The cougar approach to in-network query processing in sensor networks
•Descargar como PPT, PDF•
1 recomendación•1,549 vistas
This document presents the Cougar approach to in-network query processing in sensor networks. It introduces the concept of a database abstraction layer that allows users to interact with sensor networks using declarative queries. This abstraction layer optimizes queries for efficient in-network processing to reduce energy consumption. The presentation outlines key components of the architecture like the query proxy layer and query optimizer. It also discusses several open research problems in building such a system, including aggregation, query languages, optimization, catalog management, and multi-query optimization.
Recomendados
Más contenido relacionado
La actualidad más candente
La actualidad más candente (20)
Destacado
Similar a The cougar approach to in-network query processing in sensor networks
Similar a The cougar approach to in-network query processing in sensor networks (20)
Último
Último (20)
The cougar approach to in-network query processing in sensor networks
- 1. The Cougar Approach to In-Network Query Processing in Sensor Networks Presented By: Supervised By: Dilini A. Muthumala Dr. Jeevani Goonetillake
- 2. Authors
- 3. Yong Yao • Software Engineer at Google • Ph.D., Computer Science Cornell University (2000 – 2007) • Research Interests – Databases – Sensor Networks – Distributed Systems
- 4. Johannes Gehrke • University Professor Department of Computer Science Cornell University • Research Interests –Scalability in Computer Games and Simulations –Data Privacy –Data Mining
- 5. Motivation • “Database Abstraction Layer” for Sensor Networks • Most popular sensor data management middleware • Introduces Database Abstraction Layer Concept • Cited by 1185 (source: Google Scholar) No. of citations Year
- 6. Presentation Outline • Introduction • Database Abstraction Layer • Architecture • Research Problems • Conclusion
- 7. Introduction
- 8. Wireless Sensor Network (WSN)
- 9. Limitations • Communication • Power Consumption • Computation • Uncertainty in Sensor Readings
- 10. WSN Applications • Smart Buildings, Smart Homes
- 11. WSN Applications • Wild Life Monitoring
- 12. WSN Applications • Monitoring Vineyards
- 13. Future of WSN Is Johannes in his office? Internet
- 15. Motivation 1) Declarative queries are suited for WSN interaction SELECT Temp FROM sensors Complex Network
- 16. Motivation 2) Increasing network lifetime is the major goal of any WSN application WSN Data Repository for offline analysis
- 18. Database Abstraction Layer SELECT Temp, Humid, NodeID FROM sensors SAMPLE PERIOD 5s Base Station Node ID Temperature Humidity 1 127 44 2 119 47 3 120 45 4 123 40 5 120 46 WSN
- 19. Database Abstraction Layer • Local computations are much cheaper than communication – Pushing partial computations out into the network
- 20. Database Abstraction Layer • Retrieves data only upon user demand • No offline data storage • Energy Efficient
- 21. Architecture
- 22. Architecture - Overview Query Proxy Layer Query Optimizer
- 23. Query Proxy Layer Application Layer Query Proxy Layer Routing Layer Other Layers
- 24. Query Optimizer • Generates “Query Processing Plans” • Refers to – Catalog Information – Query Specification • Specifies – Data Flow between sensors – Computation Plan • Finally, plan is disseminated to all sensors
- 25. Example
- 26. User Query “Notify when the average temperature exceeds 35 °C” WSN
- 27. Query Optimizer “Notify when the average temperature exceeds 35 °C” Query Optimizer Query Plan
- 28. Query Plan Query Plan (QP) • Designates the Leader node – Where average value will be finalized Leader Node
- 29. Query Plan Query Plan (QP) • Two computation plans i. Leader Node ii. Non-Leader Nodes
- 30. QP for Non-Leader Node Non-Leader Node
- 31. QP for Non-Leader Node In-network Aggregation Network Interface Sensor Scan
- 32. QP for Non-Leader Node In-network Aggregation 1 Network Interface Sensor Scan Temperature = 38 °C
- 33. QP for Non-Leader Node In-network Aggregation 2 Network Interface Sensor Scan Temperature = 38 °C
- 34. QP for Non-Leader Node In-network Aggregation Network Interface Sensor Scan
- 35. QP for Non-Leader Node In-network Aggregation 2 Network Interface AVG Temperature = 35 °C Contributor Count = 1 AVG Temperature = 36 °C Contributor Count = 1 Sensor Scan Temperature = 38 °C
- 36. QP for Non-Leader Node In-network Aggregation Network Interface AVG Temperature = 35 °C Contributor Count = 1 AVG Temperature = 36 °C Contributor Count = 1 Sensor Scan Temperature = 38 °C
- 37. In-Network Aggregation AVG Temperature = 35 °C Contributor Count = 1 AVG Temperature = 36 °C Contributor Count = 1 Total Temperature No of Contributors AVG Temperature Temperature = 38 °C = 35*1 + 36*1 + 38 = 109 =3 = 109 / 3 = 36.33 AVG Temperature 36.33 °C Contributor Count = 3 =
- 38. QP for Non-Leader Node Towards the Leader AVG Temperature In-network 36.33 °C Aggregation Contributor Count = 3 Network Interface = Sensor Scan
- 39. QP for Leader Node Leader Node
- 40. QP for Leader Node Towards the Leader Select AVG > threshold Average Value Aggregate Operator (AVG) Partially aggregated results Network Interface
- 41. QP for Leader Node Towards the Leader Select AVG > threshold Average Value Aggregate Operator (AVG) Network Interface Partially aggregated results 1
- 42. QP for Leader Node Leader Node AVG Temperature 39 °C Contributor Count = 2 = AVG Temperature 36.33 °C Contributor Count = 3 =
- 43. QP for Leader Node Towards the Leader Select AVG > threshold Average Value Aggregate Operator (AVG) Partially aggregated results AVG Temperature 39 °C Contributor Count = 2 = Network Interface AVG Temperature 36.33 °C Contributor Count = 3 =
- 44. Aggregate Operator AVG Temperature 39 °C Contributor Count = 2 = AVG Temperature 36.33 °C Contributor Count = 3 Total Temperature = 39*2 + 36.33*3 = 186.99 No of Contributors = 5 AVG Temperature = 186.99 / 5 = 37.40 AVG Temperature 37.40 °C = =
- 45. QP for Leader Node Towards the Leader Select AVG > threshold Average Value AVG Temperature 37.40 °C = Aggregate Operator (AVG) Partially aggregated results Network Interface
- 46. QP for Leader Node Towards the Leader AVG Temperature 37.40 °C = Select AVG > threshold “Notify when Threshold = 35 °C the average temperature exceeds 35 °C” Average Value Aggregate Operator (AVG) Partially aggregated results Network Interface
- 47. User Query Result ALERT! Temperature exceeds 35 °C WSN
- 49. 1. Aggregation • Most popular computation and communication pattern • Two important issues – Leader Selection – Data Delivery
- 50. Leader Selection Requirements for the policy i. Dynamically-maintained Leader ii. Physically advantageous location
- 51. Leader Selection Requirements for the policy i. Dynamically-maintained Leader ii. Physically advantageous location
- 52. Data Delivery “How should the data be delivered from source nodes to the leader?” – Send all data to leader? – Should intermediate nodes participate?
- 53. 2. Query Language “What types of queries should be supported?”
- 54. 3. Query Optimization • Cost of query plan has changed • Energy should be the focus • Reactive to changes in catalog information – Changes in topology – Power level at sensor nodes
- 55. 4. Catalog Management • Maintained at the server • Provides Meta Data about the network • Question: What is the best way to main the catalog?
- 56. 5. Multi-Query Optimization • Occurs when the WSN is shared • Users may pose similar queries • Share common data among the users
- 57. Conclusion • Interacting with a WSN is made easy • Database Abstraction layer provides – Friendly Interface – Efficient scheme to reduce energy consumption • Research problems need to be carefully addressed
- 58. My Views on the Paper • Presents a concept • Easy-to-understand • Flow of the paper sometimes confuse the reader
- 59. Q&A
Notas del editor
- I think all of you are aware of the concept “Database Abstraction Layer for Sensor Networks”. As you can see in the citation-count vs. year graph, it has being most cited in 2008 Having this motivation in mind, let’s move on.
- Linking: Besides these limitations, WSNs are successfully used in a wide variety of application domains.
- At the time of writing this paper, the authors have percieved the future of
- Seeing this future, the authors identified two facts that motivated them to come up with the Database Approach for WSNs.
- The first reason is As the popularity of the WSNs grow, they may be used by technically expert users as well as non-expert users. To cater such a diversified user group, it would be useful if a middleware can provide an abstract view of the WSN that hides the underlying messy details of the WSN. By giving users a declarative query interface, they can issue queries without even knowing how the data is generated in the sensor network, how they are processed, and how the answers are computed.
- Link to the next slide Motivated by these facts and as a solution to these issues, the Database Abstraction Layer for WSNs was introduced.
- The Database Abstraction Layer allows the users to issue SQL-like queries to retrieve data from the WSN. This Layer hides all the complex and messy details of the underlying WSN by giving users a feeling like they are using a traditional database management system. Further,
- The architecture of the Database Abstraction Layer spans over two main regions: Gateway node WSN Note that here the gateway node is excluded from the WSN and it is considered as an external entity.
- Query Optimizer generates “Query Processing Plans” upon receiving a query from a user.
- The query plan for a non-leader node has 3 components:
- The query plan for a non-leader node has 3 components:
- The query plan for a non-leader node has 3 components:
- The query plan for a non-leader node has 3 components:
- The query plan for a non-leader node has 3 components:
- The query plan for a non-leader node has 3 components:
- The query plan for a non-leader node has 3 components:
- The query plan for a non-leader node has 3 components:
- The resulting aggregate value from the Aggregate Operator step will be passed to the next step: Selection
- The resulting aggregate value from the Aggregate Operator step will be passed to the next step: Selection
- Most popular computation and communication pattern for WSN This is the same operation that we considered in the example. To support aggregation we have to address two research problems: Leader Selection Data Delivery
- With the introduction of such a layer, However consequent to that several research problems have arrived that need to be addressed to achieve the fullest potential of a DB Layer.