by Andrej Bogdanov, Elitza Maneva, Samantha Riesenfeld
in IEEE Infocom
http://www.ieee-infocom.org/2004/Papers/12_5.PDF
Add To MetaCart
Abstract:
Abstract — 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. Index Terms — Sensor networks, optimization, combinatorics, graph theory
Citations
|
484
|
R.S.J.: Wireless sensor networks for habitat monitoring
– Mainwaring, Culler, et al.
- 2002
|
|
266
|
A new approach to the maximum flow problem
– Goldberg, Tarjan
- 1988
|
|
128
|
Topology, A first course
– Munkres
- 1975
|
|
115
|
Unit disk graphs
– Clark, Colbourn, et al.
- 1990
|
|
88
|
On Implementing PushRelabel Method for the Maximum Flow Problem
– Cherkassky, Goldberg
- 1995
|
|
87
|
Unreliable sensor grids: coverage, connectivity and diameter
– Shakkottai, Srikant, et al.
- 2003
|
|
87
|
Power consumption in packet radio networks
– Kirousis, Kranakis, et al.
|
|
73
|
Anantha Chandrakasan, and Hari Balakrishnan. Energy-efficient Communication Protocols for Wireless Microsensor Networks
– Heinzelman
- 2000
|
|
71
|
Heuristically optimized trade-offs: A new paradigm for power laws
– Fabrikant, Koutsoupias, et al.
- 2002
|
|
66
|
Power control and clustering in ad hoc networks
– Kawadia, Kumar
- 2003
|
|
50
|
A distributed and adaptive signal processing approach to reducing energy consumption in sensor networks
– Chou, Petrovic, et al.
- 2003
|
|
41
|
and Albert-Laszlo Barabasi. Statistical mechanics of complex networks
– Albert
|
|
37
|
On the power assignment problem in radio networks
– Clementi, Penna, et al.
|
|
29
|
Utility-Based Decision-Making in Wireless Sensor Networks
– Byers, Nasser
- 2000
|
|
19
|
A ploynomial-time approximation scheme for base station positioning in UMTS networks
– Galota, Glasser, et al.
- 2001
|
|
10
|
Daniela Rus, “Online power-aware routing in wireless ad-hoc networks
– Li, Aslam
|
|
5
|
Scheduling algorithms for wireless ad-hoc sensor networks
– Florens, McEliece
- 2002
|
|
2
|
A tractable complex network model based on the stochastic mean-field model of distance
– Aldous
- 2003
|
|
1
|
Leandros Tassiulas, “Routing for network capacity maximization in energy-constrained ad-hoc networks
– Kar, Kodialam, et al.
- 2003
|
