RFID technology (Radio Frequency IDentification) enables putting an identification label (e.g. tag) into a certain object and, by means of a reader, get the information related to it without any physical contact. The use of these tags in medical context enables a rapid and precise identification of each patient and, by means of Internet of Things (IoT), enables a ubiquitous and quick access to medical related records. These technologies, RFID and IoT, integrated within a suitable system, promote a better physician-patient interaction. A simple IoT-enabled system can send warnings to any physician, nurse or other health caregiver. Therefore, with a simple IoT architecture one may remotely monitor and control the patient's well being. This paper presents an IoT architecture, using RFID tags, able to easily and remotely establish a medication control system, beginning from physicians prescription to pharmaceutical drug administration. This paper presents the implementation and analysis of a prototype service, with a web interface, allowing for a first evaluation of the proposed service. The prototype service -based on RFID, EPC (Electronic Product Code) and ONS (Object Name Service) -and its web interface are presented and evaluated. Some use cases are presented and evaluated using this prototype service entitled “RFID-based IoT for medication control”

The use of Radio Frequency Identification technology (RFID) in medical context enables not only drug identification, but also a rapid and precise identification of patients, physicians, nurses or any other health caregiver. The combination of RFID tag identification with structured and secured Internet of Things (IoT) solutions enables ubiquitous and easy access to medical related records, while providing control and security to all interactions. This paper defines a basic security architecture, easily deployable on mobile platforms, which would allow to establish and manage a medication prescription service in mobility context making use of electronic Personal Health Records. This security architecture is aimed to be used with a mobile e-health application (m-health) through a simple and intuitive interface, supported by RFID technology. This architecture, able to support secured and authenticated interactions, will enable an easy deployment of m-health applications. The special case of drug administration and ubiquitous medication control system, along with the corresponding Internet of Things context, is presented. The security architecture and its protocols, along with a general Ambient Assisted Living secure service for medication control, is then analyzed in the context of the Internet of Things.

Among various factors contributing to performance of named data networking (NDN), the organization of caching is a key factor and has benefited from intense studies by the networking research community. The performed studies aimed at (1) finding the best strategy to adopt for content caching; (2) specifying the best location, and number of content stores (CS) in the network; and (3) defining the best cache replacement policy. Accessing and comparing the performance of the proposed solutions is as essential as the development of the proposals themselves. The present work aims at evaluating and comparing the behavior of four caching policies (i.e., random, least recently used (LRU), least frequently used (LFU), and first in first out (FIFO)) applied to NDN. Several network scenarios are used for simulation (2 topologies, varying the percentage of nodes of the content stores (5–100), 1 and 10 producers, 32 and 41 consumers). Five metrics are considered for the performance evaluation: cache hit ratio (CHR), network traffic, retrieval delay, interest re-transmissions, and the number of upstream hops. The content request follows the Zipf–Mandelbrot distribution (with skewness factor $\alpha =1.1$ and $\alpha =0.75$). LFU presents better performance in all considered metrics, except on the NDN testbed, with 41 consumers, 1 producer and a content request rate of 100 packets/s. For the level of content store from 50% to 100%, LRU presents a notably higher performance. Although the network behavior is similar for both skewness factors, when $\alpha =0.75$, the CHR is significantly reduced, as expected.

Vehicular Ad hoc Networks (VANETs) are an emerging type of network that increasingly encompass a larger number of vehicles. They are the basic support for Intelligent Transportation Systems (ITS) and for establishing frameworks which enable communication among road entities and foster the development of new applications and services aimed at enhancing driving experience and increasing road safety. However, VANETs’ demanding characteristics make it difficult to implement security mechanisms, creating vulnerabilities easily explored by attackers. The main goal of this work is to propose an Intelligent Hierarchical Security Framework for VANET making use of Machine Learning (ML) algorithms to enhance attack detection, and to define methods for secure communications among entities, assuring strong authentication, privacy, and anonymity. The ML algorithms used in this framework have been trained and tested using vehicle communications datasets, which have been made publicly available, thus providing easily reproducible and verifiable results. The obtained results show that the proposed Intrusion Detection System (IDS) framework is able to detect attacks accurately, with a low False Positive Rate (FPR). Furthermore, results show that the framework can benefit from using different types of algorithms at different hierarchical levels, selecting light and fast processing algorithms in the lower levels, at the cost of accuracy, and using more precise, accurate, and complex algorithms in nodes higher in the hierarchy.

Introductory programming courses entail students' high failure and dropout rates. In an effort to tackle this problem, we carried out a qualitative study aiming to shed some light on the programming phase that is most challenging for students, in order to elicit the specific difficulties they experience while learning to program. In doing so, distinctive cognitive abilities, differentiating subjects in terms of the way they handle programming tasks, were detected. Such aptitudes are represented in three groups of students: those who learn easily, those who never seem to fully grasp what programming requires despite true effort, and those who experience a sudden insight, making them leap from a point were they had difficulties to another where they overcome them. By interviewing teachers and students, abstraction and sequencing elaboration were found to be the two core skills for programming. These results impelled us to consider the mental models' approach, concluding that there are very specific cognitive functions that are more favorable to learn programming and that are fostered by more adequate schemas of representing reality. Some conclusions involving Problem-based learning as a fit teaching methodology to overcome students' difficulties are also presented.