Publications

2025

1. EMSOFT

   Ember: Task Wakeup Sequence–Based Energy Optimization for Mobile Web Browsing

   Seonghoon Park, Jiwon Kim, Jeho Lee, and 1 more author

   In The ACM SIGBED International Conference on Embedded Software (EMSOFT 2025)

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   Existing Android systems exhibit energy inefficiency during mobile web browsing due to the lack of awareness of application-level context. Inferring such context from system-level data alone is challenging, but one promising opportunity is using the sequence of task wakeup events, where one task activates another. These sequences show correlation with the type of webpage being used. In this paper, we present \textttEmber, a lightweight and responsive power management system for mobile web browsing using only task wakeup sequences. Ember introduces a neural network–based approach to predict optimal CPU clamping values by addressing three key challenges: (1) embedding task names, given as natural-language strings, into meaningful vectors using a Word2Vec-based embedding scheme tailored for task wakeup sequences; (2) minimizing inference overhead with a touch-driven hierarchical inference method that combines lightweight logistic regression with high-accuracy neural networks to balance responsiveness and efficiency; and (3) adapting to within-page interaction dynamics through an interaction-adaptive clamping mechanism that adjusts constraints across different user interaction phases. Implemented on commercial Android smartphones, Ember reduced power consumption by 6.2%–31.2% across a wide range of webpages while maintaining user-perceived quality of experience (QoE).

2. EMSOFT

   SecureRide: Detecting Safety-threatening Behavior of E-Scooters Using Battery Information

   Jiwon Kim*, Geon Kim*, Jeho Lee, and 2 more authors

   In The ACM SIGBED International Conference on Embedded Software (EMSOFT 2025)

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   Reckless usage of electric (e-) scooters causes many injury accidents, raising critical safety concerns. Despite newly introduced regulations, specifically, speed limits and sidewalk driving prohibitions, the number of accidents increases due to the challenges in enforcement. Therefore, a reliable method to detect safety-threatening illegal behaviors of e-scooters is essential to mitigate this growing problem. In this paper, we propose SecureRide, a system that accurately detects illegal e-scooter behaviors, i.e., speeding violation and sidewalk riding, at runtime using only battery information, without the need for additional sensors. To this end, we first design a neural network-based illegal behavior predictor that takes sequences of three battery factors, i.e., voltage, current, and capacity, as inputs. The model architecture is optimized based on time constraints, target accuracy, and resource constraints of the target devices. Next, we devise a runtime detection strategy to achieve both high accuracy and low detection time. SecureRide operates in two different modes with predictors—lightweight-quick and complex-accurate models—depending on the driving situation, ensuring both high accuracy and low detection time. We extensively validate SecureRide based on actual driving experiments. Our results show that SecureRide detects illegal behaviors with an accuracy of up to 99.77% within 1.01 seconds.

3. MobiSys

   ARIA: Optimizing Vision Foundation Model Inference on Heterogeneous Mobile Processors for Augmented Reality

   Chanyoung Jung*, Jeho Lee*, Gunjoong Kim, and 3 more authors

   In Proceedings of the 23rd Annual International Conference on Mobile Systems, Applications and Services (MobiSys 2025)

   Abs PDF 🏆 Best Paper Award

   Mobile Augmented Reality (AR) applications demand high-quality, real-time visual prediction, including pixel-level depth and semantics, to enable immersive and context-aware user experiences. Recently, Vision Foundation Models (VFMs) offer strong generalization capabilities on diverse and unseen data, supporting scalable mobile AR experiences. However, deploying VFMs on mobile devices is challenging due to computational limitations, particularly in maintaining both prediction accuracy and real-time performance. In this paper, we present ARIA, the first system that enables on-device inference acceleration of a VFM. ARIA employs the heterogeneity of mobile processors through a parallel and selective inference scheme: full-frame prediction is periodically offloaded to a processor with high parallelism capability like GPU, while low-latency updates on dynamic regions are conducted via a specialized accelerator like NPU. Implemented and evaluated using mobile devices, ARIA achieved significant improvements in accuracy and deadline success rate on diverse real-world mobile AR scenarios.

2024

1. MobiCom

   Panopticus: Omnidirectional 3D Object Detection on Resource-constrained Edge Devices

   Jeho Lee, Chanyoung Jung, Jiwon Kim, and 1 more author

   In The 30th Annual International Conference on Mobile Computing and Networking (MobiCom 2024)

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   3D object detection with omnidirectional views enables safetycritical applications such as mobile robot navigation. Such applications increasingly operate on resource-constrained edge devices, facilitating reliable processing without privacy concerns or network delays. To enable cost-effective deployment, cameras have been widely adopted as a low-cost alternative to LiDAR sensors. However, the compute-intensive workload to achieve high performance of camera-based solutions remains challenging due to the computational limitations of edge devices. In this paper, we present Panopticus, a carefully designed system for omnidirectional and camera-based 3D detection on edge devices. Panopticus employs an adaptive multi-branch detection scheme that accounts for spatial complexities. To optimize the accuracy within latency limits, Panopticus dynamically adjusts the model’s architecture and operations based on available edge resources and spatial characteristics. We implemented Panopticus on three edge devices and conducted experiments across real-world environments based on the public self-driving dataset and our mobile 360° camera dataset. Experiment results showed that Panopticus improves accuracy by 62% on average given the strict latency objective of 33ms. Also, Panopticus achieves a 2.1× latency reduction on average compared to baselines.

2. IoTJ

   HarvAR: Mobile Augmented Reality-assisted Photovoltaic Energy Harvesting Sensor Management

   Daeyong Kim, Junick Ahn, Jiwon Kim, and 2 more authors

   IEEE Internet of Things Journal, 2024

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   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 paper, 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 hours using existing techniques, and harvesting prediction is provided with less than 20% error.

3. MobiSys

   Optimizing Profitability of E-Scooter Sharing System via Battery-aware Recommendation

   Jiwon Kim, Taewoong Jung, Yonghun Choi, and 2 more authors

   In Proceedings of the 22nd Annual International Conference on Mobile Systems, Applications and Services (MobiSys 2024)

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   In e-scooter sharing systems, users randomly select and use e-scooters based on inaccurate battery information. This simple rental policy leads to low profitability on two fronts. First, inaccurate battery information causes unexpected device shutdowns, causing negative user experiences and refunds. Second, randomly selected e-scooters increase operation costs for battery management. In this paper, we propose e-scooter recommendation system, EcoRide, which provides accurate battery estimation and profitable e-scooter selection to maximize profitability of sharing systems. To this end, we propose a battery estimation considering four factors, i.e., battery state, temperature, user weight, and road slope, that affect the available battery energy in e-scooter applications. We define a parameter, dynamic voltage threshold (DVT), to represent dynamically changing battery energy, and use it to estimate battery availability. Next, to achieve cost-effective e-scooter selection, we introduce a multi-agent reinforcement learning (MARL)-based technique to learn policies that minimize operation costs. We define sharing system operation as a MARL problem with an objective function based on battery management costs. To cope with unstable training due to a wide service area and multiple requests, a centralized training technique is adopted. The proposed battery estimation and e-scooter selection technique are validated through actual driving tests and a sharing system simulator, respectively. Additionally, our case study using open data from Washington D.C. demonstrates a profit gain of up to 68% with EcoRide.

2023

1. ELL

   DNN-based Temperature Prediction of Large-Scale Battery Pack

   Jiwon Kim, and Rhan Ha

   Electronics Letters, 2023

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   Temperature monitoring is critical for estimating the available capacity of Lithium-ion batteries. In electric vehicle applications using large-scale battery packs, monitoring individual cell temperature is challenging due to difficulties in sensor management. To address this issue, a sensor-less battery temperature prediction technique is proposed that ensures both accuracy and rapid runtime execution using deep learning. A deep neural network-based temperature prediction model is introduced that utilizes short sequences of battery voltage and discharge current. An adaptive sequence length strategy is then devised to ensure high accuracy and responsiveness, covering the non-identically distributed nature of the data. The proposed technique is experimentally validated with commercial batteries, verifying its accuracy and rapid execution.

2. PMC

   Detecting Structural Anomalies of Quadcopter UAVs based on LSTM Autoencoder

   Seunghyeok Jeon, Jaeyun Kang, Jiwon Kim, and 1 more author

   Pervasive and Mobile Computing (PMC), 2023

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   Detecting a structural anomaly, such as a damaged propeller or motor, is crucial for mission-critical operation of unmanned aerial vehicles (UAVs). The existing solutions often fail to detect structural anomalies because the pre-defined parameters required for the solution are limited in reflecting the flight pattern or the external environment, such as wind conditions. In this paper, we propose a method for detecting structural anomalies in quadcopter UAVs, using only regular data and specifically considering flight patterns and runtime flight conditions. To this end, we employ a long short-term memory (LSTM) autoencoder model to learn complex features of regular flight data. While flying the UAV, the trained model estimates the degree of outlierness of the incoming data and assesses abnormal behavior of UAV by adaptively considering its movement. This way, the proposed method accurately detects structural anomalies in UAVs regardless of the runtime environment or flight mission. Our experiment results with an off-the-shelf UAV show that the proposed approach detects diverse structural anomalies by an average of 98.6% specificity and 90.3% sensitivity.

2022

1. DAC

   Voltage prediction of drone battery reflecting internal temperature

   Jiwon Kim, Seunghyeok Jeon, Jaehyun Kim, and 1 more author

   In Proceedings of the 59th ACM/IEEE Design Automation Conference (DAC 2022)

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   Drones are commonly used in mission-critical applications, and the accurate estimation of available battery capacity before flight is critical for reliable and efficient mission planning. To this end, the battery voltage should be predicted accurately prior to launching a drone. However, in drone applications, a rise in the battery’s internal temperature changes the voltage significantly and leads to challenges in voltage prediction. In this paper, we propose a battery voltage prediction method that takes into account the battery’s internal temperature to accurately estimate the available capacity of the drone battery. To this end, we devise a temporal temperature factor (TTF) metric that is calculated by accumulating time series data about the battery’s discharge history. We employ a machine learning-based prediction model, reflecting the TTF metric, to achieve high prediction accuracy and low complexity. We validated the accuracy and complexity of our model with extensive evaluation. The results show that the proposed model is accurate with less than 1.5% error and readily operates on resource-constrained embedded devices.

2. EMSOFT

   DynLiB: Maximizing Energy Availability of Hybrid Li-Ion Battery Systems

   Jiwon Kim, Sungwoo Baek, Seunghyeok Jeon, and 1 more author

   In The ACM SIGBED International Conference on Embedded Software (EMSOFT 2022)

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   Battery-powered devices commonly use Li-ion batteries due to their high energy density. Unfortunately, low ambient temperature and high discharge current significantly affect the available capacity of Li-ion batteries at runtime. One solution to handle this issue is to construct a hybrid energy storage (HES) system, taking advantage of the different performance characteristics of various Li-ion batteries. Conventional HES techniques often configure systems without considering the hardware overhead and capacity loss of batteries. Moreover, dynamic energy management in HES was not considered adequately due to severe power leakage. In this article, we propose DynLiB, a reconfigurable HES architecture that provides high energy availability in various runtime environments. DynLiB constructs HES with only two types of batteries, the main battery and the auxiliary battery, to reduce the operational cost of the hardware. The capacity of each battery is determined based on the application profile that characterizes the runtime factors. DynLiB supports energy transfer between batteries so that the available energy is maximized by dynamically transferring energy between the batteries, depending on the runtime environment. We implemented a prototype system to validate the efficacy of DynLiB. The experimental results showed that DynLiB achieved an average energy gain of 27.2% compared to a single-type battery system in various runtime environments.

3. CODES+ISSS

   PVoT: Reconfigurable Photovoltaic Array for Indoor Light Energy-Powered Batteryless Devices

   Jiwon Kim*, Eunyeong Kim*, Seunghyeok Jeon, and 3 more authors

   In The International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS 2022)

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   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.

2020

1. EMSOFT

   Optrone: Maximizing Performance and Energy Resources of Drone Batteries

   Jiwon Kim, Yonghun Choi, Seunghyeok Jeon, and 2 more authors

   In The ACM SIGBED International Conference on Embedded Software (EMSOFT 2020)

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   The optimal use of batteries in drones is a critical issue for achieving both reliable operation and maximum flight time. The key is to acquire accurate information about the state-of-charge (SoC) of the battery in runtime. Drones typically employ series-connected lithium-ion polymer (Li-Po) battery cells, whose SoC is affected by many environmental factors as well as flight patterns. In this paper, we propose a scheme, called Optrone, which maximizes the flight time of a drone while safely using the battery. Understanding the implications of the factors affecting the SoC of the drone’s battery pack, we propose a three-level SoC, which is a metric for representing the SoC of a battery in runtime. We also provide various operating policies to users to improve the safety and efficiency of operating the drone. We implemented the prototype hardware and software for Optrone, and validated its operation in controlled and real environments. The experimental results in a controlled environment showed that the proposed three-level SoC poses less than 3% error and the operating policies achieved a flight time gain of 19.4%, while guaranteeing battery safety. We also observed a flight time gain of about 10% in real outdoor experiments, where the user rightly adheres to the advised Optrone policy.

2. CODES+ISSS

   Hydrone: Reconfigurable energy storage for UAV applications

   Jiwon Kim, Sungwoo Baek, Yonghun Choi, and 2 more authors

   In The International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS 2020)

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   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.

3. EMSOFT

   Optimizing discharge efficiency of reconfigurable battery with deep reinforcement learning

   Seunghyeok Jeon, Jiwon Kim, Junick Ahn, and 1 more author

   In The ACM SIGBED International Conference on Embedded Software (EMSOFT 2020)

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   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.