“Sociological Orbits”Mobility Profiling and Routingfor Mobile Wireless Networks

Hung Q. Ngo

Computer Science & Engineering

State University of New York at Buffalo

Jul 11, 2006

Acknowledgement

On-going project with Prof. Chunming Qiao

Joy Ghosh Duc Ha S. K. Yoon Dr. Sumesh Philip (former student)

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Outline Mobility Impact on Routing Sociological ORBIT Mobility Framework Mobility Profiling Techniques and

Applications A Fundamental Routing Problem on ICMAN Sociological Orbit aware Location

Approximation and Routing (SOLAR) Conclusion

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Mobility Impact on Routing

Node Mobility Dynamic network topology

Proactive protocols (LS, DV) are inefficient Need to exchange control packets too often Leads to congestion

Reactive protocols (DSR, LAR) are better suited, however Locating a node incurs more delay Route maintenance is tricky as nodes move

To strike a balance need mobility modeling

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Mobility Models in the Literature Random Waypoint, Weighted Random Waypoint

simple, but impractical!! Entity based individual node movement

Jardosh et al., MOBICOM’03 Lin et al., INFOCOM’04

Group based collective group movement Hong et al., MSWIM’99, MCM’01

Scenario based geographical constraints Lam et al., IEEE Comm. Mag. 97 Markulidakis et al., IEEE Per. Comm. 97 Liu et al., IEEE JSAC 98

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Advantages of Node Mobility – Individual node’s view of network

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Advantages of Node Mobility – Node’s view of network through “acquaintances”

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Impact of mobility on protocol performance F. Bai, N. Sadagopan,

and A. Helmy, “Important: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks”, Proceedings of IEEE INFOCOM '03, vol. 2, pp. 825-835, March 2003.

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Our MotivationsObservations MANET is often comprised of wireless devices carried by

people living within societies Social activities impose constraints on user movements

Steps to take Study the social influence on user mobility (e.g.,

realization of special regions of some social value) Identify a macro level (thus, lightweight) mobility profile

per user Use this profile to aid macro level soft location

management and routing

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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

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Illustration of A Random Orbit Model

(Random Waypoint + Corridor Path)Conference Track 1

Conference Track 3

Cafeteria

Lounge

Conference Track 2

Conference Track 4

PostersRegistration

Exhibits

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Random Orbit Model

See Ghosh et al., Adhoc NetworksJournal, 2005.

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Hub Based Mobility Profiles and 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

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Traces Used Profiling techniques applied to ETH Zurich traces

Duration of 1 year from 4/1/04 till 3/31/05 13,620 wireless users, 391 APs, 43 buildings Grouped users into 6 groups based on degree of activity Selected one sample (most active) user from each group

Mapped APs into buildings based on AP’s coordinates, and each building becomes a “hub” Converted AP-based traces into hub-based traces

Other traces Expect similar results from Dartmouth’s traces No sufficient AP location info from other traces UMass’s traces are for buses, more predictable than users Need to obtain actual users’ traces with GPS

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Orbital Mobility Profiling Obtain each user’s daily hub lists as binary vectors Represent each hub list (binary vector) as a point in

a n-dimensional space (n = total number of hubs) Cluster these points into multiple clusters, each with

a mean Using the Expectation-Maximization (EM) algorithm to train

the model based on a Mixture of Bernoulli’s distribution Probe other classification methods: Bayesian-Bernoulli’s

Each cluster mean represents a mobility profile, described as a probabilistic hub visitation list

User’s mobility is aptly modeled using a mixture of mobility profiles with certain “mixing proportions”

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Profiling illustration

Obtain daily hub stay durations

Translate to binary hub visitation vectors

Apply clustering algorithm to find mixture of profiles

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Profile parameters for all sample users

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Hub-based Location Predictions - I Unconditional Hub-visit Prediction

Prediction Error = Incorrect hubs predicted over Total hubs SPE – Statistical based Prediction Error

SPE-ALL: (n+1)th day prediction based on hub-visit frequency from day 1 through day n

SPE-W7 : (n+1)th day prediction based on hub-visit frequency within last week, i.e., day (n-7) through day n

PPE – Profile based Prediction Error PPE-W7 : (n+1)th day prediction based on profiles of the last

week, i.e., day (n-7) through day n Prediction Improvement Ratio (PIR)

PIR-ALL = (SPE-ALL – PPE-ALL) / SPE-ALL PIR-W7 = (SPE-W7 – PPE-W7) / SPE-W7

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Unconditional Prediction Results

The profile mixing proportions vary with every window of n days

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Hub-based Location Predictions - II Conditional Hub-visit Prediction

Improvement given current profile is known/identifiable It is possible sometimes to infer profile from current hub

information alone Our method effectively leverages information when available

Sample user categoriesTarget Hub ID: will the user visit this hub?The current day in questionPredicted probability using visit frequency Indicator (Current) HubCurrent ProfilePredicted probability based on profileActually visited Ht on day D or not

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Hub-based Location Predictions - III Hub sequence prediction based on hub transitional probability

Prediction Accuracy = 1 – (incorrect predictions / total predictions) Scenario 1: only starting hub is known for sequence prediction Scenario 2: hub prediction is corrected at every hub in sequence Better performance with increasing knowledge – intuitive

Statistical based Prediction Accuracy (SPA) – no profile informationProfile based Prediction Accuracy (PPA) – no time informationTime based Prediction Accuracy (TPA) – temporal profiles

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Applications of Orbital Mobility Profiles Location Predictions and Routing within MANET and ICMAN

We will discuss an example of routing on ICMAN We have several other papers in this area (see website at the end)

Anomaly based intrusion detection unexpected movement (in time or space) sets off an alarm

Customizable traffic alerts alert only the individuals who might be affected by a specific traffic condition

Targeted inspection examine only the persons who have routinely visited specific regions

Environmental/health monitoring identify travelers who can relay data sensed at remote locations with no APs

Others

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Routing challenges in ICMAN ICMAN (Intermittently Connected MANET)

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

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On Problem’s Complexity Basic model:

G = (V,E) be a 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 subject to some constraint Basic tradeoff: overhead vs delivery probability Possible constraints to limit overhead

Constraint 1: each intermediate node forwards packets to at most k downstream neighbors

Constraint 2: G(A) has at most k edges

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On Problem’s Complexity (cont’) The centralized version: given G and k, find a delivery subgraph H where Conn2(H) is maximized, subject to |E(H)| ≤ k

Two Negative Theorems1. Computing Conn2(H) is #P-complete

2. Finding H maximizing Conn2(H) is #P-hard What do we do now?

Approximate Conn2(H) by another poly-time computable function f, then find H that maximizes f(H)

Develop heuristics routing algorithms

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Our Problem’s Setting

Slightly different from the basic problem just discussed Mobility profiles give contact probabilities But, contact probabilities do not give mobility

profiles We make use of the mobility profiles in our

routing heuristics!

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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

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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

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SOLAR-HUB Protocol Pd

nihj: 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 ni & 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

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Simulation Parameters for GloMoSim

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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

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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

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Performance – Cache Size (Only SOLAR)

• All versions fair better with more cache

• Overall D-SOLAR-KSP performs best

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Performance – Cache Timeout (Only SOLAR)

• All versions fair better with larger timeout

• Overall D-SOLAR-KSP performs best

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Conclusion Mobility has severe impact on routing performance A practical mobility framework should the sociological

influence on user movement into account Wireless users can be profiled based on their social

activities Mobility profiles are useful not only for routing (e.g.,

SOLAR protocols) but also other applications such as location prediction, resource allocation, etc.

SOLAR Project: (for more information) http://www.cse.buffalo.edu/~joyghosh/solar.html

Thank You!

Questions?