off by one power-save protocols presentation #4

Off By OnePower-Save ProtocolsPresentation #4

Corey Andalora

Keith Needels

Agenda

Final Design Graph Algorithms Test Results Future Work Conclusion

Final Design

Removed packet simulationStatistics about topology more usefulFrom statistics, latency and delivery rates can

be estimatedEasier to implement graph algorithms

Did not implement BECA / AFECAWould require a source routing algorithmGAF and Span are independent of routing

Graph Algorithms

As nodes power cycle and move, the topology graph is updated

Run periodically through lifetime of system to collect statistics.

Connected Pairs

B

A

Connected Pairs

A

C

Connected Pairs

A

C

Connected Pairs

E

D

Connected Pairs

D

F

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.

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

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.

Node Lifetime – 100 Node Network

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Span Nodes Alive (%) Unmodified Nodes Alive (%) GAF Nodes Alive (%)

Node Lifetime – 200 Node Network

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Span Nodes Alive (%) Unmodified Nodes Alive (%) GAF Nodes Alive (%)

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.

GAF – 100 vs. 400 Nodes

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Path Probability (%) Nodes Alive (%) Power Remaining (%) Path Probability (%) Nodes Alive (%) Power Remaining (%)

Dashed lines = results for 100 nodes, solid lines = results for 400 nodes.

GAF – 100 Nodes

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Average Hops # Unique Paths

GAF – 400 Nodes

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Average Hops # Unique Paths

Span – 100 vs. 400 Nodes

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Path Probability (%) Nodes Alive (%) Power Remaining (%) Path Probability (%) Nodes Alive (%) Power Remaining (%)

Dashed lines = results for 100 nodes, solid lines = results for 400 nodes.

Span – 100 Nodes

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Average Hops # Unique Paths

Span – 400 Nodes

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Average Hops # Unique Paths

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.

GAF – No Movement vs Movement

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Path Probability (%) Nodes Alive (%) Power Remaining (%)

Path Probability (%) Nodes Alive (%) Power Remaining (%)

Dashed lines = no movement, solid lines = movement.

GAF - Movement

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Average Hops # Unique Paths

Span – No Movement vs. Movement

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Connected Pairs w/ Dead(%) Nodes Alive (%) Power Remaining (%)

Path Probability (%) Nodes Alive (%) Power Remaining (%)

Dashed lines = no movement, solid lines = movement.

Span - Movement

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Average Hops # Unique Paths

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.

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.

Future Work

Varying transmit power simulations More efficient statistical algorithms Fusion algorithm

Run Span on active GAF nodes Investigate fairness

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.

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.