Sensor networks data management

In order for everyone to be on the same page, we will first review one of the main systems for query processing in sensor networks.

TinyDB: An Acquisitional Query Processing System for Sensor Networks.
Samuel Madden, Michael Franklin, Joseph Hellerstein, and Wei Hong. In TODS 30(1), 2005.

Additional references:

• Main conference for research on sensor networks: Sensys

Application domain 1: Scientific data management

Increasingly, scientists need to deploy and share common sensor infrastructures to monitor and study the physical world. We will discuss two short papers that present some of the challenges of scientific sensor data management.

Life Under your Feet: A Wireless Soil Ecology Sensor Network.
Razvan Musaloiu, Andreas Terzis, Katalin Szlavecz, Alex Szalay, Joshua Cogan, and Jim Gray. In EmNetS 2006. [Note that there is also an extended tech report version of this paper with a slightly different title].

Batch is Back: CasJobs, Serving Multi-TB Data on the Web .
William OMullane, Nolan Li, Maria Nieto-Santisteban, Alex Szalay, Ani Thakar, Jim Gray. In Proc. of the IEEE International Conference on Web Services (ICWS'05)

Additional references:

• Science In An Exponential World. Jim Gray, Alex Szalay, Nature, V. 440.23, 23 March 2006.
• The World Wide Telescope, Jim Gray, Alex Szalay, MSDN Magazine, V. 21.5, April 2006.
• Scientific Data Management in the Coming Decade. Jim Gray, David T. Liu, Maria Nieto-Santisteban, Alexander S. Szalay, David DeWitt, Gerd Heber. Technical report. MSR-TR-2005-10

Application domain 2: Monitoring cities and homes

Different from scientific deployments are sensor networks designed to monitor humans in their daily activities. These networks can be deployed at the scale of a city or at the scale of individual homes.

CarTel: A Distributed Mobile Sensor Computing System.
Bret Hull, Vladimir Bychkovskiy, Kevin Chen, Michel Goraczko, Eugene Shih, Yang Zhang, Hari Balakrishnan, Samuel Madden. In Proc. of SenSys 2006.

An Assisted Living Oriented Information System Based on a Residential Wireless Sensor Network.
G. Virone, A. Wood, L. Selavo, Q. Cao, L. Fang, T. Doan, Z. He, R. Stoleru, S. Lin, and J.A. Stankovic. In Proc. of the 1st Distributed Diagnosis and Home Healthcare (D2H2) Conference.

Additional references:

• Wireless Urban Sensing Systems. Mani Srivastava, Mark Hansen, Jeff Burke, Andrew Parker, Sasank Reddy, Ganeriwal Saurabh, Mark Allman, Vern Paxson, Deborah Estrin in CENS Technical Report #65 , April 2006.
• Recognizing Activities and Spatial Context Using Wearable Sensors. A. Subramanya, A. Raj, J. Bilmes, and D. Fox. UAI-06.
• Fine-Grained Activity Recognition by Aggregating Abstract Object Usage. D. Patterson, D. Fox, H. Kautz, and M. Philipose. ISWC-05.
• ALARM-NET: Wireless Sensor Networks for Assisted-Living and Residential Monitoring. A. Wood, G. Virone, T. Doan, Q. Cao, L. Selavo, Y. Wu, L. Fang, Z. He, S. Lin, J. Stankovic. Technical Report CS-2006-11, Department of Computer Science, University of Virginia, 2006.

30 October

27 October

Spatio-temporal data management

[Note: Rescheduled to Friday, October 27th at 2pm in CSE 605]

Sensor data requires powerful spatio-temporal data management capabilities. The spatio-temporal research area is quite wide. We will only discuss a specific example based on RFID data.

Temporal Management of RFID Data.
Fusheng Wang, Peiya Liu. VLDB 2005.

Sensor data cleaning (RFID data)

An important challenge when dealing with sensor data is handling erroneous inputs due to noise and sensor failures. We start the discussion by examining how to clean data produced by an RFID infrastructure.

Declarative Support for Sensor Data Cleaning.
Shawn R. Jeffery, Gustavo Alonso, Michael J. Franklin, Wei Hong, Jennifer Widom. Pervasive 2006.

Additional references:

• Adaptive Cleaning for RFID Data Streams. Shawn R. Jeffery, Minos Garofalakis, Michael J. Franklin. VLDB 2006
• A Deferred Cleansing Method for RFID Data Analytics. Jun Rao, Sangeeta Doraiswamy, Hetal Thakkar, Latha S. Colby. VLDB 2006

Model-based sensor data management

Continuing on the idea of noisy sensor data, we will discuss the use of correlations and models to handle uncertainty, missed reading, outliers, and more.

Using Probabilistic Models for Data Management in Acquisitional Environments.
Amol Deshpande, Carlos Guestrin, Samuel Madden. In Proceedings of CIDR, 2005.

Additional references:

• Online Outlier Detection in Sensor Data Using Non-Parametric Models. Sharmila Subramaniam, Themis Palpana, Dimitris Papadopoulos, Vana Kalogeraki, Dimitrios Gunopulos. VLDB 2006
• MauveDB: Supporting Model-Based User Views in Database Systems. Amol Deshpande, Samuel Madden. In Proceedings of SIGMOD, 2006.
• Model-based Approximate Querying in Sensor Networks. Amol Deshpande, Carlos Guestrin, Samuel Madden, Joseph Hellerstein, Wei Hong. In Proceedings of VLDB Journal, 2005.

Internet-scale infrastructures

We do not spend too much time on infrastructures because most of us studied them last Winter. Here are two good overview papers that we can use to discuss push-based vs pull-based infrastructures and their challenges.

Cache-and-Query for Wide Area Sensor Databases,
Amol Deshpande, Suman Nath, Phillip B. Gibbons, Srinivasan Seshan. ACM SIGMOD 2003.

The Design of the Borealis Stream Processing Engine.
Daniel J Abadi, Yanif Ahmad, Magdalena Balazinska, Ugur Cetintemel, Mitch Cherniack, Jeong-Hyon Hwang, Wolfgang Lindner, Anurag S Maskey, Alex,er Rasin, Esther Ryvkina, Nesime Tatbul, Ying Xing, and Stan Zdonik. CIDR 2005.

Data integration and lineage

The world-wide sensor web requires data integration across autonomous organizations. Data integration is one of the main database challenges today. There isn't any work done specifically on integrating sensor data. Instead, we will discuss the more general problem of scientific data integration.

Reconciling while Tolerating Disagreement in Collaborative Data Sharing
Nicholas Taylor, Zachary Ives. SIGMOD 2006

Additional references:

• Provenance Management in Curated Databases. Peter Buneman, Adriane Chapman, James Cheney. SIGMOD 2006.
• Report on the EDBT'02 panel on scientific data integration. Omar Boucelma and Silvana Castano and Carole Goble and Vanja Josifovski and Zoe Lacroix and Bertram Ludascher. SIGMOD Record. Volume: 31. Nb: 4. 2002.

Security and privacy

Finally, monitoring the physical world raises serious privacy and security issues.

RFID Security and Privacy: A Research Survey.
A. Juels. Condensed version to appear in 2006 in the IEEE Journal on Selected Areas in Communication.

Security and Privacy in Sensor Networks
Haowen Chan, Adrian Perrig.
IEEE Computer, 36(10), October 2003, pp 103-105