Wireless Sensor Networks Data and Databases

1. Wireless Sensor Networks Data and Databases Professor Jack Stankovic Department of Computer Science University of Virginia

2. Outline • Overview of • Database Perspective for WSN • Storage Issues • General Architectures • Queries (what they look like) • TinyDB/TAG • Example Protocol • SEAD

3. Classical DB Schema (Personnel Records) Query Query Optimizer Plan Data Indices Streams Stock Market Quotes News Feeds Database (Confidence of Data)

4. Ad Hoc WSN – DB View Temperature Map Data(i) Cluster Head More Storage Query Optimizer Plan

5. Why Different? • Amount of memory small • No disks • Highly decentralized • Volatile • Nodes sleep/awake • Nodes fail • RAM (and FLASH) • Data is transient • Data is uncertain (range queries) • Query on time/location/area

6. Why Different? • Multiple queries that follow each other • Real-Time Streams • Cost models for optimizing the plans for executing queries are difficult • Goal: Answer the query to a specified confidence level at minimum cost • Minimize energy, messages, time, …

7. Why Different? • Data is correlated • Av. Temperature in area of 10 nodes • Expensive to query all 10 • Nodes near each other have similar temperatures • Learn correlation • Sensor on a window sill x degrees warmer than center of room on sunny days

8. WSN - Data Perspective • Raw sensor readings – data • Process data into information • Example • Magnetic + acoustic + motion => vehicle

9. WSN – Data Perspective • In-network aggregation • Minimize energy used • Reduce end-to-end delay • Archive all data ?? • Handle (dynamic, periodic) queries • Disseminate queries into WSN • Raise level of abstraction • View as a database

10. Data Storage • Collect physical measurements, including data streams • Store the data - where Raw Data Detection Information Classification Sensor Node Situation Assessment Small storage Flash No disk Cache Log Mote Sensor Node Mem. Communicate Append only

11. Data Storage • In-network processing to reduce storage requirements • Send results of queries back to (multiple) users • Can be mobile • Replicate in network stored data for • Efficiency • Reliability

12. Data Storage • Tag data with confidence level • Encrypt data • Compress data • Drop data • Age data • Aggregate data (min, max, mean, …) • Blur data => privacy

13. Data Storage • Data consists of real world measurements and is inherently noisy • Exact match queries not always useful • Range-based queries more appropriate • Real-time queries • Sample rates • Deadlines • Data freshness (temporal validity) • Continuous, long running queries

14. More Complicated Scenario Tree construction: • Hierarchical Structure • Subscription Requests • Replica Placement • Mobility Management

15. Data Association • Tracking “N” targets • People, vehicles, animals • RFID tags • Known/friendly targets

16. Smart Living Space

17. Architecture (1) Base Station Data Stored here Queries performed here Data Data Data Data

18. Applications • Monitor soil moisture • Create temperature maps • …

19. Architecture (2) Base Station Queries Flood Data Stored Decentralized at Each Node

20. Applications • Number of horses in meadow • Tank appears • … • DD • RAP

21. Architecture (3) Hierarchical Network Query to Rendezvous Points Base Station Stargates/ Log motes

22. Applications • Medical • Environmental • …

23. Architecture (4) Distant WorkStation Disconnected System Data Stored Decentralized at Each Node Collected by Data Mules

24. Applications • Environmental Studies • Bridge Analysis • Structural Assessments • Difficult to access areas – use helicopters • …

25. Example of SQL Query • Retrieve, every 45 seconds, the rainfall level if it is greater than 50 mm SELECT R.Sensor.getRainfallLevel() FROM RFSensors R WHERE R.Sensor.getRainfallLevel() > 50 AND $every(45);

26. Queries - Extensions • Choose area • Choose lifetime • Aggregate data over a group of sensors • Set conditions restricting which sensors can contribute data • Correlate data from different sensors • Sound alarm whenever two sensors within 10 m of each other detect abnormality • Specify probabilities for equality tests • Ask for range data • Ask for confidence level on answer

27. Examples of Queries • Military Surveillance • ??? • Medical Domain • Assisted Living Spaces • ??? • Nursing Homes • ??? • Environmental • ???

28. Disseminating the Query • Flooding • Selective Flooding (to an area) • Spanning Tree • Multiple needed if multiple base stations • Multiple needed for different queries at same base station • Store data by name and hash to that location to retrieve the data

29. Geographic Hash Table (GHT) • Translate from a attribute to a storage location • Distribute data evenly over the network • Example: GHT system (A Geographic Hash Table for Data Centric Storage – see Ch 6.6 in text))

30. GHT • Events are named with keys • Storage and retrieval of event are performed with these keys • Key is hashed to a geographic position • Locate node closest to this position Hash to x x Closest node

31. GHT Base Station Store Tank Info Here Query

32. Disseminating the Query • Given a cost model for using the WSN • Given the request with a confidence level • Create a plan to disseminate the query at minimum cost to obtain the answer AND meet confidence in the answer

33. TinyDB • For Periodic (Environmental) Applications • Integrates query and query response with power management by scheduling sleep/wake-up times depending on the depth of the tree • Coordinate sleep/wake-up with sensing • Note the need for clock sync

34. TAG of TinyDB • 2 Phases (sleep when possible) • disseminate periodic query • collect data (scheduled) Base Station Epoch Pipelining

35. Another Issue - Indexing • Indexing • Cost of building and maintaining an index may be too high for WSN • More likely when nodes begin to have more storage/memory • Example system: DIFS (A Distributed Index for Features in Sensor Networks) • Low average search costs • Hash chooses a location within a region not over the whole system like GHT

36. Underlying Support • ELF: An Efficient Log-Structured Flash File System • Persistent storage • Appending data to file • Delivery to base station – later • Supports garbage collection • Accounts for limited number of writes to flash (e.g., 10,000 writes) • Wear leveling • API – open(); read(); write(); delete()

37. Content Distribution Mobile Monitoring Agents Information (Quality dimensions: Refresh Rate, Accuracy, …) Time Ad hoc Wireless Sensor Network Must be Minimized Energy (Computation, Communication) Environmental Measurements

38. Content Dissemination Receivers (Refresh Rate, Accuracy) Goal: Find the optimal communication path to send sensory data from a monitored source to multiple mobile sinks such that energy usage is minimized and requirements are met. Data replicas (Placement?) Information source (Aperiodic or Periodic updates)

39. Applications • Soldiers with PDA monitoring for chemical contamination • Note: • Current: 1 to n • Multiple sinks and multiple sources are possible

40. Issues • Building the dissemination tree • Maintaining it as nodes enter/leave • Disseminating the data • Maintaining linkage to mobile sinks • Save energy!!! • Meet end-to-end delay • Meet refresh rate Self-Organizing

41. Dissemination Trees  Unicast (Geographic Forwarding) Dense sensor network

42. Dissemination Trees   Unicast (Geographic Forwarding) Minimum Spanning Tree Dense sensor network

43. Dissemination Trees    Unicast (Geographic Forwarding) Steiner Tree Minimum Spanning Tree Steiner Point: replicas Dense sensor network

44. Sending the Data Regular Multicast Steiner Tree R R Steiner Point R R R Update rate = R

45. Sending the Data Asynchronous Multicast Regular Multicast r1, r2, r3, r4 are receiver refresh rates Steiner Tree Weighted Steiner Tree r4 R r3 r4 Weighted Steiner Tree ? r2 r3 R r2 Steiner Point R r1 r2 r1 R R Caching Steiner points r4 R Update rate = R Update rate = R

46. SEAD: Scalable Energy Efficient Asynchronous Dissemination Protocol • An asynchronous content distribution multicast tree is maintained • Tree is modified when • a sink joins • a sink leaves • a sink moves beyond some threshold • Cost of building tree is minimized Mobile node Access node Forwarding chain r4 Dissemination to Mobile Sinks r3 r2 r1 r2 Caching Steiner points r4

47. Assumptions • WSN is static and then mobile nodes (e.g., PDAs) enter the network • Dissemination trees are among the static nodes • Important – mobile nodes are NOT part of the tree • SEAD works with an overlay network • Source, sink representatives (access points) and Steiner points (see previous slide)

48. 4 Phases • First - Subscription Query • Mobile node attaches to nearest node as access point • Access node sends join query to source

49. Subscription Query (1) Sink 1 (access node) Sink 2 (access node) Source

50. 4 Phases • Subscription Query • Mobile node attaches to nearest node as access point • Access node sends join query to source • Second -Gate replica search • Attach new node on current tree at best gate replica