Filippo Malandra

Analytical performance analysis of a large-scale RF-mesh smart meter communication system

Advanced meter infrastructures (AMIs) are now widespread and their importance within smart grid systems continues to increase with the advent of new applications. Performance analysis of the infrastructure is key to assess the limits of application deployment. However, due to the large-scale nature of AMI networks that are often composed of tens of thousands of nodes per collector, performance analysis is often carried out in contained experimental trials. To our knowledge, no thorough mathematical performance analysis of real-sized systems has been carried out so far. In this work, we present a model to analyze the performance of a large-scale RF-AMI system and show its application to large-scale real-case scenarios.

Traffic characterization and LTE performance analysis for M2M communications in smart cities

Abstract The paper presents a model for the characterization of Machine-to-machine (M2M) traffic and the performance evaluation of LTE access to support M2M communication, embedded into a web-based application. The application enables the study of the traffic produced by realistic M2M elements in the context of smart cities. Packet generation for each machine is modeled by means of three mathematical distributions (Poisson, Beta, and Deterministic), which makes it possible to represent a wide variety of M2M applications. A case study was constructed based on the city of Montreal. Real data on the position of machines were retrieved from public datasets. The case study includes a realistic representation of the LTE infrastructure that allows the estimation of traffic load at each eNodeB, as well as other performance indexes, such as collision probability and access delay. The use of real geographic information enables visual analyses aiming at identifying bottlenecks and possible roadblocks to the M2M integration in the LTE infrastructure.

Performance Evaluation of Large-scale RF-Mesh Networks in a Smart City Context

Driven by the need of robust, cost-effective, and ready-to-use solutions to connect wirelessly thousands to million of nodes, an increasing 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 proposed 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 analysis 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 provides 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. A realistic large-scale case study of a smart city is also presented to assess the suitability of the RF-Mesh technology for some IoT and smart city applications, that do not require a very small delay.

A case study for M2M traffic characterization in a smart city environment

This paper presents a case study to characterize Machine-To-Machine (M2M) traffic in a smart city environment. Real data on the position of machines and three different probability distributions (Poisson, Beta, and deterministic) are used to model the packet generation of some realistic IoT applications. A web application is presented and allows to perform a variety of analyses on M2M traffic characterization. The island of Montreal is used as study case: realistic data on the position of machines and of eNodeB stations in a real LTE network are employed to demonstrate the possibilities of the tool. In the numerical results, the traffic generated by different M2M applications is presented and some differences between M2M and human traffic and their impact on the LTE infrastructure are highlighted.