Off By One Power-Save Protocols Presentation #4

1. Off By OnePower-Save ProtocolsPresentation #4 Corey Andalora Keith Needels

2. Agenda • Final Design • Graph Algorithms • Test Results • Future Work • Conclusion

3. Final Design • Removed packet simulation • Statistics about topology more useful • From statistics, latency and delivery rates can be estimated • Easier to implement graph algorithms • Did not implement BECA / AFECA • Would require a source routing algorithm • GAF and Span are independent of routing

4. Graph Algorithms • As nodes power cycle and move, the topology graph is updated • Run periodically through lifetime of system to collect statistics.

5. Connected Pairs A B

6. Connected Pairs C A

7. Connected Pairs C A

8. Connected Pairs D E

9. Connected Pairs F D

10. Algorithm Transition • Johnson Algorithm • Finds all pairs, shortest path • O( V2 log V + VE ) • Can’t use it because of temporary node edges • Dijkstra Algorithm performed n2 times • Finds all shortest paths to a node • O( ( E + V ) log V ) with binary heap • Again not taking advantage because of temporary node edges, therefore inefficient • Not necessary because hops are a weight of one.

11. Algorithm Transition (cont’d) • Breadth First Search performed n2 times • Determines if a path exists between two nodes • O( V + E ) • When all edges weigh the same, finds shortest path • Perfect for our needs • Ford-Fulkerson • Determines max flow of network • O( VE2 ) • Don’t need to temporarily add edges • We use this to determine number of unique paths in system

12. Test Setup • Using our AdHocSimulation program, we can run tests with a specified node type, random seed, number of nodes, radio range, maximum speed, and maximum travel distance before changing direction. • AdHocSimulation outputs a CSV file with the following columns: • Time: The time (in seconds) since the start of the simulation that this data snapshot was taken. • Path probability: The percentage of pairs of nodes that currently have a path between them. • Average Hops: The average number of hops between nodes. • Average number of unique paths between nodes: This is the average maximum flow between all pairs of nodes. • Percentage of nodes that have not drained all of their battery. • Power remaining: The amount of power left in all nodes divided by the total power capacity of all nodes in the system.

13. Node Lifetime – 100 Node Network

14. Node Lifetime – 200 Node Network

15. Basic Test Results • Both GAF and Span offer big savings in power over no power save protocol. • GAF: 190-225% increase in lifetime over unmodified. • Span: 210-280% increase in lifetime over unmodified. • Both GAF and Span have better performance with denser networks.

16. GAF – 100 vs. 400 Nodes Dashed lines = results for 100 nodes, solid lines = results for 400 nodes.

17. GAF – 100 Nodes

18. GAF – 400 Nodes

19. Span – 100 vs. 400 Nodes Dashed lines = results for 100 nodes, solid lines = results for 400 nodes.

20. Span – 100 Nodes

21. Span – 400 Nodes

22. Density Test Results • A higher node density improves performance in both protocols. • Quadrupling the node density gave a 70% increase in network lifetime for Span and a 25% increase in the network lifetime for GAF. • The GAF authors claim that in some cases quadrupling node density increases network lifetime by 300-600%, but we did not see this with our simulations.

23. GAF – No Movement vs Movement Dashed lines = no movement, solid lines = movement.

24. GAF - Movement

25. Span – No Movement vs. Movement Dashed lines = no movement, solid lines = movement.

26. Span - Movement

27. Movement Test Results • Node movement seems to improve the network lifetime of both algorithms. • Nodes in sparsely populated areas move into dense areas • Path probability can vary greatly, but GAF is the clear winner here. • Why? GAF has more nodes on, so you have a greater likelihood of another active node being nearby if your grid’s active node moves out of range.

28. Overall Test Results and Comments • Span performs better than GAF in nearly all measurements. • While GAF can have more edge disjoint paths between nodes, this is because it has more nodes on at any given time. We could modify Span to leave more nodes on and achieve similar results. • We were surprised by these results: It seems that GAF should an advantage with location awareness, but it appears that location awareness offers little to no advantage with power save protocols.

29. Future Work • Varying transmit power simulations • More efficient statistical algorithms • Fusion algorithm • Run Span on active GAF nodes • Investigate fairness

30. Conclusion • Knowing geographical information doesn’t necessarily help you in terms of power saving. • Span seems to outperform GAF in most aspects. • A power safe protocol proves most beneficial for sensor networks, where many nodes can be dedicated to routing only.

31. Questions? • Ya Xu, John Heidemann, and Deborah Estrin. “Adaptive energy-conserving routing for multi-hop ad hoc networks.” Technical Report 527, USC/Information Sciences Institute, October 2000. • Ya Xu, John Heidemann, and Deborah Estrin. “Geography-informed energy conservation for ad hoc routing,” in Proceedings of 7th Annual International Conference on Mobile Computing and Networking, pp. 70-84, July 2001. • Benjie Chen, Kyle Jamieson, Hari Balakrishnan, and Robert Morris. “Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks.” ACM Wireless Networks Journal, 8(5), 481-494, September 2002. • Stefano Basagni, Marco Conti, Silvia Giordano, and Ivan Stojmenovic. Mobile Ad Hoc Networking. John Wiley & Sons, 2004. ISBN 0-471-37313-3.