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. –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)

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)

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

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.

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)

Erasure Coding ABCD A-1 A-2 A-3 A-4 B-1 B-2 B-3C-1 C-4 D-1 ABC DA-1 A-2 A-3 A-4 B-1 B-2 B-3 B-4 C-1 C-2 C-3 C-4 D-1 D-2 D-3 D-4 Lossy Channel (r,n)=(2,4)

Overview of H-EC A-EC A-EC: erasure coding + aggressive forwarding (r=2, n=4) EC EC: erasure coding (r=2, n=4)

Overview of H-EC H-ECH-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

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

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.

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

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

Probabilistic Forwarding

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

Evaluation I: two-hop scenario Power-Low ScenarioZebraNet Scenario UCSD 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

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

Evaluation II: Variable k Scenarios

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

Evaluation II: Variable H Scenarios

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.

Thank You!