What to do about the transitional region?
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On the Implications of the Log-normal Path Loss Model: An Efficient Method to Deploy and Move Sensor Motes Yin Chen, Andreas Terzis November 2, 2011
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On the Implications of the Log-normal Path Loss Model: An Efficient Method to Deploy and Move Sensor Motes Yin Chen, Andreas Terzis November 2, 2011. Transitional region. Connected region. What to do about the transitional region? - PowerPoint PPT Presentation
Transcript of What to do about the transitional region?
On the Implications of the Log-normal Path Loss Model:
An Efficient Method to Deploy and Move Sensor Motes
Yin Chen, Andreas TerzisNovember 2, 2011
• What to do about the transitional region?– Place motes in the transitional region vs in the connected
region
Transitional region
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Connected region
Our Proposal
• Occupy the transitional region– Perform random trials to construct links with high PRR– Based on the Log-normal radio model
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Motivation: Placing Relay Nodes
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Outline
• Introduce log-normal path loss model
• Discuss pitfalls
• Present the experimental results – reality check
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Log-normal Path Loss Model
• Received signal strength at a distance is
– , is a Gaussian random variable• Due to artifacts in the environment (occlusions, multipath, etc.)
– Does not consider temporal variation
Power of the transmitted signal
Path loss at distance
Path loss exponent
Random variation
Sender Receiverdistance
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Three Regions of Radio Links
• As the distance increases, we go through 3 regions– Connected:– Transitional: – Disconnected:
• Observation– The packet reception ratio at any given location is random 7
Connected Region
• In connected region• PRR is very likely to be
high• Trying one location will
likely produce good link
Sender Receiver5 meters
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Transitional Region
• In transitional region• PRR may or may not be
high• Trying a few spots should
yield a good link
Sender Receiver15 meters
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Disconnected Region
• In disconnected region• PRR is very unlikely to be
high• Trying multiple spots
seems worthless
Sender Receiver40 meters
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Outline
• Introduce log-normal path loss model
• Discuss pitfalls
• Present the experimental results – reality check
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Pitfalls
• Log-normal path loss model is not perfect• The Gaussian variation in signal strength is a
statistical observation• Signal strengths at nearby locations are
correlated
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Reality Check
• Verify log-normal path loss model• Quantify spatial correlations• Count number of trials to construct good links• Investigate temporal variations
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Experimental Setup
• Devices– TelosB motes– iRobot with an Ebox-3854 running Linux
• Environments– Outdoor parking lot– Lawn– Indoor hallway– Indoor testbed– Two forests
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Evaluations on the Log-normal Model
• Holds well in all the environments– Example figure for the parking lot
– We can subtract the solid line from the raw RSSI readings• The residual RSSI values are samples of the random variable :
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Q-Q Plot of the Residual RSSI Values
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Reality Check
• Verify log-normal path loss model• Quantify spatial correlations• Count number of trials to construct good links• Investigate temporal variations
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Spatial Correlation
• PRR measurements at a parking lot– iRobot moves in a 2-d plane (the ground)– Black cell : PRR below 85%; Gray cell : PRR above 85%
• PRR are correlated• Trying two adjacent locations
flipping two coins
• In all of our experiments, 1 meter is sufficient to remove most correlation
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Reality Check
• Verify log-normal path loss model• Quantify spatial correlations• Count number of trials to construct good links• Investigate temporal variations
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Number of Trials - Configuration
• Grid sampling– Bernoulli trials
• Number of trials to find a good PRR is geometrically distributed
distance
1 meter
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Number of Trials - Results• Measure and compute the length of connected region
– Place motes at distances longer than
Parking Lot
Hallway 1 Hallway 2 Office Forest0
0.5
1
1.5
2
2.5
3
3.5
Number of Trials Expected Number of Trials
Parki
ng Lo
t
Hallway
1
Hallway
2Office
Forest
00.5
11.5
22.5
33.5Distance to the Sender (Normalized by lc)
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Number of Trials – Fitting Geometric Distribution
Suggests that 1 meter ensures independent trials.
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Connecting Two MotesMote A Mote B
Relay
TAR: number of trials to connect to A
TBR: number of trials to connect to B
TARB: number of trials to connect to both A and B
Hallway 1 Hallway 2 Parking Lot 1
Parking Lot 2
Parking Lot 3
00.5
11.5
22.5
33.5
44.5
TAR TBRTARB TAR multiplied by TBR
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TARB TAR TBR
Reality Check
• Verify log-normal path loss model• Quantify spatial correlations• Count number of trials to construct good links• Investigate temporal variations
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Temporal Variation
• Box plots of residual RSSI values for two forests
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Conclusion
• Log-normal model fits sensornets• Signal correlation vanishes at 1 meter separation• Easy to find good links in the transitional region
– Rule of thumb: at twice the length of connected region, number of trials is less than 5 with high probability
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Application – Placing Relay Nodes
• Number of relay nodes at large scale– Place 120 sensor motes in an area of size 800m by 800m – Run Steiner Tree algorithm to place relay nodes
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Application – Mobile Sensor Networks
• Mobile sink– If the current spot yields low PRR, move 1 meter– Minimize travel distance
• Mobile motes Signal variation in the space domain
Signal variation in the time domain
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Thank you!Questions?
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