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Doctoral thesis defense Arezu Moghadam 13 May 2011

Application platform, routing protocols and behavior models in mobile disruption-tolerant networks (DTNs). Doctoral thesis defense Arezu Moghadam 13 May 2011. Introduction. D. D. D2. D1. Internet WiFi or 3G. Communication in mobile DTNs : 1 – No knowledge of the

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Doctoral thesis defense Arezu Moghadam 13 May 2011

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  1. Application platform, routing protocols and behavior models in mobile disruption-tolerant networks (DTNs) Doctoral thesis defense Arezu Moghadam 13 May 2011

  2. Introduction D D

  3. D2 D1 Internet WiFi or 3G Communication in mobile DTNs : 1 – No knowledge of the routes beyond the immediate hop 2 – Mobility 3 – Opportunistic D2 ? ? D1 DTN: Disruption-Tolerant Networks

  4. Introduction • Applications of mobile DTNs: • Covering regions with no infrastructure, e.g. natural disasters • Retrieving data from remote sensor networks • Sharing music, news, pictures in the subway or networks of pedestrians • Collaborative ad-hoc environments • Challenges of mobile DTNs • Networking and connectivity • No application server or end-to-end communication path • Different routing requirements and models • Performance of the applications and routing algorithms relies on the mobility behavior of mobile users

  5. Problem scope Mobile DTNs Application Routing Mobility A modular app. platform Popularity-based and interest-aware communication models Markov-based mobility model and routing algorithm

  6. Problem scope Mobile DTNs Applications Routing Mobility Class of disruption- tolerant Core functional requirements A modular App platform

  7. Motivation

  8. Problem Internet 3G ?

  9. Solution • 7DS platform • Provides a class of disruption-tolerant applications • Store-carry-forward communication • Node and service discovery • Web, email, file-synchronization and bulletin-board • Modular platform for application developers Internet Suman Srinivasan, Arezu Moghadam, Se Gi Hong, Henning G Schulzrinne, "7DS - Node Cooperation and Information Exchange in Mostly Disconnected Networks", IEEE International Conference on Communications (ICC), Jun 2007.

  10. Email exchange • Mobile nodes act as mail transport agents (MTA) • Email client configuration • SMTP server is set to the 7DS local MTA in the email client • Database • TTL, relays identities to avoid loops.

  11. 7DS nodes running file-sync application (view of the nodes after sync). 7DS nodes running file-sync application (view of the nodes before sync). Discovery Sync Sync Discovery Shared folder content: test1.txt=2e6480af642eeba3;1170886792000 test2.txt=a66a86c11861cb0e;1170957333000 Shared folder content: test1.txt=2e6480af642eeba3; 1170886792000 test4.doc=c78a56b341861cd06;1170867833000 All shared folders content after sync: test1.txt=2e6480af642eeba3;1170886792000 test2.txt=a66a86c11861cb0e;1170957333000 test3.doc=a6ba76c21861db5e;1170757443000 test4.doc=c78a56b341861cd06;1170867833000 Shared folder content: test1.txt=2e6480af642eeba3; 1170886792000 test3.doc=a6ba76c21861db5e;1170757443000 Shared folder content: test1.txt=2e6480af642eeba3; 1170886792000 test2.txt=a66a86c11861cb0e;1170957333000 test4.doc=c78a56b341861cd06;1170867833000 Discovery Sync Sync Discovery File synchronization Pull-based: automatic download

  12. 7DS Access Box at 116th & Broadway 1 2 1 2 2 Bulletin board system • Push-based data sharing • Data exchange should be approved by the user • Metadata in an XML format Users can generate and share content in the spirit of Web 2.0 2. Users can search for and read bulletin board announcements. 1. User publishes announcements on the bulletin board.

  13. User interface Email Web query Bulletin Board File Synchronization APPs . Implementation of the Rsync algorithm . A more efficient use of the BW and contact opportunity . Useful when someone has a newer version of the stale file (>>) Support services Proxy server Web server Data sharing Mail Transport Agent Multicast engine Search engine APIs Discovery Module Cache manager Delta compression Fetches the locally cached web pages. Emulates a connected communication path in the absence of Internet Search the internal cache Query the local neighbors BonAHA A thin wrapper around Apple’s Bonjour > rsync 1 - Arezu Moghadam, Suman Srinivasan, Henning Schulzrinne, "7DS - A Modular Platform to Develop Mobile Disruption-tolerant Applications", Second IEEE Conference and Exhibition on Next Generation Mobile Applications, Services, and Technologies (NGMAST 2008), Sep 2008. 2 - Suman Srinivasan, Arezu Moghadam, Henning Schulzrinne, "BonAHA: Service Discovery Framework for Mobile Ad-Hoc Applications", IEEE Consumer Communications & Networking Conference 2009 (CCNC'09), Jan 2009.

  14. Problem scope Mobile DTNs Applications Routing Mobility A modular app. platform Popularity-based and interest-aware communication models Markov-based mobility model and routing algorithm

  15. Problem scope Mobile DTNs Applications Routing Mobility Lack of group communication model Popularity-based Interest-aware model

  16. Routing Problem Each edge is a contact meaning an opportunity to transfer data. • Store-carry-forward • Storage constraints • Routing objectives: • Minimize delay • Maximize throughput • Per-hop routing vs. source routing • No end-to-end path • MANET’s routing protocols fail • Proactive and reactive • No knowledge of the topology • Time varying connectivity graph • Unicast vs. Multicast u v S w D x > Routing Models

  17. Problem – lack of group communication model for mobile DTNs? • Any cast communication model • Emergencies • Traffic congestion notifications • Severe weather alerts • Traditional multicast as a group communication model  Fails! • No knowledge of the topology • No infrastructure to track group memberships • Communication with communities of interest  Even a harder problem! • Market news, sport events • Scientific articles • Advertisement about particular products Epidemic routing

  18. 3 1 D D 2 4 3 1 3 1 4 3 D Solution – interest-aware communication model • Our one-to-many communication model with communities of users • Objective: transmitting data to users who are interested in the content • Assumptions • No previous knowledge about the location of the recipients • No knowledge about the mobility behavior of users • No previous knowledge about interests of users • Uniform probability of encounter d a X X D e b Y S Y f X c X g Y wireless contact data transfer Arezu Moghadam, Henning Schulzrinne, "Interest-aware content distribution protocol for mobile disruption-tolerant networks", 10th IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks, Kos, Greece, Jun 2009.

  19. Interests – IV Interest Vector Monitoring Behavior Downloaded documents Reviewed webpages Music Restaurant reviews • User profiling for the Web • Profiles users based on their downloaded or reviewed web content, clicked hyperlinks and… • Music • The genre of the music user is playing more often • Topic and category of the documents user has downloaded

  20. 1 D i j 2 D 3 cache i cache j correlation(D , ) > : Interest-vector of node j ? Solution – interest-aware communication model

  21. Document-Term Matrix LSA • User profiling for the Web • Profiles users based on their downloaded or reviewed web content, clicked hyperlinks and… • Latent Semantic Analysis • A low-dimensional topic-based representation of web documents is obtained • Then low-dimensional representations are clustered to semantic groups > Web recommender

  22. Singular Value Decomposition (SVD) A U x x = m x n m x r r x r r x n

  23. Singular Value Decomposition (SVD) k k x x = m x n m x r r x r r x n K << r > Sim

  24. Reviews all Jazz Interest-aware music sharing app. Rock P2P Music Bulletin Board Soul Vampire weekend ? Adele Jazz Miles Davis Madonna Pop

  25. 3 1 D D 2 4 3 1 3 1 4 3 D Problem with interest-aware: Greedy! h Y 5 d a X X D e D b Y S Y f X c X g Y wireless contact data transfer

  26. Solution – PEEP T1 T2 T3 T4 T5 T6 T7 1 2 Items of interest? Others? Popular Arezu Moghadam, Henning Schulzrinne, "PEEP: Popularity-based and Energy Efficient Protocol for Data Distribution in Mobile DTNs ", CCNC'2011 - Smart Spaces and Personal Area Networks, Las Vegas, USA, Jan 2011. • Still interest-aware • Interest vectors; binary • Learning interests: feedback from user, # data items of each category, play times for music files, or LSA • Transmit-budget • Amount of data items allowed for transmission at each connection • How to divide the transmit budget? • Popularity • Should be estimated

  27. Popularity estimation T1 T2 T3 T4 T5 T6 • Contact window N • History of the users’ interests • Average or weighted average • Example: C=6, N=8 • Replace the oldest

  28. Evaluation of PEEP > Simulation details

  29. Problem scope Mobile DTNs Applications Routing Mobility A modular app. platform Popularity-based and interest-aware communication models Markov-based mobility model and routing algorithm

  30. Problem scope Mobile DTNs Applications Routing Mobility Markov models to Model users’ movement Markov-based Routing algorithm

  31. S Mobility is a crucial factor! partition D

  32. Mobility models • Mobility models usage • Application provisioning and evaluation of routing protocols • performance analysis • QoS in cellular networks • Problem: Inadequacy of the current synthetic and trace-based mobility models • Trace-based studies • Precision and granularity • Specific population of study • Our empirical analysis based on a new set of traces • Calculating patterns of human movement and using it in designing routing protocols > Levy

  33. Problem with the current models • Synthetic models mostly based on RWP • Simplified assumptions about human movement • Synthesized or trace-driven models • Cellular networks • Handoff predictions for QoS • Movement of the node is not important within the cell • Mobile DTNs • No cell-tower or AP • Impact of the mobility is higher on data propagation • Traces or models extracted for cellular networks are not fine-grained enough! • Traces from a limited number of users from a specific class • Traces from APs with not enough granularity Arezu Moghadam, Tony Jebara, Henning Schulzrinne, “A Markov Routing Algorithm for Mobile DTNs based on Spatio-Temporal Modeling of Human Movement Data ", ACM MSWiM 2011 , Miami Beach, FL, USA, Oct 2011.

  34. Spatial and Temporal Patterns 12 pm: Café X 4 pm: Coffee X 6 PM: Work 1 PM: Work 10 AM: Work 9 am: Drop kid @ school 7 pm: Shop Y 12am~8am Home 8 AM: Home 10 pm: Bar Z 8 pm: Home

  35. Sense Network’s traces • GPS traces of a wide-spectrum of mobile users • Citysense application • Nightlife discovery • Friend-finder • Privacy concerns • People are owners of their own data • GPS precision of 20 feet compared to 1~20miles cell-tower coverage • Population of 10,000 users

  36. G I J A B C D E F H K L M N O 1 2 3 4 5 6 7 8 9 10 11 12 Data presentation • Sequence of grids G1, G1, G17, G23,…, GN… • Learning mechanism • Ngrams • A subsequence of N items from a sequence • Modeling sequences in NLP, gene sequence analyzing, speech recognition • Goal: most probable future locations • Pattern • Likelihood of traversing a given sequence.

  37. Ngrams • G1 , G2 , … , Gi , … , Gn • Training • Extract bigram and trigram tables. • Testing • Calculating the likelihood of a new observation

  38. Markov chains for users’ movement 50% x x • Set of states • S = {S1, s2, …, sr} • Transition matrix • Transitions correspond to consecutive GPS pings •  users’ mobility profiles • Pattern • States should be positive recurrent • Finite hitting times with prob. 1 • Matrix of hitting times x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x 10% x x x x x 25% x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x grids (100ft) x x x x x x x x x x x x grids (100ft)

  39. Markov-based routing algorithm • Absorption (hitting) times • = number of transitions until chain arrives at state j starting @ i • Select the relay (r) with less absorption time than source (s). .0588 .7529 .625 1.0 .0882 1 2 3 4 .375 0.1 1.0

  40. Monte Carlo simulation .0588 Users’ locations after each transition .7529 .625 Delay = #transitions 1.0 .0882 Mobility Generator Engine -------------- Sampling from the Markov Chains Routing Algorithm Emulator 1 2 3 4 .375 0.1 0.05 1.0 0.7 0.6 0.3 0.3 1.0 0.15 0.2 0.3 1 2 3 4 5 0.7 0.2 0.1 Energy = #transmissions 0.2 0.6 .0.3 .625 0.4 0.6 1 2 3 .375 0.1

  41. Performance measure • Performance objective • Delay • Consumed energy • Family of α-epidemics • Measure performance curve: α = 100% R α = 70% R R α = 30% R R S R R R R R ?

  42. Evaluation of results Random Destination Popular Destination α = 0.1 α = 0.2 α = 0.3 α = 0.7 α = 1

  43. Class of disruption- tolerant Core functional requirements Classes of routing protocols Group communication model Simulations based on mobility Synthetic & synthesized models Developed a Modular Platform (Released on sourceforge) Markov-based Mobility-Model and Routing Algorithm Developed Interest-Aware, PEEP algorithms Mobile music-sharing system 1 – N-Grams to estimate future locations 2 – Routing based on Markov Model 3 – Best to route to popular locations Conclusion Mobile DTNs Applications Routing Mobility

  44. Back up slides

  45. Client Server Signatures File (to be sent to server) Signatures File (received from client) Old New File (Checksum, Hash) File (Checksum, Hash) Difference (deltas file, to be sent back to the client) … … … … Insert hash Look up hash (R0, H0) (R0, H0) … (R1, H1) (R1, H1) (R2, H2) (R2, H2) (pointer i) Copy (R3, H3) (R3, H3) (pointer i+1) Copy (R4, H4) (R4, H4) (pointer i+2) Copy (R5, H5) (R5, H5) Download (R6, H6) (R6, H6) Download … … (pointer i+4) Copy … … (pointer i+5) Copy … matching non-matching Rsync Algorithm

  46. Current routing models • Single-source single-destination (no knowledge of topology) • Flooding based protocols • Epidemic • Probabilistic routing • PROPHET[57], RPLM[79], MaxProp[21] • Context or behavior of mobile users • HiBOp[18],Profile-cast[42],MobySpace[54] • Multicast • Extends the classical model with group memberships to mobile DTNs • No infrastructure • No knowledge of the topology (e.g., no multicast routers) • Epidemic based multicast (no knowledge)

  47. Current routing models • Single-source single-destination (no knowledge of topology) • Flooding based protocols • Epidemic • Probabilistic routing • PROPHET [57], RPLM [79], MaxProp [21] • Context or behavior of mobile users • HiBOp [18], Profile-cast [42], MobySpace [54] • Multicast • Extends the classical model with group memberships to mobile DTNs • No infrastructure (e.g., no multicast routers) • No knowledge of the topology • Epidemic based multicast (no knowledge)

  48. Probabilistic routing criteria • PROPHET • Delivery predictability calculation. • Routing with Persistent Link Modeling (RPLM) • Monitors link connectivity to calculate its cost. • Dijkstra to find a minimum cost path. • MaxProp • Assigning a cost value to each destination based on probability. • Priority queue  younger messages higher chances. • MobySpace • MobyPoint  each node’s coordinates or mobility pattern. • Distance on each axes probability of contacts or presence in a location.

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