Since its emergence in late 2019, the COVID-19 pandemic has swept through many cities around the world, claiming millions of lives and causing major socio-economic impacts. The pandemic occurred at an important historical juncture when smart solutions and technologies have become ubiquitous in many cities. Against this background, in this review, we examine how smart city solutions and technologies have contributed to resilience by enhancing planning, absorption, recovery, and adaptation abilities. For this purpose, we reviewed 147 studies that have discussed issues related to the use of smart solutions and technologies during the pandemic. The results were synthesized under four themes, namely, planning and preparation, absorption, recovery, and adaptation. This review shows that investment in smart city initiatives can enhance the planning and preparation ability. In addition, the adoption of smart solutions and technologies can, among other things, enhance the capacity of cities to predict pandemic patterns, facilitate an integrated and timely response, minimize or postpone transmission of the virus, provide support to overstretched sectors, minimize supply chain disruption, ensure continuity of basic services, and offer solutions for optimizing city operations. These are promising results that demonstrate the utility of smart solutions for enhancing resilience. However, it should be noted that realizing this potential hinges on careful attention to important issues and challenges related to privacy and security, access to open-source data, technological affordance, legal barriers, technological feasibility, and citizen engagement. Despite this, this review shows that further development of smart city initiatives can provide unprecedented opportunities for enhancing resilience to the pandemic and similar future events.

https://www.mdpi.com/2071-1050/13/14/8018/pdf

Contributions of Smart City Solutions and Technologies to Resilience against the COVID-19 Pandemic: A Literature Review

Cough is a symptom in over a hundred respiratory diseases. The audio features in cough signals contain erudition about the predicament of the respiratory system. Using deep learning or signal processing, these features can be used to build an effective disease prediction system. However, cough analysis remains an area that has received scant attention from machine learning researchers. This can be attributed to several factors such as inefficient ancillary systems, high expenses in obtaining datasets, or difficulty in building classifiers. This paper categorized and reviewed the current progress on cough audio analysis for the classification of pulmonary diseases. It also explored potential future issues in research. Additionally, it proposed a model for the classification of ten serious pulmonary ailments commonly seen in Indian adolescents. The proposed model is evaluated against four existing state of the art techniques in the literature.

Towards cough sound analysis using the Internet of things and deep learning for pulmonary disease prediction

The current world faces a considerable traffic rate on roads due to the increase in various types of vehicles. It caused emergency vehicles to delay and increasing the patients' health risk factor. Internet of vehicle-based artificial neural network (IoV-ANN) has been proposed for effective health autonomous system in medical things. The proposed IoV-ANN provides a secure network to monitor and track the vehicle's motion using the global positioning system. It consists of an autonomous system which is enabled with an artificial neural network (ANN). ANN model has three layers. First layers collect the data using IoV sensors. Second or hidden layers process the sensor data, predict the road's traffic condition and reroute the emergency vehicle to an exact route. IoV-ANN helps to reduce road congestion in this article to enhance the timely functioning of an emergency vehicle. ANN categorizes the congestion networks of traffic. Traffic restrictions such as changing the queue gap in the road signals and the alternative roads are altered automatically due to congestion. It allows the government to develop ideas for alternate routes to enhance traffic control. The output layer gives commands to the driver autonomously. The simulation analysis of the proposed method proved that the system could work independently. The IoV-ANN achieves the highest performance rate of (97.89%), with a reduced error rate (9.12%) traffic congestion rate (10.31%), travel period (32 s), vehicle detection rate (93.61%), classification accuracy (95.02%), MAPE (8.4%), throughput rate (93.50%) when compared to other popular methods.

/pdf/internet-of-vehicles-and-autonomous-systems-with-ai-for-4y6hr3cjkc.pdf

Internet of vehicles and autonomous systems with AI for medical things

https://doi.org/10.1016/j.displa.2022.102235

ViDMASK dataset for face mask detection with social distance measurement

Detecting In-vehicle Intrusion via Semi-supervised Learning-based Convolutional Adversarial Autoencoders

Crowd management systems play a vital role in today's smart cities and rely on several Internet of Things (IoT) solutions to build prevention mechanisms for widespread viral diseases such as Coronavirus 2019 (COVID-19). In this article, we propose a framework to aid in preventing widespread viral diseases. The proposed framework consists of a physical distancing notification system by leveraging some existing futuristic technologies, including deep learning and the Internet of Vehicles. Each vehicle is equipped with a switching camera system through thermal and vision imaging. Afterward, using the Faster R-CNN algorithm, we measure and detect physical distancing violation between objects of the same class. We evaluate the performance of our proposed architecture with vehicle-to-infrastructure communication. The obtained results show the applicability and efficiency of our proposal in providing timely notification of social distancing violations.

/pdf/deepdist-a-deep-learning-based-iov-framework-for-real-time-2yclfoop8r.pdf

DeepDist: A Deep-Learning-Based IoV Framework for Real-Time Objects and Distance Violation Detection

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.

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

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.

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

With the outbreak of COVID-19, and its terrible and fast spread among communities, contact tracing methods have become crucial to protect people. However, conventional mechanisms, for instance based on the manual search of close contacts, lack of high efficiency due to the high time consumption. The research community is therefore exploring new ways to track contagious diseases by exploiting modern communications paradigms and technologies. In this paper, we propose a new contact tracing method based on Online Social Network (OSN) platforms. In our design, contact detection occurs in real-time by means of traditional proximity approaches. Then, a likely future contact forecast is notified through OSN communities.

TraceMe: Real-Time Contact Tracing and Early Prevention of COVID-19 based on Online Social Networks

The Internet of Vehicles (IoV) can deliver services for intelligent transportation systems. However, it typically relies on the exchange of sensitive information, including passwords and personal data, which makes it vulnerable to many security risks, such as phishing attacks, which have largely increased in the last decade. Cryptography-based security measures look as a form of protection to preserve sensitive information, but they can be bypassed by inside attackers and, in addition, they increase the burden on the network. To tackle the aforementioned issues, in this paper, we present an approach to control phishing attacks in the IoV environment, based on Machine Learning (ML) and Deep Learning (DL) techniques.

Yesin Sahraoui

Papers

DeepDist: A Deep-Learning-Based IoV Framework for Real-Time Objects and Distance Violation Detection

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

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

TraceMe: Real-Time Contact Tracing and Early Prevention of COVID-19 based on Online Social Networks

LearnPhi: a Real-Time Learning Model for Early Prediction of Phishing Attacks in IoV