• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.8
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• pp.3224-3242
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• 2020

Internet of Things (IoT) networks composed of a large number of sensors and actuators generate a huge volume of data and control commands, which should be enforced by strong data reliability. The end-to-end data reliability of IoT networks is an essential industrial enabler. Blockchain technology can provide strong data reliability and integrity within IoT networks. We designed a lightweight end-to-end blockchain network that applies to common IoT applications. Its enhanced modular architecture and lightweight consensus mechanism guarantee its practical applicability for general IoT applications. In addition, the proposed blockchain network is highly software compatible because it adopts the Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves its practicability.

• ETRI Journal
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• v.37 no.1
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• pp.186-196
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• 2015

In resource-constrained, low-cost, radio-frequency identification (RFID) sensor-based mobile ad hoc networks (MANETs), ensuring security without performance degradation is a major challenge. This paper introduces a novel combination of steps in lightweight protocol integration to provide a secure network for RFID sensor-based MANETs using error-correcting codes (ECCs). The proposed scheme chooses a quasi-cyclic ECC. Key pairs are generated using the ECC for establishing a secure message communication. Probability analysis shows that code-based identification; key generation; and authentication and trust management schemes protect the network from Sybil, eclipse, and de-synchronization attacks. A lightweight model for the proposed sequence of steps is designed and analyzed using an Alloy analyzer. Results show that selection processes with ten nodes and five subgroup controllers identify attacks in only a few milliseconds. Margrave policy analysis shows that there is no conflict among the roles of network members.

• ETRI Journal
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• v.30 no.5
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• pp.685-695
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• 2008

The Network Mobility (NEMO) and IPv6 over Low power WPAN (6LoWPAN) protocols are the two most important technologies in current networking research and are vital for the future ubiquitous environment. In this paper, we propose a compressed packet header format to support the mobility of 6LoWPAN. Also, a Lightweight NEMO protocol is proposed to minimize the signaling overhead between 6LoWPAN mobile routers and 6LoWPAN gateways by using a compressed mobility header. Performance results show that our Lightweight NEMO protocol can minimize total signaling costs and handoff signaling delay.

• The KIPS Transactions:PartC
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• v.13C no.3 s.106
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• pp.375-382
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• 2006

As various kinds of embedded systems (or devices) become widely available, research on home network middleware which can access and control embedded home appliances are actively being progressed. However, there is a significant problem in applying the home network technology to embedded systems because of their limited storage space and low computing power. In this paper, we present a lightweight middleware for home network on embedded systems. Also, we propose a mechanism for mobility management which adopts the anycast technology.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.4
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• pp.314-322
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• 2021

Skeleton-based action recognition has attracted considerable attention in human action recognition. Recent methods for skeleton-based action recognition employ spatiotemporal graph convolutional networks (GCNs) and have remarkable performance. However, most of them have heavy computational complexity for robust action recognition. To solve this problem, we propose a shuffle graph convolutional network (SGCN) which is a lightweight graph convolutional network using pointwise group convolution rather than pointwise convolution to reduce computational cost. Our SGCN is composed of spatial and temporal GCN. The spatial shuffle GCN contains pointwise group convolution and part shuffle module which enhances local and global information between correlated joints. In addition, the temporal shuffle GCN contains depthwise convolution to maintain a large receptive field. Our model achieves comparable performance with lowest computational cost and exceeds the performance of baseline at 0.3% and 1.2% on NTU RGB+D and NTU RGB+D 120 datasets, respectively.

• Annual Conference of KIPS
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• 2021.05a
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• pp.358-361
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• 2021

In order to improve the accuracy of small target detection more effectively, this paper proposes an improved single shot detector (SSD) target detection and recognition method based on cspdarknet53, which introduces lightweight ECA attention mechanism and Feature Pyramid Network (FPN). First, the original SSD backbone network is replaced with cspdarknet53 to enhance the learning ability of the network. Then, a lightweight ECA attention mechanism is added to the basic convolution block to optimize the network. Finally, FPN is used to gradually fuse the multi-scale feature maps used for detection in the SSD from the deep to the shallow layers of the network to improve the positioning accuracy and classification accuracy of the network. Experiments show that the proposed target detection algorithm has better detection accuracy, and it improves the detection accuracy especially for small targets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.476-496
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• 2018

With the advancement and deployment of wireless communication techniques, wireless body area network (WBAN) has emerged as a promising approach for e-healthcare that collects the data of vital body parameters and movements for sensing and communicating wearable or implantable healthful related information. In order to avoid any possible rancorous attacks and resource abuse, employing lightweight ciphers is most effective to implement encryption, decryption, message authentication and digital signature for security of WBAN. As a typical lightweight cryptosystem with an extended sponge function framework, the PHOTON family is flexible to provide security for the RFID and other highly-constrained devices. In this paper, we propose a differential fault analysis to break three flavors of the PHOTON family successfully. The mathematical analysis and simulating experimental results show that 33, 69 and 86 random faults in average are required to recover each message input for PHOTON-80/20/16, PHOTON-160/36/36 and PHOTON-224/32/32, respectively. It is the first result of breaking PHOTON with the differential fault analysis. It provides a new reference for the security analysis of the same structure of the lightweight hash functions in the WBAN.

• Computers and Concrete
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• v.10 no.4
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• pp.379-390
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• 2012

In this paper, the mechanical strength of different lightweight mortars made with 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 and 100 percentage of scoria instead of sand and 0.55 water-cement ratio and 350 $kg/m^3$ cement content is investigated. The experimental result showed 7.9%, 16.7% and 49% decrease in compressive strength, tensile strength and mortar density, respectively, by using 100% scoria instead of sand in the mortar. The normalized compressive and tensile strength data are applied for artificial neural network (ANN) generation using generalized regression neural network (GRNN). Totally, 90 experimental data were selected randomly and applied to find the best network with minimum mean square error (MSE) and maximum correlation of determination. The created GRNN with 2 input layers, 2 output layers and a network spread of 0.1 had minimum MSE close to 0 and maximum correlation of determination close to 1.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.55-67
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• 2023

Lightweight face recognition models, as one of the most popular and long-standing topics in the field of computer vision, has achieved vigorous development and has been widely used in many real-world applications due to fewer number of parameters, lower floating-point operations, and smaller model size. However, few surveys reviewed lightweight models and reimplemented these lightweight models by using the same calculating resource and training dataset. In this survey article, we present a comprehensive review about the recent research advances on the end-to-end efficient lightweight face recognition models and reimplement several of the most popular models. To start with, we introduce the overview of face recognition with lightweight models. Then, based on the construction of models, we categorize the lightweight models into: (1) artificially designing lightweight FR models, (2) pruned models to face recognition, (3) efficient automatic neural network architecture design based on neural architecture searching, (4) Knowledge distillation and (5) low-rank decomposition. As an example, we also introduce the SqueezeFaceNet and EfficientFaceNet by pruning SqueezeNet and EfficientNet. Additionally, we reimplement and present a detailed performance comparison of different lightweight models on the nine different test benchmarks. At last, the challenges and future works are provided. There are three main contributions in our survey: firstly, the categorized lightweight models can be conveniently identified so that we can explore new lightweight models for face recognition; secondly, the comprehensive performance comparisons are carried out so that ones can choose models when a state-of-the-art end-to-end face recognition system is deployed on mobile devices; thirdly, the challenges and future trends are stated to inspire our future works.

• Journal of Industrial Technology
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• v.25 no.B
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• pp.211-220
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• 2005

The wireless sensor network consists of a number of sensor nodes which have physical constraints. Each sensor node senses surrounding environments and sends the sensed information to Sink. The inherent vulnerability in security of the sensor nodes has promoted the needs for the lightweight security protocol. In this paper, we propose a non-hierarchical sensor network and a security protocol that is suitable for monitoring the man-made objects such as bridges. Furthermore, we present the efficient way of setting the routing path by storing IDs, MAC(message authentication code) and the location information of the nodes, and taking advantage of the two node states, Sleep and Awake. This also will result in the reduced energy consuming rate.