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UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science

Re-thinking Data Management for Storage-Centric Sensor Networks

Deepak GanesanUniversity of Massachusetts Amherst

With: Yanlei Diao, Gaurav Mathur, Prashant Shenoy

2UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer ScienceUUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science

Sensor Network Data ManagementLive Data Management: Queries on current or recent data.

Applications: Real-time feeds/queries: Weather, Fire, VolcanoDetection and Notification: Intruder, Vehicle

Techniques:Push-down Filters/Triggers: TinyDB, Cougar, Diffusion, …Acquisitional Query Processing: BBQ, PRESTO, …

Archival Data Management: Querying or Mining of past data

Applications:Scientific Analysis of past events: Weather, Seismic, …Historical trends: Traffic analysis, habitat monitoringOur focus is on designing an efficient archival data management architecture for

sensor networks


Archival Querying in Sensor Networks

Data Gathering with centralized archival query processing

Efficient for low rate, small volume sensors such as weather sensors (temp, humidity, …).

Inefficient energy-wise for “rich” sensor data (acoustic, video, high-rate vibration).

Lossless aggregation

DBMS

Internet

Gateway


Archival Querying in Sensor Networks

Acoustic stream

Store data locally at sensors and push queries into the sensor network

Flash memory energy-efficiency, cost, capacity.

Limited capabilities of sensor platforms.

Internet

Gateway

Image stream

Flash Memory

Push query to sensors


Technology Trends in Storage

Generation of Sensor Platform

CC1000

CC2420

Telos STM NOR

Atmel NOR

Communication

Storage

Micron NAND 128MB

Energy Cost

(uJ/byte)


Outline

Case for Storage-centric Sensor Networks

Challenges in a Storage-centric Sensor

Database

StonesDB Architecture

Local Database Architecture

Distributed Database Architecture

Conclusion


Optimize for Flash and RAM Constraints

Flash Memory ConstraintsData cannot be over-written, only erasedPages can often only be erased in blocks (16-64KB)Unlike magnetic disks, cannot modify in-place

Challenges:Memory: Minimize use of memory for flash database.Energy: Organize data on flash to minimize read/write/erase operationsAging: Need to efficiently delete old data items when storage is insufficient.

1. 1. Load block 2. Into Memory

3. Save block back

Eraseblock

Memory

2. Modify in-memory

~16-64 KB

~4-10 KB


SQL-style Queries: Min, max, count, average, median, top-k,

contour, track, etc

Similarity Search: Was a bird matching signature S observed last

week?

Classification Queries: What type of vehicles (truck, car, tank, …) were observed in the

field in the last month?

Wireless Sensor Network

Support Rich Archival Querying Capability

Signal Processing: Perform an FFT to find the mode of vibration signal between time

<t1,t2>?


StonesDB Goals

Our goal is to design a distributed sensor database for archival data management that:

Supports energy-efficient sensor data storage, indexing, and aging by optimizing for flash memories.

Supports energy-efficient processing of SQL-type queries, as well as data mining and search queries.

Is configurable to heterogeneous sensor platforms with different memory and processing constraints.




Example: Indexing in StonesDB

Naïve Design:Consider a value-based index on entire streamDeletion/Aging of data triggers in-place updates involving energy-intensive block read/write/erase operations.


Indexed Storage

StonesDB Design:Split data stream into partitions and build index on each partition. Age partitions as a whole cheaply.

Flash

Block


Outline

Case for Storage-centric Sensor Networks

Challenges in a Storage-centric Sensor

Database


Local Database Architecture

Distributed Database Architecture

Conclusion


StonesDB: Data Mining Queries

Similarity Search: Was a bird matching signature S observed

last week?

Proxy Cache of Image Summaries


StonesDB: System Operation

Similarity Search: Was a bird matching signature S observed

last week?

Query Engine

Partitioned Access Methods


Research Issues

Local Database LayerImpact of RAM limitations on storage organizationEnergy-optimized indexing and aging. New cost models for self-tuning energy-efficient sensor databases.

Distributed Database LayerIntelligent split of query processing between proxy and sensor tiersAdaptively tuning quality of data cached at sensor proxy based on query needs

UUNIVERSITY OF NIVERSITY OF MMASSACHUSETTSASSACHUSETTS, A, AMHERST • MHERST • Department of Computer Science Department of Computer Science

The End

STONES: STOrage-centric Networked Embedded Systems

http://sensors.cs.umass.edu/projects/stones


Sensor Data Management Taxonomy

Timeline vs Prior KnowledgeQ

uery

ing

Min

ing

Current Recent Past

Acquisitional Query Processing

(BBQ, …)

Pushdown Filters(TinyDB, Cougar, …)

Timeline of data being processed

Search/Mining on Archived Sensor Data

Type of data processing


Technology Trends in Sensor Platforms

Cyclops Camera+ Mica2 Mote128 x 128 resolution images4 KB RAM, 10 MHz microcontroller

OmniVision Camera + iMote2128 x 128 resolution images64KB - 32MB RAM, 10 MHz microcontroller

Spectrum of sensing devices with different power, capability, resource constraints.

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