Research on Task-Offloading Decision Mechanism in Mobile Edge Computing based Internet of Vehicle

Abstract

As a technology integrated with Internet of Things (IoT), mobile edge computing (MEC) can provide real-time and low latency services to the underlying network, and improve the storage and computation ability of the networks instead of central cloud infrastructure. In Mobile Edge Computing based Internet of Vehicle(MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers Based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for IoV-based edge computing is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization (PSO) is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

A Joint Task Offloading Method Based Edge-fog Computing in IoV

“…In this experiment, we set the experimental environment to be on a one-way road, the number of vehicles on the road is set to 40, 60, 80, 100 and 120, each vehicle carries a task to be processed, and the task load of the computing tasks is set to 250, 500, 1000 and 2000 respectively. In this paper, according to [21] and [31], the relevant parameters of this experiment are set as shown in Table 2. and the channel gain ℎ [𝑑𝐵] = 140.…”

Computing Offloading Decision Based on Adaptive Estimation of Distribution Algorithm in Internet of Vehicles

“…Luo, C. Li,, et al 2021). To evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making so as to avoid serious degradation of the offloading efficiency, a task-offloading decision mechanism with particle swarm optimization with adaptive inertia weight factor (Cheng, J., Guan, D. 2021). A distributed computation offloading inspired by game theory and proposed a model-free reinforcement learning algorithm.…”

Online Optimization of Animation Art Design User Virtual Perception in Mobile Edge Computing Environment

“…In terms of picture composition principles, they all focus on the creation of picture ideograms and visual centers and pay attention to the dynamic balance of the picture. e development of digital technology has brought revolutionary progress to the film industry [21,22]. Animation art design user virtual online optimization are widely involved in film production with virtual and technical characteristics, which breaks through the objective material limitations of traditional film shooting technology and shooting content and has an important impact on the development of film art.…”