1. 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
Sponsor:

2. Tempe, Fulton School of Engg & CSE

3. Department of Computer Science & Engineering, Tempe, Arizona
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
Datacenter project ?????
Sponsors

4. Pervasive Health Monitoring Criticality Aware-Systems
IMPACT: Research
Use-inspired research in pervasive computing & wireless sensor networking
Goal:
Protocols for mobile ad-hoc networks
Features:
Energy efficiency
Increased lifetime
Data aggregation
Localization
Caching
Multicasting
Sponsor:
Mobile Ad-hoc
Networks
Goal:
Protect people’s identity & consumer computing from viral threats
Features:
PKI based
Non-tamperable, non-programmable personal authenticator
Hardware and VM based trust management
Sponsor:
ID Assurance
Pervasive Health Monitoring
Criticality Aware-Systems
Thermal Management
for Data Centers
Intelligent
Container
Goal:
Pervasive Health monitoring
Evaluation of medical applications
Features:
Secure, Dependable and Reliable data collection, storage and communication
Sponsor:
Goal:
Evaluation of crisis response management
Features:
Theoretical model
Performance evaluation
Access control for crisis management
Sponsor:
Goal:
Increasing computing capacity for datacenters
Energy efficiency
Features:
Online thermal evaluation
Thermal Aware Scheduling
Sponsor:
Goal:
Container Monitoring for Homeland Security
Dynamic Supply Chain Management
Features:
Integration of RFID and environmental sensors
Energy management
Communication security
Sponsor:
Medical Devices, Mobile Pervasive Embedded Sensor Networks
BOOK: Fundamentals of Mobile and Pervasive Computing, Publisher: McGraw-Hill  Dec. 2004

5. 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
Network Characteristics
Dynamic Topology
Constrained resources
battery power B C C A A B D D
Links formed and broken with mobility

6. Routing in MANETs Routing
Proactive
Periodically maintains routes between every mobile node pair.
Predefined routes available
Low latency
Low scalability.
Hybrid
Network divided in small zones.
Intra-region Proactive Routing.
Reactive Inter-region routing.
Balances Proactive & Reactive.
Scalable.
Latency higher than proactive.
Reactive
Routes NOT maintained.
Route established only if data to transmit.
High Scalability.
No pre-defined route.
High Latency.
Slide before this……basic things about mobile ad hoc networks. What is MANTE? sequential
Real-time applications such as Disaster Relief and Battle-field
operations require Proactive Maintenance of Routes.

7. Proactive Route Maintenance
Number
of Nodes
Beacon
Interval
Energy Consumption
per bit transmitted
Average size
of beacon msg
Overhead
Periodic beacon messages for link state maintenance.
Periodic route update b’cast.
Triggered route update b’cast with each link change.
Bits transmitted due to
beaconing per unit time
E x N x logN / β
E x N2 x logN / φ
Bit transmitted per unit time
for periodic broadcast
Route Broadcast
Interval
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 β & φ to minimize energy overhead.

8. Proactive Protocol Classification
PP+BTP
PP+BP
PP+B
PP+BT
Employs Beacons,
& Periodic Updates
Employs Beacons,
& Triggered Updates
Employs only
Beacons
Employs Beacons, Periodic, & Triggered Update
FSR, IARP etc.
WRP, OLSR etc.
BFST, SS-SPST etc.
DSDV, TBRPF etc.
Research Goals:
Developing a PP+B 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 β & φ for different proactive protocols.

9. Energy-Aware Self-Stabilizing Protocol

10. 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
Valid
State
Invalid
State
Convergence
Local actions in distributed nodes.
Applied to Multicasting in MANETs

11. Self-stabilizing Multicast for MANETs
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)

12. Energy-Efficiency in Self-stabilization

13. 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. i j k l
non-intended
neighbor
intended neighbor
Ti reaches all nodes in range i Ti
No communication
schedule during
broadcast in random
access MAC
(e.g ).
Overhearing at j, k, and l
Ci = Ti + 7R
What is the additional cost if a node selects a parent?

14. 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 C D X
AdditionalCost (B → X) = TB + R
AdditionalCost (A → X) = TA + 2R
Potential Parents of X
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.

15. SS-SPST-E Execution And so on …… Tolerance to topological changes.
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 A S B 1 2 2 G 3
First Round – source (root) stabilizes
hop distance of root from itself is 0.
no additional cost.
No potential parents for any node. 1 1 H D C 2 2
Second Round – neighbors of root stabilizes
hop distance of root’s neighbors is 1.
parent of root’s 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 (S → F) = TS + 5R
AdditionalCost (C → F) = TC + 3R
Tolerance to topological changes.
Potential parent for F = {S, C}.
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.

16. 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 64Kbps.
Performance Measures:
Packet Delivery Ratio (PDR) - for reliability
Energy Consumed / Packet Delivered - for energy efficiency
Goal of the simulation

17. Simulation Results – Varying Beacon Interval
PDR decreases with less beaconing

18. Simulation Results – Varying Beacon Interval
Energy consumption per packet delivered increases due to decrease in number of packets delivered.

19. Simulation Results – Varying Node Mobility
1m/s
5m/s
10m/s
15m/s
20m/s
Low packet delivery with high dynamicity
ODMRP has high PDR due to redundant routes

20. 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

21. Simulation Results - Varying Multicast Group Size10 20 30 40 50
Self-stabilizing protocols scale better.
MAODV has highest delay due to reactive tree construction

22. Simulation Results - Varying Multicast Group Size10 20 30 40 50
ODMRP leads to high control overhead and less PDR.

23. Analytical Model

24. Periodic Broadcast based on Link Dynamics (LD)
Determines optimum φ [Samar ‘06].
periodic b’cast of route update only when link changes.
Optimum β 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.

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

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

27. Assumptions & System Model
Network Parameters
Link changes Poisson distributed (avg. rate = ) [Samar ‘06].
Avg. rate of triggered update depends on  and β.
determines overhead for triggered b’cast.
Packet loss due to delay in route reconstruction.
Link reliability assumed.
No packet re-transmission.
Disconnection 1
Triggered Update
Disconnection 2
Disconnection 3
Disconnection 4
1/  k
Average interval between consecutive triggered update
Delay in route
reconstruction
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, , ).

28. Analytical Model
Objective Function
Constraints
Optimization

29. Objective Function: Overhead Energy
Cost of
Triggered B’cast
Cost of
Periodic B’cast
Cost of Beacons
PP+BTP N d l o g e E + D N 2 1 + k d l o g e E + N 2 ' d l o g e E E O v =
PP+BP N d l o g e E + E O v = N 2 ' d l o g e E
PP+BT + D N 2 1 + k d l o g e E E O v = N d l o g e E
PP+B E O v = N d l o g e E

30. Constraints PDR Constraint Capacity Constraint
P = Probability of packet loss due to each link failure.
PDR = (1 - P)D.
(1 - P)D >= .
Find P = function of , , β, φ.
Capacity Constraint
Control Traffic.
Data Traffic. P

31. 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
Delay in route
reconstruction
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
Delay in route
reconstruction
P = P1 x prob of CASE I + P2 x prob of CASE II
=  x route-reconstruction delay
PDR Constraint: route-reconstruction delay <= [1 – 1/D] / 

32. Optimization Step 1: route-reconstruction delay in terms of β and φ.
Link
Disconnection
Valid Route
Establishment
Beacons Not
Received
Beacons
Route-reconstruction delay  k 
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 = kβ + φ + 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.

33. Optimizations for different Proactive Protocols
PP+BTP
PP+BP
PP+BT
PP+B
Employs Beacons, Periodic,
& Triggered Updates
DSDV, TBRPF etc.
1st Derivative
Quartic equation
Employs Beacons,
& Periodic Updates
FSR, IARP etc.
1st Derivative
Quadratic equation
Employs Beacons,
& Triggered Updates
WRP, OLSR etc.
Single variable
Equating PDR
constraint gives
the result
Employs Beacons
BFST, SS-SPST etc.
Single variable
Equating PDR
constraint gives
the result 1
> o p t D + d r e c k N 3 ' ' o p t = 1 D N + k ( ) o p t = 1 D k o p t = 1 D k + P i c

34. Optimum Periods w.r.t. link change
PP+BP
PP+B
PP+BT LD
PP+BTP
PP+BP
PP+BTP

35. Optimum Periods w.r.t. traffic intensity
PP+B
PP+BT LD
PP+BP
PP+BTP
PP+BP
PP+BTP

36. Optimum Periods w.r.t. Network Size
Decrease Periodic Update Frequency
Decreases broadcast.
Increases Scalability.
Increase beacon frequency to meet PDR Constraint.

37. 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 β & φ optimization for other proactive protocols
Local stabilization
Comparison with other energy-efficient protocols

38. 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 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 , 2003.

39. Questions ??