Industry Track to The First International Conference on Simulation Tools and Techniques for Communications, Networks and Systems

Research Article

Driving the Deployment of Citywide WiFi Networks

  • @INPROCEEDINGS{10.4108/ICST.SIMUTOOLS2008.3101,
        author={Christopher Thraves and Gilberto Urueta and Pablo Vidales and Marcin Solarski},
        title={Driving the Deployment of Citywide WiFi Networks},
        proceedings={Industry Track to The First International Conference on Simulation Tools and Techniques for Communications, Networks and Systems},
        publisher={ACM},
        proceedings_a={SIMULATIONWORKS},
        year={2010},
        month={5},
        keywords={Citywide WiFi networks broadband sharing Ubiquitous WiFi access},
        doi={10.4108/ICST.SIMUTOOLS2008.3101}
    }
    
  • Christopher Thraves
    Gilberto Urueta
    Pablo Vidales
    Marcin Solarski
    Year: 2010
    Driving the Deployment of Citywide WiFi Networks
    SIMULATIONWORKS
    ICST
    DOI: 10.4108/ICST.SIMUTOOLS2008.3101
Christopher Thraves1,*, Gilberto Urueta1,*, Pablo Vidales1,*, Marcin Solarski1,*
  • 1: Deutsche Telekom Laboratories Berlin, Germany 10587
*Contact email: christopher.thraves@telekom.de, gilberto.urueta@telekom.de, Pablo.Vidales@telekom.de, marcin.solarski@telekom.de

Abstract

Cost-efficiency is a critical factor for citywide deployments of WiFi networks that are being planned by telecom operators and governments around the world. Building such networks by reusing the broadband infrastructure, currently used only by the mass costumer at home, is an attractive approach to follow. We tackle the problem of selecting locations from the existing broadband infrastructure to build such an overlay WiFi access network. We use an algorithm that solves the budgeted version of the Maximum Coverage Problem to select hotspot deployment points out of the available ones in an area. These points represent premises of the broadband customers where hotspots can be installed. The hotspots are placed in such a way that the connectivity demand in the given deployment area is satisfied at the lowest possible cost. The proposed algorithm is assessed by simulations, applying it to random and real datasets representing access demand, geographical distributions, and locations where hotspots can be deployed. The key findings of this study are: The connectivity demand can be satisfied by the coverage at a cost growing faster than linearly. In fact, the cost of covering the first 85% of the demand is as much as 1/4 of the one needed to satisfy it fully. Additionally, the current broadband penetration in cities, like Berlin, makes WiFi access almost ubiquitous with an average distance between nomadic users and hotspots of 200 m.