1. Energy-Efficient and Low-Latency, Tree Based Algorithms for Convergecast in Wireless Sensor Networksby
Sarma Upadhyayula
Committee
Dr. Sandeep Gupta
Dr. Arun Sen
Dr. Hasan Davulcu

2. Outline of Presentation
Introduction
Problem statement
System model
Convergecast vs Broadcast
Tree based algorithms
Data scheduling algorithm
Results
Conclusion and future work
References

3. Introduction
Convergecast – collecting data from all or some nodes to a central node.
Flow of data – opposite of broadcast
Issues: Energy-Efficiency, Low-Latency,
Network Life time, Fault-Tolerance, Scheduling,
Data Compression.
Wendi[4] – Life time
Stephanie[5] (a.k.a Pegasis)– Energy, Life time
Bhaskar[6] – Energy (general case)
Valli[2] – Energy
Chalmerk[7] – Network Density

4. Problem Statement
GIVEN: Wireless Sensor Network (WSN), N nodes – sources, 1 base station – sink.
Definitions
Total Energy – sum of individual energies.
Total Latency –from start to reception ALL packets
Life Time – No. of rounds – before k nodes die
Energy-Efficient – high energy for communication [4]
Low-Latency – time sensitive applications
Long network life time
OBJECTIVE: Energy-Efficient, Low-Latency and High Network Life Time

5. System Model (Assumptions)
Data Routing
nodes – static and synchronized.
Graph – positive edges, bidirectional edges, connected.
Edges weight – cost (energy) for communication.
All nodes can talk to one another – but they don’t.
Multi-hop (over single-hop).
Data compression factor,  = [0,1], for intermediate nodes
Data Communication
Channels, (CDMA code, time slot), for communication
One transceiver

6. System Model Energy Model [6] Latency Model [2]i j
Energy Model [6]
Ei = (Eelec + εamp × rλ) × d
Ej = Eelec × d
Typical values[6]: Eelec = 50µJ/bit, εamp = 100nJ/m/bit, λ = 2, d = data bits
Latency Model [2]
Time Slot T = max{d1, d2, …, dn}
Network Life Time Model
No. of rounds of convergecast before death of k nodes. 1 2 n

7. Convergecast vs Broadcast
Data flow in a tree is bottom up.
Packet size varies.
Cannot maximize no. of child nodes.
Latency is determined by parallel transmissions.*
* = the concept of -constraint
Data flow is top down.
Constant packet size.
Tree – maximize no. of child nodes.
Latency is determined by longest path.

8. Convergecast Algorithm (β = 2)

9. Algorithms’ Performance
Energy gain: 5-8% over broadcast; 2% over other Valli03

10. Algorithms’ Performance (contd…)
Latency gain: 30-40% over broadcast; up to 3 times better than Valli03

11. Tree Based Algorithms Rationale for tree construction
Data Compression Factor,  = [0,1]
-constraint (ideally, for  = 1, minimum latency for  = e and for  = 0, minimum latency for  = 2)
MST & SPT based trees

12. Latency vs  for  = 0: Minimum at  = 2
Theoretical Dependence of Latency vs  for  = 1: Minimum at  = e (2.71)
Latency vs  for  = 0: Minimum at  = 2
Experimental Results of Latency vs  for  = 1: Minimum at  = 2
Latency vs  for  = 0: Minimum at  = 2

13. MST based trees ( = 2)
Consumes minimum energy when data compression,  = 0 5 7 8 4 6 ∞
Network 5 4
MST
Energy = 24J
Latency = 20 units
MST with -constraint
Energy = 26J
Latency = 9 units 5 4 7

14. SPT based trees ( = 2)
Consumes minimum energy when data compression,  = 1 5 ∞
Network 6 7 4 2 10 7 5 11
SPT
Energy = 44J
Latency = 16 units 6 10 7 5 11 12
SPT
Energy = 45J
Latency = 9 units 6

15. Data Scheduling Algorithm
Assumption: Some fixed CMDA codes are given
(code, time slot) pair = channel
Parent nodes shouldn’t be in the range of another transmitting node
Child node shouldn’t transmit when another parent node is receiving
Channel i
j(≠ i)

16. Simulation Environment
Battery Energy = 10µJ,
Data Compression  = [0,1],  = {1,2,3,N}
Node Density = 10% to 80%
Network Size = 50 – 300
Comparison with Pegasis [5]

17. ( = 1,  = 3, ND = 10%)
( = 1,  = 3, ND = 10%)
( = 1,  = 3, ND = 80%)
( = 1,  = 3, ND = 80%)

18. ( = 0,  = 3, ND = 10%)
( = 0,  = 3, ND = 10%)
( = 0,  = 3, ND = 80%)
( = 0,  = 3, ND = 80%)

19. ( = 1,  = 3, ND = 10%)
( = 1,  = 3, ND = 80%)
( = 0,  = 3, ND = 10%)
( = 0,  = 3, ND = 80%)

20. Observations and Explanations
MST:
High  > 0.75:
regardless of ND, always
low energy
Lowest Latency
Worst life time.
Low  ≤ 0.75:
performance drop - energy
wise and gets worse
Moderate life time.
High , low longevity of the packet
???
Concentrated energy expenditure
Part of packet exists after 1st hop
???
Due to poor life time of Pegasis

21. Observations and Explanations
PEGASIS:
High  > 0.75:
Low energy
Highest Latency
Good life time - drops with
increase in network size.
Low  ≤ 0.75
steep drop, energy wise
Moderate life time but
drops rapidly.
Greedy construction
Chain structure
Rotation of chain head
Very long routing distance
Static nature of MST and SPT

22. Observations and Explanations
SPT:
High  > 0.75:
More energy than other two
but difference reduces with ND
High Latency
Best life time.
Low  ≤ 0.75:
Enormous energy savings 4 6 1
network 4 6 1
SPT
11 units 4 1
MST
9 units
???
Accounts for High Latency-
High Energy contradiction
Shortest path by each packet
Considers neighbors energy
resource. Therefore energy spreads in the network

23. Results Summary  Network Density Priority (Latency or Energy)
Recommended Algorithm
Recommended 
>75%
Low(<30%)
Medium(30-60%)
High(>60%)
Energy
MST or Pegasis
 > 3
75%
MST for lower range
MST if network size > 150 nodes
SPT if size < 150
MST if network size > 200 and SPT otherwise.
SPT
= 2 for MST
> 3 for SPT
> 3

24. Results Summary (contd…)
Network Density
Priority
(Latency or Energy)
Recommended Algorithm
Recommended 
<75%
Low (10-30%)
Medium (30-60%)
High(>60%)
Energy
SPT
 > 3 -
Latency
MST
 = 2 or 3

25. Contributions and Future Work
Our contributions
Refute the notion of using broadcast trees
Emphasized the importance of 
Significance of  in latency
Solution provided for a broader problem set
network size, node density, data compression, tree structure
Issues addressed: Energy-Efficiency, Low-Latency, Network Life Time
and their inter-dependency.
Future Work
Issues NOT addressed: fault tolerance, channel reliability
More general case: k sources, 1 sink

26. References
Sarma Upadhyayula, Valliappan Annamalai and Sandeep Gupta, “A Low-Latency and Energy-Efficient Algorithm for Convergecast in Wireless Sensor Networks”, Proceedings of IEEE Global Communications Conference, 2003.
Valliappan Annamalai and Sandeep Gutpa, “On Tree-Based Convergecasting inWireless Sensor Networks”, Proceedings of IEEE Communications and Networking Conference, Louisiana, 2003.
Imrich Chlamtac and Shay Kutten, “Tree Broadcasting in Multi-Hop Radio Networks”. IEEE Transactions on Computers, Vol. C-36, No. 10, October 1987.
Wendi R. Heinzelman, Anantha Chandrakasan and Hari Balakrishnan, “Energy-Efficient Communication Protocols for Wirless Micro Sensor Networks”. Proceedings of Hawaii International Conference on System Science, January 2000.
Stephanie Lindsey, Caugili Raghavendra and Krishna M. Sivalingam, “Data Gathering Algorithms in Sensor Networks Using Energy Metrics”, IEEE Transactions on Parallel and Distributed Systems, Vol. 13, No. 9, September 2002.
Bhaskar Krishnamachari, Deborah Estrin and Stephen Wicker, “Impact of Data Aggregation in Wireless Sensor Networks”, International Workshop on Distributed Event-Based Systems, Vienna, Austria, July 2002.
Chalermek Intanagonowiwat, Deborah Estrin, Ramesh Govindan and John Heidemann, “Impact of Network Density on Data Aggregation in Wireless Sensor Networks”, Proceedings of Internationals Conference on Distributed Computing Systems, Vienna, Austria, July 2002.

27.  = e (derivation) Number of children =  Data compression =  = 1-α
log  N
Number of children = 
Data compression =  = 1-α
Total Latency = ( log N) (d ∑ (α)i)
For  = 1, Minimum Latency when  = e
For  = 0, Minimum Latency when  = 2
log N
i=0
Number of
time slots
Length of
each slot

28. Notations Notation Definition Initial Value P set of parent nodes
root node C
nodes with parents in P Φ
Adj(p)
nodes NOT part of tree and adjacent to p -
Cposs
neighbors of nodes in P
Adj(s) Ci
children of node i β
β-constraint
input
(u,v)
distance between u and v
update(X)
update set X after each round reflecting their definitions
Rx(p)
reception code of p
Tx(p)
transmission code of p
(p)
time slot for transmission
i
set of nodes with reception code i
i L
leaf nodes in the tree 
total number of codes

29. Convergecast Algorithm
Tree Construction (G, s)
while P ≠ Φ
for all c є Cposs
d = ∞
for all p є P
if (p,c) < d Λ (|Cp| < β ν |Adj(c)| = 1)
d = (p,c)
Cparent(c) = Cparent(c) – {c}
parent(c) = p
Cp = Cp  {c}
update(Adj(c)), update(Adj(parent(c)))
C = C  {c}
P = C, C = Φ, update(Cposs)