摘要 :
In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transf...
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In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly and efficiently at times when it matters most and consequentially not wasting precious resources when nothing can be observed. On the other hand we utilize machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by quantizing and pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices. A field study with our system is running on the rockfall scarp of the Matterhorn H?rnligrat at 3500 m a.s.l. since 08/2018.
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摘要 :
In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transf...
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In natural hazard warning systems fast decision making is vital to avoid catastrophes. Decision making at the edge of a wireless sensor network promises fast response times but is limited by the availability of energy, data transfer speed, processing and memory constraints. In this work we present a realization of a wireless sensor network for hazard monitoring based on an array of event-triggered single-channel micro-seismic sensors with advanced signal processing and characterization capabilities based on a novel co-detection technique. On the one hand we leverage an ultra-low power, threshold-triggering circuit paired with on-demand digital signal acquisition capable of extracting relevant information exactly and efficiently at times when it matters most and consequentially not wasting precious resources when nothing can be observed. On the other hand we utilize machine-learning-based classification implemented on low-power, off-the-shelf microcontrollers to avoid false positive warnings and to actively identify humans in hazard zones. The sensors' response time and memory requirement is substantially improved by quantizing and pipelining the inference of a convolutional neural network. In this way, convolutional neural networks that would not run unmodified on a memory constrained device can be executed in real-time and at scale on low-power embedded devices. A field study with our system is running on the rockfall scarp of the Matterhorn Hörnligrat at 3500 m a.s.l. since 08/2018.
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摘要 :
If visions and forecasts of industry come true then we will be soon surrounded by billions of interconnected embedded devices. We will interact with them in a cyber-human symbiosis, they will not only observe us but also our envir...
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If visions and forecasts of industry come true then we will be soon surrounded by billions of interconnected embedded devices. We will interact with them in a cyber-human symbiosis, they will not only observe us but also our environment, and they will be part of many visible and ubiquitous objects around us. The information that is collectively gathered and analyzed is supposed to help us in our daily live, in making faithful decisions, but it will also directly be used for actuation and it will cause changes by means of local and global control loops.
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摘要 :
If visions and forecasts of industry come true then we will be soon surrounded by billions of interconnected embedded devices. We will interact with them in a cyber-human symbiosis, they will not only observe us but also our envir...
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If visions and forecasts of industry come true then we will be soon surrounded by billions of interconnected embedded devices. We will interact with them in a cyber-human symbiosis, they will not only observe us but also our environment, and they will be part of many visible and ubiquitous objects around us. The information that is collectively gathered and analyzed is supposed to help us in our daily live, in making faithful decisions, but it will also directly be used for actuation and it will cause changes by means of local and global control loops.
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摘要 :
Wireless sensors form an integral part of the Internet of Things (IoT), standing at the edge between the cyber and physical domains. Ac-quiring and transmitting environmental data is an energy-intensive workload, especially when c...
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Wireless sensors form an integral part of the Internet of Things (IoT), standing at the edge between the cyber and physical domains. Ac-quiring and transmitting environmental data is an energy-intensive workload, especially when considering networks spanning large buildings or even cities. Many works aim to integrate energy har-vesting into wireless sensors, providing them with a greater level of energy autonomy. Initially deployed for energy-rich outdoor environments, recent advances have allowed wireless sensors to efficiently utilize the reduced energy harvested in indoor lighting conditions. This demo introduces a harvesting-based Dual Proces-sor Platform, the DPP3e, designed for energy harvesting in indoor environments. It features various sensors for advanced indoor en-vironmental sensing, e.g. air quality measurements, a low-power display for immediate visual feedback, and a powerful micro controller for energy-efficient inference of Tensorflow models. Fur-thermore, it has two separate RF interfaces: a 2.4 GHz Bluetooth Low Energy (BLE) radio for short-range communication, and a sub-GHz transceiver for long-range communication. Using configurable power domains and advanced power management, it can sustain sending BLE packets every 5 seconds while consuming only 37 µ W.
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摘要 :
With the Internet of Things (IoT) large amounts of data can be gathered at the edge, centrally collected and subsequently utilized for various application domains. Efficient and reliable synchro-nous communication protocols are es...
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With the Internet of Things (IoT) large amounts of data can be gathered at the edge, centrally collected and subsequently utilized for various application domains. Efficient and reliable synchro-nous communication protocols are essential for automated data gathering, yet they typically require a stable energy supply. En-ergy harvesting enables long-term deployments, but it imposes widely varying energy budgets on each node in the network. To re-main efficient, synchronous protocols need to consider this energy variability. We propose a selective flooding protocol that employs low-energy communication rounds for all nodes and additional high-energy rounds only for nodes with high input power thus increasing their throughput. Low-energy rounds gather data with high reliability and maintain global network synchronization, while high-energy rounds use increased transmit power to overcome potentially-broken links that depend on low-power nodes. We eval-uate our proposed method on the FlockLab testbed and build a small network composed of standalone energy harvesting nodes. The average power consumption of almost 84 µW and 177 µW for low- and high-power nodes, respectively, are fully sustainable by indoor photovoltaic harvesting.
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摘要 :
In this demo abstract we present a custom-built low-power geophone sensor node which features on-device mountaineer classification using a convolutional neural network. The execution of such a processing-heavy algorithm on an embe...
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In this demo abstract we present a custom-built low-power geophone sensor node which features on-device mountaineer classification using a convolutional neural network. The execution of such a processing-heavy algorithm on an embedded platform is enabled by optimizing the memory requirement of the neural network through advanced quantization and pipelining techniques. As a result, real-time classification with low energy consumption can be achieved.
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摘要 :
In this demo abstract we present a custom-built low-power geophone sensor node which features on-device mountaineer classification using a convolutional neural network. The execution of such a processing-heavy algorithm on an embe...
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In this demo abstract we present a custom-built low-power geophone sensor node which features on-device mountaineer classification using a convolutional neural network. The execution of such a processing-heavy algorithm on an embedded platform is enabled by optimizing the memory requirement of the neural network through advanced quantization and pipelining techniques. As a result, real-time classification with low energy consumption can be achieved.
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摘要 :
In this demo we will demonstrate an implementation of the Event-based Low-power Wireless Bus (eLWB) based on an FSK version of Glossy for the CC430. The application is an event-triggered geophone platform that uses the CC430 ComBo...
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In this demo we will demonstrate an implementation of the Event-based Low-power Wireless Bus (eLWB) based on an FSK version of Glossy for the CC430. The application is an event-triggered geophone platform that uses the CC430 ComBoard and eLWB. This application generates periodic as well as event-based traffic that is transmitted using eLWB to a central sink (base station) running on a laptop. Different network traffic patterns can be observed depending on the behavior of the application w.r.t. the sensed data. Users can interact with the demo by generating seismic events (shaking, knocking, footsteps etc.). Furthermore, we will showcase our field deployments in the Swiss Alps based on the online web interface.
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摘要 :
In this demo we will demonstrate an implementation of the Event-based Low-power Wireless Bus (eLWB) based on an FSK version of Glossy for the CC430. The application is an event-triggered geophone platform that uses the CC430 ComBo...
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In this demo we will demonstrate an implementation of the Event-based Low-power Wireless Bus (eLWB) based on an FSK version of Glossy for the CC430. The application is an event-triggered geophone platform that uses the CC430 ComBoard and eLWB. This application generates periodic as well as event-based traffic that is transmitted using eLWB to a central sink (base station) running on a laptop. Different network traffic patterns can be observed depending on the behavior of the application w.r.t. the sensed data. Users can interact with the demo by generating seismic events (shaking, knocking, footsteps etc.). Furthermore, we will showcase our field deployments in the Swiss Alps based on the online web interface.
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