Discrete- and Continuous-State Trajectory Decoders for Positioning in Wireless Networks

Rudic, Branislav; Pichler-Scheder, Markus; Efrosinin, Dmitry; Putz, Veronika; Schimbäck, Erwin; Kastl, Christian; Auer, Wolfgang

doi:10.1109/tim.2020.2973841

Cited by 5 publications

(1 citation statement)

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“…While the parametrization is defined and provided by the retail store in a preprocess step, the dynamic decoding of the MAP trajectory takes place solely on the smartphone. Compared to MAP position decoding, trajectory decoding results in a considerably better context of consecutive positions [12]. The apriori consideration of uncertainty, geometry and motion dynamics in the context of a probabilistic model results in robust position estimations.…”

Section: Localization System (Locsys)mentioning

confidence: 99%

Augmented Shopping Experience for Sustainable Consumption Using the Internet of Thing

Hormann

Putz

Rudic³

et al. 2019

IEEE Internet Things M.

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The digital world offers ample availability of data, both historic and real-time. While this capability has the potential for a better decision-making, the contrary can be the case for a human actuator. Information overflow causes mental overload rather than empowerment of choice. In the context of the traditional supermarket shopping for example, customers are exposed to unstructured and complex product information including ingredients, nutrition facts, product labels, and more. Processing all this information in the context of multiple sustainability aspects requires expert knowledge. On the other hand, the rise of digitalization and the Internet of Things can be used to assist and empower customers during this shopping process. However, an integrated solution is required to provide a high grade of usability and the crucial complexity reduction for customers. Therefore, we outline an IoT decision-support system which assists customers on the sales floor and enables a better decision-making according to personal preferences and sustainable consumption. It integrates an indoor localization system, a product information database and a ranking system considering the individual shopping preferences, where the latter is specified by the customer within an interactive smartphone application. The discussed IoT decision support system was deployed and tested in two retail stores. Its interaction with non-expert test participants was observed over months and the results are summarized in this contribution.

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Section: Localization System (Locsys)mentioning

confidence: 99%