The active badge location system
ACM Transactions on Information Systems (TOIS)
Self-Organizing Maps
Gaia: a middleware platform for active spaces
ACM SIGMOBILE Mobile Computing and Communications Review
A Context-Aware Security Architecture for Emerging Applications
ACSAC '02 Proceedings of the 18th Annual Computer Security Applications Conference
Towards Context-aware Security: An Authorization Architecture for Intranet Environments
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
A Framework for Developing Mobile, Context-aware Applications
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Guest Editors' Introduction: The Smart Phone--A First Platform for Pervasive Computing
IEEE Pervasive Computing
An Adaptation Methodology for the Deployment of Mobile Component-based Applications
PERSER '06 Proceedings of the 2006 ACS/IEEE International Conference on Pervasive Services
CARISMA: Context-Aware Reflective mIddleware System for Mobile Applications
IEEE Transactions on Software Engineering
Semantic context aware security policy deployment
Proceedings of the 4th International Symposium on Information, Computer, and Communications Security
Context-aware reconfiguration of autonomic managers in real-time control applications
Proceedings of the 7th international conference on Autonomic computing
A user-centric privacy framework for pervasive environments
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part II
The parameterless self-organizing map algorithm
IEEE Transactions on Neural Networks
Exploiting spatiotemporal and device contexts for energy-efficient mobile embedded systems
Proceedings of the 49th Annual Design Automation Conference
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Modern mobile computing devices are versatile, but bring the burden of constant settings adjustment according to the current conditions of the environment. While until today, this task has to be accomplished by the human user, the variety of sensors usually deployed in such a handset provides enough data for autonomous self-configuration by a learning, adaptive system. However, this data is not fully available at certain points in time, or can contain false values. Handling potentially incomplete sensor data to detect context changes without a semantic layer represents a scientific challenge which we address with our approach. A novel machine learning technique is presented - the Missing-Values-SOM - which solves this problem by predicting setting adjustments based on context information. Our method is centered around a self-organizing map, extending it to provide a means of handling missing values. We demonstrate the performance of our approach on mobile context snapshots, as well as on classical machine learning datasets.