The inclusion of embedded sensors into a networked system provides useful information for many applications. A Distributed Control System (DCS) is one of the clearest examples where processing and communications are constrained by the client’s requirements and the capacity of the system. An embedded sensor with advanced processing and communications capabilities supplies high level information, abstracting from the data acquisition process and objects recognition mechanisms. The implementation of an embedded sensor/actuator as a Smart Resource permits clients to access sensor information through distributed network services. Smart resources can offer sensor services as well as computing, communications and peripheral access by implementing a self-aware based adaptation mechanism which adapts the execution profile to the context. On the other hand, information integrity must be ensured when computing processes are dynamically adapted. Therefore, the processing must be adapted to perform tasks in a certain lapse of time but always ensuring a minimum process quality. In the same way, communications must try to reduce the data traffic without excluding relevant information. The main objective of the paper is to present a dynamic configuration mechanism to adapt the sensor processing and communication to the client’s requirements in the DCS. This paper describes an implementation of a smart resource based on a Red, Green, Blue, and Depth (RGBD) sensor in order to test the dynamic configuration mechanism presented.

Current opinion on undergraduate studies has led to a reformulation of teaching methodologies to base them not just on learning, but also on skills and competencies. In this approach, the teaching/learning process should accomplish both knowledge assimilation and skill development. Previous works demonstrated that a strategy that uses continuous evaluation is able to meet both objectives. However, those studies did not evaluate and quantify the additional effort required to implement such strategies. This paper evaluates the additional instructor effort required when implementing continuous evaluation in a first-year Computer Fundamentals course in the Computer Engineering degree program at the Technical University of Valencia, Spain. The experiment quantifies how instructor workload increases under different continuous evaluation strategies and how this affects the overall student grade. Both the “standard” continuous evaluation method and the intensive continuous evaluation method are analyzed, the latter being a proposal that builds upon the standard method by increasing the number of tests and examinations. The results obtained reveal that continuous evaluation improves student grades but that intensive continuous evaluation is liable to generate an excessive instructor overload without having a significant impact on student scores.

Soft skills are important for any career and are necessary to access and face the labor market. This research focuses on soft skills by exploring engineer profiles. It also determines how soft skills are developed through the study of a representative sample of 314 undergraduate engineering students from 15 different Italian universities. The instrument used is a questionnaire that investigates soft skills and is based on the Business-focused Inventory of Personality (BIP). Answers are grouped into four areas: intrapersonal, interpersonal, activity development, and impression management. Results show that these engineers have more self-confidence than the reference sample; they demonstrated a great commitment in setting job goals and pursuing projects, a good emotional adaptation to social situations, and enough attitudes in terms of problem solving and openness to change. Perception on the ability to work under pressure is in the average, and they seem ready to take on challenging tasks. The score shows that engineers from the sample are able to express positive and negative ideas and feelings in balance with the reference average, but sometimes they have difficulties in establishing personal relationships. Therefore, they are unable to understand the moods of those who around them and may also have difficulty in understanding their expectations. This results in some difficulties in teamwork. The general result underlines the opportunity of empowerment programs regarding soft skills.

Understanding the interplay between traffic optimization parameters and their alignment with sensors, control algorithms, and Sustainable Development Goals (SDGs) is essential for improving urban traffic management. The appropriate selection of parameters in urban traffic management is crucial to optimize vehicular flow and meet the Sustainable Development Goals (SDGs). To find out which parameters are most commonly used and appropriate, a comprehensive study was conducted, the results of which are presented in this article. This study uses a three-phase approach: qualitative exploration, systematic literature review, and multiple-dimensional analysis. This study’s contributions include a practical five-level framework for traffic optimization addressing congestion problems, the identification of 19 commonly used traffic control parameters, the analysis of their implementations in recent intelligent traffic control systems, and a proposal of trends to orient these parameters towards efficiency and compliance with the SDGs. The results lay the groundwork for creating new parameters or modifying existing parameters so that the parameters are oriented not only towards efficiency in control algorithms or user experience but also towards meeting the SDGs.