1. STREAMS & SENSOR NETWORKS “ Query Processing in Sensor Networks ”

2.  Introduction to Wireless Sensor Network (WSN)s  WSN Specific Issues for Query Processing  Data Storage & Data Management Issues  Stream Processing Requirements  Query Processing Techniques in WSN  Discussion & Future Work Outline

3. Introduction to WSN

4. Introduction to WMSN WMSN = Data Sensor Nodes + Stream Processing Nodes Data Sensor NodesStream Processing Nodes TemperatureStill Images PressureAudio Streams HumidityVideo Streams LocationCombination of above

5. Introduction to WSN  Initial Usage : Data-only Delay-tolerant apps.  Advances in IT  Suitable for a variety of applications such as retrieval of multimedia streams,  Store, process in real-time, correlate and fuse multimedia content.

6. Introduction to WSN

8. Properties of WSNs : Large number of tiny sensor nodes, One or more base stations (the sink), Densely deployed. AOU (Areas of Use) :  Advanced Healthcare Monitoring,  Environmental Monitoring,  Traffic and Collision Avoidance,  Surveillance Systems.

9. WSN Specific Issues  Hardware Requirements  Computational constraints,  Communication constraints,  Mobility,  Power supplies,  Objectives and Efforts*

10. WSN Specific Issues * Data Storage & Management Restricted hardware resources of sensor nodes, due to their tiny design, impede most general storage management issues to be implemented on WSN.

11. WSN Specific Issues * Data Storage & Management Approaches in Data Storage & Management  Burden of data processing on the sink Node --- Little Storage Sink --- Plenty of Storage  Storage-Centric WSN Embed nodes with high capacity storage (stemmed from energy consumption)

12. WSN Specific Issues * Data Storage & Management

14. WSN Specific Issues * Stream Processing Requirements  Multimedia streaming, requires high- bandwidth channels and timeliness…  Key Challenges  Low-power resources and computational capabilities for video coding,  Real-time requirements of the bursty video traffic,  Lossy wireless hops.

15. Requirements of multimedia streaming at each level of the stack :  At the application layer, sensor nodes require video/audio coding & compression with low complexity, producing a low output bandwidth, packet loss-tolerant and energy efficient.  At the transport layer, requirements such as bounded delay and jitter in data delivery, minimum bandwidth availability, multiple data priorities and session maintenance must be met. WSN Specific Issues * Stream Processing Requirements

16. Requirements of multimedia streaming at each level of the stack :  Network layer is crucial for QoS( Quality of Service).  MAC layer in WMSN requires two new performance criteria along with the traditional requirements :  Packet latency,  Multiple priorities to packets.  Cross-layer Requirement Optimization. WSN Specific Issues * Stream Processing Requirements

17. Query Processing in Sensor Networks

18. Query Processing in WSN  WHAT IS QP in WSN?  Unlike traditional query processing on each computational unit, WSN queries are generally high-level statements of logical interests over an entire network which may be event detection or monitoring of an area.  WHAT IS NOT QP in WSN?  Users specifying the data they want to collect through simple, declarative queries.

19. Query Processing in WSN  Differences between standard data sources and sensor based data sources:  Stream data delivery,  Garbled and unreliable data delivery.

20. WSN Specific Issues * Query Processing Techniques  Sensor node operating systems are not very useful in fault tolerance and devices (sensor nodes) are crash-prone.  Because of the limitations on implementation issues, careful resource management, transient node come&go and varying signal strengths between devices are to be considered.

21. WSN Specific Issues * Query Processing Techniques  QP in SN can be divided into two:  Shipping data to a remote server for processing(Fjording Architecture)  In-network QP (SNQP)

22. WSN Specific Issues * Shipping Data to a Remote Server  In this technique, QP is done on 2 levels:  Reassembled query plan architecture called Fjords “Framework in Java for Operators on Remote Data Streams”  Sensor-proxies.

23. WSN Specific Issues * Shipping Data to a Remote Server  Flowing data from sensors is pushed into query operators directly.  Operators do not actively pull data to process, rather, they operate when sensor data is available and are otherwise idle. Because of this passive behavior, the adaptive-query processing situation of an operator being “blocked”, waiting for input, does not happen.

24. WSN Specific Issues * Shipping Data to a Remote Server

25. WSN Specific Issues * SNQP  Data summarization techniques,  Interfaces that can be easily used by the operators of the sink,  Network monitoring tools in order to check the health status of sensor nodes have to be developed.

26. WSN Specific Issues * SNQP Components :  Server-side software running on the sink,  Sensor-side software running on the nodes.

27. WSN Specific Issues * SNQP

28.  Query inputs by the operators at the sink,  Sensor data is sampled according to the demanded intervals which is called epoch,  Tailored SQL language is used,  Query results passed forward from nodes to the sink.

29. WSN Specific Issues * SNQP  SNQP typically choose from several alternative plans and operator orderings for any given logical query according to the cost estimation.  Query optimization occurs as much possible as on the sink.

30. WSN Specific Issues * SNQP WSN Query Processing Operators OperatorDescription Select Reject readings that don’t satisfy a particular Boolean predicate. Aggregate Combine readings according to an aggregation function like sum, max etc. Join Concatenate two readings when some join predicate is satisfied. For example, the predicate PointA.light > sensors.light joins (concatenates) all the historical tuples in PointA with current sensor readings for any pair of tuples in which the current light value exceeds the historical value. Data acquisition Acquire a reading (field) from a sensor or an internal device attribute, such as a light sensor reading or free RAM in the dynamic heap.

31. WSN Specific Issues * SNQP  Aggregation operation  NOT ONLY reduces the quantity of data that must be transmitted through the network. Thus, it can reduce energy consumption and bandwidth usage,  BUT ALSO detect object tracking.

32. WSN Specific Issues * SNQP  SAMPLE QUERY CLAUSES  STOP QUERY id,  SAMPLE PERIOD and FOR clauses,  CREATE STORAGE POINT,

33. WSN Specific Issues * SNQP (Tree-Based Routing)

34. WSN Specific Issues * SNQP

35. * Sample Query in SNQP

36. WSN Specific Issues * Query Optimization Techniques  Lifetime management,  Partial aggregation,  Packet Merging for Pushing Computation These techniques all emphasize the efficient usage of the restricted resources.

37. Discussion & Future Work Data Management and Storage Issue Deployment Consideration Improvements over Protocol Stack QP Techniques

38. References [1] Wireless multimedia sensor networks: A survey, IF Akyildiz, T Melodia… - Wireless Communications, …, 2007 - ieeexplore.ieee.org [2] Rethinking data management for storage-centric sensor networks, Y Diao, D Ganesan, G Mathur… - Proceedings of the Third …, 2007 - mit.edu [3] Ultra-low power data storage for sensor networks, G Mathur, P Desnoyers, P Chukiu… - … on Sensor Networks ( …, 2009 - dl.acm.org) [4] A survey of multimedia streaming in wireless sensor networks, S Misra, M Reisslein…Communications Surveys & …, 2008 - ieeexplore.ieee.org [5] Transmitting streaming data in wireless multimedia sensor networks with holes L Shu, ZB Zhou, M Hauswirth, D Le Phuoc… - … ubiquitous multimedia, 2007 - dl.acm.org [6] Query processing in sensor networks, J Gehrke… - Pervasive Computing, IEEE, 2004 - ieeexplore.ieee.org [7] Fjording the stream: An architecture for queries over streaming sensor data, S Madden… - Data Engineering, 2002. Proceedings …, 2002 - ieeexplore.ieee.org [8] Join of multiple data streams in sensor networks, X Zhu, H Gupta… - Knowledge and Data Engineering, …, 2009 - ieeexplore.ieee.org [9] Y. Wei, J. Heidemann, and D. Estrin. An energy-efficient MAC protocol for wireless sensor networks. In Proc. IEEE INFOCOM, pages 1567-1576, June 2002

41. STREAMS & SENSOR NETWORKS “ Query Processing in Sensor Networks ”