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
minutes delay before service B makes sensor data available
online. In this case, the updating behaviour of service B may
not be suitable to be data sources as near real-time applications.
Service A Service B
Average time difference 30,679 5.433.863
(millisecond)
Number of unnecessary 0 24
requests
Total number of feedings 21 26
Table 1. Experimental results
4. CONSLUSIONS AND FUTURE WORK
We have presented a hybrid push-pull system to retrieve sensor
data in a near real-time manner. The proposed system first uses
the query aggregator to aggregate user queries and filter out
unnecessary requests. Then the adaptive feeder component
detects the updating frequency of OGC sensor web services and
retrieves sensor data with the aggregated requests in a timely
manner. As shown in the experimental results, our proposed
system can retrieve sensor data in a timely manner if the service
makes data available online as soon as it is measured. On the
other hand, if the service buffers or calibrates sensor data before
making them available online, the proposed system will
periodically request data with the detected sampling frequency
with the trade-off of redundant requests.
As we can see from the experimental results, the performance of
the proposed system is highly related to the updating behaviour
of sensor web service. Therefore, one of our future works is to
simulate sensor web services with different data updating
behaviours. The other future direction is the integration of
sensor data from different sensor web service. The current
sensor web services are heterogeneous in terms of protocol,
syntactic, and semantic. Users need to first find the services that
host the data they are interested in. However, with the growing
number of sensor web services, this discovery process becomes
a challenging task. Therefore, how to integrate sensor data to
provide a coherent view of sensor web is also one of our future
works.
REFERENCES
Birman, K. and Joseph, T., 1987. Exploiting virtual synchrony
in distributed systems, In: the 11th ACM Symposium on
Operating Systems Principles, Vol. 21, Part 5, pp. 123-138.
Bonnet, P., Gehrke, J., and Seshadri, P., 2001. Towards Sensor
Database Systems, In: International Conference on Mobile
Data Management, pp. 3-14.
Brenna, L., Demers, A., Gehrke, J., Hong, M., Ossher, J.,
Panda, B., Riedewald, M., Thatte, M., and White, W., 2007.
Cayuga: A High-Performance Event Processing Engine, In: the
2007 ACM SIGMOD, New York, USA, pp. 1100-1102.
Carzaniga, A., Rosenblum, D., Wolf, A., 2001. Design and
Evaluation of a Wide-Area Event Notification Service, ACM
Transactions on Computer Systems, Vol. 19, Part 3, pp. 332.
383.
Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, MJ.,
Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S.R.
Reiss, F., and Shah, M.A., 2003. TelegraphCQ: Continuous
Dataflow Processing, In: ACM SIGMOD, New York, USA.
Chen, J., DeWitt, D.J., Tian, F., and Wang, Y., 2000.
NiagraCQ: A Scalable Continuous Query System for Internet
Databases, In: the 2000 ACM SIGMOD, pp. 379-390.
Cugola, G., Nitto, E.D., and Fugetta, A., 2001. The JEDI Event-
based Infrastructure and Its Application to the Development of
the OPSS WFMS, /EEE Transaction on Software Engineering,
Vol. 27, Part 9, pp. 827-850.
Esper, 2012. http://esper.codehaus.org/ (05 January 2012).
Gedik, B., Andrade, H., Wu, K.L., Yu, P.S., and Doo, M., 2008.
SPADE: The System S Declarative Stream Processing Engine,
In: the 2008 ACM SIGMOD, New York, USA, pp. 1123-1134.
Hart, J.K. and Martinez, K., 2006. Environmental Sensor
Networks: A revolution in the earth system science? Earth
Science Reviews, Vol. 78, pp. 177-191.
Hsieh, T.T., 2004. Using Sensor Networks for Highway and
Traffic Applications. IEEE Potentials, Vol. 23, Part 2, pp. 13-
16.
Kassab, A., Liang, S., and Gao, Y., 2010. Real-Time
Notification and Improved Situational Awareness in Fire
Emergencies using Geospatial-based ^ Publish/Subscribe,
International Journal of Applied Earth Observation and
Geoinformation, Vol. 12, Part 6, pp. 431-438.
Liang, S.H.L., Croitoru, A., and Tao, C.V., 2005. A Distributed
Geospatial Infrastructure for Sensor Web. Computers and
Geosciences, Vol. 31, Part 2, pp. 221-231.
Madden, S. and Franklin, M.J., 2002. Fjording the Stream: An
Architecture for Queries over Streaming Sensor Data, In: the
2002 International Conference on Data Engineering, pp. 555.
Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., and
Anderson, J., 2002. Wireless Sensor Networks for Habitat
Monitoring. In: the 2002 ACM International Workshop on
Wireless Sensor Networks and Applications. Atlanta, USA.
Microsoft, 2012. “Microsoft ^ StreamInsight — 20"
http://msdn.microsoft.com/en-
us/library/hh750619(v=SQL.10).aspx (12 January 2012).
Mokbel, M.F., Xiong, X., and Aref, W.G., 2005. Continuous
Query Processing of Spatio-Temporal Data Streams in PLACE,
Geolnformatica, Vol. 9, Part 4, pp. 343-365.
Open Geospatial Consortium, 2007. “Sensor Oris
Service” http://www.opengeospatial.org/standards/sos (0
January 2012).
ghtweight
Oracle, 2009. “Oracle Complex Event Processing: Li
the Real
Modular Application Event Stream Processing in
World
http://www.oracle.com/technetwork/middleware/complex-
event-processing/overview/oracle-37.pdf ( 05 January 2012).
Internation:
Pietzuch, P.F
Middleware.
Cambridge, U]
Powell, D., 1€
the ACM, Vol.
Skeen, D., 1
Publish-Subsci
January 2012).
StreamBase,
http://www str
dex.html (05 J:
Terry, D., Go
Continuous Qu
ACM SIGMOLD
TIBCO, 1999.
http://www .tibc
(05 January 20
Xu, N., 2002.
IEEE
http://enl.usc.ec
The authors w
Innovates Tech
supports on this