The Message Queuing Telemetry Transport (MQTT) protocol is one of the most extended protocols on the Internet of Things (IoT). However, this protocol does not implement a strong security scheme by default, which does not allow a secure authentication mechanism between participants in the communication. Furthermore, we cannot trust the confidentiality and integrity of data. Lightweight IoT devices send more and more sensible data in areas of Smart Building, Smart City, Smart House, Smart Car, Connected Car, Health Care, Smart Retail, Industrial IoT (IIoT), etc. This makes the security challenges in the protocols used in the IoT particularly important. The standard of MQTT protocol strongly recommends implement it over Transport Layer Security (TLS) instead of plain TCP. Nonetheless, this option is not possible in most lightweight devices that make up the IoT ecosystem. Quite often, the constrained resources of IoT devices prevent the use of secure asymmetric cryptography algorithms implemented by themselves. In this article, we propose making a security schema in MQTT protocol using Cryptographic Smart Cards, for both challenges, the authentication schema and the trusted data confidentiality and data integrity. We carry out this security schema without modifying the standard protocol messages. And finally, we present a time results experiment using an example implementation model with JavaCard library.

The increasing popularity of distance education poses exciting new challenges. In particular, current pedagogical paradigms, such as competency-based education, require students’ continuous evaluation. That is, to master skills, students need to receive constant feedback to guide their experimentation processes. However, teaching teams are usually under-dimensioned to support the large number of students that online courses usually have. This paper presents the approach we have adopted at the National University of Distance Education to overcome this problem for the case of computer programming practices, which complements human evaluation with an automatic assessment system. The paper describes our system and reports its benefits with an empirical study from 2011 to 2018 that involved 14,944 students.

In software product line engineering, feature diagrams are a popular means to represent the similarities and differences within a family of related systems. In addition, feature diagrams implicitly model valuable information that can be used in economic models to estimate the cost savings of a product line. In particular, this paper reviews existing proposals on computing the total number of products modeled with a feature diagram and, given a feature, the number of products that implement it. This paper also reviews the economic information that can be estimated when such numbers are known. Thus, this paper contributes by bringing together previously-disparate streams of work: the automated analysis of feature diagrams and economic models for product lines.

RFID middleware is anticipated to one of the main research areas in the field of RFID applications in the near future. The Data EPC Acquisition System (DEPCAS) is an original proposal designed by our group to transfer and apply fundamental ideas from System and Data Acquisition (SCADA) systems into the areas of RFID acquisition, processing and distribution systems. In this paper we focus on how to organize and manage generic RFID sensors (edge readers, readers, PLCs, etc…) inside the DEPCAS middleware. We denote by RFID Sensors Networks Management (RSNM) this part of DEPCAS, which is built on top of two new concepts introduced and developed in this work: MARC (Minimum Access Reader Command) and RRTL (RFID Reader Topology Language). MARC is an abstraction layer used to hide heterogeneous devices inside a homogeneous acquisition network. RRTL is a language to define RFID Reader networks and to describe the relationship between them (concentrator, peer to peer, master/submaster).

This work analyzes a new method for object recognition in complex scenes combining vision-based techniques applied to the 3-D data obtained using range sensors and object identification coming from radio frequency tags (radio frequency identification (RFID) technology). Three-dimensional vision-based algorithms for object recognition have many restrictions in practical applications, i.e., uncertainty, incapability for real-time tasks, etc., but they work well for pose determination once the object is recognized. On the other hand, RFID technology allows us to detect the presence of specific objects in a scene, but it cannot provide their localization, at least not with the accuracy required in applications such as ours. In this paper, we present a new and powerful recognition method obtained by fusing both techniques. The phases of the method are described, and abundant experimentation results are included. An in-depth performance analysis has been carried out to demonstrate the recognition improvements achieved by the algorithm when RFID assistance is considered. It helps to confirm the robustness of this fusion approach and prove its effectiveness. A final discussion is included, concerning what should be the most adequate size of the object database for optimal algorithm exploitation.

Production lines face numerous challenges to meet market demands, including constant changes in products that require continuous adjustments. Efficient and rapid reconfiguration and adaptation of production processes are crucial. In cases of inadequate adaptation, bottlenecks can arise due to human errors or incorrect configurations, often introducing complexity in pinpointing the root cause and resulting in financial losses. Furthermore, improper machine maintenance contributes to this situation as well. This article seeks to establish a framework grounded in the contemporary smart factory, the IIoT, the Industry 4.0 paradigm, and Big Data. The proposed system places emphasis on leveraging real-time data analysis for predicting risks, while concurrently conducting a thorough analysis of historical data to monitor trends and enhance bottleneck identification. The defined architecture operates across multiple levels, acquiring real-time information and generating historical data for training and continuous optimization. Predictive results contribute to decision-making and assist in mitigating bottlenecks in manufacturing lines.