• Journal of IKEEE
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• v.26 no.3
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• pp.364-372
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• 2022

This paper proposes the design and implementation results for human and object classification systems utilizing frequency modulated continuous wave (FMCW) radar sensor. Such a system requires the process of radar sensor signal processing for multi-target detection and the process of deep learning for the classification of human and object. Since deep learning requires such a great amount of computation and data processing, the lightweight process is utmost essential. Therefore, binary neural network (BNN) structure was adopted, operating convolution neural network (CNN) computation in a binary condition. In addition, for the real-time operation, a hardware accelerator was implemented and verified via FPGA platform. Based on performance evaluation and verified results, it is confirmed that the accuracy for multi-target classification of 90.5%, reduced memory usage by 96.87% compared to CNN and the run time of 5ms are achieved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1873-1893
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• 2023

The development of lightweight, low energy and small-sized sensors incorporated with the wireless networks has brought about a phenomenal growth of Wireless Sensor Networks (WSNs) in its different fields of applications. Moreover, the routing of data is crucial in a wide number of critical applications that includes ecosystem monitoring, military and disaster management. However, the time-delay, energy imbalance and minimized network lifetime are considered as the key problems faced during the process of data transmission. Furthermore, only when the functionality of cluster head selection is available in WSNs, it is possible to improve energy and network lifetime. Besides that, the task of cluster head selection is regarded as an NP-hard optimization problem that can be effectively modelled using hybrid metaheuristic approaches. Due to this reason, an Improved Coyote Optimization Algorithm-based Clustering Technique (ICOACT) is proposed for extending the lifetime for making efficient choices for cluster heads while maintaining a consistent balance between exploitation and exploration. The issue of premature convergence and its tendency of being trapped into the local optima in the Improved Coyote Optimization Algorithm (ICOA) through the selection of center solution is used for replacing the best solution in the search space during the clustering functionality. The simulation results of the proposed ICOACT confirmed its efficiency by increasing the number of alive nodes, the total number of clusters formed with the least amount of end-to-end delay and mean packet loss rate.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.7
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• pp.1320-1326
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• 2017

Recently, IoT has been studying a lightweight protocol to satisfy device communication in a limited network environment. MQTT is a typical lightweight protocol. It supports small fixed headers to minimize overhead, and adopts publish/subscribe structure to guarantee real-time performance. However, MQTT does not support prioritization of important data and can not provide QoS in a specific IoT service. In this paper, we propose a message processing method to consider the priority of various IoT services in MQTT. In the proposed method, the priority flag is added to the fixed header of the MQTT in the node to transmit the message, and the broker confirms the priority of the corresponding message and processes it preferentially. Through experiment and evaluation, we confirmed the reduction of end-to-end delay between nodes according to priority.

• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.237-247
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• 2005

Ubiquitous Sensor Network(USN) is the very core of a technology for the Ubiquitous environments. There is the weakness from various security attacks such that tapping of sensor informations, flowing of abnormal packets, data modification and Denial of Service(DoS) etc. And it's required counterplan with them. Especially it's restricted by the capacity of battery and computing. By reasons of theses. positively, USN security technology needs the lightweighted design for the low electric energy and the minimum computing. In this paper, we propose lightweight USN mutual authentication methology based on trust model to solve above problems. The proposed authentication model can minimize the measure of computing because it authenticates the sensor nodes based on trust information represented by subjective logic model. So it can economize battery consumption and resultingly increse the lifetime of sensor nodes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.707-714
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• 2013

IoT, which can be regarded as an enhanced version of M2M communication technology, was proposed to realize intelligent thing to thing communications by utilizing Internet connectivity. Things in IoT are generally heterogeneous and resource constrained. Also such things are connected with each other over LLN(low power and lossy Network). Confidentiality, mutual authentication and message origin authentication are required to make a secure service in IoT. Security protocols used in traditional IP Networks cannot be directly adopted to resource constrained devices in IoT. Under the respect, a IETF standard group proposes to use lightweight version of DTLS protocol for supporting security services in IoT environments. However, the protocol can not cover up all of very constrained devices. To solve the problem, we propose a scheme which tends to support mutual authentication and session key agreement between devices that contain only a single crypto primitive module such as hash function or cipher function because of resource constrained property. The proposed scheme enhances performance by pre-computing a session key and is able to defend various attacks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4727-4741
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• 2019

The lightweight block cipher Piccolo adopts Generalized Feistel Network structure with 64 bits of block size. Its key supports 80 bits or 128 bits, expressed by Piccolo-80 or Piccolo-128, respectively. In this paper, we exploit the security of reduced version of Piccolo from the first round with the pre-whitening layer, which shows the vulnerability of original Piccolo. As a matter of fact, we first study some linear relations among the round subkeys and the properties of linear layer. Based on them, we evaluate the security of Piccolo-80/128 against the meet-in-the-middle attack. Finally, we attack 13 rounds of Piccolo-80 by applying a 5-round distinguisher, which requires $2^{44}$ chosen plaintexts, $2^{67.39}$ encryptions and $2^{64.91}$ blocks, respectively. Moreover, we also attack 17 rounds of Piccolo-128 by using a 7-round distinguisher, which requires $2^{44}$ chosen plaintexts, $2^{126}$ encryptions and $2^{125.49}$ blocks, respectively. Compared with the previous cryptanalytic results, our results are the currently best ones if considering Piccolo from the first round with the pre-whitening layer.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.93-98
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• 2021

Due to the development of deep learning and AI, the scale of the model has grown, and it has been integrated into other fields to blend into our lives. However, in environments with limited resources such as embedded devices, it is exist difficult to apply the model and problems such as power shortages. To solve this, lightweight methods such as clouding or offloading technologies, reducing the number of parameters in the model, or optimising calculations are proposed. In this paper, quantization of learned models is applied to ONNX models used in various framework interchange formats, neural network structure and inference performance are compared with existing models, and various module methods for quantization are analyzed. Experiments show that the size of weight parameter is compressed and the inference time is more optimized than before compared to the original model.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.17-22
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• 2022

In this paper, we design a lightweight embedded device that can support intelligent edge computing, and show that the device quickly detects an object in an image input from a camera device in real time. The proposed system can be applied to environments without pre-installed infrastructure, such as an intelligent video control system for industrial sites or military areas, or video security systems mounted on autonomous vehicles such as drones. The On-Device AI(Artificial intelligence) technology is increasingly required for the widespread application of intelligent vision recognition systems. Computing offloading from an image data acquisition device to a nearby edge device enables fast service with less network and system resources than AI services performed in the cloud. In addition, it is expected to be safely applied to various industries as it can reduce the attack surface vulnerable to various hacking attacks and minimize the disclosure of sensitive data.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.95-107
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• 2022

With the rapid development of Internet of Things sensor devices and big data processing techniques, Internet of Things sensor-based information systems have been applied in various industries. Depending on the industry in which the information systems are applied, the accuracy of the information derived can affect the industry's efficiency and safety. Therefore, security techniques that protect sensing data from security attacks and enable information systems to derive accurate information are essential. In this paper, we examine security threats targeting each processing step of an Internet of Things sensor-based information system and propose security mechanisms for each security threat. Furthermore, we present an Internet of Things sensor-based information system structure that is robust to security attacks by integrating the proposed security mechanisms. In the proposed system, by applying lightweight security techniques such as a lightweight encryption algorithm and obfuscation-based data validation, security can be secured with minimal processing delay even in low-power and low-performance IoT sensor devices. Finally, we demonstrate the feasibility of the proposed system by implementing and performance evaluating each security mechanism.

• Journal of the Korean Electrochemical Society
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• v.24 no.4
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• pp.93-99
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• 2021

In order to decrease the weight of stretchable energy storage devices, interest in developing lightweight materials to replace metal current collectors is increasing. In this study, nanofibers prepared by electrospinning a conductive polymer, PEDOT:PSS, were used as current collectors for lithium ion batteries. The nanofiber showed improved electrical conductivity by using DMSO, a dopant, and indicated a stretch rate of 30% or more from the elasticity evaluation result. In addition, the use of the nanofiber current collector facilitates penetration of the liquid electrolyte and exhibits the effect of increasing the electronic conductivity through the nanofiber network. The lithium-ion battery using the DMSO-doped PEDOT:PSS@PAM nanofiber current collector indicated a high discharge capacity of 135mAh g^-1, and indicated a high capacity retention rate of 73.5% after 1000 cycles. Thus, the excellent electrochemical stability and mechanical properties of conductive nanofibers showed that they can be used as lightweight current collectors for stretchable energy storage devices.