• KIPS Transactions on Software and Data Engineering
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• v.9 no.11
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• pp.377-386
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• 2020

Mobile cloud computing involves mobile or embedded devices as clients, and features small devices with constrained resource and low availability. Due to the fast expansion of smart phones and smart peripheral devices, researches on mobile cloud computing attract academia's interest more than ever. Computation offloading, or code offloading, enhances the performance of computation by migrating a part of computation of a mobile system to nearby cloud servers with more computational resources through wired or wireless networks. Code offloading is considered as one of the best approaches overcoming the limited resources of mobile systems. In this paper, we analyze the factors and the performance of code offloading, especially focusing on static program partitioning and data protection. We survey state-of-the-art researches on analyzed topics. We also describe directions for future research.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.5
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• pp.1071-1078
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• 2018

Recently, a family of lightweight block ciphers CHAM that has effective performance on resource-constrained devices is proposed. The CHAM uses a stateless-on-the-fly key schedule method which can reduce the key storage areas. Furthermore, the core design of CHAM is based on ARX(Addition, Rotation and XOR) operations which can enhance the computational performance. Nevertheless, we point out that the CHAM algorithm may be vulnerable to the fault injection attack which can reveal 4 round keys and derive the secret key from them. As a simulation result, the proposed fault injection attack can extract the secret key of CHAM-128/128 block cipher using about 24 correct-faulty cipher text pairs.

• ETRI Journal
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• v.32 no.5
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• pp.801-809
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• 2010

This paper presents a statistical model-based noise suppression approach for voice recognition in a car environment. In order to alleviate the spectral whitening and signal distortion problem in the traditional decision-directed Wiener filter, we combine a decision-directed method with an original spectrum reconstruction method and develop a new two-stage noise reduction filter estimation scheme. When a tradeoff between the performance and computational efficiency under resource-constrained automotive devices is considered, ETSI standard advance distributed speech recognition font-end (ETSI-AFE) can be an effective solution, and ETSI-AFE is also based on the decision-directed Wiener filter. Thus, a series of voice recognition and computational complexity tests are conducted by comparing the proposed approach with ETSI-AFE. The experimental results show that the proposed approach is superior to the conventional method in terms of speech recognition accuracy, while the computational cost and frame latency are significantly reduced.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.10
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• pp.1969-1975
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• 2015

The Internet of Things (IoT), in which resource constrained devices communicate with each other, requires a lightweight security protocol. In this paper, we propose a new message authentication scheme using a hash tree for lightweight message authentication in the Datagram Transport Layer Security (DTLS) protocol. The proposed scheme provides lightweight secure operations compared with those of the DTLS protocol. Besides, it provides more suitable performance than the DTLS protocol for an IoT environment, thanks to the reduced use of message authentication code.

• ETRI Journal
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• v.27 no.4
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• pp.341-354
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• 2005

The adaptation of video according to the heterogeneous and dynamic resource constraints on networks and devices, as well as on user preferences, is a promising approach for universal access and consumption of video content. For optimal adaptation that satisfies the constraints while maximizing the utility that results from the adapted video, it is necessary to devise a systematic way of selecting an appropriate adaptation operation among multiple feasible choices. This paper presents a general conceptual framework that allows the formulation of various adaptations as constrained optimization problems by modeling the relations among feasible adaptation operations, constraints, and utilities. In particular, we present the feasibility of the framework by applying it to a use case of rate adaptation of MPEG-4 video with an explicit modeling of adaptation employing a combination of frame dropping and discrete cosine transform coefficient dropping, constraint, utility, and their mapping relations. Furthermore, we provide a description tool that describes the adaptation-constraint-utility relations as a functional form referred to as a utility function, which has been accepted as a part of the terminal and network quality of service tool in MPEG-21 Digital Item Adaptation (DIA).

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.5
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• pp.107-112
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• 2020

Ultra-lightweight password CHAM is an algorithm with efficient addition, rotation and XOR operations on resource constrained devices. CHAM shows high computational performance, especially on IoT platforms. However, lightweight block encryption algorithms used on the Internet of Things may be vulnerable to side channel analysis. In this paper, we demonstrate the vulnerability to side channel attack by attempting a first power analysis attack against CHAM. In addition, a safe algorithm was proposed and implemented by applying a masking technique to safely defend the attack. This implementation implements an efficient and secure CHAM block cipher using the instruction set of an 8-bit AVR processor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5614-5633
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• 2018

Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) are both recognized as promising techniques, one is for solving the resource insufficient of mobile devices and the other is for powering the mobile device. Naturally, by integrating the two techniques, task will be capable of being executed by the harvested energy which makes it possible that less intrinsic energy consumption for task execution. However, this innovative integration is facing several challenges inevitably. In this paper, we aim at prolonging the battery life of mobile device for which we need to maximize the harvested energy and minimize the consumed energy simultaneously, which is formulated as residual energy maximization (REM) problem where the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device are all considered as key factors. To this end, we jointly optimize the offloading ratio, energy harvesting time, CPU frequency and transmission power of mobile device to solve the REM problem. Furthermore, we propose an efficient convex optimization and sequential unconstrained minimization technique based combining method to solve the formulated multi-constrained nonlinear optimization problem. The result shows that our joint optimization outperforms the single optimization on REM problem. Besides, the proposed algorithm is more efficiency.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.3
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• pp.245-251
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• 2023

Facial expression recognition plays a significant role in understanding human emotional states. With the advancement of AI and computer vision technologies, extensive research has been conducted in various fields, including improving customer service, medical diagnosis, and assessing learners' understanding in education. In this study, we develop a model that can infer emotions in real-time from a webcam using transfer learning with TensorFlow.js and MobileNet. While existing studies focus on achieving high accuracy using deep learning models, these models often require substantial resources due to their complex structure and computational demands. Consequently, there is a growing interest in developing lightweight deep learning models and transfer learning methods for restricted environments such as web browsers and edge devices. By employing MobileNet as the base model and performing transfer learning, our study develops a deep learning transfer model utilizing JavaScript-based TensorFlow.js, which can predict emotions in real-time using facial input from a webcam. This transfer model provides a foundation for implementing facial expression recognition in resource-constrained environments such as web and mobile applications, enabling its application in various industries.

• Journal of IKEEE
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• v.27 no.4
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• pp.531-536
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• 2023

This paper proposes a sliding window method with frame feature insertion for immediate attack detection on in-vehicle networks. This method guarantees real-time attack detection by labeling based on the attack status of the current frame. Experiments show that the proposed method improves detection performance by giving more weight to the current frame in CNN computation. The proposed model was designed based on a lightweight LeNet-5 architecture and it achieves 100% detection for DoS attacks. Additionally, by comparing the complexity with conventional models, the proposed model has been proven to be more suitable for resource-constrained devices like ECUs.

• Smart Structures and Systems
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• v.32 no.3
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• pp.179-193
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• 2023

This study explores an alternative to the existing centralized process for data anomaly detection in modern Internet of Things (IoT)-based structural health monitoring (SHM) systems. An edge intelligence framework is proposed for the early detection and classification of various data anomalies facilitating quality enhancement of acquired data before transmitting to a central system. State-of-the-art deep neural network pruning techniques are investigated and compared aiming to significantly reduce the network size so that it can run efficiently on resource-constrained edge devices such as wireless smart sensors. Further, depthwise separable convolution (DSC) is invoked, the integration of which with advanced structural pruning methods exhibited superior compression capability. Last but not least, quantization-aware training (QAT) is adopted for faster processing and lower memory and power consumption. The proposed edge intelligence framework will eventually lead to reduced network overload and latency. This will enable intelligent self-adaptation strategies to be employed to timely deal with a faulty sensor, minimizing the wasteful use of power, memory, and other resources in wireless smart sensors, increasing efficiency, and reducing maintenance costs for modern smart SHM systems. This study presents a theoretical foundation for the proposed framework, the validation of which through actual field trials is a scope for future work.