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An Energy-Aware Cooperative Communication Scheme for Wireless Multimedia Sensor Networks
Jeong-Oh Kim, Hyunduk Kim, Wonik Choi
Numerous clustering schemes have been proposed to increase energy efficiency in wireless sensor networks. Clustering schemes consist of a hierarchical structure in the sensor network to aggregate and transmit data. However, existing clustering schemes are not suitable for use in wireless multimedia sensor networks because they consume a large quantity of energy and have extremely short lifetime. To address this problem, we propose the Energy-Aware Cooperative Communication (EACC) method which is a novel cooperative clustering method that systematically adapts to various types of multimedia data including images and video. An evaluation of its performance shows that the proposed method is up to 2.5 times more energy-efficient than the existing clustering schemes.
Analysis of Energy Consumption and Processing Delay of Wireless Sensor Networks according to the Characteristic of Applications
Chong Myung Park, Young Tak Han, Soobin Jeon, Inbum Jung
Wireless sensor networks are used for data collection and processing from the surrounding environment for various applications. Since wireless sensor nodes operate on low computing power, restrictive battery capacity, and low network bandwidth, their architecture model has greatly affected the performance of applications. If applications have high computation complexity or require the real-time processing, the centralized architecture in wireless sensor networks have a delay in data processing. Otherwise, if applications only performed simple data collection for long period, the distributed architecture wasted battery energy in wireless sensors. In this paper, the energy consumption and processing delay were analyzed in centralized and distributed sensor networks. In addition, we proposed a new hybrid architecture for wireless sensor networks. According to the characteristic of applications, the proposed method had the optimal number of wireless sensors in wireless sensor networks.
Creating Level Set Trees Using One-Class Support Vector Machines
A level set tree provides a useful representation of a multidimensional density function. Visualizing the data structure as a tree offers many advantages for data analysis and clustering. In this paper, we present a level set tree estimation algorithm for use with a set of data points. The proposed algorithm creates a level set tree from a family of level sets estimated over a whole range of levels from zero to infinity. Instead of estimating density function then thresholding, we directly estimate the density level sets using one-class support vector machines (OC-SVMs). The level set estimation is facilitated by the OC-SVM solution path algorithm. We demonstrate the proposed level set tree algorithm on benchmark data sets.
Distributed Computing Models for Wireless Sensor Networks
Chongmyung Park, Chungsan Lee, Youngtae Jo, Inbum Jung
Wireless sensor networks offer a distributed processing environment. Many sensor nodes are deployed in fields that have limited resources such as computing power, network bandwidth, and electric power. The sensor nodes construct their own networks automatically, and the collected data are sent to the sink node. In these traditional wireless sensor networks, network congestion due to packet flooding through the networks shortens the network life time. Clustering or in-network technologies help reduce packet flooding in the networks. Many studies have been focused on saving energy in the sensor nodes because the limited available power leads to an important problem of extending the operation of sensor networks as long as possible. However, we focus on the execution time because clustering and local distributed processing already contribute to saving energy by local decision-making. In this paper, we present a cooperative processing model based on the processing timeline. Our processing model includes validation of the processing, prediction of the total execution time, and determination of the optimal number of processing nodes for distributed processing in wireless sensor networks. The experiments demonstrate the accuracy of the proposed model, and a case study shows that our model can be used for the distributed application.
A Malicious Traffic Detection Method Using X-means Clustering
Myoungji Han, Jihyuk Lim, Junyong Choi, Hyunjoon Kim, Jungjoo Seo, Cheol Yu, Sung-Ryul Kim, Kunsoo Park
Malicious traffic, such as DDoS attack and botnet communications, refers to traffic that is generated for the purpose of disturbing internet networks or harming certain networks, servers, or hosts. As malicious traffic has been constantly evolving in terms of both quality and quantity, there have been many researches fighting against it. In this paper, we propose an effective malicious traffic detection method that exploits the X-means clustering algorithm. We also suggest how to analyze statistical characteristics of malicious traffic and to define metrics that are used when clustering. Finally, we verify effectiveness of our method by experiments with two released traffic data.
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