1 research outputs found
Edge-enabled V2X Service Placement for Intelligent Transportation Systems
Vehicle-to-everything (V2X) communication and services have been garnering
significant interest from different stakeholders as part of future intelligent
transportation systems (ITSs). This is due to the many benefits they offer.
However, many of these services have stringent performance requirements,
particularly in terms of the delay/latency. Multi-access/mobile edge computing
(MEC) has been proposed as a potential solution for such services by bringing
them closer to vehicles. Yet, this introduces a new set of challenges such as
where to place these V2X services, especially given the limit computation
resources available at edge nodes. To that end, this work formulates the
problem of optimal V2X service placement (OVSP) in a hybrid core/edge
environment as a binary integer linear programming problem. To the best of our
knowledge, no previous work considered the V2X service placement problem while
taking into consideration the computational resource availability at the nodes.
Moreover, a low-complexity greedy-based heuristic algorithm named "Greedy V2X
Service Placement Algorithm" (G-VSPA) was developed to solve this problem.
Simulation results show that the OVSP model successfully guarantees and
maintains the QoS requirements of all the different V2X services. Additionally,
it is observed that the proposed G-VSPA algorithm achieves close to optimal
performance while having lower complexity.Comment: 13 pages, 16 figures (including 5 bio pictures), accepted and to be
published in IEEE Transactions on Mobile Computin