A relay-assisted parallel offloading strategy for multi-source tasks in internet of vehicles

Abstract

Internet of Vehicles (IoV) is paving the road for the new generation of Intelligent Transportation Systems (ITS), and Mobile Edge Computing (MEC) is enabling IoV to efficiently handle the computation-intensive and time-sensitive tasks. However, this has introduced new challenges such as maximizing computing resources, allocating resources fairly for multi-source tasks concurrently, and dividing tasks for parallelly processing to minimize the latency. To face these challenges, a three-dimensional road vehicle mobility model is constructed, and the problem of offloading strategy and resource allocation among multiple vehicles served by one Road Side Unit (RSU) is investigates to minimize the average latency of multi-source tasks while satisfying the quality of service requirements. To address the Non-deterministic Polynomial-time hardness (NP-hardness) of the problem, we design a Relay-Assisted Parallel Offloading (RAPO) strategy to obtain the optimization solution. Extensive experimental results show that the RAPO strategy introducing relay-assisted nodes can enhance performance in poor scenarios and ensure low-latency multi-tasking under various conditions, especially reducing latency by 39% compared to local computing.