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.Article Citation - WoS: 13Citation - Scopus: 18Graph Matching-Based Distributed Clustering and Backbone Formation Algorithms for Sensor Networks(Oxford University Press, 2010) Dağdeviren, Orhan; Erciyeş, KayhanClustering is a widely used technique to manage the essential operations such as routing and data aggregation in wireless sensor networks (WSNs). We propose two new graph-theoretic distributed clustering algorithms for WSNs that use a weighted matching method for selecting strong links. To the best of our knowledge, our algorithms are the first attempts that use graph matching for clustering. The first algorithm is divided into rounds; extended weighted matching operation is executed by nodes in each round; thus the clusters are constructed synchronously. The second algorithm is the enhanced version of the first algorithm, which provides not only clustering but also backbone formation in an energy-efficient and asynchronous manner. We show the operation of the algorithms, analyze them, provide the simulation results in an ns2 environment. We compare our proposed algorithms with the other graph-theoretic clustering algorithms and show that our algorithms select strong communication links and create a controllable number of balanced clusters while providing low-energy consumptions. We also discuss possible applications that may use the structure provided by these algorithms and the extensions to the algorithms. © The Author 2009. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.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 - Scopus: 1A Software Architecture for Shared Resource Management in Mobile Ad Hoc Networks(Springer Verlag, 2007) Dağdeviren, Orhan; Erciyeş, KayhanWe propose a three layer software architecture for shared resource management in mobile ad hoc networks(MANETs). At the lowest layer, the Merging Clustering Algorithm(MCA)[ll] partitions the MANET into a number of balanced clusters periodically. At the second layer, the Backbone Formation Algorithm(BFA) provides a virtual ring using the clusterheads found by MCA. Finally, an example resource management protocol which is a modified and scaled version of the Ricart-Agrawala algorithm implemented using the virtual ring structure is presented with the performance results.