Energy-Aware Proactive Routing in MANETs

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Energy-Aware Proactive Routing in MANETs

• SANDEEP GUPTA
• Department of Computer Science and Engineering
• School of Computing and Informatics
• Ira A. Fulton School of Engineering
• Arizona State University
• Tempe, Arizona, USA
• sandeep.gupta_at_asu.edu

Sponsor
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Tempe, Fulton School of Engg CSE
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IMPACT (Intelligent Mobile Pervasive Autonomic
Computing Technologies) LAB

• Research Goals
• Enable Context-Aware Pervasive Applications
• Dependable Distributed Sensor Networking
• Projects
• Wireless Solution for Smart Sensors Biomedical
  Applications (NSF - ITR)
• Context-Aware Middleware for Pervasive Computing
  (NSF NMI)
• Thermal Management Datacenters (SFAZ, NSF)
• Location Based Access Control (CES)
• Identity Assurance (NSF, CES)
• Mobility-Tolerant Multicast (NSF)
• Ayushman Infrastructure Testbed for Sensor
  Based Health Monitoring (Mediserve Inc.)
• Group
• Faculty Dr. Sandeep K. S. Gupta
• 1 PostDoc 7 PhD 2MS 1 UG

Department of Computer Science Engineering,
Tempe, Arizona
http//impact.asu.edu
Sponsors
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IMPACT Research

• Use-inspired research in pervasive computing
  wireless sensor networking

• Goal
• Pervasive Health monitoring
• Evaluation of medical applications
• Features
• Secure, Dependable and Reliable data collection,
  storage and communication
• Sponsor

• Goal
• Increasing computing capacity for datacenters
• Energy efficiency
• Features
• Online thermal evaluation
• Thermal Aware Scheduling
• Sponsor

• Goal
• Evaluation of crisis response management
• Features
• Theoretical model
• Performance evaluation
• Access control for crisis management
• Sponsor

Medical Devices, Mobile Pervasive Embedded Sensor
Networks
BOOK Fundamentals of Mobile and Pervasive
Computing, Publisher McGraw-Hill  Dec. 2004
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Mobile Ad hoc Networks (MANETs)

• Network Model
• mobile nodes (PDAs, laptops etc.)
• multi-hop routes between nodes
• no fixed infrastructure

• Applications
• Battlefield operations
• Disaster Relief
• Personal area networking

Multi-hop routes generated among nodes
B

• Network Characteristics
• Dynamic Topology
• Constrained resources
• battery power

C
A
C
A
B
D
D
Links formed and broken with mobility
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Routing in MANETs
Routing

• Routes NOT maintained.
• Route established only if data to transmit.
• High Scalability.
• No pre-defined route.
• High Latency.

Real-time applications such as Disaster Relief
and Battle-field operations require Proactive
Maintenance of Routes.
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Proactive Route Maintenance

• Overhead
• Periodic beacon messages for link state
  maintenance.
• Periodic route update bcast.
• Triggered route update bcast with each link
  change.

E x N x ?logN? / ß
E x N2 x ?logN? / f
E x N2 x ?logN? for each triggered update
High Energy Overhead in Maintenance Operations
Low Scalability
Reduces Applicability
Reduce maintenance operations and find optimum ß
f to minimize energy overhead.
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Proactive Protocol Classification
Employs Beacons, Triggered Updates
Employs only Beacons
Employs Beacons, Periodic Updates
Employs Beacons, Periodic, Triggered Update
WRP, OLSR etc.
BFST, SS-SPST etc.
FSR, IARP etc.
DSDV, TBRPF etc.

• Research Goals
• Developing a PPB type of protocol maintaining
  energy-efficient routes.
• Uses Self-stabilization from Distributed
  Computing.
• Improves Self-Stabilizing Shortest Path Spanning
  Tree (SS-SPST) for energy-efficiency.
• Analytical Model for determining optimum ß f
  for different proactive protocols.

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Energy-Aware Self-Stabilizing Protocol
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Self-stabilization in Distributed Computing
Topological Changes and Node Failures for MANETs.

• Self-stabilizing distributed systems
• Guarantee convergence to valid state through
  local actions in distributed nodes.
• Ensure closure to remain in valid state until any
  fault occurs.
• Can adapt to topological changes
• Is it feasible for routing in MANETs?

Fault
Closure
Invalid State
Valid State
Convergence
Local actions in distributed nodes.
Applied to Multicasting in MANETs
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Self-stabilizing Multicast for MANETs
Multicast source
Topological Change

• Maintains source-based multi-cast tree.
• Actions based on local information in the nodes
  and neighbors.
• Pro-active neighbor monitoring through periodic
  beacon messages.
• Neighbor check at each round (with at least one
  beacon reception from all the neighbors)
• Execute actions only in case of changes in the
  neighborhood.

Convergence Based on Local actions
Problem energy-efficiency
is not considered
Self-Stabilizing Shortest Path Spanning Tree
(SS-SPST)
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Energy-Efficiency in Self-stabilization
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Energy Consumption Model
Ci Ti Ni x R
Cost metric for node i
Transmission energy of node i
Reception cost at all the neighbors

• Variable through Power Control
• One transmission reaches all in range

• Reception energy at intended neighbors.
• Overhearing energy at non-intended neighbors.

intended neighbor
No communication schedule during broadcast in
random access MAC (e.g. 802.11).
Overhearing at j, k, and l
Ci Ti 7R
What is the additional cost if a node selects a
parent?
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Energy Aware Self-Stabilizing Protocol (SS-SPST-E)

• Actions at each node
• (parent selection)
• Identify potential parents.
• Estimate additional cost after joining potential
  parent.
• Select parent with minimum additional cost.
• Change distance to root.

Loop Detected
E
Not in tree
F
A
B
D
C
X
AdditionalCost (B ? X) TB R
Potential Parents of X
AdditionalCost (A ? X) TA 2R

• Action Triggers
• Parent disconnection.
• Parent additional cost not minimum.
• Change in distance of parent to root.

Select Parent with minimum Additional Cost
Minimum overall cost when parent is locally
selected
Execute action when any action trigger is on
Tree validity Tree will remain connected
with no loops.
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SS-SPST-E Execution
Multicast source

• No multicast tree
• parent of each node NULL.
• hop distance from root of each node infinity.
• cost of each node is Emax.

2
2
S
A
B
1
2
2
G
3
1
No potential parents for any node.

• First Round source (root) stabilizes
• hop distance of root from itself is 0.
• no additional cost.

1
D
C
H
2
2

• Second Round neighbors of root stabilizes
• hop distance of roots neighbors is 1.
• parent of roots neighbors is root.

Potential parent for A, B, C, D, F S.
E
F
2
AdditionalCost (S ? A, B, C, D) Ts 4R
AdditionalCost (F ? E) TF 2R AdditionalCost
(D ? E) TD 3R
AdditionalCost (D ? E) TD 3R

• And so on

Potential parent for E D, F.
AdditionalCost (S ? F) TS 5R AdditionalCost
(C ? F) TC 3R
AdditionalCost (S ? F) Ts 5R
Potential parent for F S, C.

• Tolerance to topological changes.

Convergence - From any invalid state the total
energy cost of the graph reduces after every
round till all the nodes in the system are
stabilized. Proof - through induction on round .
Closure Once all the nodes are stabilized it
stays there until further faults occur.
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Simulation Model

• Goals
• performance analysis with beacon reduction.
• study reliability energy-efficiency trade-off.
• scalability study with number of receivers.
• comparative study to verify feasibility of
  self-stabilization
• SS-SPST non-energy efficient self-stabilizing
  multicast
• MAODV tree-based multicast (non
  self-stabilizing)
• ODMRP mesh-based multicast (non
  self-stabilizing)
• NS-2 used for simulating 50 nodes placed at
  random positions
• Random way-point mobility model.
• Omni-directional antenna with power control.
• CBR packets _at_ 64Kbps.
• Performance Measures
• Packet Delivery Ratio (PDR) - for reliability

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Simulation Results Varying Beacon Interval
PDR decreases with less beaconing
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Simulation Results Varying Beacon Interval
Energy consumption per packet delivered increases
due to decrease in number of packets delivered.
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Simulation Results Varying Node Mobility
5m/s
10m/s
15m/s
20m/s
1m/s
Low packet delivery with high dynamicity
ODMRP has high PDR due to redundant routes
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Simulation Results Varying Node Mobility
1m/s
5m/s
10m/s
15m/s
20m/s
SS-SPST-E leads to energy-efficiency
ODMRP has high overhead to generate redundant
routes
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Simulation Results - Varying Multicast Group Size
10
20
30
40
50
Self-stabilizing protocols scale better.
MAODV has highest delay due to reactive tree
construction
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Simulation Results - Varying Multicast Group Size
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10
30
40
50
ODMRP leads to high control overhead and less PDR.
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Analytical Model
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Periodic Broadcast based on Link Dynamics (LD)

• Determines optimum f Samar 06.
• periodic bcast of route update only when link
  changes.
• Optimum ß and scalability needs to be considered.
• Requirement for Application Parameters
  considerations
• Traffic route maintenance can be reduced for
  low traffic.
• Reliability Requirements
• Measured in terms of Packet Delivery Ratio (PDR).
• PDR Total Number of Delivered Packets / Total
  Packets Transmitted
• Route maintenance can be reduced for low PDR.

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Goals

• Balance Proactivity based on Application
  Parameters.
• Minimize Energy Overhead.
• Maintain Reliability.
• Improve Scalability.

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Contributions

• Analytical Model determining optimum beacon and
  route update intervals.
• Analysis applied to all classes of protocols.

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Assumptions System Model

• Network Parameters
• Link changes Poisson distributed (avg. rate ?)
  Samar 06.
• Avg. rate of triggered update depends on ? and ß.
• determines overhead for triggered bcast.
• Packet loss due to delay in route reconstruction.
  
• Link reliability assumed.
• No packet re-transmission.

Average interval between consecutive triggered
update

• Application Parameters
• Bulk Poisson Traffic Model (avg. rate ?).
• Voice/Audio/Video/Media Traffic.
• PDR requirement (?) known.

Packet loss dependent on route reconstruction
delay
PROBLEM Determine optimum ? f(?, N, ?, ?) ?
g(?, N, ?, ?).
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Analytical Model

• Objective Function
• Constraints
• Optimization

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Objective Function Overhead Energy
Cost of Triggered Bcast
Cost of Periodic Bcast
Cost of Beacons

• PPBTP

PPBP


PPBT
PPB
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Constraints

• PDR Constraint
• P Probability of packet loss due to each link
  failure.
• PDR (1 - P)D.
• (1 - P)D gt ?.
• Find P function of ?, ?, ß, f.
• Capacity Constraint
• Control Traffic.
• Data Traffic.

P
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Probability of Packet Loss (P)

• CASE I Link disconnection rate greater than
  traffic generation rate
• Route-reconstruction delay MUST be less than
  consecutive link disconnections in the route.
• P1 ? x route-reconstruction delay

• CASE II Link disconnection rate less than
  traffic generation rate
• Route-reconstruction delay MUST be less than
  average interval between consecutive packets.
• P2 ? x route-reconstruction delay

P P1 x prob of CASE I P2 x prob of CASE II
? x route-reconstruction delay
PDR Constraint route-reconstruction delay lt 1
?1/D / ?
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Optimization

• Step 1 route-reconstruction delay in terms of ß
  and f.

• Step 2 take the equality of the PDR constraint
• optimum value at the boundary.
• one variable represented in terms of other.
• objective re-written as a convex function of one
  variable.

Worst case route-reconstruction delay kß f
end-to-end broadcast delay.

• Step3 non-linear optimization of the objective
• equate first order derivative to 0.
• the resulting equation solved
• second order derivative checked for ve slope.

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Optimizations for different Proactive Protocols
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Optimum Periods w.r.t. link change
PPBP
PPB
PPBT
LD
PPBTP
PPBP
PPBTP
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Optimum Periods w.r.t. traffic intensity
PPBT
PPB
LD
PPBP
PPBTP
PPBP
PPBTP
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Optimum Periods w.r.t. Network Size

• Decrease Periodic Update Frequency
• Decreases broadcast.
• Increases Scalability.
• Increase beacon frequency to meet PDR Constraint.

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Conclusions Future Work

• SS-SPST-E provides energy-efficiency and
  self-stabilization.
• High adaptability to topological changes.
• Self-stabilization leads to scalability.
• Novel analytical model presented for optimization
  of maintenance operations in Proactive Routing
  Protocols for MANETs.
• Minimizes overhead
• Maintains Reliability
• Improves Scalability.
• Reduces wastage for low traffic mobility.
• Future Work
• Application of ß f optimization for other
  proactive protocols
• Local stabilization
• Comparison with other energy-efficient protocols

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List of Publications

• T. Mukherjee, S. K. S. Gupta, and G.
  Varsamopoulos, Analytical Model for Optimizing
  Periodic Route Maintenance in Proactive Routing
  for MANETs, To appear in Proc of ACM MSWiM, Crete
  Island, Greece, Oct 2007. To appear.
• T. Mukherjee, G. Sridharan, S. K. S. Gupta,
  Energy-Aware Self-Stabilization in Mobile Ad Hoc
  Networks A Multicasting Case Study, 21st IEEE
  Int'l Parallel and Distributed Processing
  Symposium (IPDPS), Long Beach, California,
  26-30th March 2007.
• S. K. S. Gupta and P. K. Srimani,
  Self-Stabilizing Multicast Protocols for Mobile
  Ad Hoc Networks, Journal of Parallel and
  Distributed Computing, 63(1), pp. 87-96, 2003.

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Questions ??