Transcript infer: A Bayesian Inference Approach towards Energy Efficient Data Collection in

infer: A Bayesian Inference Approach
towards Energy Efficient Data Collection in
Dense Sensor Networks.
G. Hartl and B.Li
In Proc. of ICDCS 2005.
Natalia Stakhanova
cs610
Sensor networks

Applications



Battle ground surveillance
Monitoring of animal habitat
Sensors are unattended & inaccessible
→ the necessity of extending sensors’ life time

Natural approach:

Data compression/aggregation

data is highly correlated – reduce data amount
transferred to the sink
Alternative approach

Limit number of nodes to transmit data




Divide time into sensing periods of time - epochs
Select a subset of nodes to sensor & transmit
data each epoch
Other nodes are in sleeping state saving energy
Two approaches




Nodes form reverse multicast tree to the
sink
Wait for all children to transmit data
Combines all data
Assumptions

(1)
(2)
(3)
3
(1)
network is densely deployed
(2)
1
2
Naïve approach
Naïve
Data aggregation


sink
sink
{ (1)(3)(2) }
3
(1)
1
(2)
2
Data aggregation
approach
The infer algorithm

Two phases


Node selection phase
Bayesian interference phase
Node selection phase

Randomized algorithm



p is target percent of active nodes on the network at
one time
each node decides randomly to stay active with
probability p
Advanced randomized algorithm

sensed reading deviation from the average


if reading is different by some threshold set p =1
takes into account remaining energy




Xi –node’s remaining energy
N –number of neighboring nodes
X – current node’s remaining energy
δ – max deviation
Bayesian interference phase

sink infers information about missing data ymisssing
based on the received readings yobserved
y={yobserved , ymisssing}
Posterior
probability
Prior probability
(assumed to be
Gaussian)
likelihood
(simulated)
• Using reverse cdf method – simulate distribution of
missing data
• Draw n samples from this distribution
• Compute average of these data (missing readings)
• Combine average for received data with this inferred
average of missing data
Results


Network monitoring temperature
Two scenarios:





Normal distribution of sensors readings (best case)
Presence of heat source (worst case)
P =0.75
δ=30
Thing to note:




Data aggregation and Naïve approaches have 100% but huge energy consumption
Randomized algorithm – 56%, 59% energy savings
Infer algorithm – 54%, 59% energy savings, the error is negligible
Infer is better for Heat Source scenario
Conclusion



Goal of the work: to extend life of sensor network
Run until 10% run out of power
Infer extends life time by


4% in Heat Source case,
3% in Normal Distribution scenario