AIEC 2020 CFP

Artificial Intelligence (AI) became a popular wide area for the latest- generation of software-oriented solutions. Lately, AI has been considered as a hot topic due to its huge applications and it attracted attention from academia as well as industrial and end users while receiving positive media coverage. AI is advancing at considerable speed and lead to many widely beneficial applications, variant from Machine Translation to Medical Image Computing. From R&D perspectives, AI acquires incredible amounts of progress in terms funding investors devoted on AI applications.

According to AI efforts, Edge Computing (EC) has become an essential solution to overcome the strangulation of emerging technology development according to its benefits of minimizing data transmission, reducing service latency and easing cloud computing pressure. Also, the scope of EC is very diverse on large application area, such as smart grid and smart city, logistics and transportation, manufacturing and healthcare. Furthermore, the EC provide significant gains which include low-latency thus allowing close cooperation in sophisticated mobile applications and on the core network to reduce traffic volume as data streaming along the way to the distant data center is no longer necessary. The evolution of new technologies of communication such as 5G made communication so much easier where the latency is lower and memory bandwidth is higher, the border between edge infrastructure and mobile devices will be even imprecise and EC will become more attractive.

The Workshop on Artificial Intelligence & Edge Computing (AIEC) aims to bring together researchers and practitioners from both academia and industry who are working on Artificial Intelligence and Edge Computing as well as their integration, to exchange research ideas and identify new research challenges in this emerging field.
Topics

The Workshop on Artificial Intelligence & Edge Computing (AIEC) calls for contributions that address fundamental research and solutions issues in Artificial Intelligence and Edge Computing including but not limited to the following :

- Big Data mining at the Edge
- Machine Learning at the Edge
- Architectures of Edge AI for IoT
- Security on the Edge
- Graph Databases on the Edge
- Resource-friendly Edge AI Model Design
- Resource Management for Edge AI
- Applications/services for Edge AI
- Communication and Networking Protocols for Edge AI
- Software Platforms for Edge