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检索条件"任意字段=Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems"

共 105 条记录，以下是11-20 订阅

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On-NAS: On-Device Neural Architecture Search on Memory-Constrained Intelligent embedded systems 21

On-NAS: On-Device Neural Architecture Search on Memory-Const...

引用

21st acm conference on embedded networked sensor systems (SenSys)

作者： Kim, Bosung Lee, Seulki Ulsan Natl Inst Sci & Technol Ulsan South Korea

ISBN: (纸本)9798400704147

We introduce On-NAS, a memory-efficient on-device neural architecture search (NAS) solution, that enables memory-constrained embedded devices to find the best deep model architecture and train it on the device. Based on the cell-based differentiable NAS, it drastically curtails the massive memory requirement of architecture search, one of the major bottlenecks in realizing NAS on embedded devices. On-NAS first pre-trains a basic architecture block, called meta cell, by combining.. cells into a single condensed cell via two-fold meta-learning, which can flexibly evolve to various architectures, saving the device storage space.. times. Then, the offline-learned meta cell is loaded onto the device and unfolded to perform online on-device NAS via 1) expectation-based operation and edge pair search, enabling memory-efficient partial architecture search by reducing the required memory up to.. and../4 times, respectively, given.. candidate operations and.. nodes in a cell, and 2) step-by-step back-propagation that saves the memory usage of the backward pass of the..-cell architecture up to.. times. To the best of our knowledge, On-NAS is the first standalone NAS and training solution fully operable on embedded devices with limited memory. Our experiment results show that On-NAS effectively identifies optimal architectures and trains it on the device, on par with GPU-based NAS in both few-shot and full-task learning settings, e.g., even 1.3% higher accuracy on miniImageNet, while reducing the run-time memory and storage usage up to 20x and 4x, respectively.

关键词： Mobile Computing Neural Network Neural Architecture Search

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LifeLearner: Hardware-Aware Meta Continual Learning System for embedded Computing Platforms 21

LifeLearner: Hardware-Aware Meta Continual Learning System f...

引用

21st acm conference on embedded networked sensor systems (SenSys)

作者： Kwon, Young D. Chauhan, Jagmohan Jia, Hong Venieris, stylianos I. Mascolo, Cecilia Univ Cambridge Cambridge England Samsung AI Ctr Cambridge England

ISBN: (纸本)9798400704147

Continual Learning (CL) allows applications such as user personalization and household robots to learn on the fly and adapt to context. This is an important feature when context, actions, and users change. However, enabling CL on resource-constrained embedded systems is challenging due to the limited labeled data, memory, and computing capacity. In this paper, we propose LifeLearner, a hardware-aware meta continual learning system that drastically optimizes system resources (lower memory, latency, energy consumption) while ensuring high accuracy. Specifically, we (1) exploit meta-learning and rehearsal strategies to explicitly cope with data scarcity issues and ensure high accuracy, (2) effectively combine lossless and lossy compression to significantly reduce the resource requirements of CL and rehearsal samples, and (3) developed hardware-aware system on embedded and IoT platforms considering the hardware characteristics. As a result, LifeLearner achieves near-optimal CL performance, falling short by only 2.8% on accuracy compared to an Oracle baseline. With respect to the state-of-the-art (SOTA) Meta CL method, LifeLearner drastically reduces the memory footprint ( by 178.7x), end-to-end latency by 80.8-94.2%, and energy consumption by 80.9-94.2%. In addition, we successfully deployed LifeLearner on two edge devices and a microcontroller unit, thereby enabling efficient CL on resource-constrained platforms where it would be impractical to run SOTA methods and the far-reaching deployment of adaptable CL in a ubiquitous manner. Code is available at https://***/theyoungkwon/LifeLearner.

关键词： Continual Learning Meta Learning On-device Training Latent Replay Product Quantization Edge Computing Microcontrollers

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Demo abstract: A unified architecture for flexible radio power management in wireless sensor networks

Demo abstract: A unified architecture for flexible radio pow...

引用

SenSys'06: 4th International conference on embedded networked sensor systems

作者： Klues, Kevin Xing, Guoliang Lu, Chenyang Washington University in St. Louis United States

No abstract available

ISBN: (纸本)1595933433

No abstract available

关键词： Architecture Framework Radio power management Wireless sensor networks

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Demo abstract: A 6LoWPAN application environment

Demo abstract: A 6LoWPAN application environment

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5th acm International conference on embedded networked sensor systems, SenSys'07

作者： Tolle, Gilman Arch Rock Corporation 501 Second St. San Francisco CA 94107 United States

We are demonstrating a networking technology and application environment that connects highly-constrained low-power wireless embedded sensor networks with large-scale IP networks. This technology is based on the 6LoWP... 详细信息

ISBN: (纸本)9781595937636

关键词： Wireless sensor networks

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Demo abstract: A hierarchical location directory service across sensor and IP networks

Demo abstract: A hierarchical location directory service acr...

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SenSys'06: 4th International conference on embedded networked sensor systems

作者： Bhattacharya, Sangeeta Fok, Chien-Liang Lu, Chenyang Roman, Gruia-Catalin Department of Computer Science and Engineering Washington University in St. Louis United States

No abstract available

ISBN: (纸本)1595933433

No abstract available

关键词： Location directory Mobile agent sensor networks

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Quantized H_∞ Filter design for networked systems with Random sensor Delays

Quantized <i>H</i><sub>∞</sub> Filter design for Networked ...

引用

21st Chinese Control and Decision conference

作者： Che, Wei-Wei Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110004 Peoples R China

ISBN: (纸本)9781424427239

This paper considers the quantized H-infinity filtering problem of networked systems with random sensor delays. A quantized random delay model is adopted to study the relationship of the communication delay, quantization and the system performance. The resulting design guarantees that the error system exponentially mean-square stable and with a prescribed H-infinity performance bound. A numerical example is given to illustrated the effectiveness of the proposed H-infinity filtering strategy.

关键词： networked systems Quantization Random delays H-infinity filtering

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AvA: An Adaptive Audio Filtering Architecture for Enhancing Mobile, embedded, and Cyber-Physical systems 21

AvA: An Adaptive Audio Filtering Architecture for Enhancing ...

引用

21st acm/IEEE International conference on Information Processing in sensor Networks (IPSN)

作者： Xia, stephen Jiang, Xiaofan Columbia Univ New York NY 10027 USA

ISBN: (纸本)9781665496247

Audio is valuable in many mobile, embedded, and cyber-physical systems. We propose AvA, an acoustic adaptive filtering architecture, configurable to a wide range of applications and systems. By incorporating AvA into their own systems, developers can select which sounds to enhance or filter out depending on their application needs. AvA accomplishes this by using a novel adaptive beamforming algorithm called content-informed adaptive beamforming (CIBF), that directly uses detectors and sound models that developers have created for their own applications to enhance or filter out sounds. CIBF uses a novel three step approach to propagate gradients from a wide range of different model types and signal feature representations to learn filter coefficients. We apply AvA to four scenarios and demonstrate that AvA enhances their respective performances by up to 11.1%. We also integrate AvA into two different mobile/embedded platforms with widely different resource constraints and target sounds/noises to show the boosts in performance and robustness these applications can see using AvA.

关键词： Adaptation models Privacy Adaptive systems Filtering Array signal processing Detectors Filtering algorithms

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MSWiM 2018 - proceedings of the 21st acm International conference on Modeling, Analysis and Simulation of Wireless and Mobile systems

MSWiM 2018 - Proceedings of the 21st ACM International Confe...

引用

21st acm International conference on Modeling, Analysis and Simulation of Wireless and Mobile systems, MSWiM 2018

ISBN: (纸本)9781450359603

The proceedings contain 35 papers. The topics discussed include: FWB: funneling wider bandwidth algorithm for high performance data collection in wireless sensor networks;balancing energy harvesting and transmission scheduling in aggregation convergecast;routing with renewable energy management in wireless sensor networks;network alarm flood pattern mining algorithm based on multi-dimensional association;constructing an accurate and a high-performance power profiler for embedded systems and smartphones;maximizing mobiles energy saving through tasks optimal offloading placement in two-tier cloud;and delay-sensitive multiplayer augmented reality game planning in mobile edge computing.

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FIND: Faulty Node Detection for Wireless sensor Networks 09

FIND: Faulty Node Detection for Wireless Sensor Networks

引用

7th acm conference on embedded networked sensor systems (SENSYS)

作者： Guo, Shuo Zhong, Ziguo He, Tian Univ Minnesota Dept Elect & Comp Engn St Paul MN USA

ISBN: (纸本)9781605587486

Wireless sensor Networks (WSN) promise researchers a powerful instrument for observing sizable phenomena with fine granularity over long periods. Since the accuracy of data is important to the whole system's performance, detecting nodes with faulty readings is an essential issue in network management. As a complementary solution to detecting nodes with functionnal faults, this paper proposes FIND, a novel method to detect nodes with data faults that neither assumes a particular sensing model nor requires costly event injections. After the nodes in a network detect a natural event, FIND ranks the nodes based on their sensing readings as well as their physical distances from the event. FIND works for systems where the measured signal attenuates with distance. A node is considered faulty if there is a significant mismatch between the sensor data rank and the distance rank Theoretically, we show that average ranking difference is a provable indicator of possible data faults. FIND is extensively evaluated in simulations and two test bed experiments with up to 25 MicaZ nodes. Evaluation shows that FIND has a less than 5% miss detection rate and false alarm rate in most noisy environments.

关键词： Wireless sensor Networks Data Fault Detection

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Demo abstract: TinyTune, a collaborative sensor network musical instrument

Demo abstract: TinyTune, a collaborative sensor network musi...

引用

8th acm International conference on embedded networked sensor systems, SenSys 2010

作者： Newman, Blake Sanders, Joshua Hughes, Riley Jurdak, Raja School of ITEE University of Queensland St. Lucia QLD Australia CSIRO ICT Centre Pullenvale QLD Australia

ISBN: (纸本)9781450303446

This paper demonstrates the implementation of TinyTune, a collaborative musical instrument using sensor motes. The system implementation is distributed across multiple nodes and supports the basic elements of a musical instrument, such as pitch and octave selection. The communication design for realizing a collaborative musical instrument and the available user configuration options are then presented. Other topics of discussion include: the underlying system architecture, covering the advantages of our design choices;and the extensibility of the concept, which discusses how the nodes are configured in multi-instrument environments.

关键词： sensor nodes

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