Design modeling and capacity planning for micro solar power sensor networks
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Design, Modeling, and Capacity Planning for Micro-Solar Power Sensor Networks. Jay Taneja , JaeinJeong , and David Culler Computer Science Division, UC Berkeley IPSN/SPOTS 2008 Presenter: SY. Outline. Introduction Micro-Solar Planning Model And System Design Node And Network Design

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Design modeling and capacity planning for micro solar power sensor networks

Design, Modeling, and Capacity Planning for Micro-Solar Power Sensor Networks

Jay Taneja, JaeinJeong, and David Culler

Computer Science Division, UC Berkeley

IPSN/SPOTS 2008

Presenter: SY


Outline
Outline Power Sensor Networks

  • Introduction

  • Micro-Solar Planning Model And System Design

  • Node And Network Design

  • Evaluation

  • Conclusion


Motivation
Motivation Power Sensor Networks

  • They have a project – HydroWatch

    • Study hydrological cycles in forest watersheds

    • Sense temperature, humidity, and light

    • Forest environment

  • Want to design a device

    • Sense and transfer data

    • Solar powered

      • Infinite power lifetime


About this paper
About This Paper Power Sensor Networks

  • Show how they develop the micro-solar power subsystem -- systematically

    • Modeling

    • Design

    • Evaluation

  • System design experience sharing

  • Real deployment evaluation


The challenges
The Challenges Power Sensor Networks

  • Capacity Planning

    • Infinite power lifetime

  • Mechanical Design

    • Weatherproof with Correctly Exposed Sensors

  • Incorporating off-the-shelf and custom-built pieces


Outline1
Outline Power Sensor Networks

  • Introduction

  • Micro-Solar Planning Model And System Design

  • Node And Network Design

  • Evaluation

  • Conclusion


Micro solar planning model

72:1 Power Sensor Networks

Micro-Solar Planning Model

Storage Charge-Discharge

1:1

E in : E out

All Ideal Components

48:1

240:1

120:1

Regulator Efficiencies

Half Hour of Exposure Per Day

60%

50%

2%

66%


Application load
Application Load Power Sensor Networks

  • Starting point for capacity planning

  • Most time is spent sleeping (~20 uA) with short active periods (~20 mA)


Energy storage
Energy Storage Power Sensor Networks

Straightforward charging logic


Solar panel
Solar Panel Power Sensor Networks

  • Solar cells composition

    • In serial and parallel

  • The panel characterized by its IV curve

    • Open-circuit voltage, short-circuit current, and maximum power point


Solar panel1
Solar Power Sensor NetworksPanel

  • Important parameters

    • IV and PV Curves

    • Physical Dimensions

MPP: 3.11 Volts

They choose – Silicon Solar #16530(4V-100mA)


Regulators
Regulators Power Sensor Networks

  • Regulators are “glue” matching primary components

  • 50-70% efficiency for typical sensornet load range

  • Input regulator

    • Regulates voltage from solar panel to battery

    • Can be obviated by matching panel directly to storage

  • Output Regulator

    • Regulates mote voltage

    • Provides stability for sensor readings

Model estimates that load requires 28 minutes of sunlight


Outline2
Outline Power Sensor Networks

  • Introduction

  • Micro-Solar Planning Model And System Design

  • Node And Network Design

  • Evaluation

  • Conclusion


Hydrowatch weather node
HydroWatch Power Sensor Networks Weather Node


Mechanical considerations
Mechanical Considerations Power Sensor Networks

  • Enclosure design is often application-driven

    • Sensor exposure

    • Waterproofing

    • Ease-of-Deployment

    • RF in forest

    • Internal mechanicals

Temp / RH Sensor

TSR, PAR Sensors


Network architecture
Network Architecture Power Sensor Networks

Used Arch Rock Primer Pack for multi-hop network stack, database for stored readings, and web-based network health diagnosis


Forest deployment
Forest Deployment Power Sensor Networks


Outline3
Outline Power Sensor Networks

  • Introduction

  • Micro-Solar Planning Model And System Design

  • Node And Network Design

  • Evaluation

  • Conclusion


The urban neighborhood
The Urban Neighborhood Power Sensor Networks

  • 20 Nodes for 5 Days

  • Mounted on house, around trees, and on roof

  • Meant to emulate forest floor conditions

  • Important for systematic approach -- provided validation of model


Urban neighborhood energy harvested
Urban Neighborhood Energy Harvested Power Sensor Networks

Every node received enough sunlight


Three nodes three solar inputs
Three Nodes, Three Solar Inputs Power Sensor Networks


The forest watershed
The Forest Watershed Power Sensor Networks

  • 19 Nodes for over a Month

  • Mounted on 4-ft stakes throughout the area


Forest watershed site
Forest Watershed Site Power Sensor Networks


Forest watershed energy harvested
Forest Watershed Energy Harvested Power Sensor Networks

Watershed

Most nodes struggle to harvest sunlight


Three nodes at the watershed
Three Nodes at the Watershed Power Sensor Networks


Reflected light
Reflected Light Power Sensor Networks

Sunny

Overcast

Overcast

Sunny

Though only minimally, a cloudy day helps a sun-starved node harvest solar energy.


Conclusion
Conclusion Power Sensor Networks

  • Always surprises in real environment

  • Reliability is important real application

    • But difficult to achieve

  • In their work

    • Systematic approach resulted in 97% collection of an unprecedented spatiotemporal data set

    • System design experience sharing