• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.221-237
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• 2016

The wireless body area networks (WBANs) consist of wearable computing devices and can support various healthcare-related applications. There exist two crucial issues when WBANs are utilized for healthcare applications. One is the protection of the sensitive biometric data transmitted over the insecure wireless channels. The other is the design of effective medical management mechanisms. In this paper, a secure medical information management system is proposed and implemented on a TinyOS-based WBAN test bed to simultaneously address these two issues. In this system, the electronic medical record (EMR) is bound to the biometric data with a novel fragile zero-watermarking scheme based on the modified visual secret sharing (MVSS). In this manner, the EMR can be utilized not only for medical management but also for data integrity checking. Additionally, both the biometric data and the EMR are encrypted, and the EMR is further protected by the MVSS. Our analysis and experimental results demonstrate that the proposed system not only protects the confidentialities of both the biometric data and the EMR but also offers reliable patient information authentication, explicit healthcare operation verification and undeniable doctor liability identification for WBANs.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.39 no.5
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• pp.225-231
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• 2002

This paper proposes a discriminative collision resolution scheme for the wireless Medium Access Control (MAC) protocols to support the Quality of Service (QoS) requirements of real-time applications. Our scheme deals with access requests in different ways depending on their delay requirements. In our scheme, a Collision Resolution Period (CRP) is used to quickly resolve collisions for the delay sensitive traffic in order to support their delay requirements. Performance analysis and simulation results show that our algorithm may successfully meet the delay requirements of real time applications by reducing access delays and collisions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2188-2203
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• 2021

With the rapid development of mobile Internet, smart phones have been widely popularized, among which Android platform dominates. Due to it is open source, malware on the Android platform is rampant. In order to improve the efficiency of malware detection, this paper proposes deep learning Android malicious detection system based on behavior features. First of all, the detection system adopts the static analysis method to extract different types of behavior features from Android applications, and extract sensitive behavior features through Term frequency-inverse Document Frequency algorithm for each extracted behavior feature to construct detection features through unified abstract expression. Secondly, Long Short-Term Memory neural network model is established to select and learn from the extracted attributes and the learned attributes are used to detect Android malicious applications, Analysis and further optimization of the application behavior parameters, so as to build a deep learning Android malicious detection method based on feature analysis. We use different types of features to evaluate our method and compare it with various machine learning-based methods. Study shows that it outperforms most existing machine learning based approaches and detects 95.31% of the malware.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.1-11
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• 2023

Federated learning (FL) is a ground breaking machine learning paradigm that allow smultiple participants to collaboratively train models in a cloud environment, all while maintaining the privacy of their raw data. This approach is in valuable in applications involving sensitive or geographically distributed data. However, one of the challenges in FL is dealing with heterogeneous and non-independent and identically distributed (non-IID) data across participants, which can result in suboptimal model performance compared to traditionalmachine learning methods. To tackle this, we introduce FedGCD, a novel FL algorithm that employs Graph Neural Network (GNN)-based community detection to enhance model convergence in federated settings. In our experiments, FedGCD consistently outperformed existing FL algorithms in various scenarios: for instance, in a non-IID environment, it achieved an accuracy of 0.9113, a precision of 0.8798,and an F1-Score of 0.8972. In a semi-IID setting, it demonstrated the highest accuracy at 0.9315 and an impressive F1-Score of 0.9312. We also introduce a new metric, nonIIDness, to quantitatively measure the degree of data heterogeneity. Our results indicate that FedGCD not only addresses the challenges of data heterogeneity and non-IIDness but also sets new benchmarks for FL algorithms. The community detection approach adopted in FedGCD has broader implications, suggesting that it could be adapted for other distributed machine learning scenarios, thereby improving model performance and convergence across a range of applications.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06d
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• pp.369-371
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• 2012

Wireless sensor networks have taken immense interest in healthcare systems in recent years. One example of it is in an in vivo sensor that is deployed in critical and sensitive healthcare applications like artificial retina, cardiac pacemaker, drug delivery, blood pressure, internal heat calculation, glucosemonitoring etc. In vivo sensor nodes exhibit temperature that may be very dangerous for human tissues. However, existing in vivo thermal aware routing approaches suffer from hotspot creation, delay, and computational complexity. These limitations motivate us toward an in vivo virtual backbone, a small subset of nodes, connected to all other nodes and involved in routing of all nodes, -based solution. A virtual backbone is lightweight and its fault-tolerant version allows in vivo sensor nodes to disconnect hotspot paths and to use alternative paths. We have formulated the problem as m-connected k-dominating set problem with minimum temperature cost in in vivo sensor network. This is a combinatorial optimization problem and we have been motivated to use evolutionary approach to solve the problem.

• Journal of Digital Contents Society
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• v.9 no.4
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• pp.679-686
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• 2008

Wireless LAN(WLAN) is a rather mature communication technology connecting mobile terminals. IEEE 802.11 is a representative protocol among WLAN technologies. With the rising popularity of delay-sensitive real-time multimedia applications(video, voice and data) in IEEE 802.11 wireless LAN, it is important to study the MAC layer delay performance of WLANs. In this paper, performance for packet delay model that recently have been proposed schemes is analysed in wireless LAN and proved performance results via simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5252-5268
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• 2018

To support a large-scale Hadoop cluster, Hadoop heartbeat messages are designed to deliver the significant messages, including task scheduling and completion messages, via piggybacking to reduce the number of messages received by the NameNode. Although Hadoop is designed and optimized for high-throughput computing via batch processing, the real-time processing of large amounts of data in Hadoop is increasingly important. This paper evaluates Hadoop's performance and costs when the heartbeat period is controlled to support latency sensitive applications. Through an empirical study based on Hadoop 2.0 (YARN) architecture, we improve Hadoop's I/O performance as well as application performance by up to 13 percent compared to the default configuration. We offer a guideline that predicts the performance, costs and limitations of the total system by controlling the heartbeat period using simple equations. We show that Hive performance can be improved by tuning Hadoop's heartbeat periods through extensive experiments.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.527-528
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• 2009

Presuming energy as a crucial resource, several duty cycle based MAC protocol have been proposed for wireless sensor network. However, these protocols have long latency problem for paying more attention on energy efficiency. In this paper, we propose Data Predicted Wakeup Based Duty Cycle MAC (DPW-MAC) for Wireless Sensor Networks for delay sensitive periodic applications in which timely delivery of data is a major concern with the maintenance of duty cycle.

• The Transactions of the Korea Information Processing Society
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• v.2 no.6
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• pp.927-936
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• 1995

ATM Forum has defined a new service class for data applications called Available Bit Rate(ABR) Service, which has highly bursty traffic and unpredictable burst size. It is desirable that we reduce the probability of retrans mission of packets by minimizing the loss of cells because the traffic is much more sensitive to loss than delay. The Forum has also selected the Rate-Based Control for the ABR service and proposed EPRCA as the control mechanism for the service. This paper proposes the combination of EPRCA and the other feedb ack control mechanisms such as BECN and BP. The combined control mechanism control ABR traffic more efficiently and the simulation results show that the network performance can be improved by choosing the appropriate parameters.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.1
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• pp.79-95
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• 2016

In order to improve the Quality-of-Service (QoS) of latency sensitive applications in next-generation cellular networks, multi-path is adopted to transmit packet stream in real-time to achieve high-quality video transmission in heterogeneous wireless networks. However, multi-path also introduces two important challenges: out-of-order issue and reordering delay. In this paper, we propose a new architecture based on Software Defined Network (SDN) for flow aggregation and flow splitting, and then design a Multiple Access Radio Scheduling (MARS) scheme based on relative Round-Trip Time (RTT) measurement. The QoS metrics including end-to-end delay, throughput and the packet out-of-order problem at the receiver have been investigated using the extensive simulation experiments. The performance results show that this SDN architecture coupled with the proposed MARS scheme can reduce the end-to-end delay and the reordering delay time caused by packet out-of-order as well as achieve a better throughput than the existing SMOS and Round-Robin algorithms.