Indoor - A New Data Management Frontier

Much research has been conducted on the management of outdoor moving objects. In contrast, relatively little research has been conducted on indoor moving objects. The indoor setting differs from outdoor settings in important ways, including the following two. First, indoor spaces exhibit complex topologies. They are composed of entities that are unique to indoor settings, e.g., rooms and hallways that are connected by doors. As a result, conventional Euclidean distance and spatial network distance are inapplicable in indoor spaces. Second, accurate, GPS-like positioning is typically unavailable in indoor spaces. Rather, positioning is achieved through the use of technologies such as Bluetooth, Infrared, RFID, or Wi-Fi. This typically results in much less reliable and accurate positioning. This paper covers some preliminary research that explicitly targets an indoor setting. Specifically, we describe a graph-based model that enables the effective and efficient tracking of indoor objects using proximity-based positioning technologies like RFID and Bluetooth. Furthermore, we categorize objects according to their position-related states, present an on-line hash-based object indexing scheme, and conduct an uncertainty analysis for indoor objects. We end by identifying several interesting and important directions for future research.