相关论文: InfiniWolf: Energy Efficient Smart Bracelet for Ed…
This paper presents InfiniteEn, a multi-source energy harvesting platform designed for the Internet of Batteryless Things (IoBT). InfiniteEn incorporates an efficient energy combiner to combine energy from different harvesting sources. The…
Extreme edge devices or Internet-of-thing nodes require both ultra-low power always-on processing as well as the ability to do on-demand sampling and processing. Moreover, support for IoT applications like voice recognition, machine…
Modern smartwatches often include photoplethysmographic (PPG) sensors to measure heartbeats or blood pressure through complex algorithms that fuse PPG data with other signals. In this work, we propose a collaborative inference approach that…
Recently, harvesting natural energy is gaining more attention than other conventional approaches for sustainable Internet-of-Things (IoT). System on chip (SoC) power requirement for the IoT and generating higher voltages on-chip is a…
A wide range of Internet of Things (IoT) applications require powerful, energy-efficient and flexible end-nodes to acquire data from multiple sources, process and distill the sensed data through near-sensor data analytics algorithms, and…
Smart sensors are an emerging technology that allows combining the data acquisition with the elaboration directly on the Edge device, very close to the sensors. To push this concept to the extreme, technology companies are proposing a new…
We propose a novel energy-aware federated learning (FL)-based system, namely SusFL, for sustainable smart farming to address the challenge of inconsistent health monitoring due to fluctuating energy levels of solar sensors. This system…
We present Warp, a hardware platform to support research in approximate computing, sensor energy optimization, and energy-scavenged systems. Warp incorporates 11 state-of-the-art sensor integrated circuits, computation, and an…
The growing number of low-power smart devices in the Internet of Things is coupled with the concept of "Edge Computing", that is moving some of the intelligence, especially machine learning, towards the edge of the network. Enabling machine…
In the ever-growing Internet of Things (IoT) landscape, smart power management algorithms combined with energy harvesting solutions are crucial to obtain self-sustainability. This paper presents an energy-aware adaptive sampling rate…
The growing demand for intelligent applications beyond the network edge, coupled with the need for sustainable operation, are driving the seamless integration of deep learning (DL) algorithms into energy-limited, and even energy-harvesting…
Environmental monitoring is a crucial component of the smart city infrastructure. It enables informed decision making which enhances sustainability, public health and urban planning. However, the large-scale deployments of the smart sensors…
Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery…
Wearable devices are a fast-growing technology with impact on personal healthcare for both society and economy. Due to the widespread of sensors in pervasive and distributed networks, power consumption, processing speed, and system…
Advances in low-power electronics and machine learning techniques lead to many novel wearable IoT devices. These devices have limited battery capacity and computational power. Thus, energy harvesting from ambient sources is a promising…
We present hardware/software techniques to intelligently regulate supply voltage and clock frequency of intermittently-computing devices. These devices rely on ambient energy harvesting to power their operation and small capacitors as…
Long-term wildlife tracking is crucial for biodiversity monitoring, but energy limitations pose challenges, especially for animal tags, where replacing batteries is impractical and stressful for the animal due to the need to locate,…
Despite advances in practical and multimodal fine-grained Human Activity Recognition (HAR), a system that runs entirely on smartwatches in unconstrained environments remains elusive. We present WatchHAR, an audio and inertial-based HAR…
This paper introduces an effective solution for retrofitting construction power tools with low-power IoT to enable accurate activity classification. We address the challenge of distinguishing between when a power tool is being moved and…
Automatic gym activity recognition on energy- and resource-constrained wearable devices removes the human-interaction requirement during intense gym sessions - like soft-touch tapping and swiping. This work presents a tiny and highly…