Sociological Influences on Mobile Wireless Networks

Chunming Qiao, Ph.D., Professor

University at Buffalo (SUNY)Director, Laboratory for Advanced Network Design, Evaluation and Research (LANDER)

Computer Science and Engineering Department

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Sociological Orbits

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Key Concepts

Users’ movements are often socially influenced “hubs” – places of social interest to users User mobility – an “orbit” involving a list of hubs Mobility profile – a list of hubs likely to be visited Remarks

User mobility profiles exist but difficult to obtain Usefulness for routing in MANET and ICMAN and Mobile

wireless applications

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Mobility Profiling Obtain mobility traces (e.g., AP system logs) Convert AP-based traces to hub-based

movements Obtain daily hub lists for individuals Apply clustering algorithms to group hub lists

together to identify patterns in movement Represent the patterns as mobility profiles Profiles have been shown useful for hub-level

location predictions

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Profiling illustration

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Profile based Predictions

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Applications of Orbital Mobility Profiles 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 upon re-entrance.

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

Routing within MANET and ICMAN (described next)

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Profile based Routing within MANET

Build a sociological orbit based mobility model (Random Orbit)

Assume that mobility profiles are obtained Devise routing protocols to leverage

mobility information within MANET setting Key assumption – geographical forwarding

is feasible

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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

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Sociological Orbit Aware Routing - Basic

Every node knows Own coordinates, Own Hub list, All Hub coordinates

Periodically broadcasts Hello SOAR-1 : own location & Hub list SOAR-2 : own location & Hub list + 1-hop neighbor Hub lists

Cache neighbor’s Hello Build a distributed database of acquaintance’s Hub lists

Unlike “acquaintanceship” in ABSoLoM, SOAR has No formal acquaintanceship request/response its not mutual Hub lists are valid longer than exact locations lesser updates

For unknown destination, query acquaintances for destination’s Hub list (instead of destination’s location), in a process similar to ABSoLoM

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Sociological Orbit Aware Routing - Advanced

Subset of acquaintances to query Problem: Lots of acquaintances lot of query overhead Solution: 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

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Sociological Orbit Aware Routing – Illustration(Random Waypoint + P2P Linear)

Hub A

Hub B

Hub C

Hub D

Hub E

Hub I

Hub FHub G

Hub H

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Performance Analysis Metrics

Data Throughput (%) Data packets received / Data packets generated

Relative Control Overhead (bytes) Control bytes send / Data packets received

Approximation Factor for E2E Delay Observed delay / Ideal delay To address “fairness” issues!

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Performance Analysis Parameters

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Results – I.a : Throughput vs. Hubs

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Results – I.b : Overhead vs. Hubs

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Results – I.c : Delay vs. Hubs

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Routing challenges in ICMAN

May not have an end-to-end path from source to destination at any given point in time (intermittently connected)

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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User level routing strategy Deliver packets to the destination itself Intermediate users store-carry-forward the packets Mobility profiles used to compute pair wise user contact

probability (CP) to form weighted graph Apply modified Dijkstra’s to obtain k-shortest paths

(KSP) with corresponding Delivery probability (DP)

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 DP 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 Fractional data delivered to each hub proportional to the

probability of finding the destination in it SOLAR-HUB protocol

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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Data Throughput vs. Number of Users

High Data Throughput

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50 100 150

Number of Users

Data

Th

rou

gh

pu

t (%

)

SOLAR-HUB

D-SOLAR-KSP

S-SOLAR-KSP

Epidemic

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End-to-End Data Delay vs. Number of Users

Low End-to-end Data Delay

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100

150

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50 100 150

Number of Users

Avera

ge E

-2-E

Dela

y (

secs)

Epidemic

S-SOLAR-KSP

D-SOLAR-KSP

SOLAR-HUB

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Network Overhead vs. Number of Users

Much lower overhead than Epidemic!

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50 100 150

Number of Users

Netw

ork

Byte

Overh

ead

SOLAR-HUB

D-SOLAR-KSP

S-SOLAR-KSP

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Future Directions

Collect and analyze user location-based traces Apply advanced clustering/profiling techniques Optimization techniques for profile information

management Design and analyze routing algorithms Experimenting with Applications

Thank You !