Title: Sensor Networks: Implications for Database Systems and Vice-Versa
1Sensor Networks Implications for Database
SystemsandVice-Versa
Michael Franklin January 2004 http//www.cs.berke
ley.edu/franklin
2- Query-based interface to sensor networks
- Developed on TinyOS/Motes
- Benefits
- Ease of programming and retasking
- Extensible aggregation framework
- Power-sensitive optimization and adaptivity
- Sam Madden (Ph.D. Thesis) in collaboration with
Wei Hong (Intel) and guidance (?) from Franklin
and Hellerstein.
http//telegraph.cs.berkeley.edu/tinydb
3Why Database Queries?
- Declarative, Set-based approach.
- Programmer productivity.
- Robustness to change.
- Let the system manage efficiency.
- Semantics and High-level operators.
- Framework for correctness criteria.
- Pushing semantics down enables smarter
implementations, code re-use. - Natural mapping of dataflow processing.
- Query plans are networks of operators.
- Query/Data duality enables intelligent routing.
4Declarative Queries in Sensor Nets
- Many sensor network applications can be described
using query language primitives. - Potential for tremendous reductions in
development and debugging effort.
Report the light intensities of the bright
nests.
Sensors
- SELECT nestNo, light
- FROM sensors
- WHERE light gt 400
- EPOCH DURATION 1s
Epoch nestNo Light Temp Accel Sound
0 1 455 x x x
0 2 389 x x x
1 1 422 x x x
1 2 405 x x x
Epoch nestNo Light Temp Accel Sound
0 1 455 x x x
0 2 389 x x x
5Aggregation Query Example
Count the number occupied nests in each loud
region of the island.
Epoch region CNT() AVG()
0 North 3 360
0 South 3 520
1 North 3 370
1 South 3 520
SELECT region, CNT(occupied)
AVG(sound) FROM sensors GROUP BY region HAVING
AVG(sound) gt 200 EPOCH DURATION 10s
6In Network Aggregation Example Benefits
- 2500 Nodes
- 50x50 Grid
- Depth 10
- Neighbors 20
7Telegraph Monitoring Data Streams
- Streaming Data
- Network monitors
- Sensor Networks
- News feeds
- Stock tickers
- B2B and Enterprise apps
- Supply-Chain, CRM, RFID
- Trade Reconciliation, Order Processing etc.
- (Quasi) real-time flow of events and data
- Must manage these flows to drive business (and
other) processes. - Can mine flows to create and adjust business
rules. - Can also tap into flows for on-line analysis.
http//telegraph.cs.berkeley.edu
8One View of the Design Space
Archiving (provenance and schema evolution)
Filtering,Cleaning,Alerts
Monitoring, Time-series
Data mining (recent history)
Combined Stream/Disk Processing
On-the-fly processing
Disk-based processing
9Another View of the Design Space
Archiving (provenance and schema evolution)
Filtering,Cleaning,Alerts
Monitoring, Time-series
Data mining (recent history)
Central Office
Regional Centers
Several Readers
10One More View of the Design Space
Archiving (provenance and schema evolution)
Filtering,Cleaning,Alerts
Monitoring, Time-series
Data mining (recent history)
Dup Elim history hrs
Interesting Events history days
Trends/Archive history years
11HiFi Systems
- High Fan-In, globally-distributed architecture
- Think RFID-enabled supply chain/logistics
- Telegraph-like nodes internal to the network
- TinyDB-like sensor networks at the edges
- Large data volumes generated at edges
- Successive aggregation as you move into the
center - Strong spatio-temporal focus
- Would love to talk with people who have
applications that might need this kind of
infrastructure.