MICS

Simulated network topology from a

SensorScope deployment in the Génépi

Mission Increase the accuracy of environmental sensor

networks while decreasing their cost

Accuracy

Can we provide more relevant information by increasing

sampling frequency during interesting events?

Cost

Can we optimize power consumption by suppressing

(spatially or temporally) redundant information?In-­network processing allows us to

carefully choose which data we report

Case  Study:  Constraint  Chaining

Overview. Constraint Chaining1 (Conch) is a sensor

network reporting algorithm that uses spatio-­temporal

suppression to reduce in-­network communication.

Temporal. Stations only transmit sensor values if they

have changed since the last measurement.

Spatial. Stations only transmit a sensor value if its

difference with a particular adjacent station’s value (i.e.,

one that was found to be highly correlated with this

station) has changed.

Adaptation. The network sink performs periodic

optimization to ensure that Conch’s spatial component

takes advantage of patterns in the observed phenomena.

A list of correlated pairs is sent back into the network.

Performance  Evaluation

Algorithm Tx reduction Sigma

Uniform sampling 0.0% 0.0%

Temporal suppression 57.3% 6.8%

Constraint Chaining 62.2% 11.5%

Simulated Conch structure from

a SensorScope deployment in

Plaine Morte

Simulation. We have

performed extensive

simulations using real data

from several sensor network

deployments2. Temporal

suppression yields large

energy savings, while

Conch’s spatial component

only manages to bring a

small additional benefit.

Reality. We have delevoped and integrated a power

monitoring board (left) with SensorScope, a commercial

environmental sensing system (right). We are now in the

process of deploying a network of stations on EPFL’s

campus to evaluate Conch under real-­world conditions.

References

[1] Silberstein, A., Braynard, R., Yang, J.: Constraint chaining: On energy-­efficient

continuous monitoring in sensor networks. ACM SIGMOD 2006, pp. 157–168.

[2] Evans, W. C., Bahr, A., Martinoli, A. Evaluating Efficient Data Collection

Algorithms for Environmental Sensor Networks. DARS 2010.

ENAC /

Performance  Evaluation  of  Data  Collection  

Algorithms  for  Environmental  Sensor  Networks

Author William  C.  Evans,  Alexander  Bahr

Supervisor Alcherio Martinoli

All  contributors are  associated with the  Distributed Intelligent  Systems  and  Algorithms Laboratory (DISAL)

EDIC 2011