New paper “PrioDeX: a Data Exchange middleware for efficient event prioritization in SDN-based IoT systems” in ACM TOIT
Authors: Georgios Bouloukakis, Kyle Benson, Luca Scalzotto, Paolo Bellavista, Casey Grant, Valérie Issarny, Sharad Mehrotra,Ioannis Moscholios, Nalini Venkatasubramanian
ACM Transactions on Internet of Things, In press, https://hal.archives-ouvertes.fr/hal-03171358
Abstract
Real-time event detection and targeted decision making for emerging mission-critical applications require systems that extract and process relevant data from IoT sources in smart spaces. Oftentimes, this data is heterogeneous in size, relevance, and urgency, which creates a challenge when considering that different groups of stakeholders (e.g., first responders, medical staff, government officials, etc) require such data to be delivered in a reliable and timely manner. Furthermore, in mission-critical settings, networks can become constrained due to lossy channels and failed components, which ultimately add to the complexity of the problem. In this paper, we propose PrioDeX, a cross-layer middleware system that enables timely and reliable delivery of mission-critical data from IoT sources to relevant consumers through the prioritization of messages. It integrates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics through a queueing analytical model. PrioDeX proposes novel algorithms that utilize the results of this analysis to tune data exchange configurations (event priorities and dropping policies), which is necessary for satisfying situational awareness requirements and resource constraints. PrioDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate our approach using both simulated and prototype-based experiments in a smart building fire response scenario. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization.
New paper “IoT data qualification for a logistic chain traceability smart contract” in Sensors journal
Authors: Mohamed Ahmed, Chantal Taconet, Mohamed Ould, Sophie Chabridon, Amel Bouzeghoub
MDPI Sensors, 21 (6), 2021. https://hal.archives-ouvertes.fr/hal-03219609
Abstract
In the logistic chain domain, the traceability of shipments in their entire delivery process from the shipper to the consignee involves many stakeholders. From the traceability data, contractual decisions may be taken such as incident detection, validation of the delivery or billing. The stakeholders require transparency in the whole process. The combination of the Internet of Things (IoT) and the blockchain paradigms helps in the development of automated and trusted systems. In this context, ensuring the quality of the IoT data is an absolute requirement for the adoption of those technologies. In this article, we propose an approach to assess the data quality (DQ) of IoT data sources using a logistic traceability smart contract developed on top of a blockchain. We select the quality dimensions relevant to our context, namely accuracy, completeness, consistency and currentness, with a proposition of their corresponding measurement methods. We also propose a data quality model specific to the logistic chain domain and a distributed traceability architecture. The evaluation of the proposal shows the capacity of the proposed method to assess the IoT data quality and ensure the user agreement on the data qualification rules. The proposed solution opens new opportunities in the development of automated logistic traceability systems.
PhD thesis defense of David Oudart on May 7 2020: “Model driven engineering applied to Smart Grids design: Cosimulation with FMI approach”
Model driven engineering applied to Smart Grids design: Cosimulation with FMI approach
Abstract
Smart Grids are cyber-physical systems that interface power grids with information and communication technologies to monitor them, automate decision making and balance production with consumption. We want to use simulation to easily evaluate and compare several solutions before deployment in a real environment. The objective of this thesis is thus to propose tools and methods to model and simulate a Smart Grid in an industrial context. We have identified two main issues: How to combine heterogeneous models of a Smart Grid to simulate it? How to ensure consistency between the models produced by different stakeholders during the design of a Smart Grid? To address these issues, we propose a cosimulation approach, using the Functional Mockup Interface (FMI) standard. Our first two contributions are the proposal of a method to allow the exchange of discrete signals between several FMUs, and an extension of the OMNeT++ telecommunications simulation software implementing this method, called fmi4omnetpp. A third contribution is the development of the Smart Grid Simulation Framework tooled environment, which automates a number of repetitive tasks in order to ensure consistency between different simulation models. Finally, a fourth contribution is the formalization of an iterative design approach for the cosimulation of a Smart Grid, and how to integrate our Smart Grid Simulation Framework into it. To do so, we explain the different steps of the approach and the role of the actors involved in the design process, then we present its application to a real case study for which we use our Smart Grid Simulation Framework.
New paper “A Model based Toolchain for the Cosimulation of Cyber-physical Systems with FMI” at MODELSWARD’2020
A Model based Toolchain for the Cosimulation of Cyber-physical Systems with FMI by D. Oudart, J. Cantenot, F. Boulanger and S. Chabridon
Abstract Smart Grids are cyber-physical systems that interface power grids with information and communication technologies in order to monitor them, automate decision making and balance production and consumption. Cosimulation with the Functional Mock-up Interface standard allows the exploration of the behavior of such complex systems by coordinating simulation units that correspond to the grid part, the communication network and the information system. However, FMI has limitations when it comes to cyber-physical system simulation, particularly because discrete-event signals exchanged by cyber components are not well supported. In addition, industrial projects involve several teams with different skills and methods that work in parallel to produce all the models required by the simulation, which increases the risk of inconsistency between models. This article presents a way to exchange discrete-event signals between FMI artifacts, which complies with the current 2.0 version of the standard. We developed a DSL and a model-based toolchain to generate the artifacts that are necessary to run the cosimulation of the whole system, and to
detect potential inconsistencies between models. The approach is illustrated by the use case of an islanded grid implementing diesel and renewable sources, battery storage and intelligent control of the production.
