1. Distributing Queries Over Low Power Sensor Networks
Sam Madden, Robert Szewczyk, Michael Franklin, Wei Hong, Joe Hellerstein, and David Culler
Focus: Hierarchical Aggregation
Queries and Sensor Networks
Query processing a core in-network service
Networking researchers buy this
Collaborations at Berkeley, MIT, UCLA, Intel, ICIR
More DB folks should play in this space!
adaptive query processing
language and semantic issues
approximate/online algorithms
Integration of sensornets with the rest of the world’s information
Telegraph
Fundamental Questions
Architectural boundaries between query processing and other network layers?
Synergies between NW/DB ideas/outlooks/skills?
Wireless Sensor Networks
Palm Devices Linux
Aggregation natural in sensornets
The “big picture” typically interesting
Aggregation can smooth noise and loss
UDAs to do signal processing
Provides data reduction
Power/Network Reduction: in-network aggregation
Hierarchical version of parallel aggregation
Tricky design space
Metrics: power cost and answer quality
Variables: topology-selection, value-routing scheme, other tricks
Dynamic environment requires adaptive schemes
Smart Dust Motes TinyOS
Query
Simulation
A spectrum of devices
Varying degrees of power and network constraints
This demo: Mica and TinyOS
Focus on many/tiny
Toward MEMS “Smart Dust”
Off-the-shelf HW for now: Berkeley Mica Mote
Wireless, single-ported, ad-hoc network
Spanning-tree communication through “root”
Performance in Tiny SensorNets
A Query Language for Sensors
Aggregation and NW Optimization
Power consumption
Communication >> Computation
METRIC: radio wake time
Send > Receive
METRIC: messages generated
Bandwidth Constraints
Internal >> External
Volume >> surface area
Result Quality
Noisy sensors
Discrete sampling of continuous phenomena
Lossy communication channel
Continuous queries with streaming, periodic results
UDAs and UDFs
Currently compiled-in
Mote Virtual Machine (Mate) under development
Periodic nature allows for:
Scheduling of communication and sleep
Simple semantics for combining multi-hop readings
Clearly other alternatives here
E.g. sequence/timeseries/temporal query languages
An expanded taxonomy of aggregates
State
Duplicate sensitivity
Montonicity
Exemplary vs. Summary
Effects on
Value Routing
Snooping and Suppression
Caching and Presumption
Hypothesis Testing
Collapsing of the NW and QP layers!
SELECT <aggs>, <attrs> WHERE <preds> GROUP BY <expr> HAVING <preds> EPOCH DURATION <constant>
TinyDB Software On Motes
SensorNets: A Classic QP Problem!
Typical networking abstractions are inappropriate
Querying, not addressing
Don’t want to connect to a port at node ID 0x6f4623b
Want to know “what’s going on” in the sensed environment
Data, not packets
Minimize communication by processing content in-network
Data independence required!
Physical layer usually volatile, often irrelevant
Apps need to be robust to changes
Apps need a high-level language for data collection
Echoes the demise of the Network Data Model
Time to revisit these issues in the network itself
Network-Aware Aggregation
New networking regime for a query processor
Parallel/distributed DBMS assumed reliable bus network abstractions
We have an unreliable cellular network
Need to integrate NW understanding with query processor
Examples:
Value Routing Schemes
Tuple-forwarding vs. Value flow routing
Snooping and Suppression
Caching and Presumption
Hypothesis Testing
4200 lines of C Code
Runs on Mica Motes with light and temperature sensors, magnetometers and accelerometers
4Mhz Atmel Processor
4KB RAM, 40kBit radio, 512K EEPROM, 128K Flash
Ad-hoc queries
Java UI
Split-pane display
Topology visualization
Applications
Environmental, military
NW Monitoring!