1. Transport Protocols for Sensor Networks
Nischal M. Piratla
Sangeetha L. Bangolae
Tarun Banka
Computer Networking Research Laboratory
Colorado State University
December 31, 2018

2. Motivation
What is expected out of a transport protocol for sensor networks ?
Reliability, congestion control, mux/demux,……
Why can’t we use the existing protocols ?
Resource constraints – power, storage, computation complexity, data rates, …
Are these constraints common for all sensor networks ?
No, they are application specific.
December 31, 2018

3. Motivation ..contd.
Any application can have a union of the constraints that we know or yet to figure out
Spectra for known constraints:
Low data Rate High data Rate
Power limited Not Power limited
Storage limited Not Storage limited
Bursty samples Periodic samples
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4. Motivation ..contd. General notion for sensor networks
Low data Rate High data Rate
Power limited Not power limited
Storage limited Not storage limited
Sink
user
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5. Motivation ..contd. Radar application:
Range of Transport protocols is yet to be explored
ESRT, PSFQ, CODA …….……!!!!………..TRABOL
High data Rate
Not Power limited
Not Storage limited
Low data Rate High data Rate
Power limited Not Power limited
Storage limited Not storage limited
December 31, 2018

6. Event-to-Sink Reliable Transport (ESRT) for Wireless Sensor Networks
Salient Features:
Event-to-sink reliability
Self-configuration
Energy awareness [low power consumption requirement!]
Congestion Control
Variation in complexity at source and sink. [computation complexity] S
December 31, 2018

7. ESRT’s Definition of Reliability
Reliability is measured in terms of the number of packets received. Or reporting frequency i.e., number of packets/decision interval.
Observed reliability: number of received data packets in decision interval at the sink.
Desired reliability: number of packets required for reliable event detection.
Normalized reliability = observed/desired.
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8. ESRT operation
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9. Algorithm for ESRT
If congestion and low reliability: decrease reporting frequency aggressively. (exponential decrease)
If congestion and high reliability: decrease reporting to relieve congestion. No compromise on reliability (multiplicative increase)
If no congestion and low reliability: increase reporting frequency aggressively (multiplicative increase)
If no congestion and high reliability: decrease reporting slowing (half the slope)
December 31, 2018

10. Components of ESRT In sink: In source:
Normalized reliability computation
A congestion detection mechanism
In source:
Listen to sink broadcast
Overhead free local congestion detection mechanism
E.g., buffer level monitoring, CN – Congestion Notification
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11. Analytical Results
Analytical results (intuitive yet useful) .. We will skip this slide
Starting from no congestion, high reliability and with linear reliability behavior when the network is not congested, the network state remains unchanged until ESRT converges
Starting from no congestion, high reliability, and with linear reliability behavior when the network is congested, ESRT converges to optimum operating range in t[log2((-1)/)]
With linear reliability behavior when the network is not congested, the network state transition from congestion, high reliability to no congestion, low reliability.
December 31, 2018

12. Performance Results (based on simulations) please refer to the paper for graphs .. They may not be legible here
Starting with no congestion and low reliability:
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13. Performance Results contd… (based on simulations)
Starting with no congestion and high reliability:
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14. Performance Results contd… (based on simulations) please refer to the paper for graphs
Starting with congestion and high reliability:
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15. Performance Results contd… (based on simulations) please refer to the paper for graphs
Starting with congestion and low reliability:
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16. Performance Results contd… (based on simulations) please refer to the paper for graphs
Average power consumption while starting with no congestion and high reliability:
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17. We will now move to TRABOL.
Challenges with ESRT
Multiple concurrent events.
Congestion may be due to all sensor nodes. Can there be a better way to slow down the nodes causing the congestion ?
Buffer occupancy and congestion.
We will now move to TRABOL.
December 31, 2018

18. Gigabit Networking: Digitized Radar Data Transfer and Beyond Sangeetha L.Bangolae, Anura P. Jayasumana, V. Chandrasekar
Sangeetha L. Bangolae
(Sang)
Computer Networking Research Lab
Colorado State University
December 31, 2018

19. Motivation
Present a new class of high-bandwidth (64 – 384 Mbps) application – VCHILL radar
Discuss the transport protocols to satisfy real-time radar data transfer over high-speed links
Congestion Control to be TCP-friendly
* VCHILL – Virtual CHILL
December 31, 2018

20. Gigabit Networking Applications
Digital Earth
Bio-medical Tele Immersion
NASA
Virtual MechanoSynthesis
Digital Sky
VCHILL
December 31, 2018

21. VCHILL Radar Application
AIM
Transfer and Display of Digitized Radar Signals in real-time over the NGI (Next Generation Internet)
Remote Control of the radar
Data gathering process
Remote control – controlling the scan rate, pulsing schemes, polarization operation
December 31, 2018

22. VCHILL Radar Application
CHARACTERISTICS
High-bandwidth requirement for best operation; A high responsiveness to available bandwidth.
Satisfactory operation with a minimum bandwidth threshold possible; Yet increase in bandwidth provides a better display image.
Tolerance to losses and end-to-end delay high, compared to audio and video streaming media.
Smoothness (delay jitter) not critical for proper functioning.
December 31, 2018

23. CSU-CHILL Doppler Radar
11 cm wavelength
Dual-polarization
Radar
1.5m parabolic antenna, kW power, dual transmitter and receiver, PRT range – ms, pulse width – us
BW – 10 MHz
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24. Current Status
Remote Client
Shared
Memory
Digitized
Radar
Signal
Internet
TCP
Server
Multicast
RDP(TRABOL)
Processing
Display
End User
Application
High Speed Link
400/800 Mbps
Real-time video
Display: PPI/RHI (Plan Position Indicator/Range Height Indicator)
End stations: Sun Solaris based
Signal Processing: DSP Software based
December 31, 2018

25. Radar Data Format One Ray of DRS Data Each Sample size: 16000 bytes
1st Sample
2nd Sample
3rd Sample
4st Sample
Gate VVI(1)VVQ(1) VVI(2)VVQ(2) VVI(3)VVQ(3) VVI(4)VVQ(4)
HHI(1)VVQ(1) HHI(2)VVQ(2) HHI(3)HHQ(3) HHI(4)HHQ(4)
Gate VVI(1)VVQ(1) VVI(2)VVQ(2) VVI(3)VVQ(3) VVI(4)VVQ(4)
HHI(1)VVQ(1) HHI(2)VVQ(2) HHI(3)HHQ(3) HHI(4)HHQ(4)
Gate VVI(1)VVQ(1) VVI(2)VVQ(2) VVI(3)VVQ(3) VVI(4)VVQ(4)
HHI(1)VVQ(1) HHI(2)VVQ(2) HHI(3)HHQ(3) HHI(4)HHQ(4)
| | | |
Gate VVI(1)VVQ(1) VVI(2)VVQ(2) VVI(3)VVQ(3) VVI(4)VVQ(4)
HHI(1)VVQ(1) HHI(2)VVQ(2) HHI(3)HHQ(3) HHI(4)HHQ(4)
Each Sample size: bytes
December 31, 2018

26. Radar Parameter Display Image
UDP-based (RDP) DRS Transfer with no losses
* RDP: Radar Data transfer Protocol
December 31, 2018

27. Radar Parameter Display Image
UDP-based (RDP) DRS Transfer with 90% losses
December 31, 2018

28. Transport protocols and Congestion Control
VCHILL Application
TCP – too conservative, not suitable for real-time
UDP – Suitable for real-time, but No congestion control, flow control
Require a transport protocol for real-time data transfer - With Congestion control!
December 31, 2018

29. VCHILL UDP-based DRS Transfer Architecture
Feedback
Sender
Manager
Data
Transmission
Reception
Parameter
Estimation
DISPLAY
DRS Client 64 –
384 Mbps
Radar
NGI/I2
DRS Server
Acquisition D
ata
Control
Application
Layer
Transport
Layer (UDP)
Congestion
December 31, 2018

30. TRABOL: TCP-friendly Rate Adaptation Based On Loss
Source-based Rate Control based on AIMD
If (Congestion)
Decrease sending rate to MIN_RATE
If (No Congestion)
Increase sending rate towards TARGET_RATE in steps
Congestion policies based on feedback from receiver
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31. Feedback Mechanism
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32. Performance Evaluation
Sending rate (Mbps) and Loss rate (%) for
Radar Application without rate control
Datagram size = 32 KB
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33. Performance Evaluation (contd…)
Sending rate (Mbps) and Loss rate (%) for
Radar Application with memory-based TRABOL
December 31, 2018

34. Summary VCHILL as a NGI application Current Status of the Project
Transport protocols for the application
UDP-based Radar data transfer protocol
Need for congestion Control
TRABOL and Performance Evaluation
December 31, 2018

35. Thank you Questions!
December 31, 2018