TIMING-SYNC PROTOCOL FOR SENSOR NETWORKS Presented by: Shet, Deepak Rajput, Rajiv

Outline Goals Synchronization Problem Challenges in synchronization Synchronization protocol examples LTS RBS TPSN Node Based Algorithm for TDMA scheduling Application Design Results Conclusion Future work

Deepak

Goals To implement TPSN on Berkeley motes to achieve global time synchronization in a wireless sensor network Observing the effect of varying topology on synchronization errors Scheduling the motes to transmit data according to a TDMA scheme Exploring the data rate above which we need the TDMA scheduling Investigating the effect of varying time-slots on packet loss

Need for Time Synchronization in WSN Primary reasons for addressing synchronization problem in WSN are as follows: Sensor nodes need to coordinate their operations and collaborate to achieve complex sensing tasks Synchronization can be used by power saving algorithms to increase the network lifetime Scheduling algorithms like TDMA can eliminate transmission collisions and conserve energy Various sensor network applications, routing protocols, for example detection of duplicate packets need synchronization

Synchronization Problem All computing devices contain a clock C(t) = a i t + b i C(t) = approximation of real time, t a i t = clock drift. (Frequency of the clock) b i = offset of node i’s clock (Difference from the real time t) C 1 (t) = a 12 * C 2 (t) + b 12 a 12 = relative drift b 12 = relative offset between clocks of node 1 and node 2

Types of synchronization Global Synchronization Equalizing C i (t) for all i=1 to n Local Synchronization Equalizing C i (t) for some set of nodes which reside in a close proximity.

Challenges in synchronization methods Synchronization becomes difficult due to nondeterministic nature of factors such as: Send Time Access Time Propagation Time Receive Time

Challenges for synchronization (cont.) Disconnected network leading to synchronization problem

Synchronization Protocol Examples Lightweight Time Synchronization (LTS) Reference-Broadcast Synchronization (RBS) Timing-sync Protocol for Sensor Networks (TPSN)

Lightweight Time Synchronization (LTS) Traditional algorithms focus on maximizing accuracy rather than energy expenses involved LTS aims at minimizing overhead energy. Cost of synchronization can be reduced by relaxed accuracy constraints Targeted mainly at environment monitoring applications such as temperature control, traffic monitoring and surveillance

LTS (cont.) Types of Synchronization in LTS Pair-wise synchronization Multihop synchronization

LTS (cont.) (Pair wise synchronization) Packet exchange for pair wise synchronization

LTS (cont.) Multihop- Synchronization Extension of pair-wise synchronization. Two schemes for Multihop-Synchronization Centralized Distributed

LTS (cont.) Centralized Extension of single-hop synchronization Aims at constructing low-depth spanning tree Pair-wise synchronization are performed along edges of the tree Reference node initiates synchronization

LTS (cont.) Distributed Performs synchronization in distributed manner Each node decides the time for its own synchronization Nodes with lower data rate need not synchronize frequently, hence saves unnecessary synchronization effort

Reference-Broadcast Synchronization (RBS) for WSN Receiver to Receiver synchronization Nodes send reference packets to their neighbors Receivers use arrival time of the packet as reference point for comparing their clocks Removes Send Time and Access Time from the critical path Only source of error is nondeterminism in propagation time and receive time

RBS (cont.) Figure 2.3 (a) Critical Path (Traditional) (b) Critical Path (RBS) (Elson et al. 2002)

RAJIV

RBS (co-relating events) Topology requiring multi-hop synchronization E 1 = P A + 2 E 7 = P B - 4 P A = P B + 10 E 1 = E

RBS (Multihop) E 1 (R 1 ) -> E 1 (R 4 ) -> E 1 (R 8 ) -> E 1 (R 10 ) RBS extension for Multi-hop Synchronization

Timing-sync Protocol for Sensor Networks (TPSN) Conventional sender-receiver synchronization Time stamping done at the MAC layer !! Forms a hierarchical structure before synchronization Two times better than RBS (theoretical and experimental evidence on Berkeley motes)

Why TPSN perform better? By time stamping at the MAC layer - Eliminates uncertainty at the Sender completely (removing send and access time) - Removes receive time at the Receiver

Two Phases in TPSN Level Discovery Phase Synchronization Phase

Level Discovery Phase Aims at establishing a hierarchical structure Root Node initiates the phase by broadcasting level-discovery packets A node receiving a level-discovery packet: - Assigns one higher level to itself - Re-broadcasts the level-discovery packets - Ignores any further level-discovery packets

Synchronization Phase Root node initiates the phase by broadcasting synchronization packet Nodes at level 1 begin the two way message exchange with root node All the nodes at level 1 synchronize themselves to the root node through the two way message exchange Nodes at level 2 back off for some random time when they hear this message exchange, before starting their own synchronization In general, nodes at level “i” synchronize to the nodes at level “i-1” The overall time required to synchronize the whole network depends on the number

Two way message exchange Node A constructs the “sync” packet and timestamps it with T1 before sending it Node B receives “sync” packet and timestamps it with T2 Then, Node B sends an “ack” packet back to A and timestamps it with T3 just before sending it Node A receives the “ack” and timestamps it with T4 as soon as it gets it All the time stamping is done at the MAC layer

Calculating drift and propagation delay Δ = clock drift d = propagation delay

Node Based Scheduling Algorithm (for TDMA Scheduling) IN WSNs, data packets are generated at different sources but travel toward a common destination The scheduling problem is to determine conflict free assignment of time slots such that data packets constructed at each source node reach the destination sink node Two types of conflicts Primary Conflicts Secondary Conflicts

Node Based Scheduling Algorithm (cont.) The network is represented by a graph called G = (V,E) V is a set of all the nodes including the sink node and there is only one sink node known as the Access Point (AP) E is the number of undirected number of edges in the network The conflict graph GC = (V,EC) EC comprises the links or edges between the pair of nodes in G that should not transmit at the same time A pair of nodes u and v belongs to the set I if either u or v can interfere with the signal intended for the other

Node Based Scheduling Algorithm (cont.) Works in two phases: Coloring the network Scheduling the network

Coloring the network Color the network such that nodes i and j get different colors if (i,j) € EC

Scheduling the network The nodes with the same color can transmit at the same time, while the nodes with different colors have to transmit at different times Each node with at least one packet at the beginning of the super slot transmits at least one packet during the super-slot

Application Design Mote 1Mote 2Mote 3 Mote 4Mote 6 Mote 5 Base Station (level 0) Level 2 Child nodes Level 1 Parent nodes sync mssgs at time t1 sync mssgs at time t2

Packet losses with burst data

Packet losses with burst data (percentage) The average percentage loss for burst data: 25 ft = 40% 10 ft = % 5 ft =19%.

Packet loss with non-burst data (Time period = 200ms) Data rate= 40 pkts/sec Time period = 200ms The average percentage loss for burst data: 25 ft = 14.18% 10 ft = 8.54% 5 ft = 7.6%.

Packet loss with non-burst data (Time period = 100ms) Data rate= 80 pkts/sec Time period = 100ms The average percentage At 80 pkts/second: 25 ft = 57.29% 10 ft = 43.59% 5 ft = 39.62%.

Packet loss with TDMA scheduling The average percentage with TDMA: 25 ft = 7.6% 10 ft = 6.57 % 5 ft = 5.88%.

Conclusions At burst data rates, huge packet loss occurs at the base station, so we need TDMA scheduling in this case In case of non-burst data, high data rates of 80 pkts/sec (or more) at the base station, we need TDMA scheduling The synchronization error varies with varying topology The packet loss with TDMA scheduling for time slots of 500 milliseconds, 200 milliseconds, and 100 milliseconds was low. But for time periods below 100ms even TDMA scheduling was not effective A linear increase in the relative synchronization error occurred when motes were kept unsynchronized after initial synchronization. Hence, periodic resynchronization was necessary

Future work Blend the ideas of RBS and TPSN by: Time stamping at the MAC layer in RBS Trying receiver to receiver synchronization with TPSN We need more effective algorithms in the case where nodes in a WSN are continuously moving

References (Elson et al. 2002) Jeremy Elson, Lewis Girod and Deborah Estrin. “Fine-grained network time synchronization using reference broadcasts,” in ACM SIGOPS Operating Systems Review (Elson and Estrin 2001) Jeremy Elson and Deborah Estrin. “Time Synchronization for Wireless Sensor networks,” in Proceedings of the 15th International Parallel & Distributed Processing Symposium 2001 (Ergern and Varaiya 2005) Sinem Coleri Ergen and Pravin Varaiya. “TDMA Scheduling Algorithms for Sensor networks,” in INFOCOM (Ganeriwal et al. 2003) Saurabh Ganeriwal, Ram Kumar and Mani Srivastava. “Timing-sync protocol for sensor networks,” in Conference On Embedded Networked Sensor System (Greunen and Rabaey 2003) Jana van Greunen and Jan Rabaey. “Lightweight time synchronization for sensor networks,” in International Workshop on Wireless Sensor Networks and Applications (Gay et al. 2003) David Gay, Phil Levis, Rob Von Behren, Matt Welsh, Eric Brewer, and David Culler, “The nesC language: A holistic approach to networked embedded systems,” in SIGPLAN Conference on Programming Language Design and Implementation (PLDI’03), June 2003.

References (cont.) (Hill et al. 2001) Jason Hill, Philip Bounadonna, David Culler,“Active message communication for tiny network sensors,” in INFOCOM, Sivrikaya,F. Yener, B. “Time synchronization in sensor networks: a survey.” Network, IEEE (2004). Jeremy Elson and Kay Romer. “Wireless Sensor Networks: A New Regime for Time Synchronization.” ACM SIGCOMM Computer Communication Review (2003). (Li and Rus 2006) Qun Li and Daniela Rus. “Global Clock Synchronization in Sensor Networks,” in IEEE Transactions on Computers (Phil et al. 2003) Phil Levis, Nelson Lee, Matt Welsh, “TOSSIM: Accurate and scalable simulation of entire TinyOS applications,” in Proceedings of the First ACM Conference on Embedded Networked Sensor Systems (SenSys), November (Hill et al. 2000) Jason Hill, Robert Szewczyk, Alec Woo, Seth Hollar, David Culler, and Kristofer Pister, “System architecture directions for network sensors,” in Proceedings of the 9th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS-IX), Cambridge, MA, pp , November 2000.

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