Mobility Aware Routing Schemes (MARS) for Mobile Wireless Networks A Dissertation Proposal by Joy...
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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
Return
Performance Variation with Radio Hops