Browsing by Subject "Optimal locations"

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    Optimal jammer placement in wireless localization networks
    (IEEE, 2015-06-07) Gezici, Sinan; Bayram, S.; Gholami, M. R.; Jansson, M.
    The optimal jammer placement problem is proposed for a wireless localization network, where the aim is to degrade the accuracy of locating target nodes as much as possible. In particular, the optimal location of a jammer node is obtained in order to maximize the minimum of the Cramér-Rao lower bounds for a number of target nodes under location related constraints for the jammer node. Theoretical results are derived to specify scenarios in which the jammer node should be located as close to a certain target node as possible, or the optimal location of the jammer node is determined by two or three of the target nodes. In addition, explicit expressions for the optimal location of the jammer node are derived in the presence of two target nodes. Numerical examples are presented to illustrate the theoretical results. © 2015 IEEE.
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    Predicting optimal facility location without customer locations
    (ACM, 2017-08) Yilmaz, Emre; Elbaşı, Sanem; Ferhatosmanoğlu, Hakan
    Deriving meaningful insights from location data helps businesses make better decisions. One critical decision made by a business is choosing a location for its new facility. Optimal location queries ask for a location to build a new facility that optimizes an objective function. Most of the existing works on optimal location queries propose solutions to return best location when the set of existing facilities and the set of customers are given. However, most businesses do not know the locations of their customers. In this paper, we introduce a new problem setting for optimal location queries by removing the assumption that the customer locations are known. We propose an optimal location predictor which accepts partial information about customer locations and returns a location for the new facility. The predictor generates synthetic customer locations by using given partial information and it runs optimal location queries with generated location data. Experiments with real data show that the predictor can find the optimal location when sufficient information is provided. © 2017 Copyright held by the owner/author(s).