SOLAR Joy Ghosh, Sumesh J. Philip, Chunming Qiao {joyghosh, sumeshjp, qiao}@cse.buffalo

1. Key Concepts • Every user periodically visits a list of places of social interests (i.e., hubs) • Can utilize such mobility information for location approximation and routing • Examples (at right): • User 1 (green), User 2 (blue) and User 3 (red) attending a conference • User 3 queries User 2 for the hub list of User 1 • User 3 sends data to User 1 • Advantage of Macro-level (hub-based) sociological orbital mobility profile • does not require continuous location monitoring • does not depend on exact movement in time or space • acquaintance-based soft location management • captures probabilistic routing in MANET & other networks (e.g., ICN) • SOLAR Variations: Ongoing Research • Non-probabilistic – Geographic forwarding to hubs • SOLAR Sequential – to all hubs in sequence • SOLAR Simulcast – to all hubs simultaneously • SOLAR Multicast – to a multicast tree of hubs • Probabilistic – Intermittently connected networks • SOLAR-P – forward to hubs in probabilistic order • SOLAR-KSP – K-shortest paths; store & forward routing SOLAR Simulcast: Location Query and Routing Conference Track 2 • Research Issues: • Routing Objectives: • Maximize data throughput (under energy and memory constraints) • Minimize control overhead (number of location queries/updates) • Minimize number of logical hops required for each location query • Minimize number of acquaintances maintaining throughput • Minimize the end-to-end delay (location query + data delivery) • Routing Variable: • Cache size (number of acquaintances) • Logical hop threshold (acquaintance to acquaintance lookup) • Hub list discovery probability (reliability of location approximation) • Optimization problems: • What is the minimum cache size required to achieve a desired • discovery probability within a fixed number of search steps? • Given a fixed cache size, what is the minimum number of search • steps required to achieve desired reliability? • What is the probability of Hub list discovery within a fixed number • of search steps given a fixed cache size? Sociological Orbits City 2 Friends Level 3 Level 2 Home Town City 3 Relatives Outdoors Level 1 School Home Intermittently Connected Networks Cafe Cubicle Kitchen Porch Conf. Room Living MANET Conference Track 2 Conference Track 1 Exhibits • Query Optimization – 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 H’ of H such that: • This is a minimum set cover problem – NP Complete • Possible solutions: Greedy Set Cover, Primal-Dual Schema, etc. • Minimizes the number of queries and optimizes the cache size Exhibits Conference Track 1 Conference Track 2 Conference Track 1 Exhibits Hub A Performance of SOLAR vs. conventional protocols Lounge Conference Track 3 Lounge Hub B Lounge Conference Track 3 Conference Track 3 Registration Posters Registration Registration Hub E Posters Hub D Posters Conference Track 4 Conference Track 4 Conference Track 4 Cafeteria Cafeteria Cafeteria Hub C Hub F Hub Centers Green’s IHO: Hubs A, B, C IHM of individual nodes SOLAR achieves high throughput, low control (signaling) overhead, and reasonable delay (even for destinations far away) (a) Geographic forwarding of location query to acquaintance (b) Geographic forwarding of data to destination Blue’s IHO: Hubs D, F IHM: Random Waypoint; IHO: P2P Linear Red’s IHO: Hubs E, F SOLARJoy Ghosh, Sumesh J. Philip, Chunming Qiao{joyghosh, sumeshjp, qiao}@cse.buffalo.edu A Random Orbit Model and its Parameters Sociological Orbit aware Location Approximation and Routing Laboratory for Advanced Network Design, Evaluation and Research (LANDER)