Browsing by Keyword "Unconstrained optimization"
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Item A novel range-free localization algorithm to turn connectivity traces and motion data into localization information(2014-09) Cabero, José María; Olabarrieta, Ignacio; Gil-López, Sergio; Ser, Javier Del; Martín, José Luis; Tecnalia Research & Innovation; IAThis paper presents a novel range-free localization algorithm that has been originally designed to help in the characterization of human behavior by turning connectivity traces of mobile nodes into localization information. It is based on an error function that uses connectivity between nodes and information about their maximum velocity and that is solved by iterative minimization using unconstrained optimization. The resultant trajectories are evaluated with respect to the localization solution space (LSS), a multi-dimensional space consisting of all solutions that satisfy completely the conditions of the problem. The algorithm is evaluated through extensive simulations in scenarios with different levels of connectivity and under regular and irregular conditions in the communications. The complementarity with other localization algorithms is presented by using their results as initialization stage for this algorithm. Finally, it is applied to a real database that provides information about Bluetooth-based connectivity and motion of people in an office scenario, rendering satisfactory results and hence, validating the practicability of the proposed algorithm as a framework to obtain localization traces.Item Range-free localization algorithm based on connectivity and motion(2014-10-17) Cabero, José María; Olabarrieta, Ignacio; Gil-López, Sergio; Del Ser, Javier; Martín, José Luis; Tecnalia Research & Innovation; IAInformation about the position of entities is very valuable in many fields. People, animals, robots and sensors are some examples of entities that have been targeted as nodes of interest for localization purposes. Technical advances in ubiquitous computing and wireless communications properties are very valuable means to obtain localization information. This paper presents a novel range-free localization algorithm based on connectivity and motion (LACM). The core of the algorithm is an error function that measures the error of the obtained trajectories with respect to the localization solution space, a multi-dimensional space that encompasses all solutions that satisfy completely the constraints of a range-free localization problem. LACM is a centralized method that can be used standalone or as a refinement phase for other localization methods. Limited-memory Broyden–Fletcher–Goldfarb–Shanno, an unconstrained optimization algorithm, is the numerical method used to minimize the error function. The performance of LACM is validated both through extensive simulations with excellent results in scenarios with irregular communications and by transforming real Bluetooth connectivity traces into localization information.