Browsing by Author "Kantarci, A"

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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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    Gingival crevicular fluid levels of TLR-9, AIM-2, and ZBP-1 in periodontal diseases
    Yilmaz, B; Emingil, G; Öztürk, VÖ; Atmaca, H; Köse, T; Kantarci, A
    Objectives: Toll-like receptor (TLR)-9, may play a role in periodontal disease inflammation. This study measured TLR-9 and its related molecules, absence in melanoma-2 (AIM-2) and Z-DNA-binding protein-1 (ZBP-1), in gingival crevicular fluid (GCF) from patients with varying stages of periodontal disease to assess the role of pathogen-derived nucleic acids in inflammation. Materials and Methods: The study comprised 80 participants: 20 with Stage III Grade C periodontitis, 20 with Stage III Grade B periodontitis (P-Stage III-B), 19 with gingivitis, and 21 with periodontal health. Parameters including probing depth (PD), clinical attachment level (CAL), plaque index (PI), and bleeding on probing (BOP) were recorded. ELISA was used to analyze TLR-9, AIM-2, and ZBP-1 levels in GCF. Nonparametric tests were used for statistical comparisons. Results: The total amount of TLR-9 was higher in P-Stage III-B than in the healthy group (p < 0.05). Similarly, the gingivitis group exhibited elevated GCF TLR-9 levels compared to the healthy group (p < 0.05). GCF AIM-2 and ZBP-1 levels remained consistent across groups (p > 0.05). Significant correlations were found between GCF TLR-9 and CAL (p < 0.05), BOP (p < 0.05), PI (p < 0.01), and GCF volume (p < 0.001). Conclusion: These findings suggested that the TLR-9-mediated inflammatory process plays a role in periodontal disease, as evidenced by the increased levels of TLR-9 in GCF.