Pranvera Kortoçi

Bio: Pranvera Kortoçi is an academic researcher. The author has contributed to research in topics: Air quality index & Augmented reality. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Augmented Informative Cooperative Perception.

Abstract: Connected vehicles, whether equipped with advanced driver-assistance systems or fully autonomous, are currently constrained to visual information in their lines-of-sight. A cooperative perception system among vehicles increases their situational awareness by extending their perception ranges. Existing solutions imply significant network and computation load, as well as high flow of not-always-relevant data received by vehicles. To address such issues, and thus account for the inherently diverse informativeness of the data, we present Augmented Informative Cooperative Perception (AICP) as the first fast-filtering system which optimizes the informativeness of shared data at vehicles. AICP displays the filtered data to the drivers in augmented reality head-up display. To this end, an informativeness maximization problem is presented for vehicles to select a subset of data to display to their drivers. Specifically, we propose (i) a dedicated system design with custom data structure and light-weight routing protocol for convenient data encapsulation, fast interpretation and transmission, and (ii) a comprehensive problem formulation and efficient fitness-based sorting algorithm to select the most valuable data to display at the application layer. We implement a proof-of-concept prototype of AICP with a bandwidth-hungry, latency-constrained real-life augmented reality application. The prototype realizes the informative-optimized cooperative perception with only 12.6 milliseconds additional latency. Next, we test the networking performance of AICP at scale and show that AICP effectively filter out less relevant packets and decreases the channel busy time.

Value-Anticipating V2V Communications for Cooperative Perception.

Abstract: The growing penetration of on-board communication units is enabling intelligent vehicles to share their sensor data with cloud computing platforms as well as with other vehicles. Although this unlocks the possibility of a variety of emerging applications, the massive amount of data traffic in vehicular networks is expected to pose a big challenge in the long term. In this paper, we shed light on the potential of value-anticipating networking to tackle this issue. A vehicle sending a piece of information first anticipates the value of that information for potential receivers. When the network is congested, the sender may defer or even cancel transmissions of less valuable information, so that important information can be delivered to receivers more reliably. We investigate the applicability of this concept to cooperative perception, where vehicles exchange processed sensor data over vehicle-to-vehicle (V2V) networks to collaboratively improve coverage and accuracy of environmental perception. Through simulations based on realistic road traffic, we show that value-anticipating V2V communications can significantly improve the performance of cooperative perception under heavy network load.

Adoption of Environmental Information Chatbot Services Based on the Internet of Educational Things in Smart Schools: Structural Equation Modeling Approach

Abstract: The Internet of Educational Things (IoET) equips chatbots with real-time environmental information monitoring to prevent student and instructor absences and safeguard their health. Individual behavioral intention toward a chatbot service is essential for better understanding the user’s experience and acceptance of monitoring environmental elements such as PM2.5, temperature, humidity, and carbon monoxide. This study aims to apply an integration of an extended framework for smart schools developing an environmental information chatbot service (ENICS) and various users’ continued behavioral intentions toward the chatbot system based on the unified theory of acceptance and use of technology model to support health and safety in universities. The proposed framework design can incorporate Internet of Things architecture to develop and utilize the chatbot services. The key results of the partial least square test largely support the validity of the proposed model and the significant effects of IoET, performance expectation, effort expectation, social influence, facilitating conditions, health and safety, behavioral intention, and use behavior on personal environmental information chatbot utilization. This study’s findings deal with a better design for environmental system development and understanding the factors influencing an individual’s intention to continue using a chatbot service for IoET applications with low-cost information facilities in safe environmental sustainability.

A survey of COVID-19 in public transportation: Transmission risk, mitigation and prevention

Abstract: The COVID-19 pandemic is posing significant challenges to public transport operators by drastically reducing demand while also requiring them to implement measures that minimize risks to the health of the passengers. While the collective scientific understanding of the SARS-CoV-2 virus and COVID-19 pandemic are rapidly increasing, currently there is a lack of understanding of how the COVID-19 relates to public transport operations. This article presents a comprehensive survey of the current research on COVID-19 transmission mechanisms and how they relate to public transport. We critically assess literature through a lens of disaster management and survey the main transmission mechanisms, forecasting, risks, mitigation, and prevention mechanisms. Social distancing and control on passenger density are found to be the most effective mechanisms. Computing and digital technology can support risk control. Based on our survey, we draw guidelines for public transport operators and highlight open research challenges to establish a research roadmap for the path forward.

Transformational IoT sensing for air pollution and thermal exposures

Abstract: Cities today encounter significant challenges pertaining to urbanization and population growth, resource availability, and climate change. Concurrently, unparalleled datasets are generated through Internet of Things (IoT) sensing implemented at urban, building, and personal scales that serve as a potential tool for understanding and overcoming these issues. Focusing on air pollution and thermal exposure challenges in cities, we reviewed and summarized the literature on IoT environmental sensing on urban, building, and human scales, presenting the first integrated assessment of IoT solutions from the data convergence perspective on all three scales. We identified that there is a lack of guidance on what to measure, where to measure, how frequently to measure, and standards for the acceptable measurement quality on all scales of application. The current literature review identified a significant disconnect between applications on each scale. Currently, the research primarily considers urban, building, and personal scale in isolation, leading to significant data underutilization. We addressed the scientific and technological challenges and opportunities related to data convergence across scales and detailed future directions of IoT sensing along with short- and long-term research and engineering needs. IoT application on a personal scale and integration of information on all scales opens up the possibility of developing personal thermal comfort and exposure models. The development of personal models is a vital promising area that offers significant advancements in understanding the relationship between environment and people that requires significant further research.

A Radical Safety Measure for Identifying Environmental Changes Using Machine Learning Algorithms

Abstract: Due to air pollution, pollutants that harm humans and other species, as well as the environment and natural resources, can be detected in the atmosphere. In real-world applications, the following impurities that are caused due to smog, nicotine, bacteria, yeast, biogas, and carbon dioxide occur uninterruptedly and give rise to unavoidable pollutants. Weather, transportation, and the combustion of fossil fuels are all factors that contribute to air pollution. Uncontrolled fire in parts of grasslands and unmanaged construction projects are two factors that contribute to air pollution. The challenge of assessing contaminated air is critical. Machine learning algorithms are used to forecast the surroundings if any pollution level exceeds the corresponding limit. As a result, in the proposed method air pollution levels are predicted using a machine learning technique where a computer-aided procedure is employed in the process of developing technological aspects to estimate harmful element levels with 99.99% accuracy. Some of the models used to enhance forecasts are Mean Square Error (MSE), Coefficient of Determination Error (CDE), and R Square Error (RSE).