Deployment of technologies for Intelligent Transportation Systems (ITS) involves the installation of Road Side Units (RSU) on the roadway and On-Board Units (OBU) inside vehicles. In this direction, 5G technologies will make a great impulse providing low latency communication and computation at the edge of the network. First, this paper defines VMEC-in-a-Box, a smart RSU combined with a MEC station, aimed at providing edge computing for vehicular applications by enabling job offloading from vehicles. VMEC-in-a-Box is equipped with a microeolic power generator to be autonomous and self-consistent even in presence of low levels of wind. The behavior of VMEC- in-a-Box is controlled by artificial intelligence to vary its computing capacity dynamically to pursue the best tradeoff between performance and power consumption, and to cooperate by offloading jobs to each other (horizontal offload) to improve performance and reliability of the system. Hence, the paper defines a Markov model to support decisions to optimize by means of Reinforcement Learning the system behavior according to two reward functions defined at the MEC and at the Vehicular Domains. To the best of our knowledge, this is the first work proposing an integrated framework to maximize reliability and performance at both Vehicular and MEC Domains.

A Smart Road Side Unit in a Microeolic Box to Provide Edge Computing for Vehicular Applications

Christian Grasso;Sergio Palazzo;Giovanni Schembra
2022-01-01

Abstract

Deployment of technologies for Intelligent Transportation Systems (ITS) involves the installation of Road Side Units (RSU) on the roadway and On-Board Units (OBU) inside vehicles. In this direction, 5G technologies will make a great impulse providing low latency communication and computation at the edge of the network. First, this paper defines VMEC-in-a-Box, a smart RSU combined with a MEC station, aimed at providing edge computing for vehicular applications by enabling job offloading from vehicles. VMEC-in-a-Box is equipped with a microeolic power generator to be autonomous and self-consistent even in presence of low levels of wind. The behavior of VMEC- in-a-Box is controlled by artificial intelligence to vary its computing capacity dynamically to pursue the best tradeoff between performance and power consumption, and to cooperate by offloading jobs to each other (horizontal offload) to improve performance and reliability of the system. Hence, the paper defines a Markov model to support decisions to optimize by means of Reinforcement Learning the system behavior according to two reward functions defined at the MEC and at the Vehicular Domains. To the best of our knowledge, this is the first work proposing an integrated framework to maximize reliability and performance at both Vehicular and MEC Domains.
Edge computing
Vehicular Networks
Reinforcement Learning
Markov Models
Offloading
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11769/542742
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact