Pervasive and Mobile Computing
Contextual conditional models for smartphone-based human mobility prediction
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Automatically characterizing places with opportunistic crowdsensing using smartphones
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Autonomous place naming system using opportunistic crowdsensing and knowledge from crowdsourcing
Proceedings of the 12th international conference on Information processing in sensor networks
Learning and user adaptation in location forecasting
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
effSense: energy-efficient and cost-effective data uploading in mobile crowdsensing
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Reduce the Number of Sensors: Sensing Acoustic Emissions to Estimate Appliance Energy Usage
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
Presence-pattern aware service selection and composition in a smart space
Proceedings of the 5th Asia-Pacific Symposium on Internetware
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It is well known that people movement exhibits a high degree of repetition since people visit regular places and make regular contacts for their daily activities. This paper1 presents a novel framework named Jyotish2, which constructs a predictive model by exploiting the regular pattern of people movement found in real joint Wifi/Bluetooth trace. The constructed model is able to answer three fundamental questions: (1) where the person will stay, (2) how long she will stay at the location, and (3) who she will meet. In order to construct the predictive model, Jyotish includes an efficient clustering algorithm to exploit regularity of people movement and cluster Wifi access point information in Wifi trace into locations. Then, we construct a Naive Bayesian classifier to assign these locations to records in Bluetooth trace. Next, the Bluetooth trace with assigned locations is used to construct predictive model including location predictor, stay duration predictor, and contact predictor to provide answers for three questions above. Finally, we evaluate the constructed predictors over real Wifi/Bluetooth trace collected by 50 participants in University of Illinois campus from March to August 2010. Evaluation results show that Jyotish successfully constructs a predictive model, which provides a considerably high prediction accuracy of people movement.