A Sensor Data Gathering Framework for Agricultural-Fields: - - PowerPoint PPT Presentation

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A Sensor Data Gathering Framework for Agricultural-Fields: - - PowerPoint PPT Presentation

Dec 25, 2023 20 likes •283 views

A Sensor Data Gathering Framework for Agricultural-Fields: Implementation and Experiment Report Hideya Ochiai The University of Tokyo / NICT, JAPAN 1 Background Weather stations at remote sites How can we collect sensor data ?

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A Sensor Data Gathering Framework for Agricultural-Fields: Implementation and Experiment Report

Hideya Ochiai The University of Tokyo / NICT, JAPAN

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Background

  • Weather stations at remote sites
  • How can we collect sensor data ?

引用: http://www.twin.ne.jp/~saineria/flowerknowledge/flowerknowledge01.html 引用: http://www.edu.city.yokohama.jp/sch/es/hie/6-1ho-mu/page1.htm

Weather Station Mechanical observation

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<< Background >>

By making use of PHS or 3G Network

Agricultural Field

PHS Access Point

PHS PHS PHS PHS PHS

○ Easy to deploy × Communication fee for carrier □ Real-time is not always important Real-time data collection from remote sites

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<< Proposal >> DTN-Based Data Gathering

Agricultural Field

WiFi WiFi WiFi WiFi WiFi WiFi WiFi WiFi

Use the movement of truck as a network carrier ○ Easy to deploy ○ No communication fee for carrier △ Delivery delay is physically bound to the movement of the truck

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What is DTN ???

  • Delay or Disruption Tolerant Networking

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Intermittent Connectivity

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Isolated Networks Mobile Node

A B

Message

Circle: Transmission Range

Features

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Sensor Truck Data Server

To deliver sensor readings to the data server

Potential

Data Flow Data Flow

: Wireless device

Routing in DTN: Potential-Based Routing (PBR)

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Experiment Scenario

Sensor Deploy (#1) Sensor Deploy (#2)

Data Server (#99) Mobile Node (#3) Mobile Node (#4) Mobile Node (#5)

  • 1. The sensors and the server are isolated.
  • 2. The sensors continuously send data to the server.
  • 3. The movement of "mobile nodes" carries data to

the server.

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Armadillo220 Battery(6.0V 2100mAh) Power Circuit Wifi 802.11g Storage(2GByte)

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Implementation

  • Software: http://sourceforge.net/projects/pear/files/
  • IP packet buffer: 4096 entries
  • Source code: 3225 lines in C.
  • Foot print: 34kbyte (object code)
  • OS: linux-2.6.12.3-a9-15
  • Hardware: Armadillo-220 (Atmark Techno)

PEAR for sensor data gathering

PEAR: potential-based entropy adaptive routing

  • 1. Autonomously develops routing information
  • 2. Adaptively changes message delivery form according

to the physical mobility pattern.

"Mobility entropy and message routing in community-structured delay tolerant networks", ACM AINTEC 2008.

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Weather Station with DTN

RS232C(Serial)

Weather Sensor (WXT510/520)

  • Temperature
  • Humidity
  • Pressure
  • Rain Fall
  • Wind Direction
  • Wind Speed

DTN node (#1, #2)

Observe and send data to the data server (#99) (every ten second)

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Mobile Node -- Carry by hand!!

DTN node

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Data Server

50m

40 [min]

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The number of received neighbor advertisement packets. e.g., node 8 received 45 advertisement from sensor 1.

Contact Graph

Mobility Entropy2.1

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Potential for the Data Server

Potentials developed by PEAR potential construction algorithm

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Message Flow (1/2)

Red arrow: messages from sensor 1 (#1) Blue arrow: messages from sensor 2 (#2)

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Message Flow (2/2)

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Distribution of Delivery Latency

E.g., 50 = [0,100]

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Collected Temperature Data from Sensor 1

Delivery Rate = 100 %

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Collected Temperature Data from Sensor 2

Delivery Rate = 100 %

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Conclusion

  • DTN-Based Sensor Data Gathering

– Data server Truck Sensors

  • Implementation

– DTN-Communication Boxes – Potential-Based Entropy Adaptive Routing (PEAR)

  • Experiment

– in the University of Tokyo – 100% delivery (with 20min – 30min delay)

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Thank you ...

DTN Communication Boxes Sensor data delivery pattern DTN-Based Sensor Data Gathering