The impact of COVID-19 pandemic on electricity consumption and electricity demand forecasting accuracy: Empirical evidence from the state of Qatar

The goal of this study is to use machine-learning (ML) techniques and empirical big data to examine the influence of the COVID-19 pandemic on electricity usage and electricity demand forecasting accuracy in buildings in Qatar over time and across sectors. Furthermore, this study statistically investigates the relationship between building electricity consumption and the number of daily infected cases in the State of Qatar. The effect of the pandemic on electricity usage was quantified during various periods of the pandemic years. Around 1 million electricity meter readings per year were considered for six different types of building usage between the years 2010 and 2021. The findings indicate that there was a gap between the actual and simulated electricity consumption during the pandemic years. Furthermore, the results show that the fluctuation in electricity consumption was correlated with the number of daily infected cases in some socioeconomic sectors. The changes in the pattern of electricity consumption during the pandemic years (2020–2021) affected the accuracy of the ML models in predicting electricity consumption in 2022.This publication was made possible by an NPRP award [ NPRP13S-0206-200272 ] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The open access publication of this article was funded by the Qatar National Library (QNL)

Motivation, preference, socioeconomic, and building features: New paradigm of analyzing electricity consumption in residential buildings

In strategic energy planning, human-oriented factors are uncertain and lead to unpredictable challenges. Thus, decision-makers must contextualize the target society to address these uncertainties. More precisely, uncertainties lead to performance gaps between assumed and actual sustainability target outcomes. This study proposed a new framework that considers vital elements, including occupant motivation, preference, socioeconomic characteristics, and building features (MPSEB). To utilize this model, a thorough face-to-face survey questionnaire was administered to measure these elements. This study explored how these elements affect the patterns of residential energy consumption in a region with numerous expat communities of various ethnic and cultural backgrounds. In particular, the study investigated the patterns of energy behaviors and human-building interactions among the residents of Qatar by collecting empirical evidence and conducting a subsequent survey analysis. Machine learning approaches were employed to explore the survey data and determine the interdependencies between features, as well as the significance of the fundamental factors influencing human-building interactions. The XGBoost method was used to conduct a feature importance analysis to determine factors contributing to residential energy consumption. The results revealed the primary behavioral and socioeconomic factors that affect residential energy consumption, and confirmed the influence of human factors in Qatar while considering its diverse population.This publication was made possible by an NPRP award [ NPRP13S-0206–200272 ] from the Qatar National Research Fund (a member of Qatar Foundation ). The statements made herein are solely the responsibility of the authors. The open access publication of this article was funded by the Qatar National Library (QNL)

Urban Traffic Monitoring and Modeling System: An IoT Solution for Enhancing Road Safety

Qatar expects more than a million visitors during the 2022 World Cup, which will pose significant challenges. The high number of people will likely cause a rise in road traffic congestion, vehicle crashes, injuries and deaths. To tackle this problem, Naturalistic Driver Behavior can be utilised which will collect and analyze data to estimate the current Qatar traffic system, including traffic data infrastructure, safety planning, and engineering practices and standards. In this paper, an IoT-based solution to facilitate such a study in Qatar is proposed. Different data points from a driver are collected and recorded in an unobtrusive manner, such as trip data, GPS coordinates, compass heading, minimum, average, and maximum speed and his driving behavior, including driver's drowsiness level. Analysis of these data points will help in prediction of crashes and road infrastructure improvements to reduce such events. It will also be used for drivers' risk assessment and to detect extreme road user behaviors. A framework that will help to visualize and manage this data is also proposed, along with a Deep Learning-based application that detects drowsy driving behavior that netted an 82% accuracy.This publication was funded by the NPRP award [NPRP8-910-2-387] from Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

Driver Drowsiness Detection Model Using Convolutional Neural Networks Techniques for Android Application

A sleepy driver is arguably much more dangerous on the road than the one who is speeding as he is a victim of microsleeps. Automotive researchers and manufacturers are trying to curb this problem with several technological solutions that will avert such a crisis. This article focuses on the detection of such micro sleep and drowsiness using neural network-based methodologies. Our previous work in this field involved using machine learning with multi-layer perceptron to detect the same. In this paper, accuracy was increased by utilizing facial landmarks which are detected by the camera and that is passed to a Convolutional Neural Network (CNN) to classify drowsiness. The achievement with this work is the capability to provide a lightweight alternative to heavier classification models with more than 88% for the category without glasses, more than 85% for the category night without glasses. On average, more than 83% of accuracy was achieved in all categories. Moreover, as for model size, complexity and storage, there is a marked reduction in the new proposed model in comparison to the benchmark model where the maximum size is 75 KB. The proposed CNN based model can be used to build a real-time driver drowsiness detection system for embedded systems and Android devices with high accuracy and ease of use.This publication was made possible by an NPRP award [NPRP8-910-2-387] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

Applied Internet of Things IoT: Car monitoring system for Modeling of Road Safety and Traffic System in the State of Qatar

One of the most interesting new approaches in the transportation research field is the Naturalistic Driver Behavior which is intended to provide insight into driver behavior during everyday trips by recording details about the driver, the vehicle and the surroundings through an unobtrusive data gathering equipment and without experimental control. In this paper, an Internet of Things solution that collects and analyzes data based on Naturalistic Driver Behavior approach is proposed. The analyzed and collected data will be used as a comprehensive review, and analysis of the existing Qatar traffic system, including traffic data infrastructure, safety planning, engineering practices and standards. Moreover, data analytics for crash prediction and the use of these predictions for the purpose of systemic and systematic network hotspot analysis, risk-based characterization of roadways, intersections, and roundabouts are developed. Finally, an integrated safety risk solution was proposed. This latter, enables decision makers and stakeholders (road users, state agencies, and law enforcement) to identify both high-risk locations and behaviors by measuring a set of dynamic variables including event-based data, roadway conditions, and driving maneuvers. More specifically, the solution consists of a driver behaviors detector system that uses mobile technologies. The system can detect and analyze several behaviors like drowsiness and yawning. Previous works are based on detecting and extracting facial landmarks from images. However, the new suggested system is based on a hybrid approach to detect driver behavior utilizing a deep learning technique using a multilayer perception classifier. In addition, this solution can also collect data about every day trips like start time, end time, average speed, maximum speed, distance and minimum speed. Furthermore, it detects for every fifteen seconds measurements like GPS position, distance, acceleration and rotational velocity along the Roll, Pitch and Yaw axes. The main advantage of the solution is to reduce safety risks on the roads while optimizing safety mitigation costs to a society. The proposed solution has three-layer architecture, namely, the perception, network, and application layers as detailed below. I. The perception layer is the physical layer, composed from several Internet of Thing devices that uses mainly use the smart phones equipped with cameras and sensors (Magnetometer, Accelerometers Gyroscope and Thermometer, GPS sensor and Orientation sensor) for sensing and gathering information about the driver behavior roads and environment as shown in Fig. 1. II. The network layer is responsible for establishing the connection with the servers. Its features are also used for transmitting and processing sensor data. In this solution, hybrid system that collect data and store them locally before sending them to the server is used. This technique proves its efficiency in case of Poor Internet coverage and unstable Internet connection. III. The application layer is responsible for delivering application specific services to end user. It consists in sending the data collected to web server in order to be treat and analyzed before displaying it to the final end user. The web service which part of the application layer is the component responsible for collecting data not only from devices but also from other sources such General Traffic Directorate at Minister of Interior to gather the crash details. This web service stocks all stored data in database server and analyses them. Then, the stored data and analysis will be available for end user via website that has direct access to the web services. Figure 1: Architecture of Car monitoring system Keywords: Driver Monitoring System, DrowsinessDetection, Deep Learning, Real-time Deep Neural Network, Fig. 1: Architecture of IoT solutionqscienc

Blockchain Technology for Intelligent Transportation Systems: A Systematic Literature Review

The use of Blockchain technology has recently become widespread. It has emerged as an essential tool in various academic and industrial fields, such as healthcare, transportation, finance, cybersecurity, and supply chain management. It is regarded as a decentralized, trustworthy, secure, transparent, and immutable solution that innovates data sharing and management. This survey aims to provide a systematic review of Blockchain application to intelligent transportation systems in general and the Internet of Vehicles (IoV) in particular. The survey is divided into four main parts. First, the Blockchain technology including its opportunities, relative taxonomies, and applications is introduced; basic cryptography is also discussed. Next, the evolution of Blockchain is presented, starting from the primary phase of pre-Bitcoin (fundamentally characterized by classic cryptography systems), followed by the Blockchain 1.0 phase, (characterized by Bitcoin implementation and common consensus protocols), and finally, the Blockchain 2.0 phase (characterized by the implementation of smart contracts, Ethereum, and Hyperledger). We compared and identified the strengths and limitations of each of these implementations. Then, the state of the art of Blockchain-based IoV solutions (BIoV) is explored by referring to a large and trusted source database from the Scopus data bank. For a well-structured and clear discussion, the reviewed literature is classified according to the research direction and implemented IoV layer. Useful tables, statistics, and analysis are also presented. Finally, the open problems and future directions in BIoV research are summarized

Blockchain pour l'Internet des véhicules : une solution IoT décentralisée pour la communication et le paiement des véhicules en utilisant Ethereum

The concept of smart cities is increasingly gaining prominence in modern metropolises due to the emergence and spread of embedded and connected smart devices, systems, and technologies in everyday lives, which have created an opportunity to connect every “thing" to the Internet. In the upcoming era of the Internet of Things, the Internet of Vehicles (IoV) will play a crucial role in constructing a smart city. In fact, the IoV has the potential to solve various traffic problems effectively. It is critical for enhancing road utilization, reducing energy consumption and pollution, and improving road safety. Nevertheless, the primary issue regarding the IoV, and in particular to Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I), is establishing secure and instant payments and communications. To respond to this challenge, this work proposes a Blockchain-based solution for establishing secure payment and communication in order to study the use of Blockchain as middle-ware between different participants of intelligent transportation systems. The proposed framework employs Ethereum to develop a solution aimed at facilitating Vehicle-to-Everything (V2X) communications and payments. Moreover, this work qualitatively tests the performance and resilience of the proposed systems against common security attacks. Computational tests showed that the proposed solution solved the main challenges of Vehicle-to-X (V2X) communications, such as security and centralization. In addition, it guaranteed an easy data exchange between different actors of intelligent transportation systems.Le concept de villes intelligentes gagne de plus en plus en importance dans les métropoles modernes en raison de l’émergence et de la diffusion d’appareils, de systèmes et de technologies intelligents embarqués et connectés dans la vie quotidienne, qui ont créé l’opportunité de connecter chaque “chose" à Internet. Dans l'ère à venir de l'Internet des objets, l'Internet des véhicules (IoV) jouera un rôle crucial dans la construction d'une ville intelligente. En fait, l'IoV a le potentiel de résoudre efficacement divers problèmes de trafic. Il est essentiel pour améliorer l'utilisation des routes, réduire la consommation d'énergie et la pollution et améliorer la sécurité routière. Néanmoins, le principal problème concernant l'IoV, et en particulier le Véhicule-à-Véhicule (V2V) et le Véhicule-à-infrastructure (V2I), est l'établissement de paiements et de communications sécurisés et instantanés. Pour répondre à ce défi, ce travail propose une solution basée sur la Blockchain pour mettre en place un paiement et une communication sécurisés afin d'étudier l'utilisation de la Blockchain comme middleware entre différents acteurs des systèmes de transport intelligents.Dans cette étude, nous avons évalué les propriétés les plus importantes de la solution développée, à savoir la consommation de la mémoire et de l’énergie, l’immutabilité, la confidentialité, la cohérence, l’intégrité, le temps d’exécution et le coût. L’objet de cette évaluation est de vérifier la capacité de la plateforme basée sur la Blockchain à assurer une communication efficace et un paiement sécurisé avec l’IoV. Selon les résultats, cette plateforme peut contribuer à résoudre les défis les plus critiques de la communication véhicule-à-tout (V2X) en améliorant la sécurité et l’évolutivité

Blockchain pour l'Internet des véhicules : une solution IoT décentralisée pour la communication et le paiement des véhicules en utilisant Ethereum

The concept of smart cities is increasingly gaining prominence in modern metropolises due to the emergence and spread of embedded and connected smart devices, systems, and technologies in everyday lives, which have created an opportunity to connect every “thing" to the Internet. In the upcoming era of the Internet of Things, the Internet of Vehicles (IoV) will play a crucial role in constructing a smart city. In fact, the IoV has the potential to solve various traffic problems effectively. It is critical for enhancing road utilization, reducing energy consumption and pollution, and improving road safety. Nevertheless, the primary issue regarding the IoV, and in particular to Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I), is establishing secure and instant payments and communications. To respond to this challenge, this work proposes a Blockchain-based solution for establishing secure payment and communication in order to study the use of Blockchain as middle-ware between different participants of intelligent transportation systems. The proposed framework employs Ethereum to develop a solution aimed at facilitating Vehicle-to-Everything (V2X) communications and payments. Moreover, this work qualitatively tests the performance and resilience of the proposed systems against common security attacks. Computational tests showed that the proposed solution solved the main challenges of Vehicle-to-X (V2X) communications, such as security and centralization. In addition, it guaranteed an easy data exchange between different actors of intelligent transportation systems.Le concept de villes intelligentes gagne de plus en plus en importance dans les métropoles modernes en raison de l’émergence et de la diffusion d’appareils, de systèmes et de technologies intelligents embarqués et connectés dans la vie quotidienne, qui ont créé l’opportunité de connecter chaque “chose" à Internet. Dans l'ère à venir de l'Internet des objets, l'Internet des véhicules (IoV) jouera un rôle crucial dans la construction d'une ville intelligente. En fait, l'IoV a le potentiel de résoudre efficacement divers problèmes de trafic. Il est essentiel pour améliorer l'utilisation des routes, réduire la consommation d'énergie et la pollution et améliorer la sécurité routière. Néanmoins, le principal problème concernant l'IoV, et en particulier le Véhicule-à-Véhicule (V2V) et le Véhicule-à-infrastructure (V2I), est l'établissement de paiements et de communications sécurisés et instantanés. Pour répondre à ce défi, ce travail propose une solution basée sur la Blockchain pour mettre en place un paiement et une communication sécurisés afin d'étudier l'utilisation de la Blockchain comme middleware entre différents acteurs des systèmes de transport intelligents.Dans cette étude, nous avons évalué les propriétés les plus importantes de la solution développée, à savoir la consommation de la mémoire et de l’énergie, l’immutabilité, la confidentialité, la cohérence, l’intégrité, le temps d’exécution et le coût. L’objet de cette évaluation est de vérifier la capacité de la plateforme basée sur la Blockchain à assurer une communication efficace et un paiement sécurisé avec l’IoV. Selon les résultats, cette plateforme peut contribuer à résoudre les défis les plus critiques de la communication véhicule-à-tout (V2X) en améliorant la sécurité et l’évolutivité