timing-sync protocol for sensor networks
DESCRIPTION
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 - PowerPoint PPT PresentationTRANSCRIPT
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) = ait + bi C(t) = approximation of real time, t ait = clock drift. (Frequency of the clock) bi = offset of node i’s clock (Difference from the real time t)
C1(t) = a12 * C2(t) + b12
a12 = relative drift
b12 = relative offset between clocks of node 1 and node 2
Types of synchronization
Global Synchronization
Equalizing Ci(t) for all i=1 to n
Local Synchronization
Equalizing Ci(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
E1 = PA + 2E7 = PB - 4PA = PB + 10E1 = E7 + 16
RBS (Multihop)
E1(R1) -> E1(R4) -> E1(R8) -> E1(R10)
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
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
( 2 1) ( 4 3)
2( 2 1) ( 4 3)
2
T T T T
T T T Td
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 1 Mote 2 Mote 3
Mote 4 Mote 6Mote 5
Base Station (level 0)
Level 2Child nodes
Level 1Parent nodes
sync mssgs at time t1
sync mssgs at time t2
Packet losses with burst data
0
400
800
1200
1600
2000
2400
2800
60 120 180 240 300
Time (seconds)
Pa
ck
ets
Receiv
ed
5 ft range
10 ft range
25 ft range
Theoretical
Packet losses with burst data (percentage)
0
5
10
15
20
25
30
35
40
45
60 120 180 240 300
Time (seconds)
% P
ack
et lo
ss
5 ft range
10 ft range
25 ft range
The average percentage loss for burst data:
25 ft = 40%10 ft = 25.05 % 5 ft =19%.
Packet loss with non-burst data (Time period = 200ms)
0100020003000400050006000700080009000
10000110001200013000
60 120 180 240 300
Time (Seconds)
Pac
ket
s ex
pec
ted
5 ft range
10 ft range
25 ft range
Theoretical
Data rate= 40 pkts/secTime 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)
0
5000
10000
15000
20000
25000
30000
60 120 180 240 300
Time (seconds)
Pack
ets
exp
ecte
d
5 ft range
10 ft Range
25 ft Range
Theoretical
Data rate= 80 pkts/secTime 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
0
500
1000
1500
2000
2500
3000
3500
60 120 180 240 300Time (seconds)
Pack
ets
rece
ived
5 ft range
10 ft range
25 ft range
Theoretical
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 2002.
(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 2005.
(Ganeriwal et al. 2003) Saurabh Ganeriwal, Ram Kumar and Mani Srivastava. “Timing-sync protocol for sensor networks,” in Conference On
Embedded Networked Sensor System 2003.
(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 2003.
(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, 2001.
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
2006.
(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 2003.
(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 93-104, November 2000.
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