Towards an Accurate Faults Detection Approach in Internet of Medical Things Using Advanced Machine Learning Techniques.

Remotely monitoring people's healthcare is still among the most important research topics for researchers from both industry and academia. In addition, with the Wireless Body Networks (WBANs) emergence, it becomes possible to supervise patients through an implanted set of body sensors that can communicate through wireless interfaces. These body sensors are characterized by their tiny sizes, and limited resources (power, computing, and communication capabilities), which makes these devices prone to have faults and sensible to be damaged. Thus, it is necessary to establish an efficient system to detect any fault or anomalies when receiving sensed data. In this paper, we propose a novel, optimized, and hybrid solution between machine learning and statistical techniques, for detecting faults in WBANs that do not affect the devices' resources and functionality. Experimental results illustrate that our approach can detect unwanted measurement faults with a high detection accuracy ratio that exceeds the 99.62%, and a low mean absolute error of 0.61%, clearly outperforming the existing state-of-art solutions.

NOTA: a novel online teaching and assessment scheme using Blockchain for emergency cases.

Technology advancements promote a redefinition of traditional instructional methodologies, as well as the roles of teachers and learners towards an efficient e-learning ecosystem. To date, all existing solutions are combined with the conventional face-to-face learning process. However, the latter can be unexpectedly hindered in some emergency cases, like the Coronavirus (COVID-19) pandemic. To handle such unexpected scenarios, this paper presents NOTA, a novel online teaching and assessment scheme that takes advantage of Blockchain technology to maintain the expected teaching quality and assessment fairness while respecting the courses' and examinations' schedule. Besides, NOTA also motivates both learners and teachers to persist in their endeavours, even from home, through Blockchain's incentive strategies. The preliminary results taken during the CoronaVirus period showed a very high satisfaction ratio, exceeding the 90%. This made us feel very optimistic about the potential of our proposal when deployed at a larger scale.

A cooperative crowdsensing system based on flying and ground vehicles to control respiratory viral disease outbreaks

The massive increase in population density in cities has led to several urban problems, such as an increment of air pollution, traffic congestion, and a faster spread of infectious diseases. With the rapid innovation in the intelligent sensors technology, and its integration into smart vehicles and Unmanned Aerial Vehicles (UAVs), a novel sensing paradigm has been promoted, namely vehicular crowdsensing, which leverages on-board sensors to capture information from the surrounding environment. Collected data are then analyzed to take proper countermeasures. In this paper, we present a smart coordination mechanism between UAVs and ground vehicles (GVs), which sense information like body temperature and breathing rate of people, in order to support a variety of monitoring applications, including discovering the presence of infectious diseases. In our framework, namely GUAVA, aerial and ground vehicles are equipped with GPS devices and thermal cameras to monitor specific geographic areas, detect humans’ vital parameters and, at the same time, discover duplicate data by identifying matching faces in thermal video sequences with the GaussianFace algorithm. The sensing tasks in hard-to-reach places are assigned to UAVs, with the ability to power up wirelessly from the nearest GV and offload the collected monitoring images to it. Simulation results have assessed our proposed framework, showing good performance in terms of distinct Quality of Service (QoS) metrics.

Remote sensing to control respiratory viral diseases outbreaks using Internet of Vehicles

Abstract The respiratory viral diseases, such as those caused by the family of coronaviruses, can be extremely contagious and spread through saliva droplets generated by coughing, sneezing, or breathing. In humans, the most common symptoms of the infection include fever and difficulty in breathing. In order to reduce the diffusion of the current ?Coronavirus disease 2019 (COVID-19)? pandemic, the Internet of Things technologies can play an important role;for instance, they can be effectively used for implementing a real-time patient tracking and warning system at a city scale. Crucial places to install the tracking IoT devices are the public/private vehicles that, augmented with multiple connectivity solutions, can implement the Internet of Vehicles paradigm. In such ubiquitous network environment, vehicles are equipped with a variety of sensors, including regular cameras that can be replaced with thermal cameras. Therefore, this article proposes a new design for widely detecting respiratory viral diseases that leverages IoV to collect real-time body temperature and breathing rate measurements of pedestrians. This information can be used to recognize geographic areas affected by possible COVID-19 cases and to implement proactive preventive strategies that would further limit the spread of the disease.