1. 1 Presented by Jing Sun Computer Science and Engineering Department University of Conneticut

2.  +Highly scalable  O(1) route discovery  O(1) routing table  Path lengths are close to the shortest path  -Each node should node its geographic coordinates  -Greedy forwarding can be suboptimal because it does not use real connectivity info. 2

3. ◦ Simple – minimal complexity, with minimal assumptions about radio quality, presence of GPS, … ◦ Scalable – low control overhead, small routing tables ◦ Robust – node failure, wireless vagaries ◦ Efficient – low routing stretch 3

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6.  4 pieces ◦ Deriving positions ◦ Forwarding rules ◦ Beacon Maintenance ◦ Lookup: mapping node IDs  positions  Used from other work: ◦ Reverse path trees construction (Directed Diffusion) ◦ Consistent hashing to map node identities to its current coordinates 6

7.  Randomly select nodes as beacons. The beacon vectors serve as coordinates  r beacon nodes (B 0,B 1,…,B r ) flood the network;  P(q), a node q’s position, is its distance in hops to each beacon P(q) =  B 1 (q), B 2 (q),…,B r (q)   k, p, C(k,p), Node p advertises its coordinates using the k closest beacons (we call this set of beacons C(k,p))  Nodes know their own and neighbors’ positions  Nodes also know how to get to each beacon 7

8. 1. Define the distance between two nodes P and Q as 2. To reach destination Q, choose neighbor to reduce dist k (*,Q) 3. If no neighbor improves, enter Fallback mode: route towards the beacon which is closer to the destination 4. If Fallback fails, and you reach the beacon, do a scoped flood

9. 9 The sum of the differences for the beacons that are closer to the destination d than to the current routing node p The sum of the distances to the farther beacons We want to minimize:

10. 10 B1B1 B2B2 B3B3 1,2,3 0,3,3 3,0,3 2,1,2 1,3,2 3,3,0 2,3,1 3,2,1 2,2,2 Fallback towards B 1

11.  Route based on the beacons the source and destination have in common ◦ Does not require perfect beacon info.  Each entry in the beacon vector has a sequence number ◦ Periodically updated by the corresponding beacon ◦ Timeout  If the #beacons < r, non-beacon nodes nominate themselves as beacons ◦ Set a timer that is a function of its unique ID  If there’re more than r beacons ◦ A beacon stop being a beacon if there’re more than r beacons with smaller IDs 11

12.  First look up the destination coordinates by name  Hashing H: nodeid → beaconid [14] ◦ Use beacons as storage  Each node k that wants to be a destination periodically publishes its coordinates to its corresponding beacon b k = H(k)  When a node wants to route to node k, it sends a lookup request to b k  Cache the coordinates 12

13.  Assumptions for high level simulation ◦ Fixed circular radio range ◦ Ignore the network capacity and congestion ◦ Ignore packet losses  Place nodes uniformly at random in a square planner region ◦ 3200 nodes uniformly distributed in a 200 * 200 unit area ◦ Radio range is 8 units  Vary #total beacons and #routing beacons 13

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16.  At lower densities, each node has fewer immediate neighbors ◦ The performance of greedy routing drops ◦ Add a neighbor’s neighbors to the routing table, if greedy forwarding is impossible 16

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19.  Place horizontal & vertical walls with lengths of 10 or 20 units when the radio range is 8 units. BVR (True Positions) 19

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21.  Office-Net: 42 mica2dot motes in a 20m * 50m office  Univ-Net: 74 mica2dot motes deployed across multiple student offices on a single floor in a UC Berkeley building 21

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26.  Thank you! 26

27. 27 Route from 3,2,1 to 1,2,3

28.  Shortest Path ◦ Scalability O(n2) message and O(n) routing state  Hierachical ◦ Less message. O(nlogn) message and O(logn) message. ◦ Maintainence issue.  Geographic Routing ◦ O(1) and O(1) ◦ Assumes fixed radio range ◦ Require each node knows its geographic coordinates ◦ Doesn’t consider real radio connectivity 28