Department of Computer Science
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Item Multi-Pollutant based Hybrid Framework for Green Smart Cities using Machine Learning Techniques.(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Usama Masood; FA21-RCS-013; LHR TP 8676; Dr. Tariq UmerAchieving a sustainable environment is one of the most emerging issues discussed in the smart cities concept. Due to the rapid population growth in the world, the concentration of greenhouse gases is increasing day by day. A lot of research studies have focused on different techniques and technologies to reduce environmental pollution. To achieve a sustainable environment, it is important to consider the multi-pollutant factors involve in polluting the environment. In MS thesis, we proposed a multi-pollutants-based intelligent hybrid framework using machine learning that considers the multiple sources of pollution in the city environments. The research emphasizes a comprehensive comparisons of machine learning algorithm for predicting the air quality index in smart cities. In this research. The framework considers the concentration of the pollutants in the environment and will make intelligent predictions on their combined effect on the environment as well as the individual groups based on the similar characteristics of gases concerning the guidelines of WHO. The effect of the concentration of multi-pollutants on air quality and water quality will be analyzed. Machine Learning Techniques including Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, and Long Short Term Memory (LSTM) will be applied to predict air quality index and for the classification of pollutants. The evaluation measure used in this research is Accuracy, Precision, F1 score and Recall to find the accuracy of these models.Item Design and Development of a Trust-based secure Authentication scheme for Internet of Drones (IoD) Networks(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Fatima Fayyaz; FA21-RCS-004; LHR TP 8674; Dr. Tariq UmerDrone advancement is prevailing in the latest trends in various sectors. The concept and deployment of commercial drones can provide services in different fields of life. They can be very beneficial in terms of serving industrial, agricultural, construction, packet delivery, videography, and health care providing services in managing, securing, and broadcasting operations. Diversification of networks and dynamic conditions due to heterogeneousness in types of drones and their ownership, a trust factor between these drones is a very critical issue in their intercommunication. Attacker can easily capture the data from a un-secured public channel and can misuse it against participants. Internet of Drone network protection is very important and challenging, as it ensures message integrity, message authenticity and secure authorization access. The lack of this trust factor has just left the Internet of Drones (IoD) domain exposed to security and privacy threats. The drone nodes can only establish a connection to other drones if they are considered legitimate or corporate (friendly) nodes, the rest of them will not be able to make any connection as they are non-corporate (adversary) or unauthorized. The proposed trust factor is based on a security model that establishes trust by continuous authentication and monitoring access attempts between nodes located in the corporate cluster for data delivery. Hence, a trust-based network authenticated framework is much needed for overcoming these security issues. For achieving secure data decimation and intercommunication it is highly important to create a trust-based, threat-free, environment for IOD networks and to provide the strength to security framework in vulnerable authentication schemes against different type of attacks i.e.; replay and impersonation attack, this paper analyzes the research gaps and proposes a new framework for authentication schemes. Considering the previous proposed factors and techniques, this research focuses on establishing a new architecture for providing secure authentication among Internet of Drones (IoD) environment. This paper presents the more secure framework by mutual authentication and key exchange protocol in IoD with continuous chained Hash Function. System generates temporary symmetric keys to establish connection between two nodes, hence devices can mutually authenticate and establishes connection in an untraceable manner. For the verification of sender identity, chained-hashed way is presented. Session keys between devices are updated after every transaction to maintain secrecy. The evaluation of cost and effectiveness of the proposed method with previous protocol are computed. The outcomes demonstrated that our techniques are more secure than previous frameworks.Item A Digital Twin based Approach for Healthier Smart City Environment(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Khazina Naveed; FA21-RCS-001; Dr. Tariq UmerAir quality, toxic substances, climate change, pollution, and occupational hazards are the environmental factors that have a great impact on human and environmental health. The presence of pollutants and contaminants in the environment can have detrimental effects on human health, including respiratory issues, cardiovascular ailments, cancer, neurological disorders, and various other illnesses. Certain groups, such as children, older adults, and individuals with pre-existing health conditions, are more vulnerable to the health impacts of environmental hazards. Air Quality Index is used to depict the air quality of an area. This thesis delves into studying the utilization of Digital Twin models as an innovative strategy for creating a sustainable smart city environment by accurately forecasting Air Quality Index (AQI) using time series analysis. The study applies different deep learning models, namely Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and deep Artificial neural networks (ANN) for precisely predicting the AQI levels for a healthier environment. The time-series historical data of Delhi city has been gathered from the year 2015 to 2020 and is preprocessed before proceeding to train and validate different deep learning models. The six selected machine learning algorithms have been implemented and it has been observed that CNN offers unparalleled accuracy compared to other evaluated models making it highly effective for precise forecasting. The CNN-1D-2 layer yielded the best results with root mean squared error (RMSE) reaching 3.010343, the mean absolute error (MAE) reaching 1.706329, the mean absolute percentage error (MAPE) reaching 0.013216, and the R2 reaching 0.99931. The digital twin model is developed by incorporating InfluxDB and Grafana. InfluxDB is an open-source platform that has been used to store the historical and real-time data of AQI using python. The Grafana online platform is utilized for data visualization and management and facilitates real-time monitoring. The 3D model of the city is developed in Blender and then the 3D file is exported to Microsoft Azure Digital Twin explorer to develop a digital twin using Digital Twin Definition Language (DTDL). The digital twin model displays the forecasted value of AQI and different pollutants along with their previous trends in the form of graphs. The 3D model of the city is covered with different colors based on ranges or thresholds defined for AQI. The findings contribute to the advancement of smart city development by utilizing digital twin-based approaches for creating healthier and more sustainable urban environments