The 1st EAI International Conference on Smart Grid Assisted Internet of Things

Research Article

A Simulation Tool for the Performance Evaluation of Large-scale RF-Mesh Networks for Smart Grid and IoT Applications

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  • @INPROCEEDINGS{10.4108/eai.7-8-2017.152989,
        author={Filippo Malandra and Brunilde Sanso},
        title={A Simulation Tool for the Performance Evaluation of Large-scale RF-Mesh Networks for Smart Grid and IoT Applications},
        proceedings={The 1st EAI International Conference on Smart Grid Assisted Internet of Things},
        publisher={EAI},
        proceedings_a={SGIOT},
        year={2017},
        month={8},
        keywords={},
        doi={10.4108/eai.7-8-2017.152989}
    }
    
  • Filippo Malandra
    Brunilde Sanso
    Year: 2017
    A Simulation Tool for the Performance Evaluation of Large-scale RF-Mesh Networks for Smart Grid and IoT Applications
    SGIOT
    EAI
    DOI: 10.4108/eai.7-8-2017.152989
Filippo Malandra,*, Brunilde Sanso
    *Contact email: filippo.malandra@polymtl.ca

    Abstract

    Driven by the need of robust, cost-effective, and ready-to-use solutions to connect wirelessly thousands to million of nodes, an increas-ing number of applications such as Smart Grids and IoT networks use large-scale Wireless Mesh Networks as transmission support. Tools and methodologies to study the performance of such systems are constantly sought and become fundamental in the feasibility assessment of the high number of possible applications. In this paper, a simulation tool is pro-posed to study the performance of a particular kind of Wireless Mesh Network, based on the RF-Mesh technology. The modular nature of the implemented tool allows for a smooth extension to the performance anal-ysis of other types of Wireless Mesh Networks using technologies similar to RF-Mesh. The tool was implemented in the context of a large-scale Smart Grid AMI (Advanced Metering Infrastructure) system. The tool, coded in Java and Python, considers different types of traffic and pro-vides the end-to-end delay and several other performance indexes of large scale (i.e., with several thousand nodes) instances in the order of minutes.