1. OntoNav: A Semantic Indoor Navigation System Pervasive Computing Research Group, Communication Networks Laboratory (CNL), Dept. of Informatics & Telecommunications, University of Athens C. Anagnostopoulos, V. Tsetsos, P. Kikiras, and S. Hadjiefthymiades 1st Workshop on Semantics in Mobile Environments - SME’05 (in conjunction with MDM’05) May 9 2005 Ayia Napa, Cyprus

2. SME 2005 – OntoNav: A Semantic Indoor Navigation System 2 Presentation Structure  Introduction  System Design  Conclusions

3. SME 2005 – OntoNav: A Semantic Indoor Navigation System 3 Indoor Location Based Services  Traditional LBS Navigation, find nearest POIs, etc. Based on geometric location modeling  Semantic LBS Intelligent service provisioning based on ontological knowledge representation and hybrid location modeling Human-centered services, suitable for people with disabilities Can be deployed to “pervasive environments”

4. SME 2005 – OntoNav: A Semantic Indoor Navigation System 4 Motivation  Complex and unknown built environments cannot be easily explored  People with disabilities face additional difficulties and put increased effort when following paths that eventually become non-traversable  Built environments are associated with rich semantics which may lead to intelligent services if exploited appropriately  OntoNav’s goal: to assist the path selection and end-to-end guidance processes using semantic modeling techniques

5. SME 2005 – OntoNav: A Semantic Indoor Navigation System 5 Presentation Structure  Introduction  System Design  Conclusions

6. SME 2005 – OntoNav: A Semantic Indoor Navigation System 6 OntoNav Architecture  Navigation Service (NAV)  Geometric Path Computation Service (GEO)  Semantic Path Selection Service (SEM) NAV SEM GEO User Profiles Ontology Repository Spatial DB Indoor Positioning System

7. SME 2005 – OntoNav: A Semantic Indoor Navigation System 7 System Functionality Spatial DB Graph creation algorithm GEO Geometric path computation (graph traversal) Building blueprints Building representation (graph) SEM User profile (capabilities and preferences) Indoor Navigation Ontology (INO) User and destination locations all walkable paths NAV Best traversable path Feature Extraction INO instances

8. SME 2005 – OntoNav: A Semantic Indoor Navigation System 8 Indoor Navigation Ontology (INO) I  Represents complex built environments, along with user models, from a navigation perspective  Imports concepts from indoor location ontology Building, Floor, Room, Corridor, … Since no such well-established ontology exists, aggregation/merging and extensions of existing indoor location models are pursued  INO currently undergoes a model-evaluate- reengineer process

9. SME 2005 – OntoNav: A Semantic Indoor Navigation System 9 Indoor Navigation Ontology (INO) II

10. SME 2005 – OntoNav: A Semantic Indoor Navigation System 10 User profiles  Describe the capabilities and preferences of users  A user profile (UP) contains: Physical navigation rules (e.g., wheelchair) Perceptual navigation rules (e.g., child) Routing preferences (e.g., calendar-driven)  A user typically selects a predefined UP and further adjusts it  UPs are implemented as sets of rules that use the INO vocabulary and are applied to INO instances e.g., if user x cannot walk and path p contains a vertical passage v of type “stairs” then p is excluded

11. SME 2005 – OntoNav: A Semantic Indoor Navigation System 11 NAV Service  Provides the interface between end-users and OntoNav  Receives user requests  Retrieves user position and location of destination  Handles path presentation issues

12. SME 2005 – OntoNav: A Semantic Indoor Navigation System 12 GEO Service  Inputs: (a) a planar graph that accumulates the floor sub-graphs, (b) user and destination locations  Output: all possible walkable paths  Edges=corridor segments, vertices=exits and passages (i.e., each location is reduced to a set of exits or passages)  Performs a hierarchical clustering in the graph for more efficient path discovery  Walkable paths are computed with a graph traversal algorithm, since no path can be excluded a priori  Computational complexity might be a problem

13. SME 2005 – OntoNav: A Semantic Indoor Navigation System 13 SEM Service  Inputs: (a) user profile, (b) all walkable paths  Output: “best” traversable path (BTP) and its anchors  The physical navigation rules and the routing preferences of the user profile are used for exclusion of non-traversable paths  From the remaining paths, the shortest is the BTP  The physical and perceptual navigation rules are applied to BTP in order to select the most appropriate anchors (landmarks) for its presentation E.g., for a blind or illiterate user, voice-enabled anchors should be selected along the BTP

14. SME 2005 – OntoNav: A Semantic Indoor Navigation System 14 Example: GEO Destination 5 walkable paths

15. SME 2005 – OntoNav: A Semantic Indoor Navigation System 15 Example: SEM Destination 2 traversable paths BTP is not the shortest path

16. SME 2005 – OntoNav: A Semantic Indoor Navigation System 16 Implementation Issues  OntoNav is currently in the development phase using: Web Ontology Language (OWL-DL) - navigation ontology Semantic Web Rules Language (SWRL) - user profiles SweetRules v2.0 - SWRL rules engine Racer – OWL reasoning engine PostGIS – spatial database  OntoNav is an advanced application in the Semantic Web domain with increased applicability in everyday life

17. SME 2005 – OntoNav: A Semantic Indoor Navigation System 17 Presentation Structure  Introduction  System Design  Conclusions

18. SME 2005 – OntoNav: A Semantic Indoor Navigation System 18 Added Value of OntoNav  A purely user-centric navigation system, that adheres to the Inclusive Design paradigm  Based on a hybrid location model (geographic and semantic) that: Enables more advanced interpretations of distance than the Euclidean one Introduces user-defined quality metrics to the path selection process  Suitable for “intelligent context-aware environments”

19. SME 2005 – OntoNav: A Semantic Indoor Navigation System 19 Future Work  Inference of user status for assistance during the navigation process e.g., identification of lost, wandering, stationary, or deviated users  Decrease computational complexity GEO service integrates a graph traversal which is a greedy algorithm Further work: bypass the GEO service and prune non-traversable paths using only the semantic model  OntoNav URL: http://p-comp.di.uoa.gr/projects/ontonav