1. 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

2. Sensor networks → the necessity of extending sensors’ life time
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

3. Data aggregation approach
Alternative approach
sink
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
Naïve
Data aggregation
Nodes form reverse multicast tree to the sink
Wait for all children to transmit data
Combines all data
Assumptions
network is densely deployed
(1)
(2)
(3) 3
(1)
(2) 1 2
Naïve approach
sink
{ (1)(3)(2) } 3
(1)
(2) 1 2
Data aggregation approach

4. The infer algorithm Two phases Node selection phase
Bayesian interference phase

5. 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

6. 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

7. Results Network monitoring temperature Two scenarios: Thing to note:
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

8. 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