People spend large parts of their lives in indoor spaces such as office buildings, shopping centers, airports, other transport infrastructures, etc. Meanwhile, such spaces are becoming increasingly large and complex. For example, according to the statistics available in 2008, the London Underground has 268 stations and a network of 408 kilometers. Each hour, 146,000 passengers enter its tube system; the total number of daily passengers exceeds 4 million.
Tracking moving objects in indoor spaces is very useful. Large volumes of tracking data enable a range services akin to those enabled by GPS-based positioning in outdoor settings. Example services include indoor navigation, personal security, and those providing insight into how and how much the indoor space is being used, which is important in planning applications and for the pricing of advertisement space and store rentals.
Motivated by these observations, the ISA project aims at finding effective and efficient methods to track, index and query indoor moving objects. Presence sensing technologies such as RFID and Bluetooth have been assumed as the underlying positioning infrastructure. In particular, we are interested in (1) data models that support efficient indoor trajectory construction from uncertain tracking data; (2) indexing structures that facilitate fast retrieval and queries on indoor tracking data; (3) useful query types that underlie important applications on indoor moving objects.