Sound event detection (SED) based on on-device machine learning (ML) presents considerable energy challenges for ultra-low-power sensor devices. In this paper, we propose DASH, a duration-aware SED system designed for energy-constrained sensor devices in domestic environments. As repeated inferences for continuous sound events lead to unnecessary energy consumption, DASH aims to minimize unnecessary inferences by predicting the duration of sound events. However, the variability of sound event durations across different environments and scenarios poses a major challenge in developing a responsive yet energy-efficient duration-aware SED system. To address this, DASH introduces three key solutions: (1) N-probability distribution-based event duration prediction, which identifies checkpoints where new inferences are likely needed; (2) Affinity-guided event classification, which performs low-energy affinity matching at checkpoints to determine whether ML inference is necessary; and (3) Interrupt blocking-enabling cycle-based device state control, which periodically checks for event presence with minimal energy consumption at non-checkpoint times. We implemented DASH on MSP430-based sensor devices deployed in real home environments. Experimental results demonstrate that DASH reduced energy consumption by approximately 97–98% compared to evaluation baselines, with only a 4.7% error rate.

Sound event detection (SED) using lightweight sensor device has recently gained attention as a practical means to capture context and activities especially in domestic environments. However, SED applications running on sensor device are severely constrained by device’s energy capacity. One solution is to offload a portion of inference to server for reducing runtime complexity, i.e., energy consumption, of sensor device. Offloading should consider the trade-off between computation and data transmission costs adequately; more computation on sensor device reduces data to be transmitted and vice versa. To address this challenge, we propose SEDAC (Sound Event Detection with Attention-based audio Compression), a novel technique for split learning in SED that compresses data from sensor device to offload less data. SEDAC compresses the input of SED models, or Mel spectrograms, with minimal computation in sensor device. Rather than directly compressing the input, SEDAC achieves data compression by selectively capturing the key parts of sound events using an attention mechanism. The scheme also modifies an existing loss function and employs knowledge distillation to mitigate potential loss of SED accuracy due to data compression. Our evaluation shows that SEDAC outperforms the state-of-the-art data compressive split learning schemes, up to about 30%. Furthermore, our real-world deployment demonstrates that sensor devices with SEDAC successfully operate with minimal energy and memory overhead.

The capability of energy harvesting application powered by indoor photovoltaic energy is severely affected by dynamic light environments. Accordingly, accurate understanding of the target environment and deploying energy harvesting sensors is practically very hard. In this article, we propose HarvAR, which manages photovoltaic energy harvesting sensors with mobile augmented reality (AR)-empowered techniques. HarvAR utilizes the error-prone RGBD data of mobile device to construct a digital twin (DT), performing depth error compensation and estimating the optical properties of the target space. Using the DT, the proposed system predicts the harvesting capability with low overhead, and recommends adequate locations for installing or relocating harvesting sensors. We implemented the HarvAR system and evaluated its accuracy and efficiency in three indoor environments. Our experiments show that DT configuration and harvesting prediction can be performed in minutes, compared to over 10 h using existing techniques, and harvesting prediction is provided with less than 20% error.

Energy-neutral operation (ENO) aims to provide near-perpetual device operation using energy harvested from ambient environments. Existing ENO techniques, however, have two key problems. The batteries used in harvesting devices have inherently limited lifespans, and the device experiences a long cold-start time when charging the battery. In this paper, we propose a long-lasting and highly responsive batteryless system, called RENO, to solve the problems that occur in energy harvesting devices. Using a supercapacitor to store energy, RENO maximizes the responsiveness in ENO especially running in dynamic harvesting environments such as indoor light energy harvesting. Combining the intermittent characteristics of power-neutral operation (PNO), RENO allows dual-mode operation of PNO and ENO, depending on the current harvesting capability. The device works as a PNO device when charging the energy storage, solving the ENO cold-start issue, while the harvested energy is efficiently managed with ENO. For this dual-mode operation, RENO provides hardware and software that handle the switch between PNO and ENO effectively. Application developers are provided with a well-defined API, which enables energy-efficient development of applications without detailed knowledge of the target hardware. Using the API, developers simply declare a task to be executed as either PNO or ENO, and the rest is handled by the system. The prototype system is implemented, and its functionality is evaluated in controlled environments. We also validate the proposed system with two real-world applications, proving the efficacy of dual-mode batteryless operation.

Duty cycle management is critical for the energy-neutral operation of batteryless devices. Many efforts have been made to develop an effective duty cycling method, including machine-learning-based approaches, but existing methods can barely handle the dynamic harvesting environments of batteryless devices. Specifically, most machine-learning-based methods require the harvesting patterns to be collected in advance, as well as manual configuration of the duty-cycle boundaries. In this article, we propose a configuration-free duty cycling scheme for batteryless devices, called CTRL, with which energy harvesting nodes tune the duty cycle themselves adapting to the surrounding environment without user intervention. This approach combines reinforcement learning (RL) with a control system to allow the learning algorithm to explore all possible search space automatically. The learning algorithm sets the target State of Charge (SoC) of the energy storage, instead of explicitly setting the target task frequency at a given time. The control system then satisfies the target SoC by controlling the duty cycle. An evaluation based on the real implementation of the system using publicly available trace data shows that CTRL outperforms state-of-the-art approaches, resulting in 40% less frequent power failures in energy-scarce environments while achieving more than ten times the task frequency in energy-rich environments.

Transiently-powered devices rely solely on energy harvesters. Such devices typically use capacitors to store the harvested energy, but recent systems employ supercapacitors to store energy for extended operations. In a supercapacitor-based transiently-powered system, the energy-efficient estimation of the state-of-charge (SoC) of a supercapacitor is critical for practical use of the system, due to its tight energy budget. Conventional voltage-based schemes for capacitors do not provide accuracy in SoC estimation for supercapacitors. Also, the supercapacitor-specific SoC estimation which exploits its charge redistribution characteristics provides an accuracy, but the scheme demands significant overhead, and thus, is not applicable to transiently-powered systems, such as wireless sensor nodes. In this article, we specify three requirements for estimating supercapacitor SoC that should be met to function in transiently-powered systems. We then propose a scheme that meets those requirements. The proposed scheme does not require additional hardware, has a low computation cost, and operates when the system is intermittently powered. Thus, the scheme fits the energy-efficient operation of transiently-powered systems. We implemented the proposed scheme in real hardware and evaluated its functionality and accuracy. The proposed scheme estimated the SoC of supercapacitors with high accuracy, in various configurations and use scenarios, while guaranteeing operations in a typical energy harvesting environment.

Multiple photovoltaic (PV) modules are often used to provide enhanced harvesting capability for light energy-based Internet of Things (IoT) devices. PV modules facing multiple directions can lead to a situational energy loss when parts of the modules are shaded. To address this issue, existing solutions exploit reconfigurable PV arrays to acquire the optimal configuration in a given situation. However, conventional techniques are not energy efficient in estimating the harvesting capability of PV modules, and require high computation to find the optimal PV array at runtime. In this article, we propose PVoT, an energy-efficient reconfigurable PV array, which maximizes the harvesting energy for indoor IoT devices. To this end, we propose the use of photoresistors to estimate the harvesting capability with minimal energy overhead. We also provide hardware and software schemes, which perform event-driven light change detection in an energy-efficient way. Furthermore, we develop a power imbalance threshold metric to quickly find the optimal PV array at runtime. We implemented a prototype PVoT with off-the-shelf components and accompanying software. Experiments with the prototype hardware showed that PVoT achieves a gain of up to 23.9% in harvested energy compared to the existing directly connected PV array scheme.

Cell imbalance in a multicell battery occurs over time due to varying operating environments. This imbalance leads to overall inefficiency in battery discharging due to the relatively weak cells in the battery. Reconfiguring the cells in the battery is one option for addressing the problem, but relevant circuits may lead to severe safety issues. In this article, we aim to optimize the discharge efficiency of a multicell battery using safety-supplemented hardware. To this end, we first design a cell string-level reconfiguration scheme that is safe in hardware operations and also provides scalability due to the low switching complexity. Second, we propose a machine learning-based run-time switch control that considers various battery-related factors, such as the state of charge, state of health, temperature, and current distributions. Specifically, by exploiting the deep reinforcement learning (DRL) technique, we train the complex relationship among the battery factors and derive the best switch configuration in run-time. We implemented a hardware prototype, validated its functionalities, and evaluated the efficacy of the DRL-based control policy. The experimental results showed that the proposed scheme, along with the optimization method, improves the discharge efficiency of multicell batteries. In particular, the discharge efficiency gain is maximized when the cells constituting the battery are unevenly distributed in terms of cell health and exposed temperature.

Unmanned aerial vehicles (UAVs) are often used in mission-critical applications, requiring a critical criterion in flight time. Unfortunately, severe power fluctuations, caused by specific flight patterns, degrade the deliverable capacity of the battery and hamper the flight time. A common approach to mitigating power fluctuations is to employ a hybrid energy storage system using a Li-ion battery with an ultracapacitor (UC). However, the conventional scheme poses inherent problems of low-energy density and power leakage due to the use of the UC and the supplementary hardware required for hybrid storage. In this article, we propose Hydrone, a reconfigurable battery architecture that maximizes the flight time of UAVs, overcoming the previous limitations. Hydrone addresses two key challenges that arise when hybrid energy storage is utilized in UAVs: 1) capacity loss and 2) power leakage. First, the proposed scheme compromises the capacity loss of hybrid storage by using a minimal capacity UC for use as a buffer to counteract the power fluctuations. Second, the power leakage of the hybrid battery is minimized by draining power from the UC only when it is necessary. To this end, the Hydrone architecture provides reconfigurability in hardware and offers two modes of battery operation, i.e., a battery-only mode and a hybrid mode. An appropriate operation is then selected at runtime depending on the flight situation and battery status. To switch modes, we employed a reinforcement learning-based switch control, reflecting the power fluctuation adequately on the flight and battery states. We implemented a hardware prototype to demonstrate the efficiency of Hydrone. Our extensive evaluation shows that the flight time of a UAV is prolonged up to 39% in our experiment setup.