• Proceedings of the KIEE Conference
• /
• 2006.10c
• /
• pp.467-469
• /
• 2006

In wireless network environments, as users' demands on high-speed data communications due to increase of multi-media services, the necessity of new high-speed WLAN technologies has appeared. Nowaday, IEEE is standardizing a new WLAN protocol caned as IEEE 802.11n. To effectively use wireless resources, IEEE 802.11n introduces MAC aggregation function which is similar to that in IEEE 802.11e. In case of transmitting several frames without MAC aggregation, the frames include individual frame header and trailer, and their corresponding acknowledgement frames can appear on wireless link. However, if they are aggregated into single MAC frame, we can reduce the number of used bits due to frame headers/trailers and also remove redundant acknowledgement frames. In this paper, we explain two different MAC frame aggregation methods for IEEE 802.11e and IEEE 802.11n and evaluate their throughput by simulations.

• ETRI Journal
• /
• v.26 no.4
• /
• pp.351-359
• /
• 2004

Flow aggregation is a scalable method to provide quality of service (QoS) guarantees to a large number of flows economically. A round-robin scheduler is an efficient scheduling algorithm. We investigate flow aggregation using a round-robin scheduler and propose the use of periodic timer interrupts for rate control of the round-robin scheduler. The proposed flow aggregator is a single-stage scheduler compared to Cobb's two-stage flow aggregator consisting of an aggregator and non-aggregating scheduler. It is possible to implement flow aggregation in the existing routers with only a software upgrade. We also present a simulation study showing the delay behaviors of the proposed algorithm.

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

• Applied Biological Chemistry
• /
• v.29 no.2
• /
• pp.198-206
• /
• 1986

For the information on micellization at each ginsenoside level aqueous solution of purified saponin of Panax ginseng root was dialyzed through dialysis tubing (MW 12,000) or eluted through Bio-Gel P-2 (MW 200-2,000) and analysed for ginsenosides by high performance liquid chromatography. Ginsenosides can be classified into three groups depending upon molecular aggregation pattern and spatial arrangement of hydrophilic parts in molecule. Group I that is large micelle former(aggregation number: above 10) and one side hydrophilic part (HP) includes $ginsenoside\;Rb_1$, $Rb_2$, Rc and Rd (diols). Group II thai is small micelle former (aggregation number:>10-1) and semi-two sales HP includes $Rg_2$, Rf (triol) and $Rg_3$ (diol). Group III that is no micelle former (aggregation number: 1) and two sides HP includes Re and $Rg_1$ (triol).

• The KIPS Transactions:PartC
• /
• v.11C no.4
• /
• pp.527-538
• /
• 2004

In this paper, we present the flow aggregation method and the FLTC(flow lasting time control) algorithm to reduce the number of flows and solve the scalability problem in high speed IP switching networks. The flow aggregation based on the destination address could reduce the total number of flows, improve the label efficiency, and increase the total amount of the switched packets. The FLTC algorithm also eliminates the waste of label by deleting the flow binding efficiently. With the traces of real Internet traffics, we evaluate the performance of these schemes by simulation. The label efficiency, the average number of label used, and the percentage of packets switched and the number of packets switched are used as performance measures for this simulation.

• The KIPS Transactions:PartC
• /
• v.18C no.5
• /
• pp.303-316
• /
• 2011

Data aggregation is important in wireless sensor networks. However, it also introduces many security problems, one of which is that a compromised node may inject false data or drop a message during data aggregation. Most existing solutions rely on encryption, which however requires high computation and communication cost. But they can only detect the occurrence of an attack without finding the attacking node. This makes sensor nodes waste their energy in sending false data if attacks occur repeatedly. Even an existing work can identify the location of a false data injection attack but it has a limitation that at most 50% of total sensor nodes can participate in data transmission. Therefore, a novel approach is required such that it can identify an attacker and also increase the number of nodes which participate in data transmission. In this paper, we propose a monitoring-based secure data aggregation protocol to prevent against a compromised aggregator which injects false data or drops a message. The proposed protocol consists of aggregation tree construction and secure data aggregation. In secure data aggregation, we use integration of abnormal data detection with monitoring and a minimal cryptographic technique. The simulation results show the proposed protocol increases the number of participating nodes in data transmission to 95% of the total nodes. The proposed protocol also can identify the location of a compromised node which injects false data or drops a message. A communication overhead for tracing back a location of a compromised node is O(n) where n is the total number of nodes and the cost is the same or better than other existing solutions.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.11 no.10
• /
• pp.4717-4737
• /
• 2017

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.05a
• /
• pp.44-46
• /
• 2020

Aggregating data with an optimal delay, which is a critical problem in Wireless Sensor Networks applications, is proven as NP-hard. In this paper, we focus on optimizing the aggregation delay by presenting an idea for channel and timeslot co-scheduling data aggregation in MWSNs. The proposed scheme, which names Break and Join, maximizes the number of sensor nodes to be scheduled in a working period, so that the overall number of working periods and data collection delay are reduced.

• Journal of Korean Society for Geospatial Information Science
• /
• v.3 no.1 s.5
• /
• pp.91-104
• /
• 1995

The spatial aggregation of data, in transportation and other planning processes, is an important theoretical consideration because the results of any analysis are not entirely independent of the delineation of zones. Moreover, using a different spatial aggregation may lead to different, and sometimes contradictory conclusions. Two criteria have been considered as important in designing zone systems. They are scale and aggregation. The scale problem arises because of uncertainty about the number of zones needed for a study and the aggregation problem arises because of uncertainty about how the data are to be aggregated to from a given scale problem. In a transportation study, especially in the design of traffic analysis zone(TAZ), the scale problem is directly related to the number dof zones and the aggregation problem involves spatial clustering, meeting the general requirements of forming the zones system such as equal traffic generation, convexity, and the consistency with the political boundary. In this study, first, the comparative study of delineating spatial units has been given. Second, a FORTRAN-based heuristic algorithm for designing TAZ based on socio-economic data has been developed and applied to the Korean peninsula containing 132 micro parcels. The vector type ARC/INFO GIS topological data mosel has been used to provise the adjacency information between parcels. The results, however, leave some to be desired in order to overcome such problems as non-convexity of the agglomerated TAZ system and/or uneven traffic phenomenon for each TAZ.

• Journal of Electrical Engineering and Technology
• /
• v.13 no.1
• /
• pp.38-50
• /
• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.