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The Cougar Approach to In-Network Query Processing in Sensor Networks By Yong Yao and Johannes Gehrke Cornell University

The Cougar Approach to In-Network Query Processing in Sensor Networks By Yong Yao and Johannes Gehrke Cornell University. Presented by Penelope Brooks. Overview. Motivation Sensor Networks Overview Applications Sensor Data Problems in Sensor Networks Cougar Architecture Approach

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The Cougar Approach to In-Network Query Processing in Sensor Networks By Yong Yao and Johannes Gehrke Cornell University

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  1. The Cougar Approach to In-Network Query Processing in Sensor NetworksBy Yong Yao and Johannes GehrkeCornell University Presented by Penelope Brooks

  2. Overview • Motivation • Sensor Networks Overview • Applications • Sensor Data • Problems in Sensor Networks • Cougar • Architecture • Approach • Related Projects

  3. Motivation • Distributed database approach to sensor networks • Why? • Declarative queries are well-suited to sensor networks • Energy conservation in sensor networks is crucial

  4. The Big Idea Local computation is much cheaper than communication, so push computation to the network and improve energy consumption

  5. Sensor Networks Overview • Thousands of sensors connected through wireless communication • Multi-hop routing protocol used • Limited computation and storage • Limited energy supply • Sensor nodes connected to one or more physical sensors • Distributed to measure and monitor physical environment • Communication and computation biggest energy drains

  6. Challenges • Communication • Power consumption • Computation • Uncertainty in sensor readings

  7. Some Applications • Besides temperature… • Intelligent building management • Hostile environments • Battlefield • Disaster regions/Early warning systems • Tracking items in transit • Automatic target recognition and tracking

  8. Sensor Data • Uncertainty of data values • Measurements accurate within range • Addressed by aggregation • Historically - sensor networks collect data and transfer to central node for querying and analysis

  9. Problems in Sensor Networks • Aggregation • Must complete at leader node • Data has to be delivered from source nodes • Computation approaches • Send all data to leader and compute there • Some computation at nodes along path • Query Languages • Diverse applications, data, query classes • Look at properties of sensor data, abstract computational patterns that fit

  10. Problems in Sensor Networks (cont’d) • Query Optimization • Large space of possible query plans • Cost of plan is energy consumed • Make decisions with uncertainty • Catalog Management • Metadata for optimizer • Sensor position, density, connectivity, system workload, network stability • Multi-Query Optimization • Share results from similar queries

  11. Cougar Architecture • Loosely-coupled, distributed • Supports in-network computation • Query optimizer on sensor gateway • Describes data flow in network • Computation flow in each sensor • Query proxies on sensor nodes • Register query • Create local operator tree • Activate relevant sensors • Return applicable results contribution contribution

  12. Cougar Architecture Query Optimizer here Query Proxy Layer here

  13. Approach • Query presented to optimizer • Query optimizer • Merge with existing query OR • Generate new query plan

  14. Approach (cont’d) • Designate leader for computation • Methods • Fixed • Randomly selected node • Leader selection policy • Dynamically maintained in case of failure • Minimize communication distance • Two plans: leader, other • Query plans disseminated to all nodes

  15. Select Aggregate Operator Network Interface Query Plan QPL Towards the gateway QPO Towards the leader Aggregated Results In-network aggregation Partially aggregated data from other sensors Data from local sensor Partially aggregated results Network Interface Sensor scan

  16. Example • Query Q: • Monitor office temperature • Generate notification to administrator when temperature over threshold • Optimize query • Query Plan QP generated, leader identified, computation plans generated • Query plans disseminated • Query proxy actions initiated

  17. Example (cont’d) • Sensors collect temperature • Leader aggregates sensors readings, performs AVG • Aggregate value compared to initial condition of query Q • If AVG > threshold • Value sent to gateway • Administrator notified • Otherwise, sensors continue

  18. Another Example TinyDB: An Acquisitional Query Processing System for Sensor Networks SAMUEL R. MADDEN, MICHAEL J. FRANKLIN, JOSEPH M. HELLERSTEIN, and WEI HONG ACM Transactions on Database Systems, Vol. 30, No. 1, March 2005, Pages 122–173.

  19. Related Projects • CoSense - Xerox PARC • SCADDS - UCLA • WebDust - Rutgers • Agent-based Tasking of Massive Sensor Networks - Univ of MD • Reactive Sensor Networks - Penn State • TinyOS - Berkeley • Telegraph - Berkeley • Location-Centric Distributed Computation and Signal Processing - Wisconsin

  20. Wrap-Up • Cougar is one possible architecture for a sensor network • Performs in-network computation • Decreases energy consumption • One leader per query plan • Attempt to merge similar queries • Propagate results to system if condition met

  21. Questions?

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