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CFP: IEEE Workshop on Information Retrieval in Sensor Networks (IRSN 2009)
CCNC 2009- Satellite Workshop
January 13 2009, Las Vegas, Nevada, USA

                                                 Click here for all the details on this workshop.

Thanks to sensor technologies, which allow for cheaper sensors with increasing sensing capability, there is a growing trend of deploying sensor nodes in the physical world to form a network for monitoring events of interest. Critical to such a sensor network is a functionality that allows for efficient storage and fast retrieval of information. Despite various efforts, research on techniques that enable information retrieval for sensor networks is still in an infancy stage and mostly ad hoc. It is understandable, though. Compared to information retrieval systems already existing on the Internet, that designed for sensor networks poses unseen challenges due to limitations in sensor storage, processing, and communication capacities. Adding to the aforementioned issues is the curse of dimensionality. In practice, due to their sophistication, sensor events are usually identified by more than one attributes. Management of multi-dimensional data is already a difficult problem in information systems. Doing so under the resource constraints of sensor networks is even much harder.

This workshop solicits original contributions aimed to significantly advance the state of the art in the area of information retrieval in sensor networks. The topics of interest are listed below, but not limited to:

* Distributed MAC/networking/overlay protocols in support of information retrieval services for sensor networks

* Distributed storage and indexing in sensor networks

* Distributed data aggregation, query processing and other in-network processing in support of information retrieval in sensor networks

* Impacts of energy/failure/noise/ambiguity/mobility/privacy/security on design of information retrieval techniques in sensor networks

* Programming language and/or middleware support for information retrieval in sensor networks

* Simulation tools and empirical testbeds for information retrieval in sensor networks

* Case studies, implementations, and novel applications of information retrieval in sensor networks

We are also interested in survey papers, vision papers, and those well-thought ideas that could be risky yet potential to lead to a major advance in the field.

Submission Guidelines

Submitted papers must represent original material that is not currently under review in any other conference or journal, and has not been previously published.

  • Paper format should follow the standard IEEE Transactions templates (Microsoft Word or LaTeX). Paper length should not exceed five-page technical paper manuscript. The paper should be used as the basis for a 20 - 30 minute workshop presentation
  • Papers should be submitted in a .pdf or .ps format at the EDAS IRSN-2009 submission website. A separate cover sheet should show the title of the paper, the author(s) name(s) and affiliation(s), and the address (including e-mail, telephone, and fax) to which the correspondence should be sent
  • All accepted papers will be published in the conference proceedings. At least one author of accepted papers is required to register at the full registration rate

Best papers and journal special issue

Authors of select papers will be invited to submit an extended version for publication in the Special Issue on Information Retrieval in Sensor Networks with Int'l Journal for Parallel, Emergent, and Distributed Systems (IJPEDS, publication tentatively scheduled in 2nd Quarter, 2009).


Paper Submission: Sept 1, 2008 (Submission site is open)
Author Notification: Sept 12, 2008
Camera-ready Copy: Oct 10, 2008 (Firm deadline)
Author Registration Deadline: Oct 10, 2008 (Firm deadline)
Workshop date: Jan 13, 2009

General Chair
: Jun Suzuki, UMass Boston (

PC Chair: Duc A. Tran, UMass Boston (

PC Members

Cindy Chen, University of Massachusetts Lowell
Paolo Costa, Vrije Universiteit
Takahiro Hara, Osaka University, Japan
Judith Kelner, Federal University of Pernambuco, Brazil
Benyuan Liu, University of Massachusetts Lowell
Thinh Nguyen, Oregon State University
Xuanlong Nguyen, Duke University
Radu Storelu, Texas A&M University
Hong L. Truong, IBM Research Zurich
Naoki Wakamiya, Osaka University, Japan
Vladimir Zadorozhny, Univ of Pittsburgh


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