A distributed and energy-efficient framework for Neyman-Pearson detection of fluctuating signals in large-scale sensor networks

To address the challenges inherent to a problem of practical interest - of Neyman-Pearson detection of fluctuating radar signals using wireless sensor networks, we propose in this paper a distributed and energy-efficient framework. Such framework is scalable with respect to the network size, and is able to greatly reduce the dependence on the central fusion center. It assumes a clustering infrastructure, and addresses signal processing and communications related issues arising from different layers. This framework includes a distributed scheduling protocol and a distributed routing protocol, which enable sensor nodes to make their own decisions about information transmissions, without requiring the knowledge of the network global information. In this framework, energy efficiency manifests itself at different network layers in a distributed fashion, and a balance between the detection performance and the energy efficiency is also attained.

[1]  Yang Yang.  Signal processing and communications for radar sensor networks , 2009 .

[2]  S. Srivastava,et al.  A Survey and Classification of Distributed Scheduling Algorithms for Sensor Networks , 2007, 2007 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007).

[3]  Anantha P. Chandrakasan,et al.  An application-specific protocol architecture for wireless microsensor networks , 2002, IEEE Trans. Wirel. Commun..

[4]  Lang Tong,et al.  Cooperative routing for distributed detection in large sensor networks , 2007, IEEE Journal on Selected Areas in Communications.

[5]  Baruch Awerbuch,et al.  Distributed algorithms for multicommodity flow problems via approximate steepest descent framework , 2007, SODA '07.

[6]  G. Parmigiani.  Large Deviation Techniques in Decision, Simulation and Estimation , 1992 .

[7]  Rick S. Blum,et al.  Energy-efficient Routing for Signal Detection under the Neyman-Pearson Criterion in Wireless Sensor Networks , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[8]  Ness B. Shroff,et al.  Opportunistic transmission scheduling with resource-sharing constraints in wireless networks , 2001, IEEE J. Sel. Areas Commun..

[9]  Dimitri P. Bertsekas,et al.  Second Derivative Algorithms for Minimum Delay Distributed Routing in Networks , 1984, IEEE Trans. Commun..

[10]  Rick S. Blum,et al.  Energy Efficient Signal Detection in Sensor Networks Using Ordered Transmissions , 2008, IEEE Transactions on Signal Processing.

[11]  Randall Berry,et al.  Exploiting multiuser diversity for medium access control in wireless networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[12]  Robert G. Gallager,et al.  A Minimum Delay Routing Algorithm Using Distributed Computation , 1977, IEEE Trans. Commun..

[13]  Cem Ersoy,et al.  MAC protocols for wireless sensor networks: a survey , 2006, IEEE Communications Magazine.

[14]  J. J. Garcia-Luna-Aceves,et al.  A simple approximation to minimum-delay routing , 1999, SIGCOMM '99.

[15]  Lang Tong,et al.  Opportunistic Carrier Sensing for Energy-Efficient Information Retrieval in Sensor Networks , 2005, EURASIP J. Wirel. Commun. Netw..

[16]  Claude Castelluccia,et al.  Differentiation mechanisms for IEEE 802.11 , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[17]  S. Kullback,et al.  Information Theory and Statistics , 1959 .

[18]  Rick S. Blum,et al.  Energy-Efficient Routing for Signal Detection in Wireless Sensor Networks , 2009, IEEE Transactions on Signal Processing.