IEEE INFOCOM 2023
Wireless Charging
Utilizing the Neglected Back Lobe for Mobile Charging
Meixuan Ren, Dié Wu and Jing Xue (Sichuan Normal University, China); Wenzheng Xu and Jian Peng (Sichuan University, China); Tang Liu (Sichuan Normal University, China)
Speaker
Concurrent Charging with Wave Interference
Yuzhuo Ma, Dié Wu and Meixuan Ren (Sichuan Normal University, China); Jian Peng (Sichuan University, China); Jilin Yang and Tang Liu (Sichuan Normal University, China)
Speaker
Roland: Robust In-band Parallel Communication for Magnetic MIMO Wireless Power Transfer System
Wangqiu Zhou, Hao Zhou, Xiang Cui and Xinyu Wang (University of Science and Technology of China, China); Xiaoyan Wang (Ibaraki University, Japan); Zhi Liu (The University of Electro-Communications, Japan)
Speaker
Charging Dynamic Sensors through Online Learning
Yu Sun, Chi Lin, Wei Yang, Jiankang Ren, Lei Wang, Guowei WU and Qiang Zhang (Dalian University of Technology, China)
Speaker
Network Applications
Latency-First Smart Contract: Overclock the Blockchain for a while
Huayi Qi, Minghui Xu and Xiuzhen Cheng (Shandong University, China); Weifeng Lv (Beijing University of Aeronautics and Astronautics, China)
Speaker Huayi Qi (Shandong University)
Huayi Qi received his bachelor's degree in computer science from Shandong University in 2020. He is working toward a Ph.D. degree in the School of Computer Science and Technology, Shandong University, China. His research interests include blockchain privacy and security.
On Design and Performance of Offline Finding Network
Tong Li (Renmin University of China, China); Jiaxin Liang (Huawei Technologies, China); Yukuan Ding (Hong Kong University of Science and Technology, Hong Kong); Kai Zheng (Huawei Technologies, China); Xu Zhang (Nanjing University, China); Ke Xu (Tsinghua University, China)
experience in OFN is closely related to the success ratio (possibility) of finding the lost device, where the latency of the prerequisite stage, i.e., neighbor discovery, matters. However, the crowd-sourced finder devices show diversity in scan modes due to different power modes or different manufacturers, resulting in local optima of neighbor discovery performance. In this paper, we present a brand-new broadcast mode called ElastiCast to deal with the scan mode diversity issues. ElastiCast captures the key features of BLE neighbor discovery and globally optimizes the broadcast mode interacting with diverse scan modes. Experimental evaluation results and commercial product deployment experience demonstrate that ElastiCast is effective in achieving stable and bounded neighbor discovery latency within the power budget.
Speaker
WiseCam: Wisely Tuning Wireless Pan-Tilt Cameras for Cost-Effective Moving Object Tracking
Jinlong E (Renmin University of China, China); Lin He and Zhenhua Li (Tsinghua University, China); Yunhao Liu (Tsinghua University & The Hong Kong University of Science and Technology, China)
Speaker
Effectively Learning Moiré QR Code Decryption from Simulated Data
Yu Lu, Hao Pan, Guangtao Xue and Yi-Chao Chen (Shanghai Jiao Tong University, China); Jinghai He (University of California, Berkeley, China); Jiadi Yu (Shanghai Jiao Tong University, China); Feitong Tan (Simon Fraser University, Canada)
Speaker
Session Chair
Qinghua Li
Crowdsourcing
Multi-Objective Order Dispatch for Urban Crowd Sensing with For-Hire Vehicles
Jiahui Sun, Haiming Jin, Rong Ding and Guiyun Fan (Shanghai Jiao Tong University, China); Yifei Wei (Carnegie Mellon University, USA); Lu Su (Purdue University, USA)
Speaker Haiming Jin (Shanghai Jiao Tong University)
AoI-aware Incentive Mechanism for Mobile Crowdsensing using Stackelberg Game
Mingjun Xiao, Yin Xu and Jinrui Zhou (University of Science and Technology of China, China); Jie Wu (Temple University, USA); Sheng Zhang (Nanjing University, China); Jun Zheng (University of Science and Technology of China, China)
Speaker
Spatiotemporal Transformer for Data Inference and Long Prediction in Sparse Mobile CrowdSensing
En Wang, Weiting Liu and Wenbin Liu (Jilin University, China); Chaocan Xiang (Chongqing University, China); Bo Yang and Yongjian Yang (Jilin University, China)
Speaker
Crowd2: Multi-agent Bandit-based Dispatch for Video Analytics upon Crowdsourcing
Yu Chen, Sheng Zhang, Yuting Yan, Yibo Jin, Ning Chen and Mingtao Ji (Nanjing University, China); Mingjun Xiao (University of Science and Technology of China, China)
Speaker
Session Chair
Qinghua Li
Distributed Learning
Accelerating Distributed K-FAC with Efficient Collective Communication and Scheduling
Lin Zhang (Hong Kong University of Science and Technology, Hong Kong); Shaohuai Shi (Harbin Institute of Technology, Shenzhen, China); Bo Li (Hong Kong University of Science and Technology, Hong Kong)
Speaker
PipeMoE: Accelerating Mixture-of-Experts through Adaptive Pipelining
Shaohuai Shi (Harbin Institute of Technology, Shenzhen, China); Xinglin Pan and Xiaowen Chu (Hong Kong Baptist University, Hong Kong); Bo Li (Hong Kong University of Science and Technology, Hong Kong)
Speaker
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization
Peiwen Qiu, Yining Li and Zhuqing Liu (The Ohio State University, USA); Prashant Khanduri (University of Minnesota, USA); Jia Liu and Ness B. Shroff (The Ohio State University, USA); Elizabeth Serena Bentley (AFRL, USA); Kurt Turck (United States Air Force Research Labs, USA)
Speaker
DAGC: Data-aware Adaptive Gradient Compression
Rongwei Lu (Tsinghua University, China); Jiajun Song (Dalian University of Technology, China); Bin Chen (Harbin Institute of Technology, Shenzhen, China); Laizhong Cui (Shenzhen University, China); Zhi Wang (Tsinghua University, China)
In this study, we first derive a function from capturing the correlation between the number of training iterations for a model to converge to the same accuracy, and the compression ratios at different workers; This function particularly shows that workers with larger data volumes should be assigned with higher compression ratios to guarantee better accuracy. Then, we formulate the assignment of compression ratios to the workers as an n-variables chi-square nonlinear optimization problem under fixed and limited total communication constrain. We propose an adaptive gradients compression strategy called DAGC, which assigns each worker a different compression ratio according to their data volumes. Our experiments confirm that DAGC can achieve better performance facing highly imbalanced data volume distribution and restricted communication.
Speaker
Session Chair
Yanjiao Chen
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