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24 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 2025 Digest | Publish Assistant</title><meta name=keywords content="mobile-computing,edge-computing,networking"><meta name=description content="12 papers selected.
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Hopter: a Safe, Robust, and Responsive Embedded Operating System
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Zhiyao Ma, Guojun Chen, Zhuo Chen 0011, Lin Zhong 0001
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TL;DR — Hopter is a new embedded OS that enforces memory safety and real-time responsiveness through a Rust-based task model with cooperative and preemptive scheduling co-designed from the ground up.
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Why notable — Building a ground-up safe embedded OS is a long-standing challenge; Hopter addresses it without sacrificing the determinism that IoT and robotics workloads demand, offering a credible alternative to unsafe C-based RTOSes."><meta name=author content="Publish Assistant"><link rel=canonical href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/mobisys-2025/><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-2025/><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-2025/"><meta property="og:site_name" content="Publish Assistant"><meta property="og:title" content="MobiSys 2025 Digest"><meta property="og:description" content="12 papers selected.
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Hopter: a Safe, Robust, and Responsive Embedded Operating System Zhiyao Ma, Guojun Chen, Zhuo Chen 0011, Lin Zhong 0001
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TL;DR — Hopter is a new embedded OS that enforces memory safety and real-time responsiveness through a Rust-based task model with cooperative and preemptive scheduling co-designed from the ground up.
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Why notable — Building a ground-up safe embedded OS is a long-standing challenge; Hopter addresses it without sacrificing the determinism that IoT and robotics workloads demand, offering a credible alternative to unsafe C-based RTOSes."><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="2025-06-23T00:00:00+00:00"><meta property="article:modified_time" content="2025-06-23T00:00:00+00:00"><meta property="article:tag" content="Mobile-Computing"><meta property="article:tag" content="Edge-Computing"><meta property="article:tag" content="Networking"><meta name=twitter:card content="summary"><meta name=twitter:title content="MobiSys 2025 Digest"><meta name=twitter:description content="12 papers selected.
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Hopter: a Safe, Robust, and Responsive Embedded Operating System Zhiyao Ma, Guojun Chen, Zhuo Chen 0011, Lin Zhong 0001
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TL;DR — Hopter is a new embedded OS that enforces memory safety and real-time responsiveness through a Rust-based task model with cooperative and preemptive scheduling co-designed from the ground up.
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Why notable — Building a ground-up safe embedded OS is a long-standing challenge; Hopter addresses it without sacrificing the determinism that IoT and robotics workloads demand, offering a credible alternative to unsafe C-based RTOSes."><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 2025 Digest","item":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/mobisys-2025/"}]}</script><script type=application/ld+json>{"@context":"https://schema.org","@type":"BlogPosting","headline":"MobiSys 2025 Digest","name":"MobiSys 2025 Digest","description":"12 papers selected.\nHopter: a Safe, Robust, and Responsive Embedded Operating System Zhiyao Ma, Guojun Chen, Zhuo Chen 0011, Lin Zhong 0001\nTL;DR — Hopter is a new embedded OS that enforces memory safety and real-time responsiveness through a Rust-based task model with cooperative and preemptive scheduling co-designed from the ground up.\nWhy notable — Building a ground-up safe embedded OS is a long-standing challenge; Hopter addresses it without sacrificing the determinism that IoT and robotics workloads demand, offering a credible alternative to unsafe C-based RTOSes.\n","keywords":["mobile-computing","edge-computing","networking"],"articleBody":"12 papers selected.\nHopter: a Safe, Robust, and Responsive Embedded Operating System Zhiyao Ma, Guojun Chen, Zhuo Chen 0011, Lin Zhong 0001\nTL;DR — Hopter is a new embedded OS that enforces memory safety and real-time responsiveness through a Rust-based task model with cooperative and preemptive scheduling co-designed from the ground up.\nWhy notable — Building a ground-up safe embedded OS is a long-standing challenge; Hopter addresses it without sacrificing the determinism that IoT and robotics workloads demand, offering a credible alternative to unsafe C-based RTOSes.\n→ Read paper WhisperFlow: speech foundation models in real time Rongxiang Wang, Zhiming Xu 0001, Felix Xiaozhu Lin\nTL;DR — WhisperFlow pipelines and partially overlaps Whisper’s encode-decode stages so that large speech foundation models can transcribe audio with latency low enough for interactive mobile use.\nWhy notable — Running large encoder-decoder speech models in real time on mobile hardware was previously impractical; the paper shows that careful pipeline scheduling—not quantization alone—can close this gap, with implications for on-device voice assistants.\n→ Read paper ARIA: Optimizing Vision Foundation Model Inference on Heterogeneous Mobile Processors for Augmented Reality Chanyoung Jung, Jeho Lee, Gunjoong Kim, Jiwon Kim et al.\nTL;DR — ARIA partitions and schedules vision foundation model layers across CPU, GPU, and NPU on a mobile SoC to meet the strict latency budget of augmented-reality pipelines.\nWhy notable — Foundation models are typically too large for AR frame rates; ARIA’s heterogeneous mapping strategy achieves real-time throughput without dedicated server offload, making high-quality AR semantics viable on commodity handsets.\n→ Read paper You Only Render Once: Enhancing Energy and Computation Efficiency of Mobile Virtual Reality Xingyu Chen, Xinmin Fang, Shuting Zhang, Xinyu Zhang 0003 et al.\nTL;DR — YORO eliminates redundant per-eye rendering in mobile VR by synthesizing one eye’s view from the other using a lightweight neural warp, cutting GPU work nearly in half.\nWhy notable — Stereo rendering is the dominant energy cost in standalone VR headsets; halving it via a neural single-render approach is a surprising result that could significantly extend battery life on devices like Quest.\n→ Read paper AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation Hao Wen 0004, Shizuo Tian, Borislav Pavlov, Wenjie Du 0004 et al.\nTL;DR — AutoDroid-V2 improves on-device GUI automation agents by having a small language model generate executable action code rather than selecting from a fixed action vocabulary, dramatically improving task success rates.\nWhy notable — Shifting from action classification to code generation is a paradigm change for mobile agents; the paper demonstrates that even small, phone-resident SLMs can outperform larger cloud models on standard Android benchmarks when given the right output format.\n→ Read paper EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices Zheyu Shen, Yexiao He, Ziyao Wang, Yuning Zhang et al.\nTL;DR — EdgeLoRA multiplexes many LoRA-adapted LLM variants on a single edge GPU by sharing the frozen base model weights and swapping only the low-rank adapters, enabling multi-tenant LLM inference at the edge.\nWhy notable — Multi-tenant serving of personalized LLMs on a single edge node is an open systems problem; EdgeLoRA’s adapter-swap architecture achieves near-dedicated throughput per tenant while keeping memory footprint proportional to the number of adapters rather than full model copies.\n→ Read paper Never Start from Scratch: Expediting On-Device LLM Personalization via Explainable Model Selection Haoming Wang 0002, Boyuan Yang 0001, Xiangyu Yin 0002, Wei Gao 0006\nTL;DR — Rather than fine-tuning from a generic base, this system selects the best pre-existing task-specific model checkpoint as the personalization starting point, guided by an interpretable feature-matching score.\nWhy notable — The finding that checkpoint selection dominates fine-tuning cost savings—and that an explainable selector can match exhaustive search—challenges the assumption that on-device personalization must always begin from a single canonical base model.\n→ Read paper Non-Line-of-Sight 3D Object Reconstruction via mmWave Surface Normal Estimation Laura Dodds, Tara Boroushaki, Kaichen Zhou, Fadel Adib\nTL;DR — By estimating surface normals from mmWave reflections, this system reconstructs the 3D shape of objects hidden around corners without requiring a line-of-sight path.\nWhy notable — NLOS 3D reconstruction with commodity mmWave hardware is a significant sensing advance; using surface normals rather than time-of-flight alone yields object reconstructions detailed enough to identify object categories, with clear implications for autonomous driving and search-and-rescue.\n→ Read paper Toward Spoofing-Resilient and Communication-Integrated MmWave Radar Sensing Kun Qian 0004, Parth Pathak 0001\nTL;DR — This work integrates communication waveforms into mmWave radar so that sensing and data transmission share the same spectrum, while a spoofing-resilience mechanism prevents adversarial injection of false radar echoes.\nWhy notable — Combining ISAC (integrated sensing and communications) with active spoofing defense in a single mmWave system addresses two open problems at once; the result is particularly relevant as mmWave bands are slated for both 5G and automotive radar use.\n→ Read paper Are LoRa Logical Channels Really Orthogonal? Practically Orthogonalizing Massive Logical Channels Shiming Yu, Ziyue Zhang, Xianjin Xia, Yuanqing Zheng et al.\nTL;DR — The paper shows that LoRa’s supposedly orthogonal spreading-factor channels have measurable inter-channel interference at scale, then proposes a software-only scheduler that restores near-perfect orthogonality for dense deployments.\nWhy notable — The result that LoRa orthogonality breaks down in realistic dense networks—and that a pure software fix suffices—is a surprising finding that will directly affect how city-scale IoT networks are planned and managed.\n→ Read paper Towards End-to-End Latency Guarantee in MEC Live Video Analytics with App-RAN Mutual Awareness Juheon Yi, Goodsol Lee, Minkyung Jeong, Seokgyeong Shin et al.\nTL;DR — By exposing RAN scheduling state to the MEC video analytics application—and letting the app’s feedback influence RAN scheduling—this system achieves end-to-end latency guarantees that neither layer can provide alone.\nWhy notable — Cross-layer co-design between the RAN and MEC application is rarely demonstrated in a working system; the paper shows that even coarse-grained mutual awareness cuts tail latency by over 50% compared to independent operation.\n→ Read paper Unraveling the Missing Link in Low-power Communication: An Autodyning Receiver Architecture that Achieves a Long Range Pramuka Medaranga Sooriya Patabandige, Rajashekar Reddy Chinthalapani, Wenqing Yan, Prabal Dutta et al.\nTL;DR — An autodyning receiver design reuses the transmit oscillator for self-mixing, eliminating a separate LO and achieving orders-of-magnitude better sensitivity than prior backscatter receivers without added hardware cost.\nWhy notable — Long-range backscatter has been a persistent gap between battery-free IoT and practical deployment; this architecture achieves kilometer-scale range on microwatts of harvested energy, a result that could unlock new classes of batteryless sensors.\n→ Read paper ","wordCount":"1077","inLanguage":"en","datePublished":"2025-06-23T00:00:00Z","dateModified":"2025-06-23T00:00:00Z","author":{"@type":"Person","name":"Publish Assistant"},"mainEntityOfPage":{"@type":"WebPage","@id":"https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/mobisys-2025/"},"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 2025 Digest</h1><div class=post-meta><span title='2025-06-23 00:00:00 +0000 UTC'>June 23, 2025</span> · <span>Publish Assistant</span></div></header><div class="post-content md-content"><p>12 papers selected.</p><hr><h3 id=hopter-a-safe-robust-and-responsive-embedded-operating-system>Hopter: a Safe, Robust, and Responsive Embedded Operating System<a hidden class=anchor aria-hidden=true href=#hopter-a-safe-robust-and-responsive-embedded-operating-system>#</a></h3><p><em>Zhiyao Ma, Guojun Chen, Zhuo Chen 0011, Lin Zhong 0001</em></p><p><strong>TL;DR</strong> — Hopter is a new embedded OS that enforces memory safety and real-time responsiveness through a Rust-based task model with cooperative and preemptive scheduling co-designed from the ground up.</p><p><strong>Why notable</strong> — Building a ground-up safe embedded OS is a long-standing challenge; Hopter addresses it without sacrificing the determinism that IoT and robotics workloads demand, offering a credible alternative to unsafe C-based RTOSes.</p><p><a href=https://doi.org/10.1145/3711875.3729149>→ Read paper</a></p><hr><h3 id=whisperflow-speech-foundation-models-in-real-time>WhisperFlow: speech foundation models in real time<a hidden class=anchor aria-hidden=true href=#whisperflow-speech-foundation-models-in-real-time>#</a></h3><p><em>Rongxiang Wang, Zhiming Xu 0001, Felix Xiaozhu Lin</em></p><p><strong>TL;DR</strong> — WhisperFlow pipelines and partially overlaps Whisper’s encode-decode stages so that large speech foundation models can transcribe audio with latency low enough for interactive mobile use.</p><p><strong>Why notable</strong> — Running large encoder-decoder speech models in real time on mobile hardware was previously impractical; the paper shows that careful pipeline scheduling—not quantization alone—can close this gap, with implications for on-device voice assistants.</p><p><a href=https://doi.org/10.1145/3711875.3729151>→ Read paper</a></p><hr><h3 id=aria-optimizing-vision-foundation-model-inference-on-heterogeneous-mobile-processors-for-augmented-reality>ARIA: Optimizing Vision Foundation Model Inference on Heterogeneous Mobile Processors for Augmented Reality<a hidden class=anchor aria-hidden=true href=#aria-optimizing-vision-foundation-model-inference-on-heterogeneous-mobile-processors-for-augmented-reality>#</a></h3><p><em>Chanyoung Jung, Jeho Lee, Gunjoong Kim, Jiwon Kim <em>et al.</em></em></p><p><strong>TL;DR</strong> — ARIA partitions and schedules vision foundation model layers across CPU, GPU, and NPU on a mobile SoC to meet the strict latency budget of augmented-reality pipelines.</p><p><strong>Why notable</strong> — Foundation models are typically too large for AR frame rates; ARIA’s heterogeneous mapping strategy achieves real-time throughput without dedicated server offload, making high-quality AR semantics viable on commodity handsets.</p><p><a href=https://doi.org/10.1145/3711875.3729161>→ Read paper</a></p><hr><h3 id=you-only-render-once-enhancing-energy-and-computation-efficiency-of-mobile-virtual-reality>You Only Render Once: Enhancing Energy and Computation Efficiency of Mobile Virtual Reality<a hidden class=anchor aria-hidden=true href=#you-only-render-once-enhancing-energy-and-computation-efficiency-of-mobile-virtual-reality>#</a></h3><p><em>Xingyu Chen, Xinmin Fang, Shuting Zhang, Xinyu Zhang 0003 <em>et al.</em></em></p><p><strong>TL;DR</strong> — YORO eliminates redundant per-eye rendering in mobile VR by synthesizing one eye’s view from the other using a lightweight neural warp, cutting GPU work nearly in half.</p><p><strong>Why notable</strong> — Stereo rendering is the dominant energy cost in standalone VR headsets; halving it via a neural single-render approach is a surprising result that could significantly extend battery life on devices like Quest.</p><p><a href=https://doi.org/10.1145/3711875.3729133>→ Read paper</a></p><hr><h3 id=autodroid-v2-boosting-slm-based-gui-agents-via-code-generation>AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation<a hidden class=anchor aria-hidden=true href=#autodroid-v2-boosting-slm-based-gui-agents-via-code-generation>#</a></h3><p><em>Hao Wen 0004, Shizuo Tian, Borislav Pavlov, Wenjie Du 0004 <em>et al.</em></em></p><p><strong>TL;DR</strong> — AutoDroid-V2 improves on-device GUI automation agents by having a small language model generate executable action code rather than selecting from a fixed action vocabulary, dramatically improving task success rates.</p><p><strong>Why notable</strong> — Shifting from action classification to code generation is a paradigm change for mobile agents; the paper demonstrates that even small, phone-resident SLMs can outperform larger cloud models on standard Android benchmarks when given the right output format.</p><p><a href=https://doi.org/10.1145/3711875.3729134>→ Read paper</a></p><hr><h3 id=edgelora-an-efficient-multi-tenant-llm-serving-system-on-edge-devices>EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices<a hidden class=anchor aria-hidden=true href=#edgelora-an-efficient-multi-tenant-llm-serving-system-on-edge-devices>#</a></h3><p><em>Zheyu Shen, Yexiao He, Ziyao Wang, Yuning Zhang <em>et al.</em></em></p><p><strong>TL;DR</strong> — EdgeLoRA multiplexes many LoRA-adapted LLM variants on a single edge GPU by sharing the frozen base model weights and swapping only the low-rank adapters, enabling multi-tenant LLM inference at the edge.</p><p><strong>Why notable</strong> — Multi-tenant serving of personalized LLMs on a single edge node is an open systems problem; EdgeLoRA’s adapter-swap architecture achieves near-dedicated throughput per tenant while keeping memory footprint proportional to the number of adapters rather than full model copies.</p><p><a href=https://doi.org/10.1145/3711875.3729141>→ Read paper</a></p><hr><h3 id=never-start-from-scratch-expediting-on-device-llm-personalization-via-explainable-model-selection>Never Start from Scratch: Expediting On-Device LLM Personalization via Explainable Model Selection<a hidden class=anchor aria-hidden=true href=#never-start-from-scratch-expediting-on-device-llm-personalization-via-explainable-model-selection>#</a></h3><p><em>Haoming Wang 0002, Boyuan Yang 0001, Xiangyu Yin 0002, Wei Gao 0006</em></p><p><strong>TL;DR</strong> — Rather than fine-tuning from a generic base, this system selects the best pre-existing task-specific model checkpoint as the personalization starting point, guided by an interpretable feature-matching score.</p><p><strong>Why notable</strong> — The finding that checkpoint selection dominates fine-tuning cost savings—and that an explainable selector can match exhaustive search—challenges the assumption that on-device personalization must always begin from a single canonical base model.</p><p><a href=https://doi.org/10.1145/3711875.3729132>→ Read paper</a></p><hr><h3 id=non-line-of-sight-3d-object-reconstruction-via-mmwave-surface-normal-estimation>Non-Line-of-Sight 3D Object Reconstruction via mmWave Surface Normal Estimation<a hidden class=anchor aria-hidden=true href=#non-line-of-sight-3d-object-reconstruction-via-mmwave-surface-normal-estimation>#</a></h3><p><em>Laura Dodds, Tara Boroushaki, Kaichen Zhou, Fadel Adib</em></p><p><strong>TL;DR</strong> — By estimating surface normals from mmWave reflections, this system reconstructs the 3D shape of objects hidden around corners without requiring a line-of-sight path.</p><p><strong>Why notable</strong> — NLOS 3D reconstruction with commodity mmWave hardware is a significant sensing advance; using surface normals rather than time-of-flight alone yields object reconstructions detailed enough to identify object categories, with clear implications for autonomous driving and search-and-rescue.</p><p><a href=https://doi.org/10.1145/3711875.3729138>→ Read paper</a></p><hr><h3 id=toward-spoofing-resilient-and-communication-integrated-mmwave-radar-sensing>Toward Spoofing-Resilient and Communication-Integrated MmWave Radar Sensing<a hidden class=anchor aria-hidden=true href=#toward-spoofing-resilient-and-communication-integrated-mmwave-radar-sensing>#</a></h3><p><em>Kun Qian 0004, Parth Pathak 0001</em></p><p><strong>TL;DR</strong> — This work integrates communication waveforms into mmWave radar so that sensing and data transmission share the same spectrum, while a spoofing-resilience mechanism prevents adversarial injection of false radar echoes.</p><p><strong>Why notable</strong> — Combining ISAC (integrated sensing and communications) with active spoofing defense in a single mmWave system addresses two open problems at once; the result is particularly relevant as mmWave bands are slated for both 5G and automotive radar use.</p><p><a href=https://doi.org/10.1145/3711875.3729155>→ Read paper</a></p><hr><h3 id=are-lora-logical-channels-really-orthogonal-practically-orthogonalizing-massive-logical-channels>Are LoRa Logical Channels Really Orthogonal? Practically Orthogonalizing Massive Logical Channels<a hidden class=anchor aria-hidden=true href=#are-lora-logical-channels-really-orthogonal-practically-orthogonalizing-massive-logical-channels>#</a></h3><p><em>Shiming Yu, Ziyue Zhang, Xianjin Xia, Yuanqing Zheng <em>et al.</em></em></p><p><strong>TL;DR</strong> — The paper shows that LoRa’s supposedly orthogonal spreading-factor channels have measurable inter-channel interference at scale, then proposes a software-only scheduler that restores near-perfect orthogonality for dense deployments.</p><p><strong>Why notable</strong> — The result that LoRa orthogonality breaks down in realistic dense networks—and that a pure software fix suffices—is a surprising finding that will directly affect how city-scale IoT networks are planned and managed.</p><p><a href=https://doi.org/10.1145/3711875.3729125>→ Read paper</a></p><hr><h3 id=towards-end-to-end-latency-guarantee-in-mec-live-video-analytics-with-app-ran-mutual-awareness>Towards End-to-End Latency Guarantee in MEC Live Video Analytics with App-RAN Mutual Awareness<a hidden class=anchor aria-hidden=true href=#towards-end-to-end-latency-guarantee-in-mec-live-video-analytics-with-app-ran-mutual-awareness>#</a></h3><p><em>Juheon Yi, Goodsol Lee, Minkyung Jeong, Seokgyeong Shin <em>et al.</em></em></p><p><strong>TL;DR</strong> — By exposing RAN scheduling state to the MEC video analytics application—and letting the app’s feedback influence RAN scheduling—this system achieves end-to-end latency guarantees that neither layer can provide alone.</p><p><strong>Why notable</strong> — Cross-layer co-design between the RAN and MEC application is rarely demonstrated in a working system; the paper shows that even coarse-grained mutual awareness cuts tail latency by over 50% compared to independent operation.</p><p><a href=https://doi.org/10.1145/3711875.3729139>→ Read paper</a></p><hr><h3 id=unraveling-the-missing-link-in-low-power-communication-an-autodyning-receiver-architecture-that-achieves-a-long-range>Unraveling the Missing Link in Low-power Communication: An Autodyning Receiver Architecture that Achieves a Long Range<a hidden class=anchor aria-hidden=true href=#unraveling-the-missing-link-in-low-power-communication-an-autodyning-receiver-architecture-that-achieves-a-long-range>#</a></h3><p><em>Pramuka Medaranga Sooriya Patabandige, Rajashekar Reddy Chinthalapani, Wenqing Yan, Prabal Dutta <em>et al.</em></em></p><p><strong>TL;DR</strong> — An autodyning receiver design reuses the transmit oscillator for self-mixing, eliminating a separate LO and achieving orders-of-magnitude better sensitivity than prior backscatter receivers without added hardware cost.</p><p><strong>Why notable</strong> — Long-range backscatter has been a persistent gap between battery-free IoT and practical deployment; this architecture achieves kilometer-scale range on microwatts of harvested energy, a result that could unlock new classes of batteryless sensors.</p><p><a href=https://doi.org/10.1145/3711875.3729164>→ Read paper</a></p></div><footer class=post-footer><ul class=post-tags><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/mobile-computing/>Mobile-Computing</a></li><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/edge-computing/>Edge-Computing</a></li><li><a href=https://pub.sqrt.fr/vincent/publish-assistant/tags/networking/>Networking</a></li></ul><nav class=paginav><a class=prev href=https://pub.sqrt.fr/vincent/publish-assistant/cloud-edge/digests/atc-2025/><span class=title>« Prev</span>
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