Minimizing sum distortion for static and mobile fusion center placement in underwater sensor networks

The problem of optimizing the position of a mobile fusion center in a sensor network is considered. The optimization criterion of interest is the sum distortion for the communication of all the information from each of the nodes to the fusion center. Transmission losses along underwater links are modeled and a time-multiplexing architecture imposed on the sensor nodes for communication with the fusion center. Classical results from the information theory literature are leveraged and the optimization problem is formulated and solved analytically for the case when the fusion center is at one fixed location and numerically for the case when the fusion center is mobile. It is observed that in the low node power regime, with the fusion center at a fixed location, the fusion center selectively communicates with a few nodes while turning the others off. In the high power regime, the time allocated to the nodes is a function of the information they need to transmit to the destination and the distance from the node to the fusion center. Further, when the fusion center is mobile, in the low power regime, the fusion center is placed close to the node with the largest information content while for higher powers the difference from a fixed fusion center declines.

[1]  D. Caron,et al.  Networked Aquatic Microbial Observing System , 2006 .

[2]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[3]  R. Nowak,et al.  Backcasting: adaptive sampling for sensor networks , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[4]  M. Stojanovic,et al.  Underwater Acoustic Communications: Design Considerations on the Physical Layer , 2008, 2008 Fifth Annual Conference on Wireless on Demand Network Systems and Services.

[5]  James Preisig,et al.  Acoustic propagation considerations for underwater acoustic communications network development , 2006, Underwater Networks.

[6]  Milica Stojanovic,et al.  On the relationship between capacity and distance in an underwater acoustic communication channel , 2007, MOCO.

[7]  Edward Y. Chang,et al.  Adaptive sampling for sensor networks , 2004, DMSN '04.

[8]  Thor I. Fossen,et al.  Formation Control of Marine Surface Vessels Using the Null-Space-Based Behavioral Control , 2006 .

[9]  D. Caron,et al.  Design and Development of a Wireless Robotic Networked Aquatic Microbial Observing System , 2007 .

[10]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[11]  M. Stojanovic,et al.  Underwater acoustic networks , 2000, IEEE Journal of Oceanic Engineering.

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

[14]  Gaurav S. Sukhatme,et al.  Bioscope: actuated sensor network for biological science , 2005 .

[15]  Urbashi Mitra,et al.  A constrained channel coding approach to joint communication and channel estimation , 2008, 2008 IEEE International Symposium on Information Theory.

[16]  Gaurav S. Sukhatme,et al.  Adaptive Sampling for Estimating a Scalar Field using a Robotic Boat and a Sensor Network , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[17]  Giri Narasimhan,et al.  Resource-constrained geometric network optimization , 1998, SCG '98.

[18]  Yasutada Oohama,et al.  The Rate-Distortion Function for the Quadratic Gaussian CEO Problem , 1998, IEEE Trans. Inf. Theory.

[19]  Nicholas Roy,et al.  Global A-Optimal Robot Exploration in SLAM , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[20]  Harry L. Van Trees,et al.  Detection, Estimation, and Modulation Theory, Part I , 1968 .

[21]  James C. Preisig Acoustic propagation considerations for underwater acoustic communications network development , 2007 .

[22]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[23]  Charles F. Hockett,et al.  A mathematical theory of communication , 1948, MOCO.

[24]  Urbashi Mitra,et al.  Sensor Selection and Power Allocation for Distributed Estimation in Sensor Networks: Beyond the Star Topology , 2008, IEEE Transactions on Signal Processing.

[25]  Wolfram Burgard,et al.  Coastal Navigation { Robot Motion with Uncertainty , 1998, AAAI 1998.

[26]  C. Guestrin,et al.  Near-optimal sensor placements: maximizing information while minimizing communication cost , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[27]  Naomi Ehrich Leonard,et al.  Cooperative Control for Ocean Sampling: The Glider Coordinated Control System , 2008, IEEE Transactions on Control Systems Technology.

[28]  David R. Karger,et al.  Approximation algorithms for orienteering and discounted-reward TSP , 2003, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings..