Both the idea and technology for connecting sensors and actuators to a network to remotely monitor and control physical systems have been known for many years and developed accordingly. However, a little more than a decade ago the concept of the Internet of Things (IoT) was coined and used to integrate such approaches into a common framework. Technology has been constantly evolving and so has the concept of the Internet of Things, incorporating new terminology appropriate to technological advances and different application domains. This paper presents the changes that the IoT has undertaken since its conception and research on how technological advances have shaped it and fostered the arising of derived names suitable to specific domains. A two-step literature review through major publishers and indexing databases was conducted; first by searching for proposals on the Internet of Things concept and analyzing them to find similarities, differences, and technological features that allow us to create a timeline showing its development; in the second step the most mentioned names given to the IoT for specific domains, as well as closely related concepts were identified and briefly analyzed. The study confirms the claim that a consensus on the IoT definition has not yet been reached, as enabling technology keeps evolving and new application domains are being proposed. However, recent changes have been relatively moderated, and its variations on application domains are clearly differentiated, with data and data technologies playing an important role in the IoT landscape.

Biosensors are small analytical devices incorporating a biological recognition element and a physico-chemical transducer to convert a biological signal into an electrical reading. Nowadays, their technological appeal resides in their fast performance, high sensitivity and continuous measuring capabilities; however, a full understanding is still under research. This paper aims to contribute to this growing field of biotechnology, with a focus on Glucose-Oxidase Biosensor (GOB) modeling through statistical learning methods from a regression perspective. We model the amperometric response of a GOB with dependent variables under different conditions, such as temperature, benzoquinone, pH and glucose concentrations, by means of several machine learning algorithms. Since the sensitivity of a GOB response is strongly related to these dependent variables, their interactions should be optimized to maximize the output signal, for which a genetic algorithm and simulated annealing are used. We report a model that shows a good generalization error and is consistent with the optimization.

In the last decade, Process Mining has established as an emerging discipline for being oriented to the discovery, monitoring, and improvement of processes. This article presents the results of a Systematic Literature Review that reveals the state of the art of Process Mining perspectives in Software Engineering from 2010 to 2020, with the purpose of adequately characterizing software processes that are mostly complex. Twelve primary studies were selected and classified according to their type of publication, conceptual definition of Process Mining perspectives, their implementation in case studies and the successful contributions to the challenges and opportunities of implementing the perspectives identified in Software Engineering. The results identify four Process Mining perspectives (control- flow, organizational, case, and time), highlighting the use of the control-flow perspective in the cases of discovery of process models (flows and order of execution activities) and the organization- al perspective in the identification of human resource interaction. Although Process Mining perspectives have been applied in the Software Engineering domain, the lack of a standardized definition for the term Process Mining perspective and, in turn, for each of them, is still highlighted.