Abstract
Ensuring dependable quality of service (QoS) and quality of experience
(QoE) for computation-intensive and delay-sensitive applications in
vehicles can be a challenging task that impacts performance. While
multi-access edge computing (MEC) based vehicular edge computing network
(VECN) and vehicular cloudlets (VC) enable task offloading, but their
prompt and optimal accessibility is another challenge. The conventional
wireless technologies may not suffice to meet the stringent ultra-low
latency and cost constraints of such applications. Nonetheless, the
combination of different wireless technologies can enhance network
performance and satisfy these requirements. Focusing on the
computational efficacy of VECN, this paper proposes a mobility,
contact, and computational load-aware (MCLA) task offloading scheme for
heterogeneous VECN. The MCLA scheme dynamically considers the mobility,
contact, and computational load of vehicles for making task offloading
decisions. To optimize the performance, the MCLA scheme integrates the
Mode-1 and Mode-2 of the 5G-NR-V2X standard, along with mmWave
communications. The MCLA scheme provides an opportunistic switching
mechanism between these modes and heterogeneous radio access
technologies (RATs) to reduce communication delays and costs. Moreover,
the MCLA scheme leverages public vehicles (i.e., public buses), in
proximity by using their computational power to manage computational
latency and cost. Furthermore, it also considers the shareable
computations from passengers’ mobile equipment within the public vehicle
to improve the computation capacity of the public vehicles. Extensive
evaluations and numerical results show that the proposed MCLA scheme
significantly improves the task turnover ratio by 4%-15% with
4.7%-29.8% lower transmission and computation costs.