Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object Citation - Scopus: 5A Distributed Wakening Based Target Tracking Protocol for Wireless Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2010) Alaybeyoğlu, Ayşegül; Dağdeviren, Orhan; Kantarcı, Aylin; Erciyeş, KayhanWe 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.Conference Object Citation - WoS: 13Citation - Scopus: 14Performance Evaluation of Cluster-Based Target Tracking Protocols for Wireless Sensor Networks(Institute of Electrical and Electronics Engineers Inc., 2009) Alaybeyoğlu, Ayşegül; Dağdeviren, Orhan; Erciyeş, Kayhan; Kantarcı, AylinTarget tracking is an important application type for wireless sensor networks (WSN). Recently, various approaches [1-11] are proposed to maintain the accurate tracking of the targets as well as low energy consumption. Clustering is a fundamental technique to manage the scarce network resources [12-19]. The message complexity of an application can be significantly decreased when it is redesigned on top of a clustered network. Clustering has provided an efficient infrastructure in many existing studies [1-8]. The clusters can be constructed before the target enters the region which is called the static method [1-4] or clusters are created by using received signal strength (RSS) from target which is called the dynamic method [5-8]. In this paper we provide simulations of static and dynamic clustering algorithms against various mobility models and target speeds. The mobility models that we applied are Random Waypoint Model, Random Direct Model and Gauss Markov Model. We provide metrics to measure the tracking performance of both approaches. We show that the dynamic clustering is favorable in terms of tracking accuracy whereas the energy consumption of static clustering is significantly smaller. We also show that the target moving with Gauss Markov Model can be tracked more accurately than the other models.Conference Object Citation - WoS: 4Citation - Scopus: 4Merging Clustering Algorithms in Mobile Ad Hoc Networks(Springer Verlag, 2005) Dağdeviren, Orhan; Erciyeş, Kayhan; Çokuslu, DenizClustering is a widely used approach to ease implementation of various problems such as routing and resource management in mobile ad hoc networks (MANET)s. We first look at minimum spanning tree(MST) based algorithms and then propose a new algorithm for clustering in MANETs. The algorithm we propose merges clusters to form higher level clusters by increasing their levels. We show the operation of the algorithm and analyze its time and message complexities.