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Subject: CfP] The 31st IEEE International Symposium on Industrial Electronics (ISIE), Special Session on Intelligent Edge Learning for Industrial IoT, June 1-3, 2022, Anchorage, Alaska, USA, Submission deadline January 15, 2022
[Apologies if you receive multiple copies of this message] [Call for Papers] ===================================================== The 31st IEEE International Symposium on Industrial Electronics (ISIE) Special Session on Intelligent Edge Learning for Industrial IoT June 1-3, 2022, Anchorage, Alaska, USA https://www.ieee-isie2022.org ===================================================== [Accepted and presented papers will be copyrighted to IEEE and published in conference proceedings and in the IEEE Xplore Digital Library, EI indexed] IMPORTANT DATES Submission deadline: January 15, 2022 Acceptance Notification: February 15, 2022 Camera Ready Submission: March 15, 2022 The annual IEEE ISIE gathers researchers, academics, industry experts, and practitioners from more than 100 countries to share their ideas and experiences surrounding frontier technologies, breakthroughs, innovative solutions, research results, as well as initiatives related to industrial electronics and their applications. The symposium will showcase world-class keynote and plenary speakers, tutorials, special sessions, and industry sessions to create an exceptional forum to foster innovation and entrepreneurship. [Special Session on Intelligent Edge Learning for Industrial IoT] SCOPE Academics and industry experts are advocating for going from large-centralized cloud computing infrastructures to computing nodes located at the edge of the network in the industrial Internet of Things (IIoT). The intelligent edge computing paradigm for IIoT is taking its shape where we envision it as an edge computing system that realizes the computing, communication, and caching resources using data analytic platforms and harvesting the potential bene?ts of arti?cial intelligence to improve the system utility. There will be new opportunities for, i.e., supporting real-time manufacturing and intelligent smart factory. However, many fundamental questions arise at the same time. For example, how and where to process the sensor and control data from distributed IIoT objects are significant issues due to the real-time controlling remote robotic objects requirements in IIoT. Therefore, many researchers in several technologies are actively investigating edge learning is yet requi! red to be intelligent, which motiva tes intelligent edge learning. On the one hand, distributed learning algorithms, such as federated learning, are required to be supported in network edge close to mobile objects. On the other hand, edge learning algorithms are desired to intelligently process distributed and shared data for real-time remote factory logistics control. The special session aims to provide a forum for scientists, engineers, and researchers to discuss and exchange novel ideas, results, experiences, and work-in-progress on all aspects of intelligent edge learning for industrial IoT. Topics of interest include, but are not limited to: o Edge, fog, and mobile edge computing for Industrial IoT o Machine learning and computational intelligence for handling big data in Industrial applications o Information-centric networking and software-defined network for edge intelligence o Intelligent edge-based mobile computing and analysis o Real-time communication interfaces and protocols o Intelligent infrastructures at the edge o Hardware testbed or field trial for AI-driven intelligent edge computing for industrial applications o Security and related considerations in intelligent edge computing o Intelligent routing and load balancing for edge computing o Network protocols for distributed machine learning SUBMISSION LINK https://www.ieee-isie2022.org/paper-submissions During the submission process, an appropriate Special Session must be selected, select SS07 - Intelligent Edge Learning for Industrial IoT ORGANIZERS Mithun Mukherjee Nanjing University of Information Science and Technology, China Mian Guo Guangdong Polytechnic Normal University, China Jaime Lloret Universitat Politecnica de Valencia, Valencia, Spain
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