Ad Hoc Networks. 7th International Conference, AdHocHets 2015, San Remo, Italy, September 1–2, 2015, Proceedings

Research Article

Evaluation of Different Signal Propagation Models for a Mixed Indoor-Outdoor Scenario Using Empirical Data

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  • @INPROCEEDINGS{10.1007/978-3-319-25067-0_1,
        author={Oleksandr Artemenko and Adarsh Nayak and Sanjeeth Menezes and Andreas Mitschele-Thiel},
        title={Evaluation of Different Signal Propagation Models for a Mixed Indoor-Outdoor Scenario Using Empirical Data},
        proceedings={Ad Hoc Networks. 7th International Conference, AdHocHets 2015, San Remo, Italy, September 1--2, 2015, Proceedings},
        proceedings_a={ADHOCNETS},
        year={2015},
        month={9},
        keywords={Path loss Signal propagation models Signal strength Experiment},
        doi={10.1007/978-3-319-25067-0_1}
    }
    
  • Oleksandr Artemenko
    Adarsh Nayak
    Sanjeeth Menezes
    Andreas Mitschele-Thiel
    Year: 2015
    Evaluation of Different Signal Propagation Models for a Mixed Indoor-Outdoor Scenario Using Empirical Data
    ADHOCNETS
    Springer
    DOI: 10.1007/978-3-319-25067-0_1
Oleksandr Artemenko1,*, Adarsh Nayak1,*, Sanjeeth Menezes1,*, Andreas Mitschele-Thiel1,*
  • 1: Technische Universität Ilmenau
*Contact email: Oleksandr.Artemenko@tu-ilmenau.de, Adarsh.Nayak@tu-ilmenau.de, Sanjeeth.Menezes@tu-ilmenau.de, Mitsch@tu-ilmenau.de

Abstract

In this paper, we are choosing a suitable indoor-outdoor propagation model out of the existing models by considering path loss and distance as parameters. Path loss is calculated empirically by placing emitter nodes inside a building. A receiver placed outdoors is represented by a Quadrocopter (QC) that receives beacon messages from indoor nodes. As per our analysis, Stanford University Interim (SUI) model, COST-231 Hata model, Green-Obaidat model, Free Space model, Log-Distance Path Loss model and Electronic Communication Committee 33 (ECC-33) models are chosen and evaluated using empirical data collected in a real environment. The aim is to determine if the analytically chosen models fit our scenario by estimating the minimal standard deviation from the empirical data.