An increasing number of mobile applications rely on Machine Learning (ML) routines for analyzing data. Executing such tasks at the user devices saves the energy spent on transmitting and processing large data volumes at distant cloud-deployed servers. However, due to memory and computing limitations, the devices often cannot support the required resource-intensive routines and fail to accurately execute the tasks. In this work, we address the problem of edge-assisted analytics in resource-constrained systems by proposing and evaluating a rigorous selective offloading framework. The devices execute their tasks locally and outsource them to cloudlet servers only when they predict a significant performance improvement. We consider the practical scenario where the offloading gain and resource costs are time-varying; and propose an online optimization algorithm that maximizes the service performance without requiring to know this information. Our approach relies on an approximate dual subgradient method combined with a primal-averaging scheme, and works under minimal assumptions about the system stochasticity. We fully implement the proposed algorithm in a wireless testbed and evaluate its performance using a state-of-the-art image recognition application, finding significant performance gains and cost savings.
Pocket RAG: On-Device RAG for First Aid Guidance in Offline Mobile Environment
Ustechlab Research Institute（Ustechlab研究院）；Department of Computer Science, Hanyang University（汉阳大学计算机系）；Department of Business Administration, Ewha Womans University（梨花女子大学工商管理系）；Department of Computer Science, The University of Texas at Austin（德克萨斯大学奥斯汀分校计算机系）；Department of Computer Science, Kookmin University（国民大学计算机系）
Ad hoc移动场景需要一个轻量级的路由协议来传播快速变化的数据可达性信息在一个高度动态的环境。我们正在开发一个距离矢量路由协议，使每个节点有选择地传播一个数据可达性矢量包含命名数据前缀当前可到达他们的邻居。在本报告中，我们描述了NDVR（NDN距离矢量路由）的实现，讨论了协议设计选择的基本原理，并演示了该协议的用例，以说明路由协议如何帮助NDN应用程序，特别是在移动ad-hoc场景中。
FluxShard: Motion-Aware Feature Cache Reuse for Collaborative Video Analytics in Mobile Edge Computing
The University of Hong Kong（香港大学）；The Southern University of Science and Technology（南方科技大学）；City University of Hong Kong（香港城市大学）；Edith Cowan University（伊迪丝·考恩大学）；Nanyang Technological University（南洋理工大学）
ViTMAlis: Towards Latency-Critical Mobile Video Analytics with Vision Transformers
School of Computing Science, Simon Fraser University（计算科学系，西蒙·弗雷泽大学）；Global College, Shanghai Jiao Tong University（全球学院，上海交通大学）；School of Science and Engineering (SSE) and The Future Network of Intelligence Institute (FNii), The Chinese University of Hong Kong, Shenzhen（科学与工程学院（SSE）和未来智能网络研究所（FNii），香港中文大学（深圳））；Computer Science Department, University of Illinois Urbana-Champaign（计算机科学系，伊利诺伊大学厄巴纳-香槟分校）
Adaptive Probabilistic Skyline Analytics for Mobile Edge-Cloud Systems via State-Aware Deep Reinforcement Learning
National Chung Cheng University（中正大学）；Advanced Institute of Manufacturing with High-tech Innovations (AIM-HI), National Chung Cheng University（中正大学先进制造与高科技创新研究院）
LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality
Toyota Motor North America (TMNA) R&D InfoTech Labs（丰田汽车北美（TMNA）研发信息技术实验室）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；University of Houston（休斯顿大学）；Kyung Hee University（庆熙大学）
