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ing SOS wrapper and
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by using the calibration
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by executing Structured
nents. The SOS wrapper
1, which helps in the real
d Application Clients i
Capabalities,
FeaturaOfinterast,
Observations, etc.
ervice
Crop Database
FieldServer
(Wi-Fi based WSN)
tecture for GeoSense
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ols (GWT, 2011), which
or data on the web by
ests (e.g. GetCapabilities,
database (Figure 6). The
» has the ability to locate
ce (e.g. Google Web Map
ms and conditions on the
given time interval in the
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
The back-end consists of Postgre SQL database server version-
84 (PostgreSQL, 2011) and to support geographic objects,
PostGIS version-1.5 (PostGIS, 2011) spatial database plug-in /
template has been used. The Integrated Development
Environment (IDE) Eclipse release-Helios (Eclipse, 2011) has
been used with Google Web Toolkit plug-in (GWT, 2011) to
convert source Java code into web-based JavaScript code.
Apache Tomcat (Apache Tomcat, 2011) container processing
application for Java Servlet and JavaServer Pages have been
used to test and implement the distributed application client.
4.3 Modelling Service
The SOS has the ability to access, storage and retrieval of real/
near-real time sensory data. It has been found that due to high
temporal resolution of sensory data (one observation per
minute), there are few difficulties in processing and retrieval
data on client side. In addition to all basic operations only
orginal GetObservation (Arthur and Priest 2007: OGC
Standards, 2011) request was modified and statistical analysis
was obtained as a response.
Through Remote Procedure Call (RPC) mechanism requests
were processed and generated response was asynchronously
transferred to the service client (GWT, 2011). The response for
specified phenomenon would consist of observation values
grouped by user specified time interval along with statistical
analysis (minimum, maximum, mean, standard deviation, etc).
Figure 7, shows request and response mechanism of the
modified GetObservation operation. This would help to
understand the exact degree of change occurred in agro-
meteorological parameters with detection of sensor defects and
malfunction if any.
n Sod Ind Distributed Application Clients :
Modified Modified
GetObservation | Observation
Request parameters | Response parameters
1. Phenomenon id i 1. Phenomenon id
(e.g. temperature} (e.g. temperature)
2. Time interval 2. Basisof analysis (unit/ day)
(fromdayx today x.) | 3. Values
3. Grouping duration (dayx min, max, mean, SD, etc.;
(group by day) dayx, min, max, mean, SD, etc.:
gay Xx. min, max, mean, SD, etc.)
Sensor Observation Service
Figure 7. Request and response mechanism of modified
GetObservation operation
The SensorML process chains has been implemented in order
{0 obtain values for composite phenomenon (e.g. Reference
Evapotranspiration (ET), Irrigation requirement, etc.).
SensorML for computation of Reference ET by Hargreaves
method (Allen et al., 1989) is shown in Figure 8.
Through integration of various data processing equations (e.g.
Penman-Monteith, FAO-56, crop water balance, etc) SOS can
be Improved and made available as a location based service to
439
formulate strategies for agricultural resource utilization and
management.
Process SensorML Method SensorML
computation process chain computation Method
Y
Documentation
Link to source of Ref. ET equation
Reference Evapotranspiration (Ref. ET) [nee Evapotranspiration (Ref. A
input
independent Parameters
Atmospheric Condition Rule Set
Temp in Degree Celsius
Date in Julian day (n=1 for 1* Jan.)
Sensor Location in Decimal Degrees
Sensor location (Lat, Lon)
Daily Temperature (min, max}
Dependent Parameter
Radiation (mm/day)
Algorithm
Hargreaves Equation
ET; = 0.0023 Tınean + 17.8) Try — ufa
Implementation
Link to Source and compiled binary
Figure 8. SensorML Process and Method for
Reference Evapotranspiration calculation
Output
Reference Evapotranspiration
(mm/day)
5. CONCLUSIONS
Two different architectures of WSN having common
application in agriculture are combined through the SWE
framework. Through SensorML it provides performance
characteristics (e.g. accuracy, threshold, lineage, etc.) and
explicit description of the process by which an observation is
obtained. This also ensures capacity to connect many sensor
networks over internet and provide sensor observation
information in support of data discovery. The implementation
of the standards based SOA indicates that the sensor data can
be efficiently accessed and utilized by researchers and various
other user communities (students, government organizations,
etc.) from web-based platform. Further, it is cost effective to
transform the pre-existing WSN system into OGC Standards by
using open source tools.
Implementation of Sensor Web Enablement standards
facilitates the interoperability and sensor data discovery, and it
is possible to improve the research in WSN application fields
(e.g. Agriculture, Environment, etc.) through this architecture.
The GeoSense architecture has been improved through the
application of SWE framework to work in ubiquitously in an
interoperable manner and with web-based geo-visualization
capability.
6. FUTURE SCOPE
It is possible to improve the capabilities of SOA through
implementation of additional process chains and enable
application based requests (e.g. crop disease risk prediction,
etc.). There is need for implementation of efficient and robust
algorithms to process the high resolution spatio-temporal
distributed sensory data (15 minute interval for AS and 01
minute for FS) and use in various applications (e.g. quantifying
deep percolation of irrigation water, correlating agro-
meteorological parameters with crop diseases, etc.). Through
multi-modal communication (by implementing Sensor Event
Service) enabling participatory decision making and advisory.
7. REFERENCES
Adinarayana J., Sudharsan D. and A. K. Tripathy, 2009. Geo-
information Services to Rural Extension Community for Rural
