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Nowadays, wireless sensor networks (WSNs) emerge as an active research area in which challenging topics involve energy consumption, routing algorithms, selection of sensors location according to a given premise, robustness, efficiency, and so forth. Despite the open problems in WSNs, there are already a high number of applications available. In all cases for the design of any application, one of the main objectives is to keep the WSN alive and functional as long as possible. A key factor in this is the way the network is formed. This survey presents most recent formation techniques and mechanisms for the WSNs. In this paper, the reviewed works are classified into distributed and centralized techniques. The analysis is focused on whether a single or multiple sinks are employed, nodes are static or mobile, the formation is event detection based or not, and network backbone is formed or not. We focus on recent works and present a discussion of their advantages and drawbacks. Finally, the paper overviews a series of open issues which drive further research in the area.

This paper is concerned with the analysis of the observability of the discrete event systems (DES) modeled by interpreted Petri nets (IPN). This paper presents three major contributions on the field of the observability of DES. First, an observability definition for IPN is proposed. This definition is more precise than previous ones because it deals with the possibility of determining the system's initial state, using the knowledge of the system's inputs, outputs, and structure. Later, a novel characterization of the IPN exhibiting the observability property that is based on the IPN structure is presented. Finally, a method for designing asymptotic observers is discussed. The main advantage over other methods is that the observer presented herein is given as an IPN, allowing further analysis of the system-observer pair.Index Terms-Discrete event systems (DES), observability, observers, Petri nets (PN).

Analogous to the identification of continuous dynamical systems, identification of discrete-event systems (DESs) consists of determining the mathematical model that describes the behaviour of a given ill-known or eventually unknown system from the observation of the evolution of its inputs and outputs. First, the paper overviews identification approaches of DES found in the literature, and then it provides a comparative analysis of three recent and innovative contributions.

This paper deals with the identification of discrete event manufacturing systems that are automated using a programmable logic controller (PLC). The behavior of the closed loop system (PLC and Plant) is observed during its operation and is represented by a single long sequence of observed input/output (I/O) signals vectors. The proposed method follows a black-box and passive identification approach that allows addressing large and complex industrial DES and yields compact and expressive interpreted Petri net (IPN) models. It consists of two complementary stages; the first one obtains, from the I/O sequence, the reactive part of the model composed by observable places and transitions. The I/O sequence is also mapped into a sequence of the created transitions, from which the second stage builds the non observable part of the model including places that ensure the reproduction of the observed input output sequence. This method, based on polynomial-time algorithms on the size of the input data, has been implemented as a software tool that generates and draws the IPN model; it has been tested with input/output sequences obtained from real systems in operation. The tool is described and its application is illustrated through a case study. Note to practitioners-Automated modeling of controlled discrete manufacturing systems can be achieved by efficient identification algorithms that cope with large and complex plants performing concurrent and repetitive tasks a priori unknown. The black-box identification procedure processes an input/output sequence recorded during the system functioning for a long period of time, and then yields a comprehensive model of the closed-loop controlled system; this model approximates closely the actual behavior of the compound system controllerplant. A tool based on identification algorithms constitutes an excellent resource for computer-aided reverse engineering of controlled manufacturing systems. The method proposed herein allows processing sequences composed by thousands of I/O vectors in few seconds.