Predicting length of stay at WiFi hotspots

Justin Manweiler, Naveen Santhapuri, Romit Roy Choudhury, Srihari Nelakuditi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Today's smartphones provide a variety of sensors, enabling high-resolution measurements of user behavior. We envision that many services can benefit from short-term predictions of complex human behavioral patterns. While enablement of behavior awareness through sensing is a broad research theme, one possibility is in predicting how quickly a person will move through a space. Such a prediction service could have numerous applications. For one example, we imagine shop owners predicting how long a particular customer is likely to browse merchandise, and issue targeted mobile coupons accordingly - customers in a hurry can be encouraged to stay and consider discounts. Within a space of moderate size, WiFi access points are uniquely positioned to track a statistical framework for user length of stay, passively recording metrics such as WiFI signal strength (RSSI) and potentially receiving client-uploaded sensor data. In this work, we attempt to quantity this opportunity, and show that human dwell time can be predicted with reasonable accuracy, even when restricted to passively observed WiFi RSSI.

Original languageEnglish (US)
Title of host publication2013 Proceedings IEEE INFOCOM 2013
Number of pages9
StatePublished - Sep 2 2013
Externally publishedYes
Event32nd IEEE Conference on Computer Communications, IEEE INFOCOM 2013 - Turin, Italy
Duration: Apr 14 2013Apr 19 2013

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Other32nd IEEE Conference on Computer Communications, IEEE INFOCOM 2013

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering


Dive into the research topics of 'Predicting length of stay at WiFi hotspots'. Together they form a unique fingerprint.

Cite this