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Sociological Orbit aware Location Approximation and Routing (SOLAR) in MANET

This paper presents the concept of Sociological Orbit (SOLAR) in Mobile Ad hoc Networks (MANET), which utilizes the sociological orbital movement of users to improve location approximation and routing. The SOLAR protocol combines the use of acquaintances' hub lists and query-based routing to achieve high throughput, low overhead, and low delay. Performance comparison with other routing protocols is also discussed. Current work involves probabilistic routing techniques in intermittently connected mobile ad hoc networks.

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Sociological Orbit aware Location Approximation and Routing (SOLAR) in MANET

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  1. Sociological Orbit aware Location Approximation and Routing (SOLAR) in MANET Joy Ghosh, Sumesh J. Philip, Chunming Qiao Laboratory for Advanced Network Design, Evaluation and Research (LANDER)

  2. Outline • Sociological orbital movement • Random orbit model • Social acquaintance based query • SOLAR protocol concept • Performance Comparison • Summary • Current work University at Buffalo

  3. What is a Sociological Orbit? • “List” of special places (hubs) for most users • Periodic visits – in any sequence • Substantial stay time • E.g., Places with Internet Access Points, Academic buildings, Libraries, Residential Complex, Coffee shops, etc. • Fair and economical assumption • User nodes have GPS (~ $80) or equivalent localization techniques to record the hubs visited • Broader context of pervasive/ubiquitous computing University at Buffalo

  4. Time and Space based hierarchy(e.g., life of a graduate student!!) City 2 Friends Level 3 Orbit Level 2 Orbit Home Town City 3 Relatives Outdoors Level 1 Orbit School Home Potential DTN Cafeteria Cubicle Kitchen Porch/Yard Conference Room Living Room Potential MANET University at Buffalo

  5. Example mobility in Conference Scenario Conference Track 2 Conference Track 1 Exhibits Lounge Conference Track 3 Registration Posters Conference Track 4 Cafeteria University at Buffalo

  6. Random Orbit model and parameters University at Buffalo

  7. Sociological acquaintance based query • Acquaintance Based Soft Location Management(ABSoLoM) • Our prior work (WCNC 2004) on formation and maintenance of “acquaintances” • Use of acquaintances to query for unknown destination • Inspired by the 1967 “small world experiment” by Stanley Milgram • Random US citizens were seen to be connected by an average of six acquaintances – “six degrees of separation” • Sharing/caching location information via Hello packets • Build a distributed database of acquaintance’s Hub lists • Unlike “acquaintanceship” in ABSoLoM, in SOLAR we find • No formal acquaintanceship request/response  its not mutual • Hub lists are valid longer than exact locations  lesser updates • No limit on number of acquaintances  more flexible • For unknown destination, query acquaintances for destination’s Hub list, instead of destination’s location • “Query hop threshold” limits the process of query propagation University at Buffalo

  8. Sociological Orbit aware Location Approximation and Routing (SOLAR) protocol - Concept • Subset of acquaintances to query • Challenge: Lots of acquaintances  lot of query overhead • Formulation: Query a subset such that all the Hubs that a node learns of from its acquaintances are covered • Packet Transmission to a Hub List • All packets (query, response, data, update) are sent to node’s Hub list • To send a packet to a Hub, geographically forward to Hub’s center • If “current Hub” is known – unicast packet to current Hub • Default – simulcast separate copies to each Hub in list • On reaching Hub, do Hub local flooding if necessary • Improved Data Accessibility – Cache data packets within Hub • Data Connection Maintenance • Two ends of active session keep each other informed • Such location updates generate “current Hub” information University at Buffalo

  9. SOLAR Protocol – Illustration Hub E Hub A Hub H Hub D Hub B Hub G Hub F Hub I Hub C University at Buffalo

  10. Performance Analysis Metrics • Data Throughput (%) • Data packets received / Data packet generated • Relative Control Overhead (bytes) • Control bytes send / Data packets received • Approximation Factor for E2E Delay • Observed delay / Ideal delay  “fairness” issues! University at Buffalo

  11. Routing Protocols(without location services) • Dynamic Source Routing (DSR) – basic flooding • Location Aided Routing (LAR) – location aware • SOLAR with “query hop threshold” set to 2 • SOLAR-1: nodes only share their own hub lists • SOLAR-2: nodes also share 1-hop neighbor’s hub lists University at Buffalo

  12. Simulation Parameters (GloMoSim) University at Buffalo

  13. Results – Ia : Throughput vs. No of Hubs University at Buffalo

  14. Results – Ia : Overhead vs. No of Hubs University at Buffalo

  15. Results – Ia : Delay vs. No of Hubs University at Buffalo

  16. Summary • User mobility exhibits “orbital” pattern • Macro-level hub based random orbit model • Use acquaintances to disseminate hub lists • Query destination’s hub list & route to hubs • High throughput, low overhead, low delay University at Buffalo

  17. Current Work – I (Probabilistic Routing) • Intermittently Connected Mobile Ad hoc Network (ICMAN) with Sociological Orbits • No contemporaneous path from source to destination through peers • Store-n-forward routing techniques in addition to normal multihop transmissions • Probabilities associated with hubs visited • Study of offline and online K-shortest path algorithms and other SOLAR variations • Analytical model for contact probabilities via Continuous Markov Chains • Submitted to Infocom 2006 University at Buffalo

  18. Current Work – II (Mobility Trace Analysis) • ETH Zurich, Dept. of Computer Science • Event logs from Access Points (4/1/04 – 3/31/05) • Dr. Thomas Gross, Cristian Tuduce • Dartmouth NH, Dept. of Computer Science • Syslogs and SNMP Data from APs (2003 & 2004) • Dr. David Kotz, Dr. Minkyong Kim • Setting up data collection in University at Buffalo • SOLAR specific analysis • Periodic hub visits  existence of hub lists • Hub list size distribution  memory constraints • Hub list change distribution  bound on updates University at Buffalo

  19. Sociological Orbit aware Location Approximation & Routing (SOLAR) in MANET Suggestions & Comments Joy Ghosh, Sumesh J Philip, Chunming Qiao Laboratory for Advanced Network Design, Evaluation and Research (LANDER) University at Buffalo

  20. Subset of acquaintances to query • Acquaintance Ai has a Hub list Hi = {h1, h2, …, hm} where hi is a Hub • H = {H1, H2, …, Hn} is the set of Hub lists covered by A1, A2, …, An • C = H1 U H2 U … U Hn is the set of all Hubs covered by A1, A2, …, An • Objective: find a minimum subset • This is a minimum set cover problem – NP Complete • We use the Quine-McCluskey optimization technique Return University at Buffalo

  21. Quine-McCluskey optimization • Acquaintance • _ • a • Example: A = {1,2}, B = {2,3,4}, C = {1,3} • A, B, C are Prime acquaintances • B is an Essential Prime acquaintance • Choose all the Essential Prime acquaintances first • If any Hub is still uncovered, iteratively choose non-essential Prime acquaintances that cover the max number of remaining Hubs, till all Hubs are covered Return University at Buffalo

  22. Performance variation with Radio Hops Return University at Buffalo

  23. Results – II : Hub Size variations University at Buffalo

  24. Results – III : Node Speed variations University at Buffalo

  25. Results – IV : Radio Range variations University at Buffalo

  26. Results – V : No. of Nodes variations University at Buffalo

  27. A Random Orbit model • Rectangular hubs placed at random in terrain • Inter-hub Orbit (IHO) for each user (node) • Number of hubs bounded by Hub List Size • Time spent in hubs bounded by Hub Stay Time • IHO Timeout – allows for hub lists to change • Mobility pattern involves two different parts • Inter-hub: Point-to-Point Linear • Intra-hub: Random Waypoint Any practical mobility model can be chosen for either or both of the two parts mentioned above!! University at Buffalo

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