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 → 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

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

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: 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

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