Mobility Aware Routing Schemes (MARS) for Mobile Wireless Networks

A Dissertation Proposalby Joy Ghosh

LANDERcse@buffalo

Outline

Geographic forwarding + AcquaintancesAcquaintance Based Soft Location

Management (ABSoLoM)Hierarchical Sociological OrbitsSociological Orbit Aware Routing (SOAR)Proposed Research

Mobility makes routing challenging!

Node Mobility Dynamic network topology Proactive protocols are inefficient

Need to exchange control packets too often Leads to congestion E.g., Distance Vector, Link State

Reactive protocols are better suited, but Locating a node incurs more delay Route maintenance is tricky as nodes move E.g., Dynamic Source Routing (DSR), Location Aided

Routing (LAR)

Greedy Geographic Forwarding

Pros Less affected by mobility than source routes Smaller header size (no path cached)

Cons Nodes need to know own location Needs sufficient node density

Workarounds for local maxima Broadcast Planar graph perimeter routing (e.g., GPSR)

Strict Location Management

Efficiently determine destination’s location Map node id to location servers Every node keeps its server updated Other nodes query server to locate node Needs some formalized methods:

Form grids optional Assign server nodes (or, server regions) Requires sufficient node density for simplicity Higher overhead in protocol maintenance E.g, GLS, SLURP, SLALoM, HGRID

Is there a less formal method?

Individual node’s view of network

Node’s view of network through “acquaintances”

Acquaintance Based Soft Location Management (ABSoLoM)

Forming and maintaining acquaintances Limit number of acquaintances Keep updating acquaintances of location Query acquaintances for destination location Limit query propagation by logical hops On learning of destination, use geographic

forwarding to send packets to destination Nosy Neighbors

Can respond to query if destination’s location is known Caches node locations while forwarding certain packets

Performance Analysis

Simulated in GloMoSim LAR & DSR borrowed from the GloMoSim distribution Implementation of SLALoM by Sumesh Philip (author) ABSoLoM parameters

Number of friends = 3 Maximum logical hops = 2

100 nodes in 2000m x 1000m for 1000s Random Waypoint mobility

Velocity = 0m/s-10m/s; Pause = 15s Random CBR connections varied in simulation

50 packets per connection; 1024 bytes per packet

Results – I.a: Throughput vs. Load

Results – I.b: Overhead vs. Load

Simulation Results - II

Framework for analyzing 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.

Parallel growth of models and protocols

Practical mobility models Random Waypoint simple, but impractical!! Entity based individual node movement Group based collective group movement Scenario based geographical constraints

Mobility pattern aware routing protocols Mobility tracking and prediction Link break estimation Choice of next hop

Our Motivation

Not to suggest a practical mobility model MANET is comprised of wireless devices carried by

people living within societies Society imposes constraints on user movements Study the social influence on user mobility Realization of special regions of some social value Identify a macro level mobility profile per user Use this profile to aid macro level soft location

management and routing

Hierarchical Sociological Orbits(e.g., life of a graduate student!!)

Living Room

Kitchen Porch/Yard

Conference Room

Cafeteria Cubicle

HomeSchool

OutdoorsHome Town

City 2Friends

City 3Relatives

Potential MANET

Level 3 Orbit

Level 2 Orbit

Level 1 Orbit

Potential DTN

ORBIT Framework – NOT a mobility model!!

A Random Orbit Model(Random Waypoint + Corridor Path)

Conference Track 1

Conference Track 3

Cafeteria

Lounge

Conference Track 2

Conference Track 4

PostersRegistration

Exhibits

Random Orbit Model

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

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

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

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!

Performance Analysis Parameters

Results – I.a : Throughput vs. Hubs

Results – I.b : Overhead vs. Hubs

Results – I.c : Delay vs. Hubs

Results – II : Hub Size variations

Results – III : Node Speed variations

Results – IV : Radio Range variations

Results – V : No. of Nodes variations

Summary of Preliminary Work

Conferences:

[1] Joy Ghosh, Sumesh J. Philip, Chunming Qiao, "Acquaintance Based Soft Location Management (ABSLM) in MANET" - Proceedings of IEEE Wireless Communications a nd Networking Conference 2004 (March)

[2] Joy Ghosh, Sumesh J. Philip, Chunming Qiao, “Sociological Orbit Aware Routing in MANET" – Submitted to Mobihoc 2005

Technical Reports:

[1] Joy Ghosh, Sumesh J. Philip, Chunming Qiao, " ORBIT Mobility Framework and Orbit Based Routing (OBR) Protocol for MANET " - CSE Dept. TR # 2004-08, State University of New York at Buffalo,   2004 (July)

[2] Joy Ghosh, Sumesh J. Philip, Chunming Qiao, " Performance Analysis of Mobility Based Routing Protocols in MANET " - CSE Dept. TR # 2004-14, State University of New York at Buffalo,   2004 (Sept)

Outline of Proposed Research

Identification of Issues in SOARThe Problem formulation for MANETExplore probabilistic Hub level routingImplication of Orbital movement in DTNAnalytical modeling with graph theoryPractical applications and scenarios

Issues with SOAR in MANET

No definite method to select acquaintances Any node with known Hub list is an acquaintance

No constraints on memory per user device E.g., Nodes in SOAR-2 cache 1 & 2 hop neighbors

No measures on reliability of data delivery Hub list discovery is not guaranteed May effectively resort to flooding with a high value for

query packet’s logical hops

Problem formulation for SOAR in MANET

Assumptions Enough Hubs to ensure sufficient node density

throughout terrain to do geographic forwarding without 100% guarantee due to geographic holes

Hub coordinates and dimensions are common knowledge

The delay for data packets to go from one hub to another (via geo forward) may be estimated

Optional: time related information of a node’s visit to a Hub, and the Hub stay duration

Problem formulation for SOAR in MANET

Problem to be solved Efficient routing of data packets to nodes in ‘orbital’ motion

Sub-problem Hub list discovery (location approximation) of the destination

via ‘acquaintances’

Difference from peer-to-peer networks Require information about a single node, unlike several nodes

in p2p networks, which contain some required information In p2p networks, queries are propagated over logical links,

whereas in our case, each logical hop (i.e., node to its acquaintance) may require multiple physical hops

Problem formulation for SOAR in MANET

Routing Objectives Maximize data throughput Minimize control overhead Minimize end-to-end delay

Routing variables (from the identified issues) The number of entries in the acquaintance table

(cache size) The maximum number of search steps

(logical hop threshold) The probability of finding the destination’s Hub list

(reliability)

Problem formulation for SOAR in MANET

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

Possible approaches to solution Central / Global knowledge Analytical modeling, ILP Local / Distributed knowledge Heuristic

Probabilistic Hub level Routing

Nodes may orbit Hubs in some probabilistic sequence Each Hub in the Hub list of a node has an assigned

probability for containing the node

Further assumptions may be made about time related information regarding the Hub visits

Explore probabilistic routing schemes under these assumptions

‘Orbit’ in Delay Tolerant Networks (DTN)

DTN is a network overlaid on regional networks Supports inter-operability between regions Network is intermittently connected

Geographic forwarding will not apply Source routing will not work

Network is delay tolerant Explore ‘store and forward’ of packets

E.g., mobile nodes are satellites, busses.

‘Orbit’ in Delay Tolerant Networks (DTN)

Movement is more continuous Nodes do not stay at one place for long Hubs may need to refer to ‘points of contact’ Probabilistic contact {time, duration, capacity} information

Movement may be more deterministic Explore knowledge vs. performance relationship

Assign probabilities to Paths instead of Hubs Consideration of wired overlay networks (multi-path) Explore graph theoretical approaches for analytical

modeling of orbital routing in DTN

Questions & Answers

Source Routing (DSR, LAR)Return

Geographic Forwarding may help(nodes must know own location)

Return

Forming & maintaining acquaintances

Non Acqntnce Pending Acqntnce Accepted Acqntnce

Return

Querying AcquaintancesReturn

Random Waypoint mobility model

Parameters Pause time = p Max velocity = vmax

Min velocity = vmin

Description Pick a random point within terrain Select a velocity vi such that vmin ≤ vi ≤ vmax Move linearly with velocity vi towards the chosen point On reaching the destination, pause for specified time p Repeat the steps above for entire simulation

Return

Entity based mobility model examples

Random Walk Mobility Model (including its many derivatives) A simple mobility model based on random directions and speeds.

Random Waypoint Mobility Model A model that includes pause times between changes in destination and

speed. Random Direction Mobility Model

A model that forces MNs to travel to the edge of the simulation area before changing direction and speed.

A Boundless Simulation Area Mobility Model A model that converts a 2D rectangular simulation area into a torus-shaped

simulation area. Gauss-Markov Mobility Model

A model that uses one tuning parameter to vary the degree of randomness in the mobility pattern.

A Probabilistic Version of the Random Walk Mobility Model A model that utilizes a set of probabilities to determine the next MN position.

City Section Mobility Model A simulation area that represents streets within a city.

Return

Group based mobility model examples

Exponential Correlated Random Mobility Model A group mobility model that uses a motion function to create

movements. Column Mobility Model

A group mobility model where the set of MNs form a line and are uniformly moving forward in a particular direction.

Nomadic Community Mobility Model A group mobility model where a set of MNs move together

from one location to another. Pursue Mobility Model

A group mobility model where a set of MNs follow a given target.

Reference Point Group Mobility Model A group mobility model where group movements are based

upon the path traveled by a logical center.Return

Scenario based mobility model examples

Freeway model Manhattan model City Area, Area Zone,

Street Unit METMOD, NATMOD,

INTMOD

Return

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

This is a minimum set cover problem – NP Complete We use the Quine-McCluskey optimization technique

Subset of acquaintances to query

Return

Quine-McCluskey optimization

Acquaintance

_ a

Example: A = {1,2}, B = {2,3,4}, C = {1,3} A, B, C are Prime acquaintances B is an Essential Prime acquaintance

Choose all the Essential Prime acquaintances first If any Hub is still uncovered, iteratively choose non-essential Prime

acquaintances that cover the max number of remaining Hubs, till all Hubs are covered

Return

Performance variation with Radio Hops

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Performance Variation with Radio Hops