Evaluation of body sensor network platforms
This presentation is the property of its rightful owner.
Sponsored Links
1 / 26

Evaluation of Body Sensor Network Platforms PowerPoint PPT Presentation


  • 67 Views
  • Uploaded on
  • Presentation posted in: General

Evaluation of Body Sensor Network Platforms. A Design Space and Benchmarking Analysis. Sidharth Nabar , Radha Poovendran Network Security Lab (NSL), University of Washington, Seattle. Ayan Banerjee , Sandeep K.S. Gupta IMPACT Lab, Arizona State University. Outline.

Download Presentation

Evaluation of Body Sensor Network Platforms

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Evaluation of body sensor network platforms

Evaluation of Body Sensor Network Platforms

A Design Space and Benchmarking Analysis

SidharthNabar,RadhaPoovendran

Network Security Lab (NSL),

University of Washington, Seattle

Ayan Banerjee, Sandeep K.S. Gupta

IMPACT Lab,

Arizona State University


Outline

Outline

  • Background and Motivation

  • Proposed Evaluation Framework

    • Design Space Determination

      • Design Coordinates, Metrics and Benchmarking

    • Design Space Exploration

  • Case Study

  • Conclusion and Future Work


Evaluation of body sensor network platforms

Temperature Tele-sensor

@ Oak Ridge National Lab

Nano-scale Blood Glucose level detector @ UIUC

L:ifeshirt @ Vivometrics

Pervasive Health Monitoring

  • Services Enabled

  • Continuous, remote patient monitoring: No time & space restrictions

  • Utilize wearable and in-vivo medical sensors

  • Reduced medical errors

  • Early detection of ailments and actuation through automated health data analysis

Components Used

Miniature sensors, gateway device

Applications

Computer Assisted

Rehabilitation

Medical Facility

Management

Sports Health

Management

Home-based care

Principal enabling technology:

Body Sensor Networks (BSNs)


Bsn platforms

BSN Platforms

Imote2

BSN node v3

TelosB

Shimmer

Radio – CC2420 + Surface mount antenna

Processor –

Intel Xscale

Size – 36mm x 48mm x 9mm

Radio – CC2420 +

Inverted-F antenna

Processor –

MSP430

Size – 65mm x 31mm x 6mm

Radio – CC2420+ miniaturized chip antenna

Processor – MSP430

Size – 26mm x 16mm x 2mm

Radio – CC2420 (802.15.4) + RN-42 (Bluetooth)

Processor – MSP430

Size – 53mm x 32mm x 15mm

Diversity in available platforms- How to choose?

Components: Microprocessor, radio, onboard memory, power supply interface, etc.

Applications: Used for BSN research experiments and clinical trials.


Job hiring example

Job Hiring Example

EMPLOYER

CANDIDATES

  • Knows job requirements

  • Defines candidate qualifications

  • Reviews multiple candidates

  • Checks performance

  • Selects most suitable candidate

  • Provide resume

  • Understand market requirements and peer competition

  • Acquire new skills to improve

Standard evaluation method and well-understood performance metrics enable candidate selection


Bsn platform selection

BSN Platform Selection

BSN PLATFORM USER

PLATFORM DESIGNERS

  • Provide datasheet

  • Need to improve design based on new, emerging applications

  • Need to objectively compare performance of multiple platforms

  • Uses BSN platforms for research or clinical trials

  • Knows application requirements

  • Need to map to platform specifications

  • Need to quantify platform performance

Lack of standard evaluation method and

performance metrics


Main idea

Main Idea

Application Requirements

Available Platforms

Common set of parameters

Desirable parameter values

Select platform satisfying most/all constraints


Research challenges

Research Challenges

  • Mapping diverse platforms to common evaluation ground

    • Design Coordinate: A feature of the BSN platform that determines its performance, e.g. Available Memory.

    • Design Space: The space defined by the design coordinates

  • Quantify design coordinates, performance parameters

    • Evaluation Metrics, e.g. kB of RAM, W of power consumed

  • Measure performance in real application scenarios

    • Develop benchmarks based on BSN applications

  • Search design space for suitable platform


Goal and contributions

Goal and Contributions

  • Goal: Evaluation Framework for BSN platforms

  • Contributions:

    • Identify design coordinates for BSN platforms

    • Map a given platform to a point in the design space:

      • Use metrics to quantify design coordinates, definetwonew metricsfor BSN platforms.

      • Develop BSN-specific benchmarking suite: BSNBench

    • Provide a method to search the design space


Proposed evaluation framework

Proposed Evaluation Framework

Set of BSN Platforms

Application Requirements

DESIGN SPACE DETERMINATION

PROPOSED EVALUATION FRAMEWORK

DESIGN

COORDINATES

EVALUATION METRICS

BSNBENCH, DATASHEET, MODELS

DESIGN SPACE EXPLORATION

SET OF APPLICATION REQUIREMENTS

CONSTRAINTS ON DESIGN COORDINATES

ELIMINATE PLATFORMS VIOLATING CONSTRAINTS

Most suitable BSN Platform for application


Example design space determination

Example:Design Space Determination

P1

Set of available platforms

P2

P3

P4

Design

Coordinates

Average Radio Power Consumption

Form Factor

On-board data memory

mW

Volume (mm3 )

kBof RAM

Metrics

BSNBench

Datasheet

Datasheet

Evaluation

Method


Example design space exploration

Example:Design Space Exploration

Average Radio Power Consumption (mW)

Form Factor (mm3)

P1

P2

P4

P3

Design coordinate axis

RAM Availability (KB)

Constraints on design coordinates

Sensor platforms

Appropriate region in the design space


Design coordinates

Design Coordinates

(Processor, data and program memory, signal processing capability)

COMPUTATION

(Radio reliability, average power consumption, interoperability)

WIRELESS COMMUNICATION

DESIGN COORDINATES

(Battery, energy scavenging support)

ENERGY SOURCE

(Form factor, thermal safety, sensor integration)

PHYSICAL ASPECTS

Based on typical BSN application requirements

Decompose platform functionality into individual modules


Evaluation metrics

Evaluation Metrics

[1] A. Natarajan, B. Silva, K. Yap, and M. Motani. To hop or not to hop: Network architecture for body sensor networks. In IEEE SECON, 2009.

  • Use suitable metrics to quantify design coordinates:

  • Some traditional metrics are independent of the target applications. e.g. MIPS, MIPS/W

  • Consider BSN application characteristicsto develop more suitable metrics.

    • For example, processor speed measured in units of samples processed per second


Spsw metric for processor

SPSW Metric for Processor

(No. of Samples Processed)

SPSW =

(Time taken) (Power consumed)

  • SPSW (Samples Processed per Second per Watt) is defined as:

  • Captures tradeoff between processor speed and power consumption.

  • “Processing a sample” is application-specific.

  • For example, a platform motes calculates mean of 1000 data samples in 100 ms and consume 25 mWpower. Then,

    SPSW = 1000/(100 X 25) = 400 samples/mJ


Epc metric for radio

EPC Metric for Radio

Fraction of time in state S *

Power consumed in state S

EPC =

all states

  • Radio power consumption depends on radio specifications as well as duty cycle.

  • EPC (Expected Power Consumption) is defined as:

  • For example, if radio transmits for 5% time, with power draw 10 mW and is in SLEEP state for remaining 95% time, with power 0.1 mW,

    EPC = 0.05 * 10 + 0.95 * 0.1 = 0.595 mW


Bsnbench a bsn specific benchmark

BSNBench: A BSN-specific benchmark

  • Key Observation: In spite of diversity in BSN applications, some basic tasks are common.

  • Type of benchmark: Microbenchmark

  • Composition:

    • Data Operations (Statistics, Differential Encoding)

    • Signal Processing (FFT, Peak detection)

    • Radio Communication (Duty-cycled handshake)

    • Sensor Interface (Sensed Data Query)

  • Implemented in TinyOS 2.0


Evaluation framework workflow

Evaluation Framework Workflow

DESIGN SPACE DETERMINATION

DESIGN

COORDINATES

EVALUATION METRICS

BSNBENCH, DATASHEET, MODELS

DESIGN SPACE EXPLORATION

CONSTRAINTS ON DESIGN COORDINATES

ELIMINATE

PLATFORMS VIOLATING CONSTRAINTS

PRIORITIZE DESIGN COORDINATES


Design space exploration

Design Space Exploration

Constraints on Design Coordinates

Application Requirements

Define subspace of

design space

Prioritize design coordinates

Set of suitable platforms

Identify most suitable platform


Case study

Case Study

  • We consider two typical BSN applications:

  • Continuous Glucose Monitoring (CGM):

    • Long term monitoring application

    • Sensor measures the blood glucose level and transmits this data to a gateway device.

  • Epileptic Seizure Detection (ESD):

    • Detect onset of epileptic seizures using an ECG sensor.

    • Perform peak detection on ECG signal to calculate RR intervals.

    • Intervals are converted to FFT coefficients and sent to the gateway device.


Case study cgm

Case Study: CGM

iMote2

35.62 mW

Mica2

EPC (W)

BSN node v3

TelosB

(50 X 50 X 50)

Form Factor (mm3)

Set of platforms: TelosB, Mica2, Imote2 and BSN v3

Constraints on EPC and Form Factor coordinates


Case study esd

Case Study: ESD

Imote2

BSN node v3

TelosB

256 - point FFT

Available RAM

Mica2

128 – point FFT

0.7

On-body PDR

Constraints on signal processing capability and communication reliability (on-body PDR)


Conclusion and future work

Conclusion and Future Work

  • Conclusion

    • Proposed design space approach for evaluation framework

    • Identified design coordinates for BSN platforms

    • Developed novel, BSN-specific metrics

    • Proposed benchmarking suite for BSNs

  • Future work

    • Extend BSNBench by including data privacy tasks

    • Complex objective functions in design space exploration.

    • Extend set of design coordinates: fault tolerance, etc.

    • Explore metrics for human centric evaluation


Thank you

Thank You!

Questions and Comments?


Bsnbench composition

BSNBench: Composition


References

References

[1] http://ubimon.doc.ic.ac.uk/bsn/a1875.html

[2] K. Lorincz, B. Kuris, S. Ayer, S. Patel, P. Bonato, and M. Welsh. Wearable wireless sensor network to assess clinical status in patients with neurological disorders. In Proceedings of the 6th international conference on Information processing in sensor networks. ACM, 2007.

[3] C. Park, J. Liu, and P. Chou. Eco: an ultra-compact low-power wireless sensor node for real-time motion monitoring. In IPSN 2005., pages 398–403.

[4] M. Hempstead, M. Welsh, and D. Brooks. TinyBench: The case for a standardized benchmark suite for TinyOS based wireless sensor network devices. 2004.

[5] L. Nazhandali, M. Minuth, and T. Austin. SenseBench: toward an accurate evaluation of sensor network processors. In Workload Characterization Symposium, 2005. Proceedings of the IEEE International, pages 197–203

[6] A. Natarajan, B. Silva, K. Yap, and M. Motani. To hop or not to hop: Network architecture for body sensor networks. In IEEE SECON, 2009.


  • Login