Download SenProbe: Path Capacity Estimation in Wireless Sensor Networks

SenProbe: Path Capacity Estimation
in Wireless Sensor Networks
Tony Sun, Ling-Jyh Chen, Guang Yang
M. Y. Sanadidi, Mario Gerla
Outline
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Motivation
Background
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Design Consideration
What do we want to measure?
Effective Capacity
Previous Method
Proposed Approach: SenProbe
Simulation Results
Conclusion
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Motivation
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Mobile computing platforms may interact with
ambient sensor environment establishing
“Opportunistic wireless networks”
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Evaluation and measurement of wireless path
capacity in sensor network is of realistic interest
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(i.e. Capacity planning, protocol design, performance
analysis, system deployment, assess applicability of
deployment)
Need a tool that can monitor and measure
opportunistic path capacity in wireless sensor
networks
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Outline
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
Motivation
Background
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Design Consideration
What does SenProbe actually measure?
Effective Capacity
Previous Method
Proposed Approach: SenProbe
Simulation Results
Conclusion
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Design Consideration
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CSMA-CA and variant schemes still popularly
used in sensor network for its simplicity
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(IEEE 802.15.4 beaconless mode, Berkeley motes,
etc)
Basic CSMA-CA doesn’t incorporate
RTS/CTS mechanisms
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Send packet when an idle channel is detected
Smaller packet overhead if idle channel can be
detected quickly
Suffers from hidden terminal problem
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What do we want to measure? (1)
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The effective end-to-end rate is defined as
the maximum achievable data rate in the
absence of any cross traffic connection.
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It is smaller than the raw data rate at the
physical layer due to
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Packet Overhead
Interference between multiple packets in the
pipeline
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What do we want to measure? (2)
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In fact, path capacity in wireless net also
varies with:
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MAC protocol and link scheduling
Link interference
S/N ratio;
Tx power
Encoding/modulation scheme
Number of antennas (eg MIMO)
Antenna directionality
etc
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Neighborhood Example
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If Dr=Di=250m , nodes {3,4,5} are within the same n-hood, C’=C/3
If Dr=250m, Di=500m, nodes {2,3,4,5,6} are in n-hood, C’=C/4
Dr= effective receive range from node 4
(solid-line circle)
Di = interference range caused by node
4 (dotted-line circle)
Distance between nodes: 200m
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Effective Capacity of CSMA-CA
Enabled Wireless Channel
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The effective capacity of a one-hop link can
be calculated as
C
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TPacket
TPacket
 CP
 TACK  TOverhead
TPacket 
S Packet
CP
TACK 
S ACK
CP
For the CSMA environment in our study (if
ACKs are used)
C
6ms
 2Mbps  1.89Mbps
6ms  0.156ms  0.188ms
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Previous Work (Morris et al)
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Dr=250m, Di=500m
Use UDP flows to probe the maximum achievable throughput
(brute force method)
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Outline
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
Motivation
Background

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




Design Consideration
What does SenProbe actually measure?
Effective Capacity
Previous Method
Proposed Approach: SenProbe
Simulation Results
Conclusion
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CapProbe Concept
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Key insight: a packet pair that gets through with zero queueing
delay yields the exact estimate
Capacity
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Issues: Compression and Expansion
• Queueing delay on the first packet => compression
• Queueing delay on the second packet => expansion
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SenProbe
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Path capacity estimation tool specially designed for the multi-hop
CSMA based wireless networks.
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One-way estimation technique, based on CapProbe concepts
Aimed to simplify the path capacity estimation process
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A back-to-back packet train technique designed to overcome the
hidden terminal effects in CSMA environment
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SenProbe measures end-to-end effective capacity in wireless ad hoc
networks.
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SenProbe is simple, fast and less intrusive to comparative
techniques.
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SenProbe Algorithm(1)
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Instead of using back-to-back packet pairs, SenProbe
relies on back-to-back packet train to overcome the
effect of hidden terminal in CSMA-CA
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The length of this back-to-back packet train depends on
the interference range and the transmission range of the
specific radio technology under question
 InterferenceRange 
NTRAIN  
2
Tranmissio
nRange


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SenProbe Algorithm(2)
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The receiver measures the OWD of
every packet in kth packet train
received as the difference between
time received and time sent
OWD[k , i]  Trcv [k , i]  Tsend [k , i], 1  i  NTRAIN
the minimum OWDSUM is kept for
OWDSUM [k ]  min OWD [k , i]  OWD [k , j ]
the kth packet train. The “good”
1i  j  N
dispersion sample r (i.e. samples
r arg min OWDSUM [k ]
encountering no cross traffic) is the
k
sample with the minimum OWD
sum
TRAIN
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Dispersion of the good sample
calculated, and used to estimation
capacity
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T  OWD [r , v]  OWD [r , u ]
P
C
T
17
SenProbe-Visualization
4 3 2
1
1)
4 3
2
1
4
3
1
4
1
2)
3)
4)
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Outline
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Motivation
Background

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




Design Consideration
What does SenProbe actually measure?
Effective Capacity
Previous Method
Proposed Approach: SenProbe
Simulation Results
Conclusion
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Simulation Results (1)
End-to-End Capacity (Mbps)
2.5
Packet-pair technique
FTP
2
1.5
1
0.5
0
1
2
3
4
5
6
7
8
Number of hops
Path Capacity measured via FTP connection and Packet-Pair
technique (one way CapProbe)
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Simulation Results (2)
End-to-End Capacity (Mbps)
2.5
SensorProbe
Theoretical
2
1.5
1
0.5
0
1
2
3
4
5
6
7
8
Number of hops
Path capacity of adhoc multi-hop forwarding chain in CSMA-CA
wireless environment
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Simulation Results (3)
End-to-End Capacity (Mbps)
2.5
Theoretical
SensorProbe
2
1.5
1
0.5
0
1
2
3
4
5
6
7
8
Number of hops
End-to-end capacity estimation of multi-hop connections within the
same collision domain
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Simulation Results (4)
End-to-End Capacity (Mbps)
2.5
Theoretical
SensorProbe
2
1.5
1
0.5
0
1
2
3
4
5
Number of hops
6
7
8
Capacity estimates along a multi-hop forwarding chain for CSMA-CA
with ACK enabled wireless sensor network
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Conclusion
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SenProbe uses back-to-back packet trains, and
relies on packet dispersion between the packet
trains to measure the path capacities in a one-way
fashion.
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SenProbe estimates e2e path capacity in CSMA
enabled wireless sensor networks.
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SenProbe is a simple and non-intrusive technique
that can accurately reflects the effective path
capacity
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Thanks!
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