Indoor Next Location Prediction with Wi-Fi
Boon-Khai Ang, Ziheng Lin Daniel Dahlmeier
Abstract
Boon-Khai Ang, Ziheng Lin Daniel Dahlmeier
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.
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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