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

What to do about the transitional region?What to do about the transitional region? –Place motes in the transitional region vs in the connected region Transitional region 2 Connected region

Our Proposal Occupy the transitional regionOccupy the transitional region –Perform random trials to construct links with high PRR –Based on the Log-normal radio model 3

Motivation: Placing Relay Nodes 4

Outline Introduce log-normal path loss model Discuss pitfalls Present the experimental results – reality check 5

Log-normal Path Loss Model Power of the transmitted signal Path loss exponent Random variation Sender Receiver 6

Three Regions of Radio Links 7

Connected Region In connected region PRR is very likely to be high Trying one location will likely produce good link Sender Receiver 5 meters 8

Transitional Region In transitional region PRR may or may not be high Trying a few spots should yield a good link Sender Receiver 15 meters 9

Disconnected Region In disconnected region PRR is very unlikely to be high Trying multiple spots seems worthless Sender Receiver 40 meters 10

Outline Introduce log-normal path loss model Discuss pitfalls Present the experimental results – reality check 11

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 12

Reality Check Verify log-normal path loss model Quantify spatial correlations Count number of trials to construct good links Investigate temporal variations 13

Experimental Setup Devices –TelosB motes –iRobot with an Ebox-3854 running Linux Environments –Outdoor parking lot –Lawn –Indoor hallway –Indoor testbed –Two forests 14

Evaluations on the Log-normal Model 15

Q-Q Plot of the Residual RSSI Values 16

Reality Check Verify log-normal path loss model Quantify spatial correlations Count number of trials to construct good links Investigate temporal variations 17

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% 18

Reality Check Verify log-normal path loss model Quantify spatial correlations Count number of trials to construct good links Investigate temporal variations 19

Number of Trials - Configuration Grid sampling –Bernoulli trials Number of trials to find a good PRR is geometrically distributed 1 meter 20

Number of Trials - Results 21

Number of Trials – Fitting Geometric Distribution Suggests that 1 meter ensures independent trials. 22

Connecting Two Motes Mote 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 23

Reality Check Verify log-normal path loss model Quantify spatial correlations Count number of trials to construct good links Investigate temporal variations 24

Temporal Variation Box plots of residual RSSI values for two forests 25

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 26

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 27

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 28

Thank you! Questions? 29