A pragmatic value-of-information approach for intruder tracking sensor networks

Sensor networks are distributed systems where nodes embedded in the environment collect readings through their sensors and transmit data to customers. The overall goal of these systems can be stated as maximizing a metric of the sensing quality while limiting the consumption of a set of scarce resources. In this paper we consider an intruder detection and tracking system where the sensing quality is a metric of the pragmatic value of the information provided by the network. This metric depends not only on the quantity and accuracy of information, but also on when and how the customers will use this information. We design a system which adapts its information transmission to the disruptive decisions made by the user, including a consideration for the cost of incorrect decisions.

[1]  Ramesh Govindan,et al.  The Sensor Network as a Database , 2002 .

[2]  Victor R. Lesser,et al.  Distributed sensor network for real time tracking , 2001, AGENTS '01.

[3]  Krishnendu Chakrabarty,et al.  Distributed Mobility Management for Target Tracking in Mobile Sensor Networks , 2007, IEEE Transactions on Mobile Computing.

[4]  Prasant Mohapatra,et al.  Power conservation and quality of surveillance in target tracking sensor networks , 2004, MobiCom '04.

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

[6]  Chen-Khong Tham,et al.  Energy Efficient Multiple Target Tracking in Wireless Sensor Networks , 2007, IEEE Transactions on Vehicular Technology.

[7]  Donald F. Towsley,et al.  Target tracking with packet delays and losses - QoI amid latencies and missing data , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[8]  Kin K. Leung,et al.  A letter soup for the quality of information in sensor networks , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[9]  Gang Zhou,et al.  VigilNet: An integrated sensor network system for energy-efficient surveillance , 2006, TOSN.

[10]  Dharma P. Agrawal,et al.  Intrusion Detection in Homogeneous and Heterogeneous Wireless Sensor Networks , 2008, IEEE Transactions on Mobile Computing.

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

[12]  Stephan Olariu,et al.  ANSWER: AutoNomouS netWorked sEnsoR system , 2007, J. Parallel Distributed Comput..

[13]  Guoliang Xing,et al.  Analysis of Quality of Surveillance in fusion-based sensor networks , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[14]  Duncan Fyfe Gillies,et al.  Probabilistic Approaches to Estimating the Quality of Information in Military Sensor Networks , 2010, Comput. J..

[15]  Tian He,et al.  Differentiated surveillance for sensor networks , 2003, SenSys '03.

[16]  Vikram Krishnamurthy,et al.  Algorithms for optimal scheduling and management of hidden Markov model sensors , 2002, IEEE Trans. Signal Process..

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

[18]  Mani B. Srivastava,et al.  Building principles for a quality of information specification for sensor information , 2009, 2009 12th International Conference on Information Fusion.