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Mobility Aware Routing Schemes (MARS) for Mobile Wireless Networks. A Dissertation Proposal by Joy Ghosh LANDER cse@buffalo. Outline. Geographic forwarding + Acquaintances Acquaintance Based Soft Location Management (ABSoLoM) Hierarchical Sociological Orbits

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mobility aware routing schemes mars for mobile wireless networks

Mobility Aware Routing Schemes (MARS) for Mobile Wireless Networks

A Dissertation Proposal

by Joy Ghosh



  • Geographic forwarding + Acquaintances
  • Acquaintance Based Soft Location Management (ABSoLoM)
  • Hierarchical Sociological Orbits
  • Sociological Orbit Aware Routing (SOAR)
  • Proposed Research
mobility makes routing challenging
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
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
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
  • Is there a less formal method?
acquaintance based soft location management absolom
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
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
framework for analyzing impact of mobility on protocol performance
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
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
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
Hierarchical Sociological Orbits(e.g., life of a graduate student!!)

City 2


Level 3 Orbit

Level 2 Orbit

Home Town

City 3



Level 1 Orbit



Potential DTN





Conference Room

Living Room

Potential MANET

a random orbit model random waypoint corridor path
A Random Orbit Model(Random Waypoint + Corridor Path)

Conference Track 2

Conference Track 1



Conference Track 3



Conference Track 4


sociological orbit aware routing basic
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
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
Sociological Orbit Aware Routing – Illustration(Random Waypoint + P2P Linear)

Hub E

Hub A

Hub H

Hub D

Hub B

Hub G

Hub F

Hub I

Hub C

performance analysis metrics
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!
summary of preliminary work
Summary of Preliminary Work


[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
Outline of Proposed Research
  • Identification of Issues in SOAR
  • The Problem formulation for MANET
  • Explore probabilistic Hub level routing
  • Implication of Orbital movement in DTN
  • Analytical modeling with graph theory
  • Practical applications and scenarios
issues with soar in manet
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
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 manet36
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 manet37
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 manet38
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
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
‘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 dtn41
‘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
forming maintaining acquaintances
Forming & maintaining acquaintances


Non Acqntnce

Pending Acqntnce

Accepted Acqntnce

random waypoint mobility model
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


entity based mobility model examples
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.


group based mobility model examples
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.


scenario based mobility model examples
Scenario based mobility model examples
  • Freeway model
  • Manhattan model
  • City Area, Area Zone, Street Unit


subset of acquaintances to query
Subset of acquaintances to query
  • 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


quine mccluskey optimization
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