Data Aggregation Through Web Service Composition in Smart Camera Networks

1. Data Aggregation Through Web Service Composition in Smart Camera Networks Rajapaksage Jayampathi Advisor : Dr Sushil K Prasad Master Thesis Defense November 19, 2010

2. Outline • Introduction • Web Service Enabled Smart Camera Network Architecture • Data Aggregation through Web Service Composition • SQL Query Primitives For Smart Camera Network • Producer/Consumer Pattern • Results and Analysis • Conclusion

3. Introduction [Contd.] • Smart Camera • Smart camera is a powerful sensor that can process complex information such as image and video streams • Output can range from abstract data to video streams

4. Introduction • Smart Camera Networks • Real-Time embedded systems • Perform computer vision using multiple cameras • Central processing and distributed processing • Also called Distributed Smart Camera Networks (DSC Networks)

5. Introduction [Contd.] • Specific Requirements of Smart Camera Networks: • Resource availability requirement • Image processing requirement • Communication bandwidth requirement • Automated load distribution requirement • Challenges in Smart Camera Networks: • Complex image processing algorithm development • Collaborative image analysis/decision making • Image/Video Data Aggregation • High level query API development

6. Introduction [Contd.] • Applications: • interactive • dynamic • stream based • computationally demanding and • need real time or near real time guarantees • Applied Areas: • law enforcement • habitat monitoring • security surveillance – gang activity detection etc. • Designing, implementing, and deploying applications for DSCs is a complex and challenging task due to the multiple and heterogeneous nature of the DSC networks

7. Scope of the Thesis • Problem Statement Our thesis focuses on following research problems in Smart Camera Networks: • Problem 1 : Providing data aggregation for complex image and video streams • Problem 2: Providing scalable operation by exposing functionality and data produced by the nodes in highly interoperable manner • Problem 3: Providing High Level Language Primitives for querying DSC networks

8. Scope of the Thesis • Research Contributions We develop a novel SOA based middleware framework on a DSC network. • Contribution1: Scalable operation of DSC through web service enablement using Distributed OSGi • Contribution2: Data aggregation through web service composition • Contribution3: Develop an SQL API for Image Data Analysis • Contribution4: Efficient Resource Management in DSCs using OSGI Life Cycle To best of our knowledge this is the first time above has been done for a DSC environment

9. Web Service Enabled Smart Camera Network Architecture • Environmental Description • Hierarchical network architecture • Server receives queries from user, send queries to camera nodes and sends results back to the user • Camera nodes with higher number of children are more powerful in terms of processing capability, memory capacity etc

10. Web Service Enabled Smart Camera Network Architecture [Contd.] • Proposed Layered Middleware Architecture of a Camera Node:

11. Web Service Enabled Smart Camera Network Architecture [Contd.] • OSGi and Distributed OSGi: • Third party components that are commercially available • Relatively mature to serve our purpose • Used to model dynamic behaviour of the network • Every Smart Camera node is an OSGi Container • OSGi • Component based framework specification • Brings modularity to the Java platform • Enables creating highly cohesive, loosely coupled deployable modules called Bundles • Bundles offer services • Bundles offer lifecycle management

12. Web Service Enabled Smart Camera Network Architecture [Contd.] • OSGi framework enable SOA by : • Every Smart Camera node is an OSGi Container • Service producer bundles register services • Consumer bundles consumeservices. • Services are published and consumed within the same JVM. (SOA in a JVM) • Distributed OSGi • Allow OSGI service bundles to be published as web services (distribution capability) • We use Apache cxf-dosgi as D-OSGi framework

13. Services Offered by a Smart Camera Node Services offered for a monitoring application based on face detection:

14. Sample WSDL file (WinMax Service)

15. LS LS LS Proposed Smart Camera Architecture WS WS - Web Services LS - Local Service

16. Producer/Consumer Pattern for Asynchronous Communication

17. Data Aggregation through Web Service Composition Aggregate Query: • SELECT MAX(node_id,num_faces, epoch) • FROM CAMERAS • SAMPLE PERIOD 5s FOR 10s Query Max= <3,20,3> <0,10,1>, 0 Max =13 Max =10 0 <0, 6,2>, 0 Query Query <0,13,3>, 1 Max= <3,20,3>,1 Max=<2,12,1>, 1 <1,8,1>, 0 Max =8 <1,6,2>, 0 <2,12,1>,0 Max =12 1 2 <1,7,3>, 1 <2,10,2>,0 Query Query Query <2,12,3>,1 Max= <5,3,2>, 1 Max=<3, 20,3>, 1 Max= <4,5,1>, 1 <3,5,1>,0 3 4 <4,6,1>, 0 Max =6 5 <5,3,1>, 0 Max =3 Max =5 Max =20 <3,4,2>,0 <4,4,2>, 0 <5,3,2>, 0 <3,20,3>,1 <4,4,3>, 1 <5,2,3>, 1

18. Data Aggregation through Web Service Composition [Contd.]

19. 0s 0s 5s 5s 10s 10s 15s 15s 20s 20s 0 1 2 3 4 5 6 7 8 9 10 C C C C C S C S C S C S C S C S C C C S C C S C C S C C C S C S SQL Query Primitives For Smart Camera Network • SQL Query Primitives for Smart Camera Network: C - Collect S - Send

20. Dynamic Loading/Unloading of Services in Smart Camera Network • Problem: • In a resource constrained environment it is a waste of energy to run all available services • Solution: • Dynamically load/start relevant services when user issues a query • Dynamically unload/stop started services once user gets results • Saves energy of services running and listening to ports all the time • Maximize network lifetime

21. Dynamic Loading of Services in Smart Camera Network

22. Implementation and Integration System Setup Environment Smart Camera Network Parameters

23. Result and Analysis • Performance metrics: • Turnaround Time • Time taken between the query issue at root and the arrival of the last response message for that query back to the root. • Measures the communication cost in terms of time. • Bytes Transferred • Total bytes transferred within the network for a given query. • Measures the communication cost in terms of bandwidth consumption. • Service Startup Time • Time taken between the service startup request triggered by the root node and the arrival of the confirmation of successful startup of all camera nodes in the network at the root. • Memory Consumption • Memory consumption of a single camera node as a result of running services.

24. Results and Analysis [Contd.] • Turnaround Time – SELECT • Duration =20s, Sample interval =1s,2s,3s,4s,5s , Depth = 1,2,3 • Turnaround time increases with a lower sample interval • Increase is more apparent in larger network sizes • Turnaround Time – MAX • Same parameters as previous • Same pattern of behaviour observed for SELECT • Turnaround time is less for MAX than for SELECT ( due to in-node processing) • Turnaround Time – WINMAX • Duration=20s, Sample interval=1s, Response time=1s,2s,3s,4s,5s, Depth =1,2,3 • Higher turnaround times when response interval is lowest

25. Total Bytes Transferred – MAX • Duration =20s, Sample rate =1s Depth = 3 • 55.40% reduction in total bytes transferred • Service Startup Time • Depth =1,2,3 • More nodes resulted in higher startup times • Memory Consumption – Local Services • Single node, number of local services running=0,1,2,3 • OSGi container consumes 25224 kilo Bytes without any services running. • Memory consumption increase w.r.t deployed local services • Memory Consumption – Web Services • Single node, number of web services running= 1-8 • Memory consumption increase w.r.t deployed web services Results and Analysis [Contd.]

26. Related Work

27. Conclusion • In this thesis, we develop a novel SOA based middleware framework on a DSC network that uses Distributed OSGi to expose DSC network services as web services. • We also develop a novel web service composition scheme that aid in data aggregation and a SQL query interface for DSC networks that allow sophisticated query processing. • Future Work • Event based queries, network health queries • Resource monitoring algorithms and queries • Collaborative Decision Making • New algorithms for on-the-fly creation of the hierarchical aggregation tree.

28. Bibliography • Constantin Timm, Jens Schmutzler Peter Marwedel, Christian Wietfeld, Dynamic Web Service Orchestration applied to the Device Profile for Web Services in Hierarchical Networks, 2009. • Samuel Madden , Michael J. Franklin , Joseph M. Hellerstein , Wei Hong ,TAG: a Tiny AGgregation service for ad-hoc sensor networks, 5th Symposium on Operating Systems Design and Implementation, 2002 • Rinner, B.; Jovanovic, M.; Quaritsch, M., Inst. for Tech. Inf., Graz Univ. of Technol.Embedded Middleware on Distributed Smart Cameras, Acoustics, Speech and Signal Processing,ICASSP 2007. • Samuel R. Madden et al, Tinydb: An Acquisitional Query Processing System For Sensor Networks, 2005.

29. Questions/Comments?