1. Beacon Vector Routing: Scalable Point-to-Point Routing in Wireless Sensornets

2. Motivation Most existing protocols only support basic many-to- one or one-to-many routing primitives (e.g., Directed diffusion, TAG, …) Most existing protocols only support basic many-to- one or one-to-many routing primitives (e.g., Directed diffusion, TAG, …) More point-to-point routing protocols have recently been proposed More point-to-point routing protocols have recently been proposed Applications: Pursuer-evader game, spatial queries, reactive tasking, multi-dimensional range queries, data centric storage, … Applications: Pursuer-evader game, spatial queries, reactive tasking, multi-dimensional range queries, data centric storage, … No practical and broadly applicable implementation of point-to-point routing in WSNs No practical and broadly applicable implementation of point-to-point routing in WSNs

3. Design Goals Develop & implement a point-point routing protocol: Develop & implement a point-point routing protocol: That is simple – minimal complexity That is simple – minimal complexity That makes minimal assumptions about radio quality, presence of GPS, … That makes minimal assumptions about radio quality, presence of GPS, … Use TinyOS tree construction prtocol Use TinyOS tree construction prtocol

4. Key Ideas Randomly select a few beacon nodes Randomly select a few beacon nodes Construct trees from the beacons to every other node Construct trees from the beacons to every other node Every node knows its distance (#hops) to every beacon by using the standard reverse path tree construction Every node knows its distance (#hops) to every beacon by using the standard reverse path tree construction These beacon vectors serve as coordinates These beacon vectors serve as coordinates Apply simple greedy, geographic forwarding Apply simple greedy, geographic forwarding

5. Approach Nodes periodically send a local broadcast to announce their coordinates Nodes periodically send a local broadcast to announce their coordinates Distance function δ(p, d) to measure how good p would be as a next hop to reach the destination d? Distance function δ(p, d) to measure how good p would be as a next hop to reach the destination d? A node q’s position P(q) = where qi is #hops from node q to beacon i A node q’s position P(q) = where qi is #hops from node q to beacon i Move towards a beacon when the destination is closer to the beacon than the current node Move towards a beacon when the destination is closer to the beacon than the current node Move away from a beacon when the destination is further from the beacon than the current node Move away from a beacon when the destination is further from the beacon than the current node

6. Fallback mode If a node cannot make a progress towards the destination itself, it forwards the packet to the parent in the corresponding beacon tree If a node cannot make a progress towards the destination itself, it forwards the packet to the parent in the corresponding beacon tree A packet will eventually reach the beacon node closest to d A packet will eventually reach the beacon node closest to d If the closest beacon still cannot find the destination, it does scoped flooding If the closest beacon still cannot find the destination, it does scoped flooding

7. Beacon maintenance Route based on the beacons the source and destination have in common Route based on the beacons the source and destination have in common Does not require perfect beacon info. Does not require perfect beacon info. Each entry in the beacon vector has a sequence number Each entry in the beacon vector has a sequence number Periodically updated by the corresponding beacon Periodically updated by the corresponding beacon Timeout Timeout If the #beacons < r, non-beacon nodes nominate themselves as beacons If the #beacons < r, non-beacon nodes nominate themselves as beacons

8. Location directory First look up the destination coordinates by name First look up the destination coordinates by name Hashing H: nodeid → beaconid [14] Hashing H: nodeid → beaconid [14] Use beacons as storage 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) 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 When a node wants to route to node k, it sends a lookup request to b k Cache the coordinates Cache the coordinates

9. Simulation Results Assumptions for high level simulation Assumptions for high level simulation Fixed circular radio range Fixed circular radio range Ignore the network capacity and congestion Ignore the network capacity and congestion Ignore packet losses Ignore packet losses Place nodes uniformly at random in a square planner region Place nodes uniformly at random in a square planner region 3200 nodes uniformly distributed in a 200 * 200 unit area 3200 nodes uniformly distributed in a 200 * 200 unit area Radio range is 8 units Radio range is 8 units Average node degree is 16 Average node degree is 16 Vary #total beacons and #routing beacons Vary #total beacons and #routing beacons

10. Greedy success rate

11. Success ratio given 10 routing beacons

12. On-demand two hop neighbor acquisition At lower densities, each node has fewer immediate neighbors At lower densities, each node has fewer immediate neighbors The performance of greedy routing drops The performance of greedy routing drops Add a neighbor’s neighbors to the routing table, if greedy forwarding is impossible Add a neighbor’s neighbors to the routing table, if greedy forwarding is impossible

13. #beacons required to achieve less than 5% scoped floods

14. Performance under obstacles Place horizontal & vertical walls with lengths of 10 or 20 units when the radio range is 8 units Place horizontal & vertical walls with lengths of 10 or 20 units when the radio range is 8 units

15. Transmission stretch over the shortest path

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

17. Link quality vs. distance Orthogonal! (in Office-Net) Orthogonal! (in Office-Net) Contradicts to circular radio assumptions made by geographic routing protocols Contradicts to circular radio assumptions made by geographic routing protocols BVR is connectivity based BVR is connectivity based

18. Routing performance in Office-Net

19. Routing performance in Univ-Net

20. Office-Net success rate

21. Beacon failure TOSSIM – TinyOS simulator TOSSIM – TinyOS simulator 100 motes with 8 beacons 100 motes with 8 beacons Expected node degree of 12 Expected node degree of 12 TOSSIM’s lossy link generator TOSSIM’s lossy link generator Based on empirical data to simulate lossy and asymmetric connectivity Based on empirical data to simulate lossy and asymmetric connectivity

23. Related Work DSDV computes the shortest path between all possible pair of source and destination DSDV computes the shortest path between all possible pair of source and destination Scalibility problem Scalibility problem On-demand route discovery On-demand route discovery Poor performance when many source-destination pair want to communicate Poor performance when many source-destination pair want to communicate Landmark routing Landmark routing Hierarchical set of landmark nodes periodically send scoped route discovery messages Hierarchical set of landmark nodes periodically send scoped route discovery messages +Each node self-configures its address – concatenation of the closest landmark at each level of the hierarchy +Each node self-configures its address – concatenation of the closest landmark at each level of the hierarchy -Landmark maintenance -Landmark maintenance -How to tune the landmark scope? -How to tune the landmark scope?

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

25. Questions?