Task unloading method for sensing mobility and service availability in Internet of Vehicles

Abstract

The invention belongs to the technical field of the Internet of Things, and particularly relates to a mobility and service availability sensing task unloading method in the Internet of Vehicles. According to the invention, the calculation of the link survival time # imgabs0 # considers traffic environments such as driving speed, included angle and the like. The method focuses on a two-way multi-lane scene, and more complex road traffic environments are considered. In the scene, the vehicle needs to cope with higher vehicle speed and more complex traffic flow. Compared with a single-lane scene, the task unloading scheme of the invention needs a more refined strategy to adapt to a dynamically changing traffic environment. According to the invention, the problem that in a dynamic Internet of Vehicles environment, detailed mobility modeling cannot be carried out when an unloading decision is made in the prior art, so that the V2V service availability cannot be evaluated, and the unloading reliability and low delay cannot be ensured is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of Internet of Things, and in particular relates to a task offloading method for mobility and service availability awareness in a vehicle network. Background Art

[0002] With the rapid development of VANETs (Vehicular Ad Hoc Networks) technology, vehicle-to-vehicle (V2V) communication has become an important component of intelligent transportation systems. V2V task offloading is to transfer computationally intensive or delay-sensitive tasks from local processing of vehicles to other vehicles to reduce the vehicle computing burden and improve system performance. However, task offloading in a two-way multi-lane environment faces unique challenges. First, the contact time and communication quality between vehicles are unstable, especially in high-density and complex traffic environments, which greatly affects the reliability and real-time performance of task offloading. Second, due to the traffic mobility of two-way lanes, the decision of task offloading must not only consider the upstream and downstream relationship of a single lane, but also the cross-lane movement of vehicles, which in turn affects the allocation of offloading tasks and the availability of resources.

[0003] Mirza et al. from Beijing University of Posts and Telecommunications proposed a vehicle edge computing and task offloading solution in a single-lane scenario. In this technology, vehicle mobility and service availability are dynamically considered to optimize offloading decisions. The solution reduces latency and costs by switching between multiple wireless access technologies, while improving computing efficiency by utilizing the computing power of nearby public vehicles. Shi et al. from Tsinghua University proposed a task offloading strategy that considers one-way multi-lanes to improve the efficiency and reliability of task offloading between vehicles. In this framework, the overall computing power of the system is improved by sharing idle computing resources. Effectively improve the reliability and efficiency of vehicle-to-vehicle task offloading.

[0004] In summary, most existing task offloading methods are optimized in relatively static or single-lane traffic environments, and fail to fully consider the high-speed mobility and complex lane changes of vehicles in two-way multi-lane scenarios. In particular, for dynamic changes in vehicle cross-lane movement and vehicle speed, the task offloading strategies in existing technologies often rely on relatively simple predictions of vehicle contact time and position, and cannot adjust the offloading decision in real time, resulting in the inability to guarantee the reliability and low latency of offloading in high-density and changeable traffic environments. Summary of the invention

[0005] The present invention proposes a task offloading method based on mobility and service availability awareness in the Internet of Vehicles, which solves the problem that in a dynamic Internet of Vehicles environment, the existing technology is unable to perform detailed mobility modeling when making offloading decisions, and further evaluate the V2V service availability, and cannot guarantee the reliability and low latency of offloading.

[0006] The technical solution of the present invention is achieved in this way:

[0007] A mobility and service availability aware task offloading method in a vehicle network comprises the following steps:

[0008] S1. Vehicle mobility modeling: Determine whether the user vehicle is within the IVCP transmission range based on the vehicle's location information. If it is within the transmission range, calculate the link lifetime. Link lifetime The calculation of is as follows:

[0009]

[0010] in, represents the link lifetime of the same-direction link, Indicates the lifetime of the opposite link, v n represents the speed of vehicle n, v k represents the speed of vehicle k, d nk represents the distance between vehicle n and vehicle k, represents the link survival time of vehicle k in front of vehicle n, represents the link survival time of vehicle k behind vehicle n, θ represents the angle between the vehicle line and the horizontal direction, R n represents the transmission radius of vehicle n;

[0011] S2, V2V service availability evaluation: If the transmission delay is lower than the link lifetime, the service availability is evaluated;

[0012] use To represent the service vehicle k for the task The service availability is expressed as:

[0013]

[0014] Among them, ∈ k is the probability that vehicle k accepts the unloading task, τ represents the task transmission delay;

[0015] S3. Select a suitable V2V offloading object according to the evaluation result of the evaluation service availability.

[0016] Optionally, in step S2, the service ratio of the service vehicle

[0017]

[0018] Among them, d nk is the transmission distance, C constant is a constant, is the task complexity coefficient, The proportion of computing resources reserved for local tasks, yes The weight coefficient of Indicates the unloading request of other vehicles except vehicle n;

[0019] Assume that each car has the same The threshold value is The probability that vehicle k accepts the unloading task can be expressed as Define a circle with vehicle k as the center and radius Once any vehicle other than vehicle n appears in the virtual area and sends an unloading request to vehicle k, the will drop to Below, the radius of the virtual area is obtained as follows:

[0020]

[0021] The probability that Vn's offload task is rejected by Vk is expressed as:

[0022]

[0023] Where ρ is the vehicle density corresponding to the number of vehicles per unit distance, is a constant,

[0024] Finally, we got

[0025] Optionally, in step S3, under the constraint of the link lifetime, that is, the task transmission time is less than the link lifetime, the IVCP with the highest service availability is selected as the offloading object.

[0026] After adopting the above technical solution, the beneficial effects of the present invention are:

[0027] Link survival time in the present invention The calculation takes into account traffic environment such as driving speed and angle. Adding an angle-based calculation method to the link lifetime can cover the situation of multiple lanes. Calculating the link lifetime before unloading can estimate whether the task can complete the task transmission before the link is disconnected, so as to adapt to the highly dynamic Internet of Vehicles environment. The present invention focuses on two-way multi-lane scenarios and takes into account more complex road traffic environments. In this scenario, vehicles need to cope with higher speeds and more complex traffic flows. Compared with single-lane scenarios, the task unloading scheme of the present invention requires more refined strategies to adapt to dynamically changing traffic environments. Compared with single-lane scenarios, the task unloading scheme of the present invention requires more refined strategies to adapt to dynamically changing network states and task requirements of different vehicles.

[0028] The technical solution of the present invention has important application significance in the collaborative computing of vehicle-to-vehicle edge computing platform (IVCP). Vehicle-to-vehicle edge computing is an important component of future intelligent driving systems. Vehicles distribute computing tasks to IVCP to improve decision-making efficiency. However, due to the differences in the dynamic mobility of vehicles, how to achieve efficient collaborative computing and make reliable unloading decisions has always been a challenge. The present invention can dynamically evaluate the lifetime and service availability of the V2V link and optimize task unloading decisions. Specifically, it can ensure that tasks can be unloaded to the appropriate IVCP in a timely manner, improve the collaborative efficiency of vehicle-to-vehicle edge computing, and enhance the stability and reliability of the intelligent driving system. In addition, as the scale of intelligent transportation systems expands, the IVCP selection provided by the present invention can support the efficient collaboration of large-scale vehicle-to-vehicle edge computing networks and ensure the efficient and stable operation of the entire system. This technical solution not only improves the collaborative ability in intelligent driving, but also provides technical guarantees for the safety and efficiency of intelligent transportation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0030] Figure 1 is a flow chart of a task offloading method for mobility and service availability awareness in connected vehicles;

[0031] Figure 2 It is a schematic diagram of vehicle mobility modeling in two-way multi-lane;

[0032] Figure 3 This is a comparison chart of the task completion rate experiment (the number of IVCPs is 20);

[0033] Figure 4 This is a comparison chart of the task completion rate experiment (the number of IVCPs is 30);

[0034] Figure 5 This is a comparison chart of the task completion rate experiment (the number of IVCPs is 40). DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] An embodiment of the present application discloses a task offloading method based on mobility and service availability awareness in a connected vehicle network.

[0037] Example

[0038] according to Figures 1 to 5 As shown, a mobility and service availability aware task offloading method in connected vehicles.

[0039] 1. Link survival time prediction model

[0040] The link lifetime prediction model includes a vehicle mobility modeling module and a V2V service availability evaluation module.

[0041] Vehicle mobility modeling module, the main function of the vehicle mobility modeling module is to improve the stability of communication and data transmission efficiency by predicting the lifetime of the link. By accurately predicting the link duration, more stable connections can be given priority, thereby reducing communication interruptions and data loss, thereby improving the reliability of routing decisions. In addition, the prediction of link lifetime can also help save energy and network resources, optimize the allocation of network resources, and improve the network's adaptability in high-speed mobile environments. Especially in application scenarios that require continuous connection (such as autonomous driving), it is crucial to ensure the stability of the link, which can effectively support real-time data transmission and safe operation. Through this module, the Internet of Vehicles can better guarantee communication quality and system performance in complex dynamic environments.

[0042] V2V service availability evaluation module,The function of the V2V service availability evaluation module is to comprehensively evaluate the service availability of all vehicles around the task vehicle to determine which vehicles are suitable for task offloading. In the intelligent driving system, not all vehicles within the communication range of the task vehicle have sufficient computing resources or stable communication links. Some vehicles may not be able to effectively undertake the offloading task due to insufficient computing power or the duration of the V2V link is shorter than the time required for task execution. Therefore, this module screens out vehicles suitable for offloading tasks by evaluating factors such as the task complexity and link duration of surrounding vehicles, ensuring that the task can be offloaded between suitable vehicles, thereby improving the efficiency and reliability of task offloading.

[0043] A mobility and service availability aware task offloading method in a vehicle network comprises the following steps:

[0044] (1) Vehicle mobility modeling: Based on the vehicle’s location information, determine whether the user vehicle is within the IVCP transmission range. If it is within the transmission range, calculate the link lifetime. The calculation of is as follows:

[0045]

[0046] The link lifetime between vehicle n and vehicle k is Divide it into the link survival time of the same-direction link and the lifetime of the opposite link More specifically, it is divided into the link survival time of vehicle k behind vehicle n and the link lifetime of vehicle k in front of vehicle n The link lifetime is determined by the speed v of vehicle n and vehicle k n 、v k , vehicle distance d nk , the angle θ between the vehicle line and the horizontal direction and the transmission radius R of vehicle n n When the transmission initiating vehicle’s speed is equal to the receiving vehicle’s, the link lifetime is infinite. When the transmission initiating vehicle’s speed is less than the receiving vehicle’s speed, and θ = 0, d nk =R n When the link survives, it will obtain a larger value, such as Figure 2 Example (a) and example (b) are shown in FIG. Figure 2 Examples (a) and (b) show V n and The same driving direction, however, in reality, V n and They do not necessarily travel in the same direction, but may travel in opposite directions, such as Figure 2 Example (c) and Example (d), therefore, the present invention also considers the link lifetime when driving in opposite directions. During the driving in opposite directions, the link lifetime is not affected by V n and The speed is affected by who is bigger and who is smaller.

[0047] (2) V2V service availability evaluation: If the transmission delay is lower than the link lifetime, the probability of accepting the offload task is calculated and the service availability is further evaluated;

[0048] use To represent the service vehicle k for the task The service availability is denoted as

[0049]

[0050] Among them, ∈ k is the probability that vehicle k accepts the unloading task, and τ represents the task transmission delay. Function (x) + =max(x,0) ensures that the service availability is non-negative. Represents the link lifetime. Considering that the service vehicle may receive multiple unloading requests from other vehicles at the same time, and due to limited computing power, it cannot perform all unloading tasks at the same time, so some unloading requests must be rejected. Service ratio of service vehicles

[0051] Among them, d nk is the transmission distance, C constant is a constant, is the task complexity coefficient, The proportion of computing resources reserved for local tasks, yes The weight coefficient of represents the unloading request of other vehicles except vehicle n. In addition, it is assumed that each vehicle has the same The threshold value is Then the probability of vehicle k accepting the unloading task can be expressed as In order to estimate ∈ k The value of defines a radius with vehicle k as the center. Once any vehicle other than vehicle n appears in the virtual area and sends an unloading request to vehicle k, the will drop to Below, the radius of the virtual area is obtained as follows:

[0052]

[0053] The probability that Vn’s offload task is rejected by Vk can be expressed as:

[0054]

[0055] Where ρ is the vehicle density corresponding to the number of vehicles per unit distance, is a constant,

[0056] Then got

[0057] Finally, we get the formula Evaluate service availability.

[0058] (3) Select a suitable V2V offloading object based on the evaluation results; under the constraint of link lifetime, that is, the task transmission time is less than the link lifetime, select the IVCP with the highest service availability as the offloading object.

[0059] 3. Verification

[0060] The present invention simulates a two-way road with a length of 300 meters, where user vehicles are randomly distributed and generate multiple tasks. Each task requires an IVCP to process, and each service vehicle can process multiple tasks with a service range of 20 meters. Three different task processing methods are compared in the experiment:

[0061] Solution 1: The method proposed by the present invention takes into account two-way multi-lane.

[0062] Solution 2, DVETA (Dynamic Vehicle Environment Task Assignment): A task scheduling framework that takes into account the mobility of one-way multi-lane vehicles.

[0063] Scheme 3, MCLA (Mobility, Contact, and Load Aware Task Offloading Schem): a task scheduling framework that takes into account the mobility of single-lane vehicles.

[0064] The experimental results show the performance of the three solutions in terms of task completion rate when the number of lanes is fixed to 6. Figure 3 , Figure 4 and Figure 5 The task completion rates when the number of IVCPs is 20, 30, and 40 respectively. The horizontal axis represents the number of user vehicles, and the vertical axis represents the task completion rate. It can be observed from the figure that with the increase in the number of user vehicles, the task completion rate has decreased to varying degrees. This phenomenon is mainly due to the increase in the number of user vehicles, which leads to an increase in the amount of tasks that need to be processed, thereby increasing the system load and reducing the task completion rate. However, when the number of IVCPs increases, this downward trend is significantly alleviated, indicating that more IVCPs can effectively alleviate the pressure caused by the increase in the amount of tasks.

[0065] Among the three schemes, the task completion rate based on the mobility management method proposed in the present invention is always at a high level. This is because the method can effectively take into account the task scheduling and management of two-way multi-lane, ensuring that the tasks of each lane can be processed in a timely manner. In contrast, although the MCLA method can manage IVCPs in different lanes, it is limited to the management of multiple lanes in the same direction. Therefore, there are certain limitations in task scheduling, resulting in performance slightly inferior to the scheme of the present invention. The DVET method only considers the IVCP arrived at by the vehicle, which significantly limits its performance in scenarios with high lane complexity, resulting in poor task completion rate performance. Overall, with the increase in the number of IVCPs, the method proposed in the present invention shows stronger adaptability and superiority in processing multi-lane tasks.

[0066] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A mobility and service availability aware task offloading method in a vehicle network, characterized in that: The following steps are involved: S1. Vehicle mobility modeling: Determine whether the user vehicle is within the IVCP transmission range based on the vehicle's location information. If it is within the transmission range, calculate the link lifetime. Link lifetime The calculation of is as follows: in, represents the link lifetime of the same-direction link, Indicates the lifetime of the opposite link, v n represents the speed of vehicle n, v k represents the speed of vehicle k, d nk represents the distance between vehicle n and vehicle k, represents the link survival time of vehicle k in front of vehicle n, represents the link survival time of vehicle k behind vehicle n, θ represents the angle between the vehicle line and the horizontal direction, R n represents the transmission radius of vehicle n; S2, V2V service availability evaluation: If the transmission delay is lower than the link lifetime, the service availability is evaluated; use To represent the service vehicle k for the task The service availability is expressed as: Among them, ∈ k is the probability that vehicle k accepts the unloading task, τ represents the task transmission delay; S3. Select a suitable V2V offloading object according to the evaluation result of the evaluation service availability.

2. The method for task offloading based on mobility and service availability awareness in a connected vehicle network according to claim 1, characterized in that: In step S2, the service ratio of the service vehicle Among them, d nk is the transmission distance, C constant is a constant, is the task complexity coefficient, The proportion of computing resources reserved for local tasks, yes The weight coefficient of Indicates the unloading request of other vehicles except vehicle n; Assume that each car has the same The threshold value is The probability that vehicle k accepts the unloading task can be expressed as Define a circle with vehicle k as the center and radius Once any vehicle other than vehicle n appears in the virtual area and sends an unloading request to vehicle k, the will drop to Below, the radius of the virtual area is obtained as follows: The probability that Vn's offload task is rejected by Vk is expressed as: Where ρ is the vehicle density corresponding to the number of vehicles per unit distance, is a constant, Finally, we got 3. The method for task offloading based on mobility and service availability awareness in a connected vehicle network according to claim 1, characterized in that: In step S3, under the constraint of link lifetime, that is, the task transmission time is less than the link lifetime, the IVCP with the highest service availability is selected as the offloading object.

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