Difference between revisions of "Resource:Paper Carnival 2022"

@@ Line 1: / Line 1: @@
 paper carnival 2022
+'''Visible Light Communication--Wenliang'''
+[Sensys 2021] [https://dl.acm.org/doi/pdf/10.1145/3485730.3485934 CurveLight: An Accurate and Practical Indoor Positioning System]
+[Sensys 2021] [https://dl.acm.org/doi/pdf/10.1145/3485730.3485948 SpiderWeb: Enabling Through-Screen Visible Light Communication]
+[Kaiwen][ICNP2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9155474 X-MAC: Achieving High Scalability via Imperfect-Orthogonality Aware Scheduling in LPWAN]
+'''Response to Mobility--Luwei'''
+[Infocom2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796811 Enabling QoE Support for Interactive Applications over Mobile Edge with High User Mobility]
+[Infocom2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796968 User Experience Oriented Task Computation for UAV-Assisted MEC System]
+[TMC2022] [https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9343712 ECHO: Efficient Zero-Control-Packet Broadcasting for Mobile Ad Hoc Networks]
+[Zhuoliu][MobiCom21] [https://www.microsoft.com/en-us/research/uploads/prod/2021/09/Visage_Mobicom_2021.pdf Visage: enabling timely analytics for drone imagery]
+'''Offloading, Delivery--Wenjie'''
+[Infocom2022] [https://ieeexplore.ieee.org/document/9796843 An Efficient Two-Layer Task Offloading Scheme for MEC Networks with Multiple Services Providers]
+[Infocom2022] [https://ieeexplore.ieee.org/document/9796714/ Two Time-Scale Joint Service Caching and Task Offloading for UAV-assisted Mobile Edge Computing]
+[Infocom2022] [https://ieeexplore.ieee.org/document/9796763/ AoDNN: An Auto-Offloading Approach to Optimize Deep Inference for Fostering Mobile Web]
+[TMC2022] [https://ieeexplore.ieee.org/document/9238459 A Force-Directed Approach to Seeking Route Recommendation in Ride-on-Demand Service Using Multi-Source Urban Data]
+[Xinyu][INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796908/ IoTMosaic: Inferring User Activities from IoT Network Traffic in Smart Homes]
+[Jiajun][INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796661/ Kalmia: A Heterogeneous QoS-aware Scheduling Framework for DNN Tasks on Edge Servers]
+'''Video Service in Edge Networks--Congrong'''
+[SigComm 2022] [https://dl.acm.org/doi/pdf/10.1145/3544216.3544218 NeuroScaler: neural video enhancement at scale]
+[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796984/ FlexPatch: Fast and Accurate Object Detection for On-device High-Resolution Live Video Analytics]
+[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796657/ DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation]
+[MobiHoc 2021] [https://dl.acm.org/doi/pdf/10.1145/3466772.3467034 Task Offloading with Uncertain Processing Cycles]
+'''Edge, offloading, caching--Qingyong'''
+[Infocom 2022] [https://ieeexplore.ieee.org/document/9796969/ Online File Caching in Latency-Sensitive Systems with Delayed Hits and Bypassing]
+[Infocom 2022] [https://dl.acm.org/doi/10.1109/INFOCOM48880.2022.9796799 Distributed Cooperative Caching in Unreliable Edge Environments]
+[TMC 2022] [https://ieeexplore.ieee.org/abstract/document/9832640 Reverse Auction-based Computation Offloading and Resource Allocation in Mobile Cloud-Edge Computing]
+[YuanQi][NSDI 2022] [https://www.microsoft.com/en-us/research/uploads/prod/2021/07/nsdi22spring-final74.pdf Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers]
+[Wangkun][Infocom 2022][https://ieeexplore.ieee.org/document/9796884/ Joint Resource Management and Flow Scheduling for SFC Deployment in Hybrid Edge-and-Cloud Network]
+'''Communication-Efficient Federated Learning--Jianqi'''
+[ICML 2022] [https://arxiv.org/pdf/2111.00465.pdf DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning]
+[ICML 2022] [https://proceedings.mlr.press/v162/yi22a/yi22a.pdf QSFL: A Two-Level Uplink Communication Optimization Framework for Federated Learning]
+[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796982/ Optimal Rate Adaption in Federated Learning with Compressed Communications]
+'''Crowdsensing-Xianyang'''
+[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796960/ Learning for Crowdsourcing: Online Dispatch for Video Analytics with Guarantee]
+[INFOCOM 2022] [https://ieeexplore.ieee.org/document/9796743/ Worker Selection Towards Data Completion for Online Sparse Crowdsensing]
+[IoTJ] [https://ieeexplore.ieee.org/document/9828398/ Nondeterministic Mobility based Incentive Mechanism for Efficient Data Collection in Crowdsensing]
+[Jiangshu][SIGCOMM2022] [https://dl.acm.org/doi/pdf/10.1145/3544216.3544238 From Luna to Solar: The Evolutions of the Compute-to-Storage Networks in Alibaba Cloud]