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On Using Probabilistic Forwarding to Improve HEC-based Data Forwarding in Opportunistic Networks

On Using Probabilistic Forwarding to Improve HEC-based Data Forwarding in Opportunistic Networks. Ling-Jyh Chen 1 , Cheng-Long Tseng 2 and Cheng-Fu Chou 2 1 Academia Sinica 2 National Taiwan University. Motivation. There are numerous opportunistic networking applications.

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On Using Probabilistic Forwarding to Improve HEC-based Data Forwarding in Opportunistic Networks

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  1. On Using Probabilistic Forwarding to Improve HEC-based Data Forwarding in Opportunistic Networks Ling-Jyh Chen1, Cheng-Long Tseng2 and Cheng-Fu Chou2 1Academia Sinica 2National Taiwan University

  2. Motivation • There are numerous opportunistic networking applications. • wireless sensor network, underwater sensor network, pocket switched network, people network, and transportation network • Traditional data forwarding algorithms are not suitable for opportunistic networks. • Scheduled optimal routing method • Mobile relay approaches (Message ferry)

  3. Related work • Replication-based approaches • The messages are replicated. Several identical copies are transmitted over the networks to mitigate the effects of a single path failure. • For example: • Epidemic Routing, • Controlled Flooding, • mobility pattern-based scheme (Prophet)

  4. Related work • Coding-based approaches • Transforming a message into another format prior to transmission. • For example: • Erasure coding (EC), Aggressive Erasure Coding (A-EC), Hybrid Erasure Coding (H-EC) • Network Coding

  5. Our Contribution • We propose a message scheduling algorithm, Probabilistic Forwarding, to improve H-EC scheme. • Using a set of simulations, we show the proposed approach can provide better data delivery performance.

  6. Overview of H-EC • Erasure Coding: • Providing better fault-tolerance by adding redundancy without the overhead of strict replication. • Reed-Solomon, • Low-Density Parity-Check (LDPC) based coding (Gallager, Tornado, and IRA codes)

  7. A-1 A-3 B-1 B-3 C-1 C-3 D-1 D-3 A-2 A-4 B-2 B-4 C-2 C-4 D-2 D-4 Lossy Channel A-1 A-3 B-1 B-3 C-1 D-1 A-2 A-4 B-2 C-4 D A B C Erasure Coding (r,n)=(2,4) A B C D

  8. Overview of H-EC • H-EC: Hybrid of EC and A-EC • First copy is sent using EC • Second copy is sent using A-EC during the residual contact duration after sending the first EC block

  9. The Purposed Method: HEC-PF • Probabilistic forwarding • The HEC-PF scheme dost NOT enter the aggressive forwarding phase unless a newly encountered node has a higher likelihood of successfully forwarding the message to the destination node that the current nodes. • Delivery Probability

  10. Delivery Probability • Based on the observed contact history • Take the contact frequency and contact volume into consideration. • The proportion of time that the two nodes are in contact in the last T time units.

  11. the aggregated contact volume between the node pair Xi and Xj in the last T time units The source Node The ith Node The Destination Node Delivery Probability One-hop delivery probability K: number of nodes in the network Xi: the i-th node tXi;Xj:the aggregated contact volume between the node pair Xi and Xj in the last T time units

  12. Delivery Probability Two-hop delivery probability Three-hop delivery probability k-hop delivery probability

  13. Probabilistic Forwarding

  14. Evaluation • DTNSIM: A Java-based DTN simulator • Performance metric: • Delay performance • Transmission overhead • Evaluating Scenarios:

  15. Evaluation I: two-hop scenario Evaluate the delay performance of the HEC-PF scheme for message delivery. Maximum message delivery distance (hops) H=2, The transitive property of message delivery (hops) K=2 UCSD Scenario Power-Low Scenario ZebraNet Scenario

  16. Evaluation II: Variable k Scenarios We evaluate the performance with various k values (k = 2,3,4,5) ZebraNet Scenario UCSD Scenario

  17. Evaluation II: Variable k Scenarios

  18. Evaluation III:Variable H Scenarios We evaluate the performance with various maximum forwarding distance settings (H = 2,3,4,5) UCSD Scenario ZibraNet Scenario

  19. Evaluation II: Variable H Scenarios

  20. Conclusion • We purposes a new scheme for data forwarding by incorporating the basic H-EC scheme with a new feature, Probabilistic Forwarding. • Using simulations as well as both synthetic and realistic network traces, we show that the proposed has better performance in terms of delivery latency and completion ratio. • We show that the completion ratio improves as the maximum forwarding distance or the considered hop distance of the delivery probability increases.

  21. Thank You!

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