Abstract
Developing mobile geo-information systems for sensor web applications involves technologies that can access linked geographical and semantically related Internet information. Additionally, in tomorrow’s Web 4.0 world, it is envisioned that trillions of inexpensive micro-sensors placed throughout the environment will also become available for discovery based on their unique geo-referenced IP address. Exploring these enormous volumes of disparate heterogeneous data on today’s location and orientation aware smartphones requires context-aware smart applications and services that can deal with “information overload”. 3DQ (Three Dimensional Query) is our novel mobile spatial interaction (MSI) prototype that acts as a next-generation base for human interaction within such geospatial sensor web environments/urban landscapes. It filters information using “Hidden Query Removal” functionality that intelligently refines the search space by calculating the geometry of a three dimensional visibility shape (Vista space) at a user’s current location. This 3D shape then becomes the query “window” in a spatial database for retrieving information on only those objects visible within a user’s actual 3D field-of-view. 3DQ reduces information overload and serves to heighten situation awareness on constrained commercial off-the-shelf devices by providing visibility space searching as a mobile web service. The effects of variations in mobile spatial search techniques in terms of query speed vs. accuracy are evaluated and presented in this paper.
Highlights
Geospatial information is increasingly recognized as the common denominator in both today’s―web 2.0‖ peer-to-peer social network era and tomorrow’s ―web 4.0‖—where it is envisioned that the Internet becomes connected to trillions of micro-sensors placed into real-world objects of all types, all with their own unique 128 bit IP address [1]
In a 3D scenario, where flat 2D footprints no longer contain enough detail of the physical objects they represent, accurate geometries of different floors of a building are re-constructed to provide links to floor level and even window/door level detailed information. To prepare such 3D vector datasets of Dublin Institute of Technology (DIT), we attached non-spatial attributes to the various floors, windows, and doors of a selection of buildings, plus affixed a range of simulated environmental sensors to other scattered locations on a building’s façade. Together this provides the beginnings of a future Internet of Things (iThings) type environment for human interaction testing of our mobile 2D/3D visibility-based spatial search techniques in detailed sensor web landscapes (Figure 4)
The datasets used include 2D footprints of DIT campus with actual heights stored as a non-spatial attribute, 3D wireframe building outlines stored as 3D multi-polygons, and extruded 3D solids from the 2D footprints up to known building heights
Summary
Geospatial information is increasingly recognized as the common denominator in both today’s. In mobile spatial interaction (MSI) research, how to address the problem of optimizing information to personal needs through intelligent retrieval and display is essentially at the heart of this process Previous work in this regard proposed a Local Visibility Model which searches for information by considering a user’s actual field-of-view [2]. The result is a more accurate and expected search result for mobile LBS applications by returning information on only those objects visible within a user’s actual 3D field-of-view (Figure 1). Contrary to this visibility query, retrieving objects that are just out of sight would be possible by subtracting the visible resultset from a standard 2D range query cast at the same location.
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