PINSPOT: An oPen platform for INtelligent context-baSed Indoor POsiTioning

This work proposes PINSPOT; an open-access platform for collecting and sharing of context, algorithms and results in the cutting-edge area of indoor positioning. It is envisioned that this framework will become reference point for knowledge exchange which will bring the research community even closer and potentially enhance collaboration towards more effective and efficient creation of indoor positioning-related knowledge and innovation. Specifically, this platform facilitates the collection of sensor data useful for indoor positioning experimentation, the development of novel, self-learning, indoor positioning algorithms, as well as the enhancement and testing of existing ones and the dissemination and sharing of the proposed algorithms along with their configuration, the data used, and with their results

Contextual location in the home using Bluetooth Beacons

Location sensing is a key enabling technology for Ubicomp to support contextual interaction. However, the laboratories where calibrated testing of location technologies is done are very different to the domestic situations where “context” is a problematic social construct. This study reports measurements of Bluetooth beacons, informed by laboratory studies, but done in diverse domestic settings. The design of these surveys has been motivated by the natural environment implied in the Bluetooth beacon standards relating to the technical environment of the beacon to the function of spaces within the home. This research method can be considered as a situated, “ethnographic” technical response to the study of physical infrastructure that arises through social processes. The results offer insights for the future design of “seamful” approaches to indoor location sensing, and to the ways that context might be constructed and interpreted in a seamful manner

An Open Platform for Studying and Testing Context-Aware Indoor Positioning Algorithms

This paper presents an open platform for studying and analyzing indoor positioning algorithms. While other such platforms exist, our proposal features novelties related to the collection and use of additional context data. The platform is realized in the form of a mobile client, currently implemented on Android. It enables manual collection of radio-maps—i.e. fingerprints of Wi-Fi signals—while also allowing for amending the fingerprints with various context data which could help improve the accuracy of positioning algorithms. While this is a research-in-progress platform, an initial experiment was carried out and its results were used to justify its applicability and relevance