Presentation at Mobile Deployment of Semantic Technologies Workshop at the International Semantic Web Conference. Abstract: In the past few decades, the field of ecology has grown from a collection of disparate researchers who collected data on their local phenomenon by hand, to large ecosystems-oriented projects partially fueled by automated sensor networks and a diversity of models and experiments. These modern projects rely on sharing and integrating data to answer questions of increasing scale and complexity. Interpreting and sharing the big data sets generated by these projects relies on information about how the data was collected and what the data is about, typically stored as metadata. Metadata ensures that the data can be interpreted and shared accurately and efficiently. Traditional paper-based metadata collection methods are slow, error-prone, and non-standardized, making data sharing difficult and inefficient. Semantic technologies offer opportunities for better data management in ecology, but also may pose a challenging learning curve to already busy researchers. This paper presents a mobile application for recording semantic metadata about sensor network deployments and experimental settings in real time, in the field, and without expecting prior knowledge of semantics from the users. This application enables more efficient and less error-prone in-situ metadata collection, and generates structured and shareable metadata.

1. 1
Automating Semantic Metadata
Collection in the Field with
Mobile Application
Laura Kinkead*, Paulo Pinheiro,
Deborah L. McGuinness
Tetherless World Constellation
Rensselaer Polytechnic Institute
* Now at Athena Health

2. Motivation: Next Generation Monitored
Ecosystems
The Jefferson Project (JP) is a joint effort between Rensselaer
Polytechnic Institute (RPI), IBM and the Fund for Lake
George aimed at creating an instrumented water ecosystem
along with an appropriate cyberinfrastructure that can serve as
a global model for ecosystem monitoring, exploration,
understanding, and prediction.

3. 3
Historical Sampling to Sensors, Models, Experiments
• Sampling at 12 locations
• Only water chemistry was previously measured
• Key previous results:
 Salt levels increasing – now dominant in the lake
 Chlorophyll slowly increasing
 Hypoxia in Caldwell Basin changed little
• Key resulting hypotheses:
 Increasing salt levels and organic nutrients may favor dominance of
cyanobacteria in the phytoplankton
 Ca levels may limit spread of invasive zebra mussels
 Chlorophyll increase may be caused by nutrient loading
 Food web mostly driven by “bottom-up” factors (i.e. nutrients, growing
season length)
Moving to sensors, streaming data, and a smarter, instrumented
lake with the goal of providing a foundation to form and evaluate
hypotheses much more effectively enabling a new generation of
strategic science dedicated to fuller understanding of the Lake's
ecological health.

4. 4
Science to Inform Solutions
To Realize a truly Smart Lake:
We need an integrative approach to
understanding lake stressors,
identifying correlations, hypothesizing
causation, experimentally testing
hypotheses, and proposing actions
Science-based
Solutions:
Leveraging deep
understanding of
multiple communities
and their research
content to propose
solutions along with
evidence
informs
Cyberinfrastructure/Data
Platform/Viz Lab
Semantic Data
Model
Current focus has been on
observations &sensor networks

5. 5
Traditional Data Collection
Notes
Notes taken in
the field with the
use of pen and
paper
Notes are
rarely
attached to
data
There is no community-
wide consensus on how
to take and reuse field
notes

6. 6
Mobile Context Capture for Sensor
Networks (MOCCASN)
COLLECT
METADATA
One single mobile
application capable of
taking field notes and
connect the notes to
data as semantic
annotations

7. SOLR-CCSVSOLR-CCSV
CCSV-LoaderCCSV-Loader
data
Static
metadata
CSV2CCSV
(ICS)
CSV2CCSV
(ICS)
CCSV-Annotator*CCSV-Annotator*
MOCASSNMOCASSN
HASNetO-LoaderHASNetO-Loader
Dynamic
metadata
Sensor
network
technician scientist
data user
(incl. scientists)
maintains
reports
human
Interventions
(deployments,
sensor config,
calibrations)
Single
instrument
data (csv)
ccsv
data (csv)
Spreadsheet
of static metadata
ccsv
static metadata
turtle
SPARQL and
Lucene queries
CCSV BrowserCCSV Browser
SPARQL and
Lucene queries
Faceted search
annotated
csv
Dynamic
metadata
IN-SITU
DATA-SITE
DATA-SITE
WWW
uses
reports
needs
Dynamic
metadata
Ontologies
(HASnetO, OBOE,
PROV, VSTO)
* Tool to be developed
metadata metadata
mainly data flow
mainly metadata flow

8. 8
a simple example

9. Human-Aware Sensor Network Ontology (HASNetO)
9
vstoi:
Detector
vstoi:
Instrument
vstoi:
Platform
hasneto:
Sensing
Perspective
oboe:
Characteristic
oboe:
Entity
vstoi:
Detachable
Detector
vstoi:
Attached
Detector
*
*
*
1
0..1
*
hasPerspective
Characteristic
perspectiveOf
prov:
Activity
hasneto:
DataCollection
vstoi:
Deployment
xsd:dateTime
xsd:dateTime
hasData
Collection
1*
prov:
Agent
wasAssociatedWith startedAtTime
endedAtTime
1
1
*
*
*
*
oboe:
Measurement
of-characteristic
hasneto:
hasMeasurement
1
1
*
*
Platform 3952
Instrument 3
D 38 D 94

10. 10
Platform 3952
Instrument 3
D 38 D 94
RFID Type Parent Deployed Location Start Time End Time
3952 Platform NA TRUE 43.1, -73.2 2014-10-
27T12:00
NA
3 Instrument 3952 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
38 Detector 3 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
94 Detector 3 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
Example Knowledge Base

11. 11
Platform 3952
Instrument 5
D 74
Instrument 3
D 38 D 94
RFID Type Parent Deployed Location Start Time End Time
3952 Platform NA TRUE 43.1, -73.2 2014-10-
27T12:00
NA
3 Instrument 3952 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
38 Detector 3 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
94 Detector 3 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
Example Knowledge Base
New instrument
deployment

12. 12
Platform 3952
Instrument 5
D 74
Instrument 3
D 38 D 94
38 94
3952
74
5
3
RFID Type Parent Deployed Location Start Time End Time
3952 Platform NA TRUE 43.1, -73.2 2014-10-
27T12:00
NA
3 Instrument 3952 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
38 Detector 3 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
94 Detector 3 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
Example Knowledge Base

13. 13
Platform 3952
Instrument 5
D 74
Instrument 3
D 38 D 94
RFID Type Parent Deployed Location Start Time End Time
3952 Platform NA TRUE 43.1, -73.2 2014-10-
27T12:00
NA
3 Instrument 3952 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
38 Detector 3 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
94 Detector 3 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
5 Instrument 3952 TRUE 43.1, -73.2 2014-10-
27T16:30
NA
74 Detector 5 TRUE 43.1, -73.2 2014-10-
27T16:30
NA
Example Knowledge Base

14. 14
Platform 3952
Instrument 5
D 74
Instrument 3
D 38 D 94
RFID Type Parent Deployed Location Start Time End Time
3952 Platform NA TRUE 43.1, -73.2 2014-10-
27T12:00
NA
3 Instrument 3952 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
38 Detector 3 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
94 Detector 3 TRUE 43.1, -73.2 2014-10-
27T12:00
NA
5 Instrument 3952 TRUE 43.1, -73.2 2014-10-
27T16:30
NA
74 Detector 5 TRUE 43.1, -73.2 2014-10-
27T16:30
NA
Example Knowledge Base

15. 15
a more complicated
example – taking an
instrument out of
service and adding
instruments

16. 16
RFID Type Parent Deployed Location Start Time End Time
9754 Platform NA TRUE 43.2,
-73.1
2014-10-
01T11:00
NA
8 Instrument 9754 TRUE 43.2,
-73.1
2014-10-
01T11:00
NA
43 Detector 8 TRUE 43.2,
-73.1
2014-10-
01T11:00
NA
Platform 9754
Instrument 8
D 43
Example Knowledge Base

17. 17
Platform 9754
Instrument 2
D 61
Instrument 6
D 09
RFID Type Parent Deployed Location Start Time End Time
9754 Platform NA TRUE 43.2, -73.1 2014-10-
01T11:00
NA
8 Instrument 9754 TRUE 43.2, -73.1 2014-10-
01T11:00
NA
43 Detector 8 TRUE 43.2, -73.1 2014-10-
01T11:00
NA
Example Knowledge Base
Undeploy one
instrument (8)
(with one
detector(43)) and
deploy 2 new
instruments (each
with a detector)

18. 18
Platform 9754
Instrument 2
D 61
Instrument 6
D 09
09 61
9754
6
2
RFID Type Parent Deployed Location Start Time End Time
9754 Platform NA TRUE 43.2, -73.1 2014-10-
01T11:00
NA
8 Instrument 9754 TRUE 43.2, -73.1 2014-10-
01T11:00
NA
43 Detector 8 TRUE 43.2, -73.1 2014-10-
01T11:00
NA
Example Knowledge Base

19. 19
Platform 9754
Instrument 2
D 61
Instrument 6
D 09
09
61
6
2
Does Detector 09
belong to Instrument
2?
Yes No
RFID Type Parent Deployed Location Start Time End Time
9754 Platform NA TRUE 43.2, -73.1 2014-10-
01T11:00
NA
8 Instrument 9754 TRUE 43.2, -73.1 2014-10-
01T11:00
NA
43 Detector 8 TRUE 43.2, -73.1 2014-10-
01T11:00
NA
Example Knowledge Base

20. 20
RFID Type Parent Deployed Location Start Time End Time
9754 Platform NA FALSE 43.2, -73.1 2014-10-
01T11:00
2014-10-
27T17:00
8 Instrument 9754 FALSE 43.2, -73.1 2014-10-
01T11:00
2014-10-
27T17:00
43 Detector 8 FALSE 43.2, -73.1 2014-10-
01T11:00
2014-10-
27T17:00
9754 Platform NA TRUE 43.2, -73.1 2014-10-
27T17:00
NA
2 Instrument 9754 TRUE 43.2, -73.1 2014-10-
27T17:00
NA
6 Instrument 9754 TRUE 43.2, -73.1 2014-10-
27T17:00
NA
61 Detector 2 TRUE 43.2, -73.1 2014-10-
27T17:00
NA
9 Detector 6 TRUE 43.2, -73.1 2014-10-
27T17:00
NA
Platform 9754
Instrument 2
D 61
Instrument 6
D 09
Does Detector 09
belong to Instrument
2?
Yes No
Example Knowledge Base

21. 21
RFID Type Parent Deployed Location Start
Time
End Time
9754 Platform NA FALSE 43.2,
-73.1
2014-10-
01T11:00
2014-10-
27T17:00
8 Instrument 9754 FALSE 43.2,
-73.1
2014-10-
01T11:00
2014-10-
27T17:00
43 Detector 8 FALSE 43.2,
-73.1
2014-10-
01T11:00
2014-10-
27T17:00
9754 Platform NA TRUE 43.2,
-73.1
2014-10-
27T17:00
NA
2 Instrument 9754 TRUE 43.2,
-73.1
2014-10-
27T17:00
NA
6 Instrument 9754 TRUE 43.2,
-73.1
2014-10-
27T17:00
NA
61 Detector 2 TRUE 43.2,
-73.1
2014-10-
27T17:00
NA
9 Detector 6 TRUE 43.2,
-73.1
2014-10-
27T17:00
NA
Platform 9754
Instrument 2
D 61
Instrument 6
D 09
Example Knowledge Base
Automatic update
from answering one
simple question:
lightweight use of
semantics

22. 22
Conclusion
• Automated Metadata capture can enable current and next generation
sensor-based science by enabling ubiquitous capture of contextual
information – helps eliminate forgetting to annotate
• Mobile technology should and can enable contextual capture even
without connectivity
• Relatively light weight semantics can significantly
• Improve deployment quality by using semantic constraints to check
for inconsistencies and help identify / resolve ambiguities
• Enable integration
• Enable discovery
Questions? Interested in collaborating?
dlm@cs.rpi.edu pinhep@rpi.edu

23. Extras
23

24. 24

25. 25
Recognized Challenges
• What do you do when there’s no cell
service?
• How do you make sure the instruments on
the boat are excluded?

26. 26
Intelligent Deployment of
Sensor Networks
• Automates the collection of metadata
‣ faster
‣ harder to forget to do
‣ less error-prone

27. The Human-Aware Sensor Network Ontology
27

28. Science to Inform Solutions