Emergency message (EM) dissemination is a significant process in vehicular ad hoc networks (VANETs) and plays a vital role in road safety. Nonetheless, EMs’ dissemination while avoiding broadcast storms poses a consi...
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Air pollution has been on the rise for quite a while now and with it is the increasing number of cases involving respiratory diseases. These respiratory diseases range from the mild ones to the most severe ones. There...
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Hydrothermal carbonization (HTC) was employed to convert cannabis waste into valuable solid fuel (hydrochar) under different operating conditions, including reaction temperature (170–230 °C), biomass-water ...
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Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by the Wo...
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Introduction: Vehicle crashes can be hazardous to public safety and may cause infrastructure damage. Risky driving significantly raises the possibility of the occurrence of a vehicle crash. As per statistics by the World Health Organization (WHO), approximately 1.35 million people are involved in road traffic crashes resulting in loss of life or physical disability. WHO attributes events like over-speeding, drunken driving, distracted driving, dilapidated road infrastructure and unsafe practices such as non-use of helmets and seatbelts to road traffic accidents. As these driving events negatively affect driving quality and enhance the risk of a vehicle crash, they are termed as negative driving attributes. Methods: A multi-level hierarchical fuzzy rules-based computational model has been designed to capture risky driving by a driver as a driving risk index. Data from the onboard telematics device and vehicle controller area network is used for capturing the required information in a naturalistic way during actual driving conditions. Fuzzy rules-based aggregation and inference mechanisms have been designed to alert about the possibility of a crash due to the onset of risky driving. Results: On-board telematics data of 3213 sub-trips of 19 drivers has been utilized to learn long term risky driving attributes. Furthermore, the current trip assessment of these drivers demonstrates the efficacy of the proposed model in correctly modeling the driving risk index of all of them, including 7 drivers who were involved in a crash after the monitored trip. Conclusion: In this work, risky driving behavior has been associated not just with rash driving but also other contextual data like driver’s long-term risk aptitude and environmental context such as type of roads, traffic volume and weather conditions. Trip-wise risky driving behavior of six out of seven drivers, who had met with a crash during that trip, was correctly predicted during evaluation. Similarly, for the other 12
In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuat...
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Network function virtualization (NFV), a novel network architecture, promises to offer a lot of convenience in network design, deployment, and management. This paradigm, although flexible, suffers from many risks enge...
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Counting the turning movements in a four-leg roundabout is a challenging task and often executed by vehicle recognition and tracking on traffic videos. In order to obtain accurately all the 12 flow values of the origi...
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An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet...
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In Mobile sensor Networks, Each and every wireless node dynamically changes its location due to its mobility nature. The routing information is maintained by the master node to deliver a data packet from source to des...
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