Data Communications (Sensor Network) 1

Energy-Efficient Routing with Reliability Constraint

Team 2

Hojoong Kwon

Taehyun Kim

Data Communications (Sensor Network) 2

“Routing for Maximum System Lifetime in Wireless Ad-hoc Networks ”

Annual Allerton Conference on Communication 1999

J.-H. Chang and L. Tassiulas

Data Communications (Sensor Network) 3

Contents Problem Suggestion Solutions

Optimal Energy Consumption FR(Flow Redirection) MREP(Maximum Residual Energy Path Routing)

Simulation Results Conclusion Comments

Data Communications (Sensor Network) 4

Problem Suggestion

Many routing algorithms are focused on minimum energy dissipating path.

Nodes in that path will be drained out quickly

To maximize the system lifetime, energy dissipating load should be distributed to all nodes in the network

Minimum energy path

I’m DIEIN

G!

Data Communications (Sensor Network) 5

Solutions How can we distribute energy dissipating load?

Using multiple paths! FR (Flow Redirection)

Data route which causes a early system halt is redirected by using other nodes.

Maximum Residual Energy Path Routing Optimal Energy Consumption

This is computed by linear programming

Data Communications (Sensor Network) 6

Optimal Energy Consumption

Linear programming problem

Data Communications (Sensor Network) 7

Flow Redirection Algorithm FR is motivated by the following observation Theorem 1(Necessary optimality condition) - If the

minimum lifetime over all nodes is maximized then the minimum lifetime of each path flow from the origin to the destination with positive flow has the same value as the other paths.

Data Communications (Sensor Network) 8

Flow Redirection – con’t

Outgoing flow redirection Procedure

Determine the from which path to which path Calculate the amount of redirection (εi) Redirect the flow properly

+εi

-εi

Multihop routes

Giver

Taker

Data Communications (Sensor Network) 9

Flow Redirection – con’t

Firstly, define followings

Data Communications (Sensor Network) 10

Flow Redirection – con’t Determine the from which path to which path

If (node i’s lifetime should be increased),

If ,

Data Communications (Sensor Network) 11

Flow Redirection – con’t Calculate the amount of redirection flow (εi)

Constrains

Data Communications (Sensor Network) 12

Flow Redirection – con’t Redirect the flow properly

Add & Subtract ei properly Adding ei is easy, but subtracting is not easy since there may be

some links in the path whose flow is less than ei. Subtracting procedure

Subtract ei from qig If qjk (g<j<d, j<k<d) < ei , subtract qjk from node j to node k

recursively Subtract ei -n· qjk

Data Communications (Sensor Network) 13

MREP Routing Maximum Residual Energy Path Routing Define Lp as a vector whose elements are the reciprocal of the

residual energy for each link in the path after the route has been used by a unit flow Element of Lp for link (j,k) is

: Residual Energy : Unit flow

The largest element (the least energy node) comparing

Data Communications (Sensor Network) 14

Simulation Results Performance Measure of algorithm X

Random graph generation 5x5 square space 20 nodes(5 origins & 2 destinations) Initial energy = 1, generation rate Q = 1 TX range : 2.5 Energy expenditure per bit TX from i to j is

Data Communications (Sensor Network) 15

Simulation Results MTE(Minimum Transmitted Energy routing)

The shortest path algorithm based on energy expenditures per bit transmission.

The performance comparison

Data Communications (Sensor Network) 16

Conclusion To maximize the lifetime, the traffic is routed so that the energy

consumption is balanced.

FR & MREP algorithm are local and amenable to distributed implementation with close to optimal performance.

Data Communications (Sensor Network) 17

Comments Bellman-Ford algorithm is used

Stationary topology is needed to be assumed Setting up procedure has to be preceded for lifetime & energy

consumption calculation.

Is MREP better than FR? If so, what can we gain by using FR?

Routing overhead is not considered Maybe it is quite serious.

Author did not mention how to distribute this algorithm

Data Communications (Sensor Network) 18

Reliability Definition

End-to-end, event-to-sink, sink-to-sources Reliable data transmission, reliable event detection

Management MAC layer vs. transport layer hop-by-hop recovery vs. end-to-end recovery

Our approach Energy-efficient routing algorithm while guaranteeing reliable

end-to-end transmission

Data Communications (Sensor Network) 19

“Providing Application QoS through Intelligent Sensor Management”

WSNA 2003

“Optimal Sensor Management Under Energy and Reliability Constraints”

WCNC 2003

M. Perillo and W. B. Heinzelman

Data Communications (Sensor Network) 20

Introduction Maximize lifetime while meeting application-specific

QoS (reliability)

Only certain subsets of sensors may satisfy reliability constraint.

Two strategies Turn off redundant sensors Energy-efficient routing

Data Communications (Sensor Network) 21

Example Problem Wish to detect the presence of phenomena

anywhere in the observation space

Feasible sensor sets

• F1 = { S1, S2 }

• F2 = { S1, S5, S6 }

• F3 = { S2, S3, S4 }

Data Communications (Sensor Network) 22

Problem Formulation Given

Feasible set : Feasible set makeup

Path makeup

: sensing power and transmission power : receiving, processing and transmission power

( when routing sensor Sj2’s data )

}},...,1{,{ Fi NiFF

0

1ija

0

121 ljjr

Sensor Sj is in set Fi

Else

Sensor Sj1 is included on Sj2‘s lth path

Else

jsP ,

21, jjrP

Data Communications (Sensor Network) 23

Problem Formulation Find

Total time that the set Fi is used :

Fraction of time that path l is used to route Sj’s data during the time that Fi is used :

Energy constraint

Maximize

iT

jliF

1

2

2,

222121111 1 1

,1

, j

N

i

N

j

N

liijlijjjrljj

N

iijsij ETafPrTPa

F S jPF

FN

iiT

1

0

1,

1

jPN

ljlif

data sink not in Sj’s tx range

otherwise

Data Communications (Sensor Network) 24

Maximum Flow Graph Problem

ds

S4

S1

S2

S3

F3

F1

F2

P211

P431

P432

R21

R43

Energy

Time

Data Communications (Sensor Network) 25

Maximum Flow Graph Problem

S3

4

2

1

S3

4

2

1

S3

4

2

1

1F 2F 3F

Data Communications (Sensor Network) 26

Maximum Flow Graph Problem

s

S4

S1

S2

S3

Capacities on arcs from initial energy constraint

4E

F3

4,34

1

sPa

Arc multipliers to convert energy to time

P431

P432

S4 has 2 valid routes

41,31

1

rPa

Arc multipliers to normalize time contribution

R4

d

2

12

1

1

Energy

Time

Data Communications (Sensor Network) 27

Simulation Results Lifetime vs. transmission range

Environment dimension : 100m x 100m Number of sensors : 100

Data Communications (Sensor Network) 28

Simulation Results Lifetime vs. number of sensors

Transmission range : 25m

Data Communications (Sensor Network) 29

Simulation Results Lifetime vs. field size

Node density : 0.01 node/m2

Data Communications (Sensor Network) 30

Conclusions & Comments Joint optimization of sensor scheduling and data

routing

Optimally balance the tradeoff between application performance and network lifetime

Centralized solution using global information High computation and signaling cost

It may not be easy to find the feasible sensor sets.