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A Congestion Control Scheme Considering Traffic in Large-Scale Wireless Sensor Networks
Moon-Sang Kwak, Young Sik Hong
Large-scale wireless sensor networks are constructed by using a large number of sensor nodes that are non-uniformly deployed over a wide area. As a result, the data collected by the sensor nodes are similar to that from one another since a high density of the sensor nodes may cause an overlap. As a result of the characteristics of the traffic, data is collected from a plurality of sensor nodes by a sink node, and when the sensor nodes transmit their collected data to the sink node, the sensor nodes around the sink node have a higher amount of traffic than the sensor nodes far away from the sink node. Thus, the former sensor encounter bottlenecks due to traffic congestion and have an energy hole problem more often than the latter ones, increasing energy consumption. This paper proposes a congestion control scheme that considers traffic flows in order to control traffic congestion of the sensor nodes that are non-uniformly deployed over a large-scale wireless sensor network.
Exploiting Friend’s Username to De-anonymize Users across Heterogeneous Social Networking Sites
Nowadays, social networking sites (SNSs), such as Twitter, LinkedIn, and Tumblr, are coming into the forefront, due to the growth in the number of users. While users voluntarily provide their information in SNSs, privacy leakages resulting from the use of SNSs is becoming a problem owing to the evolution of large data processing techniques and the raising awareness of privacy. In order to solve this problem, the studies on protecting privacy on SNSs, based on graph and machine learning, have been conducted. However, examples of privacy leakages resulting from the advent of a new SNS are consistently being uncovered. In this paper, we propose a technique enabling a user to detect privacy leakages beforehand in the case where the service provider or third-party application developer threatens the SNS user’s privacy maliciously.
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.
Modeling and Analysis of Vehicle Detection Using Roadside Ultrasonic Sensors in Wireless Sensor Networks
To address the problems of existing traffic information acquisition systems such as high cost and low scalability, wireless sensor networks (WSN) based traffic information acquisition systems have been studied. WSN based systems have many benefits including high scalability and low maintenance cost. Recently, various sensors are studied for traffic surveillance based on WSN, such as magnetic, acoustic, and accelerometer sensors. However, ultrasonic sensor based systems have not been studied. There are many issues for WSN based systems, such as battery driven operation and low computing power. Thus, power saving methods and specific algorithms with low complexity are necessary. In this paper, we introduce optimal methodologies for power saving of ultrasonic sensors based on the modeling and analysis in detail. Moreover, a new vehicle detection algorithm for low complexity using ultrasonic data is presented. The proposed methodologies are implemented in a tiny microprocessor. The evaluation results show that our algorithm has high detection accuracy.
Contents Routing in the OpenFlow-based Wireless Mesh Network Environment
Won Suk Kim, Sang Hwa Chung, Hyun suk Choi, Mi Rim Do
The wireless mesh network based on IEEE 802.11s provides a routing based on a destination address as it inherits legacy internet architecture. However, this architecture interested in not ‘what’ which is originally the users goal but ‘where’. Futhermore, because of the rapid increase of the number of mobile devices recently, the mobile traffic increases geometrically. It reduces the network effectiveness as increasing many packets which have same payload in the situation of many users access to the same contents. In this paper, we propose an OpenFlow based contents routing for the wireless mesh network(WMN) to solve this problem. We implement contents layer to the legacy network layer which mesh network uses and the routing technique based on contents identifier for efficient contents routing. In addition we provide flexibility as we use OpenFlow. By using this, we implement caching technique to improve effectiveness of network as decreasing the packet which has same payload in WSN. We measure the network usage to compare the flooding technique, we measure the delay to compare environment using caching and non caching. As a result of delay measure it shows 20% of performance improve, and controller message decrease maximum 89%.
Spammer Detection using Features based on User Relationships in Twitter
Twitter is one of the most famous SNS(Social Network Service) in the world. Twitter spammer accounts that are created easily by E mail authentication deliver harmful content to twitter users. This paper presents a spammer detection method that utilizes features based on the relationship between users in twitter. Relationship based features include friends relationship that represents user preferences and type relationship that represents similarity between users. We compared the performance of the proposed method and conventional spammer detection method on a dataset with 3% to 30% spammer ratio, and the experimental results show that proposed method outperformed conventional method in Naive Bayesian Classification and Decision Tree Learning.
Para-virtualized Library for Bare-metal Network Performance in Virtualized Environment
Dongwoo Lee, Youngjoong Cho, Young Ik Eom
Now, virtualization is no more emerging research area, and we can easily find its application in our circumstance. Nevertheless, I/O workloads are reluctant to be applied in virtual environment since they still suffer from unacceptable performance degradation due to virtualization latency. Many previous papers identified that virtual I/O overhead is mainly caused by exits and redundant I/O stack, and proposed several techniques to reduce them. However, they still have some limitations. In this paper, we introduce a novel I/O virtualization framework which improves I/O performance by exploiting multicore architecture. We applied our framework to the virtual network, and it improves TCP throughput up to 169%, and decreases UDP latency up to 38% on the network with the 10Gbps NIC.
Effective Importance-Based Entity Grouping Method in Continual Graph Embedding
http://doi.org/10.5626/JOK.2025.52.7.627
This study proposed a novel approach to improving entity importance evaluation in continual graph embeddings by incorporating edge betweenness centrality as a weighting factor in a Weighted PageRank algorithm. By normalizing and integrating betweenness centrality, the proposed method effectively propagated entity importance while accounting for the significance of information flow through edges. Experimental results demonstrated significant performance improvements in MRR and Hit@N metrics across various datasets using the proposed method compared to existing methods. Notably, the proposed method showed enhanced learning performance after the initial snapshot in scenarios where new entities and relationships were continuously added. These findings highlight the effectiveness of leveraging edge centrality in promoting efficient and accurate learning in continual knowledge graph embeddings.
VNF Anomaly Detection Method based on Unsupervised Machine Learning
Seondong Heo, Seunghoon Jeong, Hosang Yun
http://doi.org/10.5626/JOK.2022.49.9.780
By applying virtualization technology to telecommunication networks, it is possible to reduce hardware dependencies and provide flexible control and management to the operators. In addition, since Capital Expenditure (CAPEX) and Operating Expenditure (OPEX) can be reduced by utilizing the technology, modern telco operators and service providers are using Software-Defined Networking(SDN) and Network Function Virtualization (NFV) technology to provide services more efficiently. As SDN and NFV are widely used, cyber attacks on Vitualized Network Functions (VNF) that degrade the quality of service or cause service denial are increasing. In this study, we propose a VNF anomaly detection method based on unsupervised machine learning techniques that models the steady states of VNFs and detects abnormal states caused by cyber attacks.
Super Resolution-based Robust Image Inpainting for Large-scale Missing Regions
Jieun Lee, SeungWon Jung, Jonghwa Shim, Eenjun Hwang
http://doi.org/10.5626/JOK.2022.49.9.708
Image inpainting is a method of filling missing regions of an image with plausible imagery. Even though the performance of recent inpainting methods has been significantly improved owing to the introduction of deep learning, unnatural results can be obtained when an input image has a large-scale missing region, contains a complex scene, or is a high-resolution image. In this study, we propose a super resolution-based two-stage image inpainting method, motivated by the point that inpainting performance in low-resolution images is better than in high-resolution images. In the first step, we convert a high-resolution image into a low-resolution image and then perform image inpainting, which results in the initial output image. In the next step, the initial output image becomes the final output image, with the same resolution as the original input image using the super resolution model. To verify the effectiveness of the proposed method, we conducted quantitative and qualitative evaluations using the high-resolution Urban100 dataset. Furthermore, we analyzed the inpainting performance depending on the size of the missing region and demonstrated that the proposed method could generate satisfactory results in a free-form mask.
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