The Internet of Things (IoT) allows physical devices that live inside smart homes, offices, roads, electricity networks and city infrastructures to seamlessly communicate through the Internet while the forthcoming Web of Things (WoT) ensures interoperability at the application level through standardized Web technologies and protocols. In this presentation, we explain the concepts of the IoT and the WoT and their potential through various applications in the aforementioned domains. Then, we examine how the IoT/WoT can be used in the agri-food industry in order to enable novel smart farming technologies and applications,considering the recent technological opportunities for big data analysis.

1. 1
Enabling the physical world
to the Internet and potential
benefits for agriculture
Andreas Kamilaris

2. About me
2
B.Sc.
M.Sc
.
Ph.D.
Postdoc Postdoc Marie
Curie Fellow
Senior PostdocProject Manager
Lecturer
2003 2007
2007 2009
2009 2012
2013 2014
2014 2015 2016
2015 Now 2016 Now

3. 3
Talk Schedule

4. Motivation
4

5. Motivation
5
• Sensors everywhere
• Communication technologies based on Internet
• Prices for embedded hardware have effectively dropped.
• High heterogeneity in pervasive environments.
How do we bridge these
technologies together?
How can heterogeneous physical
things communicate and interact?

6. 6
The Internet of Things

7. Internet of Things
7
A network of objects, where all things are uniquely and
universally addressable, identified and managed by
computers in the same way humans can.

8. 8
Internet of Things: Communication

9. 9
Internet of Things: Software
uIPv6

10. 10
Internet of Things: Hardware

11. 11
Internet of Things: Hardware

12. 12
Internet of Things: Potential
X10 KNX ZigBee IPv6
Network Size: 2^8 2^16 2^16 2^64 per subnet
Data Rate: 20b/s 9.6kb/s 20-250kb/s 250kb/s...1Gb/s
Interface: custom
solutions
app-level
gateway
app-level
gateway
UDP, TCP,
RESTful Web
Cost: low high medium low
Installation Overhead: low high low low
Connectivity: low medium medium high
Security: none high medium medium
Internet technology, utilizing IPv6, will become the future
standard in home automation.

13. 13
Internet of Things: Still problems?
IoT is definitely cool!
Connectivity at the network layer
is nice…
… but what about the application
layer?

14. 14
The Web of Things

15. Web of Things
15
• Interconnecting embedded devices in application level.
• The Web of Things reuses Web principles to interconnect
embedded devices, built into smart things.
• The Web as a pervasive and scalable platform.
The WoT practice:
1. Connect embedded devices to the Internet, via IPv4 or IPv6.
2. Embed Web servers on the devices.
3. Model their services in a resource-oriented way (REST).

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Web of Things
REST is a lightweight architectural style which defines how to
properly use the HTTP protocol as an application interface.
It is about four concepts:
1. Resources.
2. Their names (URIs).
3. The links between them.
4. Their representations (HTML, JSON, XML).
Resources can be manipulated with:
1. GET to retrieve a representation of a resource.
2. POST represents an insert or update.
3. PUT to alter the state of a resource.
4. DELETE to delete resources.

17. Web of Things
17

18. Web of Things: Challenges
18
• Local discovery of devices/services
• Global discovery of devices/services on the web.
• Description of services and data.
• Fast alerting and notifications.
• Multiple users interacting with same devices.
• Security of devices against misuse.
• Privacy of owners and users of the devices.
• Semantics of sensor information.
• Sharing of devices among friends, colleagues etc.
• Extraction of knowledge from raw data.
• Data management and data analysis.
• Composition of new services.
• Fairness

19. Web of Things: Examples
19
Smart Homes
HomeWeb Client Application
Restlet-GWT
Restlet
Web API
Server Application Framework
XML JSON

20. Web of Things: Examples
20
Smart Homes

21. Web of Things: Examples
21
Smart Homes

22. Web of Things: Examples
22
Eco-Feedback of Energy Consumption

23. Web of Things: Examples
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Sharing through Social Networks

24. Web of Things: Examples
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Competitions between Flats for Energy Savings

25. Web of Things: Examples
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A social, collaborative platform for energy management

26. 300,000 domestic premises
2-month periods
3-year historical information
10.900,000 electricity measurements

27. Web of Things: Examples
A social, collaborative platform for energy management

28. Web of Things: Examples
A social, collaborative platform for energy management

29. Web of Things: Examples
A social, collaborative platform for energy management

30. Web of Things: Examples
30
Smart Grid Applications: Load Shedding

31. Web of Things: Examples
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Smart Grid Applications: Demand Response

32. Web of Things: Examples
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Smart Offices: Targeting office occupants for energy savings
• 20-30% improvement in behavior
• Behavioral change continued 13 weeks
after feedback was removed!

33. Web of Things: Examples
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Smart Logistics
Electronic Product Code Information Services (EPCIS) is an open
public standard used to track the progress of objects as they move
through the supply chain, using RFID.

34. Web of Things: Examples
34
Smart Cities: Urban Mashups and the UrbanRadar mobile app

35. Web of Things: Examples
35
Smart Cities: Large-scale data analytics

36. Web of Things: Examples
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Global Discovery of Semantic IoT Data

37. Web of Things: Examples
37
Global Discovery of Semantic IoT Data

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Smart Agriculture

39. Smart Agriculture
39

40. Smart Agriculture: Ideas
40
More visibility, transparency and security along the food life cycle
Product tagging/addressing

41. Smart Agriculture: Ideas
41
More visibility, transparency and security along the food life cycle
Control of micro-
climate conditions
Trucks’ management
Multi-actors real-time
communication
Identification
of products:
faster & better
delivery

42. Smart Agriculture: Ideas
42
More visibility, transparency and security along the food life cycle
Consumer
transparency
Product information
combined with user
data, e.g. to identify
allergies

43. Smart Agriculture: Ideas
43
More visibility, transparency and security along the food life cycle
Product scanning
could reveal
information over
its proper waste
management

44. Smart Agriculture: Ideas
44
More direct relationship between producer and consumer

45. Smart Agriculture: Ideas
45
Online social platforms bringing together producers with traders

46. Smart Agriculture: Ideas
46
Open Datasets and easier discovery through the web

47. Smart Agriculture: Ideas
47
Semantically annotated data

48. Smart Agriculture: Ideas
48
Farm management apps that seamlessly integrate data
from various (heterogeneous) sources

49. Smart Agriculture: Ideas
49
Social networking of the farm environment

50. Smart Agriculture: Ideas
50
Social networking of the farm environment
• Nine workers at the farm - two weeks
Impressions:
• “The application is easy to be used.”
• “Excited with controlling the greenhouse
while amusing with friends.”
• “Notifications are difficult to understand.”
• “User must be online to be notified!”
• “I increased my monitoring activity.”
• “I became more aware about the farm.”
• “Cost to fully automate the farm?”

51. Smart Agriculture: Ideas
51
Twitter real-time data for products’ quality assessment
Product B
was
contaminated
#productB
Product A
was not as
the one
advertised
#productA
Product C did
not solve my
health issue
as promised
#productC

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Big Data

53. 53
Big Data: The 5 “Vs”
 Volume: The size of data collected for analysis
 Velocity: The time window in which data is useful,
accurate and relevant.
 Variety: Multi-source (e.g. images, videos, sensing data),
multi-temporal (e.g. collected on different dates), and
multi-resolution (e.g. different spatial resolution images).
 Veracity: The quality, reliability and potential of the data,
as well as their accuracy, reliability and confidence.
 Valorization: The ability of big data to propagate
knowledge, appreciation and innovation.

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Big Data: Sources for Agri
• Cameras
• GPS sensors
• Physical sensors
• Weather stations
• Remote sensing from drones and other UAV
• Remote sensing from airplanes and satellites
• Web data from online web services
• Feeds from social media
• Crowdsourcing-based techniques from mobile phones
• Static historical information: databases and statistics
Humans as sensors

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Big Data: Analysis Techniques
• Image processing
• Machine learning
• Cloud-based Platforms for large-scale information storing,
analysis and computation
• Geographical information systems (GIS)
• Big databases
• Message-oriented middleware
• Modeling and simulation
• Statistical tools
• Time-series analysis

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Big Data: Data Collection Platforms
Online, global sensor platforms enable people to share, discover and
monitor in real-time environmental data from objects, sensors,
satellites connected to the Web, from around the world.
Examples: Xively, Evrythng, SenseWeb, IrisNet, G-Sense.

57. Big Data: Data Collection Platforms
57

58. Big Data: Data Analysis Platforms
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AgriPulse:Big data analysis in the agro-food sector

59. Big Data: Data Analysis Platforms
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Plantix

60. Big Data: Data Analysis Platforms
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mooCall

61. Big Data: The P-Sphere Project
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How can we accurately measure the
environmental impact of agriculture using
big data analysis?
• Large-scale
• Heterogeneous – various sources
• Temporal dimension
• Less accurate

62. Big Data: The P-Sphere Project
62
Data Sources
• Crops
• Water
• Soil
• Satellites
• Drones
• Machinery
• Operations
• GIS
• Historical data
• Humans as sensors

63. Big Data: The P-Sphere Project
63
• Quantify impact – focus on
manure management
• Propose solutions based on
ICT technologies
• Examine “what if”…
scenarios
• A complete geo-information
inventory/model that could be
used by agriculture scientists
• Circular economy – waste
management

64. 64
Concluding Remarks

65. Conclusion
65
• Sensor technology transforms agriculture into high-tech industry.
• Agri-data is being produced faster than ever.
• Storage, analysis, performance, visualization.
• Semantics will facilitate data integration and reuse.
• More productivity, better yields
• Multi-actor communication and collaboration
• Consumer transparency
• Food security
• Sustainability and less impact on the environment

66. 66
Conclusion

67. 67
Challenges-Barriers
• Digital divide between developed and developing
• Big data collection efforts benefit big and well-educated farmers
• Privacy issues and fairness
• Monopolies such as Monstanto and dependence of the farmers
on large corporations.
• Hedge funds or big companies might use big data to speculate in
commodity markets.
• Competitive advantages to Wall Street analysts and managers
• Scale and heterogeneity, lack of structure, lack of governance
• Remote sensing systems have still some weaknesses.
• Big data is not only about volume!

68. Open for Collaboration
68
Big data
available?

69. 69
Thank you!
Andreas Kamilaris
andreas.kamilaris@irta.cat