Dept. of Computer Science Distributed Computing Group Asymptotically Optimal Mobile Ad-Hoc Routing Fabian Kuhn Roger Wattenhofer Aaron Zollinger.

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Presentation transcript:

Dept. of Computer Science Distributed Computing Group Asymptotically Optimal Mobile Ad-Hoc Routing Fabian Kuhn Roger Wattenhofer Aaron Zollinger

Distributed Computing Group Overview Introduction Model Face Routing Adaptive Face Routing Lower Bound Conclusion

Distributed Computing Group Model I (Ad-Hoc Network) Nodes are points in R 2 All nodes have the same transmission range (normalized to 1) ) network is a unit disk graph Distance between any two nodes is lowerbounded by a constant d 0 )  (1) -model

Distributed Computing Group Model II (Geometric Routing) Nodes know the geometric positions of themselves and of their neighbors Source s knows the coordinates of destination t Nodes not allowed to store anything In the message, only O(logn) additional bits can be stored

Distributed Computing Group Cost Model I Cost of sending a message over a link (edge) e is c(e) Cost of a path p is the sum over the costs of its edges Cost of a routing algorithm A is the sum over the costs of the traversed edges

Distributed Computing Group Cost Model II 3 different cost metrics: Link distance metric ( c (e) ´ 1 ) Euclidean distance ( c d (e) ) Energy metric ( c E (e):=c d 2 (e) ) more general: c E (e):=c d  (e) for an  ¸ 2

Distributed Computing Group Costs are Equivalent Lemma: In the  (1) -model the link, Euclidean, and energy metrics of a path or an algorithm are equivalent up to a constant factor.

Distributed Computing Group Face Routing geometric routing algorithm for planar graphs Compass Routing on Geometric Networks [Kranakis, Singh, Urrutia; 1999]

Distributed Computing Group Face Routing

Distributed Computing Group Face Routing (Faces)

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing st

Distributed Computing Group Face Routing (Analysis) Lemma: Face Routing always finds a path to the destination. The total cost of Face Routing is O(n).

Distributed Computing Group Face Routing (Analysis) Proof Sketch: each face is explored at most once each edge is traversed at most four times ) there are at most 3n-6 edges (Euler formula)

Distributed Computing Group Problem of Face Routing Face Routing always reaches destination However, even if source and destination are close to each other, Face Routing can take O(n) steps. We would like to have an algorithm, whose cost is a function of the cost of an optimal path.

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st Ellipse is too small, go back!

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group Adaptive Face Routing st

Distributed Computing Group AFR on the Unit Disk Graph AFR needs a planar graph, UDG is not planar need a planar subgraph of UDG: –simple distributed construction –spanner for link distance, Euclidean, and energy metric

Distributed Computing Group Two nodes u and v are connected by an edge iff the circle with uv as diameter contains no other node. Gabriel Graph Definition: u v

Distributed Computing Group Properties of GG Å UDG For each pair of nodes, the GG ( Å UDG) contains an energy optimal path (on UDG). ) spanner for link, Eucl. dist., energy (  (1) -model) planar no additional communication ) meets all our requirements

Distributed Computing Group AFR Complexity Theorem 1: Let c * be the cost (link, Euclidean, or energy) of an optimal path between two nodes on the UDG. Applying AFR on GG Å UDG then terminates with cost O(c * 2 ).

Distributed Computing Group AFR Complexity, Proof I Lemma: For each used ellipse E, the cost is linear in the number of nodes in E. Collorary: In the  (1) -model, for each used ellipse E, the cost is linear in area covered by E.

Distributed Computing Group AFR Complexity, Proof II Lemma: The cost, AFR needs to route a packet, is linear in the area covered by the last used ellipse. Ellipses grow exponentially 

Distributed Computing Group AFR Complexity, Proof III Lemma: Using an ellipse E, AFR finds a path from s to t iff there is such a path inside E. Lemma: All paths of (Euclidean) length smaller or equal to c are inside an ellipse whose area is in O(c 2 ).

Distributed Computing Group AFR Complexity, Proof IV All the lemmas together now prove Theorem 1.

Distributed Computing Group Lower Bound Theorem 2: Let c * be the cost (link, Euclidean, or energy) of an optimal path between two nodes on a UDG G. For each geometric routing algorithm, there is a graph for which the cost is  (c * 2 ).

Distributed Computing Group Lower Bound, Proof w t s Cost of optimal path: Exp. cost for an algorithm A :

Distributed Computing Group Main Theorem Theorem 3: On the Unit Disk Graph in the  (1) -model, AFR is asymptotically optimal. (follows directly from Theorems 1 and 2)

Distributed Computing Group Conclusion I works fine for link and Euclidean distance (still  (c * 2 ) ) For energy, it can be shown that the cost of a geometric routing alg. cannot be bounded by a function of c * alone.  (1) restriction can be dropped by clustering:

Distributed Computing Group Conclusion II The lower bound holds for all routing algorithms which have only local knowledge at the beginning. if coordinates of dest. are not known, but if each node can store some bits: Then there is a simple flooding variant which achieves O(c * 2 ).