Profit Maximization of Independent Task Offloading in MEC-Enabled 5G Internet of Vehicles

The development of the Internet of Vehicles (IoVs) has attracted much attention due to the increasing number of connected cars. IoV refers to the interconnection of vehicles with other devices through the internet to enable information sharing and interaction. The advent of 5G mobile communication technologies has provided high-speed, low-latency, and high-reliability communication services, which have gone a long way in solving the communication problems associated with IoV. Additionally, the Multi-Access Edge Computing (MEC) technology has placed computing resources on edge nodes closer to the users, thus enabling faster, more reliable, and more secure computing services to meet the vehicles’ computing resource requirements. However, task offloading and resource allocation issues of 5G-connected vehicles enabled by Mobile edge computing remain a significant challenge when it comes to computing tasks and data related to IoVs. Our study proposes a Lyapunov Based Profit Maximum (LBPM) task offloading algorithm, which utilizes the Lyapunov optimization theory to maximize the time-averaged profit as the optimization objective. The algorithm uses the drift plus penalty optimization framework to establish the Lyapunov function and transforms the optimization goal into making a reasonable offloading decision at each time slot to optimize the upper bound of the function. We also compare the LBPM algorithm with existing algorithms for simulation experiments and performance analysis. The experimental results indicate that the LBPM algorithm increases the time-averaged profit by over 15
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