Edge intelligence is an emerging paradigm for real-time training and
inference at the wireless edge, thus enabling mission-critical applications.
Accordingly, base stations (BSs) and edge servers (ESs) need to be densely
deployed, leading to huge deployment and operation costs, in particular the
energy costs. In this article, we propose a new framework called
Mobility-Enhanced Edge inTelligence (MEET), which exploits the sensing,
communication, computing, and self-powering capabilities of intelligent
connected vehicles for the smart and green 6G networks. Specifically, the
operators can incorporate infrastructural vehicles as movable BSs or ESs, and
schedule them in a more flexible way to align with the communication and
computation traffic fluctuations. Meanwhile, the remaining compute resources of
opportunistic vehicles are exploited for edge training and inference, where
mobility can further enhance edge intelligence by bringing more compute
resources, communication opportunities, and diverse data. In this way, the
deployment and operation costs are spread over the vastly available vehicles,
so that the edge intelligence is realized cost-effectively and sustainably.
Furthermore, these vehicles can be either powered by renewable energy to reduce
carbon emissions, or charged more flexibly during off-peak hours to cut
electricity bills.Comment: This paper has been accepted by IEEE Communications Magazin