• Journal of the Korea Institute of Information and Communication Engineering
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• 제13권6호
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• pp.1175-1180
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• 2009

Frame Aggregation is a promissing technology for improving MAC throughput in IEEE 802.11n. In IEEE 802.11n, two frame aggregation schemes, Aggregate MSDU (A-MSDU) and Aggregation MPDU (A-MPDU), are defined. In this paper, we analyze the performance the two-level frame aggregation scheme where A-MSDU and A-MPDU are combined. We develop the analytical model for the two-level frame aggregation scheme and present numerical results on the effect of bit error rate, aggregation size, and the number of nodes.

• Journal of Institute of Control, Robotics and Systems
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• 제22권11호
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• pp.984-990
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• 2016

In wireless mesh sensor networks (WMSNs), sensor nodes are connected in the form of a mesh topology and transfer sensor data by multi-hop routing. A data aggregation method for WMSNs is required to minimize the number of routing hops and the energy consumption of each node with limited battery power. This paper presents a shortest path data aggregation method for WMSNs. The proposed method utilizes a simple hash function based on shuffled row major indexing for addressing sensor nodes. This allows sensor data to be aggregated without complex routing tables and calculation for deciding the next hop. The proposed data aggregation algorithms work in a fractal fashion with different mesh sizes. The method repeatedly performs gathering and moves sensor data to sink nodes in higher-level clusters. The proposed method was implemented and simulations were performed to confirm the accuracy of the proposed algorithms.

• Journal of KIISE
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• 제42권6호
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• pp.781-790
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• 2015

In recent times, microblogs have become popular owing to the development of the Internet and mobile environments. Among the various types of microblog data, those containing location data are referred to as spatial social Web objects. General aggregations of such microblog data include data aggregation per user for a single piece of information. This study proposes a spatial aggregation algorithm that combines a general aggregation with spatial data and uses the Geohash and MapReduce operations to perform spatial social analysis, by using microblog data with the characteristics of a spatial social Web object. The proposed algorithm provides the foundation for a meaningful spatial social analysis.

• Journal of Korean Society of Transportation
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• 제35권1호
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• pp.63-78
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• 2017

The purpose of this study is to determine the optimal aggregation interval to increase the reliability when estimating representative value of individual vehicle travel time collected by DSRC equipment in interrupted traffic flow section in National Highway. For this, we use the bimodal asymmetric distribution data, which is the distribution of the most representative individual vehicle travel time collected in the interrupted traffic flow section, and estimate the MSE(Mean Square Error) according to the variation of the aggregation interval of individual vehicle travel time, and determine the optimal aggregation interval. The estimation equation for the MSE estimation utilizes the maximum estimation error equation of t-distribution that can be used in asymmetric distribution. For the analysis of optimal aggregation interval size, the aggregation interval size of individual vehicle travel time was only 3 minutes or more apart from the aggregation interval size of 1-2 minutes in which the collection of data was normally lost due to the signal stop in the interrupted traffic flow section. The aggregation interval that causes the missing part in the data collection causes another error in the missing data correction process and is excluded. As a result, the optimal aggregation interval for the minimum MSE was 3~5 minutes. Considering both the efficiency of the system operation and the improvement of the reliability of calculation of the travel time, it is effective to operate the basic aggregation interval as 5 minutes as usual and to reduce the aggregation interval to 3 minutes in case of congestion.

• The KIPS Transactions:PartC
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• 제18C권5호
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• pp.303-316
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• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• 제33권5B호
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• pp.326-333
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• 2008

In wireless sensor networks, a sensor node with in-network aggregation adjusts the timeout which is a waiting time to receive messages from child nodes. This paper proposes a novel timeout scheduling scheme for data aggregation in wireless sensor networks, which adaptively configures its timeout according to changing data patterns in order to improve energy efficiency and data accuracy during data aggregation. The proposed scheme decreases the timeout when the temporal difference of collected data(data variation) from children is lower than a pre-defined threshold because there is no occurrence of critical events. Conversely, it increases the timeout when the data variation is higher than the pre-defined threshold in order to fulfill more accurate data aggregation. Extensive simulation reveals that the proposed scheme outperforms the cascading-based scheme in terms of energy consumption and data accuracy.

• Proceedings of the Korea Information Processing Society Conference
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.175-177
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• 2020

In multi-channel wireless sensor networks, optimizing data aggregation delay without any channels and timeslots conflicts has been concerned these days. The aggregation delay can be reduced by using different aggregation tree construction methods or scheduling in different methods in bottom-up or top-down manners. In this paper, we propose a new way of constructing aggregation tree purposing to decrease the total aggregation delay. The result shows that our proposed scheme can improve up to 64% comparing with state-of-the-art schemes.

• Journal of the Korea Institute of Information and Communication Engineering
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• 제17권7호
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• pp.1715-1724
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• 2013

The maximum lifetime data aggregation problem is to maximize the network lifetime as minimizing the transmission energy of all deployed nodes in wireless sensor networks. In this paper, we propose a simulated annealing algorithm to solve efficiently the maximum lifetime data aggregation problem on the basis of meta-heuristic approach in wireless sensor networks. In order to make a search more efficient, we propose a novel neighborhood generating method and a repair function of the proposed algorithm. We compare the performance of the proposed algorithm with other existing algorithms through some experiments in terms of the network lifetime and algorithm computation time. Experimental results show that the proposed algorithm is efficient for the maximum lifetime data aggregation problem in wireless sensor networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 한국해양정보통신학회 2009년도 춘계학술대회
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• pp.473-476
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• 2009

Frame Aggregation is a promissing technology for improving MAC throughput in IEEE 802.11n. In IEEE 802.11n, two frame aggregation schemes, Aggregate MSDU (A-MSDU) and Aggregate MPDU (A-MPDU), are defined. In this paper, we analyze the performance the two-level frame aggregation scheme where A-MSDU and A-MPDU are combined. We develop the analytical model for the two-level frame aggregation scheme and present numerical results on the effect of bit error rate, aggregation size, and the number of nodes.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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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.