Beacon Location Service A Location Service for Point-to-Point Routing in Wireless Sensor Networks

1. Jorge Ortiz, Chris R. Baker, Daekyeong Moon, Rodrigo Fonseca, and Ion Stoica EECS DepartmentUniversity of California, Berkeley Beacon Location ServiceA Location Service for Point-to-Point Routing in Wireless Sensor Networks IPSN/SPOTS 2007

2. What's Missing in Point-to-point Routing for Sensornets? Address space derived from network topology Routing performed over address space Application wants to route over name space Names do not change Location Service maps names to addresses

3. Point-to-point routing classes Geographic coordinates GPSR, GOAFR+ Virtual coordinates No-Geo, GEM, BVR, S4 Hierarchical Landmark Shortest path based (flood path-discovery) AODV, DSDV DHT-based VRR These need a location service!

4. Location Service Requirements • Provide a mechanism that closes the loop for point-to-point routing schemes • Added location service traffic should not overload the network • Point-to-point schemes usually have some amount of maintenance traffic • Maintain a high overall routing success rate from the point of view of the application

5. BLS Design Assumptions Dynamic, topologically derived addresses Each node has a unique name There exists a set of anchor nodes (beacons) that all nodes in the network know how to route to These nodes are used as the location servers

6. BLS Basic Design Single rendezvous point Distributed set of lookup servers B | addr(B) LS Reply(addr(B)) Publish(B, addr(B)) Query(B) A B Send(addr(A),addr(B),data)

7. BLS Implementation • Uses Beacon Vector Routing [Fonseca et al, NSDI '05] as underlying routing layer • Beacons used as location servers • Hash-based rendezvous with global hash function • Same hash function used to publish and query • TinyOS implementation • ~2.3K memory footprint (BLS) • ~3500 lines of Code (BVR + BLS)

8. BLS Example beacon1 3 | addr(3) 7 | addr(7) 10 | addr(10) 1 hash(3)=1 2 beacon2 hash(4)=2 3 2 hash(6)=2 4 5 6 2 | addr(2) | addr(6) 12 | addr(12) 9 | addr(9) hash(9)=3 hash(7)=1 hash(1)=3 hash(9)=3 1 9 Reply(addr(8),addr(1),addr(9)) 7 hash(12)=2 Send(addr(1),addr(9),data) Query(addr(1), addr(8), 9) 12 beacon3 3 hash(10)=1 hash(11)=3 10 8 11 1 | addr(1) 9 | addr(9) 11 | addr(11)

9. Overview • Problem statement and motivation • BLS Design: Assumptions and Choices • Implementation decisions and example • Evaluation • Metrics • Simulation • Parameter tuning and results • Testbed • Overall Performance • Comparison Study: TinyAODV and VRR • Conclusion

10. Evaluation Metrics • Routing success rate • Location service (query and reply) • Total (query and reply and data) • Minimize message overhead on the network • Minimize query + reply hopcount distribution

11. Evaluation Simulation Scale, Parameter Tuning, different topologies Up to 400 nodes Random beacon placement Testbed Real environment, verify simulation results Berkeley sMote testbed: 35 mica2dots, 3-4 hop diameter Beacons chosen a priori

12. Testbed Topology and Location Server Selection

13. Experimental Setup BVR Warm-up phase (wait for >90% routing success) Local cache disabled No misses at beacon cache

14. Naïve Implementation Results Poor scalability: 200 nodes and 8 beacons yield low success rate High success rate with BVR alone for similar setups Conjecture: Beacons overloaded Added beacons: 200 nodes and up to 32 beacons Results remained poor Logs indicated overall congestion in both cases Set out to reduce lookup traffic Aggressive in-network caching

15. Eavesdropping Example beacon1 3 | addr(3) 7 | addr(7) 10 | addr(10) 9 | addr(9) 1 2 hash(9)=3 beacon2 3 2 Query + Reply 4 5 9 | addr(9) 6 9 | addr(9) 2 | addr(2) | addr(6) 12 | addr(12) 9 | addr(9) hash(9)=3 Send(addr(3),addr(9),data) 1 9 Reply(addr(8),addr(1),addr(9)) 7 hash(12)=2 Send(addr(1),addr(9),data) Query(addr(1), addr(8), 9) 12 beacon3 3 10 8 11 9 | addr(9) 1 | addr(1) 9 | addr(9) 11 | addr(11)

16. Eavesdropping Improves Performance and Scalability Original With Eavesdropping 16.4

17. Eavesdropping Reduces Message Overhead (TOSSIM Results) 100 Nodes 35 Nodes 26.8% Average Msg Reduction 16.4% Average Msg Reduction

18. BLS Success Rate (Testbed)

19. BLS Hopcount Distribution (Testbed) Query Reply Data

20. BLS/BVR vs TinyAODV Comparison Experimental Setup AODV part of ZigBee standard Bounded destination set size at 7 to prevent cache-entry replacement Vary the number of senders [1, 5, 15, 28] First request to send to a destination node invokes path-discovery process (flooding)

21. BLS/BVR Outperforms TinyAODV TinyAODV BLS

22. BLS/BVR vs VRR Experimental Setup • Virtual Ring Routing [Caesar et al, SIGCOMM '06] • Routes directly to names • Route data packet from random source to random destination • Varying send rates

23. BLS/BVR Performance Comparable to VRR Hopcount distribution for simulated cache hit rates Amortized send cost decreases with increased cache hit rate

24. Conclusion • A subset of point-to-point routing schemes need a location service • Simple design yields comparable performance to state-of-the-art point-to-point routing schemes • BLS can be used over various beacon-based routing schemes • Code available soon

25. Questions? Thank you jortiz@cs.berkeley.edu

26. BLS Hopcount Distribution (Testbed)

27. Summary of Message Overhead with Eavesdropping TOSSIM: 16.4% reduction in message overhead for 35 nodes TOSSIM: 26.8% reduction in message overhead for 100 nodes Testbed (35 motes): 11% reduction in message overhead per node

28. DHT Application Successfully Written over BLS/BVR 99% success rate for application send requests

29. Eavesdrop Reduces Message Overhead (Testbed Results)

30. Future work Caching and Workloads Mobility and churn BLS over various beacon-based routing schemes More optimizations

31. Extra BLS and BVR - ~3500 lines of code BLS and BVR ~3700 bytes of RAM LRU cache replacement

32. New Title Management and control of specific nodes (i.e. network health monitoring) Data Centric Storage Pursuer-Evader application Derived from the topology

33. Experimental Setup (TOSSIM) Local cache lookup turned off (each send request invokes the BLS lookup process) Beacon cache size set to large enough to fit all registered nodes Warm-up necessary for BVR establishment (link estimator, neighbor selection, coordinate setup) >=90% BVR routing success before BLS experiments started Beacons randomly placed in TOSSIM simulations

34. Experimental setup (Testbed) BVR Warmup period >= 90% BVR routing success rate UC Berkeley sMote testbed – 35 mica2dots Network diameter between 3 and 4 hops Beacons chosen a priori

35. Eavesdropping message overhead 35 Nodes 100 Nodes Testbed Results TOSSIM

36. Naïve Implementation Results Results obtained in TOSSIM Paths to beacons congested Added Beacons to spread load Eavesdropping implemented

37. Tuned Parameters

38. Overview Point-to-point routing motivation Location service assumptions and design BLS design and implementation Experimental results Conclusion and questions

39. Overview Point-to-point routing motivation Location service assumptions and design BLS design and implementation Experimental results Conclusion and questions

40. Overview Point-to-point routing motivation Location service assumptions and design BLS design and implementation Experimental results Conclusion and questions

41. Overview Point-to-point routing motivation Location service assumptions and design BLS design and implementation Experimental results Conclusion and questions