WLAN Location Determination via Clustering and Probability Distributions
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
The Location Stack: A Layered Model for Location in Ubiquitous Computing
WMCSA '02 Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications
Employing User Feedback for Fast, Accurate, Low-Maintenance Geolocationing
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Practical robust localization over large-scale 802.11 wireless networks
Proceedings of the 10th annual international conference on Mobile computing and networking
InfoRadar: group and public messaging in the mobile context
Proceedings of the third Nordic conference on Human-computer interaction
ACM SIGMOBILE Mobile Computing and Communications Review
Zone-based rss reporting for location fingerprinting
PERVASIVE'07 Proceedings of the 5th international conference on Pervasive computing
Automatic mitigation of sensor variations for signal strength based location systems
LoCA'06 Proceedings of the Second international conference on Location- and Context-Awareness
TraX: a device-centric middleware framework for location-based services
IEEE Communications Magazine
Designing middleware for context awareness in agriculture
Proceedings of the 5th Middleware doctoral symposium
Enhancing mobile interaction using WLAN proximity
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: context diversity - Volume Part III
Determining and locating the closest available resources to mobile collaborators
Expert Systems with Applications: An International Journal
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Detecting proximity and separation among mobile targets is a basic mechanism for many location-based services (LBSs) and requires continuous positioning and tracking. However, realizing both mechanisms for indoor usage is still a major challenge. Positioning methods like GPS cannot be applied there, and for distance calculations the particular building topology has to be taken into account. To address these challenges, this paper presents a novel approach for indoor proximity and separation detection, which uses location fingerprinting for indoor positioning of targets and walking distances for modeling the respective building topology. The approach applies efficient strategies to reduce the number of messages transmitted between the mobile targets and a central location server, thus saving the targets' battery power, bandwidth, and other resources. The strategies are evaluated in terms of efficiency and application-level accuracy based on numerous emulations on experimental data.