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Geographic Routing in City Scenarios C. Lochert, M. Mauve, H. Fler,H. Hartenstein Mobile Computing and Communications Review 9(1): 69-72, 2005 . Slide by Jihyuk Choi ( Partly from authors poster presentation at MobiCom 2004). Introduction. Greedy Perimeter Stateless Routing (GPSR)
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Geographic Routing in City ScenariosC. Lochert, M. Mauve, H. Fler,H. HartensteinMobile Computing and Communications Review 9(1): 69-72, 2005 Slide by JihyukChoi ( Partly from authors poster presentation at MobiCom 2004)
Introduction • Greedy Perimeter Stateless Routing (GPSR) • Well worked for inter-vehicle communication on highways. • Not suitable for city environment due to radio obstacles • How do we use Position-based routing in urban areas?
GPSR: Assumptions • Nodes know • Their own position • Position of neighbors acquired beaconing • Position of the destination
Closest to D A GPSR: Greedy Routing S D • Find neighbors who are the closer to the destination • Forward the packet to the neighbor closest to the destination
GPSR: Benefits • A node only needs to remember the location info of one-hop neighbors • Routing decisions can be dynamically made
GPSR: Local optimum D u v x S No neighbor exists which is closer to the destination than itself
y D x w b u v a S GPSR: Repair Strategy Perimeter Forwarding
Reaches a local optimum GPSR Greedy Routing Repair Strategy have left a local optimum greedy works greedy fails
Greedy Perimeter Coordinator Routing • Assumption • Nodes do know if they are located on a junction • This information is sent within a beacon message
Detecting Junctions • Assumption • Node’s neighbor table is sent within a beacon message
GPCR NT • Comparing neighbor tables of direct neighbors (NT)
GPCR CC • Calculating correlation coefficient
Conclusion • GPCR combines position-based routing with local topological knowledge • In simulations, GPCR outperforms GPSR significantly and independently of a given junction detection algorithm • Calculating the correlation coefficient is sufficient to detect a junction