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1
Energy-Efficiency in MANETs
Task 4: Energy-Efficient Networks
Katia Obraczka
University of California, Santa Cruz
[email protected]://inrg.cse.ucsc.edu/
3
Efficient Protocols for Power-Constrained Heterogeneous MANETs
Novel energy-efficient data collection algorithms using spatial and temporal data locality.
Novel flexible interconnection protocol to accommodate device heterogeneity and application requirements.
I. Solis, Efficient Protocols for Power-Constrained Heterogeneous Wireless Ad-Hoc Networks, PhD Dissertation, UCSC, 2005.
I. Solis and K. Obraczka, In-Network Aggregation Trade-offs for Data Collection in Wireless Sensor Networks, International Journal on Sensor Networks (IJSNet), Vol 1, No 2, 2006.
4
Robust Routing for Network Fault-Tolerance and Security (Task 3)
Novel game-theoretic stochastic routing framework as proactive alternative to today's reactive approaches to route repair.
Collaboration with Prof. J. Hespanha, affiliated with UCSB’s ICB (Army UARC) and Prof. S. Bohacek at UDel, funded by the Communications&Networking Army CTA.
Integrated and flexible approach to secure routing in MANETs.
C. Lim, Scalable Multi-path Routing for Robust Communication, PhD Dissertation, USC, 2006.
G. Huang, Robust and Secure Routing in MANETs, MSc Theis, UCSC, 2006. C. Lim, S. Bohacek, J. Hespanha and K. Obraczka, Hierarchical Max-Flow
Routing, IEEE Globecom 2005. S. Bohacek, J. Hespanha, J. Lee, C. Lim and K. Obraczka, A New TCP for
Persistent Packet Reordering, IEEE/ACM Transactions on Networking, Vol. 14, No.2, April 2006.
R. Guru, G. Huang and K. Obraczka, An Integrated and Flexible Approach to Robust and Secure Routing in MANETs, IEEE IC3N, August 2005.
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Modeling Data Networks with Hybrid Systems (Task 5)
New approach to modeling, analyzing, and simulating networks using hybrid systems which combine both continuous-time dynamics as well as discrete-time logic.
Collaboration with Prof. J. Hespanha, affiliated with UCSB’s ICB (Army UARC) and Prof. S. Bohacek at UDel, funded by the Communications&Networking Army CTA.
J. Lee, A Hybrid Systems Modeling Framework for Transport Protocols, PhD Dissertation, USC, 2005.
S. Bohacek, J. Lee, J. Hespanha and K. Obraczka, Modeling Data Communication Networks Using Hybrid Systems, IEEE/ACM Transaction on Networks, 2006, to appear.
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Mobility Models for Wireless Networks New approach to modeling mobility in wireless
networks using statistical equivalent models (SEMs).
Collaboration with Profs. B. Sanso and A. Kottas, UCSC Applied Math.
K. Viswanath and K. Obraczka, Modeling the Performance of Flooding in MANETs (Extended Version), Computer Communications Journal (CCJ) 2005.
K. Viswanath, K. Obraczka, A. Kottas, B. Sanso, A Statistical Equivalent Model for Random Waypoint Mobility: A Case Study, IEEE SMC SPECTS 2006.
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Energy Consumption Modeling, Characterization, and Prediction
Models for energy consumption and network lifetime prediction.
Collaboration with Prof. R. Manduchi, UCSC CE.
C. Margi, Energy Consumption Trade-Offs in Power-Constrained Networks, PhD Dissertation, UCSC, 2006.
C. Margi, K. Obraczka, R. Manduchi, Characterizing System Level Energy Consumption in Mobile Computing Platforms, IEEE WirelessCom 2005, June 13-16, 2005
C. Margi, V. Petkov, K. Obraczka, R. Manduchi Characterizing Energy Consumption in a Visual Sensor Network Testbed, IEEE/Create-Net TridentCom 2006, March 1-3, 2006.
Energy Consumption Trade-offs in Visual Sensor Networks”. C. B. Margi, R. Manduchi , K. Obraczka. SBRC 2006, May 29 - June 02, 2006.
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Energy-Efficient Medium Access Novel efficient and flexible medium access
framework form MANETs. Collaboration with Prof. J.J. Garcia-Luna.
V. Rajendran, Medium Access Control Protocols for MANETs, PhD Dissertation, UCSC, 2006.
V. Rajendran, K. Obraczka and J.J. Garcia-Luna, Application-Aware Medium Access for Sensor Networks, 2nd IEEE International Conference on Mobile Ad-hoc and Sensor Systems (MASS), November 2005.
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Energy Consumption Modeling, Characterization, and Prediction
In collaboration with Roberto Manduchi
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Motivation
Understand energy trade-offs between computation and communication. Why? Make application-level decisions, e.g.,
data processing in the node vs. transmission of all data.
Make resource management decisions, e.g., wake up more often.
Main assumption for sensor nets: communication dominates energy consumption. True?
Heterogeneity in MANETs: Platforms. Sensors. Application requirements.
Mote
Stargate
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Contributions
Duty cycle energy consumption prediction based on elementary task composition.
Simple lifetime prediction model based on elementary task composition considering different duty cycles.
Case study: visual sensor network.
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Duty cycle energy consumption prediction
Duty cycle as the node’s “execution unit”. E.g., image acquisition duty cycle.
Duty cycle composed of “elementary tasks”. E.g., capture image, transmit image.
13
Approach: task composition Compose elementary task
consumption to obtain duty cycle consumption. Compose elementary tasks differently for
different duty cycles. Average current does not provide
enough information by itself. Need better granularity:
Charge & duration of a task.
14
Ti: task i. q(Ti): average charge for task i. d(Ti): average duration for task i. Qdc−j: average charge of duty cycle j. Ddc−j: average duration of duty cycle j. n: number of tasks in duty cycle j.
n
1iij-dc )q(T Q
n
1iij-dc )d(T D
Hypothesis
15
First step: task characterization
Thorough energy consumption characterization. Steady state AND transitory behavior.
Case study: Visual sensor network.
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Sensing/Processing: Acquire image; Acquire/save (raw) image; Acquire/compress/save image; Acquire/process image:
No object; Object: then must compress & save sub-image.
Communication: Transmit image:
Raw image (200KB); Full compressed image (48KB); Compressed sub-image (3 different sizes).
Elementary tasks
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Transitions: Tasks:
Activate/deactivate webcam; Activate/deactivate wireless card; Sleep/wakeup.
Elementary tasks
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Elementary tasks: duration
0.0000
0.2000
0.4000
0.6000
0.8000
1.0000
1.2000
1.4000
1.6000
Du
rati
on
(s)
Results are the average of 20 different measurements.
20
0.0000
0.0200
0.0400
0.0600
0.0800
0.1000
0.1200
0.1400
Incr
emen
tal
Ch
arg
e (C
)
Elementary tasks: charge
21
Duty cycles 6 different duty cycles. 2 types:
Deterministic: image acquisition/compression.
Conditional: event detection. If no event is detected, the system is put in
sleep or idle mode for T1 = 5 seconds. Otherwise, the system remains idle for T2 =
3 seconds.
22
Duty Cycle (b)Duty Cycle (c)Duty Cycle (a)
Image
Tx
Wait
Image
Tx
Wait
Deterministic duty cyclesIm
age
Tx
Wait
23
Conditional duty cycles
Duty Cycle (d) Duty Cycle (e) Duty Cycle (f)
Imag
e
Tx
Wait
Imag
e
Tx
Wait
Tx
Wait
Imag
e
Yes Yes Yes
24
Duty cycles: duration & charge
0
2
4
6
8
10
12
14
16
(a) (b) (c) (d) - noevent
(d) -event
(e) - noevent
(f) - noevent
(f) -event
tim
e (s
)
Predicted
Measured
0
0.5
1
1.5
2
2.5
3
3.5
4
(a) (b) (c) (d) - noevent
(d) -event
(e) - noevent
(f) - noevent
(f) -event
Ch
arg
e (C
)
Predicted
Measured
Relative error:Ed: -6.6% to 3.4%.
Eq: -9.4% to 9.1%.
Results are the average of 20 measurements.
predicted
measuredd D
DE 1
25
Errors Webcam & wireless card activation:
Must add 1 second delay after in the duty cycle scripts after issuing commands!
But it is not 1 second in idle! Hardware still working!!
Linux as the OS: Does not allow complete control.
Wireless interference: TX task has variable duration; Transmission errors.
27
Simple lifetime prediction model Follows same hypothesis. Lifetime:
Lx: node lifetime. Qb: charge available from the battery. Qd: average charge for each duty cycle. Dd: average duration of each duty cycle.
dd
bx D *
Q
Q L
28
Simple lifetime prediction model
Expand formula to include duty cycle prediction:
For conditional duty cycles:
n
1iin
1ii
bx )d(T *
)q(T
Q L
)P - (1 * Q P * Q )E(Q ev-dno-dev-dev-dd
29
Experiments Same duty cycles presented. Duty cycle runs continuously, until
1000 mAh is used. No control on event generation (node
was placed in our lab).
30
Simple lifetime prediction model
Prediction vs. Experiments
Relative error for prediction based on duty cycle model is less than 13%.
Results are the average of 20 measurements.
0
5000
10000
15000
20000
25000
(a) (b) (c) (d) (e) (f)
Lif
etim
e (s
)
Based on DC measurements
Based on DC Prediction
Measured Lifetime (s)
Duty cycle duration:
(a): 6.4 s
(b): 11.6 s
(c): 13.9 s
(d) - no object: 11.0 s
(e) - no object: 8.3 s
(d) & (e) – object: 9.5 s
(f) - no object: 6.2 s
(f) - object: 4.4 s
31
Lifetime prediction model:summary
Simple. Relative error < 13%. Allows lifetime estimation for new
deployments. Allows duty cycle trade-off analysis.
32
Future Directions (1)
Formalize lifetime prediction model to include non-deterministic sequence of tasks: Set of known tasks. Sequence of tasks not known a priori. Inputs: info from neighboors, battery, etc. Could we use it to implement a “resource
manager” on the node?
33
Future Directions (2)
Validate this approach using different hardware platforms and sensor network applications. Would a simpler hardware platform allow
better accuracy?
35
Contributions
First traffic-adaptive MAC protocol. TRAMA.
Application-aware MAC. FLAMA (Motes testbed). MFLAMA.
Framework for energy-efficient, application-aware, multi-channel medium access. DYNAMMA (UWB radio testbed).
36
MAC state-of-the-art
Far from addressing challenges posed by: Taking advantage of higher PHY data
rates. Accommodating different applications. And still achieve energy efficiency.
37
Approach: DYNAMMA (Dynamic Medium Access Framework) Flexible, energy-efficient, application-aware
framework. Flexible and efficient traffic announcement
mechanisms. Slot structure with reduced idle duration and
inter-frame spacing. Scheduled access (including signalling).
Avoids collisions. Multi-channel.
Spatial re-use for improved channel utilization.
38
Time slot organization
Superframe NSuperframe N+1
Signaling Slots
Burst Data Slots
Base Data Slots
Collision-free signalingGather neighbor Information
Collision-free data exchangeBurst data frame exchange
Collision-free data exchangeSingle frame exchange
39
DYNAMMA: components
Collision-free signalling.
Traffic characterization. Different class of flows
based on flow arrival / service rate.
Each flow class contends for a “subset” of the channel access slots – prevents idle slot allocation.
Multi-channel, collision-free scheduling. One transmitter per
channel in the 2-hop. Channel selection based
on flow priorities. Ensures that a node
does not transmit to a node sleeping or listening on other channels.
40
DYNAMMA performance Performance analysis by extensive
simulations using QualNet and testbed experiments using UWB radio/MAC platform.
Different application scenarios Synthetic – random exponential traffic (worst
case scenario for DYNAMMA, large number of flows).
Data gathering application.
42
Simulation Setup
16 nodes, square grid topology (18m). 4000 bytes packet. DYNAMMA Parameters:
SignalingSlot = 16. BaseSlots = 16. BurstSlots = 240, framesPerSlot = 2.
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Delivery Ratio
Packet losses forDYNAMMA and TRAMAdue to queue drops.
Packet losses for 802.11due to collisions.
Multiple channels helpreducing the queuingdelay.
45
Energy savings
DYNAMMA implementsidle receive timeouts -i.e. shut off receiver if thetransmission does not startwithin a timeout.
Idle receive timeoutsimproves energy savings.
46
UWB Testbed
Instruction / Data BRAM64 KB
PLB Controller
PowerPC 405
DCR Interface
PLB Controller
Processor Local B
us
DC
R B
us
Master PLB
Master DCR
PLB to OPB Bridge
OP
B B
us
RS232 Interface
FPGA Setup
clk
rst
Lower MAC
MPI
RS232TX
RS232RX
Radio Mode Control Schedulers
Timers
Transmit / receive DMA
47
Testbed experiments
Basic slot duration: 644 us Signal slot duration: 161
us Superframe: 16 signal
slots, 256 base slots = 167.440 ms
SIFS = 10 us, MIFS = 1.875 us
Time base using a 66.66 Mhz crystal with < 10ppm drift. Total drift per superframe 1 us.
Three nodes – saturated throughput analysis
Two data rates – 53.3 Mbps, & 200 Mbps 4000 bytes payload for
53.3 Mbps 3400 bytes payload and
“4” burst per slot for 200 Mbps
49
Experimental results
53.3 2000
5
10
15
20
25
30
35
40
Per-flow Throughput
A-BA-CB-AB-CC-AC-B
Data rate (Mbps)
Thro
ughput
(Mbps)
A
C
B
53.3 2000
10
20
30
40
50
60
70
80
90
100
Sleep / Utilization
Sleep
Utilization
Data rate (Mbps)
Perc
enta
ge
50
Conclusion
Flexible framework for energy-efficient, application-aware medium access.
Significant improvements in delay and reliability.
Significant improvements in channel utilization by the use of multiple channels.