1. Model-Driven Data Acquisition in Sensor Networks - Amol Deshpande et al., VLDB ‘04 Jisu Oh March 20, 2006 CS 580S Paper Presentation

2. Problems – Sensornet is not a database! Databases - complete, authoritative sources of information - Answer a query “CORRECTLY” based upon all the available data Sensornets - Misrepresentations of data :only samples acquired, not random :need to complement the sensornet readings - Inefficient approximate queries :Existing query processing from a completist’s approach is costly

3. Solutions – Model-driven data acquisition Use a statistical model which maps the raw sensor readings onto physical reality - in order to robustly interpret sensor readings - and provide a framework for optimizing the acquisition of sensor readings

4. Proposed approaches - Architecture Architecture for model-based querying in sensor networks

5. Proposed approaches (cont.) - Probability density function (pdf) Probability density function (pdf) - Based on time-varying multivariate Gaussians - Estimates sensor readings in the current time period - Properties : correlation between different attributes : cost differential - Constraints: need historical data and training with them

6. Key concepts – Probabilistic queries BBQ query processing (static probabilistic model)  A user requests a range query that ask if an attribute Xi is in the range [ai, bi] with confidence (1-α)  Marginalize a prior density (probability density function), p(Xi, …, Xn) to a density over only attribute Xi, p(x i )  Compute P(Xi ∈ [a i, b i ]) = ∫ ai bi p(x i )dx i  Answer true if p>1-α, false if p> α  Otherwise, move on conditioning step

7. Key concepts – Probabilistic queries (cont.) BBQ query processing (conditioning)  Acquire new sensor readings, o, a set of observations  Marginalize a posterior density (conditional probability density function), p(X i, … X j-1, X j+1, … X n | x j ) to a density over only attribute X j p(x j |o)  Compute P(Xi ∈ [a i, b i ] | x j ) = ∫ ai, bi p(x i |o)dx i  Answer true if p>1-α, false if p> α

8. Key concepts – Probabilistic queries (cont.) BBQ query processing (dynamic probabilistic model)  describe the evolution of the system over time  for time evolved attributes  use transition model p(X 1 t+1,.. X n t+1 | X 1 t.. X n t ) for conditioning  marginalize transition model then obtain p(X 1 t+1,.. X n t+1 | O 1…t )

9. Key concepts (cont.) – Choosing on observation plan Choose a data acquisition plan for the sensornet to best refine the query anser.  Cost(O) = C a (O) + C t (O)  R i (O), statistical benefit of acquiring a reading  Minimize O ⊆ {1, …, n} C(O), such that R(O) ≥ 1-α

10. Contribution integrate a database system with a correlation- aware probabilistic model build the model from historical readings and improve it from current readings answer approximately SQL queries by consulting the model + shield from faulty sensors + reduce # expensive sensor readings + reduce # radio transmissions

11. Experiment results

13. Conclusions Probabilistic model-driven data acquisition Help to provide approximations with probabilistic confidences, significantly more efficient to compute in both time and energy Strong assumptions and constraints - The model must be trained - based on static network topology - all sensor nodes are well synchronized

14. Questions?