Clustering intelligent transportation sensors using public transportation

Tejswaroop Geetla, Rajan Batta (), Alan Blatt, Marie Flanigan and Kevin Majka
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Tejswaroop Geetla: University at Buffalo (SUNY)
Rajan Batta: University at Buffalo (SUNY)
Alan Blatt: Center for Transportation Injury Research, CUBRC
Marie Flanigan: Center for Transportation Injury Research, CUBRC
Kevin Majka: Center for Transportation Injury Research, CUBRC

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 2016, vol. 24, issue 3, No 4, 594-611

Abstract: 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; 90CXX; 65K05; 00AXX (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11750-016-0410-7

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