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It is our great pleasure to welcome you all to the 7th International Workshop on Data Management for Sensor Networks (DMSN'10), which takes place in Singapore on September 13, 2010. The annual DMSN workshop is a leading international forum that covers all important aspects of sensor data management, including data acquisition, processing, and storage in remote wireless networks; the handling of uncertain sensor data; and the management of heterogeneous and sometimes sensitive sensor data in databases. It brings together a wide range of researchers, practitioners, and users to explore and share scientific and industrial challenges that arise in the aforementioned contexts. We hope you find the workshop academically stimulating and the location interesting and enjoyable.
One of our main objectives was to bring forward an exciting research program, spanning both predominant and emerging fields in data management for sensor networks. DMSN'10 received 10 submissions for research papers, out of those we accepted only 6 papers. The accepted papers were thematically organized in the following categories: Data Provenance, Query Processing, Mobile Sensor Networks and Outlier Detection in Sensor Networks. In addition to research contributions, DMSN'10 features an exciting keynote talk by Prof. Kian-Lee Tan (National University of Singapore, Singapore), with title: "What's NExT? Sensor + Cloud!?" Finally, the program also features a panel discussion with title "Future Directions in Sensor Data Management: A Panel Discussion", with panelists Dr. Yanlei Diao (University of Massachusetts Amherst, USA), Prof. Le Gruenwald (National Science Foundation, USA), Prof. Christian S. Jensen (Aarhus University) and Prof. Kian-Lee Tan (National University of Singapore, Singapore)
Besides authors that provided the content of the program, several other people have contributed to the successful organization of DMSN'10. In particular, we would like to thank our technically and geographically diverse Technical Program Committee (TPC), which enabled us to make high quality decisions. Our TPC comprised of 32 members that spanned the following continents: North America (50%), Europe (31%) and Asia (19%). Our TPC board came from both Academia (87%) and Industrial Research Labs (13%). We owe our sincere gratitude to all of these members for their excellent work in reviewing the papers and providing valuable feedback under a tight schedule. Every paper was reviewed at least by 3 TPC members. We would like to thank Microsoft for granting us permission to use the Microsoft Conference Management System (CMT) and the entire CMT support team, for their help in setting up and managing the online review process. The latest features in CMT made it extremely easy to cope with virtually all aspects of the paper evaluation process.
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What's NExT?: Sensor + Cloud!?
Today, we are witnessing a number of interesting phenomena. First, there is an increasing adoption of sensing technologies (e.g., RFID, cameras, mobile phones) in many industries. Second, the internet has become a source of realtime information (e.g., ...
Provenance-based trustworthiness assessment in sensor networks
As sensor networks are being increasingly deployed in decision-making infrastructures such as battlefield monitoring systems and SCADA (Supervisory Control and Data Acquisition) systems, making decision makers aware of the trustworthiness of the ...
Facilitating fine grained data provenance using temporal data model
E-science applications use fine grained data provenance to maintain the reproducibility of scientific results, i.e., for each processed data tuple, the source data used to process the tuple as well as the used approach is documented. Since most of the e-...
Processing nested complex sequence pattern queries over event streams
- Mo Liu,
- Medhabi Ray,
- Elke A. Rundensteiner,
- Daniel J. Dougherty,
- Chetan Gupta,
- Song Wang,
- Ismail Ari,
- Abhay Mehta
Complex event processing (CEP) has become increasingly important for tracking and monitoring applications ranging from health care, supply chain management to surveillance. These monitoring applications submit complex event queries to track sequences of ...
Query-driven data collection and data forwarding in intermittently connected mobile sensor networks
In sparse and intermittently connected Mobile Sensor Networks (MSNs), the base station cannot easily get the data objects acquired by the mobile sensors in the field. When users query the base station for specific data objects, the base station may not ...
DEMS: a data mining based technique to handle missing data in mobile sensor network applications
In Mobile Sensor Network (MSN) applications, sensors move to increase the area of coverage and/or to compensate for the failure of other sensors. In such applications, loss or corruption of sensor data, known as the missing sensor data phenomenon, ...
PAO: power-efficient attribution of outliers in wireless sensor networks
Sensor nodes constitute inexpensive, disposable devices that are often scattered in harsh environments of interest so as to collect and communicate desired measurements of monitored quantities. Due to the commodity hardware used in the construction of ...
Future directions in sensor data management: a panel discussion
We will soon celebrate 10 years of research and development in the area of sensor networks. During this decade, we have witnessed the emergence of specialized embedded systems, operating systems, data-oriented management systems as well as programming ...
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Acceptance Rates
Year | Submitted | Accepted | Rate |
---|---|---|---|
DMSN '09 | 16 | 6 | 38% |
Overall | 16 | 6 | 38% |