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, Volume 24, Issue 3, pp 594–611 | Cite as

Clustering intelligent transportation sensors using public transportation

  • Tejswaroop Geetla
  • Rajan Batta
  • Alan Blatt
  • Marie Flanigan
  • Kevin Majka
Original Paper
First Online:
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Accepted:
  • 263 Downloads

Abstract

Advanced transportation sensors use a wireless medium to communicate and use data fusion techniques to provide complete information. Large-scale use of intelligent transportation sensors can lead to data bottlenecks in an ad-hoc wireless sensor network, which needs to be reliable and should provide a framework to sensors that constantly join and leave the network. A possible solution is to use public transportation vehicles as data fusion nodes or cluster heads. This paper presents a mathematical programming approach to use public transportation vehicles as cluster heads. The mathematical programming solution seeks to maximize benefit achieved by covering both mobile and stationary sensors, while considering cost/penalty associated with changing cluster head locations. A simulation is developed to capture realistic considerations of a transportation network. This simulation is used to validate the solution provided by the mathematical model.

Keywords

Sensor placement Data fusion Simulation Optimization methods 

Mathematics Subject Classification

90CXX 65K05 00AXX 
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Notes

Acknowledgments

The authors would like to thank the two anonymous who provided us with excellent advice and guidance to strengthen and improve our paper. This material is based on work supported by the FHWA under Cooperative Agreement No. DTFH61-07-H-00023, awarded to the Center for Transportation Injury Research, CUBRC, Inc., Buffalo, NY. Any opinions, findings and conclusions are those of the author(s) and do not necessarily reflect the view of the FHWA.

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Copyright information

© Sociedad de Estadística e Investigación Operativa 2016

Authors and Affiliations

  • Tejswaroop Geetla
    • 1
  • Rajan Batta
    • 1
    • 2
  • Alan Blatt
    • 2
  • Marie Flanigan
    • 2
  • Kevin Majka
    • 2
  1. 1.Department of Industrial and Systems EngineeringUniversity at Buffalo (SUNY)BuffaloUSA
  2. 2.Center for Transportation Injury Research, CUBRCBuffaloUSA

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