29 lines
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29 lines
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<!doctype html><html lang=en dir=auto data-theme=auto><head><meta charset=utf-8><meta http-equiv=X-UA-Compatible content="IE=edge"><meta name=viewport content="width=device-width,initial-scale=1,shrink-to-fit=no"><meta name=robots content="index, follow"><title>MobiSys 2024 Digest | Publish Assistant</title><meta name=keywords content><meta name=description content="13 papers selected.
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WAIS: Leveraging WiFi for Resource-Efficient SLAM
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Aditya Arun 0002, William Hunter, Roshan Sai Ayyalasomayajula, Dinesh Bharadia
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TL;DR — Demonstrates that commodity WiFi signals can replace LiDAR for simultaneous localization and mapping, dramatically cutting the resource cost of robot/AR navigation.
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UWB-Fi: Pushing Wi-Fi towards Ultra-wideband for Fine-Granularity Sensing
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Xin Li 0070, Hongbo Wang, Zhe Chen 0015, Zhiping Jiang et al.
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TL;DR — Extends standard Wi-Fi to UWB-class sensing resolution without hardware changes, enabling centimeter-level gesture and motion detection on existing infrastructure."><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/mobisys-2024/><link crossorigin=anonymous href=/vincent/publish-assistant/assets/css/stylesheet.d72f07832e13c592b3edba91680bfe70f01daac396179bcace0ac36e8e0494c6.css integrity="sha256-1y8Hgy4TxZKz7bqRaAv+cPAdqsOWF5vKzgrDbo4ElMY=" rel="preload stylesheet" as=style><link rel=icon href=https://pub.sqrt.fr/vincent/publish-assistant/favicon.ico><link rel=icon type=image/png sizes=16x16 href=https://pub.sqrt.fr/vincent/publish-assistant/favicon-16x16.png><link rel=icon type=image/png sizes=32x32 href=https://pub.sqrt.fr/vincent/publish-assistant/favicon-32x32.png><link rel=apple-touch-icon href=https://pub.sqrt.fr/vincent/publish-assistant/apple-touch-icon.png><link rel=mask-icon href=https://pub.sqrt.fr/vincent/publish-assistant/safari-pinned-tab.svg><meta name=theme-color content="#2e2e33"><meta name=msapplication-TileColor content="#2e2e33"><link rel=alternate hreflang=en href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/mobisys-2024/><noscript><style>#theme-toggle,.top-link{display:none}</style><style>@media(prefers-color-scheme:dark){:root{--theme:rgb(29, 30, 32);--entry:rgb(46, 46, 51);--primary:rgb(218, 218, 219);--secondary:rgb(155, 156, 157);--tertiary:rgb(65, 66, 68);--content:rgb(196, 196, 197);--code-block-bg:rgb(46, 46, 51);--code-bg:rgb(55, 56, 62);--border:rgb(51, 51, 51);color-scheme:dark}.list{background:var(--theme)}.toc{background:var(--entry)}}</style></noscript><script>localStorage.getItem("pref-theme")==="dark"?document.querySelector("html").dataset.theme="dark":localStorage.getItem("pref-theme")==="light"?document.querySelector("html").dataset.theme="light":window.matchMedia("(prefers-color-scheme: dark)").matches?document.querySelector("html").dataset.theme="dark":document.querySelector("html").dataset.theme="light"</script><meta property="og:url" content="https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/mobisys-2024/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="MobiSys 2024 Digest"><meta property="og:description" content="13 papers selected.
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WAIS: Leveraging WiFi for Resource-Efficient SLAM Aditya Arun 0002, William Hunter, Roshan Sai Ayyalasomayajula, Dinesh Bharadia
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TL;DR — Demonstrates that commodity WiFi signals can replace LiDAR for simultaneous localization and mapping, dramatically cutting the resource cost of robot/AR navigation.
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UWB-Fi: Pushing Wi-Fi towards Ultra-wideband for Fine-Granularity Sensing Xin Li 0070, Hongbo Wang, Zhe Chen 0015, Zhiping Jiang et al.
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TL;DR — Extends standard Wi-Fi to UWB-class sensing resolution without hardware changes, enabling centimeter-level gesture and motion detection on existing infrastructure."><meta property="og:locale" content="en_us"><meta property="og:type" content="article"><meta property="article:section" content="cloud-edge"><meta property="article:published_time" content="2024-01-01T00:00:00+00:00"><meta property="article:modified_time" content="2024-01-01T00:00:00+00:00"><meta name=twitter:card content="summary"><meta name=twitter:title content="MobiSys 2024 Digest"><meta name=twitter:description content="13 papers selected.
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WAIS: Leveraging WiFi for Resource-Efficient SLAM Aditya Arun 0002, William Hunter, Roshan Sai Ayyalasomayajula, Dinesh Bharadia
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TL;DR — Demonstrates that commodity WiFi signals can replace LiDAR for simultaneous localization and mapping, dramatically cutting the resource cost of robot/AR navigation.
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UWB-Fi: Pushing Wi-Fi towards Ultra-wideband for Fine-Granularity Sensing Xin Li 0070, Hongbo Wang, Zhe Chen 0015, Zhiping Jiang et al.
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TL;DR — Extends standard Wi-Fi to UWB-class sensing resolution without hardware changes, enabling centimeter-level gesture and motion detection on existing infrastructure."><script type=application/ld+json>{"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Edge and Cloud Systems","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/"},{"@type":"ListItem","position":2,"name":"Digests","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/"},{"@type":"ListItem","position":3,"name":"MobiSys 2024 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/mobisys-2024/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"MobiSys 2024 Digest","name":"MobiSys 2024 Digest","description":"13 papers selected.\nWAIS: Leveraging WiFi for Resource-Efficient SLAM Aditya Arun 0002, William Hunter, Roshan Sai Ayyalasomayajula, Dinesh Bharadia\nTL;DR — Demonstrates that commodity WiFi signals can replace LiDAR for simultaneous localization and mapping, dramatically cutting the resource cost of robot/AR navigation.\nUWB-Fi: Pushing Wi-Fi towards Ultra-wideband for Fine-Granularity Sensing Xin Li 0070, Hongbo Wang, Zhe Chen 0015, Zhiping Jiang et al.\nTL;DR — Extends standard Wi-Fi to UWB-class sensing resolution without hardware changes, enabling centimeter-level gesture and motion detection on existing infrastructure.\n","keywords":[],"articleBody":"13 papers selected.\nWAIS: Leveraging WiFi for Resource-Efficient SLAM Aditya Arun 0002, William Hunter, Roshan Sai Ayyalasomayajula, Dinesh Bharadia\nTL;DR — Demonstrates that commodity WiFi signals can replace LiDAR for simultaneous localization and mapping, dramatically cutting the resource cost of robot/AR navigation.\nUWB-Fi: Pushing Wi-Fi towards Ultra-wideband for Fine-Granularity Sensing Xin Li 0070, Hongbo Wang, Zhe Chen 0015, Zhiping Jiang et al.\nTL;DR — Extends standard Wi-Fi to UWB-class sensing resolution without hardware changes, enabling centimeter-level gesture and motion detection on existing infrastructure.\nRadarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry Emerson Sie, Xinyu Wu, Heyu Guo, Deepak Vasisht\nTL;DR — Introduces a Doppler-derived odometry method that generalizes radar-based SLAM across environments and radar hardware without per-deployment retraining.\nChirpTransformer: Versatile LoRa Encoding for Low-power Wide-area IoT Chenning Li, Yidong Ren, Shuai Tong, Shakhrul Iman Siam et al.\nTL;DR — Redesigns LoRa chirp encoding with a transformer-based scheme that simultaneously improves throughput, range, and coexistence for large-scale IoT deployments.\nWillow: Practical WiFi Backscatter Localization with Parallel Tags Jinyan Jiang, Jiliang Wang, Yijie Chen, Shuai Tong et al.\nTL;DR — Enables concurrent localization of multiple passive backscatter tags over commodity WiFi, making large-scale battery-free asset tracking practical.\nPantheon: Preemptible Multi-DNN Inference on Mobile Edge GPUs Lixiang Han, Zimu Zhou, Zhenjiang Li\nTL;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.\nARISE: High-Capacity AR Offloading Inference Serving via Proactive Scheduling Z. Jonny Kong, Qiang Xu 0006, Y. Charlie Hu\nTL;DR — Proactively schedules AR inference offloading by predicting gaze and scene dynamics, significantly increasing server capacity while meeting strict latency budgets.\nCACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference Mohammad Mehdi Rastikerdar, Jin Huang, Shiwei Fang, Hui Guan 0001 et al.\nTL;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.\nEmpowering 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.\nTL;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.\nFedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients Leming Shen, Qiang Yang 0018, Kaiyan Cui, Yuanqing Zheng et al.\nTL;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.\nSoilCares: Towards Low-cost Soil Macronutrients and Moisture Monitoring Using RF-VNIR Sensing Juexing Wang, Yuda Feng, Gouree Kumbhar, Guangjing Wang 0001 et al.\nTL;DR — Combines RF and near-infrared sensing in a low-cost handheld device to measure soil nutrients and moisture, demonstrating real agricultural field deployments.\nMobiAir: 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.\nTL;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.\nJoey: Supporting Kangaroo Mother Care with Computational Fabrics Qijia Shao, Jiting Liu, Emily Bejerano, Ho-Man Colman Leung et al.\nTL;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.\n","wordCount":"564","inLanguage":"en","datePublished":"2024-01-01T00:00:00Z","dateModified":"2024-01-01T00:00:00Z","author":{"@type":"Person","name":"Publish Assistant"},"mainEntityOfPage":{"@type":"WebPage","@id":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/mobisys-2024/"},"publisher":{"@type":"Organization","name":"Publish Assistant","logo":{"@type":"ImageObject","url":"https://pub.sqrt.fr/vincent/publish-assistant/favicon.ico"}}}</script></head><body id=top><header class=header><nav class=header-nav><div class=logo><a href=https://pub.sqrt.fr/vincent/publish-assistant/ accesskey=h title="Publish Assistant (Alt + H)">Publish Assistant</a>
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<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather feather-chevron-right"><polyline points="9 18 15 12 9 6"/></svg></nav><h1 class="post-title entry-hint-parent">MobiSys 2024 Digest</h1><div class=post-meta><span title='2024-01-01 00:00:00 +0000 UTC'>January 1, 2024</span> · <span>Publish Assistant</span></div></header><div class="post-content md-content"><p>13 papers selected.</p><hr><h3 id=wais-leveraging-wifi-for-resource-efficient-slam>WAIS: Leveraging WiFi for Resource-Efficient SLAM<a hidden class=anchor aria-hidden=true href=#wais-leveraging-wifi-for-resource-efficient-slam>#</a></h3><p><em>Aditya Arun 0002, William Hunter, Roshan Sai Ayyalasomayajula, Dinesh Bharadia</em></p><p><strong>TL;DR</strong> — Demonstrates that commodity WiFi signals can replace LiDAR for simultaneous localization and mapping, dramatically cutting the resource cost of robot/AR navigation.</p><hr><h3 id=uwb-fi-pushing-wi-fi-towards-ultra-wideband-for-fine-granularity-sensing>UWB-Fi: Pushing Wi-Fi towards Ultra-wideband for Fine-Granularity Sensing<a hidden class=anchor aria-hidden=true href=#uwb-fi-pushing-wi-fi-towards-ultra-wideband-for-fine-granularity-sensing>#</a></h3><p><em>Xin Li 0070, Hongbo Wang, Zhe Chen 0015, Zhiping Jiang <em>et al.</em></em></p><p><strong>TL;DR</strong> — Extends standard Wi-Fi to UWB-class sensing resolution without hardware changes, enabling centimeter-level gesture and motion detection on existing infrastructure.</p><hr><h3 id=radarize-enhancing-radar-slam-with-generalizable-doppler-based-odometry>Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry<a hidden class=anchor aria-hidden=true href=#radarize-enhancing-radar-slam-with-generalizable-doppler-based-odometry>#</a></h3><p><em>Emerson Sie, Xinyu Wu, Heyu Guo, Deepak Vasisht</em></p><p><strong>TL;DR</strong> — Introduces a Doppler-derived odometry method that generalizes radar-based SLAM across environments and radar hardware without per-deployment retraining.</p><hr><h3 id=chirptransformer-versatile-lora-encoding-for-low-power-wide-area-iot>ChirpTransformer: Versatile LoRa Encoding for Low-power Wide-area IoT<a hidden class=anchor aria-hidden=true href=#chirptransformer-versatile-lora-encoding-for-low-power-wide-area-iot>#</a></h3><p><em>Chenning Li, Yidong Ren, Shuai Tong, Shakhrul Iman Siam <em>et al.</em></em></p><p><strong>TL;DR</strong> — Redesigns LoRa chirp encoding with a transformer-based scheme that simultaneously improves throughput, range, and coexistence for large-scale IoT deployments.</p><hr><h3 id=willow-practical-wifi-backscatter-localization-with-parallel-tags>Willow: Practical WiFi Backscatter Localization with Parallel Tags<a hidden class=anchor aria-hidden=true href=#willow-practical-wifi-backscatter-localization-with-parallel-tags>#</a></h3><p><em>Jinyan Jiang, Jiliang Wang, Yijie Chen, Shuai Tong <em>et al.</em></em></p><p><strong>TL;DR</strong> — Enables concurrent localization of multiple passive backscatter tags over commodity WiFi, making large-scale battery-free asset tracking practical.</p><hr><h3 id=pantheon-preemptible-multi-dnn-inference-on-mobile-edge-gpus>Pantheon: Preemptible Multi-DNN Inference on Mobile Edge GPUs<a hidden class=anchor aria-hidden=true href=#pantheon-preemptible-multi-dnn-inference-on-mobile-edge-gpus>#</a></h3><p><em>Lixiang Han, Zimu Zhou, Zhenjiang Li</em></p><p><strong>TL;DR</strong> — Provides a preemptible scheduling runtime for concurrent DNN workloads on edge GPUs, achieving low-latency inference without sacrificing throughput under mixed real-time demands.</p><hr><h3 id=arise-high-capacity-ar-offloading-inference-serving-via-proactive-scheduling>ARISE: High-Capacity AR Offloading Inference Serving via Proactive Scheduling<a hidden class=anchor aria-hidden=true href=#arise-high-capacity-ar-offloading-inference-serving-via-proactive-scheduling>#</a></h3><p><em>Z. Jonny Kong, Qiang Xu 0006, Y. Charlie Hu</em></p><p><strong>TL;DR</strong> — Proactively schedules AR inference offloading by predicting gaze and scene dynamics, significantly increasing server capacity while meeting strict latency budgets.</p><hr><h3 id=cactus-dynamically-switchable-context-aware-micro-classifiers-for-efficient-iot-inference>CACTUS: Dynamically Switchable Context-aware micro-Classifiers for Efficient IoT Inference<a hidden class=anchor aria-hidden=true href=#cactus-dynamically-switchable-context-aware-micro-classifiers-for-efficient-iot-inference>#</a></h3><p><em>Mohammad Mehdi Rastikerdar, Jin Huang, Shiwei Fang, Hui Guan 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — 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.</p><hr><h3 id=empowering-in-browser-deep-learning-inference-on-edge-through-just-in-time-kernel-optimization>Empowering In-Browser Deep Learning Inference on Edge Through Just-In-Time Kernel Optimization<a hidden class=anchor aria-hidden=true href=#empowering-in-browser-deep-learning-inference-on-edge-through-just-in-time-kernel-optimization>#</a></h3><p><em>Fucheng Jia, Shiqi Jiang 0002, Ting Cao 0003, Wei Cui <em>et al.</em></em></p><p><strong>TL;DR</strong> — 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.</p><hr><h3 id=fedconv-a-learning-on-model-paradigm-for-heterogeneous-federated-clients>FedConv: A Learning-on-Model Paradigm for Heterogeneous Federated Clients<a hidden class=anchor aria-hidden=true href=#fedconv-a-learning-on-model-paradigm-for-heterogeneous-federated-clients>#</a></h3><p><em>Leming Shen, Qiang Yang 0018, Kaiyan Cui, Yuanqing Zheng <em>et al.</em></em></p><p><strong>TL;DR</strong> — 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.</p><hr><h3 id=soilcares-towards-low-cost-soil-macronutrients-and-moisture-monitoring-using-rf-vnir-sensing>SoilCares: Towards Low-cost Soil Macronutrients and Moisture Monitoring Using RF-VNIR Sensing<a hidden class=anchor aria-hidden=true href=#soilcares-towards-low-cost-soil-macronutrients-and-moisture-monitoring-using-rf-vnir-sensing>#</a></h3><p><em>Juexing Wang, Yuda Feng, Gouree Kumbhar, Guangjing Wang 0001 <em>et al.</em></em></p><p><strong>TL;DR</strong> — Combines RF and near-infrared sensing in a low-cost handheld device to measure soil nutrients and moisture, demonstrating real agricultural field deployments.</p><hr><h3 id=mobiair-unleashing-sensor-mobility-for-city-scale-and-fine-grained-air-quality-monitoring-with-airbert>MobiAir: Unleashing Sensor Mobility for City-scale and Fine-grained Air-Quality Monitoring with AirBERT<a hidden class=anchor aria-hidden=true href=#mobiair-unleashing-sensor-mobility-for-city-scale-and-fine-grained-air-quality-monitoring-with-airbert>#</a></h3><p><em>Yuxuan Liu 0010, Haoyang Wang 0012, Fanhang Man, Jingao Xu <em>et al.</em></em></p><p><strong>TL;DR</strong> — 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.</p><hr><h3 id=joey-supporting-kangaroo-mother-care-with-computational-fabrics>Joey: Supporting Kangaroo Mother Care with Computational Fabrics<a hidden class=anchor aria-hidden=true href=#joey-supporting-kangaroo-mother-care-with-computational-fabrics>#</a></h3><p><em>Qijia Shao, Jiting Liu, Emily Bejerano, Ho-Man Colman Leung <em>et al.</em></em></p><p><strong>TL;DR</strong> — 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.</p></div><footer class=post-footer><ul class=post-tags></ul><nav class=paginav><a class=prev href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/jpdc-2024/><span class=title>« Prev</span>
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