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Dental decay is the most prevalent oral disease worldwide since more than 2.4 billion people suffer from caries of permanent teeth. Therefore, any details about its progression into the hard-dental tissues could contribute to unravelling the mechanisms underlying this process. We have analyzed dental tissue sections with and without caries in order to detect structure differences corelating them with clinical aspects observable from the tooth surface. Our working hypothesis was based on finding a link between the process of tertiary dentin laying (as a response to coronal caries) and the subsequent obliteration of dentin tubules. We have selected N=10 extracted teeth with/without coronal caries, resin-embedded and sectioned them. A specific software was used to digitally quantify the density of unobliterated dentin tubules reaching the dentin-enamel junction (DEJ), considering as positive threshold criteria the presence of the odontoblast process inside the analyzed tubule. This study showed the differences between the healthy and carious-affected hard-dental coronal tissues. More odontoblast processes reached the DEJ in unaffected teeth. Using specific software, we have quantified their density decrease near a lesion. We have studied the dynamics of the carious study and measured the consequent structural modifications of the dentin. In conclusion, there is a significant difference between the number of dentin tubules containing odontoblast processes that reach the DEJ in healthy∕altered tissues. The tooth reacts not only by production of tertiary dentin to protect the pulp chamber, but also by obliteration of dentin tubules, thus reducing the number of odontoblast processes reaching the DEJ. This pilot study could serve as the starting point in developing a dedicated software that could deliver a personalized pattern for decay progression by analyzing one single tooth and extrapolate the result to all the patient's remaining ones.

Charcot foot or Charcot neuropathy is a rare disease found in patients with diabetes mellitus and is characterized by bone damage to the foot leading to deformities, instability, functional impotence and even amputation. The mechanism is still under discussion, without a consensus regarding the pathophysiology of this condition. The treatment is a complex one, non-surgical and surgical, the non-surgical one addressing especially the acute phase of the disease, the surgical one being complex and ranging from osteotomy, debridement, arthrodesis, internal or external fixation or even amputation. A good management of diabetes, of its peripheral complications, an early recognition of the Charcot type foot, prevents the evolution towards this serious condition.

The fast-paced adoption of smart devices has changed the Internet of Things (IoT) landscape, leading to the growth of smart environments and inclusion in many aspects of our society. In IoT applications, data collected from sensors and mobile devices are aggregated, processed, and analyzed to extract useful information and develop intelligent services. If the collected data is not trustworthy due to the damage or malicious input of some sensors, the quality of the service will be impacted. For reliable data collection and mining, it is mandatory to define robust security and trust models, suitable for the IoT application context. In this paper, we propose RESFIT, a platform that implements a reputation-based trust mechanism and an advanced application level firewall to cope with the above mentioned issues. Having a gateway-centric architecture, the proposed platform ensures minimal resource consumption at the node layer, and an integrated overview and control of the system state, through the cloud component and smartphone management application.

Mobile crowdsensing has emerged as a new paradigm in the IoT world, exploiting users’ mobility in conjunction with advanced capabilities and proliferation of mobile devices. Smartphones, tablets and smartwatches are now typically equipped with sensing and wireless capabilities, enabling them to produce and upload data for different IoT applications. The mobile crowdsensing approach has the advantage of being cost-effective, while also providing real-time data. However, a number of challenges should be addressed in order for mobile crowdsensing to reach its full potential. Security, privacy and reliability of the data provided by mobile devices are the most important ones. In this paper, we propose a security framework with a multi-layer architecture that addresses the trust evaluation of sensing devices based on reputation scores calculated using a naive Bayes algorithm.

Data gathering from wireless sensors using unmanned aerial vehicles has been a subject of interest in the recent years. Scientific literature shows that this method has not reached its peak and results are promising, as can be seen from Section 2. Sensors were included in different areas with the sole purpose of optimizing results (traffic management, crop field surveillance and many more). Beside obvious advantages that they bring, they offer also limitations in terms of hardware/software capabilities. For this reason, this paper addresses the need of reducing the transmission time and energy consumption due to the limited capacities of wireless sensors. The most important part of the proposed optimization relies in the dynamic construction of the trajectory that an unmanned aerial vehicle uses for gathering data from wireless sensors. The trajectory will be built based on a file that contains the positions of all sensors in the field. The file would be created in a previous step, when the unmanned aerial vehicle would fly on a predefined pattern and discover sensors on the ground. Based on the locations of the sensors, grouping and optimizations are done to obtain the best trajectory for the given location of sensors. Simulations were made with Network Simulator 2 and results show that the proposed objectives have been reached.

The rapid growth of Internet of Things (IoT) connected devices will have a great impact on people's personal lives. Even if, at the beginning of the technological advance, IoT was used only in the industrial applications, now it has reached regular consumers, enabling Smart-Home applications. In the IoT landscape, Smart-Home applications need to meet certain requirements, such as enriching the user experience or allowing the integration with an IoT Cloud platform and a smartphone management solution. In a Smart-Home architecture, remote access, whether or not using a trusted Cloud, requires secure authentication and authorization mechanisms in order to protect against unauthorized actions. This paper proposes a Smart-Home security framework which consists in a Secure Cloud platform and a smartphone based command authorization solution. The Secure Cloud acts as proxy between the IoT devices and a third-party Cloud vendor and the smartphone authorization mechanism allows the user to control the commands received by the IoT device.

Ambient intelligence is a new paradigm in the Internet of Things (IoT) world that brings smartness to living environments to make them more sensitive, adaptive, and personalized to human needs. A critical area where ambient intelligence can be used is health and social care, where it can improve and sustain the quality of life without increasing financial costs. The adoption of this new paradigm for health and social care largely depends on the technology deployed (sensors and wireless networks), the software used for decision-making and the security, privacy and reliability of the information. IoT sensors and wearables collect sensitive data and must respond in a near real-time manner to input changes. An IoT security framework is meant to offer the versatility and modularization needed to sustain such applications. Our framework was designed to easily integrate with different health and social care applications, separating security tasks from functional ones and being designed with independent modules for each layer (Cloud, gateway and IoT device), that offer functionalities relative to that layer.