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.

Poza-Lujan, J.; Calafate, CT.; Posadas-Yagüe, J.; Cano, J. (2016). Assessing the impact of continuous evaluation strategies: tradeoff between student performance and instructor effort. IEEE Transactions on Education. 59(1):17-23. doi:10.1109/TE.2015.2418740. Abstract-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 on 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.

This paper presents the extension of a meta-model (MAM5) and a framework based on the model (JaCalIVE) for developing intelligent virtual environments. The goal of this extension is to develop augmented mirror worlds that represent a real and virtual world coupled, so that the virtual world not only reflects the real one, but also complements it. A new component called a smart resource artifact, that enables modelling and developing devices to access the real physical world, and a human in the loop agent to place a human in the system have been included in the meta-model and framework. The proposed extension of MAM5 has been tested by simulating a light control system where agents can access both virtual and real sensor/actuators through the smart resources developed. The results show that the use of real environment interactive elements (smart resource artifacts) in agent-based simulations allows to minimize the error between simulated and real system.

Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2–4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.

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