program stored thereon, wherein the program when executed by a processor implements the steps of the method of claim 1.
4. A task unloading method adopting an unmanned aerial vehicle in an edge network is characterized by comprising the following steps:
determining at least two anchor points of a first type with data quantity exceeding a set condition;
planning a primary flight path of the unmanned aerial vehicle traversing each first type of anchor point;
screening the residual nodes with the distance smaller than a set threshold value on the primary flight path of the unmanned aerial vehicle as second-class anchor points;
planning a non-anchor node to select a route path with the minimum cost reaching the first class of anchor points and send a task to the corresponding anchor points;
correcting the preliminary flight path to obtain a final flight path which is included in the second type of anchor points;
and instructing the unmanned aerial vehicle to sequentially collect tasks from the corresponding first class anchor points and second class anchor points according to the final flight path.
5. A task offloading system employing drones in an edge network, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of the method of claim 4.
6. A computer storage medium having a computer program stored thereon, wherein the program when executed by a processor implements the steps of the method of claim 4.
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