Sensor Networks: Implications for Database Systems and ViceVersa

1
Sensor Networks Implications for Database
SystemsandVice-Versa

• UCB Sensor Day

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
3
Why 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.

4
Declarative 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 400
• EPOCH DURATION 1s

5
Aggregation Query Example
Count the number occupied nests in each loud
region of the island.
SELECT region, CNT(occupied)
AVG(sound) FROM sensors GROUP BY region HAVING
AVG(sound) 200 EPOCH DURATION 10s
6
In Network Aggregation Example Benefits

• 2500 Nodes
• 50x50 Grid
• Depth 10
• Neighbors 20

7
Telegraph 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
8
One 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
9
Another 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
10
One 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
11
HiFi 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.