Post on 17-Jan-2016
1Segal’s Law
A man with a watch knows what time it is.
A man with two watches is never sure.
2Fasika Assegei
Decentralized Frame Synchronization of a TDMA-based
Wireless Sensor Network
Fasika Assegei
Advisors: Frits van der Wateren – Chess B.V. dr.ir. Peter Smulders – TU/e
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This research is ….
DevLab project,
a project to build a WSN for research on protocols, power management, programming models, and security.
What is MyriaNed ?
• Conducted at Chess B.V. in Haarlem , the Netherlands.
• Part of MyriaNed project.
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Outline of presentation
Synchronization in Wireless Sensor Networks
What is the flaw with the Median algorithm ?
Proposed algorithms
Simulation Results
Energy consumption
Conclusion and future work
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Why synchronization?
TDMA Slots
Data Integration
NTP
Having the same notion of time
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Isn’t this a solved problem by now ??? NTP, time broadcasts (GPS, WWVB), high-stability oscillators
(Rubidium, Cesium)
New problems arise in Wireless Sensor Networks Important assumptions no longer hold
(fewer resources -- such as energy, good connectivity, infrastructure, size, and cost -- are available)
Sensor apps have stronger requirements (…but we have to do better than the Internet anyway)
What now ?
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Previous works on synchronization of adhoc networks …
Average / Median of the phase errors with the neighbors…..unstable in dynamic networks
Decentralized synchronization using topology as a metric……….higher cost
Interference elimination…..less time and costly
Correlation of a sequence ……high cost
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What is done in this research …
Decentralized synchronization
Using estimation
Tolerant to dynamic situations
Energy efficient
Unnecessary synchronization wastes energy, and insufficient synchronization leads to poor performance.
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Terms used in …
Phase error Time difference between the clocks
Frequency error The difference in the rates of the
clocks
Clock cycle (clk) The time between adjacent pulses of
the oscillator
Wakeup time The time that the node starts to listen
Sleep Sleep Sleep
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Where does the error come from ?
Oscillator characteristics: Accuracy: Difference between ideal frequency and
actual frequency of the oscillator. Stability: Tendency of the oscillator to stay at the same
frequency over time. Caused by different factors like temperature, aging,
noise, …
Network and System Parameters Receive and Transmit Delay: Time duration between
message generation and network injection. Propagation Delay: Time to travel from sender to
receiver. Access Delay: Time to access the channel.
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Nodes communicate ……
txguardslot Ttt
1T
Tsync
Synchronization period: The period in which the network can stay synchronized without the application of the synchronization algorithm.
2/guardt 2/guardttxT
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)()()( nj
ni
nij ttt io
n
ni
ni tTt )()(
)()()1( ni
ni
ni
ni Ttt
)( )()( nij
ni tf
Representation ……
What is f ???
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Median as a method for synchronization
Simple method.
Calculates the Median of the phase errors
Adjusts the offset in the next wakeup time of the node.
With the simplicity, …
Not stable in a highly dynamic networks.
)( iji tmedian for all j neighbors ..
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What is the flaw with Median ?
Got message from Node 3
Calculating offset
Got message from Node 10
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Node 9 drifted from
its neighbors
A WSN scenario for Median contd.
Algorithms are proposed …..
Less time to synchronize …
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Building blocks of a sync protocol…
Synchronization Parameter Space:(max error, lifetime, scope, convergence,
stability,…)
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Weighted Measurements
ijtbij ae
1ij
The larger the phase error is, the lower the pre-weight factor it is assigned.
1.0ij
0 Nijt
1 Nijt
if
if
Uses the weighted average of the phase errors
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Weighted Measurements Contd.Two scenarios …..
Case I Case II
ijw,1 ij
,ij
Weight factor determination:
5.0)( ijmean
5.0)( ijmean .1ijwwhere
N
j
nijij
ni
ni
ni twTtt
0
)()()()1(
Offset calculation:
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)log(),( 21 ii xxf
),)...(,(),,(),,( 332211 nn yxyxyxyx
n
iirS
1
2),( iii xfyr
jkj
kj 1
Least Squares approach ...
Set of n phase errors from n neighbors :
Minimizing the squares of error :
where:
Iteration of parameters:
Model Curve:
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Least Squares contd.
Upper bound fits in the guard time ….
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Discrete time kalman filter
Kalman filter estimates a process by using a form of feed forward
control
the filter estimates the process state at some time then obtains feedback in the form of noisy measurements.
Time update equations
Measurement Update equations
Dynamic and recursive
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1
0lim
HKk
Rk
0lim0
k
PK
k
Kalman Filter Contd.
A - relates the state at the previous time step to the current stateH - relates the current state to the measurementR - process noise covarianceQ - measurement noise covarianceP - estimate error covariance
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Simulation setup
Synchronization error : the maximum difference between the clock times in the neighborhood.
clk : Clock cycle
precision : metric to express the performance of the synchronization error
• KF – Kalman Filter
• WM – Weighted Measurements
• LS – Least Squares.
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Simulation results
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Simulation results contd.
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Putting in perspective ….Computing energy cost…
Communicating energy gain…
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Putting in perspective …
Tradeoff ?
Comparison of the energy consumption and gain
per RX slot
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Active Idle
CPU 3.5mA 0.01mA
Radio 11.3mA(TX) 900nA
Radio 12.3mA(RX) 900nA
Tradeoff? Directly comparing computation/communication energy cost not
possible but: put them into perspective! Energy ratio of “listening for 1 clk ” vs. “computing instruction for 1
clk”: greater than 4 times
Communication is more expensive than computing.
The downside in implementation ……
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Conclusion
Decentralized synchronization algorithms for a TDMA-based WSN are proposed using KF, WM and LS.
WM and LS have a very good tolerance in a dynamic Wireless Sensor Network.
KF performs very good in all synchronization space, both in static as well as dynamic environments.
Reducing the communication cost and increasing cost of computing is worthy bait.
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To be explored …
Software power minimization techniques to reduce the power consumption of the algorithms.
Implementation of the algorithms on the MyriaNode and further investigation...
Additional tools for frequency error minimization, using the available resources like temperature sensor.
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Thank you!
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Vraag?
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Not a perfect clock …
1)(
1dt
tdC
The nodes clock time is thus bounded as :
where ρ is the maximum clock drift.
Factors affecting ..
• Temperature
• Aging
• Noise
• …
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Energy Consumption …..
For 5 clk, an increase in battery life of up to half a year can be obtained.
Battery power : 2400 mAh No of slots: 10
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Reducing the duty cycle ……..
xslot TxT 2
T
NTD slot
T
TxND x )2(
T
TxND xn
))(2(
T
ND
)2( A decrease in the duty cycle:
For a performance improvement:
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Wireless Sensor Networks: Why different ?
Energy limitation Limited battery life
Dynamic nature of the network, as well as inaccessibility Mobility …
Diverse applications Relative and absolute reference
Cost factor ... Large scale deployment …
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Lower bound of synchronization
where n is the number of nodes in the network. This means that synchronization is not only a local
property, in the sense that the clock skew between two nodes depends not only on the distance between the nodes
but also on the size of the network.
))1log()1(8
log(
)1log(
)1(8
n
ndL
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What causes the clock to drift ?
The frequency of the clock is given as :
a is the aging factor fe is the environmental factor (temperature..) fr is the noise instability
fo is the nominal frequency
)()()()( 0 tftfttaftf reo
where :
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What is synchronization ?
Having the same time of reference and count the same time [either global (UTC) or local (relative)]
Operate a system in unison
Same notion of time
We are synchronized!!!!
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Wireless Sensor Networks
Wireless network of low cost sensors. Nodes communicate by broadcasting. Multihop communication needed in order to reach
far-away destinations.
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Simulation setup
Mixim Octave
Input
Output
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Having no comment after the presentation is unacceptable.
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Synchronization Methods- Previously
Centralized synchronization Central reference time Global or Relative
Receiver-Receiver RBS ( Receiver Broadcast Synchronization)
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Synchronization Methods- Previously
Decentralized synchronization Estimation of the other’s clock time
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Abstraction
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Simulation results contd.
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Simulation results contd.
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Simulation results contd.
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Simulation results contd.
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Simulation results contd.
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Simulation results contd.