Mining WiFi Data for Business Intelligence

Abstract

The WiFi networks provide an ease of accessing email, Web, and other Internet applications while on the move. However, deploying additional WiFi hotspots that can provide both increased coverage and enhance user quality of service largely depends upon the number of access points already existing and user densities. Extracting usage patterns and information from the available data has the potential to answer several business-focussed questions. In this paper, we show that by plotting WiFi locations in a two-dimensional space of incoming (downloading) and outgoing (uploading) data amount, in conjunction with the simple k-means clustering, it is possible to gain insight into the basic data usage patterns. When combined with information about geographic location of the WiFi hotspots such analysis can answer questions related to spatial patterns of data usage and make informed business decisions including charging customers at selected locations for WiFi service.

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