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.
翻译:因此,基地站和边缘服务器需要密集部署,导致巨大的部署和运行成本,特别是能源成本。在本条中,我们提出一个新的框架,称为“移动-强化电磁在教学中(MEET)”,利用智能和绿色6G网络智能连通车辆的感测、通信、计算和自力能力,使智能和绿色6G网络的智能连通车辆的部署和运行能力。具体地说,操作者可以将基础设施车辆作为可移动的BS或ES,以更灵活的方式安排它们与通信和计算交通波动保持一致。与此同时,机会型车辆的剩余计算资源被用于边际培训和推断,在边际培训和推断中,机动性可以带来更精密的资源、通信机会和多种数据,从而进一步增强边际智能。这样,部署和运营成本就分散在广大可用的车辆上,从而实现边际情报的成本效益和可持续性。此外,这些车辆可以通过可再生能源发电来减少碳排放,或者在离岸期间以更灵活的方式将电价收费到离岸。