A new approach to developing Web-enabled environments for remote diagnostics, maintenance, and experimentation in engineering is based on a middleware layer that uses a Java-lntemet-Labview server to provide communication between Java programs and Labview virtual instruments. The authors illustrate their technique by applying it to the development of a complete Web-enabled application for remote control of a thermal process.

Internet of Things (IoT) aims at facilitating access to all devices that are connected to the internet, both wired and wireless. This scenario can initially seem interesting. Nevertheless, it has a lack of privacy and danger of malicious interaction with the devices. Therefore, IoT, as it stands, is not suitable for companies to make their data and devices accessible through the internet, since they could find an untidy cloud, made up of devices without the necessary control of use. This paper proposes the use of the cloud computing advantages to develop a secure access global system based on a cloud. The company will decide the controlled access to the chosen devices and data, both by employees and external people. The developed system can be used from different scenarios such as: a public cloud; an Infrastructure as a Service (IaaS); and a private cloud. To illustrate the operation of the developed system, a representative network of heterogeneous multiprotocol devices has been designed.

Continuous authentication systems have been proposed as a promising solution to authenticate users in smartphones in a non-intrusive way. However, current systems have important weaknesses related to the amount of data or time needed to build precise user profiles, together with high rates of false alerts. Voice is a powerful dimension for identifying subjects but its suitability and importance have not been deeply analyzed regarding its inclusion in continuous authentication systems. This work presents the S3 platform, an artificial intelligence-enabled continuous authentication system that combines data from sensors, applications statistics and voice to authenticate users in smartphones. Experiments have tested the relevance of each kind of data, explored different strategies to combine them, and determined how many days of training are needed to obtain good enough profiles. Results showed that voice is much more relevant than sensors and applications statistics when building a precise authenticating system, and the combination of individual models was the best strategy. Finally, the S3 platform reached a good performance with only five days of use available for training the users’ profiles. As an additional contribution, a dataset with 21 volunteers interacting freely with their smartphones for more than sixty days has been created and made available to the community.

In recent years, acoustic pollution caused by noise has considerably increased in many countries. Particularly in Spain, the noisiest country in Europe. It is sometimes difficult to predict the noise levels that a new installation or an expansion of industrial equipment will cause in the surroundings. This work introduces a new methodology for the prediction, evaluation, and analysis of industrial noise sources, as well as a novel tool for predicting and categorizing outdoor noise from its measurement at their sources. A Wastewater Treatment Plant (WWTP) has been used to demonstrate the applicability and validity of this methodology. The continuous level of acoustic pressure equivalent has been measured in different points of the plant using an integrating sound level meter. From these values, noise maps have been built to obtain detailed information of the industrial noise generated in the installation. Also, the typical frequency patterns of each type of source have been used for the calculation of source noise apportionments. To achieve this objective, several noise sources have been selected to provide information about their contribution to the industrial noise in the WWTP surrounding area. Finally, predictions have been validated using actual measurements. This methodology is a useful tool to predict personal exposure to noise and the impact on the environment. This information can be used, in particular, to propose mitigation actions.

Continuous authentication (CA) is a promising approach to authenticate workers and avoid security breaches in the industry, especially in Industry 4.0, where most interaction between workers and devices takes place. However, introducing CA in industries raises the following unsolved questions regarding machine learning (ML) models: its precision and performance; its robustness; and the issue about if or when to retrain the models. To answer these questions, this article explores these issues with a proposed supervised versus nonsupervised ML-based CA system that uses sensors, applications statistics, or speaker data collected by the operator’s devices. Experiments show supervised models with equal error rates of 7.28% using sensors data, 9.29% with statistics, and 0.31% with voice, a significant improvement of 71.97, 62.14, and 97.08%, respectively, over unsupervised models. Voice is the most robust dimension when adding new workers, with less than 2% of false acceptance rate even if workforce size is doubled.