• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.8
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• pp.1663-1668
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• 2005

There have been numerous researches regarding the QoS guarantee in packet switching networks. IntServs, based on a signaling mechanism and scheduling algorithms, suggesting promising solutions, yet has the crucial complexity problem so that not enough real implementations has been witnessed. Flow aggregation is suggested recently to overcome this issue. In order to aggregated flows fairly so that the latency of the aggregated flows is bound, however, a non-work conserving scheduler is necessary, which is not very popular because of its another inherent complexity. We suggest a non-work conserving scheduler, the Round Robin with Virtual Flow (RRVF), which is a variation of the popular Deficit Round Robin (DRR). We study the latency of the RRVF, and observe that the non-work conserving nature of the RRVF yields a slight disadvantage in terms of the latency, but after the aggregation the latency is greatly reduced, so that e combined latency is reduced. We conclude that the flow aggregation through RRVF can actually reduce the complexity of the bandwidth allocation as well as the overall latency within a network.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.4
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• pp.289-293
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• 2009

In sensor networks, many methods have been proposed to process in-network aggregation effectively. Contrary to normal aggregation queries, skyline query processing that compare multi-dimension data for producing result is very hard. It is important to filter unnecessary data for energy-efficient skyline query processing. Existing approach like MFTAC restricts unnecessary data transitions by deploying filters to whole sensors. However, network lifetime is reduced by energy consumption for filters transmission. In this paper, we propose a lazy filtering-based skyline query processing algorithm of in-network for reducing energy consumption by filters transmission. The proposed algorithm creates the skyline filter table (SFT) in the data gathering process which sends from sensor nodes to the base station and filters out unnecessary transmissions using it. The experimental results show that the proposed algorithm reduces false positive by 53% and improves network lifetime by 44% on average over MFTAC.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.65-74
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• 2014

IEEE 802.11n standard has introduced the new schemes in MAC and PHY layers to improve network throughput. Frame aggregation and Block ACK are mainly defined to increase the efficiency of the MAC layer. There exists still problem in IEEE 802.11n. When block ACK request and/or response frames are missing or received in error, the sender does not know the status (success/failure) of each frame in the aggregated large frame and retransmits all the frames. This can cause a lower network performance. To solve this problem, we propose a new effective scheme, called reduced retransmission of MPDUs (RRM) scheme. In the proposed scheme, when a sender does not receive a block ACK response frame, it just transmits a next data frame and requests a block ACK. Therefore, it can retransmits the erroneous frames. Performance of the proposed scheme is investigated by simulation. Our results show that the proposed scheme is very effective and improves the performance under a wide range of channel error conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1527-1552
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• 2018

The rapidly-evolving demand of applications using wireless sensor networks in several areas such as building and industrial automation or smart cities, among other, makes it necessary to determine and provide QoS support mechanisms which can satisfy the requirements of applications. In this paper we propose a mechanism that establishes different QoS levels, based on Publish/Subscribe model for wireless networks to meet application requirements, to provide reliable delivery of packet and timeliness. The first level delivers packets in a best effort way. The second one intends to provide reliable packet delivery with a novel approach for Retransmission Timeout (RTO) calculation, which adjusts the RTO depending on the subscriber Packet Delivery Ratio (PDR). The third one provides the same reliable packet delivery as the second one, but in addition, it provides data aggregation trying to be efficient in terms of energy consumption and the use of network bandwidth. The last one provides timeliness in the packet delivery. We evaluate each QoS Level with several performance metrics such as PDR, Message Delivery Ratio, Duplicated and Retransmitted Packet Ratio and Packet Timeliness Ratio to demonstrate that our proposal provides significant improvements based on the increase of the PDR obtained.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.2
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• pp.1-11
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• 2020

As an important basic building block of the smart grid environment, smart meter provides real-time electricity consumption information to the utility. However, ensuring information security and privacy in the smart meter data aggregation process is a non-trivial task. Even though the secure data aggregation for the smart meter has been a lot of attention from both academic and industry researchers in recent years, most of these studies are not secure against internal attackers or cannot provide data integrity. Besides, their computation costs are not satisfactory because the bilinear pairing operation or the hash-to-point operation is performed at the smart meter system. Recently, blockchains or distributed ledgers are an emerging technology that has drawn considerable interest from energy supply firms, startups, technology developers, financial institutions, national governments and the academic community. In particular, blockchains are identified as having the potential to bring significant benefits and innovation for the electricity consumption network. This study suggests a distributed, privacy-preserving, and simple secure smart meter data aggregation system, backed up by Blockchain technology. Smart meter data are aggregated and verified by a hierarchical Merkle tree, in which the consensus protocol is supported by the practical Byzantine fault tolerance algorithm.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.870-872
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• 2007

Sensor networks have been widely applied for data collection. Due to the energy limitation of the sensor nodes and the most energy consuming data transmission, we should allocate as much work as possible to the sensors, such as data compression and aggregation, to reduce data transmission and save energy. Querying extreme values is a general query type in wireless sensor networks. In this paper, we propose a novel querying method called Two-Level Filter (TLF) for querying extreme values in wireless sensor networks. We first divide the whole sensor network into domains using the Distributed Data Aggregation Model (DDAM). The sensor nodes report their data to the cluster heads using push method. The advantages of two-level filter lie in two aspects. When querying extreme values, the number of pull operations has the lower boundary. And the query results are less affected by the topology changes of the wireless sensor network. Through this method, the sensors preprocess the data to share the burden of the base station and it combines push and pull to be more energy efficient.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.67-75
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• 2009

IEEE 802.11 WLAN(Wireless Local Area Network) standard is currently developing with increased wireless internet demand. Study trends of IEEE 802.11n for high throughput show two aspects, enhanced system throughput using aggregation among packets in MAC(Medium Access Control) layer, and better data rates adapting MIMO(Multiple-Input Multiple-Output) in PRY(Physical) layer. But, no one demonstrates IEEE 802.11n system performance results considering MAC and PRY connection. This paper adapts A-MPDU(Aggregation-MAC Protocol Data Unit) method in MAC layer and MIMO in PRY layer for IEEE 802.11n system. Consequently, Simulation results show enhanced throughput and data rates compared to existing system. Also, We use NS-2(Network Simulator-2) considering MAC and PRY connection for reality.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.115-122
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• 2017

In this paper, we propose an adaptive data aggregation and compression scheme for wireless sensor networks with energy-harvesting nodes, which increases the amount of data arrived at the sink node by efficient use of the harvested energy. In energy-harvesting wireless sensor networks, sensor nodes can have more than necessary energy because they harvest energy from environments continuously. In the proposed scheme, when a node judges that there is surplus energy by estimating its residual energy, the node compresses and transmits the aggregated data so far. Conversely, if the residual energy is estimated to be depleted, the node turns off its transceiver and collects only its own sensory data to reduce its energy consumption. As a result, this scheme increases the amount of data collected at the sink node by preventing the blackout of relay nodes and facilitating data transmission. Through simulation, we show that the proposed scheme suppresses the occurrence of blackout nodes and collect the largest amount of data at the sink node compared to previous schemes.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.259-262
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• 2000

In this paper, an Adaptive neuro-fuzzy Inference system(ANFIS) using fuzzy min-max network(FMMN) is proposed. Fuzzy min-max network classifier that utilizes fuzzy sets as pattern classes is described. Each fuzzy set is an aggregation of fuzzy set hyperboxes. Here, the proposed method transforms the hyperboxes into gaussian menbership functions, where the transformed membership functions are inserted for generating fuzzy rules of ANFIS. Finally, we applied the proposed method to the classification problem of iris data and obtained a better performance than previous works.