A scalable architecture for heterogeneous sensor management

Thanks to their miniature size and high autonomy, sensors have become an integral part of our life. They are being successfully used in many fields (military, house automation, medical, urban environments, etc.). However, a lot of work still needs to be done on querying and system management of large sets of sensors. Scalable solutions supporting continuous data stream management are required. This paper presents a scalable architecture for management of data streams coming from a large set of heterogeneous sensors. Our proposal is at the boundaries of two traditional approaches. The first approach uses centralized or slightly distributed platforms to manage continuous data streams, while the second one allows data processing in a strongly distributed way and is based on networks of "intelligent" sensors. Our hybrid approach proposed in this article offers scalable solutions combining the advantages of these two approaches.

[1]  E. B. Moss,et al.  Nested Transactions: An Approach to Reliable Distributed Computing , 1985 .

[2]  Rajeev Motwani,et al.  Load shedding for aggregation queries over data streams , 2004, Proceedings. 20th International Conference on Data Engineering.

[3]  Divesh Srivastava,et al.  On computing correlated aggregates over continual data streams , 2001, SIGMOD '01.

[4]  Hamid Pirahesh,et al.  Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals , 1996, Data Mining and Knowledge Discovery.

[5]  A. N. Wilschut,et al.  Dataflow query execution in a parallel main-memory environment , 1991, Distributed and Parallel Databases.

[6]  Michael Stonebraker,et al.  Monitoring Streams - A New Class of Data Management Applications , 2002, VLDB.

[7]  Lukasz Golab,et al.  Issues in data stream management , 2003, SGMD.

[8]  Daniel J. Abadi,et al.  An Integration Framework for Sensor Networks and Data Stream Management Systems , 2004, VLDB.

[9]  Ying Xing,et al.  The Design of the Borealis Stream Processing Engine , 2005, CIDR.

[10]  Michael Stonebraker,et al.  Load Shedding in a Data Stream Manager , 2003, VLDB.

[11]  Frederick Reiss,et al.  Data Triage: an adaptive architecture for load shedding in TelegraphCQ , 2005, 21st International Conference on Data Engineering (ICDE'05).

[12]  David J. DeWitt,et al.  NiagaraCQ: a scalable continuous query system for Internet databases , 2000, SIGMOD '00.

[13]  Yong Yao,et al.  The cougar approach to in-network query processing in sensor networks , 2002, SGMD.

[14]  Frederick Reiss,et al.  TelegraphCQ: Continuous Dataflow Processing for an Uncertain World , 2003, CIDR.

[15]  Johannes Gehrke,et al.  Query Processing in Sensor Networks , 2003, CIDR.

[16]  Deborah Estrin,et al.  Directed diffusion for wireless sensor networking , 2003, TNET.

[17]  Joseph M. Hellerstein,et al.  Eddies: continuously adaptive query processing , 2000, SIGMOD '00.

[18]  Wei Hong,et al.  TinyDB: an acquisitional query processing system for sensor networks , 2005, TODS.

[19]  Samuel Madden,et al.  Fjording the stream: an architecture for queries over streaming sensor data , 2002, Proceedings 18th International Conference on Data Engineering.

[20]  Mohamed A. Sharaf,et al.  Balancing energy efficiency and quality of aggregate data in sensor networks , 2004, The VLDB Journal.

[21]  Gio Wiederhold,et al.  Mediators in the architecture of future information systems , 1992, Computer.

[22]  Samuel Madden,et al.  Continuously adaptive continuous queries over streams , 2002, SIGMOD '02.

[23]  Jennifer Widom,et al.  STREAM: The Stanford Stream Data Manager , 2003, IEEE Data Eng. Bull..

[24]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[25]  A. Min Tjoa,et al.  Proceedings of the 12th International Workshop on Database and Expert Systems Applications , 2001 .