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检索条件"任意字段=Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems"

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ePerceptive-Energy Reactive embedded Intelligence for Batteryless sensors 20

ePerceptive-Energy Reactive Embedded Intelligence for Batter...

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18th conference on embedded networked sensor systems

作者： Montanari, Alessandro Sharma, Manuja Jenkus, Dainius Alloulah, Mohammed Qendro, Lorena Kawsar, Fahim Nokia Bell Labs Cambridge England Univ Washington Seattle WA 98195 USA Newcastle Univ Newcastle Upon Tyne Tyne & Wear England Univ Cambridge Cambridge England Delft Univ Technol Delft Netherlands Nokia Bell Labs Cambridge Pervas Syst Dept Cambridge England

ISBN: (纸本)9781450375900

For long, we have studied tiny energy harvesters to liberate sensors from batteries. With remarkable progress in embedded deep learning, we are now re-imagining these sensors as intelligent compute nodes. Naturally, we are approaching a crossroad where sensor intelligence is meeting energy autonomy enabling maintenance-free swarm intelligence and unleashing a plethora of applications ranging from precision agriculture to ubiquitous asset tracking to infrastructure monitoring. One of the critical challenges, however, is to adapt intelligence fidelity in response to available energy to maximise the overall system availability. To this end, we present the design and implementation of ePerceptive: a novel framework for best-effort embedded intelligence, i.e., inference fidelity varies in proportion to the instantaneous energy supplied. ePerceptive operates on two core principles. First, it enables training a single deep neural network (DNN) to operate on multiple input resolutions without compromising accuracy or incurring memory overhead. Second, it modifies a DNN architecture by injecting multiple exits to guarantee valid, albeit lower-fidelity inferences in the event of energy interruption. the combination of these techniques offers a smooth adaptation between inference latency and recognition accuracy while matching the computational load to the available power budget. We report the manifestation of ePerceptive in designing batteryless cameras and microphones built with TI MSP430 MCU and off-the-shelf RF and solar energy harvesters. Our evaluation of these batteryless sensors with multiple vision and acoustic workloads suggest that the dynamic adaptation of ePerceptive can increase the inference throughput by up to 80% compared to a static baseline while ensuring a maximum accuracy drop of less than 6%.

关键词： Energy autonomous embedded intelligence batteryless devices

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Battery-less zero-maintenance embedded sensing at the mithræum of circus maximus 20

Battery-less zero-maintenance embedded sensing at the mithr...

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18th acm conference on embedded networked sensor systems, SenSys 2020

作者： Afanasov, Mikhail Bhatti, Naveed Anwar Campagna, Dennis Caslini, Giacomo Centonze, Fabio Massimo Dolui, Koustabh Maioli, Andrea Barone, Erica Alizai, Muhammad Hamad Siddiqui, Junaid Haroon Mottola, Luca Politecnico di Milano Italy RI.SE Sweden Lums Pakistan Air University Pakistan Microsoft Italy Ku Leuven Belgium Credit Suisse Poland

ISBN: (纸本)9781450375900

We present the design and evaluation of a 3.5-year embedded sensing deployment at the Mithræum of Circus Maximus, a UNESCO-protected underground archaeological site in Rome (Italy). Unique to our work is the use of energy harvesting through thermal and kinetic energy sources. the extreme scarcity and erratic availability of energy, however, pose great challenges in system software, embedded hardware, and energy management. We tackle them by testing, for the first time in a multi-year deployment, existing solutions in intermittent computing, low-power hardware, and energy harvesting. through three major design iterations, we find that these solutions operate as isolated silos and lack integration into a complete system, performing suboptimally. In contrast, we demonstrate the efficient performance of a hardware/software co-design featuring accurate energy management and capturing the coupling between energy sources and sensed quantities. Installing a battery-operated system alongside also allows us to perform a comparative study of energy harvesting in a demanding setting. Albeit the latter reduces energy availability and thus lowers the data yield to about 22% of that provided by batteries, our system provides a comparable level of insight into environmental conditions and structural health of the site. Further, unlike existing energy-harvesting deployments that are limited to a few months of operation in the best cases, our system runs with zero maintenance since almost 2 years, including 3 months of site inaccessibility due to a COVID19 lockdown. © 2020 acm.

关键词： Energy harvesting

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Capricorn: Towards Real-Time Rich Scene Analysis Using RF-Vision sensor Fusion 22

Capricorn: Towards Real-Time Rich Scene Analysis Using RF-Vi...

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proceedings of the 20th acm conference on embedded networked sensor systems

作者： Ziqi Wang Ankur Sarker Jason Wu Derek Hua Gaofeng Dong Akash Deep Singh Mani Srivastava University of California

ISBN: (纸本)9781450398862

Video scene analysis is a well-investigated area where researchers have devoted efforts to detect and classify people and objects in the scene. However, real-life scenes are more complex: the intrinsic states of the objects (e.g., machine operating states or human vital signals) are often overlooked by vision-based scene analysis. Recent work has proposed a radio frequency (RF) sensing technique, wireless vibrometry, that employs wireless signals to sense subtle vibrations from the objects and infer their internal states. We envision that the combination of video scene analysis with wireless vibrometry form a more comprehensive understanding of the scene, namely "rich scene analysis". However, the RF sensors used in wireless vibrometry only provide time series, and it is challenging to associate these time series data with multiple real-world objects. We propose a real-time RF-vision sensor fusion system, Capricorn, that efficiently builds a cross-modal correspondence between visual pixels and RF time series to better understand the complex natures of a scene. the vision sensors in Capricorn model the surrounding environment in 3D and obtain the distances of different objects. In the RF domain, the distance is proportional to the signal time-of-flight (ToF), and we can leverage the ToF to separate the RF time series corresponding to each object. the RF-vision sensor fusion in Capricorn brings multiple benefits. the vision sensors provide environmental contexts to guide the processing of RF data, which helps us select the most appropriate algorithms and models. Meanwhile, the RF sensor yields additional information that is originally invisible to vision sensors, providing insight into objects' intrinsic states. Our extensive evaluations show that Capricorn real-timely monitors multiple appliances' operating status with an accuracy of 97%+ and recovers vital signals like respirations from multiple people. A video (https://***/b-5nav3Fi78) demonstrates the capabil

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milliEgo: Single-chip mmWave Radar Aided Egomotion Estimation via Deep sensor Fusion 20

milliEgo: Single-chip mmWave Radar Aided Egomotion Estimatio...

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18th conference on embedded networked sensor systems

作者： Lu, Chris Xiaoxuan Saputra, Muhamad Risqi U. Zhao, Peijun Almalioglu, Yasin de Gusmao, Pedro P. B. Chen, Changhao Sun, Ke Trigoni, Niki Markham, Andrew Univ Edinburgh Edinburgh Midlothian Scotland Univ Oxford Oxford England Univ Calif San Diego San Diego CA 92103 USA

ISBN: (纸本)9781450375900

Robust and accurate trajectory estimation of mobile agents such as people and robots is a key requirement for providing spatial awareness for emerging capabilities such as augmented reality or autonomous interaction. Although currently dominated by optical techniques e.g., visual-inertial odometry, these suffer from challenges with scene illumination or featureless surfaces. As an alternative, we propose milliEgo, a novel deep-learning approach to robust egomotion estimation which exploits the capabilities of low-cost mmWave radar. Although mmWave radar has a fundamental advantage over monocular cameras of being metric i.e., providing absolute scale or depth, current single chip solutions have limited and sparse imaging resolution, making existing point-cloud registration techniques brittle. We propose a new architecture that is optimized for solving this challenging pose transformation problem. Secondly, to robustly fuse mmWave pose estimates with additional sensors, e.g. inertial or visual sensors we introduce a mixed attention approach to deep fusion. through extensive experiments, we demonstrate our proposed system is able to achieve 1.3% 3D error drift and generalizes well to unseen environments. We also show that the neural architecture can be made highly efficient and suitable for real-time embedded applications.

关键词： Egomotion Estimation Indoor localization Millimeter wave radar

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Continuous Sensing on Intermittent Power 19

Continuous Sensing on Intermittent Power

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19th acm/IEEE International conference on Information Processing in sensor Networks (IPSN)

作者： Majid, Amjad Yousef Schilder, Patrick Langendoen, Koen Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781728154978

the main obstacles to achieve truly ubiquitous sensing are (i) the limitations of battery technology - batteries are short-lived;hazardous, bulky, and costly - and (ii) the unpredictability of ambient power. the latter causes sensors 10 operate intermittently;violation the availability requirements of many real-world applications. In this paper, we present the Coalesced Intermittent sensor (CIS), art intermittently-powered "sensor" that senses continuously! Although a single node will frequently be off charging, a group of nodes can in principle sense 24/7 provided that their awake limes are spread apart. As communication is 100 expensive, we rely on inherent component variations that induce small differences in power cycles. this basic assumption has been verified through measurements of different nodes and power sources. However, desynchronizing nodes is not enough. An important finding is that a CIS designed for certain (minimal) energy conditions will become synchronized when the available energy exceeds the design point Nodes employing a sleep mode (to extend their availability) do wake up collectively at some event, process it, and return 10 charging as the remaining energy is typically too low to handle another event. this results in multiple responses (bad) and missing subsequent events (worse) due to the synchronized charging. To counter this undesired behavior we designed an algorithm to estimate the number of active neighbors and respond proportionally to an event. We show that when intermittent nodes randomize their responses to events, in favorable energy conditions, the CIS reduces the duplicated captured events by 50% and increases the percentage of capturing entire bursts above 85%.

关键词： embedded systems Energy harvesting Ubiquitous computing Intermittent systems

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Activity recognition through intermittent distributed processing by energy harvesting PIR sensors: Demo abstract 20

Activity recognition through intermittent distributed proces...

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18th acm conference on embedded networked sensor systems, SenSys 2020

作者： Misaki, Shinya Stirapongsasuti, Sopicha Matsui, Tomokazu Suwa, Hirohiko Yasumoto, Keiichi Nara Institute of Science and Technology Ikoma Nara Japan

ISBN: (纸本)9781450375900

As an increasing demand for human activity monitoring in many smart services such as elderly monitoring, there is a keen need of an activities of daily living (ADL) recognition system. which can be easily deployed in ordinary homes and does not require periodic maintenance such as battery replacement for long time. In this paper, we propose an ADL recognition system which can run continuously without feeding power from outlets by intermittent sensing and distributed processing of energy harvesting (EH) sensor modules. Specifically, we have designed and developed an EH sensor node composed of (i) a micro-controller board with an analog PIR sensor which senses human activity as analog signals and form a BLE mesh network with other sensor nodes and (ii) an energy harvest module with solar panels and a rechargeable battery. We have also implemented a simple distributed random forest (RF) classifier consisting of multiple RF classifiers trained independently and running on different nodes which exchange the classification results with each other via BLE and make a final decision based on majority vote. through experiments with five sensor nodes deployed in our smart home testbed, the distributed RF classifier classified the collected data of up to five different activities with average accuracy of over 90%. © 2020 acm.

关键词： sensor nodes

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Neuroplex: Learning to Detect Complex Events in sensor Networks through Knowledge Injection 20

Neuroplex: Learning to Detect Complex Events in Sensor Netwo...

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18th conference on embedded networked sensor systems

作者： Xing, Tianwei Garcia, Luis Vilamala, Marc Roig Cerutti, Federico Kaplan, Lance Preece, Alun Srivastava, Mani Univ Calif Los Angeles Los Angeles CA 90095 USA Univ Southern Calif ISI Marina Del Rey CA USA Cardiff Univ Cardiff S Glam Wales Univ Brescia Brescia Italy CCDC Army Res Lab Adelphi MD USA

ISBN: (纸本)9781450375900

Despite the remarkable success in a broad set of sensing applications, state-of-the-art deep learning techniques struggle with complex reasoning tasks across a distributed set of sensors. Unlike recognizing transient complex activities (e.g., human activities such as walking or running) from a single sensor, detecting more complex events with larger spatial and temporal dependencies across multiple sensors is extremely difficult, e.g., utilizing a hospital's sensor network to detect whether a nurse is following a sanitary protocol as they traverse from patient to patient. Training a more complicated model requires a larger amount of data-which is unrealistic considering complex events rarely happen in nature. Moreover, neural networks struggle with reasoning about serial, aperiodic events separated by large quantities in the spatial-temporal dimensions. We propose Neuroplex, a neural-symbolic framework that learns to perform complex reasoning on raw sensory data with the help of high-level, injected human knowledge. Neuroplex decomposes the entire complex learning space into explicit perception and reasoning layers, i.e., by maintaining neural networks to perform low-level perception tasks and neurally reconstructed reasoning models to perform high-level, explainable reasoning. After training the neurally reconstructed reasoning model using human knowledge, Neuroplex allows effective end-to-end training of perception models with an additional semantic loss using only sparse, high-level annotations. Our experiments and evaluation show that Neuroplex is capable of learning to efficiently and effectively detect complex events-which cannot be handled by state-of-the-art neural network models. During the training, Neuroplex not only reduces data annotation requirements by 100x, but also significantly speeds up the learning process for complex event detection by 4x.

关键词： Neural symbolic system Resource-efficient learning Neural networks Complex event detection Mobile sensing

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Shepherd: A Portable Testbed for the Batteryless IoT 19

Shepherd: A Portable Testbed for the Batteryless IoT

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17th acm conference on embedded networked sensor systems (SenSys)

作者： Geissdoerfer, Kai Chwalisz, Mikolaj Zimmerling, Marco Tech Univ Dresden Networked Embedded Syst Lab Dresden Germany TU Berlin Telecommun Networks Grp Berlin Germany

ISBN: (纸本)9781450369503

Collaboration of batteryless nodes is essential to their success in replacing traditional battery-based systems. Energy-harvesting sensor nodes experience spatio-temporal fluctuations of energy availability. these fluctuations become especially critical when sensor nodes do not have sufficient energy storage to compensate for them. Understanding the challenges and opportunities of operating groups of batteryless sensor nodes requires to record and reproduce spatio-temporal characteristics of real energy environments. We thus present SHEPHERD, a testbed for the batteryless IoT. SHEPHERD allows to record synchronized energy traces with a resolution of 3 mu A and 50 mu A at a rate of 100 kHz, and to faithfully replay these traces to any number of sensor nodes to study their behavior. We release SHEPHERD as an open-source tool for the community, facilitating research into time synchronization, wireless networking, and other distributed algorithms for batteryless systems.

关键词： Energy harvesting Batteryless Intermittent power Transient computing Intermittent networking Testbed Recording Emulation

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***: A Zero-Energy Neural Network Accelerator for embedded Sensing and Inference systems 19

***: A Zero-Energy Neural Network Accelerator for Embedded S...

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17th acm conference on embedded networked sensor systems (SenSys)

作者： Lee, Seulki Nirjon, Shahriar Univ N Carolina Chapel Hill NC 27515 USA

ISBN: (纸本)9781450369503

We introduce *** a co-processor architecture consisting of a main microcontroller (MCU) that executes scaled-down versions of a deep neural network(1) (DNN) inference task, and an accelerator microcontroller that is powered by harvested energy and follows the intermittent computing paradigm [76]. the goal of the accelerator is to enhance the inference performance of the DNN that is running on the main microcontroller. *** opportunistically accelerates the run-time performance of a DNN via one of its four acceleration modes: extended inference, expedited inference, ensemble inference, and latent training. To enable these modes, we propose two sets of algorithms: (1) energy and intermittence-aware DNN inference and training algorithms, and (2) a fast and high-precision adaptive fixed-point arithmetic that beats existing floating-point and fixed-point arithmetic in terms of speed and precision, respectively, and achieves the best of both. To evaluate ***, we implement low-power image and audio recognition applications and demonstrate that their inference speedup increases by 1.6x and 1.7x, respectively, and the inference accuracy increases by 10% and 16%, respectively, when compared to battery-powered single-MCU systems.

关键词： Accelerator Deep neural networks Zero energy Batteryless

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Moving Target Defense for embedded Deep Visual Sensing against Adversarial Examples 19

Moving Target Defense for Embedded Deep Visual Sensing again...

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17th acm conference on embedded networked sensor systems (SenSys)

作者： Song, Qun Yan, Zhenyu Tan, Rui Nanyang Technol Univ Interdisciplinary Grad Sch Energy Res Inst Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore

ISBN: (纸本)9781450369503

Deep learning-based visual sensing has achieved attractive accuracy but is shown vulnerable to adversarial example attacks. Specifically, once the attackers obtain the deep model, they can construct adversarial examples to mislead the model to yield wrong classification results. Deployable adversarial examples such as small stickers pasted on the road signs and lanes have been shown effective in misleading advanced driver-assistance systems. Many existing countermeasures against adversarial examples build their security on the attackers' ignorance of the defense mechanisms. thus, they fall short of following Kerckhoffs's principle and can be subverted once the attackers know the details of the defense. this paper applies the strategy of moving target defense (MTD) to generate multiple new deep models after system deployment, that will collaboratively detect and thwart adversarial examples. Our MTD design is based on the adversarial examples' minor transferability across different models. the post-deployment dynamically generated models significantly increase the bar of successful attacks. We also apply serial data fusion with early stopping to reduce the inference time by a factor of up to 5. Evaluation based on four datasets including a road sign dataset and two GPU-equipped Jetson embedded computing platforms shows the effectiveness of our approach.

关键词： Neural networks adversarial examples moving target defense

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