Agriculture: Methods and Experiences

Wireless Sensor Networks for Precision

Wireless Sensor Networks for Precision

Agriculture: Methods and Experiences

Novel Sensor Technologies for Plant Phenotyping

S t b 13th ₁₄th ₂₀₁₂

September 13th _{– 14}th _{, 2012}

Wageningen, The Netherlands

Antonio Javier Garcia Sanchez Antonio-Javier Garcia-Sanchez

Index

 What is a WSN?

 WSN A li ti P i i A i lt  WSN Applications: Precision Agriculture  Novel Devices

 Design: Integrated WSN Solution for Precision Agriculture (ISSPA)  Deployment –Trial Scenario

 Results  Results

 Recent Works

 How can WSNs apply to the Plant Phenotyping?  C l i

What is a WSN?

Wireless Sensor Networks (WSNs) consist of multiple unassisted

embedded devices (nodes) which process and transmit data collected from different on-board physical sensors (temperature, humidity, pressure, etc.)

What are the WSNs?

Main WSN Features: Main WSN Features:  Low Cost

 Low Cost

 Low Power Consumption  Small Size

 Wireless comunications ranging 100 meters

 Resource-constrained nodes (memory and processing)  Energy constrained!!

WSN Applications

Video Services

WSN Applications:Precision Agriculture

To monitor small-size and scattered crops

Video-surveillance

Crop yield improvement Crop yield improvement

“Wireless Sensor Network deployment for integrating video-surveillance and data-monitoring in precision agriculture over distributed crops”, in Computers and Electronics in Agriculture, Vol. 75, pp. 288-303. 2011

Precision Agriculture: Devices

Monitoring node node MDA100 board Front PIR Sensor Moisture Sensor MicaZ mainboard Back Top Detection ITS400 Imote2 and Detection node ITS400 board Imote2 and battery boards

Precision Agriculture:Devices

Camera Battery board Camera sensor Voltage Regulator Identification node Imote2 Mainboard PhotoTransistor Battery IMB400 board XBee Module Imote2 Module

Design: Integrated WSN Solution for

Features to remark:

Precision Agriculture (ISSPA)

Features to remark:

 Topology

Communications

 Efficient Energy design at devices  Crop parameters monitoring

 Detection functionality  Detection functionality  Video Surveillance

Design: Cluster-Tree Topology

PAN Coordinator/FarmerCoop-Gateway

Coordinator/Crop-p Gateway

Device (Monitoring, Detection or Identification node)

Design: Integrated WSN Solution for

Precision Agriculture (ISSPA)

Features to remark: Features to remark:  Topology

 Communications

 Efficient Energy design at devices  Crop parameters monitoring

 Detection functionality  Detection functionality  Video Surveillance

Design: Communications under

IEEE 802.15.4 standard

2 b t 1 b t 4 b t 0 b t 105 b t 2 b t 2 b t 1 b t 4 b t 0 b t 105 b t 2 b t 2 b t 1 b t 4 b t 0 b t 105 b t 2 b t Frame Control Sequence Number Addressing Field Auxiliary Security Number Data Payload FCS 2 bytes 1 byte 4 bytes 0 bytes 105 bytes 2 bytes

Frame Control Sequence Number Addressing Field Auxiliary Security Number Data Payload FCS 2 bytes 1 byte 4 bytes 0 bytes 105 bytes 2 bytes

Frame Control Sequence Number Addressing Field Auxiliary Security Number Data Payload FCS 2 bytes 1 byte 4 bytes 0 bytes 105 bytes 2 bytes

BI = aBaseSuperFrameDuration x 2 BO BI = aBaseSuperFrameDuration x 2 BO BI = aBaseSuperFrameDuration x 2 BO BI = aBaseSuperFrameDuration x 2 BO CAP CFP (GTS) INACTIVE e ac on e acon CAP CFP (GTS) INACTIVE e ac on e acon CAP CFP (GTS) INACTIVE e ac on e acon CAP CFP (GTS) INACTIVE e ac on e acon SD B S F D ti 2 SO B e B e SD B S F D ti 2 SO B e B e SD B S F D ti 2 SO B e B e SD B S F D ti 2 SO B e B e SD= aBaseSuperFrameDuration x 2 SO SD= aBaseSuperFrameDuration x 2 SO SD= aBaseSuperFrameDuration x 2 SO SD= aBaseSuperFrameDuration x 2 SO

Note that BO and SO delimited the Superframe size and Note that BO and SO delimited the Superframe size and the active duration interval (CAP+CFP), respectively

Design: Integrated WSN Solution for

Precision Agriculture (ISSPA)

Features to remark: Features to remark:  Topology

Communications

 Efficient Energy design at devices

 Crop parameters monitoring

 Detection functionality

 Detection functionality

 Video Surveillance

Design: Integrated WSN Solution for

Precision Agriculture (ISSPA)

Features to remark: Features to remark:  Topology

Communications

 Efficient Energy design at devices  Crop parameters monitoring

 Detection functionality  Detection functionality  Video Surveillance

Implementation and deployment

SOFTWARE: TinyOS and Open Zigbee

SOFTWARE: TinyOS and Open-Zigbee

DEPLOYMENT: Broccoli crop placed in the Valle de Ricote Southern Spain

Results. Energy Simulation

Battery board Camera sensor State Consumption Imote2 Mainboard C₁ Cp C₂ 1.86 mW 48.63 mJ/ 253 msec. 253.71 mW IMB400 board C_R C_3/4 6.63 uJ/194 usec. 331 mW The camera captures an image. CPU turns 104 Mhzon Receive (e.g beacon frame) or Transmit image Device at deep-sleep mode C1 C2 C3/4 Cp CR C₂CR C3/4 g e r C ons um pt io n Transition states Schedule Pow e IDENTIFICATION-NODE

Results. Energy Simulation

State Consumption D₁ Dp 17.34 mW 2.2 uJ/ 12.4 msec. D₂ D_R D 194.52 mW 6.63 uJ/194 usec. 271 34 W D_3/4 271.34 mW The device detects an intruder. CPU turns 104 Mhzon Receive _{or Transmit} event Device is at standby state. The trigger-driven mode is activated um pt io n D1 D₂ D3/4 D_p DR D2DR D3/4 Transitionstates Power Cons Schedule DETECTION- NODE

Results. Energy Simulation

State Consumption M₁ Mp M₂ M 0.7125 mW 10.3 uJ/ 22 msec 8 mA 110 A/1 8 The sensor Device at sleep The device turns at activate mode for MONITORING- NODE M_HP

HidraProbe 110 mA/1.8 sec

M_SE

Sensorex 40 mA/2 sec

C_R 6.63 uJ/194 usec. e se so captures a measurement. mode Receive or Transmit MHP or mode for developing measurements MHP or s um p ti o n MDA100 board MDA100 board M₃ 71.28 (Rx) mW M₄ 66.67 (Tx) mW C_R M1 M2 M3/4 Mp M_{R M} 2 MR M3/4 Transition states or MSE or MSE Po we r Co n s Moisture Sensor MicaZ mainboard Moisture Sensor MicaZ mainboard (a) (b)

Results. Analysis

1 1 1 1             n ca _CW P ) 1 ( * 2 1 _       ca a p CW 5 * ) 1 2 ( * * 2 _max max      P_ca BE _backoff BE 1 2 1 ) 2 2 ( * * 1 1      m a for lot numberCAPs a m p_{a BO} 1 , , 2 , 1   m a for 

Experimental Results

Experimental Results

5 * ) 1 2 ( * * 2 _max max      P_ca BE _backoff BE (a) (b)

Recent works: Large crop area or

forest monitoring

Tª Humidity CO2 CO2

Recent works

Requirements

Physical sensors to monitor diverse parameters Physical sensors to monitor diverse parameters

(temperature, humidity, pressure, gases, soil moisture, etc.)

Available multimedia services, in particular, video.

To guarantee different paths between source and sink To guarantee different paths between source and sink

End-user must access multimedia/data information with the best performancep

… and , of course, the requirements of WSNs

Mesh Topology IEEE 802.15.5 Optimization

techniques

SOLUTIONS

Recent Researches. IEEE 802.15.5

 IEEE 802.15.5 provides, in a single recommendation, all the

distinctive features for transmitting data efficiently in a mesh distinctive features for transmitting data efficiently in a mesh topology, including an effective energy saving procedure.

 Synchronous Energy Saving (SES) is part of the IEEE 802 15 5  Synchronous Energy Saving (SES) is part of the IEEE 802.15.5 standard planned to provide energy saving to scheduled

communications with strict temporal requirements that, a priori, facilitate the development of delay-sensitive applications (video). facilitate the development of delay sensitive applications (video).

 The problem of SES and, therefore, IEEE 802.15.5 is that the synchronization scheme introduces variable delays in the y y

dissemination of information that reduce lifetime of the nodes and the entire network.

 Solution: A new synchronization approach is designed. This issue is dealt with the research “On the synchronization of IEEE 802.15.5 wireless mesh sensor networks: shortcomings and improvements”, EURASIP Journal on Wireless Communications and Networking, 2012:198

Recent Researches. Optimization

STEP 1. Theoretical Study

 In a mesh network where video and monitoring  In a mesh network where video and monitoring nodes dispatch data, Optimization techniques to achieve the best network performance.

achieve the best network performance. In particular, our group has carried out investigations that lead to maximize the set throughput and lifetime metrics of the network together

together.

STEP 2. Real Deployment STEP 2. Real Deployment

To design and implement a new load-balancing algorithm which obtains results that are close to the optimization study, validating this theoretical work.

Recent Researches. Optimization

STEP 1 STEP 1 1 5 kbps 90 100% 0 kbps 0.1 0.5 1 0% 20 50 70 90

Recent Researches. Optimization

STEP 2

Recent Researches. Optimization

(b) (a)

Antonio-Javier Garcia-Sanchez, Felipe Garcia-Sanchez, David Rodenas-Herraiz, Joan Garcia-Haro, “On the Optimization of Wireless Multimedia Sensor Networks: A Goal Programming approach”, accepted in Sensors, vol.12, no. 09, 2012, ISSN 1424-8220.

Conclusions

 We presented an integrated WSN-based system for small size and scattered crops monitoring video

small-size and scattered crops monitoring, video-surveillance and process of cultivations control. This network implies an innovative deployment of precision agriculture using the IEEE 802.15.4 cost-effective

technology.

 We also showed our recent works addressed to  We also showed our recent works addressed to monitor large crops including value-added services as video.

 Our works are conducted from a twofold perspective: (i) theoretical studies (analysis and simulation) and (ii) real experiments including novel devices sensor integration experiments including novel devices, sensor integration, trial scenarios, test-beds, etc.

 Plant Phenotyping is an emerging technology that, in our humble opinion, would profit from WSN advances in precision agriculture.

Thanks!

Thanks!

Questions?