Power-aware base station positioning for sensor networks

We consider the problem of positioning data collecting base stations in a sensor network. We show that in general, the choice of positions has a marked influence on the data rate, or equivalently, the power efficiency, of the network. In our model, which is partly motivated by an experimental environmental monitoring system, the optimum data rate for a fixed layout of base stations can be found by a maximum flow algorithm. Finding the optimum layout of base stations, however, turns out to be an NP-complete problem, even in the special case of homogeneous networks. Our analysis of the optimum layout for the special case of the regular grid shows that all layouts that meet certain constraints are equally good. We also consider two classes of random graphs, chosen to model networks that might be realistically encountered, and empirically evaluate the performance of several base station positioning algorithms on instances of these classes. In comparison to manually choosing positions along the periphery of the network or randomly choosing them within the network, the algorithms tested find positions, which significantly improve the data rate and power efficiency of the network.

[1]  Andrzej Pelc,et al.  Power consumption in packet radio networks , 2000, Theor. Comput. Sci..

[2]  Robert J. McEliece,et al.  Packets distribution algorithms for sensor networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[3]  Robert J. McEliece,et al.  Scheduling algorithms for wireless ad-hoc sensor networks , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[4]  Qun Li,et al.  Online power-aware routing in wireless Ad-hoc networks , 2001, MobiCom '01.

[5]  Kannan Ramchandran,et al.  A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[6]  Leandros Tassiulas,et al.  Routing for network capacity maximization in energy-constrained ad-hoc networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[7]  John W. Byers,et al.  Utility-based decision-making in wireless sensor networks , 2000, 2000 First Annual Workshop on Mobile and Ad Hoc Networking and Computing. MobiHOC (Cat. No.00EX444).

[8]  Leandros Tassiulas,et al.  Fast Approximate Algorithms for Maximum Lifetime Routing in Wireless Ad-hoc Networks , 2000, NETWORKING.

[9]  R. Srikant,et al.  Unreliable sensor grids: coverage, connectivity and diameter , 2005, Ad Hoc Networks.

[10]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[11]  Leandros Tassiulas,et al.  Energy conserving routing in wireless ad-hoc networks , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[12]  Andrea E. F. Clementi,et al.  On the Power Assignment Problem in Radio Networks , 2004, Mob. Networks Appl..

[13]  Andrew V. Goldberg,et al.  On Implementing Push-Relabel Method for the Maximum Flow Problem , 1995, IPCO.

[14]  James R. Munkres,et al.  Topology; a first course , 1974 .

[15]  Charles J. Colbourn,et al.  Unit disk graphs , 1991, Discret. Math..

[16]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[17]  Wendi Heinzelman,et al.  Energy-efficient communication protocol for wireless microsensor networks , 2000, Proceedings of the 33rd Annual Hawaii International Conference on System Sciences.

[18]  Heribert Vollmer,et al.  A polynomial-time approximation scheme for base station positioning in UMTS networks , 2001, DIALM '01.

[19]  David J. Aldous.  A Tractable Complex Network Model based on the Stochastic Mean-field Model of Distance , 2003 .

[20]  Andrew V. Goldberg,et al.  A new approach to the maximum flow problem , 1986, STOC '86.

[21]  Kannan Ramchandran,et al.  A distributed and adaptive signal processing approach to exploiting correlation in sensor networks , 2004, Ad Hoc Networks.

[22]  Christos H. Papadimitriou,et al.  Heuristically Optimized Trade-Offs: A New Paradigm for Power Laws in the Internet , 2002, ICALP.

[23]  Vikas Kawadia,et al.  Power control and clustering in ad hoc networks , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).