• Journal of Information Processing Systems
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• v.16 no.1
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• pp.83-95
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• 2020

Because sensor nodes have limited resources in wireless sensor networks, data aggregation can efficiently reduce communication overhead and extend the network lifetime. Although many existing methods are particularly useful for data aggregation applications, they incur unbalanced communication cost and waste lots of sensors' energy. In this paper, we propose a privacy-preserving, energy-saving data aggregation scheme (EBPP). Our method can efficiently reduce the communication cost and provide privacy preservation to protect useful information. Meanwhile, the balanced energy of the nodes can extend the network lifetime in our scheme. Through many simulation experiments, we use several performance criteria to evaluate the method. According to the simulation and analysis results, this method can more effectively balance energy dissipation and provide privacy preservation compared to the existing schemes.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.607-608
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• 2012

Data aggregation is a fundamental problem in wireless sensor networks which attracts great attention in recent years. Delay and energy efficiencies are two crucial issues of designing a data aggregation scheme. In this paper, we propose a distributed, energy efficient algorithm for collecting data from all sensor nodes with the minimum latency called Delay-aware Power-efficient Data Aggregation algorithm (DPDA). The DPDA algorithm minimizes the latency in data collection process by building a time efficient data aggregation network structure. It also saves sensor energy by decreasing node transmission distances. Energy is also well-balanced between sensors to achieve acceptable network lifetime. From intensive experiments, the DPDA scheme could significantly decrease the data collection latency and obtain reasonable network lifetime compared with other approaches.

• Proceeding of EDISON Challenge
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• 2015.03a
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• pp.70-75
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• 2015

Fasciclin 1 (FAS1) is an extracellular protein whose aggregation in cornea leads to visual impairment. While a number of FAS1 mutants have been studied that exhibit enhanced/decreased aggregation propensity, no structural information has been provided so far that is associated with distinct aggregation potential. In this study, we have investigated the structural and thermodynamic characteristics of the wild-type FAS1 and its two mutants, R555Q and R555W, by using molecular dynamics simulations and three-dimensional reference interaction site model (3D-RISM) theory. We find that the hydrophobic solvent accessible surface area increases due to hydrophobic core repacking in the C-terminus caused by the mutation. We also find that the solvation free energy of the mutants increases due to the enhanced non-native H-bonding. These structural and thermodynamic changes upon mutation contribute to understand the aggregation of these mutants.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.94-97
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• 2022

The emerging energy harvesting technology, which has been successfully integrated into Wireless Sensor Networks, enables sensor batteries to be charged using renewable energy sources. In the meantime, the problem of Minimum Latency Aggregation Scheduling (MLAS) in battery-powered WSNs has been well studied. However, because sensors have limited energy harvesting capabilities, captured energy is limited and varies greatly between nodes. As a result, all previous MLAS algorithms are incompatible with Battery-Free Wireless Sensor Networks (BF-WSNs). We investigate the MLAS problem in BF-WSNs in this paper. To make the best use of the harvested energy, we build an aggregation tree that leverages the energy harvesting rates of the sensor nodes with an intuitive explanation. The aggregation tree, which determines sender-receiver pairs for data transmission, is one of the two important phases to obtain a low data aggregation latency in the BF-WSNs.

• Journal of Information Processing Systems
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• v.10 no.3
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• pp.412-428
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• 2014

Request oriented sensor networks have stricter requirements than conventional event-driven or periodic report models. Therefore, in this paper we propose a minimum energy data aggregation (MEDA), which meets the requirements for request oriented sensor networks by exploiting a low power real-time scheduler, on-demand time synchronization, variable response frame structure, and adaptive retransmission. In addition we introduce a test bed consisting of a number of MEDA prototypes, which support near real-time bidirectional sensor networks. The experimental results also demonstrate that the MEDA guarantees deterministic aggregation time, enables minimum energy operation, and provides a reliable data aggregation service.

• Korean Chemical Engineering Research
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• v.56 no.1
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• pp.14-23
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• 2018

The effect of dodecylbenzene sulfonic acid (DBSA) with different addition amount of DBSA ($M_{DBSA}$), temperatures and solvent-to-oil ratio (SOR, v/v) on asphaltene aggregation in a solvent deasphalting system was investigated. Increasing the $M_{DBSA}$ at SOR 10 and $55^{\circ}C$ caused the asphaltene removal ratio (ARR) to increase first, then maximize at 1 wt% of $M_{DBSA}$ and then decrease continuously. Based on the SARA (saturate, aromatic, resin, asphaltene) composition, the adsorption amount of DBSA on the asphaltene surface and the self-aggregation of the DBSA, the reason for the change in ARR with $M_{DBSA}$ was found due to the adsorption mechanism. In addition, the asphaltene-resin-DBSA colloidal size confirmed the change of adsorption behavior between the asphaltene and DBSA. Based on the results of this study, a hypothetical adsorption mechanism of DBSA on asphaltene aggregation in the solvent deasphalting system was conceived of and proposed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1057-1059
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• 2016

A construction method of energy-efficient data aggregation tree is proposed by considering a tradeoff between acquisition time and energy consumption in wireless sensor networks. This proposed method constructs the data aggregation tree to minimize the link cost between the connected nodes for reducing energy consumption, while minimizing the maximum distance between sensor nodes and a sink node for rapid information gathering. Simulation results show that the proposed aggregation tree can be generated with low complexity and achieves high energy efficiency compared to conventional methods.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.998-1007
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• 2018

The advent of Wireless Sensor Networks (WSN) has led to their use in numerous applications. Sensors are autonomous in nature and are constrained by limited resources. Designing an autonomous topology with criteria for economic and energy conservation is considered a major goal in WSN. The proposed honey-hive clustering consumes minimum energy and resources with minimal transmission delay compared to the existing approaches. The honey-hive approach consists of two phases. The first phase is an Intra-Cluster Min-Max Discrepancy (ICMMD) analysis, which is based on the local honey-hive data gathering technique and the second phase is Inter-Cluster Frequency Matching (ICFM), which is based on the global optimal data aggregation. The proposed data aggregation mechanism increases the optimal connectivity range of the sensor node to a considerable degree for inter-cluster and intra-cluster coverage with an improved optimal energy conservation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.2
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• pp.209-218
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• 2009

In a wireless sensor network(WSN) each sensor node deals with numerous sensing data elements. For the sake of energy efficiency and network lifetime, sensing data must be handled effectively. A technique used for this is data aggregation. Sending/receiving data involves numerous steps such as MAC layer control packet handshakes and route path setup, and these steps consume energy. Because these steps are involved in all data communication, the total cost increases are related to the counts of data sent/received. Therefore, many studies have proposed sending combined data, which is known as data aggregation. Very effective methods to aggregate sensing data have been suggested, but there is no means of deciding how long the sensor node should wait for aggregation. This is a very important issue, because the wait time affects the total communication cost and data reliability. There are two types of data aggregation; the data counting method and the time waiting method. However, each has weaknesses in terms of the delay. A hybrid method can be adopted to alleviate these problems. But, it cannot provide an optimal point of aggregation. In this paper, we suggest a stochastic-based data aggregation scheme, which provides the cost(in terms of communication and delay) optimal aggregation point. We present numerical analysis and results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.9B
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• pp.1295-1304
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• 2010

This paper proposes a fault-tolerant data aggregation which provides energy efficient and reliable data collection in wireless sensor networks. The traditional aggregation scheme does not provide the countermeasure to packet loss or the countermeasure scheme requires a large amount of energy. The proposed scheme applies caching and re-transmission based on the track topology to the adaptive timeout scheduling. The proposed scheme uses a single-path routing based on the traditional tree topology at normal, which reduces the dissipated energy in sensor nodes without any countermeasure against packet loss. The proposed scheme, however, retransmits the lost packet using track topology under event occurrences in order to fulfill more accurate data aggregation. Extensive simulation work under various workloads has revealed that the proposed scheme decrease by 8% in terms of the dissipated energy and enhances data accuracy 41% when the potential of event occurrence exists as compared with TAG data aggregation. And the proposed scheme decrease by 53% in terms of the dissipated energy and shows a similar performance in data accuracy when the potential of event occurrence exists as compared with PERLA data aggregation.