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An Interference Reduction Scheme Using AP Aggregation and Transmit Power Control on OpenFlow-based WLAN
Mi-Rim Do, Sang-Hwa Chung, Chang-Woo Ahn
Recently, excessive installations of APs have caused WLAN interference, and many techniques have been suggested to solve this problem. The AP aggregation technique serves to reduce active APs by moving station connections to a certain AP. Since this technique forcibly moves station connections, the transmission performance of some stations may deteriorate. The AP transmit power control technique may cause station disconnection or deterioration of transmission performance when power is reduced under a certain level. The combination of these two techniques can reduce interference through AP aggregation and narrow the range of interferences further through detailed power adjustment. However, simply combining these techniques may decrease the probability of power adjustment after aggregation and increase station disconnections upon power control. As a result, improvement in performance may be insignificant. Hence, this study suggests a scheme to combine the AP aggregation and the AP transmit power control techniques in OpenFlow-based WLAN to ameliorate the disadvantages of each technique and to reduce interferences efficiently by performing aggregation for the purpose of increasing the probability of adjusting transmission power. Simulations reveal that the average transmission delay of the suggested scheme is reduced by as much as 12.8% compared to the aggregation scheme and by as much as 18.1% compared to the power control scheme. The packet loss rate due to interference is reduced by as much as 24.9% compared to the aggregation scheme and by as much as 46.7% compared to the power control scheme. In addition, the aggregation scheme and the power control scheme decrease the throughput of several stations as a side effect, but our scheme increases the total data throughput without decreasing the throughput of each station.
A Priority Based Multipath Routing Mechanism in the Tactical Backbone Network
Yongsin Kim, Sang-heon Shin, Younghan Kim
The tactical network is system based on wireless networking technologies that ties together surveillance reconnaissance systems, precision strike systems and command and control systems. Several alternative paths exist in the network because it is connected as a grid to improve its survivability. In addition, the network topology changes frequently as forces and combatants change their network access points while conducting operations. However, most Internet routing standards have been designed for use in stable backbone networks. Therefore, tactical networks may exhibit a deterioration in performance when these standards are implemented. In this paper, we propose Priority based Multi-Path routing with Local Optimization(PMPLO) for a tactical backbone network. The PMPLO separately manages the global and local metrics. The global metric propagates to other routers through the use of a routing protocol, and it is used for a multi-path configuration that is guaranteed to be loop free. The local metric reflects the link utilization that is used to find an alternate path when congestion occurs, and it is managed internally only within each router. It also produces traffic that has a high priority privilege when choosing the optimal path. Finally, we conducted a simulation to verify that the PMPLO can effectively distribute the user traffic among available routers.
Message Delivery Techniques using Group Intimacy Information among Nodes in Opportunistic Networks
Seohyang Kim, Hayoung Oh, Chongkwon Kim
In opportunistic networks, each message is delivered to the destination by repeating, storing, carrying, and forwarding the message. Recently, with the vitalization of social networks, a large number of existing articles have shown performance improvement when delivering the message and considering its social relational networks. However, these works only deliver messages when they find nodes, assuming that every node cooperates with each other unconditionally. Moreover, they only consider the number of short-term contacts and local social relations, but have not considered each node’s average relation with the destination node. In this paper, we propose novel message sending techniques for opportunistic networks using nodes’ social network characteristics. In this scheme, each message is delivered to the destination node with fewer copies by delivering it mostly through nodes that have high intimacy with the destination node. We are showing that our proposed scheme presents a 20% performance increase compared to existing schemes.
An Extended DDN based Self-Adaptive System
Misoo Kim, Hohyeon Jeong, Eunseok Lee
In order to solve problems happening in the practical environment of complicated system, the importance of the self-adaptive system has recently begun to emerge. However, since the differences between the model built at the time of system design and the practical environment can lead the system into unpredictable situations, the study into methods of dealing with it is also emerging as an important issue. In this paper, we propose a method for deciding on the adaptation time in an uncertain environment, and reflecting the real-time environment in the system’s model. The proposed method calculates the Bayesian Surprise for the suitable adaptation time by comparing previous and current states, and then reflects the result following the performed policy in the design model to help in deciding the proper policy for the actual environment. The suggested method is applied to a navigation system to confirm its effectiveness.
A Re-configuration Scheme for Social Network Based Large-scale SMS Spam
Sihyun Jeong, Giseop Noh, Hayoung Oh, Chong-Kwon Kim
The Short Message Service (SMS) is one of the most popular communication tools in the world. As the cost of SMS decreases, SMS spam has been growing largely. Even though there are many existing studies on SMS spam detection, researchers commonly have limitation collecting users" private SMS contents. They need to gather the information related to social network as well as personal SMS due to the intelligent spammers being aware of the social networks. Therefore, this paper proposes the Social network Building Scheme for SMS spam detection (SBSS) algorithm that builds synthetic social network dataset realistically, without the collection of private information. Also, we analyze and categorize the attack types of SMS spam to build more complete and realistic social network dataset including SMS spam.
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.
The YouTube Video Recommendation Algorithm using Users" Social Category
With the rapid progression of the Internet and smartphones, YouTube has grown significantly as a social media sharing site and has become popular all around the world. As users share videos through YouTube, social data are created and users look for video recommendations related to their interests. In this paper, we extract users" social category based on their social relationship and social category classification list using YouTube data. We propose the YouTube recommendation algorithm using the extracted users" social category for more accurate and meaningful recommendations. We show experiment results of its validation.
An Improved Depth-Based TDMA Scheduling Algorithm for Industrial WSNs to Reduce End-to-end Delay
Hwakyung Lee, Sang-Hwa Chung, Ik-Joo Jung
Industrial WSNs need great performance and reliable communication. In industrial WSNs, cluster structure reduces the cost to form a network, and the reservation-based MAC is a more powerful and reliable protocol than the contention-based MAC. Depth-based TDMA assigns time slots to each sensor node in a cluster-based network and it works in a distributed manner. DB-TDMA is a type of depth-based TDMA and guarantees scalability and energy efficiency. However, it cannot allocate time slots in parallel and cannot perfectly avoid a collision because each node does not know the total network information. In this paper, we suggest an improved distributed algorithm to reduce the end-to-end delay of DB-TDMA, and the proposed algorithm is compared with DRAND and DB-TDMA.
MOnCa2: High-Level Context Reasoning Framework based on User Travel Behavior Recognition and Route Prediction for Intelligent Smartphone Applications
MOnCa2 is a framework for building intelligent smartphone applications based on smartphone sensors and ontology reasoning. In previous studies, MOnCa determined and inferred user situations based on sensor values represented by ontology instances. When this approach is applied, recognizing user space information or objects in user surroundings is possible, whereas determining the user’s physical context (travel behavior, travel destination) is impossible. In this paper, MOnCa2 is used to build recognition models for travel behavior and routes using smartphone sensors to analyze the user’s physical context, infer basic context regarding the user’s travel behavior and routes by adapting these models, and generate high-level context by applying ontology reasoning to the basic context for creating intelligent applications. This paper is focused on approaches that are able to recognize the user’s travel behavior using smartphone accelerometers, predict personal routes and destinations using GPS signals, and infer high-level context by applying realization.
An Intention-Response Model based on Mirror Neuron and Theory of Mind using Modular Behavior Selection Networks
Although service robots in various fields are being commercialized, most of them have problems that depend on explicit commands by users and have difficulty to generate robust reactions of the robot in the unstable condition using insufficient sensor data. To solve these problems, we modeled mirror neuron and theory of mind systems, and applied them to a robot agent to show the usefulness. In order to implement quick and intuitive response of the mirror neuron, the proposed intention-response model utilized behavior selection networks considering external stimuli and a goal, and in order to perform reactions based on the long-term action plan of theory of mind system, we planned behaviors of the sub-goal unit using a hierarchical task network planning, and controled behavior selection network modules. Experiments with various scenarios revealed that appropriate reactions were generated according to external stimuli.
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