Internet of Things combined with Radio Frequency IDentification technology enable a whole new context for smart objects that are able to combine their physical and virtual existences. Radio Frequency IDentification, putting an identification label into every object, enables a smart system to get information, either real-time or virtual-linked information, without any physical contact. Information retrieved from such an object, turns it into a potential smart object, certainly able to auto identify itself and, if security problems are suitably treated, most probably able to connect to the global Internet. This way, one can get an ubiquitous framework to access, monitor and control any of those smart objects over an Internet of connected things. RFID tags in medical context enable a rapid and precise identification of each smart entity, enabling a ubiquitous and quick access to Personal Health Records over an Internet of Things. The use of smart phones with Internet access, along with strong security concerns - such as authenticity, privacy, confidentiality, integrity, data origin authentication, entity authentication and non-repudiation - turn this whole context into a decentralized and mobile healthcare system. Using the simple IoT architecture presented, combining smart objects, the security solution and mobile communications, one may remotely take care of patients’ well being, establishing an ubiquitous Ambient Assisted Living for Mobile Health applications. As an application example, a prototype m-health service, its security mechanisms and web based application, establish a use case scenario for the evaluation of the proposed architecture

Intelligent Transportation Systems (ITSs) are systems that aim to provide innovative services for road users in order to improve traffic efficiency, mobility and safety. This aspect of safety is of utmost importance for Vulnerable Road Users (VRUs), as these users are typically more exposed to dangerous situations, and their vehicles also possess poorer safety mechanisms when in comparison to regular vehicles on the road. Implementing automatic safety solutions for VRU vehicles is challenging since they have high agility and it can be difficult to anticipate their behavior. However, if equipped with communication capabilities, the generated Vehicle-to-Anything (V2X) data can be leveraged by Machine Learning (ML) mechanisms in order to implement such automatic systems. This work proposes a VRU (motorcyclist) collision prediction system, utilizing stacked unidirectional Long Short-Term Memorys (LSTMs) on top of communication data that is generated using the VEINS simulation framework (coupling the Simulation of Urban MObility (SUMO) and Network Simulator 3 (ns-3) tools). The proposed system performed well in two different scenarios: in Scenario A, it predicted 96% of the collisions, averaging 4.53 s for Average Prediction Time (s) (APT) and with a Correct Decision Percentage (CDP) of 41% and 78 False Positives (FPs); in Scenario B, it predicted 95% of the collisions, with a 4.44 s APT, while the CDP was 43% with 68 FPs. The results show the effectiveness of the approach: using ML methods on V2X data allowed the prediction of most of the simulated accidents. Nonetheless, the presence of a relatively high number of FPs does not allow for the usage of automatic safety features (e.g., emergency breaking in the passenger vehicles); thus, collision avoidance must be achieved manually by the drivers.

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.

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.