Browsing by Author "Alaybeyoglu A."
Now showing 1 - 9 of 9
Results Per Page
Sort Options
Item A distributed wakening based target tracking protocol for wireless sensor networks(2010) Alaybeyoglu A.; Dagdeviren O.; Kantarci A.; Erciyes K.We propose a two layer protocol for tracking fast targets in sensor networks. At the lower layer, the Distributed Spanning Tree Algorithm (DSTA) [12] partitions the network into clusters with controllable diameter and constructs a spanning tree backbone of clusterheads rooted at the sink. At the upper layer, we propose a target tracking algorithm which wakes clusters of nodes by using the estimated trajectory beforehand, which is different from existing studies [3] in which target can be detected only when the nodes close to the target are awake. We provide the simulation results and show the effect of fore-waking operation by comparing error and miss ratios of existing approaches with our proposed target tracking algorithm. © 2010 IEEE.Item A Dynamic Distributed Tree Based Tracking Algorithm for Wireless Sensor Networks(2010) 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. © Springer-Verlag Berlin Heidelberg 2010.Item Energy efficient target tracking with particle filtering technique in wireless sensor networks(2011) Alaybeyoglu A.In this study, a new target tracking algorithm is proposed for wireless sensor networks. The aim of the algorithm is to decrease energy consumption of the system by decreasing the ratio of target misses. Next location of the target is predicted by using Particle Filtering (PF) technique which aims to represent the posterior density function by a set of random samples with associated weights. Nodes are deployed according to the hexagon shaped network topology in which each of the hexagons represents a cluster with a predetermined leader node. In order to decrease the ratio of target misses, nodes that are closer to the target's predicted location are woken up to make them ready to detect the target. This increases the probability of detecting the target by one of the neighboring hexagons when the target makes sudden turns or unexpected movements. Tracking performance of the proposed algorithm is evaluated by comparing with KNearest Cluster Tracking (KNCT), Wakening Based Target Tracking Algorithm (WBTA)[10] and Generic Static Tracking Approach (GSTA) in terms of miss ratio and energy consumption metrics. © Association for Scientific Research.Item An adaptive cone based distributed tracking algorithm for a highly dynamic target in wireless sensor networks(Inderscience Publishers, 2013) 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. © 2013 Inderscience Enterprises Ltd.Item A new distributed location-based routing algorithm for wireless sensor networks(2014) 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. Copyright © 2014 Taylor & Francis Group, LLC.Item A dynamic lookahead tree based tracking algorithm for wireless sensor networks using particle filtering technique(Elsevier Ltd, 2014) 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. © 2013 Elsevier Ltd. All rights reserved.Item A distributed image compression algorithm for wireless multimedia sensor networks(Old City Publishing, 2015) 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. © 2015 Old City Publishing, Inc.Item Performance evaluation of learning styles based on fuzzy logic inference system(John Wiley and Sons Inc., 2016) Ozdemir A.; Alaybeyoglu A.; Mulayim N.; Balbal K.F.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. © 2016 Wiley Periodicals, Inc Comput Appl Eng Educ 24:853–865, 2016; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21754. © 2016 Wiley Periodicals, Inc.Item An intelligent system for determining learning style(International Journal of Research in Education and Science, 2018) Ozdemir A.; Alaybeyoglu A.; Mulayim N.; Uysal M.In this study, an intelligent system which determines learning style of the students is developed to increase success in effective and easy learning. The importance of the proposed software system is to determine convenience degree of the student’s learning style. Personal information form and Dunn Learning Style Preference Survey are used to collect the data which are analyzed using the techniques of mean, standard deviation and t-test. Gender, age, and year of education are selected as the metrics for evaluation of the system performance. © 2018, International Journal of Research in Education and Science. All rights reserved.