1. MNISIKLIS: Indoor LBS for All Vassilis Papataxiarhis, V.Riga, V. Nomikos, O.Sekkas, K.Kolomvatsos, V.Tsetsos, P. Papageorgas, S.Vourakis, S.Hadjiefthymiades, and G. Kouroupetroglou vpap@di.uoa.gr Department of Informatics and Telecommunications University of Athens – Greece LBS-2008, 26-28 Nov. 2008, Salzburg

2. Introduction MNISIKLIS - Project details National Project (July ’06 – December ‘07) Consortium Main Goal Provide universal indoor LBSs focusing on navigation Unisystems S.A. National and Kapodistrian University of Athens (NKUA) Technological Educational Institute of Piraeus

3. Architecture

4. Services Static Navigation Dynamic Navigation Where-Am-I Service Exploration Service Nearest Points-Of-Interest (POI)

5. Positioning Subsystem  Sensing technologies UHF RFIDs (i.e tags and reader) WiFi access points (RSSI) dead reckoning for pedestrian users 3-axis electronic compass 3-axis accelerometer  Fusion techniques two levels of data fusion in the Location Server 1 rst level: RFID tags and WLAN measurements 2 nd level: 1 rst level output + DR estimation

6. Sensing Components Sensor unit  attached to the user’s belt  data transfer through Bluetooth to the PDA Dead reckoning  filter raw accelerometer data  step detection  step length estimation  predicted walking distance of m steps Data collector (PDA)  executes the DR algorithm  collects RFID and RSSI measurements  data transfer through WLAN to the Location Server

7. Location Server (1/3)  N symbolic locations L i  bidirectional communication with the data collector  two levels of fusion  final estimation of user’s position

8. Location Server (2/3) Communication component:  communication with the data collector  validation of received data  quantization of the WLAN RSSI values  received vector’s format: 1 rst level fusion engine:  Dynamic Bayesian Network (DBN)  based on previous estimation of user's position  probability distributions - XMLBIF file  Output:

9. Location Server (3/3) DR data converter:  2D Euclidean distance (d i )  definition of threshold d t  probability reversely proportional to d i 2  Output: 2 nd level fusion engine:  weights w b +w c =1  combination formula: P i =w b *P bi +w c *P ci  Output: location with the highest probability  feedback for the DR and DBN  update database

10. Metadata Spatial Database Instances Creation Algorithm Ontology Instances Expressed in terms of OWL ontologies Spatial model instantiation through GIS metadata

11. Core Navigation Algorithm Hybrid rule-based algorithm. Takes into account : Route complexity Euclidean route distance User profile (capabilities and preferences) Steps: Create “user compatible” building graph based on user profile and application of access rules (in terms of SWRL) E.g. WheelChaired_User(?x) ^ Stairway(?y) isObstacleFor(?y,?x) Find the k-simplest paths Assign the total cost of each path as a function of rewards and penalties of the total path distance, preferences and perceptual rules

12. User Interaction Subsystem Main user groups Non-disabled Elderly With vision loss Locomotive disabled Multimodal Interfaces Visual, Audio and Haptic Modality Devices PDA, Tablet PC, Smart Phone, Mobile Phone Head-mounted screen, braille display, earphones

13. Functionality of UI subsystem Turn-by-turn algorithm Left/right turns Distances Info about near doors SVG map capabilities E.g., zooming, turning, moving Related photo 3 levels of detail

14. User Evaluation 20 users (5 per user group) 3 predefined scenarios Evaluation through questionnaires Positive Comments Dynamic Navigation Menu Sufficient instructions Negative Comments Delay in the delivery of instructions

15. Implementation Details ESRI ArcGIS software PostGIS spatial DB Batik SVG Toolkit (Apache Foundation) Protégé Ontology Editor Knowledge Representation Languages Ontology models in OWL-DL SWRL rules Jess for reasoning over SWRL Mascopt Library for graph creation and path search

16. Contributions and Future Work Main Contributions UHF RFID for proximity sensing Multi-sensor fusion process Multimodal user interfaces Human centered service logic Future Work Landmark-based navigation Kalman filtering for DR Path prediction techniques

17. QUESTIONS? Thank you! http://speech.di.uoa.gr/mnisiklis