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Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC- ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) [email protected] Wireless Sensor Networks: Minimum-energy communication

Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) [email protected] Wireless Sensor Networks:

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Page 1: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Mario Čagalj

supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA)

and prof. Christian Enz (EPFL-DE-LEG, CSEM)

[email protected]

Wireless Sensor Networks: Minimum-energy communication

Page 2: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

2

Large number of heterogeneous sensor devices Ad Hoc Network

Sophisticated sensor devices communication, processing, memory capabilities

Wireless Sensor Networks

Page 3: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

3

Project Goals Devise a set communication mechanisms

s.t. they Minimize energy consumption Maximize network nodes’ lifetimes Distribute energy load evenly throughout a

network Are scalable (distributed)

Page 4: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

4

Minimum-energy unicast

Page 5: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

5

cA B

C

cA EDEA

B

cE D

cB C

cC D

1

C

DEA

B

1

1

1

1

Unicast communication model Link-based model

each link weighed how to chose a weight?

Power-Aware Metric [Chang00] Maximize nodes’ lifetimes

include remaining battery energy (Ei)

21)0

(x

iEiE

xrijeijc

receivingin spent energy 0

ttingin transmispent energy

rije

Page 6: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

6

Unicast problem description Definitions

undirected graph G = (N, L) links are weighed by costs the path A-B-C-D is a minimum cost path from

node A to node D, which is the one-hop neighbour of the sink node

minimum costs at node A are total costs aggregated along minimum cost paths

Minimum cost topology Minimum Energy Networks [Rodoplu99] optimal spanning tree rooted at one-hop

neighbors of the sink node each node considers only its closest neighbors -

minimum neighborhood AB

C

D

Page 7: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

7

Building minimum cost topology Minimum neighborhood

notation: - minimum neighborhood of node P1: minimum number of nodes enough to ensure

connectivity P2: no node falls into the relay space of any other

node

Finding a minimum neighborhood nodes maintain a matrix of mutual link costs among

neighboring nodes (cost matrix) the cost matrix defines a subgraph H on the network

graph G

Ni iN

iN iN

1

1

1

1

1

54535251

45434241

35343231

25242321

15141312

cccc

cccc

cccc

cccc

cccc

AB

C

Page 8: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

8

Finding minimum neighborhood We apply shortest path algorithm to find optimal

spanning tree rooted at the given node

Theorem 1: The nodes that immediately follow the root node constitute the minimum neighborhood of the root node

Theorem 2: The minimum cost routes are contained in the minimum neighborhood

Each node considers just its min. neighborhood

subgraph H

Page 9: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

9

Distributed algorithm

Each node maintains forwarding table E.g. [originator ¦ next hop ¦ cost ¦ distance]

Phase 1: find minimum neighborhood

Phase 2: each node sends its minimum cost to it neighbors upon receiving min. cost update forwarding table

Eventually the minimum cost topology is built

Page 10: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

10

An example of data routing

Properties energy efficiency

scalability

increased fault-tolerance

Different routing policies different packet priorities

nuglets [Butt01]

packets flow toward nodes with

lower costs

Page 11: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

11

Minimum-energy broadcast

Page 12: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

12

Broadcast communication model

a c

b

Eab

Eac

Ebc

Omnidirectional antennas By transmitting at the power level max{Eab,Eac} node

a can reach both node b and node c by a single transmission

Wireless Multicast Advantage (WMA) [Wieselthier et al.]

Power-aware metric include remaining battery energy (Ei) embed WMA (ej/Nj)

Trade-off between the spent energy and the number of newly reached nodes

set uncovered s' node

and nodes ofset goverlappin

oodneighbourh s' node

jU

jiO

jN

j

ij

j

3

2

1

b

)(X

X

j

jXj

jjU

E

Ee

c

Every node j is assigned a broadcast cost bjc

Page 13: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

13

Broadcast cover problem (BCP) Set cover problem

)}({minarg*cover Find

)()(

with associated )(

..

},,...,1{

:

:

iCcostC

jScostCcost

CjSjScost

jSNtsFCCovering

NjSmSSF

i

Cj

Sj

Cj

Sj

C

S 1 S2 S3

S 4

S 5 )()( ,

)()( ,

21

21

2

1

CcostCcost

CcostCcost

C

C

C1={S1, S2, S3}

C2={S3, S4, S5}

C*=

Example:

originatorat rooted treea tobelong nodes forwarding ofset The

costcover broadcast minimizes cover that Find

costcover broadcast )(

)(

Ccost

ejScost

NS

j

jj

BCP Greedy algorithm:

at each iteration add the set Sj that minimizes ratio cost(Sj)/(#newly covered nodes)

3

2

1

b

)(X

X

j

jXj

jjU

E

Ee

c

Page 14: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

14

Distributed algorithm for BCP

Phase 1: learn neighborhoods (overlapping sets)

Phase 2: (upon receiving a bcast msg)

1: if neighbors covered HALT

2: recalculate the broadcast cost

3: wait for a random time before re-broadcast

4: if receive duplicate msg in the mean time goto 1:

Random time calculation random number distributed uniformly between 0 and

b

b

i

cc

0

Page 15: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

15

Simulations GloMoSim [UCLA]

scalable simulation environment for wireless and wired networks

average node degree ~ 6

average node degree ~ 12

Page 16: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

16

Simulation results (1/2)

Page 17: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

17

Simulation results (2/2)

Page 18: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

18

Conclusion and future work Power-Aware Metrics

trade-off between residual battery capacity and transmission power are necessary

Scalability each node executes a simple localized algorithm

Unicast communication link based model

Broadcast communication node based model Can we do better by exploiting WMA properly?

Page 19: Mario Čagalj supervised by prof. Jean-Pierre Hubaux (EPFL-DSC-ICA) and prof. Christian Enz (EPFL-DE-LEG, CSEM) mario.cagalj@epfl.ch Wireless Sensor Networks:

Wireless Sensor Networks: Minimum-energy communication

19

Minimum-energy broadcast Propagation model: Omnidirectional antennas Wireless Multicast Advantage (WMA) [Wieselthier et

al.]a c

b

Pab

Pac

Pbc

if (Pac – Pab < Pbc) then transmit at Pac

Minimum-energy broadcast:

]6..2[ , abab kdP

Challenges: As the number of destination increases the complexity of this formulation increases rapidly. Requirement for distributed algorithm.

What are good criteria for selecting forwarding nodes? Broadcast Incremental Power (BIP) [Wieselthier et al.] Add a node at minimum additional cost Centralized Cost (BIP) <= Cost (MST)

Improvements? Take MST as a reference Branch exchange heuristic… … to embed WMA in MST

109

4

1

3

2

86

57

15

8

4

2

2 5

5

4

- forwarding nodes