Fine-Grained Geographic Communication (Geocast) Nexus Workshop - PowerPoint PPT Presentation

Fine-Grained Geographic Communication (Geocast) Nexus Workshop Frank Dürr 23.07.2003 1

Overview � Motivation � Requirements for Fine-Grained Geocast � Location Model for Fine-Grained Geographic Addressing � Summary � Related Work � Future Work University of Stuttgart 2 Center of Excellence 627

Motivation for Fine-Grained Geocast “Keep windows shut because � Geocast = Sending messages of toxic smoke!“ to users in certain geographic area Navtech Quelle: Maporama, � Messages can be addressed � Geometrically � Polygons, circles, cubes, etc. � Arbitrary areas � Geometric pos. sys., e.g. GPS circle(48.7340540N, 9.11159641E, 100) � Symbolically � Building/room numbers, etc. � Intuitive to use � Symbolic pos. sys, e.g. IR- based � Hybrid Send this PowerPoint presentation University of Stuttgart 3 Center of Excellence 627 to everyone in room 0 351

Requirements for Fine-Grained Geocast � Fine-grained geographic addressing � Geometric addressing � Symbolic addressing � Hybrid addressing � Mobile target areas, e.g. trains, ships, etc. � Requires fine-grained hybrid location model � Efficient Geocast Routing � Efficient message forwarding � Scalability � Easy integration in existing IP infrastructure � Fault tolerance � Routing protocols for fine-grained geocast University of Stuttgart 4 Center of Excellence 627

Overview � Motivation � Requirements for Fine-Grained Geocast � Location Model for Fine-Grained Geographic Addressing � Summary � Related Work � Future Work University of Stuttgart 5 Center of Excellence 627

Role of Location Model for Geocast � Target area definition � Client position/area definition � Key question: “Is client inside target area?“ � Comparison of target area and client position required � Problems � Inaccurate client positions � Probabilities for client being in target area � Heterogeneous target area and client areas � Translation of target area or client area University of Stuttgart 6 Center of Excellence 627

Hierarchical Symbolic Location Model � Building contains floors; floors contain rooms � Hierarchy of locations B B W 1 W 2 F 1 F 2 F 2 R 3 R 4 R 5 R 1 R 2 R 3 R 4 R 5 F 1 R 1 R 2 • Rooms are contained in floors and wings • Floors are not contained in wings; wings not in floors • Tree cannot reflect reality � Need of more powerful model University of Stuttgart 7 Center of Excellence 627

Lattice-Based Symbolic Location Model everywhere B B F 1 W 1 W 2 F 2 W 1 W 2 F 1 W 1 F 1 W 2 F 2 W 1 F 2 W 2 F 2 R 3 R 4 R 5 F 1 R 1 R 2 R 1 R 2 R 3 R 4 R 5 nowhere � Set L of symbolic locations � Partial order ≤ defined by the spatial contains relationship, i.e. for two locations l 1 , l 2 ∈ L it holds l 1 ≤ l 2 , iff l 2 contains l 1 . � Hierarchy is a lattice For every pair l 1 , l 2 ∈ L , there exists a supremum sup ({ l 1 , l 2 }) and an infimum University of Stuttgart 8 inf ({ l 1 , l 2 }). Center of Excellence 627

Symbolic Addressing (1) addr.: de Country � Path in lattice determines addr.: berlin address: City <targetarea> <symbol>loc:/de/berlin/ addr.: keplerstrasse keplerstrase/8</symbol> Street </targetarea> addr.: 8 Building addr.: floor2 addr.: wing1 Floor Wing addr.: floor2 addr.: wing1 Location addr.: 72 addr.: 69 Room Room University of Stuttgart 9 Center of Excellence 627

Symbolic Addressing (1) addr.: de Country � Path in lattice determines addr.: berlin address: City <targetarea> <symbol>loc:/de/berlin/ addr.: keplerstrasse keplerstrasse/8</symbol> target Street area </targetarea> addr.: 8 � Comparison of target area t Building and client area c : addr.: floor2 addr.: wing1 � intersection = inf({ t , c }) Floor Wing � intersection = c � client inside t � intersection = nowhere addr.: floor2 addr.: wing1 � client outside t Location client addr.: 72 addr.: 69 area Room Room University of Stuttgart 10 Center of Excellence 627

Symbolic Addressing (2) client area � Comparison of target area t and client area c : Building � intersection = inf({ t , c }) addr.: floor1 addr.: floor2 � intersection = c p=0.5 p=0.5 � client inside t Floor Floor � intersection = nowhere � client outside t addr.: 72 addr.: 69 p=0.1 p=0.2 � intersection != c,nowhere Room Room � calculate client‘s probability p for being at intersection � deliver message if p > p = 0.5*0.1 = 0.05 target area threshold University of Stuttgart 11 Center of Excellence 627

Geometric Addressing � Geometric figures describe locations: point 5 height point 6 point 3 point 4 � 2D point 2 point 1 � 2.5D (2D + alt. + height) altitude � Geometric address: � Comparison of target area t and <targetarea> client area c: <polygon> <vertex> 9.126052E 48.721938N </vertex> ... </polygon> ( ∩ A c t ) = p with A(X) : area of figure X </targetarea> A ( c ) University of Stuttgart 12 Center of Excellence 627

Heterogeneous Addressing � Example Hybrid model of � Geometrically addressed building in Berlin message to Berlin (WGS84) � Symbolic user position: Building floor1/room72 in a building in extent=polygon<…> Berlin (ActiveBadge) addr.: floor2 addr.: floor1 � Question : How to compare Floor Floor these locations? extent= � Answer : Translate one addr.: room72 location to other Room representation. ... extent= � Associate symbolic locations approximated with geometric extent geometry University of Stuttgart 13 Center of Excellence 627

Hybrid Addressing scope of local reference system <targetarea> global geometric geometric <refsys> ref. sys. scope of <scope> city symb. ref. sys. <polygon>...</polygon> geometric </scope> building 9 <name>sys_building9</name> addr="floor2" </refsys> floor 1 floor 2 symbolic Name of local addr="room72" reference system <symbol> ... ... room2.72 loc:floor2/room72 symbolic geometric scope of geometric area </symbol> geometric inside room 2.72 coordinates relative ref. sys.=floor2/room72 </targetarea> to local reference system University of Stuttgart 14 Center of Excellence 627

Summary � Fine-grained geocast requires geometric and symbolic geographic addressing � Hybrid location model for addressing associated � Hierarchical symbolic locations (lattice-based) geometric and symbolic � Geometric locations: 2, 2.5D information � Local reference systems � Comparison of target area and client position: � Probabilities for inclusion of client position in target area � Translation of heterogeneous addresses University of Stuttgart 15 Center of Excellence 627

Related Work � Wolfgang Kainz and Max J. Egenhofer and Ian Greasley: Modeling spatial relations and operations with partially ordered sets . In International Journal of Geographic Information Systems, 7(3), 1993. � Ulf Leonhardt: Supporting location-awareness in open distributed systems. Imperial College London, Department of Computing, PhD thesis, 1998. � Max J. Egenhofer, Robert D. Franzosa: Point-set topological spatial relations , International Journal of Geographical Information Systems 5(2), 1991 � D. A. Randell, A. G. Cohn: Modelling topological and metrical properties in physical processes , Proceedings of the First International Conference on the Principles of Knowledge Representation and Reasoning, 1989 � Changhao Jiang and Peter Steenkiste: A hybrid location model with a computable location identifier for ubiquitous computing . In Proceedings of the Fourth International Conference on Ubiquitous Computing (UbiComp 2002), Sep. 2002. University of Stuttgart 16 Center of Excellence 627

Future Work � Geocast � Routing algorithms for fine-grained geocast � Geographic multicast � Addressing groups of users inside geographic area � Realiable geocast � Nexus in general � Integrate symbolic addressing � Further extensions of location model, e.g. graph-based approach University of Stuttgart 17 Center of Excellence 627

Discussion Thank you very much for your attention! Further information about location model for geocast: � Frank Dürr, Kurt Rothermel: On a location model for fine-grained geocast. To appear in Proceedings of the Fifth International Conference on Ubiquitous Computing (UbiComp 2003), Oct. 2003 � frank.duerr@informatik.uni-stuttgart.de University of Stuttgart 18 Center of Excellence 627