Browsing by Author "Alaybeyoglu, A"

Now showing 1 - 7 of 7
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    Performance Evaluation of Learning Styles Based on Fuzzy Logic Inference System
    Ozdemir, A; Alaybeyoglu, A; Mulayim, N; Balbal, KF
    Determining best convenient learning style in accordance with the individual's capabilities and personalities is very important for learning rapidly, easily, and in high quality. When it is thought that each individual has different personality and ability, it can be recognized that each individual's best convenient learning style will be different. Because of the importance of lifelong learning, many methods and approaches have been developed to determine learning styles of the individuals. In this study, a rule based fuzzy logic inference system is developed to determine best convenient learning styles of the engineering faculty stuffs and the students. During studies, two different learning style models namely Honey&Mumford and McCarthy are used in implementations. This study is carried out with a total number of 60 and 26 engineering faculty students and stuffs, respectively. The personal information form and Learning Style Preference Survey of Honey&Mumford and McCarthy are used to collect the data which are analyzed using the techniques of frequency, percentage, mean, standard deviation, and t-test. While Honey&Mumford learning style classifies engineering faculty students and stuffs as Activist, Reflector, Theorist, and Pragmatist; McCarthy learning style classifies them as Innovative, Analytic, Common Sense, and Dynamic. Gender, age, and department are selected as the metrics for evaluation of the system performance. (C) 2016 Wiley Periodicals, Inc
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    A dynamic lookahead tree based tracking algorithm for wireless sensor networks using particle filtering technique
    Alaybeyoglu, A; Kantarci, A; Erciyes, K
    In this study, five different algorithms are provided for tracking targets that move very fast in wireless sensor networks. The first algorithm is static and clusters are formed initially at the time of network deployment. In the second algorithm, clusters that have members at one hop distance from the cluster head are provided dynamically. In the third algorithm, clustered trees where members of a cluster may be more than one hop distance from the cluster head are provided dynamically. In the fourth, algorithm lookahead trees are formed along the predicted trajectory of the target dynamically. Linear, Kalman and particle filtering techniques are used to predict the target's next state. The algorithms are compared for linear and nonlinear motions of the target against tracking accuracy, energy consumption and missing ratio parameters. Simulation results show that, for all cases, better performance results are obtained in the dynamic lookahead tree based tracking approach. (C) 2013 Elsevier Ltd. All rights reserved.
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    An adaptive cone based distributed tracking algorithm for a highly dynamic target in wireless sensor networks
    Alaybeyoglu, A; Erciyes, K; Kantarci, A
    Accurate tracking of a target is imperative in military as well as civil applications. In this study, we propose a distributed cone based tracking algorithm for a target that can move with highly dynamic kinematics along linear and nonlinear trajectories. The algorithm provides wakening of a group of nodes in a cone shaped region along the trajectory of the target and particle filtering is used in the prediction of the next state of the target to decrease the target missing ratios. Algorithm used is adaptive in that the shape of the cone is determined dynamically in accordance with the target kinematics. We compared our algorithm with traditional tracking approaches, a recent tracking algorithm (Semi-Dynamic Clustering (SDC)) and other tracking algorithms that we have previously proposed. Simulation results show that, in terms of target missing ratios, our algorithm is superior to all of these algorithms. Secondly, lower target missing ratios lead to less frequent execution of recovery mechanisms which in turn results in lower energy consumptions.
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    A Dynamic Distributed Tree Based Tracking Algorithm for Wireless Sensor Networks
    Alaybeyoglu, A; Kantarci, A; Erciyes, K
    We propose a dynamic, distributed tree based tracking algorithm for very fast moving targets in wireless sensor networks, with speeds much higher than reported in literature. The aim of our algorithm is to decrease the miss ratio and the energy consumption while tracking objects that move in high speeds. In order to do this, the root node which is determined dynamically in accordance with the node's distance to the target, forms lookahead spanning trees along the predicted direction of the target. As the miss ratio decreases, the usage of recovery mechanisms which are employed to detect a target again that is moving away from the predicted trajectory also decreases. This decrease reduces the energy consumption and increases the network lifetime. We describe all the phases of the algorithm in detail and show by simulations that the proposed algorithm performs well to track very fast moving targets. We also compare the algorithm with the generic cluster, generic tree and dynamic multi cluster based tracking algorithms in terms of miss ratio and energy consumption.
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    A New Distributed Location-Based Routing Algorithm for Wireless Sensor Networks
    Basaran, I; Alaybeyoglu, A
    Searching and routing procedures are important in order to ensure communication in wireless sensor networks (WSN). Although naive flooding-based searching is simple to implement, it costs a high number of message transmissions and results in high energy consumption. In this study, we propose a new distributed location-based routing algorithm for WSN. Our goal was to decrease the number of message transmissions and to increase coverage by constructing relay zones. Directed broadcast, relay zone, and broadcast suppression constitute the backbone of our algorithm. We compared our algorithm with a flooding-based approach, and saw that our algorithm performs much better for several parameters.
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    A Distributed Image Compression Algorithm for Wireless Multimedia Sensor Networks
    Alaybeyoglu, A
    Recent advances in multimedia sensor networks made the image sensors applicable in many different types of sensor network applications such as environmental monitoring, surveillance and target tracking. Especially for image based applications, image data is needed to be compressed due to the requirement of large amount of data for representing the visual data. Although image compression is made for gaining from the energy consumption, this process requires very high computational power when an only single node takes this process on. Because of the constraints in energy, memory and the computational power, nodes should share the image processing tasks to balance the computational load on the sensor networks. In order to achieve this, we propose a distributed image compression algorithm which is based on Discrete Wavelet Transform for wireless multimedia sensor networks. We compared the proposed algorithm both with the centralized and the distributed approaches. Simulation results show that the proposed algorithm performs better than the compared algorithms for energy consumption, system lifetime and the image quality parameters.