1. 8/18/2015 Mobile Ad hoc Networks COE 549 Synchronization Tarek Sheltami KFUPM CCSE COE http://faculty.kfupm.edu.sa/coe/tarek/coe549.htm 1

2. 2 Outline Overview Key Issues Traditional approaches Fine-grained approaches Coarse-grained approaches 8/18/2015

3. 3 Overview Distributed wireless sensor networks need time synchronization for a number of good reasons, some of which are described below: 1. Time-stamping measurements: data collection applications of sensor networks often require that sensor readings from different sensor nodes be provided with time stamps in addition to location information 2. In-network signal processing: Time stamps are needed to determine which information from different sources can be fused/aggregated within the network 3. Localization: TDoA-based ranging techniques used in node localization require good time synchronization 4. Cooperative communication: Some physical layer multi-node cooperative communication techniques, which have the potential to provide significant energy savings, but require tight synchronization

4. 8/18/20154 Overview 5. Medium-access: TDMA-based medium-access schemes also require that nodes be synchronized so that they can be assigned distinct slots for collision free communication 6. Sleep scheduling: synchronization is needed to coordinate the sleep schedules of neighboring devices, so that they can communicate with each other efficiently 7. Coordinated actuation: Advanced applications in which the network includes distributed actuators in addition to sensing require synchronization

5. 8/18/20155 Key Issues The clock at each node consists of timer circuitry, often based on quartz crystal oscillators The clock is incremented after each K ticks/interrupts of the timer Practical timer circuits, particularly in low-end devices, are unstable and error prone Crystal oscillators, such as those used in digital watches, experience a shift in frequency as a function of temperature A model for clock non-ideality Where f 0 is ideal frequency, ∆f is the frequency offset, d f is the drift in the frequency, r f (t) an additional random error process and f i (t) is the instantaneous oscillator frequency

6. 8/18/20156 Key Issues.. Frequency Drift is undesired change in frequency caused by component aging and environmental changes Frequency Offset is the difference between a measured frequency and the nominal frequency Assuming t=0 as the initial reference time, the clock reads time C i (t) at time t as:

7. 8/18/20157 Key Issues.. Frequency drift and random error terms may be neglected to derive a simpler linear model for clock non-ideality: Where is the clock offset at the reference time t = 0 and β i the clock drift The more stable and accurate the clock, the closer is to 0, and the closer β i is to 1 A clock is said to be fast if β i is greater than 1, and slow otherwise Manufactured clocks are often specified with a maximum drift rate parameter ρ, such that 1− ρ ≤ β i ≤1+ ρ Motes, typical sensor nodes, have ρ values on the order of 40 ppm (parts per million), which corresponds to a drift rate of ±40μs per second

8. 8 Ordering of events “  ” is a partial ordering [ total ordering: for any two events a,b (a  b) either a  b or b  a parting ordering: a,b can be concurrent ] 8/18/2015

9. 9 Definition The relation  on the set of events of a system is the smallest relation satisfying the following 3 conditions: 1) if a,b events in same process, and a comes before b, then a  b 2) if a is sending a message and b is receipt of same message by a different process, then a  b 3) if a  b and b  c, than a  c assume a  a if a  b and b  a, than a,b are concurrent 8/18/2015

10. 10 Logical Clock C i =logical clock (counter) at process i C[b] = reading of C j when event b occurs at process j Clock condition for any events a, b IF a  b THEN C[a] < C[b] No relationship to physical time 8/18/2015

11. 11 Clock Condition If a and b are events in process i and a comes before b, then C i [a] < C i [b] If a is the sending of a message by process i and b is the receipt of that message by process j, then C i [a] < C j [b] C1C1 C2C2 If a  b THEN C[a] < C[b] can be satisfied if the following conditions hold: 8/18/2015

12. 12 Note: C i [a] < C j [b]  a  b Note: a,b concurrent  C i [a] = C j [b] Note: C i [a] = C j [b]  a,b concurrent 8/18/2015

13. 13 Traditional Approaches Lamport’s Algorithm Provides a consistent ordering of all events in a distributed system Labeling each event x with a distinct time stamp L x, such that: Lx ≠ Ly ∀ unique events x & y If event x precedes event y in within a node  Lx < Ly If x transmitting and y receiving at two different nodes  Lx < Ly Drawbacks: Doesn’t provide true causality, assume the true time of event x is indicated as T x ; then, while it is true that T x <T y  L x <L y, it is not true that L x <L y  T x <T y

14. 8/18/201514 Traditional Approaches.. Cristian’s Algorithm A node A sends a request to node B (which has the reference clock) and receives back the value of B’s clock, T B Node A records locally both the transmission time T 1 and the reception time T 2

15. 8/18/201515 Traditional Approaches.. Cristian’s Algorithm.. There are many sources of uncertainty and delay, which impact its accuracy: Send time – which includes any processing time and time taken to assemble and move the message to the link layer Access time – which includes random delays while the message is buffered at the link layer due to contention and collisions Propagation time – which is the time taken for point-to-point message travel. While negligible for a single link, this may be a dominant term over multiple hops if there is network congestion. Receive time – which is the time taken to process the message and record its arrival.

16. 8/18/201516 Traditional Approaches.. Cristian’s Algorithm.. The simplest estimate is to approximate the message propagation time as (T 2 −T 1 )/2 If the processing delay is known to be I, then a better estimate is (T 2 − T 1 − I )/2. More sophisticated approaches take several round-trip delay samples and use minimum or mean delays after outlier removal.

17. 8/18/201517 Fine-grained clock synchronization Reference broadcast synchronization (RBS) Two nodes receive the beacon of one node in the same broadcast area The two receivers record the local time when the reference signal was received. The two receivers exchange this local time stamp through separate messages

18. 8/18/201518 Fine-grained clock synchronization.. Time-Sync Protocol for Sensor Networks (TPSN)

19. 8/18/201519 Fine-grained clock synchronization.. Linear parameter-based synchronization Assuming the same pair-wise message exchange as in TPSN for nodes A and B, we have that the transmission time T 1 and reception time T 4 are measured in node A’s local clock, while reception time T 2 and transmission time T 3 are measured in node B’s local clock. We therefore get the following temporal relationships:

20. 8/18/201520 Fine-grained clock synchronization.. Linear parameter-based synchronization

21. 8/18/201521 Fine-grained clock synchronization.. Flooding time synchronization protocol (FTSP) 1.Interrupt handling time: This is the delay in waiting for the processor to complete its current instruction before transferring the message in parts to the radio 2.Modulation/encoding time: This is the time taken by the radio to perform modulation and encoding at the transmitter, and the corresponding demodulation and decoding at the receiver  FTSP uses a broadcast from a single sender to synchronize multiple receivers  Each broadcast provides a synchronization point (a global–local time pair) to each receiver

22. 8/18/201522 Coarse-grained clock synchronization wireless sensor network system for structural- response data acquisition  Assume that:

23. 8/18/201523 References 1.L. Lamport, “Time, Clocks, and the Ordering of Events in a Distributed System,”Communications of the ACM, 21, 7, July 1978, 558–565. 2.F. Cristian, “A Probabilistic Approach to Distributed Clock Synchronization,” DistributedComputing, 3, 1989, 146–158. 3.J. Elson, L. Girod, and D. Estrin, “Fine-Grained Network Time Synchronization using Reference Broadcasts,” in Proceedings of the Fifth Symposium on Operating Systems Design and Implementation (OSDI), December 2002. 4.S. Ganeriwal, R. Kumar, and M. B. Srivastava, “Timing-Sync Protocol for Sensor Networks,” Proceedings of ACM SenSys’03, November 2003. 5.M. L. Sichitiu and C. Veerarittiphan, “Simple, Accurate Time Synchronization for Wireless Sensor Networks,” Proceedings of IEEE Conference on Wireless Communications and Networking (WCNC), March 2003. 6.M. Maroti, B. Kusky, G. Simon, and A. Ledeczi, “The Flooding Time Synchronization Protocol,” Proceedings of ACM SenSys, November 2004. 7.N. Xu, S. Rangwala, K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin, “A Wireless Sensor Network for Structural Monitoring,” Proceedings of ACM Conference on Embedded Networked Sensor Systems (SenSys), November 2004