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Juzheng (Alex) Li and Sol M. Shatz Concurrent Software System Laboratory

Toward Using Node Mobility to Enhance Greedy Forwarding in Geographic Routing for Mobile Ad Hoc Networks. Juzheng (Alex) Li and Sol M. Shatz Concurrent Software System Laboratory Department of Computer Science University of Illinois at Chicago. Introduction.

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Juzheng (Alex) Li and Sol M. Shatz Concurrent Software System Laboratory

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  1. Toward Using Node Mobility to Enhance Greedy Forwarding in Geographic Routing for Mobile Ad Hoc Networks Juzheng (Alex) Li and Sol M. Shatz Concurrent Software System Laboratory Department of Computer Science University of Illinois at Chicago

  2. Introduction Geographic routing, especially the greedy forwarding mode in geographic routing is rapidly gaining its reputation in the context of wireless sensor networks and mobile ad hoc networks. Generally, it does not require and store global network topology information at each node. Resilient to frequent unpredictable topology changes

  3. An example Source Source Destination Destination

  4. But… … Greedy forwarding may not always work. It may fail simply due to a lack of “closer” neighbors.

  5. Perimeter routing Then the second phase of geographic routing is engaged: perimeter routing Planar graph construction & Face routing

  6. Motivation Keep the desired features: greedy forwarding Avoid undesired features: perimeter routing

  7. Observation • To deliver a packet, there are two ways: • Transmission hops (like shown previously) • Node mobility

  8. Our approach Revisit of greedy forwarding itself Progress vs Potential Motion Potential Motion Potential assisted greedy forwarding

  9. Our approach Revisit of greedy forwarding itself Progress vs Potential Motion Potential Motion Potential assisted greedy forwarding

  10. Revisit of greedy forwarding

  11. Our approach Revisit of greedy forwarding itself Progress vs Potential Motion Potential Motion Potential assisted greedy forwarding

  12. Progress vs. Potential • Progress: direct progress • Progress region • Potential: indirect contribution • Potential region

  13. Our approach Revisit of greedy forwarding itself Progress vs Potential Motion Potential Motion Potential assisted greedy forwarding

  14. Motion potential The “strength” to move closer to the destination.

  15. Motion potential calculation • Case 1: Node can move into destination’s direct communication range; • Case 2: Node will not achieve direct communication with destination • (Both based on node’s current motion)

  16. Motion potential calculation

  17. Our approach Revisit of greedy forwarding itself Progress vs Potential Motion Potential Motion Potential assisted greedy forwarding

  18. Mobility based Adaptive Greedy Forwarding (MAGF)

  19. Two cases Case 1: Source will keep the packet Source node has the highest motion potential score Case 2: Source will pass the packet to a “potential” node Source node’s score is not the highest

  20. Case 1 Tcache

  21. Case 2 Tcache + T0

  22. Simulation setup • Size of environment 2400 * 2000 • Node’s communication range 250 • GPSR: Greedy Perimeter Stateless Routing (standard geographic routing) • MAGF: Mobility based adaptive greedy forwarding

  23. Simulation results Delivery rate Network density Node mobility

  24. Simulation results • Average hop count: • Network density • Node mobility

  25. Future work (1) Delay - Energy tradeoff study (2) Sophisticated node mobility predication and corresponding motion potential calculation

  26. Thank you Any questions? & Any advice?

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