Lab for Advanced Network Design, Evaluation and Research Mobility Profile based Routing Within Intermittently Connected Mobile Ad hoc Networks (ICMAN)

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


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


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


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


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


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 (= 0.6x0.5 + 0.4+0.9) 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 (= 0.6x0.5 + 0.4+0.9), 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


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 Conn2(G(A))

Two-terminal reliability was established by Valiant to be #P-complete Our aim is to find a routing algorithm A to maximize Conn2(G(A))

– we suspect it to be #P-hard (ongoing work) 2 Possible approaches

– Approximate Conn2(G(A)) by another polynomial time function– Develop heuristics for A for which Conn2(G(A)) can be approximated

We adopt the second approach now, keeping the first one for future


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


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


SOLAR-HUB Protocol

Pdnihj: delivery probability (DP) of user ni to hub hj

Ptnihj: probability of user ni to travel to hub hj

h(ni): hub that user ni is going to visit next Pc

nink(hj): probability of contact between users n i & nj in hub hj

N(ni): neighbors of user ni

Pdnihj = max(Pt

nihj, maxk(Pcnink(h(ni))*Pt

nkhj)) Source ns will pick ni as next hop to hub hj as:

– {ni | max(Pdnihj), ni Є N(ns)} iff P

dnihj > Pd

nshj

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


Simulation Parameters for GloMoSim


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


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


Performance – Cache Size (Only SOLAR)

• All versions fair better with more cache



Performance – Cache Timeout (Only SOLAR)

• All versions fair better with larger timeout



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


Thank You !

Questions ?

- Dr. Hung Q. Ngo

Documents

Lab for Advanced Network Design, Evaluation and Research Mobility Profile based Routing Within Intermittently Connected Mobile Ad hoc Networks (ICMAN)