Optimal Deployment Control for a Heterogeneous Mobile Sensor Network

This paper proposes a hierarchical architecture for a heterogeneous sensor network consisting of both mobile and fixed agents. The hierarchy contains three layers. The lowest level, Layer 0, is the sensing layer and consists of fixed sensors deployed in an environment of interest according to a spatial Poisson distribution of intensity measure A. Layer 1, the next level in the hierarchy, is the aggregation layer and involves mobile agents (e.g., autonomous vehicles) gathering the sensed data from the Layer 0 nodes based on a nearest neighbour rule. The highest level of the hierarchy is the query layer, which represents requests for the data gathered by the sensors to be transmitted, via the Layer 1 agents, to a mobile base station (e.g., a flying aircraft periodically visiting the area of interest). We construct for this network a utility function which basically captures the cost associated with communication, as seen from the point of view of the mobile agents of Layer 1. Based on a gradient descent strategy we propose a motion coordination scheme to maneuver the mobile nodes from an arbitrary initial arrangement to one that minimizes the utility function. The application of the proposed algorithm in an elementary settings is illustrated via numerical simulations

[1]  Petter Ögren,et al.  Cooperative control of mobile sensor networks:Adaptive gradient climbing in a distributed environment , 2004, IEEE Transactions on Automatic Control.

[2]  Atsuyuki Okabe,et al.  Spatial Tessellations: Concepts and Applications of Voronoi Diagrams , 1992, Wiley Series in Probability and Mathematical Statistics.

[3]  Sonia Martínez,et al.  Coverage control for mobile sensing networks , 2002, IEEE Transactions on Robotics and Automation.

[4]  F. Bullo,et al.  On collective motion in sensor networks: sample problems and distributed algorithms , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[5]  Bruno Sinopoli,et al.  Distributed control applications within sensor networks , 2003, Proc. IEEE.

[6]  François Baccelli,et al.  Stochastic geometry and architecture of communication networks , 1997, Telecommun. Syst..

[7]  Pei-Ling Chiu,et al.  A near-optimal sensor placement algorithm to achieve complete coverage-discrimination in sensor networks , 2005, IEEE Communications Letters.

[8]  Brian D. Ripley.  Stochastic Geometry and its Applications. , 1988 .

[9]  Catherine Rosenberg,et al.  A minimum cost heterogeneous sensor network with a lifetime constraint , 2005, IEEE Transactions on Mobile Computing.

[10]  Gaurav S. Sukhatme,et al.  Adaptive sampling for environmental robotics , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[11]  Chee-Yee Chong,et al.  Sensor networks: evolution, opportunities, and challenges , 2003, Proc. IEEE.

[12]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[13]  Gustavo de Veciana,et al.  Minimizing energy consumption in large-scale sensor networks through distributed data compression and hierarchical aggregation , 2004, IEEE Journal on Selected Areas in Communications.

[14]  Deborah Estrin,et al.  The impact of data aggregation in wireless sensor networks , 2002, Proceedings 22nd International Conference on Distributed Computing Systems Workshops.

[15]  Qiang Du,et al.  Probabilistic methods for centroidal Voronoi tessellations and their parallel implementations , 2002, Parallel Comput..

[16]  S. Fossy,et al.  On a Voronoi Aggregative Process Related to a Bivariate Poisson Process , 1996 .

[17]  Qiang Du,et al.  Centroidal Voronoi Tessellations: Applications and Algorithms , 1999, SIAM Rev..

[18]  David L. Neuhoff,et al.  Quantization , 2022, IEEE Trans. Inf. Theory.