As a supplement of traditional cloud computing technology, mobile edge computing (MEC) has recently emerged as a promising computing paradigm that offers end-users with low latency in their access to applications deployed at the edge of the cloud, e.g., smart assistant, driverless cars, smart manufacturing, etc. However, continuous monitoring and data collection by the smart devices in MEC clients or servers have been generating an unprecedented volume of data which create a main source of big data. How to deal with the big data from MEC applications in an efficient, economical and secure manner is still a fundamental challenge.
Recently, machine learning powered Artificial Intelligence (AI) has been recognized as a key technology to realize intelligent data analyses. Therefore, AI has provided a promising way to cope with the massive and heterogeneous data produced by MEC terminals. However, the adaptation of AI-based approaches is highly demanded to achieve their full potentials in supporting the MEC applications, as MEC service systems often suffer from limited computing capabilities, high energy cost and fast-changing context environment. Therefore, it still requires challenging efforts to minimize the gap between MEC applications and AI technology.
This Collection aims to highlight the cutting-edge research and applications related to the “Mobile Edge Computing Meets AI”. Specific topics of interest include but are not limited to the following:
- AI-based algorithms for MEC systems
- Smart collection, pre-processing and integration of MEC data
- Multi-modality MEC data fusion based on AI
- Intelligent scheduling or offloading of MEC data/resources
- QoS modeling and optimization of MEC services
- Smart communication among MEC clients and servers
- Security, trust and privacy-preservation of MEC applications
- Blockchain-powered MEC applications
- Architecture, models and protocols of hybrid Cloud-Fog-Edge
- AI-powered energy optimization and cost minimization in MEC
