TY - GEN
T1 - A V2X-Enabled Framework for Delay-Sensitive Task Offloading in LAPS-Aided Internet of Vehicles
AU - Wang, Hongyan
AU - Wang, Dawei
AU - He, Yixin
AU - Xu, Qian
AU - Yang, Xin
AU - Zhou, Fuhui
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This paper explores the applications of mobile edge computing (MEC) and low altitude platform station (LAPS) in the vehicle-to-everything (V2X)-enabled Internet of Vehicles (IoV). In the LAPS-assisted IoV, vehicular applications consist of multiple interdependent subtasks, and vehicles can offload some subtasks to LAPS for further computation to alleviate the computing resources shortage at vehicles. To minimize the computing time, we propose a novel offloading scheme and formulate a comprehensive task latency minimization problem that optimizes offloading decision and resource allocation, taking into account subtask dependencies, imperfect channel state information (CSI), and energy consumption. Given the non-convex of the problem, we separate it into task offloading subproblem and resource allocation subproblem and design a novel algorithmic framework to determine the optimal task offloading and collaborative communication strategy. First, we derive the optimal spectrum reuse scheme and accordingly adjust the transmission power of vehicles adopting the proposed optimal resource allocation algorithm. Then, we introduce a task offloading algorithm that considers directed acyclic graphs (DAGs) to ensure that the dependencies among subtasks are maintained. Simulation results show that our strategy significantly reduces task processing latency and improves the quality of service (QoS) for vehicle users compared to existing approaches.
AB - This paper explores the applications of mobile edge computing (MEC) and low altitude platform station (LAPS) in the vehicle-to-everything (V2X)-enabled Internet of Vehicles (IoV). In the LAPS-assisted IoV, vehicular applications consist of multiple interdependent subtasks, and vehicles can offload some subtasks to LAPS for further computation to alleviate the computing resources shortage at vehicles. To minimize the computing time, we propose a novel offloading scheme and formulate a comprehensive task latency minimization problem that optimizes offloading decision and resource allocation, taking into account subtask dependencies, imperfect channel state information (CSI), and energy consumption. Given the non-convex of the problem, we separate it into task offloading subproblem and resource allocation subproblem and design a novel algorithmic framework to determine the optimal task offloading and collaborative communication strategy. First, we derive the optimal spectrum reuse scheme and accordingly adjust the transmission power of vehicles adopting the proposed optimal resource allocation algorithm. Then, we introduce a task offloading algorithm that considers directed acyclic graphs (DAGs) to ensure that the dependencies among subtasks are maintained. Simulation results show that our strategy significantly reduces task processing latency and improves the quality of service (QoS) for vehicle users compared to existing approaches.
KW - low altitude platform station (LAPS)
KW - mobile edge computing (MEC)
KW - resource allocation
KW - Vehicle-to-everything (V2X)
UR - http://www.scopus.com/inward/record.url?scp=85206475010&partnerID=8YFLogxK
U2 - 10.1109/ICCC62479.2024.10681683
DO - 10.1109/ICCC62479.2024.10681683
M3 - 会议稿件
AN - SCOPUS:85206475010
T3 - 2024 IEEE/CIC International Conference on Communications in China, ICCC 2024
SP - 485
EP - 490
BT - 2024 IEEE/CIC International Conference on Communications in China, ICCC 2024
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE/CIC International Conference on Communications in China, ICCC 2024
Y2 - 7 August 2024 through 9 August 2024
ER -