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延期到9-15-[CCF-C, EI, �？赸 HPCC 2021 Workshop 征稿 - 分布式智能、無(wú)人系統(tǒng)
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【截稿日期延期到9月15日，歡迎大家繼續(xù)投稿，謝謝！】 投稿時(shí)請(qǐng)選擇session 2 Distributed Intelligence for Future High Performance Unmanned Mobile Systems 歡迎各位老師和同學(xué)不吝賜稿至ccf推薦會(huì)議23rd ieee hpcc 舉辦的special session: distributed intelligence for future high performance unmanned mobile systems截稿日期2021-9-15，錄用稿件與主會(huì)同收錄，投稿方式請(qǐng)見官網(wǎng)說(shuō)明https://www.ieee-hpcc.org/2021/ 投稿時(shí)通過(guò) https://www.edas.info/newpaper.php?c=28862 選擇該 special session 5 即可。12月相聚美麗�？冢惺軠嘏�。 please submit your papers at https://www.edas.info/newpaper.php?c=28862 choose Special Session 2: Distributed Intelligence for Future High Performance Unmanned Mobile Systems each paper is limited to 6 pages (or 7 pages with overlength charge) including figures and references using ieee computer society proceedings manuscripts style (https://www.ieee.org/conferences ... ng/templates.html). the papers will be required to meet the same standards as the main conference papers and will be published in the conference proceedings, available on ieee xplore, once accepted (indexed by ei). at least one of the authors of any accepted paper is requested to register the paper at the conference. --- due to the explosive development of unmanned system components, such as autonomous cars, unmanned aerial vehicles, and edge devices, designing future mobile and communication systems becomes much more challenging in terms of independent control and decision making, efficient autonomous system operations, distributed system architectures, mobility issues, etc. recently, machine learning (ml) and artificial intelligence (ai) enabled distributed intelligence has become a promising approach to address the aforementioned intelligent control challenges in the premise of large-scale decentralized unmanned and mobile systems. moreover, distributed intelligence approaches exploit the computational capabilities of edge components (e.g., uavs and edge devices) in such networks. unmanageable network optimization and underutilized computational power of system components within centralized system architectures, can be efficiently solved by applying distributed intelligence approaches, e.g., crowd collaboration and intelligence, distributed consensus, multi-agent learning, and edge computing enabled high performance computing. the capability of edge can be efficiently exploited. decentralized data processing, learning, and data security approaches can be studies and implemented, such as collaborative learning, federated learning, and blockchain. the goal of this full-day workshop is to bring together researchers and experts from academia, industry, and governmental agencies to discuss and promote the research and development needed to overcome the major challenges in this research direction. the scope of this workshop includes but not limited to: • theories, frameworks, and algorithms of distributed intelligence for high performance unmanned mobile networks • phy infrastructure for distributed intelligence for unmanned mobile systems • intelligent access control and resource allocation for unmanned mobile systems • distributed intelligence empowered future unmanned mobile networks • edge intelligence designs and algorithms design for future unmanned systems • distributed and collaborative machine learning for unmanned mobile systems • distributed control and data privacy, security, attack/defense, and data persistence approaches in unmanned mobile systems • distributed intelligence enabled green and energy-efficient unmanned mobile systems • network economics for unmanned mobile systems with distributed intelligence • explainable and responsible distributed artificial intelligence for unmanned mobile systems • simulations, experiments, and testbeds of distributed intelligence for high performance unmanned mobile systems • use cases and application scenarios highlighting distributed intelligence for high performance unmanned mobile systems |
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