Precision-Adaptive Task Offloading and Resource Allocation for Efficient Positioning and Sensing in Near-Field IoV Systems

With the rapid advancement of sixth-generation (6G) network communication technology, improvements in data transmission rates, latency, and reliability have driven substantial growth in Internet of Vehicles (IoV) applications. Among them, the integration of extremely large-scale antenna arrays (ELAAs) 6G has extended the range of near-field (NF) communication, enabling its application in IoV to facilitate efficient and accurate environmental sensing. Through NF communication, vehicles can achieve high-accuracy localization and perception by analyzing signal phase, channel state information, and beamforming calculations. However, positioning and sensing tasks place substantial computational and energy demands on edge devices, often exceeding traditional capacity limits. To address this challenge, task offloading has emerged as a solution, with mobile edge computing (MEC) offering a lower-latency alternative to centralized cloud computing by processing tasks at the network edge. Despite MEC’s advantages, its limited resources present challenges as the number of connected vehicles increases. Existing approaches to resource allocation often overlook the varied accuracy requirements of IoV tasks, where high-accuracy tasks like indoor navigation require stringent accuracy, while lower-accuracy tasks may tolerate reduced accuracy to save resources. Motivated by this, we propose a accuracy-based classification scheme for IoV positioning and sensing tasks, dynamically adjusting accuracy to reduce delay and energy consumption. Our approach maps total energy, accuracy loss, and delay to an overall Quality of Service (QoS) metric, and employs an optimization algorithm leveraging gradient descent and greedy strategies to balance resource allocation and accuracy selection. Extensive simulations demonstrate the effectiveness of the proposed scheme in reducing delay and energy consumption while maintaining high accuracy, outperforming benchmark strategies.