Browsing by Author "Ambarkutuk, Murat"

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    A Grid based Indoor Radiolocation Technique Based on Spatially Coherent Path Loss Model
    Ambarkutuk, Murat (Virginia Tech, 2017)
    This thesis presents a grid-based indoor radiolocation technique based on a Spatially Coherent Path Loss Model (SCPL). SCPL is a path loss model which characterizes the radio wave propagation in an environment by solely using Received Signal Strength (RSS) fingerprints. The propagation of the radio waves is characterized by uniformly dividing the environment into grid cells, followed by the estimation of the propagation parameters for each grid cell individually. By using SCPL and RSS fingerprints acquired at an unknown location, the distance between an agent and all the access point in an indoor environment can be determined. A least-squares based trilateration is then used as the global fix of location the agent in the environment. The result of the trilateration is then represented in a probability distribution function over the grid cells induced by SCPL. Since the proposed technique is able to locally model the propagation accounting for attenuation of non-uniform environmental irregularities, the characterization of the path loss in the indoor environment and radiolocation technique might yield improved results. The efficacy of the proposed technique was investigated with an experiment comparing SCPL and an indoor radiolocation technique based on a conventional path loss model.
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    A Multi-Sensor Passive Occupant Localization
    Ambarkutuk, Murat (Virginia Tech, 2024-11-25)
    Indoor localization has emerged as a critical technology for enhancing the functionality and efficiency of smart environments. This dissertation focuses on vibro-localization, a novel IOL methodology that determines occupant positions by analyzing floor vibrations caused by footfall patterns. Unlike traditional localization techniques that rely on visual or radio-based sensing, vibro-localization leverages accelerometers fixed to the floor to capture vibro-measurements, offering a cost-effective and privacy-preserving alternative. The primary objective of this research is to address significant limitations in existing vibro-localization approaches, including sensor imperfections, measurement uncertainty, and complex wave dynamics. To this end, we develop comprehensive models that characterize both random and systematic errors introduced by accelerometers, integrating these models into the localization framework to enhance accuracy. Furthermore, we quantify the uncertainty in vibro-measurements and elucidate their contribution to localization errors, providing a robust foundation for error mitigation strategies. A key contribution of this work is the introduction of an information-theoretic Byzantine Sensor Elimination (BSE) algorithm. This algorithm assesses the reliability of vibro-measurement vectors by categorizing sensors into consistent and divergent subsets, thereby minimizing the impact of external uncertainties such as reflections and dispersion. Additionally, we propose multi-sensor vibro-localization techniques that aggregate data from multiple accelerometers, enhancing robustness against individual sensor inaccuracies and environmental variabilities. To accurately model wave propagation, this dissertation advances parametric models that account for dispersion, attenuation, and material inhomogeneities in the floor structure. These models facilitate precise occupant localization even with low-spectral resolution in transfer function estimates. Empirical validation using controlled experimental data demonstrates significant improvements in localization accuracy and precision over baseline methods, highlighting the efficacy of the proposed techniques. The outcomes of this research contribute to the development of economically feasible and ethically sound IOL technologies, broadening their applicability across various domains such as smart homes, healthcare, and energy management. By addressing critical challenges in sensor reliability and wave dynamics, this dissertation paves the way for more accurate, reliable, and scalable indoor localization systems.
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    A Multi-Sensor Stochastic Energy-Based Vibro-Localization Technique with Byzantine Sensor Elimination
    Ambarkutuk, Murat; Alajlouni, Sa’ed; Tarazaga, Pablo Alberto; Plassmann, Paul E. (MDPI, 2023-11-21)
    This paper presents an occupant localization technique that determines the location of individuals in indoor environments by analyzing the structural vibrations of the floor caused by their footsteps. Structural vibration waves are difficult to measure as they are influenced by various factors, including the complex nature of wave propagation in heterogeneous and dispersive media (such as the floor) as well as the inherent noise characteristics of sensors observing the vibration wavefronts. The proposed vibration-based occupant localization technique minimizes the errors that occur during the signal acquisition time. In this process, the likelihood function of each sensor—representing where the occupant likely resides in the environment—is fused to obtain a consensual localization result in a collective manner. In this work, it becomes evident that the above sources of uncertainties can render certain sensors deceptive, commonly referred to as “Byzantines.” Because the ratio of Byzantines among the set sensors defines the success of the collective localization results, this paper introduces a Byzantine sensor elimination (BSE) algorithm to prevent the unreliable information of Byzantine sensors from affecting the location estimations. This algorithm identifies and eliminates sensors that generate erroneous estimates, preventing the influence of these sensors on the overall consensus. To validate and benchmark the proposed technique, a set of previously conducted controlled experiments was employed. The empirical results demonstrate the proposed technique’s significant improvement (3~0%) over the baseline approach in terms of both accuracy and precision.