Title: UltraLow Power Data Storage for Sensors
1Ultra-Low Power Data Storage for Sensors Gaurav
Mathur Peter Desnoyers Deepak Ganesan Prashant
Shenoy IPSN'06
2Motivation
- WSNs
- Local storage is required in a lot of WSN
applications - Sensor nodes have limited capabilities
- Current storage subsystems on sensor network
platforms do not exploit technology trends
- Flash memories
- High capacity
- Energy efficiency
- Low price
GOAL Minimize energy consumption
Communication VS Computation VS Storage Is there
any energy-efficient platform for Sensor
Networks? What is the relation between storage
and communication energy cost?
3Overview and Contributions
- Examine available flash-based storage options for
sensor platforms and find the most
energy-efficient for sensor networks - Parallel NAND flash
- Compare storage energy cost with communication
cost - Storage cost can be two orders of magitude less
than communication cost - Examine implications for Sensor Network design,
evaluate impact and quantify energy reduction
achieved sensor network services - Reduction of one order of magitude for
communication, data aggregation, and lower cost
for localization.
4Questions?
5Introduction
- Wireless Sensor Networks area of significant
research - Limited resources
- Optimizations are needed to ensure long lifetime
- Computation vs Communication trade-off
influenced - Algorithm design
- Sensor network platform design
6What about storage?
- Storage subsystem has undergone little change
- Mica motes provide limited storage (lt1MB)
- Energy cost equivalent to or greater than that
of communication. - What is the cost of storage?
- Is it rational to use in-network storage
techniques? - If storage consumes more energy than
communication -gt focus on centralized
data-collection systems - If communication requires more energy than
storage then storage techniques should be
exploited.
7Understanding trade-offs
- What is the most energy-efficient storage
platform for sensor devices ? - How does the energy cost of storage compare to
that of computation and communication ? - What are the implications of an ultra-low power
storage subsystem on sensor net design ?
8Methodology
- Find which is the most energy-efficient and
flash-based storage device for sensor networks - Measure active and sleep-mode energy consumption
- Compare communication, computation and storage
costs - Examine energy reduction for energy-efficient
local storage for services - Communication
- Data aggregation
9Flash Memory
- Flash memory is suitable for Sensor Network
devices - low energy consumption
- ultra-low idle current
- high capacity
- Available as component for circuit assembly or as
standardized removable device (MMC, SD). - Interfaces for flash devices
- Serial transfering one bit at a time
- Parallel transfering one byte at a time (8 bits)
10Flash Memory contd.
- MMCs
- translate the parallel NAND interface to serial.
- microcontroller for erasure, page remapping,
ECC, wear leveling, - ()simplifies system design
- (-) increases power consumption due to the
additional internal circuitry. - Surface-mount NAND devices
- ()Eliminate above overhead
- Memory managment performed in SW or HW
11Evaluation of Flash Devices
- Serial NOR
- Atmel 512KB used on Mica motes and STM TelosB
- MMC
- Designed and fabricated MMC adapter for Mica
series - Drivers in TinyOS
- Tested four MMC devices and report the results
for the best performing one (Hitachi MMC) - NAND flash
- Designed and built parallel NAND flash board
- Drivers in TinyOS
- Devices Toshiba, Micron
12Evaluation of Flash Devices
- Measure power consumption in
- active mode when performing reads/writes/erasur
es - sleep mode current drawn by the device in its
lowest power - Measurement of all devices was performed on a
Mica2 mote.
13Evaluation of Flash Devices - Results
14Evaluation of Flash Devices - Results
- Total Energy Consumption for Flash and CPU
- Perform ECC for NAND flash in software
- Four times the energy consumption of the flash
- Special-purpose hardware can reduce overhead
- Data transfer cost from RAM to Flash (software
implementation) - Can be reduced using hardware support(access to
SPI port, DMA controller)
15Evaluation of Flash Devices - Summary
- Parallel NAND flash is 21 times more efficient
than Telos STM flash and 407 times Mica2 Atmel
flash. - MMC is based on NAND technology BUT internal
microcontroller increases idle current as well as
energy consumption for reads/writes/erasures - Byte-wide interface of parallel NAND
- () significantly faster, more efficient than
bit-serial ones. - (-) supporting large number of I/O
pins of parallel interface may be difficult on
low power embedded systems. - An ideal storage solution
- combine the performance of the parallel NAND
flash with the lower pin count of serial
interfaces. - Platform-level optimizations needed
- Hardware support for data transfer reduce ECC
overheads
16Comparison of computation, communication and
storage costs
- Research in wireless sensor systems was focused
on the trade-off between computation and
communication. Storage was ignored. - Computation lt storage ltlt communication
- Challenges conventional wisdom of trade-offs
- Example Seismic monitoring application
- optimized to last for two years running on the
MicaZ. - generates 512 bytes of data/sec, stores them on
NAND flash storage, - The lifetime of each sensor would reduce by
only 6 weeks, having stored 28GB of data.
17Impact of Storage in WSN Applications
- In-network Query Processing
- Existing sensor network deployments
- centralized data collection for query processing
- In-network storage not exploited
- With parallel NAND flash
- Higher degree of local data archival and indexing
for in-network query mechanisms - Use of history for efficient network-level
compression - More history More accurate models
- Network-level Compression
- In-network data aggregation schemes rely on
hashtables to perform duplicate packet
suppression. - Hashtables (too large for RAM), can be
constructed on flash storage. - Flash-based data management schemes (MicroHash)
- Custody Transfer for Delay Tolerant Networks
18Impact of Flash Storage on Communication Costs
- GOAL Minimize the number of times the radio is
power cycled (radio startup and shutdown costs
are high!) - BMAC uses a per-packet preamble
- high per-packet transmission cost
- Efficient local storage allows
- usage of simple batching mechanisms
- amortize radio startup and shutdown energy costs
over a larger number of data bytes. - BUT increased latency of data collection
- Smaller percentage of duty cycling means larger
preamble - Batching Approach reduces communication energy
costs up to 58x
19Impact of Flash Storage on Data Aggregation
- High-capacity energy-efficient local storage
allows - larger amounts of data to be accumulated and
compressed at once - more efficient compression leading to lower
transmission costs. - Lossless Compression (Huffman encoding)
computationally expensive, better when amount of
data increases - Lossy Compression low computational cost, high
compression ratio - Feature Extraction or Event Detection low
complexity
Assume 60N common computational complexity for
all Schemes
20Conclusions
- Parallel NAND flash the most energy efficient
storage device for sensor networks. - 100-fold more energy efficient than serial NOR
flash - Storage energy reduction motivates re-examination
of computation-communication-storage trade-offs, - Storage cost is two orders of magnitude less
than for communication - Evaluate impact on Sensor Network design
- One order of magitude reduction for communication
and data aggregation.