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Yayın Force-directed approaches to sensor localization(SIAM, 2006) Efrat, Alon; Forrester, David; Iyer, Anand; Kobourov, Stephen G.; Erten, CesimWe consider the centralized, anchor-free sensor localization problem. We consider the case where the sensor network reports range information and the case where in addition to the range, we also have angular information about the relative order of each sensor's neighbors. We experimented with classic and new force-directed techniques. The classic techniques work well for small networks with nodes distributed in simple regions. However, these techniques do not scale well with network size and yield poor results with noisy data. We describe a new force-directed technique, based on a multi-scale dead-reckoning, that scales well for large networks, is resilient under range errors, and can reconstruct complex underlying regions.Yayın Biclustering expression data based on expanding localized substructures(Springer-Verlag Berlin Heidelberg, 2009) Erten, Cesim; Sözdinler, MelihBiclustering gene expression data is the problem of extracting submatrices of genes and conditions exhibiting significant correlation across both the rows and the columns of a data matrix of expression values. We provide a method, LEB (Localize-and-Extract Biclusters) which reduces the search space into local neighborhoods within the matrix by first localizing correlated structures. The localization procedure takes its roots from effective use of graph-theoretical methods applied to problems exhibiting a similar structure to that of biclustering. Once interesting structures are localized the search space reduces to small neighborhoods and the biclusters are extracted from these localities. We evaluate the effectiveness of our method with extensive experiments both using artificial and real datasets.Yayın A robust biclustering method based on crossing minimization in bipartite graphs(Springer-Verlag Berlin, 2009) Erten, Cesim; Sözdinler, Melih[No abstract available]Yayın Force-directed approaches to sensor localization(Assoc Computing Machinery, 2010-09) Efrat, Alon; Forrester, David; Iyer, Anand; Kobourov, Stephen G.; Erten, Cesim; Kılıç, Yasin OzanAs the number of applications of sensor networks increases, so does the interest in sensor network localization, that is, in recovering the correct position of each node in a network of sensors from partial connectivity information such as adjacency, range, or angle between neighboring nodes. In this article, we consider the anchor-free localization problem in sensor networks that report possibly noisy range information and angular information about the relative order of each sensor's neighbors. Previously proposed techniques seem to successfully reconstruct the original positions of the nodes for relatively small networks with nodes distributed in simple regions. However, these techniques do not scale well with network size and yield poor results with nonconvex or nonsimple underlying topology. Moreover, the distributed nature of the problem makes some of the centralized techniques inapplicable in distributed settings. To address these problems we describe a multiscale dead-reckoning (MSDR) algorithm that scales well for large networks, can reconstruct complex underlying topologies, and is resilient to noise. The MSDR algorithm takes its roots from classic force-directed graph layout computation techniques. These techniques are augmented with a multiscale extension to handle the scalability issue and with a dead-reckoning extension to overcome the problems arising with nonsimple topologies. Furthermore, we show that the distributed version of the MSDR algorithm performs as well as, if not better than, its centralized counterpart, as shown by the quality of the layout, measured in terms of the accuracy of the computed pairwise distances between sensors in the network.
