2014Unpublished venueRequires access

Indoor Next Location Prediction with Wi-Fi

Boon-Khai Ang, Ziheng Lin Daniel Dahlmeier

Open publisher page 8 citations

Abstract

Indoor Location Intelligence is a novel application that relates indoor localization technology to business data to allow for better decision making for retail businesses. In this context, Wi-Fi technology has a big potential for localization of customers who move through the store. With this information, retailers are able to analyze shopper movement behavior when formalizing their business strategies. This paper evaluates the accuracy of next location prediction based on a Markov-chain model for forecasting the next location of a customer in a shop based on the last n locations he has visited. We report experiments on a real data set and achieve prediction accuracies of up to 37 % for n=1 and 49 % for n=2.

About this research paper

What this paper is about

Indoor Location Intelligence is a novel application that relates indoor localization technology to business data to allow for better decision making for retail businesses. In this context, Wi-Fi technology has a big potential for localization of customers who move through the store. With this information, retailers are able to analyze shopper movement behavior when formalizing their business strategies. This paper evaluates the accuracy of next location prediction based on a Markov-chain model for forecasting the next location of a customer in a shop based on the last n locations he has visited. We report experiments on a real data set and achieve prediction accuracies of up to 37 % for n=1 and 49 % for n=2.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Indoor Location Intelligence is a novel application that relates indoor localization technology to business data to allow for better decision making for retail businesses. In this context, Wi-Fi technology has a big potential for localization of customers who move through the store. With this information, retailers are able to analyze shopper movement behavior when formalizing their business strategies. This paper evaluates the accuracy of next location prediction based on a Markov-chain model for forecasting the next location of a customer in a shop based on the last n locations he has visited. We report experiments on a real data set and achieve prediction accuracies of up to 37 % for n=1 and 49 % for n=2.

Key concepts: Computer science, Context (archaeology), Big data, Hidden Markov model, Business intelligence, Location data, Set (abstract data type), Markov chain

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