4.4 KiB
title, venue, year, date, tags, paper_count, draft
| title | venue | year | date | tags | paper_count | draft |
|---|---|---|---|---|---|---|
| MobiSys 2024 Digest | MobiSys | 2024 | 2024-01-01 | 13 | false |
13 papers selected.
WAIS: Leveraging WiFi for Resource-Efficient SLAM
Aditya Arun 0002, William Hunter, Roshan Sai Ayyalasomayajula, Dinesh Bharadia
TL;DR — Demonstrates that commodity WiFi signals can replace LiDAR for simultaneous localization and mapping, dramatically cutting the resource cost of robot/AR navigation.
UWB-Fi: Pushing Wi-Fi towards Ultra-wideband for Fine-Granularity Sensing
Xin Li 0070, Hongbo Wang, Zhe Chen 0015, Zhiping Jiang et al.
TL;DR — Extends standard Wi-Fi to UWB-class sensing resolution without hardware changes, enabling centimeter-level gesture and motion detection on existing infrastructure.
Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry
Emerson Sie, Xinyu Wu, Heyu Guo, Deepak Vasisht
TL;DR — Introduces a Doppler-derived odometry method that generalizes radar-based SLAM across environments and radar hardware without per-deployment retraining.
ChirpTransformer: Versatile LoRa Encoding for Low-power Wide-area IoT
Chenning Li, Yidong Ren, Shuai Tong, Shakhrul Iman Siam et al.
TL;DR — Redesigns LoRa chirp encoding with a transformer-based scheme that simultaneously improves throughput, range, and coexistence for large-scale IoT deployments.
Willow: Practical WiFi Backscatter Localization with Parallel Tags
Jinyan Jiang, Jiliang Wang, Yijie Chen, Shuai Tong et al.
TL;DR — Enables concurrent localization of multiple passive backscatter tags over commodity WiFi, making large-scale battery-free asset tracking practical.
Pantheon: Preemptible Multi-DNN Inference on Mobile Edge GPUs
Lixiang Han, Zimu Zhou, Zhenjiang Li
TL;DR — Provides a preemptible scheduling runtime for concurrent DNN workloads on edge GPUs, achieving low-latency inference without sacrificing throughput under mixed real-time demands.
ARISE: High-Capacity AR Offloading Inference Serving via Proactive Scheduling
Z. Jonny Kong, Qiang Xu 0006, Y. Charlie Hu
TL;DR — Proactively schedules AR inference offloading by predicting gaze and scene dynamics, significantly increasing server capacity while meeting strict latency budgets.
CACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference
Mohammad Mehdi Rastikerdar, Jin Huang, Shiwei Fang, Hui Guan 0001 et al.
TL;DR — Deploys a family of tiny context-aware classifiers on microcontrollers that switch at runtime to match workload context, cutting energy by orders of magnitude versus monolithic models.
Empowering In-Browser Deep Learning Inference on Edge Through Just-In-Time Kernel Optimization
Fucheng Jia, Shiqi Jiang 0002, Ting Cao 0003, Wei Cui et al.
TL;DR — Uses JIT kernel specialization to close the performance gap between browser-based and native DNN inference on edge devices, enabling high-throughput on-device AI in web apps.
FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients
Leming Shen, Qiang Yang 0018, Kaiyan Cui, Yuanqing Zheng et al.
TL;DR — Proposes learning directly over model parameters rather than data, allowing federated learning to work across radically heterogeneous IoT devices without sharing raw data or requiring uniform architectures.
SoilCares: Towards Low-cost Soil Macronutrients and Moisture Monitoring Using RF-VNIR Sensing
Juexing Wang, Yuda Feng, Gouree Kumbhar, Guangjing Wang 0001 et al.
TL;DR — Combines RF and near-infrared sensing in a low-cost handheld device to measure soil nutrients and moisture, demonstrating real agricultural field deployments.
MobiAir: Unleashing Sensor Mobility for City-scale and Fine-grained Air-Quality Monitoring with AirBERT
Yuxuan Liu 0010, Haoyang Wang 0012, Fanhang Man, Jingao Xu et al.
TL;DR — Leverages mobile sensors on vehicles and pedestrians with a BERT-style spatio-temporal model to achieve city-scale, fine-grained air quality maps at a fraction of the cost of static sensor networks.
Joey: Supporting Kangaroo Mother Care with Computational Fabrics
Qijia Shao, Jiting Liu, Emily Bejerano, Ho-Man Colman Leung et al.
TL;DR — Embeds soft physiological sensors directly into a wearable fabric wrap to monitor premature infants during skin-to-skin care, demonstrating a compelling real-world clinical deployment.