Systems of IoT Systems for Smart Food and Farming
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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.
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Systems of IoT Systems for Smart Food and Farming
- 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
- 12. USE OF THE DIFFERENT IOT FUNCTIONS 12
- 13. CONTROL CYCLES OF THE USE CASES
- 14. DATA MODEL ANALYSIS: A LOT OF DIVERSITY 14
- 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
Editor's Notes
- The core of the project lies within 5 trials. These cover 5 sectors (arable, dairy, fruits, vegetables and meat). To showcase each of the trials, the project is organized around 19 use cases.
- Information-centric Provides user with up-to-date and historic sensor data for better decision making In architectures: Dashboards, descriptive analytics Task-oriented Decision Support Actively supports users in specific, task oriented decisions using intervention suggestions, i.e. irrigation map, machine settings. User decides. In architectures: Statistical Models, Algorithms, Predictive Analytics Automated Control and/or Planning The system (semi-) autonomously intervenes based on sensor data to reach preset objectives or optimize selected outcome variables Farmer in principle accepts systems interventions, but may override/adjust In architectures: Actuation based on models, prescriptive analytics