EAI NextGenEdge 2023 : EAI Artificial Intelligence-enabled Next Generation Edge Computing Systems for Emerging Smart Applications

Recent advances in mobile technologies have enabled a new class of smart city and fifth-generation (5G) network applications, such as merged reality and real-time situation analyses. These applications have diverse requirements, such as low latencies, high data rates, significant amounts of computing and storage resources, and access to sensors and actuators. To address the requirements of these applications, several edge computing systems, such as cloudlet computing, mobile edge computing, and fog computing, have been proposed. The deployment of edge computing systems requires the addition of new infrastructure or the updating of existing infrastructure. Edge computing systems also do not utilize the capabilities of end devices, such as smartphones, mobile robots, and smart vehicles, which are equipped with multicore central and graphical processing units, several sensors, or multiple wireless communication technologies. To overcome the drawbacks of conventional edge computing systems, next generation edge computing infrastructures such as a local or private edge cloud consisting of conventional and high-end devices such as robots and smart phones are required.

This conference invites contributions focusing on the design of intelligent edge computing architectures, middleware platforms, network protocols, and resource management algorithms leveraging for instance approaches based on machine learning, software-defined network, or container technologies to support the emerging smart city, IoT, and 5G network applications.

Topics
We welcome contributions from the following fields:

Personal cloud systems
Private micro data-centers
Cloudlet computing systems
Edge clouds Integration of edge clouds with 5G and 6G networks
Edge and fog computing systems and architectures
Edge computing for vehicular systems
Multi-network management protocols and methods
Multipath routing protocols for heterogeneous multilayer networks
Multilayer network traffic management techniques
Intelligent load management and failure prediction methods
Machine learning-based resource management methods
Intelligent resource scheduling algorithms for local edge clouds
Intelligent middleware platforms for edge clouds
Software-defined network or machine learning-based methods for private edge clouds