160 lines
8.4 KiB
Markdown
160 lines
8.4 KiB
Markdown
---
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title: MobiSys 2025 Digest
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venue: MobiSys
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year: 2025
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date: '2025-06-23'
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tags:
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- mobile-computing
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- edge-computing
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- networking
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paper_count: 12
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draft: false
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---
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12 papers selected.
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---
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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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729149)
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### WhisperFlow: speech foundation models in real time
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*Rongxiang Wang, Zhiming Xu 0001, Felix Xiaozhu Lin*
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729151)
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### ARIA: Optimizing Vision Foundation Model Inference on Heterogeneous Mobile Processors for Augmented Reality
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*Chanyoung Jung, Jeho Lee, Gunjoong Kim, Jiwon Kim *et al.**
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729161)
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### You Only Render Once: Enhancing Energy and Computation Efficiency of Mobile Virtual Reality
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*Xingyu Chen, Xinmin Fang, Shuting Zhang, Xinyu Zhang 0003 *et al.**
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729133)
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---
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### AutoDroid-V2: Boosting SLM-based GUI Agents via Code Generation
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*Hao Wen 0004, Shizuo Tian, Borislav Pavlov, Wenjie Du 0004 *et al.**
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729134)
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---
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### EdgeLoRA: An Efficient Multi-Tenant LLM Serving System on Edge Devices
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*Zheyu Shen, Yexiao He, Ziyao Wang, Yuning Zhang *et al.**
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729141)
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---
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### Never Start from Scratch: Expediting On-Device LLM Personalization via Explainable Model Selection
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*Haoming Wang 0002, Boyuan Yang 0001, Xiangyu Yin 0002, Wei Gao 0006*
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729132)
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---
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### Non-Line-of-Sight 3D Object Reconstruction via mmWave Surface Normal Estimation
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*Laura Dodds, Tara Boroushaki, Kaichen Zhou, Fadel Adib*
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729138)
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### Toward Spoofing-Resilient and Communication-Integrated MmWave Radar Sensing
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*Kun Qian 0004, Parth Pathak 0001*
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729155)
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---
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### Are LoRa Logical Channels Really Orthogonal? Practically Orthogonalizing Massive Logical Channels
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*Shiming Yu, Ziyue Zhang, Xianjin Xia, Yuanqing Zheng *et al.**
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729125)
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### Towards End-to-End Latency Guarantee in MEC Live Video Analytics with App-RAN Mutual Awareness
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*Juheon Yi, Goodsol Lee, Minkyung Jeong, Seokgyeong Shin *et al.**
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729139)
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---
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### Unraveling the Missing Link in Low-power Communication: An Autodyning Receiver Architecture that Achieves a Long Range
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*Pramuka Medaranga Sooriya Patabandige, Rajashekar Reddy Chinthalapani, Wenqing Yan, Prabal Dutta *et al.**
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**TL;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.
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**Why 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.
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[→ Read paper](https://doi.org/10.1145/3711875.3729164)
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