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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
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2
3
4
5
6
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5
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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'])
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2
3
4
5
6
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2. Heuristic(2) Partitioning active nodes into m subsets
1
2
3
4
5
6
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1
2
3
4
5
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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
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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
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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
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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