The onset of the corona virus disease 2019 (COVID-19) pandemic caused shortages in mechanical ventilators (MVs) essential for the intensive care unit (ICU) in the hospitals. The increasing crisis prompted the investigation of ventilators which is low cost and offers lower health complications. Many researchers are revisiting the use of negative pressure ventilators (NPVs), due to the cost and complications of positive pressure ventilators (PPVs). This paper summarizes the evolution of the MVs, highlighting the limitations of popular positive and negative pressure ventilators and how NPV can be a cost-effective and lower health complication solution. This paper also provides a detailed investigation of the structure and material for the patient enclosure that can be used for a cost-effective NPV system using ANSYS simulations. The simulation results can confirm the selection and also help in developing a low cost while based on readily available materials. This can help the manufacturer to develop low-cost NPV and reduce the pressure on the healthcare system for any pandemic situation similar to COVID-19.
Wireless enabling technologies in critical infrastructures are increasing the efficiency of communications. In the era of 5G and beyond, more technologies will be allowed to connect to mobile networks, enabling the Internet of Things (IoT) on a massive scale. Most of these technologies are vulnerable to physical-layer security attacks, namely jamming. Jamming attacks are among the most effective techniques to attack and compromise the availability of these wireless technologies. Jamming is an interfering signal that limits the intended receiver from correctly receiving the messages. Once the adversary deploys a jammer in a wireless network, jammer detection becomes difficult, if not impossible, due to the inaccessibility of the affected devices in the network. This paper extends the state-of-the-art jamming detection and classification methods by proposing an effective IoT Tiny Machine Learning (TinyML)-based approach, where a trained deep learning model is deployed on an IoT edge device, namely a Raspberry Pi. The model is built using TensorFlow and deployed on the IoT device using TensorFlow lite. The trained model encompasses two commonly known jamming types: constant and periodic, in addition to the normal channel state. The Raspberry Pi is connected to a Software Defined Radio (SDR) that continuously senses the WiFi channel and acquires Received Signal Strength (RSS) readings which the TinyML model evaluates to detect the presence of jamming and its type. We release both the procedure and collected dataset for the different types of jamming as open source. Finally, we conducted an extensive testing campaign to test, evaluate, and illustrate the effectiveness of the proposed TinyML-based detection on the edge scheme.
The Internet of Vehicles (IoV) paradigm aims to improve road safety and provide a comfortable driving experience for Internetconnected vehicles, by transmitting early warning and infotainment signals to Internet-connected vehicles in the network. The unique characteristics of the IoV, such as their mobility and pervasive Internet connectivity, expose such networks to many cyberattacks. In particular, jamming attacks represent a considerable risk to their performance, as they can significantly affect vehicles' functionality, possibly leading to collisions in dense networks. This paper presents a new scheme enabling the detection and localization of jamming attacks carried out within an IoV network. We consider several scenarios, e.g., where the Internet-connected vehicles and the jammer are statically positioned, as when parked on a street, moving in the same direction and with variable speeds, and moving in opposite directions. We leverage the physical-layer characteristics of the received signals, particularly the Received Signal Strength (RSS), and devise a solution minimizing the jammer localization error based on a set of antennas deployed on the vehicle. Specifically, we compute the power emitted by the jammer and received by the arrays of omnidirectional antennas and we use such values to estimate the location of the jammer in the previous-cited scenarios. Through an extensive simulation campaign, we provide a thorough study of our algorithm, evaluating the effect of several system and channel parameters on the measurement error. The results obtained for all scenarios show a significant localization accuracy, i.e., ranging from 0.23 meters to 13 meters, depending on the channel conditions. CCS CONCEPTS• Networks → Network properties; • Mobile and wireless security; • Security and privacy → Systems security;
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