2. Performance Evaluation of Time-based and Hop-based TTL Schemes in Partially Connected Ad Hoc Neworks Wing Ho Yuen and Henning Schulzrinne Department of Computer Science Columbia University

3. Application Driven Networking Research Networking research community has tradition of focusing on topics that find sparse applications –E.g. QoS, multicast, active networks Most recent advances in networking driven by applications –Email in 80’s, Web in 90’s, p2p and voip in 00’s The playfield –Cellular data is ubiquitous, but fairly expensive –Wi-Fi available only in limited locations What niche should ad hoc networks fits in?

4. 7DS Application Enable communications beyond Wi-Fi locations using ad hoc networks Node density insufficient to maintain connected networks in many scenarios –Instead of dropping packets, revert to message based communication To emulate Internet services when users are disconnected –Email delivery, web access and novel applications such as localization

5. 3 Categories of 7DS Application Email delivery P2p music exchanges Subway map download

6. Email Delivery Application Message replication speeds up email delivery Message purge mechanism removes replicas Time-based Scheme (TB) –Each message has a TTL field –Replicas purged at all nodes when TTL expires Hop-based Scheme (HB) –Each message carries of TTL tuple (b,h) –b decrements every time a node transmits –When b=0, message replica is purged –When h=0, a node does not Tx in a node encounter

7. 3,22,2 3,1 3,0 1,1  0,0 3,0 3,1 1,2 0,2 Hop-based Scheme HB(b=3,h=2) 2,1 3,0

8. Availability of Feedback Channel Notify all nodes upon delivery –Exploit paging channel of cellular network –Replicas at all nodes immediately purged when a replica reaches an AP Four schemes under examination –TB –HB –TB/FB –HB/FB

9. 3,22,2 3,1 3,0 1,1  0,0 3,1 1,2 0,2 HB/FB (b=3,h=2) 2,1

10. System Model and Performance Metrics Delivery of one email message X n : # message replicas at time n : node encounter rate is Poisson n = (1-X n /n) is effective node encounter rate  : AP encounter rate is Poisson n: number of 7DS users n  holds when X n «n Storage/Energy cost C: Storage-time cost S: XnXn

11. Contributions and Sim Setup Derive E[C TB ],Var[C TB ], E[C HB ] and Var[S HB ] for non-feedback schemes Simulation results only for feedback schemes Each scenario corresponds to a given node encounter rate and AP encounter rate  –3 scenarios: node encounter is constant, =1/20 –vary , with  =1/1000,  =1/2000,  =1/5000 –Consider schemes with prob. Delivery P d  1 –No physical node mobility –Node encounters generated non-homogeneous Poisson process n

12. Comparison of TB and HB TB inferior to all HB schemes for both storage and storage-time cost C HB decreases with b max S HB increases with b max

13. Comparison of TB/FB and HB/FB C TB dramatically reduces with FB C HB decreases with b max, good to be aggressive Similar storage-time cost S TB and S HB

14. Expected Stopping time E[T s ] Defined as time when all replicas are purged stopping time = message delivery time Message delivery in ~100sec for all schemes E[T s ] of HB similar to TB, at much smaller cost WITH FB, TB trades off small E[T s ] for more storage Not as meaningful as storage-time cost WITH no FB,

15. Conclusion HB(b max,2) superior, exhibit smaller expected cost and small variance (storage cost and storage-time cost) –b max should be selected such that P d  1 Expedited (100sec) and reliable message delivery (P d = 1) possible at moderate resource overhead (50 replicas at  =1/1000, i.e. 17 minutes to AP) Feedback reduces cost further by two or three times

16. Questions?