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Optimizing Data Collection Capacity in Wireless Networks

Reference work entry

Abstract

Data collection is an important operation of wireless sensor networks (WSNs). The performance of data collection can be measured by its achievable network capacity. Most existing works focus on the capacity of unicast, multicast, or/and snapshot data collection (SDC) in single-radio single-channel wireless networks,and no works dedicatedly consider the continuous data collection (CDC) capacity for WSNs under the protocol interference model. In this chapter, firstly,a multipath scheduling algorithm for SDC in single-radio multichannel WSNs is proposed.Theoretical analysis of the multipath scheduling algorithm shows that its achievable network capacity is at least \(\frac{W} {2\left \lceil (1.8{1\rho }^{2}+c_{1}\rho +c_{2})/H\right \rceil }\), where W is the channel bandwidth, H is the number of available orthogonal channels, ρ is the ratio of the interference radius over the transmission radius of a node, \(c_{1} = \frac{2\pi } {\sqrt{3}} + \frac{\pi } {2} + 1\), and \(c_{2} = \frac{\pi } {\sqrt{3}} + \frac{\pi } {2} + 2\), which is a tighter lower bound compared with the previously best result which is \(\frac{W} {{8\rho }^{2}}\) (Chen et al. (2010) Capacity of data collection in arbitrary wireless sensor networks. In: IEEE INFOCOM, San Diego, USA). For CDC, an intuitive method is to pipeline existing SDC methods. However, such an idea cannot actually improve the network capacity. The reason for this failure is discussed and a novel pipeline scheduling algorithm for CDC in dual-radio multichannel WSNs is proposed. This pipeline scheduling algorithm significantly speeds up the data collection process and achieves a capacity of
$$\left \{\begin{array}{@{}l@{}} \dfrac{nW} {12M\left \lceil \dfrac{3.6{3\rho }^{2} + c_{3}\rho + c_{4}} {H} \right \rceil },\ \ \mbox{ if}\ \ \Delta _{e} \leq 12 \\ \dfrac{nW} {M\Delta _{e}\left \lceil \dfrac{3.6{3\rho }^{2} + c_{3}\rho + c_{4}} {H} \right \rceil },\ \ \mbox{ if}\ \ \Delta _{e} > 12 \end{array} \right.,$$
where n is the number of the sensors, M is a constant value and usually Mn, \(\Delta _{e}\) is the maximum number of the leaf nodes having a same parent in the routing tree (i.e., data collection tree), \(c_{3} = \frac{8\pi } {\sqrt{3}} +\pi +2\), and \(c_{4} = \frac{8\pi } {\sqrt{3}} + 2\pi + 6\). Furthermore, for completeness, the performance of the proposed pipeline scheduling algorithm in single-radio multichannel WSNs is also analyzed, which shows that for a long-run CDC, the lower bound of the achievable asymptotic network capacity is
$$\left \{\begin{array}{@{}l@{}} \dfrac{nW} {16M\left \lceil \dfrac{3.6{3\rho }^{2} + c_{3}\rho + c_{4}} {H} \right \rceil },\ \ \mbox{ if}\ \ \Delta _{e} \leq 12 \\ \dfrac{nW} {M(\Delta _{e} + 4)\left \lceil \dfrac{3.6{3\rho }^{2} + c_{3}\rho + c_{4}} {H} \right \rceil },\ \ \mbox{ if}\ \ \Delta _{e} > 12 \end{array} \right..$$
Extensive simulation results indicate that the proposed algorithms improve thenetwork capacity significantly compared with existing works.

Keywords

Sensor Node Time Slot Schedule Algorithm Data Packet Channel Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors would like to thank the editors and reviewers for their time and valuable comments that help to improve the quality of this chapter. This chapter is also partly supported by the NSF under Grant No. CCF-0545667.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA
  2. 2.Department of Computer ScienceGeorgia State UniversityAtlantaUSA
  3. 3.Department of Computer ScienceKennesaw State UniversityKennesawUSA

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