1. Data Collection and Dissemination

2. Learning Objectives
Understand Trickle – an data dissemination protocol for WSNs
Understand data collection protocols in low-duty-cycled WSNs with unreliable links

3. Prerequisites Basic concepts of Computer Network protocols
Basic concepts of protocol optimization

4. Outline Data Dissemination Data Collection
Estrablishing eventual consistency on a shared variable
Trickle – Address single packet
Data Collection
DSF

5. Data Dissemination - Trickle

6. Simple Broadcast Retransmission
Broadcast Storm Problem
Redundant rebroadcasts
Severe contention
Collision

7. Trickle Motivation Challenges WSNs require network code propagation
WSNs exhibit highly transient loss patterns, susceptible to environmental changes
WSNs network membership is not static
Motes must periodically communicate to learn when there is new code
Periodical metadata exchange is costly

8. Trickle Requirement
Low Maintenance
Rapid Propagation
Scalability

9. Trickle An algorithm for code propagation and maintenance in WSNs
Based on “Polite Gossip”
Each node only gossip about new things that it has heard from its neighbors, but it won’t repeat gossip it has already heard, as that would be rude
Code updates “trickle” through the network

10. Trickle Within a node time period
If a node hears older metadata, it broadcasts the new data
If a node hears newer metadata, it broadcasts its own metadata (which will cause other nodes to send the new code)
If a node hears the same metadata, it increases a counter
If a threshold is reached, the node does not transmit its metadata
Otherwise, it transmits its metadata

11. Trickle – Main Parameters
Counter c: Count how many times identical metadata has been heard
k: threshold to determine how many times identical metadata must be heard before suppressing transmission of a node’s metadata
t: the time at which a node will transmit its metadata. t is in the range of [0, τ]

12. Trickle Maintenance – One Example
Assume
No packet loss
Perfect interval synchronization
How to relax these assumptions?
[Dissemination_1]: Figure 3

13. Trickle – Impact of Packet Loss Rates

14. Trickle Maintenance without Synchronization – Short Listen Problem
Mote B selects a small t on each of its three intervals
Although other motes transmit, mote B’s transmissions are never suppressed
The number of transmissions per intervals increases significantly
[Dissemination_1]: Figure 5

15. Trickle – Impact of Short Listen Problem

16. Solution to Short Listen Problem
Instead of picking a t in the range [0, τ], t is selected in the range [τ/2, τ]

17. [Dissemination_1]: Section 5
Propagation
Tradeoff between different values of τ
A large τ
Low communication overhead
Slowly propagates information
A small τ
High communication overhead
Propagate more quickly
How to improve?
Dynamically adjust τ
Lower Bound τl
Upper Bound τh
[Dissemination_1]: Section 5

18. Trickle Complete Algorithm

19. Data Collection

20. Data Collection Link-Quality based Data Forwarding
Wireless communication links are extremely unreliable
ETX: to find high-throughput paths on multiple
Sleep-Latency Based Forwarding
Duty Cycling: sensor nodes turn off their radios when not needed
Idle listening waste much energy
[Collection_2]

21. Sleep Latency in Low Duty-Cycle Sensor Networks
Sleep now. Wake up in 57seconds
Sleep now. Wake up in 35 seconds D B
57s latency
35s latency A
13s latency
4s latency E C
Sleep now. Wake up in 4 seconds
Sleep now. Wake up in 13 seconds
[Collection_2] 21

22. Unreliable Radio Links
70%
90% A
50%
95% C E 22

23. State-of-the-art Solutions: ETX
ETX only considers link quality
ETX = 1/ /0.5 = 4 B
50%, 100s
50%, 100s
Expected E2E delay is 400s
Sole link quality based solutions cannot help reduce E2E delay in extremely low-duty cycle sensor networks! A D
Expected E2E delay is 50s
40%, 10s
40%, 10s C
ETX = 1/ /0.4 = 5

24. State-of-the-art Solutions: DESS
DESS = = 20s
DESS only considers sleep latency B
10%, 10s
10%, 10s
Expected E2E delay is 200s
Sole sleep latency based solutions cannot help reduce E2E delay in extremely low-duty cycle sensor networks! D A
Expected E2E delay is 40s
100%, 20s
100%, 20s C
DESS = = 40s

25. End-to-End Delay vs. Duty Cycle
Suppose one fixed forwarding node
Suffer excessive delivery delays when waiting for the fixed receiver to wake up again if the ongoing packet transmission fails

26. End-To-End Delay vs. Average Link Quality
Given bad link quality, the end to end delay increases dramatically

27. Sensor States Representation
Scheduling Bits
( )*
Switching Rate
0.5HZ 16s round time 1 1 1 1 1
Off On

28. Data Delivery Process 1 2 3 4 End to End (E2E) Delay is 6 ( 1 )* ( 1)* ( 1 )* ( 1 )* ( 1 )* 1 2 3 4
Sleep latency is 1
Sleep latency is 2
Sleep latency is 3
End to End (E2E) Delay is 6

29. Main Idea
Sleep latency is 1
We should try a sequence of forwarding nodes instead of a fixed forwarding node! ( 1 )* ( 1 )* ( 1 )* ( 1 )* 1 2 3 4 ( 1 )* 5
1st attempt: Sleep latency is 1
Dynamic Switching-based Forwarding (DSF) is important in extremely low duty-cycle sensor networks.
ith attempt: Sleep latency is * (i-1)
2nd attempt: Sleep latency is =11
2nd attempt: Sleep latency is =2 29

30. Optimization Objectives
EDR: Expected Delivery Ratio
EED: Expected End-to-End Delay
EEC: Expected Energy Consumption

31. Optimization Objectives(1) : EDR
Forwarding Sequence
EDR: Expected Delivery Ratio. 2
60%
(0100)*
EDR = 70%
(1000)* 1 3
EDR for node 1 is (EDR1):
(0010)*
EDR = 80%
50%
0.6*0.7
+ (1-0.6)*0.5*0.8
40%
+ (1-0.6)*(1-0.5)*0.4*0.9=0.652 4
(0001)*
EDR = 90%
See Equation (3)

32. Optimization Objectives(2) : EED
Forwarding Sequence 2
60%,2
EED: Expected E2E Delay.
(001000)*
EDR = 70%,
EED = 10
(100000)* 1 3
EED for node 1 is (EED):
(000010)*
EDR = 80%, EED = 12
50%, 3
40%,5
0.6*1/0.7 * (2 + 10) + (1-0.6) * 0.5 * 1 /0.8 * (3 + 12) + (1-0.6)*(1-0.5)*0.4*1/0.9 * (5 + 9) 4
(000001)*
EDR = 90%,
EED = 9
See Equation (4)

33. Optimizing EDR
Shall we try all available neighbors?
If both node 2 and node 3 are selected as forwarding nodes:
EDR1 = 1 * 0.7 = 0.7 2
(010)*
EDR = 70%
100%
(100)*
We should only choose a subset of neighboring nodes as forwarding nodes! 1
100%
If only node 3 is selected as forwarding node:
EDR1 = 1 * 0.8 = 0.8 3
(001)*
EDR = 80%

34. Optimizing EDR with dynamic programming
Try or skip 2
Select only a subset of neighbors as forwarders
(010)*
EDR = 70%
(100)*
60%
Try or skip
Node 4 has to be selected 1 3
(001)*
EDR = 80%
50%
Then we attempt to add more nodes into the forwarding sequence backwardly.
40%
Try or drop 4
(100)*
EDR = 90%

35. Distributed Implementation
EDRb(Ø) = 1
The sink node has no packet loss
EEDb(Ø) = 0
The sink node has no delay
EECb(Ø) = 0
The sink node has no energy consumption

36. Distributed Implementation
EDR = 99%, EED = 15, EEC = 2
EDR = 98%, EED = 2, EEC = 1 1 3
EDR = 100%, EED = 0, EEC = 0
sink 2 4
EDR = 97%, EED = 20, EEC = 5
EDR = 90%, EED = 90, EEC = 12

37. Complete Protocol Implementation at Node e