Maximizing Network Lifetimevia 3G Gateway Assignment

in Dual-Radio Sensor NetworksLCN 2012, 10/24/2012

Cisco Systems: Jaein JeongAustralian Nat’l Univ: Xu Xu, Weifa Liang CSIRO: Tim Wark

Third party network

IntroductionA Remote Monitoring Scenario

• Deployed far away from the monitoring center• Network Model: Dual-Radio• Goal: Maximize Network Lifetime

Sensor networkSensor

Gateway

Monitoring Center

IEEE 802.15.4 link

3G link

Base station

(Low Power , 3G)

2

IntroductionChallenges

• Explore main components of energy cons.– For gateways– For slave nodes

• Identify gateways among all deployed sensors– m gateways– Network lifetime maximized

• Route data to m gateways energy-efficiently– Throughput requirement– Delay requirement

3

Organization

1. Modeling– Energy consumption– Network lifetime

2. Heuristics– Establish routing trees– Determine network lifetime

3. Performance Evaluation4. Related Work

4

1. ModelingSystem Parameters

Parameter Value

5

Parameter ValueGraph G(V, E) V Set of sensor nodes (N = |V|)

E Set of links between sensors (M = |E|)rs Data generation rate

Location of sesnors

Parameter ValueGraph G(V, E) V Set of sensor nodes (N = |V|)

E Set of links between sensors (M = |E|)rs Data generation rate

Location of sesnorsGateway /

SlavesGateway IEEE 802.15.4 and 3G radios

Slave nodes IEEE 802.15.4 radiom Number of gateways

Parameter ValueGraph G(V, E) V Set of sensor nodes (N = |V|)

E Set of links between sensors (M = |E|)rs Data generation rate

Location of sesnorsGateway /

SlavesGateway IEEE 802.15.4 and 3G radios

Slave nodes IEEE 802.15.4 radiom Number of gateways

QoS metrics α Network throughputD Delivery delay

1. ModelingEnergy Cost

Flash memory

buffer3G radio

802.15.4 radio MCU

osGbufng EDvdrPPPvec /1)()()( 3

)()( vdrPvec sns

otherwise0

gateway a is if)(

slave activean is if)(

)( vvec

vvec

vec g

s

s

Param DescPn Pwr by radio, MCU

P3G Pwr by 3G

Pbuf Pwr by buffering

d(v) #-descendants

Eo Overhead for sync6

1. ModelingNetwork Lifetime

• Time before the base station is no longer able to receive data from α percentage of sensors

Round1

τ

Network Lifetime: L

Round2

τRound

r

τRound

R

τRound

R+1

τ‘ (<= τ)

7

1. ModelingNetwork Lifetime

gateways ofset :GW

nodes ing transmittofset :)(' gVr

nodes active ofset :)(' gVrGWg

RrgVvve

ver

GWgc

r

1),(',)(

)(

vver ofenergy residual:)(

vvec ofn consumptioenergy :)(

vve

ve

c

r of lifetime residual:)(

)(

)(',)(

)(min' 1 gVv

ve

veR

GWgc

r

11 , )('

RrNgVGWg

r

1τ

2τ

rτ

Rτ

R+1τ‘

8

1. ModelingProblem Definition

• Periodic assignment of gateways

– Identify m gateways– Selecting nodes to send

data to these gateways– Route data from these

nodes to gateways1τ

2τ

rτ

Rτ

R+1τ‘

9

Organization

1. Modeling– Energy consumption– Network lifetime

2. Heuristics– Establish routing trees– Determine network lifetime

3. Performance Evaluation4. Related Work

10

2. HeuristicEstablishing the Routing Forest

• Routing trees should span at least α *N nodes.

1) Identify the smallest set of active nodes

2) Partition the active nodes into m subsets

3) Find the routing tree

1

2

3

4

5

6

78

910 11

1

2

3

4

5

6

78

910 11

1

2

3

4

5

6

78

910 11

11

2. Heuristic(1) Identifying Active Nodes

• Choose sensors with high er(v)

• m-component constraint– CC(G[V’]) <= m– Or, some nodes may not

reach a gateway.vver ofenergy residual:)(

high

low

12

2. Heuristic(1) Identifying Active Nodes

identified be tonodes active:'V

)'()'()'()'( 1 Nrjrirr veveveve (v)eVv' rby sSort

', v', vi N1min from nodes first Choose

Nαi min

components ed:#-connectCC(G[V'])

mCC(G[V'])

1

2

3

4

5

6

78

910 11

13

2. Heuristic(2) Partitioning active nodes into m subsets

1

2

3

4

5

6

78

910 11

1

2

3

4

5

6

78

910 11

CC(G[V’])=m’m’<= m

CC(G[V])=m

PartitionG[V’] into G[V]

14

2. Heuristic(2) Partitioning active nodes into m subsets

• F = {S1, S2, …, Sm’} collection of vertex sets.

• Select a set with the largest #-vertices, Sl

• Partition Sl into Sl1, Sl2 s.t. ||Sl1|-||Sl2|| is minimized.

• Repeat until m’ = m.

1

2

3

4

5

6

78

910 11

SlSl2

Sl1

15

2. Heuristic(3) Finding routing tree – max-min tree

• Find max-min tree Ti(v) for each connected graph Gi and given root v.

• The tree Ti rooted at a node with the longest lifetime is selected [9].

1

2

3

4

5

6

78

910 11

Sl2

Sl1

[9] W. Liang and Y. Liu. On-line data gathering for maximinizing network lifetime in sensor networks. IEEE Trans. on Mobile Computing, 6:2–11, 2007. 16

2. HeuristicDetermining the Network Lifetime

• For each tree Ti, evaluate lmin at round r.

• If Imin > τ– L = L + τ– er(v) = er(v) – τ*ec(v) – edelta

• If lmin <= τ– τ’ = lmin

– L = L + τ’– Terminate the loop

miTVvve

vel i

c

r 1 ,)( ,)(

)(minmin

1

2

3

4

5

6

78

910 11

1τ

2τ

rτ

Rτ

R+1τ‘

17

2. HeuristicComplexity

• O(MN2) for N = |V|, M = |E|• Proof

1) Finding #-connected components:O(M) using BFS or DFS

2) Partitioning active nodes:O(N3logN) [8]

3) Building a max-min tree rooted at a given node:O(MN2) [9]

4) For any G(V, E):M=O(N2)[8] D. R. Karger and C. Stein. A new approach to the minimum cut problem.

Journal of the ACM, 43:601–640, 1996.[9] W. Liang and Y. Liu. On-line data gathering for maximinizing network lifetime in sensor networks.

IEEE Trans. on Mobile Computing, 6:2–11, 2007.18

3. Performance EvaluationAssumptions

Parameters Values#-Sensors 100 – 300Tx Range 100 m

Initial Energy Cap 200 JEnergy Consumption Param CC2420 radio for IEEE 802.15.4 radio

MO6012 radio for 3G radioNAND flash memory

Data Generation Rate (rs) 1 bit / sData Delivery Latency (D) 1 hr

Network Throughput Threshold (α) 0.7

19

3. Performance EvaluationResidual Energy over Time

• N = 100, m = 5, τ = 2 hr25τ 50τ 175τ 186τ 75τ 100τ 125τ 150τ

20

3. Performance EvaluationResidual Energy over Time

• N = 100, m = 5, τ = 2 hr

‒ In the first 75 rounds:For all, E > 0.5Einit

‒ In the 175th round:44 nodes, E < 0.2Einit

‒ In the last round:37 nodes, E = 0Throughput req isn’t met

25τ 50τ 175τ 186τ 75τ 100τ 125τ 150τ

21

3. Performance EvaluationLifetime over Constraint Parameters

• Network throughput α: steadily decreases, then rapidly falls

• #-nodes N: decreases more with smaller α

• Duration of round τ: first increases, then decreases

• #-gateways m: first increases, then decreases

• Delivery delay D: increases

• Vary α from 0.3 to 1• N = 100, m = 5, τ = 2 hr• Vary N from 100 to 300• N = 100, m = 5, τ = 2 hr

• Vary τ from 1 hr to 10 hr• m = 5• Vary m from 2 to 20• τ = 2 hr• Vary D to 10, 20, 30, 60 and 120

min• m = 5, τ = 2 hr

22

3. Performance EvaluationThree Algorithms

Algorithm Gateway Selection Time

Gateway Selection Criteria

StaticAlg

LEACH [7]

DynamicAlg

Algorithm Gateway Selection Time

Gateway Selection Criteria

StaticAlg Once in lifetime

LEACH [7] Each round

DynamicAlg Each round

Algorithm Gateway Selection Time

Gateway Selection Criteria

StaticAlg Once in lifetime Random

LEACH [7] Each roundRandom &

Time spent as gatewaysin previous rounds

DynamicAlg Each round Residual energy

23

[7] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy efficient communication protocol for wireless microsensor networks.Proc. of HICSS. IEEE, 2000.

3. Performance EvaluationThree Algorithms and Lifetime Delivered

• In general, Dynamic > LEACH > Static

• Superiority of Dynamic and LEACH over Static– More balanced energy

consumption

• Advantages of Dynamic over LEACH– More efficient gateway

identification– More advanced routing

forest establishment

τ=1hr τ=2hr τ=3hr τ=4hr τ=5hr 0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

Network lifetime to τ

DynamicAlg LEACH StaticAlg Axis Title

m=4 m=6 m=8 m=10 m=12 0

200,000400,000600,000800,000

1,000,0001,200,0001,400,0001,600,0001,800,000

Network Lifetime to m

DynamicAlg LEACH StaticAlg

Net

wor

k Li

fetim

e

24

4. Related Work

[6] J. Gummeson, D. Ganesan, M. D. Corner, and P. Shenoy. An adaptive link layer for heterogeneous multi-radio mobile sensor networks.IEEE Journal on Selected Areas in Communications, 28:1094–1104, 2010.

[10] D. Lymberopoulos, N. B. Priyantha, M. Goraczko, and F. Zhao. Towards efficient design of multi-radio platforms for wireless sensor networks. Proc. of IPSN. IEEE, 2008.

[12] C. Sengul, M. Bakht, A. F. Harris, T. Abdelzaher, and R. Kravets. Improving energy conservation using bulk transmission over high-power radios in sensor networks. Proc. of ICDCS. IEEE, 2008.

[13] T. Stathopoulos, M. Lukac, D. Mclntire, J. Heidemann, D. Estrin, and W. J. Kaiser. End-to-end routing for dual-radio sensor networks.Proc. of INFOCOM. IEEE, 2007.

Hierarchical PowerManagement Ours

Assumptions

Methods

Examples

Hierarchical PowerManagement Ours

Assumptions • Use both high low BW radios• Optimize their use

• Sensornet within the network• 3G only for remote data

Methods

Examples

Hierarchical PowerManagement Ours

Assumptions • Use both high low BW radios• Optimize their use

• Sensornet within the network• 3G only for remote data

Methods• Minimize time for high BW radio while low BW radio is always on.

• Schedule gateways to achieve max lifetime

Examples

Hierarchical PowerManagement Ours

Assumptions • Use both high low BW radios• Optimize their use

• Sensornet within the network• 3G only for remote data

Methods• Minimize time for high BW radio while low BW radio is always on.

• Schedule gateways to achieve max lifetime

Examples [6, 10, 12, 13]

25

4. Related Work

[7] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy efficient communication protocol for wireless microsensor networks.Proc. of HICSS. IEEE, 2000.

LEACH Ours

Goals

GatewaySelection

Energy-awareRouting

LEACH Ours

Goals • Maximizes lifetime by perio-dically changing the set of GWs

• Maximizes lifetime by perio-dically changing the set of GWs

GatewaySelection

Energy-awareRouting

LEACH Ours

Goals • Maximizes lifetime by perio-dically changing the set of GWs

• Maximizes lifetime by perio-dically changing the set of GWs

GatewaySelection

• Random & Time spent as GWs in previous rounds • Residual energy.

Energy-awareRouting

LEACH Ours

Goals • Maximizes lifetime by perio-dically changing the set of GWs

• Maximizes lifetime by perio-dically changing the set of GWs

GatewaySelection

• Random & Time spent as GWs in previous rounds • Residual energy.

Energy-awareRouting

• Total distance minimization in LEACH does not necessarily lead to the min energy cons.

• Data routing is designed to balance the energy cons.

26

Conclusion

• Maximizing lifetime of a dual-radio sensor network.– Proposed a model for energy cons and lifetime.– Proposed heuristics that maximizes lifetime

• Identifies gateways• Finds the data routing structure

– Experiment Results• Our heuristic outperforms other cluster methods.

• Future works– Distributed algorithm– Experiments with real energy consumption 27

Backup Slides

28

3. Performance EvaluationLifetime over Throughput Threshold α

• Lifetime (L) steadily falls down before α = 0.6, rapidly falls after that.

• For α <= 0.6– Additional nodes are

used for connected components.

• For α > 0.6– Additional nodes

increase required active nodes and energy cons.

• Vary α from 0.3 to 1• N = 100, m = 5, τ = 2 hr

29

3. Performance EvaluationLifetime of #-Nodes N

• L starts to drop from a smaller α as N gets larger.

• With higher node density,‒ Smaller # of required

extra nodes for m-component

‒ The more distinct impact of α on lifetime.

‒ Higher traffic cause shorter lifetime.

• Vary N from 100 to 300• N = 100, m = 5, τ = 2 hr

30

3. Performance EvaluationLifetime over Duration of Period τ

• Generally, the larger τ, the shorter L.– Frequent identification

balances energy better.

• From 1 to 2-3hr, L slightly increases as τ increases.‒ Too frequent routing

• With fixed τ, L gets smaller as N increases.

• Vary τ from 1 hr to 10 hr• m = 5

31

3. Performance EvaluationLifetime over #-gateways m

• Lifetime first increases and then decreases.– Before a turning point:

Better energy balancing– After passing the point:

More energy cons on 3G

• Vary m from 2 to 20• τ = 2 hr

32

3. Performance EvaluationLifetime over Delivery Delay D

• A smaller value of D leads to a shorter L– Frequent on-and-off

switching of the 3G radios results in energy overheads

• Vary D to 10, 20, 30, 60 and 120 min

• m = 5, τ = 2 hr

33