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Edge intelligence is an emerging paradigm for real-time training andinference at the wireless edge, thus enabling mission-critical applications.Accordingly, base stations (BSs) and edge servers (ESs) need to be denselydeployed, leading to huge deployment and operation costs, in particular theenergy costs. In this article, we propose a new framework calledMobility-Enhanced Edge inTelligence (MEET), which exploits the sensing,communication, computing, and self-powering capabilities of intelligentconnected vehicles for the smart and green 6G networks. Specifically, theoperators can incorporate infrastructural vehicles as movable BSs or ESs, andschedule them in a more flexible way to align with the communication andcomputation traffic fluctuations. Meanwhile, the remaining compute resources ofopportunistic vehicles are exploited for edge training and inference, wheremobility can further enhance edge intelligence by bringing more computeresources, communication opportunities, and diverse data. In this way, thedeployment 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 reducecarbon emissions, or charged more flexibly during off-peak hours to cutelectricity bills.

MEET: Mobility-Enhanced Edge inTelligence for Smart and Green 6G Networks