The paper deals with a Wireless Sensor Network (WSN) as a reliable solution for capturing the kinematics of a fire front spreading over a fuel bed. To provide reliable information in fire studies and support fire fighting strategies, a Wireless Sensor Network must be able to perform three sequential actions: 1) sensing thermal data in the open as the gas temperature; 2) detecting a fire i.e., the spatial position of a flame; 3) tracking the fire spread during its spatial and temporal evolution. One of the great challenges in performing fire front tracking with a WSN is to avoid the destruction of motes by the fire. This paper therefore shows the performance of Wireless Sensor Network when the motes are protected with a thermal insulation dedicated to track a fire spreading across vegetative fuels on a field scale. The resulting experimental WSN is then used in series of wildfire experiments performed in the open in vegetation areas ranging in size from 50 to 1,000 m².

Discrete event modeling and simulation and reinforcement learning are two frameworks suited for cyberphysical system design, which, when combined, can give powerful tools for system optimization or decision making process for example. This paper describes how discrete event modeling and simulation could be integrated into reinforcement learning concepts and tools in order to assist in the realization of reinforcement learning systems, more specially considering the temporal, hierarchical, and multi-agent aspects. An overview of these different improvements are given based on the implementation of the Q-Learning reinforcement learning algorithm in the framework of the Discrete Event system Specification (DEVS) and System Entity Structure (SES) formalisms.

The modeling and simulation (M&S) of complex systems often requires models described at different levels of detail characterized by differences in abstraction hierarchies and/or time granularity. The discrete-event system specification (DEVS) is a framework based on mathematical systems theory that offers a computational basis for application of M&S to systems engineering and that has become widely adopted for its support of discrete-event, continuous, and hybrid applications. A fundamental representation of DEVS hierarchical modular model structures is the system entity structure (SES), which represents a design space via the elements of a system and their relationships in a hierarchical and axiomatic manner. As has been described in a number of publications, the SES supports development, pruning, and generation of DEVS simulation models. The goal of this paper is to propose an extension of SES in order to integrate both the concepts of abstraction hierarchies and time granularity into DEVS. This paper explains in detail: (i) the concepts of abstraction hierarchies and time granularity; (ii) the extension of SES in order to take into account these concepts; (iii) DEVS M&S of complex systems according to different levels of detail (abstraction hierarchies and time granularity); (iv) the use of a Python DEVS simulator (DEVSimPy) to implement the management of abstraction hierarchies and time granularity. A real case study is given to illustrate the proposed approach, and follow-on research needed to implement the concepts is discussed.

This paper presents a modeling and simulation approach in order to perform a generative analysis of folktales aimed at validating Claude Lévi-Strauss’ theory and method. To this aim, a discrete-event simulation is proposed. The simulation is based on the development of a set of discrete-event models dedicated to generating a set of folktales from an initial one, according to Claude Lévi-Strauss’ structural analysis based on symmetry and double twist transformations. This paper describes in detail how these discrete-event models have been implemented in the framework of the DEVSimPy software environment by using myths of Native American mythology and folktales of Corsican oral literature. The validation involved the following steps: (i) definition of a reference folktale (according to Claude Lévi-Strauss’ methodology) (ii) generation of a set of folktales by performing their own transformations (iii) generation of a graph allowing to analyze the links that have been created after performing a set of folktales transformations. Finally, the computational validation of Lévi-Strauss’s method is intended to ground a new research that may reformulate structural analysis and elaborate a neo-structural model of canonical formalization based on transformational morphodynamics. The aim is to conceptualize and measure recursively the structural dynamics and the recurrent patterns of current identity transformations in liberal democracies, especially in US and EU contexts where ethnic/racial divisions and migration challenges are becoming more acute than ever.

The ambition and proposal for data modeling of myths presented in this paper is to link contemporary technical affordances to some canonical projects developed in structural anthropology. To articulate the theoretical promise and innovation of this proposal, we present a discrete-event system specification modeling and simulation approach in order to perform a generative analysis and a dynamic visualization of selected narratives, aimed at validating and revitalizing the transformational and morphodynamic theory and methodology proposed by Claude Lévi-Strauss in his structural analysis of myth. After an analysis of Lévi-Strauss’s transformational methodology, we describe in detail how discrete-event system specification models are implemented and developed in the framework of a DEVSimPy software environment. The validation of the method involves a discrete-event system specification simulation based on the extension of discrete-event system specification models dedicated to provide a dynamic Google Earth visualization of the selected myth. Future work around the discrete-event system specification formalism in anthropology is described as well as future applications regarding the impact of computational models (discrete-event system specification formalism, Bayesian inferences, and object-oriented features) to new contemporary anthropological domains.

In the Mediterranean area, the wildfire is a very important and critical problem. Environmental monitoring by using of sensors network seems to be a significant alternative of current detection techniques. Wireless sensor networks have been recognized as one of the most important technologies for this Century. Wireless sensor network represents a real and adapted solution to this problem. Test showed the sensor capacity to transmit the data wildfire in real time. However, we don't know if this capacity is reliable with a large number of nodes. We introduce the DEVS discrete event formalism, and to apply it to the modelling and simulation of a great sensor network. The improvement of routing protocol and data transmission quality in the wildfire phenomenon is the base of our research.