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Environment-Aware Clock Skew Estimation and Synchronization for Wireless Sensor Networks Zhe Yang (UVic, Canada), Lin Cai (University of Victoria, Canada), Yu Liu (University of New Orleans, USA), Jianping Pan (University of Victoria, Canada) Infocom 2012 MengLin, 2012 1

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Outline Introduction Clock skew measurements AMKF Clock skew estimation Environment-aware clock synchronization Performance study Conclusions 2

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Introduction Clock synchronization is important for network systems such as scheduling Improper to do synchronization frequently in WSN due to dynamic and unpredictable environment, ex: sync failure and overhead Estimate clock skew accurately can prolong synchronization period while suffering from temperature variance 3

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Introduction It is full of noise to measure clock skew directly so they use temperature measurements to assist clock skew estimation, called additional information aided multi-model Kalman lter (AMKF) algorithm, then using this to propose an environment-aware clock synchronization (EACS) scheme to dynamically compensate clock skew 4

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Terminology Clock offset is the difference between the time reported by two or more clocks Clock skew is the differential coefcient of the clock offset, the tick duration difference 5

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Constant Environment 6

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Dynamic Environment 254010401025 4010401025 7

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Outline Introduction Clock skew measurements AMKF Clock skew estimation Environment-aware clock synchronization Performance study Conclusions 8

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Measurement Setting One laptop Two Mica2 nodes – One senses temperature and send to the other one – The other one sends timestamp containing the temperature information to laptop through UART Use heater to increase and a fan to reduce the environment temperature 9

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Measurement Results and Analysis 10

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Measurement Results and Analysis 11

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Outline Introduction Clock skew measurements AMKF clock skew estimation Environment-aware clock synchronization Performance study Conclusions 12

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What is AMKF AMKF is an additional information aided multi-model Kalman lter, which can estimate the model probability for one process using the model probability of another related process of which the model probability is easier to obtain Different from the traditional approaches, where the decision is based on the estimated process itself only. 13

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Why Choose AMKF? Clock skew is not a stationary random process Their previous work utilized an IMM Kalman filter to tackle the model uncertainty in clock skew – Combine and use the weighted sum of several filters output as system output – Use Markov chain with preset transition probabilities – In each iteration, every model processes the measurement data and likelihood function independently The measurement noise for temperature is much smaller than that for clock skew 14

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Outline Introduction Clock skew measurements AMKF Clock skew estimation Environment-aware clock synchronization Performance study Conclusions 15

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How to Do EACS? 16

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Outline Introduction Clock skew measurements AMKF Clock skew estimation Environment-aware clock synchronization Performance study Conclusions 17

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Evaluation of Model Determination 18 Temperature measurements and the probability of constant velocity model

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Clock Skew Estimation 19 The estimation results

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Error Estimation Root mean square error (RMSE) Cramér–Rao bound – Determine the lower bound on the variance of estimators which can be used to indicate the estimation accuracy in this paper 20 RMSE of clock skew estimation

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Performance Evaluation by Simulation Below 2ms during whole 8000 s simulation 21 Simulation settingClock offset with compensation schemes

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Performance Evaluation by Experiment Below 8ms over the 7200 s test 22 Verification traceVerification results

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Outline Introduction Clock skew measurements AMKF Clock skew estimation Environment-aware clock synchronization Performance study Conclusions 23

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Conclusion Based on AMKF, using EACS to conduct clock synchronization and compensation, which prolongs the synchronization period Good article organization Too less analysis in real test The scenario for comparison between simulation and experiment is different 24

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Q&A Thanks you! 25

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Reference Kalman filter – http://en.wikipedia.org/wiki/Kalman_filter http://en.wikipedia.org/wiki/Kalman_filter Stationary process – http://en.wikipedia.org/wiki/Stationary_process http://en.wikipedia.org/wiki/Stationary_process – http://cnx.org/content/m10684/latest/ http://cnx.org/content/m10684/latest/ 26

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