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Time Synchronization - using Reference-Broadcast Synchronization Fine-Grained Network Time Synchronization using Reference Broadcasts by Jeremy Elson,

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Presentation on theme: "Time Synchronization - using Reference-Broadcast Synchronization Fine-Grained Network Time Synchronization using Reference Broadcasts by Jeremy Elson,"— Presentation transcript:

1 Time Synchronization - using Reference-Broadcast Synchronization Fine-Grained Network Time Synchronization using Reference Broadcasts by Jeremy Elson, Lewis Girod and Deborah Estrin Presentation by Vivek Vaidyanathan CS 691, Winter 2003

2 Outline Introduction Concept of Traditional Time Synchronization Concept of Reference Broadcast Synchronization Kind of latency in TTS and RBS RBS algorithm for: Single Broadcast Network Multi-Hop Network Analysis of RBS algorithms Advantages and Limitations of RBS

3 Introduction Time synchronization is highly critical in sensor networks for purposes such as: Data Diffusion Coordinated Actuation Object Tracking

4 Purpose To Synchronize all the nodes in the sensor network using a method that: Eliminates error efficiently Energy conservative Provides tight synchronization

5 What is Data Diffusion? Merging individual sensor readings into a high level sensing result

6 Applications of Time Synchronization Secure cryptographic schemes Coordination of future action Ordering logged events during system debugging

7 Concept of TTS- Traditional Time Synchronization The sender periodically sends a message with its current clock as a timestamp to the receiver Receiver then synchronizes with the sender by changing its clock to the timestamp of the message it has received from the sender (if the latency is small compared to the desired accuracy) Sender calculates the phase error by measuring the total round trip-time by sending and receiving the respective response from the receiver (if the latency is large compared to the desired accuracy)

8 Illustration of TTS S R (a) latency is small compared to desired accuracy S R (b) latency is large compared to desired accuracy

9 Concept of RBS – Reference-Broadcast Synchronization Reference broadcasts do not have an explicit timestamp Receivers use reference broadcasts arrival time as a point of reference for comparing nodes clocks Receivers synchronizes with one another using the messages timestamp (which is different from one receiver to another)

10 Illustration of RBS A

11 RBS vs. TTS RBS - Synchronizes a set of receivers with one another Traditional - Senders synchronizes with receivers RBS – Supports both single hop and multi hop networks Traditional – mostly supports only single hop networks

12 RBS vs. TTS TTS RBS Example: NTP (Network Time Protocol)

13 Types of errors that TTS should detect and eliminate Send Time Latency - time spent at the sender to construct the message Access Time Latency - time spent at the sender to wait for access to transmit the message Prorogation Time Latency - time spent by the message in traveling from the sender to the receiver Receive Time Latency - time spent at the receiver to receive the message from the channel and to notify the host

14 Types of errors that RBS should detect and eliminate Phase error - due to nodes clock that contains different times Clock skew - due to nodes clock that run at different rate Therefore, We go for RBS!!!

15 RBS algorithm for single broadcast domain (assuming no clock skew) Basic idea to estimate phase offset: - Transmitter broadcasts a reference packet to two receivers - Each receiver records the time that the reference was received, according to its local clock - The receivers exchange their observations

16 RBS algorithm for single broadcast domain (assuming no clock skew) Basic idea to estimate phase offset for non- deterministic receivers: - Transmitter broadcasts m reference packets - Each of the n receivers records the time that the reference was received, according to its local clock - The receivers exchange their observation - Each Receiver i can compute its phase offset to any other receiver j

17 RBS algorithm for single broadcast domain (assuming no clock skew) Formula for calculating the phase offset of receiver i with other receiver j: n : number of receivers m : number of reference broadcasts T r,b : rs clock when it received broadcast b {r n, b m} m i n, j n : Offset[i,j] = 1/m k=1 (T j,k – T i,k ) Then the receiver changes its clock by the calculated phase offset

18 Analysis of RBS algorithm for single broadcast domain (no clock skew) Mean group dispersion from the average of 1000 simulated trials for: - 20-receiver group (top) - 2-receiver group (bottom) 2-D view:

19 Analysis of RBS algorithm for single broadcast domain (no clock skew) Mean group dispersion from the average of 1000 simulated trials for the same data set, from 2 to 20 receivers (inclusive) 3-D view:

20 RBS algorithm for single broadcast domain (with clock skew) AMATHEMATICAL APPROACH The phase offset with the clock skew is estimated by: - Least-squares linear regression graph - From the best-fit line of the graph, following can be inferred: - Slope of the line : Clock skew of the nodes clock - Intercept of the line : Phase of the nodes clock

21 RBS algorithm for single broadcast domain (with clock skew) Basic idea to estimate phase offset and clock skew for non-deterministic receivers: - Transmitter broadcasts m reference packets - Each of the n receivers records the time that the reference was received, according to its local clock - The receivers exchange their observation - Each Receiver i can compute its phase offset to any other receiver j

22 RBS algorithm for single broadcast domain (with clock skew) Formula for calculating the phase offset and clock skew of receiver r 1 with other receiver r 2 : T r,b : rs clock when it received broadcast b, for each pulse k that was received by receivers r 1 and r 2, we plot a graph : x = T r1, k y = T r2,k – T r1,k Diagonal line drawn through the points represents the best linear fit to the data

23 RBS algorithm for single broadcast domain (with clock skew) Diagonal line minimizes the residual error (RMS). Therefore, we go for calculating the slope and intercept of the diagonal line Time value of r 1 is converted to time value of r 2 by combining the slope and intercept data obtained

24 Analysis of RBS algorithm for single broadcast domain (with clock skew) Synchronization of the Motes internal clock Vertical impulses show the distance of each point from the best-fit line – RMS error Phase offset (usec) Time (sec) Fit error (usec)

25 Analysis of RBS algorithm for single broadcast domain (with clock skew) Phase offset (usec) Time (sec) Synchronization of clocks on PC104-compatible single board computers using Mote as NIC

26 Why RBS is the best? Comparison of RBS with NTP and NTP-Offset: Hardware implementation RBS as a UNIX daemon UDP datagrams as Motes Testbed: - StrongARM-based Compaq IPAQs - Lucent Technologies 11 Mbit wireless Ethernet adapters - All Ethernet adapters connected to a wireless base station

27 Why RBS is the best? Test implemented in two different scenarios: Light network load - Minimal load generated by synchronization scheme Heavy network load - Two additional IPAQs configured as traffic generators - Each IPAQ sent randomly sized UDP datagrams of 500 to 15,000 bytes - Inter-packet delay: 10 msec

28 Test Results Light traffic scenario: - RBS performed more than 8 times better than NTP and NTP-Offset - RBS : average of sec error NTP : average of sec error - RBS : 95% of trails : sec error NTP : 95% of trails : sec error

29 Test Results For Light traffic:

30 Test Results Heavy traffic scenario: - RBS – almost completely unaffected NTP – suffered a 30 fold degradation - RBS : 95% of trails : sec error NTP : 95% of trails : 3,889 sec error

31 Test Results For Heavy traffic:

32 Working of RBS in multi hop network Obtained by mathematical conversion of output obtained in available single hop networks in the multi-hop network. Least square linear regression graph – used to synchronize all the single hop networks in the multi-hop network The values are then formulated and converted accordingly for all the nodes in the multi-hop network

33 Illustration of Multi-Hop Synchronization Mathematical conversion obtained through the common node 4

34 Algorithm for Calculating Phase Offset in Multi-Hop Network Events E 1 and E 7 – observed by R 1 and R 7 respectively Best-fit line calculated by R 4 using As broadcast E 1 (R 4 ) => E 1 (R 1 ) : R 1 synchronized with R 4 by A Best-fit line calculated by R 4 using Bs broadcast E 1 (R 4 ) => E 1 (R 7 ) : R 4 synchronized with R 7 by B R 1 synchronizes with R 7 using R 4 All nodes in Multi-hop are synchronized similarly

35 Analysis of Multi-Hop RBS Same test applied to Multi-Hop as the Single-Hop Test Results: - If average per-hop error = - Hop path = n - Average path error of n-hop =. Sqrt(n)

36 Advantages of RBS Can be used without external timescales Energy conservative Does not require tight coupling between sender and its network interface Covers much wider area Applicable in both wired and wireless networks Largest resources of latency (that exists in TTS) is removed from critical path Allows tighter synchronization

37 How RBS is energy conservative? Nodes stay in sleep mode until an event of interest occurs – post-facto sync

38 Limitations of RBS Works only with broadband communication Does not support point to point communication (as time synchronization is done among a set of receivers. In point-to-point – only one receiver exists)

39 Applications Acoustic Motes: Acoustic Ranging implemented in Berkeley Motes Collaborative Signal Detection

40 References Fine-Grained Network Time Synchronization using Reference Broadcasts. Jeremy Elson, Lewis Girod and Deborah Estrin, UCLA Power point presentation on Fine-Grained Network Time Synchronization using Reference Broadcasts. Jeremy Elson, Lewis Girod and Deborah Estrin, UCLA. Available at : OSDI-2002-Dec9.ppthttp://lecs.cs.ucla.edu/~jelson/talks/timesync/RBS- OSDI-2002-Dec9.ppt Wireless Sensor Networks: A New Regime for Time Synchronization. Jeremy Elson and Kay Romer, UCLA Time Synchronization for Wireless Sensor Networks. Jeremy Elson and Deborah Estrin, UCLA


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