Lab for Advanced Network Design, Evaluation and Research Mobility Profile based Routing Within Intermittently Connected Mobile Ad hoc Networks (ICMAN) Joy Ghosh Hung Q. Ngo Chunming Qiao

Lab for Advanced Network Design, Evaluation and Research Outline Routing challenges in ICMAN Sociological Orbit Framework Mobility Profiling Overview Note on Delivery Probability Complexity User level Routing in ICMAN Hub level Routing in ICMAN Performance Comparison Results Future Direction

Lab for Advanced Network Design, Evaluation and Research Routing challenges in ICMAN ICMAN  Features of DTN/ICN + MANET Lack of infrastructure and any central control May not have an end-to-end path from source to destination at any given point in time Conventional MANET routing strategies fail User mobility may not be deterministic or controllable Devices are constrained by power, memory, etc. Applications need to be delay/disruption tolerant

Lab for Advanced Network Design, Evaluation and Research Mobile Users influenced by social routines visit a few “hubs” / places (outdoor/indoor) regularly “orbit” around (fine to coarse grained) hubs at several levels Sociological Orbit Framework

Lab for Advanced Network Design, Evaluation and Research Mobility Profiling Overview Obtain mobility traces (e.g., AP system logs) Convert AP-based traces to hub-based movements Obtain daily hub lists for individuals Represent hub lists as binary vectors of H bits each, where H is the number of hubs Each hub list is seen as a point in H-dimension plane Apply clustering algorithms to group hub list points together to identify patterns in movement Represent the patterns as mobility profiles Profiles have been shown useful for hub-level location predictions

Lab for Advanced Network Design, Evaluation and Research Hub Based Mobility Profiles & Prediction On any given day, a user may regularly visit a small number of “hubs” (e.g., locations A and B) Each mobility profile is a weighted list of hubs, where weight = hub visit probability (e.g., 70% A and 50% B) In any given period (e.g., week), a user may follow a few such “mobility profiles” (e.g., P1 and P2) Each profile is in turn associated with a (daily) probability (e.g., 60% P1 and 40% P2) Example: P1={A=0.7, B=0.5} and P2={B=0.9, C=0.6} – On an ordinary day, a user may go to locations A, B and C with the following probabilities, resp.: 0.42 (=0.6x0.7), 0.66 (= 0.6x ) and 0.24 (=0.4x0.6) – 20% more accurate than simple visit-frequency based prediction – Knowing exactly which profile a user will follow on a given day can result in even more accurate prediction On any given day, a user may regularly visit a small number of “hubs” (e.g., locations A and B) Each mobility profile is a weighted list of hubs, where weight = hub visit probability (e.g., 70% A and 50% B) In any given period (e.g., week), a user may follow a few such “mobility profiles” (e.g., P1 and P2) Each profile is in turn associated with a (daily) probability (e.g., 60% P1 and 40% P2) Example: P1={A=0.7, B=0.5} and P2={B=0.9, C=0.6} On an ordinary day, a user may go to locations A, B & C with the following probabilities: 0.42 (=0.6x0.7), 0.66 (= 0.6x ), 0.24 (=0.4x0.6) 20% more accurate than simple visit-frequency based prediction Knowing exactly which profile a user will follow on a given day can result in even more accurate prediction

Lab for Advanced Network Design, Evaluation and Research Delivery Probability Complexity Objective: maximize delivery probability from nodes s to t under various constraints G = (V,E) be a complete directed graph – V = ICMAN users; E = probabilistic contact between users Let A be a routing algorithm and G(A) be the delivery sub-graph induced by A Delivery probability is then s,t-connectedness probability (two- terminal reliability) denoted by Conn 2 (G(A)) Two-terminal reliability was established by Valiant to be #P-complete Our aim is to find a routing algorithm A to maximize Conn 2 (G(A)) – we suspect it to be #P-hard (ongoing work) 2 Possible approaches – Approximate Conn 2 (G(A)) by another polynomial time function – Develop heuristics for A for which Conn 2 (G(A)) can be approximated We adopt the second approach now, keeping the first one for future

Lab for Advanced Network Design, Evaluation and Research User level routing strategies Deliver packets to the destination itself Intermediate users store-carry-forward the packets Mobility profiles used to compute pair wise user contact probability P(u,v) via Markov Process Form weighted graph G with edge weights w(u,v) = log (1/P(u,v)) Apply modified Dijkstra’s on G to obtain k-shortest paths (KSP) with corresponding Delivery probability under following constraints – Paths are chosen in increasing order of total weights (i.e., minimum first) – Each path must have different next hop from source S-SOLAR-KSP (static) protocol – Source only stores set of unique next-hops on its KSP – Forwards only to max k users of the chosen set that come within radio range within time T D-SOLAR-KSP (dynamic) protocol – Source always considers the current set of neighbors – Forwards to max k users with higher delivery probability to destination

Lab for Advanced Network Design, Evaluation and Research Hub level routing strategy Deliver packets to the hubs visited by destination Intermediate users store-carry-forward the packets Packet stored in a hub by other users staying in that hub (or using a fixed hub storage device if any) Mobility profiles used to obtain delivery probabilities (DP), not the visit probability, of a user to a given hub – i.e. user may either directly deliver to hub by traversing to the hub, or may pass onto other users who can deliver to the hub Fractional data delivered to each hub proportional to the probability of finding the destination in it Routing Strategy  SOLAR-HUB protocol

Lab for Advanced Network Design, Evaluation and Research SOLAR-HUB Protocol P d n i h j : delivery probability (DP) of user n i to hub h j P t n i h j : probability of user n i to travel to hub h j h(n i ): hub that user n i is going to visit next P c n i n k (h j ): probability of contact between users n i & n j in hub h j N(n i ): neighbors of user n i P d n i h j = max(P t n i h j, max k (P c n i n k (h(n i ))*P t n k h j )) Source n s will pick n i as next hop to hub h j as: – {n i | max(P d n i h j ), n i Є N(n s )} iff P d n i h j > P d n s h j Packet Delivery Scheme – Source transmits up to k copies of message k/2 to neighbors with higher DP to “most visited” hub k/2 to neighbors with higher DP to “2nd most visited” hub – Downstream users forward up to k users with higher DP to the hub chosen by upstream node

Lab for Advanced Network Design, Evaluation and Research Simulation Parameters for GloMoSim

Lab for Advanced Network Design, Evaluation and Research Performance – Number of Hubs Overhead of EPIDEMIC is much more than others and had to be omitted from plot Overall D-SOLAR-KSP performs best

Lab for Advanced Network Design, Evaluation and Research Performance – Number of Users Overhead of EPIDEMIC is much more than others and had to be omitted from plot Overall D-SOLAR-KSP performs best like before because it is the most opportunistic in forwarding to any of its current neighbors

Lab for Advanced Network Design, Evaluation and Research Performance – Cache Size (Only SOLAR) All versions fair better with more cache Overall D-SOLAR-KSP performs best

Lab for Advanced Network Design, Evaluation and Research Performance – Cache Timeout (Only SOLAR) All versions fair better with larger timeout Overall D-SOLAR-KSP performs best

Lab for Advanced Network Design, Evaluation and Research Future Directions Collect and analyze user location-based traces at University at Buffalo Apply advanced clustering/profiling techniques for identifying profile changes Optimization techniques for distributed profile information management Build analytical framework for routing algorithms  approximation algorithms

Lab for Advanced Network Design, Evaluation and Research Thank You ! Questions ? - Dr. Hung Q. Ngo