Browsing by Author "Kantarci A."
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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 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 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.