Global Sensor Networks (GSN) is a middleware for managing sensor data streams from various sources. It helps deploy sensor networks, publish and make data discoverable and reusable. GSN processes streaming data using Virtual Sensors defined in XML. It collects data from different sources in a distributed way and stores it locally or on the web. GSN is used in several projects involving environmental sensing, smart agriculture and manufacturing, traffic monitoring, and air quality measurements. It provides open data repositories and helps access research datasets.

1. Global Sensor Networks
An evolving middleware for sensor data stream
processing
Jean-Paul Calbimonte, Julien Eberle, Sofiane Sarni, Karl
Aberer
LSIR EPFL
Mountain Observatories 2014, Reno Nevada
16.07.2014
http://gsn.epfl.ch

2. Sensor deployments everywhere
Mountains
Glaciers
Snow regions
Sea
Coastal
Agriculture
…
DIY
Mobile
Participatory

3. We want the data
• Open data repositories
• Accessible research datasets
• Discoverability
• Reuse datasets
• Metadata

4. GSN: Global Sensor Networks

5. GSN: Global Sensor Networks
Help managing sensor datasets
Help publishing the data
Help making the data discoverable and reusable

6. GSN in a nutshell
• Middleware: Sensor network deployment
• Virtual Sensor (VS): Process streaming data
• Hosts & manages multiple VSs
6

7. Where is GSN?
7
• Sensor Network:
• Sensing
• In network
• Data/query Processing
• Filtering
• Aggregation
• Data Management:
• Data:
• Management.
• Publishing.
• Expensive processing.
• Archives.
Data
Management
Sensor
Network
GSN goes here

8. Collecting data from different sources
8
• GSN works in a distributed fashion
• Data can be kept locally
• Break data silos
• Put sensor data on the web
GSN nodes

9. GSN Distributed Deployment
9
Integrity Service
Access Control
GSN/Web/Web-Services
Notification Manager
Query Processor
Query Repository
Storage Manager
Virtual Sensor Manager
Input Stream Manager
Stream Quality Manager
Life Cycle Manager
Pool Of Sensing Devices

10. GSN Virtual Sensors
10
• A virtual sensor, any kind of data producer
• a real sensor, a wireless camera, a desktop computer, GPS sensor, network traffic,
etc.
• combination of other virtual sensors.
• Logical view of the sensor network.
• Described in an XML file:
• Functional/non-function properties.
Source 1
Source 2
…
Source n
Application logic
and processing
Output Stream
VirtualSensor

11. Virtual Sensor configuration
11
<virtual-sensor name="room-monitor" >
<addressing>
<predicate key="geographical">BC143</predicate>
<predicate key="usage">
room monitoring</predicate>
</addressing>
<life-cycle pool-size="10" />
<output-structure>
<field name="image" type="binary:image/jpeg" />
<field name="temp" type="int" />
</output-structure>
<storage permanent="true" history-size="10h" />
<input-streams>
<input-stream name="cam">
<stream-source alias="cam" storage-size="1“
sampling-rate=“1”>
<address wrapper=“tinyos2.x">
<predicate key=“host">tinybox.epfl.ch
</predicate>
<predicate key=“port">9001</predicate>
</address>
select * from WRAPPER
</stream-source>
<stream-source alias="temperature1“
storage-size="1m“ sampling-rate=“1”>
<address wrapper="remote">
<predicate key="type">temperature</predicate>
<predicate key="geographical">BC143-N
</predicate>
</address>
select AVG(temp1) as T1 from WRAPPER
</stream-source>
<stream-source alias="temperature2“
storage-size="1m“>
<address wrapper="remote">
<predicate key="type“>temperature</predicate>
<predicate key="geographical“>BC143-S
</predicate>
</address>
select AVG(temp2) as T2 from WRAPPER
</stream-source>
<query>
select cam.picture as image, temperature.T1
as temp from cam, temperature1
where temperature1.T1 > 30 AND
temperature1.T1 = temperature2.T2
</query>
</input-stream>
</input-streams>
</virtual-sensor>

12. Data in GSN through Wrappers
12
Common abstractions, independent of applications, hardware
Simple integration & data correlation.
5140
GSN
Various
Applications
Plug & Play
deployment
On-the-fly
reconfiguration
GSN
GSN

13. Some available mappings
13
• HTTP generic wrapper
• devices accessible via HTTP GET or POST requests, e.g., the AXIS206W wireless camera
• Serial forwarder wrapper
• enables interaction with TinyOS compatible motes (standard access in TinyOS)
• USB camera wrapper
• local USB connection.
• supports cameras with OV518 and OV511 chips.
• RFID wrapper
• access to Texas Instruments Series 6000 S6700 multi-protocol RFID readers
• Alien Technologies long range RFID reader 8950 EU.
• WiseNode wrapper
• access to WiseNode sensors (CSEM, Switzerland, http://www.csem.ch/)
• Generic UDP wrapper
• any device using the UDP protocol
• Generic serial/bluetooth wrapper
• supports sensing devices which send data through the serial port, e.g., EPuck robots, etc.

14. Wrappers: Lines of Code
14
50RFID reader (TI)
50Generic HTTP
300Wired camera
180Generic serial
45Generic UDP
75WiseNode
160TinyOS
Lines of codeWrapper type

15. Open source project: Available in Github
• Open Source License
• Mainly in Java
• Community Support
• Used in several projects

16. Releases available in Github

17. So what can I do with it?
• Get data from my sensors (API, web interface)
• Store and archive the data
• Put it online, available for download
• Put it online, available for discovery and querying
• Apply post-processing to the data
• Combine different data sources
• Use the data from an R script
• More stuff…

18. Data Validation through Measurements and Modelling over Multiple Scales
Lagrangian
Dispersion Model
High resolution urban
atmospheric pollution maps
Model Input
Terrain, meteorology, source strength, background
Sensor Data
Crowd-sensors, mobile sensors, monitoring stations

19. GSN Storage
19
• Centralized RDBMS
 Trends: Data & Users
 Evaluate a NoSQL solution
Scalability Fault-tolerance Performance

20. GSN Storage Extension
20
LSIR-Cloud
HBASE Java
API Client
Experimental
Platform
CPU: 24 cores
x 2.3GHz
Mem: 64 GB
Disk: 2.8 TB
Nodes: 8
CPU: 8 x 12 cores
x 2.3GHz
Mem: 32 GB
DFS Disk: 43 TB
Network: 1 Gbps
HDFS Cluster
Put / Get
HBASE
Exporter
HBASE
Wrapper
HBASE Query
Handler
User Requests
VS data
Store VS data
Read VS data
Execute Query

21. SSN Ontology with other ontologies
21
W3C SSN Ontology
tool for modeling our sensor data
combine with domain ontologies

22. GSN Access Control (AC)
• VS has an owner: decides user access
22
VS: Virtual Sensor
AC ISSUES REASON
Private VS Features not visible VS Availability should be provided
No Notifications Faster responses, if notified
No Access Time Limitations Enable owner to control access
Manual VS management Automation of the VS activation
No AC in REST services Enable alternative data access

23. More things we’re doing
• Integration: integrate with Geo-enabled repositories
(e.g. GeoNetworks)
• Standards: NetCDF, OGC standards, OpenDAP
• Metadata: add semantics to the data
• Web standards: RDF and Linked Data
• tinyGSN: for mobile devices

24. Some example of Use
• OpenIoT: Smart agriculture, manufacturing, etc.
• SwissExperiment: environmental sensing
• PlanetData: traffic data observation
• OpenSense: air quality measurements
• Permasense: mountain and snow observatory
• Etc..

25. OSPER - Swiss Experiment
Open support platform for environmental research
Multidisciplinary research team
• Real world data + problems
Facilitating research in:
• Precipitation patterns in mountains
• Evaporation in Africa
• Return periods of Natural Hazards
• Stream flows in Alpine catchments
• Permafrost in the Alps
managing environmental
sensor data &metadata
Platform
http://swiss-experiment.ch
Data
heterogeneous
sensing devices
summarization, filtering,
compression, interpolation
continuous processing,
streaming, geospatial, aggregation
pattern discovery,
correlation, regression
metadata management, semantics
data services, visualization, standards
acquisition
processing
querying
analysis
discovery
provision

26. OpenSense2
global concern
highly location-dependent
time-dependent
Crowdsourcing High-Resolution Air Quality Sensing
Air Pollution
Accurate location-dependent and real-time information on air pollution is needed
Integrated air quality measurement platform
 Heterogeneous devices and data
 Human activity assessment, lifestyle and health data
• Link high-quality and low-quality data
• Integration of pure statistical models and physical
dispersion models
• Better coverage through crowdsensing
• Incentives for crowd data provision
• Finer temporal and spatial resolutions
• Utilitarian approach for trade-off between model
complexity, privacy and accuracy
• Higher accuracy of pollution maps models
http://opensense.epfl.ch
Institutional stations
OpenSense infrastructure
Personal mobile sensors CrowdSense

27. OpenIoT FP7
Open Source Cloud solution for the Internet of Things
http://openiot.eu
Established Open-source platform for IoT
• Integrate sensors & things with cloud computing
• Configure, deploy and use IoT services
• Auditing/assessing privacy of IoT apps in the cloud
• Semantic annotations of internet-connected objects
• Energy-efficient data harvesting
• Publish/subscribe for continuous processing and
sensor data filtering
• Mobility of sensors and QoS aspects in IoT
https://github.com/OpenIotOrg/openiot
Use cases and validation scenarios
Smart
Manufacturing Campus Guide Air Monitoring
Agriculture
Sensing

28. Thanks a lot!
Global Sensor Networks
Jean-Paul Calbimonte
LSIR EPFL
http://gsn.epfl.ch