NSF-Funded Collaborative Research on Building an Intelligent, Uncertainty-Resilient Detection and Tracking Sensor Network


Detection, identification, and tracking of CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive) plumes can be accomplished by combining the modalities of sensor and cyber networks. The sensor network provides information about physical-space activities, e.g., locations and movements of the plume sources. The cyber network provides storage and computational resources to analyze and infer where the plume originated, the trajectory of its movement, and the prediction of its future movement. The challenges in realizing such a sensor cyber network include intelligent sensing (intelligent sensor selection and coverage) and the capability to deal with uncertainties (uncertainty in measurement as well as in modeling). This project proposes to leverage the convergence between the sensor and the cyber networks to achieve these goals. In particular, the plan is to carry out three synergistic research tasks: 1) network formation by sensor selection, placement, and coverage; 2) sensor tasking protocol with temporal/spatial uncertainty management; 3) protocols for reliable sensor-cyber communication supporting the above two tasks. The PIs will prototype the research results and integrate the prototypes for different components to build the sensor cyber network for plume detection, identification and tracking. They will also evaluate the sensor cyber network using various test scenarios in collaboration with Oak Ridge National Lab. If successful, the project will provide technology for building detection and tracking sensor networks that can give great protection to people and the environment against harmful plumes.

Awarded Partners

Florida International University
Louisiana State University & Agricultural and Mechanical College
Computer Science Department, Purdue University
University of Florida

Collaborative Partners

Indian Institute of Science
Oak Ridge National Laboratory
University of Hong Kong
Zhejiang University

For more information, visit the project web page at: http://nets.cis.fiu.edu/.



CFP: Real-Time Control in a Big Sensor Data Network

Call for Papers

A large number of important real-time applications depend on the analysis of big sensor data interfacing with the real world. These applications include military, medical, manufacturing, transportation, environmental planning, and disaster management systems. Many have been difficult to realize because of the problems involved with inputting big data from sensors directly into automated systems. Real-time control in a big sensor network has a strong impact on many of the previously mentioned applications especially in the context of processing, monitoring, and the immediate followup and detecting of complex events in many of these systems.

The main focus of this special issue will be on the new and existing real-time applications of big data sensor analytics to problems of global importance. This special issue will also become an international forum for researchers to highlight the most recent development and ideas in the field with a special emphasis on real-time applications. Potential topics include, but are not limited to:

  • Real-time analytics applications
  • Real-time visualization
  • Algorithms for map reduce
  • Data streaming
  • Sensor data storage
  • Sensor network mobility and social networks
  • Sensor cloud
  • Programmability tools
  • Security in big data
  • Data aggregation and protocols in large-scale networks
  • Big sensor platforms/architectures for real-time applications
  • Integration between sensor networks and business systems
  • Deployment, data aggregation, and energy optimization network protocols
  • Large-scale data sensor fusion
  • Machine learning on big data

Before submission authors should carefully read over the journal’s Author Guidelines, which are located at http://www.hindawi.com/journals/js/guidelines/. Prospective authors should submit an electronic copy of their complete manuscript through the journal Manuscript Tracking System athttp://mts.hindawi.com/submit/journals/js/rtc/ according to the following timetable:

Manuscript Due Friday, 14 June 2013
First Round of Reviews Friday, 6 September 2013
Publication Date Friday, 1 November 2013

Lead Guest Editor

  • S. S. Iyengar, School of Computing and Information Sciences, FIU, Miami, FL, USA

Guest Editors

  • Niki Pissinou, School of Computing and Information Sciences, Florida International University, Miami, FL, USA
  • N. Balakrishnan, Supercomputer Education and Research Centre, Indian Institute of Science, Bangalore, Karnataka, India

6/10/13: Peter Scheuermann (Northwestern University)

Title: Processing Spatio-Temporal Queries for Uncertain Trajectories
Time: 2pm on June 10, 2013
Space: ECS 212

ABSTRACT: The ( location, time) data capturing the motion of moving objects is subject to uncertainty due to a number of reasons, such as the imprecision of GPS devices and the fact that we cannot record this data for every time-instant. As a consequence of uncertainty the results of continuous queries become probabilistic in nature, i.e., an objects or trajectory is associated with a qualification probability that indicates how likely it is the answer of the query. We discuss first different models for modeling uncertainty of moving objects in free space and on road networks. Next we formalize two of the most popular types of continuous queries, range queries and nearest neighbor (NN) queries, under the free space and road network models. Next , we elaborate on a methodology for processing NN-queries for the sheared cylinders model used to represent time-parameterized motion in free space. We demonstrate that by using the convolution technique from probability theory we can transform the original problem into a much simpler one, where the query trajectory becomes crisp.

BIOGRAPHY: Peter Scheuermann is a Professor of Electrical Engineering and Computer Science at Northwestern University. He has held visiting professor positions with the Free University of Amsterdam, the Technical University of Berlin, the Swiss Federal Institute of Technology, Zurich and University of Melbourne. During 1997-1998 he served as Program Director for Operating Systems at the NSF. Dr. Scheuermann has served on the editorial board of the Communications of ACM, The VLDB Journal, IEEE Transactions on Knowledge and Data Engineering and is currently an associate editor of Data and Knowledge Engineering, Wireless Network and the new ACM Trans. on Spatial Algorithms and Systems. Among his professional activities, he has served as General chair of the ACM-SIGMOD Conference in 1988, General Chair of the ER ‘2003 Conference and more recently as Program Co-Chair of the ACM-SIGPATIAL conference in 2009. He was a member of the ACM-SIGMOD advisory board, and prior to this he chaired the ACM-SIGMOD awards committee His research interests are in spatio-temporal databases, mobile computing, sensor networks and data mining. He has published more than 140 journal and conference papers. His research has been funded by NSF, NASA, HP, Northrop Grumman, and BEA, among others. Peter Scheuermann is a Fellow of IEEE and AAAS (American Association for the Advancement of Science).