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proceedings of the 23rd acm conference on Embedded Networked Sensor systems

作者： Saibing Han Yanling Bu Yanchao Zhao Lei Xie College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9798400714795

Image stitching is often affected by obstructions such as pedestrians and vehicles. Traditional methods often ignore obstructions or retain them, leading to artifacts. They also cause edge distortion due to fixed-perspective stitching. To address above issues, this poster presents IIS, an IMU-assisted Image Stitching method. It uses IMU data for perspective pre-alignment, projecting images to an intermediate perspective to reduce edge distortion. It simultaneously removes obstructions and mitigating their impact. A weighted target scene stitching strategy is integrated to further enhance stitching quality. IIS effectively reduces artifacts and maintains high computational efficiency.

关键词： image stitching

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Where did my 256 GB go? A measurement Analysis of Storage Consumption on Smart Mobile Devices 21

Where did my 256 GB go? A Measurement Analysis of Storage Co...

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2021 acm sigmetrics / International conference on measurement and modeling of computer systems, sigmetrics 2021

作者： Bijlani, Ashish Ramachandran, Umakishore Campbell, Roy Georgia Institute of Technology Atlanta United States University of Illinois at Urbana-Champaign Urbana United States

ISBN: (纸本)9781450380720

This work presents the first-ever detailed and large-scale measurement analysis of storage consumption behavior of applications (apps) on smart mobile devices. We start by carrying out a five-year longitudinal static analysis of millions of Android apps to study the increase in their sizes over time and identify various sources of app storage consumption. Our study reveals that mobile apps have evolved as large monolithic packages that are packed with features to monetize/engage users and optimized for performance at the cost of redundant storage consumption. We also carry out a mobile storage usage study with 140 Android participants. We built and deployed a lightweight context-aware storage tracing tool, called cosmos, on each participant's device. Leveraging the traces from our user study, we show that only a small fraction of apps/features are actively used and usage is correlated to user context. Our findings suggest a high degree of app feature bloat and unused functionality, which leads to inefficient use of storage. Furthermore, we found that apps are not constrained by storage quota limits, and developers freely abuse persistent storage by frequently caching data, creating debug logs, user analytics, and downloading advertisements as needed. Finally, drawing upon our findings, we discuss the need for efficient mobile storage management, and propose an elastic storage design to reclaim storage space when unused. We further identify research challenges and quantify expected storage savings from such a design. We believe our findings will be valuable to the storage research community as well as mobile app developers. © 2021 Owner/Author.

关键词： Android (operating system)

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Mitigating In-Transit Vision Noise for Enhanced Vehicle Safety 25

Mitigating In-Transit Vision Noise for Enhanced Vehicle Safe...

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proceedings of the 23rd acm conference on Embedded Networked Sensor systems

作者： Yichen Luo Junzhou Chen Xinyu Chen Sidi Lu Computer Science William & Mary Williamsburg Virginia USA

ISBN: (纸本)9798400714795

Software-defined vehicles (SDVs) rely on cameras for intelligent and safety-critical applications but face challenges from dynamic environmental noise, including weather and occlusions. Unlike static sensors, SDV cameras encounter noise patterns influenced by driving speed, a factor often overlooked in prior research. To address this gap, we conduct a quantitative analysis of the in-transit noise impact using data from public datasets, the CARLA simulator, a robotic vehicle, and a real vehicle. Our findings suggest that maintaining a speed below 40km/h may serve as a threshold for ensuring reliable camera-based applications under noisy urban conditions. In addition, we propose TransitNet, a novel model designed to mitigate in-transit camera noise and enhance driving safety, particularly at higher speeds. Compared to multiple baselines, experimental results show that TransitNet improves the F-measure by 5.1%, mAP@50 by 3.6%, and increases FPS by 56.7% across all datasets. We also provide detailed observations and insights from extensive testing.

关键词： software-defined vehicles

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INT-MC: Low-Overhead In-Band Network-Wide Telemetry Based on Matrix Completion 25

INT-MC: Low-Overhead In-Band Network-Wide Telemetry Based on...

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Abstracts of the 2025 acm sigmetrics International conference on measurement and modeling of computer systems

作者： Xin Zeng Kun Xie Yanbiao Li Ke Xu Gaogang Xie Jigang Wen Yinchuan Cong Wei Liang College of Computer Science and Electronic Engineering Hunan University Changsha China Computer Network Information Center Chinese Academy of Sciences Beijing China and School Computer Science and Technology University of Chinese Academy of Sciences Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan Hunan China College of Computer Science and Electronic Engineering Hunan University Changsha Hunan China

ISBN: (纸本)9798400715938

In-band Network Telemetry (INT) enhances real-time, high-resolution network monitoring capabilities by incorporating fine-grained internal state information into packets. Utilizing INT for network-wide visualization can significantly bolster network management and operation. Although existing studies have made significant contributions, they have not been able to simultaneously meet the following two objectives: 1) Comprehensive visibility of the network's internal state, which refers to obtaining information on all switch ports and their corresponding link states; 2) Low measurement overhead, which involves reducing measurement bandwidth overhead and minimizing the impact of the measurement process on the network. We designed INT-MC to meet both of these objectives. INT-MC is an efficient and cost-effective network-wide telemetry solution based on matrix completion. By modeling link metadata as a matrix and leveraging its low-rank property, INT-MC selectively measures certain links. It then employs matrix completion algorithms to infer information about unmeasured links, thereby achieving low-overhead network-wide telemetry. Designing paths to cover the target links involves an NP-complete problem, and the high computational complexity may delay the measurement tasks. To circumvent this, we propose an improved scheme based on Eulerian digraph decomposition, transforming the path calculation problem into a high-information path selection problem, significantly reducing computational *** have implemented an INT-MC prototype within the NSFNet topology, consisting of 14 Tofino switches and 10 end-hosts, and conducted extensive experiments and evaluations. The results indicate that INT-MC incurs only 16% of the measurement overhead compared to existing network-wide telemetry solutions, while achieving nearly identical accuracy. Even under high-frequency measurements of 20 times per second, the bandwidth overhead of INT-MC is approximately 0.075% of the total bandwi

关键词： in-band network telemetry

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UMusic: In-car Occupancy Sensing via High-resolution UWB Power Delay Profile 25

UMusic: In-car Occupancy Sensing via High-resolution UWB Pow...

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proceedings of the 23rd acm conference on Embedded Networked Sensor systems

作者： Shuai Wang Yunze Zeng Vivek Jain Parth Pathak School of Computer Science and Engineering Southeast University Nanjing Jiangsu China Bosch Research Sunnyvale California USA Department of Computer Science George Mason University Fairfax Virginia USA

ISBN: (纸本)9798400714795

Occupancy sensing is essential for vehicle safety and security applications such as seat belt reminders, airbag deployment, intrusion detection, and child-left-behind alerts. This paper presents UMusic, a novel in-car occupancy sensing system that reuses the ultra-wideband (UWB) devices already installed for access control in modern vehicles. However, due to the compact size and metal structure, the in-car environment is full of reflected propagation paths, which cannot be precisely resolved even with UWB's wide-bandwidth feature. To overcome this challenge, UMusic introduces a reflected-path decomposition technique to extract a high-resolution power delay profile (PDP) from the channel impulse response (CIR) provided by commodity UWB devices, enabling precise environmental perception. By comparing PDPs in empty and occupied conditions, UMusic is able to detect the occupancy status in both a sedan and an SUV with multiple passengers across various scenarios. Our results show that UMusic achieves a 90.2% detection rate using a single CIR measurement collected within 50 ms, outperforming the state-of-the-art by 15.7%. When aggregating six consecutive CIR measurements, UMusic reaches 99.4% accuracy, demonstrating its effectiveness for real-world deployment.

关键词： power-delay profile

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Poster Abstract: Low-Cost Soil Sensing and Two-Level Classification for Early Stress Detection in Avocado Plants 25

Poster Abstract: Low-Cost Soil Sensing and Two-Level Classif...

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proceedings of the 23rd acm conference on Embedded Networked Sensor systems

作者： Abdulrahman Bukhari Bullo Mamo Mst. Shamima Hossain Ziliang Zhang Mohsen Karimi Daniel Enright Patricia Manosalva Hyoseung Kim Electrical and Computer Engineering Department University of California - Riverside Riverside CA USA Microbiology and Plant Pathology Department University of California - Riverside Riverisde CA USA Computer Science and Engineering Department University of California - Riverside Riverisde CA USA Electrical and Computer Engineering Department University of California - Riverside Riverisde CA USA

ISBN: (纸本)9798400714795

We present a systematic evaluation of low-cost soil sensors for early stress and disease detection in avocado plants. Our monitoring system was deployed across 72 plants divided into four treatment categories within a controlled greenhouse environment collecting data over six months. We developed a two-level hierarchical classifier leveraging soil electrical conductivity (EC) and moisture data to improve classification accuracy. The proposed classifier achieved 75--86% accuracy across different avocado genotypes, outperforming conventional machine learning approaches by over 20%. Our findings demonstrate that while low-cost sensors exhibit certain limitations in field conditions, strategic classification techniques can significantly enhance their utility for precision agriculture.

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Exploiting Operator-Level Concurrency Control to Guide Deployment for Real-Time Tasks in Edge AI Cluster 25

Exploiting Operator-Level Concurrency Control to Guide Deplo...

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proceedings of the 23rd acm conference on Embedded Networked Sensor systems

作者： Changyao Lin Ziyang Zhang Jie Liu School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang China NESLab Politecnico di Milano Milano Italy National Key Laboratory of Smart Farm Technologies and Systems Harbin Institute of Technology Harbin Heilongjiang China International Research Institute for Artificial Intelligence Harbin Institute of Technology Shenzhen Guangdong China

ISBN: (纸本)9798400714795

Existing task deployment frameworks for edge clusters optimize at the model-level, lacking fine-grained resource awareness and concurrency control, where the urgent tasks are frequently blocked and miss their deadlines. Therefore, we propose a multi-level collaborative deployment framework Coconut for real-time deep learning tasks in the typical heterogeneous edge GPU cluster. Coconut collaboratively optimizes model deployment and fine-grained concurrency control. To address the high complexity of multi-level collaborative optimization, we employ an efficient learning-based search algorithm. Based on the operator-level information, we also pre-train an accurate latency predictor for each device, enabling centralized optimization to further accelerate the search. We conduct a preliminary evaluation in an edge cluster and validate the effectiveness of Coconut.

关键词： edge computing

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Beaver: A High-Performance and Crash-Consistent File System Cache via PM-DRAM Collaborative Memory Tiering 25

Beaver: A High-Performance and Crash-Consistent File System ...

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Abstracts of the 2025 acm sigmetrics International conference on measurement and modeling of computer systems

作者： Qinglin Pan Ji Qi Jiatai He Heng Zhang Jiageng Yu Yanjun Wu Institute of Software Chinese Academy of Sciences Beijing China and University of Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China and Department of Computer Science The University of Hong Kong Hong Kong China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400715938

The in-memory cache layer is crucial in building a file system. Crash-consistency is highly desirable for applications running on the file system, ensuring that data is written in an all-or-none fashion during unexpected system failures or crashes. However, existing works fail to achieve both high read and write performance in constructing a crash-consistent cache layer. In this paper, we propose Beaver, a new in-memory file system cache that achieves both crash-consistency and high read/write performance. Beaver exploits a read/write distinguishable memory hierarchy involving both PM and DRAM. Beaver uses the PM to serve write operations for crash-consistency and high write performance, and uses the DRAM to serve read operations for high read performance. We also introduce a kprefetcher to asynchronously move data from PM to DRAM off the critical path of write operations. We extensively evaluated Beaver with the latest file system cache using real-world applications and popular benchmarks. Application evaluation results show that Beaver outperformed other file system caches by up to 16.1% and 11.0% on RocksDB and MinIO, respectively. Beaver's source code is available at https://***/PanQL/Beaver.

关键词： crash consistency

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SHADE-AD: An LLM-Based Framework for Synthesizing Activity Data of Alzheimer’s Patients 25

SHADE-AD: An LLM-Based Framework for Synthesizing Activity D...

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proceedings of the 23rd acm conference on Embedded Networked Sensor systems

作者： Heming Fu Hongkai Chen Shan Lin Guoliang Xing Department of Electrical and Computer Engineering Stony Brook University Stony Brook New York USA Department of Information Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798400714795

Alzheimer's Disease (AD) has become an increasingly critical global health concern, which necessitates effective monitoring solutions in smart health applications. However, the development of such solutions is significantly hindered by the scarcity of AD-specific activity datasets. To address this challenge, we propose SHADE-AD, a Large Language Model (LLM) framework for Synthesizing Human Activity Datasets Embedded with AD features. Leveraging both public datasets and our own collected data from 99 AD patients, SHADE-AD synthesizes human activity videos that specifically represent AD-related behaviors. By employing a three-stage training mechanism, it broadens the range of activities beyond those collected from limited deployment settings. We conducted comprehensive evaluations of the generated dataset, demonstrating significant improvements in downstream tasks such as Human Activity Recognition (HAR) detection, with enhancements of up to 79.69%. Detailed motion metrics between real and synthetic data show strong alignment, validating the realism and utility of the synthesized dataset. These results underscore SHADE-AD's potential to advance smart health applications by providing a cost-effective, privacy-preserving solution for AD monitoring.

关键词： large language model (LLM)

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Where did my 256 GB go? A measurement Analysis of Storage Consumption on Smart Mobile Devices

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proceedings OF THE acm ON measurement AND ANALYSIS OF COMPUTING systems 2021年第2期5卷 1–28页

作者： Bijlani, Ashish Ramachandran, Umakishore Campbell, Roy Georgia Inst Technol 266 Ferst Dr Atlanta GA 30313 USA Univ Illinois 201 North Goodwin Ave Urbana IL 61801 USA

This work presents the first-ever detailed and large-scale measurement analysis of storage consumption behavior of applications (apps) on smart mobile devices. We start by carrying out a five-year longitudinal static analysis of millions of Android apps to study the increase in their sizes over time and identify various sources of app storage consumption. Our study reveals that mobile apps have evolved as large monolithic packages that are packed with features to monetize/engage users and optimized for performance at the cost of redundant storage consumption. We also carry out a mobile storage usage study with 140 Android participants. We built and deployed a lightweight context-aware storage tracing tool, called COSMOS1, on each participant's device. Leveraging the traces from our user study, we show that only a small fraction of apps/features are actively used and usage is correlated to user context. Our findings suggest a high degree of app feature bloat and unused functionality, which leads to inefficient use of storage. Furthermore, we found that apps are not constrained by storage quota limits, and developers freely abuse persistent storage by frequently caching data, creating debug logs, user analytics, and downloading advertisements as needed. Finally, drawing upon our findings, we discuss the need for efficient mobile storage management, and propose an elastic storage design to reclaim storage space when unused. We further identify research challenges and quantify expected storage savings from such a design. We believe our findings will be valuable to the storage research community as well as mobile app developers.

关键词： storage management mobile smartphones

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