Faster Convergence for Unknown-Game Bandits
ZHIMING HUANG, Jianping Pan
INFOCOM 2025 Papers
IEEE International Conference on Computer Communications
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272papers
Counted from the official paper list; may differ slightly from the number the conference announced.
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TitleTypeTopic
Theory of Mixture-of-Experts for Mobile Edge Computing
Hongbo Li, Lingjie Duan
LoFi: Physical-layer CTC from LoRa to WiFi with IEEE 802.11ax
Demin Gao, Wenchao Jiang, Ruofeng Liu, Weizheng Wang, Zhaoyang Han, Yunhuai Liu
BAROC: Concealing Packet Losses in LSNs with Bimodal Behavior Awareness for Livecast Ingestion
Haoyuan Zhao, Jianxin Shi, Guanzhen Wu, Hao Fang, Yi Ching Chou, Long Chen, Feng Wang, Jiangchuan Liu
PDStream: Slashing Long- Tail Delay in Interactive Video Streaming via Pseudo-Dual Streaming
Xuedou Xiao, Yingying Zuo, Mingxuan Yan, Kezhong Liu, Wei Wang
AeroEcho: Towards Agricultural Low-power Wide-area Backscatter with Aerial Excitation Source
Yidong Ren, Gen Li, Yimeng Liu, Younsuk Dong, Zhichao Cao
ALPHAS: Adaptive Bitrate Ladder Optimization for Multi-Live Video Streaming
Farzad Tashtarian, Mahdi Dolati, Daniele Lorenzi, Mojtaba Mozhganfar, Sergey Gorinsky, Ahmad Khonsari, Christian Timmerer, Hermann Hellwagner
Justitia: An Incentive Mechanism Towards the Fairness of Cross-Shard Transactions
Jian Zheng, Huawei Huang, Yinqiu Liu, Taotao Li, Hong-Ning Dai, Zibin Zheng
SPIN: Accelerating Large Language Model Inference with Heterogeneous Speculative Models
Fahao Chen, Peng Li, Tom H. Luan, Zhou su, Jing Deng
m3ASL: ASL Gesture Recognition with Moving mmWave Radar
Guiyun Fan, Rong Ding, Xiaocheng Wang, Yichen Zhu, Haiming Jin
A Balanced Tuple Partitioning Method for Packet Classification with High-Performance and Scalability
Neng Ren, Yanbiao Li, Chunyang Zhang, Jing Hu, Lingbo Guo, Gaogang Xie
mmWave-Based Relay Reflector Reconstruction for LiDAR-Free Around-Corner Human Sensing
Jiaxi Lv, Guiyun Fan, Xinyue Fu, Jiahui Sun, Rong Ding, Haiming Jin
HyperJet: Joint Communication and Computation Scheduling for Hypergraph Tasks in Distributed Edge Computing
Kang Huang, Chao Qiu, Chenxuan Hou, Xiuhua Li, Xiaofei Wang
Adonis: Neural-enhanced Fine-grained Leaf Wetness Sensing with Efficient mmWave Imaging
Yimeng Liu, Maolin Gan, Gen Li, Younsuk Dong, Zhichao Cao
Robust Contextual Combinatorial Multi-Armed Bandits for Unreliable Network Systems
Junkai Wang, Xutong Liu, Jinhang Zuo, Yuedong Xu
Link Configuration for Fidelity-Constrained Entanglement Routing in Quantum Networks
Qiaolun Zhang, Nicola Di Cicco, Memedhe Ibrahimi, Raul C. Almeida, Alberto Gatto, Raouf Boutaba, Massimo Tornatore
C2F: Enabling Context-Aware Edge-Cloud Collaborative Inference for Foundation Models
Mingyue Zhao, Jiayi Shi, Zhengyuan Zhang, Yue Ling, Guanzhou Zhu, Dong Zhao, Huadong Ma
Learning to Accelerate Traffic Allocation Over Large-Scale Networks
Zhaoxing Yang, Guiyun Fan, Anjie Cao, Yuchen Guo, Wenlong Li, Tianyuan Liu, Haiming Jin
Network CoPilot: Intent-Driven Network Configuration Updating for Service Guarantee
Rongxin Han, Jingyu Wang, Haifeng Sun, Zengteng Jiang, Qi Qi, Zirui Zhuang, Yuan Zhang, Jianxin Liao
An Analytical Latency Model of the Data Distribution Service in ROS 2
Hyungseok Park, Sanghoon Lee, Doosik Um, Hyunho Ryu, Kyung-Joon Park
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After its initial release in 2007, the robot operating system (ROS) has been widely adopted as an open-source robotics middleware suite. In 2017, ROS 2 is introduced to offer enhanced performance, and it has since become the de facto standard for robot software development. In this paper, we propose an analytical latency model for the data distribution service (DDS) in ROS 2. DDS operates at the application layer on top of the user datagram protocol (UDP). In ROS 2, retransmissions for reliable data delivery are handled at the application layer, resulting in latency characteristics different from those of the transmission control protocol (TCP). We derive a closed-form analytical model to characterize the latency of DDS reliable data delivery in ROS 2, taking into account key parameters such as the packet delivery ratio, the data period, and the heartbeat period in DDS. Our extensive empirical study shows that the proposed model matches well with the empirical data, with an average error of 6.88 % across 35 different scenarios.