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Future Directions in Sensor Data Management: A Panel Discussion

Future Directions in Sensor Data Management: A Panel Discussion. Demetris Zeinalipour University of Cyprus. Sponsored by :. Panel Objective. To provide views on the following:

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Future Directions in Sensor Data Management: A Panel Discussion

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  1. Future Directions in Sensor Data Management: A Panel Discussion Demetris Zeinalipour University of Cyprus Sponsored by :

  2. Panel Objective • To provide views on the following: • To what extend the vision of applying data management techniques to sensor network research has been successful over the years • e.g., Adoption of ideas proposed by the community • To examine the significance of recent advances and to identify new directions that can foster research in sensor data management

  3. DMSN’10 Panelists… Yanlei Diao (Univ. of Massachusetts Amherst, USA) DMSN’08 PC Chair Christian S. Jensen (Aarhus University, Denmark) DMSN’08 PC Chair Le Gruenwald (National Science Foundation, USA) Kian-Lee Tan (National University of Singapore, Singapore)

  4. Wireless Sensor Networks • We will soon celebrate 10 years of research and developments in the area of sensor networks. • 1999: Kristofer Pister (UCB) introduces the Smartdust vision • “a hypothetical wireless network of tiny microelectromechanicalsensors (MEMS), robots, or devices, that can detect (for example) light, temperature, or vibration.” wikipedia.org • 2000: First release of TinyOS for Rene – Crossbow partners • 2001: Berkeley develops MICA • 2002: TinyOS in nesC is released • 2003: TinyDB launch and 1st SenSys Conference • 2004: First DMSN workshop in Toronto

  5. Research Emphasis of DMSN(based on titles since 2004) High DMSN Aim: “All important aspects of sensordata management, including data acquisition, processing, and storage in remote wireless networks” • Query Processing • Frameworks • Monitoring • Energy-efficiency • Models • Systems • Applications • Storage • …. Low

  6. Important Research Areas of DMSN(based on citations since 2004) Total Citations since 2004: 778

  7. What is the current state? • In recent years, we have been witnessing a paradigm shift from the initial target of Sensor Networks that focused on “Low Power Embedded Sensing devices” and “Environmental Monitoring Applications” • Nowadays Sensor Devices are packed with more hardware (e.g., i-Mote2) and software capabilities (e.g., running Linux) • Additionally, even traditional applications are much more diverse (e.g., using camera boards for urban monitoring, etc.)

  8. What is a Sensor Network today? A Network of Mobile Sensors? Artifacts created by the distributed robotics and low power embedded systems areas. Characteristics • Small-sized, wireless-capable, energy-sensitive, as their stationary counterparts. • Feature explicit (e.g., motor) or implicit (sea/air current) mechanisms that enable movement. SensorFlock (U of Colorado Boulder) LittleHelis (USC) MilliBots (CMU) CotsBots (UC-Berkeley)

  9. What is a Sensor Network today? Example: Chemical Dispersion Sampling Identify the existence of toxic plumes. Micro Air Vehicles (UAV – Unmanned Aerial Vehicles) Ground Station Graphic courtesy of: J. Allred et al. "SensorFlock: An Airborne Wireless Sensor Network of Micro-Air Vehicles", In ACM SenSys 2007.

  10. What is a Sensor Network today? • A Network of Smartphones? • Sensor: • Proximity Sensor (turn off display when getting close to ear) • Ambient Light Detector (brighten display when in sunlight) • Accelerometer (identify rotation and digital compass) • Camera, Microphone, Geo-location on GPS, WIFI, Cellular Towers,… • Network: • Bluetooth: Peer-to-Peer applications / services • WLAN, WCDMA/UMTS(3G) / HSPA(3.5G): broadband access. • Actuators: Notification Light, Speaker.

  11. Sensys’09 Best Paper Intelligent Transportation Systems with VTrack • Better manage traffic by estimating roads taken by users using WiFi beams (instead of GPS) . Graphics courtesy of: A .Thiagarajan et. al. “Vtrack: Accurate, Energy-Aware Road Traffic Delay Estimation using Mobile Phones, In Sensys’09, pages 85-98. ACM, (Best Paper) MIT’s CarTel Group

  12. Complementary Technologies? • Several complementary research fields in the area of data management seek to solve problems similar to those addressed by the DMSN community e.g., • Stream Processors • i.e., for processing real-time data flows. • Cloud Data Analytic Frameworks • e.g., Map-reduce for analyzing massive data. • Semantic Web Technologies • e.g., Sensorweb for structuring Internet-scale sensor data repositories

  13. Complementary Technologies? • Several complementary research fields in the area of data management seek to solve problems similar to those addressed by the DMSN community e.g., • Stream Processors • i.e., for processing real-time data flows. • Cloud Data Analytic Frameworks • e.g., Map-reduce for analyzing massive data. • Semantic Web Technologies • e.g., Sensorweb for structuring Internet-scale sensor data repositories Future Directions for Sensor Data Management?

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