This document discusses a system of systems approach for IoT systems to support smart food and farming use cases. It analyzes 19 use case architectures for meat, arable crops, vegetables, fruits and dairy. There are many commonalities across the use cases in terms of the types of data collected on animals, crops, weather conditions and soil. Technically, there are also commonalities in connectivity standards, device architectures and platforms used. The document recommends developing reusable components, reference data models and joint business models to promote synergies across the different but related use cases.

1. SYSTEMS OF IOT SYSTEMS FOR
SMART FOOD AND FARMING
Cor Verdouw, Jeroen van Grondelle, Robbert Robbemond, Sjaak Wolfert
Wageningen University & Research
AgEng2018, July 10th 2018, Wageningen, The Netherlands

2. 2
MEAT
ARABLE
VEGETABLES
FRUITS
DAIRY
A SYSTEM OF 19 USE CASE SYSTEMS

3. IOF2020 > SUM OF THE USE CASES
Synergies across use cases crucial for large-scale take-up
3

4. OUTLINE OF THE PRESENTATION
4
Systems of Systems approach
Analysis Use Case Architectures
Main Commonalities
Recommended synergy actions

5. SYSTEM OF SYSTEMS
arrangement of systems that results when independent
and useful systems are integrated into a larger system
that delivers unique capabilities
(Tekinerdogan, 2017)
5
Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products
are transforming competition. Harvard Business Review, 92(11), 64-88.

6. CRITERIA SYSTEM OF SYSTEMS (MAIER 1998)
• Operational Independence of Elements
• Managerial Independence of Elements
• Evolutionary Development
• Emergent Behavior
• Geographical Distribution of Elements
6
Heterogeneity
Not prescribing
particular tools or
technologies!
Convergence through Synergy and Reuse
In all life cycle phases

7. OUR SYSTEM OF SYSTEMS APROACH

8. OUTLINE OF THE PRESENTATION
8
Systems of Systems approach
Analysis Use Case Architectures
Main Commonalities
Recommended synergy actions

9. 9

10. ANALYSIS APPROACH
• Tag at the level of use cases first
• Logical (functions and data) and
technical dimension
• Validation by the use case owners
10
Measures: Humidity
Predicts: Crop Growth
Controls: Water supply
(to influence: Yield)
Lora Connectivity
Deployed near Animals

11. IOT FUNCTIONS
Measure
Ingest
Derive
Predict
11
Monitor
Control
Plan
Report
Alert
DATA

12. USE OF THE DIFFERENT IOT FUNCTIONS
12

13. CONTROL CYCLES OF THE USE CASES

14. DATA MODEL ANALYSIS: A LOT OF DIVERSITY
14

15. MAIN DATA CATEGORIES
15

16. PRELIMINARY DATA MODEL ANALYSIS
• We see common underlying information modeling questions
• How to model time and location of sensor measurements
• Time dimension
• Continuous time series from static sensors
• Measurements from incidentally/nomadic deployed sensors
• Measurements from machine operation
• Location, measurements of different granularity
• Crop growth/m2, rain measurements for complete field
• Crop growth measurement after each weeding, rain every 15 minutes
• Comparing/correlating data from different resolution granularity
• Critical for sharing and reusing data for new use cases
16

17. CONNECTIVITY MODELS
13 of the UCs use some
form of LPWAN or LR-
WPAN
Existing tech such as
Wifi, Bluetooth, Ethernet
and Serial Bus remain
very relevant
NB-IoT not used

18. DEVICE ARCHITECTURES
Majority uses
mature, of-the-shelf
sensors
In line with
expected technical
readiness level of
project

19. LOCATION
Mainly GPS for outdoor
positioning
Opportunities for new
localization approaches
Lora/Sigfox based
Beacons

20. Large diversity!
Quite a few platforms
undecided (at time of
drafting D3.2)
Strong base in FIWARE
stack
IOT PLATFORMS

21. OUTLINE OF THE PRESENTATION
21
Systems of Systems approach
Analysis Use Case Architectures
Main Commonalities
Recommended synergy actions

22. MAIN LOGICAL COMMONALITIES
• Animal Characteristics
• Health, Fertility, Weight, Feed Intake,
Movements, Genetics, …
• Crop Characteristics
• Incl. Growth, Water Stress, Fertilizer
Need, Pests, ....
• Outdoor Conditions
(weather)
• Incl. Temperature, Solar Radiation,
Rainfall, Wind, …
• Soil Characteristics
• Incl. Moisture, Temp, EC, Nutrients, …
22
• Indoor/Storage/Trans-
portation Conditions
• Incl. Temp, Humidity, Noise,
CO2, …
• Location
• Incl. Position of Animals, Crops,
Trees, Equipment, RTIs, ....
• Food Product
• Incl. Grain Humidity,
Chemical/Microbiological Quality
Dairy Products, Wine
Temperature, Soya Protein
Levels, Carcass Parameters, ....

23. MAIN COMMONALITIES TECH PERSPECTIVE
• Connectivity
• Low Power Long Range
Connectivity (Lora/Sigfox)
• Short range (LR-WPANs
• Localization
• GPS
• Lora/Sigfox localization
• Beacons
• Device architecture
• autonomous
deployment/solar/battery
23
• IoT data management
• FIWARE Context Broker
• FIWARE IoT Agent
• EPCIS
• 365FarmNet
• Supporting IoT
capabilities
• Device management
• Security

24. RECOMMENDED NEXT STEPS
24
Catalogue &
Market Place
Task Forces &
Guidelines
Identify Gaps
for Open Call
Develop
Reusable
Components
Reference Data
Models
Joint business models/
exploitation of solutions

25. MANY THANKS FOR
YOUR ATTENTION!
Cor Verdouw, Jeroen van Grondelle, Robbert Robbemond, Sjaak Wolfert