学生のCUI Zhihanさん（博士後期課程3年、次世代デジタル社会基盤研究領域、リム研究室）が、令和6年度に開催された一般社団法人電子情報通信学会（IEICE）センサネットワークとモバイルインテリジェンス（SeMI）研究専門委員会主催の研究会において、優秀発表賞を受賞しました。

　SeMI研究専門委員会は、モバイルセンサを中心に、センシングやモビリティ技術、モバイル・ユビキタスコンピューティングの研究を牽引しています。また、これらをネットワーク技術によって結びつけることで「インテリジェントな環境」の構築を目指すとともに、そこから生み出されるアプリケーションについても発表・議論の場を提供しています。
　優秀発表賞は、同研究会での発表において特にインパクトが大きく、議論を喚起する優れた発表に対して贈られるものです。

※参考：SeMI研究専門委員会

■受賞年月日
　令和7年7月31日

■研究題目、論文タイトル等
Broad Learning System Scheme for Multi-server Wireless Networks

■研究者、著者
　CUI Zhihan、CHI Jiancheng、リム勇仁、丹康雄

■受賞対象となった研究の内容
Multi-access edge computing (MEC) that can support context aware and delay sensitive applications allows the cloud service computation to run at the edge of the network. MEC can utilize a wireless network to connect all kinds of IoT devices to rapidly analyze data in real-time manner. However, the explosion of connected devices to deliver data will results low net- work capacity and high latency due to the uncertainty of devices choosing the right MEC server. In this paper, we define multi-MEC server wireless networks (MSWNs) as a type of wireless communication network where each device in the network can communicate with a MEC server directly. This paper explores the use of the broad learning system (BLS) for optimizing the network performances in MSWNs. Besides that, we introduce three different strategies based on the BLS scheme: BLS with minimum time delay (BLS/MT), BLS with nearest MEC server (BLS/NS), and BLS with maximum signal-to-interference-plus-noise ratio (BLS/MS), aimed at enhancing network capacity and reducing latency. Through simulations, we analyze the performance of these strategies by varying different network conditions. Our results reveal that, particularly in dense network environments, by effectively selecting the maximum SINR in the wireless networks, BLS/MS can significantly outperform the network capacity and network latency of BLS/MT and BLS/NS.

■受賞にあたって一言
It is a great honor to receive this award from the IEICE Technical Committee on Sensor Network and Mobile Intelligence (SeMI). This award recognizes my research on AI-driven optimization for multi-server wireless multihop networks. I am truly grateful to my supervisors, Professor Yuto Lim and Professor Yasuo Tan, for their continuous support and guidance, and to JAIST for providing a stimulating research environment. This recognition encourages me to continue advancing my work and to contribute to the development of next-generation communication networks that benefit both academia and society.

令和7年10月14日