A mobility and service availability aware task offloading method in internet of vehicles

Abstract

The present application belongs to the technical field of Internet of Things, and particularly relates to a task offloading method for mobility and service availability sensing in vehicle networking. In the present application, the calculation of link lifetime considers traffic environment such as driving speed and included angle. The present application focuses on a two-way multi-lane scene and considers more complex road traffic environment. In this scene, vehicles need to cope with higher vehicle speed and more complex traffic flow. Compared with a single-lane scene, the task offloading scheme of the present application needs more refined strategies to adapt to the dynamically changing traffic environment. The present application solves the problem that the existing technology cannot make detailed mobility modeling when making offloading decisions, and further cannot evaluate V2V service availability, and cannot guarantee the reliability and low delay of offloading in a dynamic vehicle networking environment.

Description

Technical Field

[0001] This invention belongs to the field of Internet of Things (IoT) technology, and specifically relates to a task offloading method for mobility and service availability awareness in vehicle networking. Background Technology

[0002] With the rapid development of VANETs (Vehicle-to-Vehicle) technology, vehicle-to-vehicle (V2V) communication has become an important component of intelligent transportation systems. V2V task offloading reduces the computational burden on vehicles and improves system performance by transferring computationally intensive or latency-sensitive tasks from local processing in one vehicle to other vehicles. 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, complex traffic environments, significantly impacting the reliability and real-time performance of task offloading. Second, due to the traffic flow in two-way lanes, task offloading decisions must consider not only the upstream and downstream relationships within a single lane but also vehicle cross-lane movement, thus affecting task allocation and resource availability.

[0003] Mirza et al. from Beijing University of Posts and Telecommunications proposed a vehicle edge computing and task offloading scheme for single-lane scenarios. This technology dynamically considers vehicle mobility and service availability to optimize offloading decisions. The scheme reduces latency and cost by switching between multiple wireless access technologies, while improving computational efficiency by utilizing the computing power of nearby public vehicles. Shi et al. from Tsinghua University proposed a task offloading strategy considering unidirectional multi-lane scenarios 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 enhancing the reliability and efficiency of vehicle-to-vehicle (V2V) task offloading.

[0004] In summary, most existing task offloading methods are optimized for relatively static or single-lane traffic environments, failing to fully consider the high-speed mobility of vehicles and complex lane changes in two-way multi-lane scenarios. In particular, for vehicle movement across lanes and dynamic changes in vehicle speed, existing task offloading strategies often rely on relatively simple predictions of vehicle contact time and position, making it impossible to adjust offloading decisions in real time. This results in a failure to guarantee the reliability and low latency of offloading in high-density, variable traffic environments. Summary of the Invention

[0005] This invention proposes a task offloading method for mobility and service availability awareness in the Internet of Vehicles (IoV). It solves the problem that in the dynamic IoV environment, existing technologies cannot perform detailed mobility modeling when making offloading decisions, and thus cannot evaluate V2V service availability, and cannot guarantee the reliability and low latency of offloading.

[0006] The technical solution of this invention is implemented as follows:

[0007] A method for offloading tasks related to mobility and service availability awareness in a vehicle-to-everything (V2X) network includes the following steps:

[0008] S1. Vehicle Mobility Modeling: Based on the vehicle's location information, determine whether the user's vehicle is within the IVCP transmission range. If it is within the transmission range, calculate the link lifetime. The calculation is as follows:

[0009]

[0010] in, This indicates the lifetime of a link traveling in the same direction. Indicates the lifetime of the peer link, v n v represents the speed of vehicle n. k Let d represent the speed of vehicle k. nk This represents the distance between vehicle n and vehicle k. This represents the link lifetime of vehicle k in front of vehicle n. R represents the link lifetime of vehicle k behind vehicle n, θ represents the angle between the vehicle connection and the horizontal direction, and R represents the link lifetime of vehicle k behind vehicle n. n Indicates the transmission radius of vehicle n;

[0011] S2 and V2V service availability assessment: If the transmission latency is lower than the link lifetime, then service availability is assessed;

[0012] use Let k represent the service vehicle for the task. Service availability is represented as:

[0013]

[0014] Where, ∈ k Let τ represent the probability that vehicle k will accept the unloading task, and let τ represent the task transmission delay.

[0015] S3. Based on the assessment results of service availability, select a suitable V2V offloading object.

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

[0017]

[0018] Where, d nk For transmission distance, C constant It is a constant. It is the task complexity coefficient. The proportion of computing resources reserved for local tasks. yes The weighting coefficients, This indicates an unloading request for vehicles other than vehicle n;

[0019] Assume each car has the same Threshold, denoted as The probability that vehicle k accepts the unloading task can be expressed as: Define a vector vector k centered at vehicle k with radius k. In the virtual area, once any vehicle other than vehicle n appears in the virtual area and sends an unloading request to vehicle k, vehicle k's... Will drop to The radius of the virtual region is obtained as follows:

[0020]

[0021] The probability that Vn's unloading 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. It is a constant.

[0024] Finally, I got

[0025] Optionally, in step S3, under the constraint of link lifetime, i.e., 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 lifetime in this invention The calculation takes into account traffic conditions such as driving speed and angle. Incorporating an angle-based calculation method into the link lifetime allows for coverage of multi-lane scenarios. Calculating the link lifetime before offloading can predict whether the task can be transmitted before the link is disconnected, adapting to the highly dynamic vehicular network environment. This invention focuses on bidirectional multi-lane scenarios, considering more complex road traffic environments. In this scenario, vehicles need to cope with higher speeds and more complex traffic flows. Compared to single-lane scenarios, the task offloading scheme of this invention requires a more refined strategy to adapt to dynamically changing traffic environments. Compared to single-lane scenarios, the task offloading scheme of this invention requires a more refined strategy to adapt to dynamically changing network states and the task requirements of different vehicles.

[0028] The technical solution of this invention has significant application value in collaborative computing of vehicle-to-vehicle edge computing platforms (IVCPs). Vehicle edge computing is a crucial component of future intelligent driving systems, where vehicles improve decision-making efficiency by assigning computing tasks to IVCPs. However, due to the differences in vehicle dynamics, achieving efficient collaborative computing and making reliable offloading decisions has always been a challenge. This invention can dynamically assess the lifetime and service availability of V2V links, optimizing task offloading decisions. Specifically, it ensures that tasks can be offloaded to appropriate IVCPs in a timely manner, improving the collaborative efficiency of vehicle edge computing and enhancing the stability and reliability of intelligent driving systems. Furthermore, as intelligent transportation systems expand in scale, the IVCP selection provided by this invention can support efficient collaboration in large-scale vehicle-to-vehicle edge computing networks, ensuring the efficient and stable operation of the entire system. This technical solution not only enhances collaborative capabilities in intelligent driving but also provides technical guarantees for the safety and efficiency of intelligent transportation systems. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart of the task offloading method for mobility and service availability awareness in the Internet of Vehicles;

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

[0032] Figure 3 This is a comparison chart of task completion rate experiments (IVCP count is 20);

[0033] Figure 4 This is a comparison chart of task completion rate experiments (IVCP count is 30);

[0034] Figure 5 This is a comparison chart of task completion rate experiments (IVCP count is 40). Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] This application discloses a task offloading method for mobility and service availability awareness in the Internet of Vehicles.

[0037] Example

[0038] according to Figures 1 to 5 As shown, a task offloading method for mobility and service availability awareness in the Internet of Vehicles (IoV) is presented.

[0039] 1. Link Survival Time Prediction Model

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

[0041] The vehicle mobility modeling module's main function is to improve communication stability and data transmission efficiency by predicting link lifetimes. Accurate prediction of link duration allows for the prioritization of more stable connections, reducing communication interruptions and data loss, and thus improving the reliability of routing decisions. Furthermore, predicting link lifetimes helps conserve energy and network resources, optimizes network resource allocation, and enhances the network's adaptability in high-speed mobile environments. Especially in applications requiring continuous connectivity (such as autonomous driving), ensuring link stability is crucial for effectively supporting real-time data transmission and safe operation. Through this module, vehicle-to-everything (V2X) networks can better guarantee communication quality and system performance in complex dynamic environments.

[0042] The V2V service availability assessment module comprehensively evaluates the service availability of all vehicles surrounding the task vehicle to determine which vehicles are suitable for task offloading. In intelligent driving systems, not all vehicles within the task vehicle's communication range possess sufficient computing resources or stable communication links. Some vehicles may be unable to effectively undertake the offloading task due to insufficient computing power or a V2V link duration shorter than the task execution time. Therefore, this module evaluates factors such as the task complexity and link duration of surrounding vehicles to select suitable vehicles for offloading, ensuring that the task can be offloaded among appropriate vehicles, thereby improving the efficiency and reliability of task offloading.

[0043] A method for offloading tasks related to mobility and service availability awareness in the Internet of Vehicles (IoV) includes the following steps:

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

[0045]

[0046] The link lifetime between vehicle n and vehicle k is Divide it into link lifetimes for links traveling in the same direction. and peer link lifetime More specifically, it is further divided into the link lifetime of vehicle k behind vehicle n. Link lifetime of vehicle k in front of vehicle n Link lifetime is determined by the speeds 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 This is jointly determined. When the speed of the initiating vehicle equals that of the receiving vehicle, the link lifetime is infinite. When the speed of the initiating vehicle is less than that of the receiving vehicle, and θ = 0, d nk =R n At that time, the link will reach a larger value during its survival period, such as Figure 2 Examples (a) and (b) are shown. Figure 2 Examples (a) and (b) demonstrate V n and When traveling in the same direction, however, in reality, V n and They may not be traveling in the same direction; they could be traveling in opposite directions. Figure 2 Examples (c) and (d) are provided, therefore the present invention also considers the link lifetime during reciprocating travel, in which the link lifetime is not affected by V. n and The impact of who has a greater or smaller speed.

[0047] (2) V2V service availability assessment: If the transmission delay is lower than the link lifetime, calculate the probability of accepting the offloading task and further assess the service availability;

[0048] use Let k represent the service vehicle for the task. Service availability is denoted as

[0049]

[0050] Where, ∈ k Let τ represent the probability that vehicle k will accept the unloading task, and let τ represent the task transmission delay. The function (x) + =max(x,0) guarantees that service availability is non-negative. This indicates the link lifetime. Considering that a service vehicle may receive multiple unloading requests from other vehicles simultaneously, and due to limited computing power, it cannot execute all unloading tasks concurrently, therefore some unloading requests must be rejected. Service ratio of the service vehicle.

[0051] Where, d nk For transmission distance, C constant It is a constant. It is the task complexity coefficient. The proportion of computing resources reserved for local tasks. yes The weighting coefficients, This represents unloading requests from vehicles other than vehicle n. Additionally, it is assumed that each vehicle has the same... Threshold, denoted as The probability that vehicle k accepts the unloading task can be expressed as: To estimate ∈ k The value is defined as a value centered on vehicle k with a radius of . The virtual area. Once any vehicle other than vehicle n appears in the virtual area and sends an unloading request to vehicle k, vehicle k's Will drop to The radius of the virtual region is obtained as follows:

[0052]

[0053] The probability that Vn's unloading task will be rejected by Vk can be expressed as:

[0054]

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

[0056] Then I got

[0057] Finally, we obtain the formula. Assess service availability.

[0058] (3) Select a suitable V2V offloading object based on the evaluation results; under the constraint of link lifetime, i.e., 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] This invention simulates a two-way road, 300 meters long, where user vehicles are randomly distributed and generate multiple tasks. Each task requires one IVCP (In-Vehicle Processing Unit) for processing, and each service vehicle can handle multiple tasks within a 20-meter service range. The experiment compared three different task processing methods:

[0061] Option 1, the method proposed in this invention, takes into account two-way multi-lane.

[0062] Option 2, DVETA (Dynamic Vehicle Environment Task Assignment): A task scheduling framework that considers the mobility of vehicles in one direction and multiple lanes.

[0063] Option 3, MCLA (Mobility, Contact, and Load Aware Task Offloading Scheme): A task scheduling framework that considers the mobility of vehicles in a single lane.

[0064] The experimental results demonstrate the performance of the three schemes in terms of task completion rate when the number of lanes is fixed at 6. Figure 3 , Figure 4 and Figure 5 The graph shows the task completion rates for IVCPs of 20, 30, and 40, respectively. The horizontal axis represents the number of user vehicles, and the vertical axis represents the task completion rate. As can be observed from the graph, the task completion rate decreases to varying degrees as the number of user vehicles increases. This phenomenon is mainly because the increased number of user vehicles leads to a greater workload of tasks, thus increasing the system load and consequently lowering the task completion rate. However, this downward trend is significantly alleviated when the number of IVCPs increases, indicating that more IVCPs can effectively alleviate the pressure caused by the increased workload.

[0065] Among the three approaches, the mobility management method proposed in this invention consistently maintains a high task completion rate. This is because the method effectively balances task scheduling and management across multiple lanes in both directions, ensuring that tasks in each lane are processed promptly. In contrast, while the MCLA method can manage IVCPs in different lanes, it is limited to managing multiple lanes in the same direction, thus limiting its task scheduling capabilities and resulting in slightly inferior performance compared to the proposed method. The DVET method only considers IVCPs reached by the vehicle itself, which significantly restricts its performance in scenarios with high lane complexity, leading to a poor task completion rate. Overall, with the increase in the number of IVCPs, the method proposed in this invention demonstrates stronger adaptability and superiority in handling multi-lane tasks.

[0066] The above are merely 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 within the protection scope of the present invention.

Claims

1. A method for task offloading in a vehicle-to-everything (V2X) network that is aware of mobility and service availability, characterized in that, Includes the following steps: S1. Vehicle Mobility Modeling: Based on the vehicle's location information, determine whether the user's vehicle is within the transmission range of IVCP. If it is within the transmission range, calculate the link lifetime. Here, IVCP stands for Vehicle-to-Vehicle Edge Computing Platform. Link Lifetime The calculation is as follows: ； in, This indicates the lifetime of a link traveling in the same direction. Indicates the lifetime of the peer link. This represents the speed of vehicle n. This represents the speed of vehicle k. This represents the distance between vehicle n and vehicle k. This represents the link lifetime of vehicle k in front of vehicle n. This represents the link lifetime of vehicle k following vehicle n. This indicates the angle between the line connecting the vehicles and the horizontal direction. Indicates the transmission radius of vehicle n; S2 and V2V service availability assessment: If the transmission latency is lower than the link lifetime, then service availability is assessed; use Let k represent the service vehicle for the task. Service availability is represented as: ； in, Let k be the probability that vehicle k accepts the unloading task. Indicates task transmission delay; S3. Based on the assessment results of service availability, select a suitable V2V offloading object.

2. The task offloading method for mobility and service availability awareness in a vehicle-to-everything (V2X) network according to claim 1, characterized in that, In step S2, the service ratio of service vehicles : ； in, For transmission distance, It is a constant. It is the task complexity coefficient The proportion of computing resources reserved for local tasks. yes The weighting coefficients, This indicates an unloading request for vehicles other than vehicle n; Assume each car has the same Threshold, denoted as The probability that vehicle k accepts the unloading task is expressed as: Define a region centered at vehicle k with a radius of k. In the virtual region, once any vehicle other than vehicle n appears in the virtual region and sends an unloading request to vehicle k, vehicle k's... Will drop to The radius of the virtual region is obtained as follows: ； The probability that Vn's unloading task is rejected by Vk is expressed as: ； Where ρ is the vehicle density corresponding to the number of vehicles per unit distance. It is a constant. Finally, I got .

3. The task offloading method for mobility and service availability awareness in a vehicle-to-everything (V2X) network according to claim 1, characterized in that, In step S3, under the constraint of link lifetime, i.e., 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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