Transmission Control Policy design for decentralized detection in sensor networks

A Wireless Sensor Network (WSN) deployed for detection applications has the distinguishing feature that sensors cooperate to perform the detection task. Therefore, the decoupled design approach typically used to design communication networks, where each network layer is designed independently, does not lead to the desired optimal detection performance. Recent work on decentralized detection has addressed the design of MAC and routing protocols for detection applications by considering independently the Quality of Information (QoI), Channel State Information (CSI), and Residual Energy Information (REI) for each sensor. However, little attention has been given to integrate the three quality measures (QoI, CSI, REI) in the complete system design. In this work, we pursue a cross-layer approach to design a QoI, CSI, and REI-aware Transmission Control Policy (TCP) that coordinates communication between local sensors and the fusion center, in order to maximize the detection performance. We formulate and solve a constrained nonlinear optimization problem to find the optimal TCP design variables. We compare our design with the decoupled approach, where each layer is designed separately, in terms of the delay for detection and WSN lifetime.

[1]  Maurizio Longo,et al.  Quantization for decentralized hypothesis testing under communication constraints , 1990, IEEE Trans. Inf. Theory.

[2]  M. Hata,et al.  Empirical formula for propagation loss in land mobile radio services , 1980, IEEE Transactions on Vehicular Technology.

[3]  J.-F. Chamberland,et al.  Cross-Layer Optimization and Information Assurance in Decentralized Detection over Wireless Sensor Networks , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[4]  T. Duman,et al.  Decentralized detection over multiple-access channels , 1998 .

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

[6]  Ananthram Swami,et al.  Wireless sensor networks : signal processing and communications perspectives , 2007 .

[7]  Moshe Kam,et al.  Distributed decision fusion with a random-access channel for sensor network applications , 2004, IEEE Transactions on Instrumentation and Measurement.

[8]  Lang Tong,et al.  The interplay between signal processing and networking in sensor networks , 2006, IEEE Signal Processing Magazine.

[9]  Leonidas J. Guibas,et al.  Wireless sensor networks - an information processing approach , 2004, The Morgan Kaufmann series in networking.

[10]  Pramod K. Varshney,et al.  Distributed detection with multiple sensors I. Fundamentals , 1997, Proc. IEEE.

[11]  Pramod K. Varshney,et al.  Data‐Centric and Cooperative MAC Protocols for Sensor Networks , 2007 .

[12]  J. Tsitsiklis Decentralized Detection' , 1993 .

[13]  Chong-Yung Chi,et al.  Channel-Aware Random Access Control for Distributed Estimation in Sensor Networks , 2008, IEEE Transactions on Signal Processing.

[14]  Lang Tong,et al.  Asymptotic Detection Performance of Type-Based Multiple Access Over Multiaccess Fading Channels , 2007, IEEE Transactions on Signal Processing.

[15]  Yao-Win Peter Hong,et al.  Exploiting Data-Dependent Transmission Control and MAC Timing Information for Distributed Detection in Sensor Networks , 2008, IEEE Transactions on Signal Processing.

[16]  Jean-Francois Chamberland,et al.  Detection in Sensor Networks , 2007 .

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

[18]  Michael Gastpar,et al.  Uncoded transmission is exactly optimal for a simple Gaussian "sensor" network , 2008, 2007 Information Theory and Applications Workshop.

[19]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.