The exponential growth of the Internet of Things has precipitated a revolution in Intelligent Transportation Systems, notably in urban environments. An ITS leverages advancements in communication technologies and data analytics to enhance the efficiency and intelligence of transport networks. At the same time, these IoT-enabled ITSs generate a vast array of complex data classified as Big Data. Traditional data analytics frameworks need help to efficiently process these Big Data due to its sheer volume, velocity, variety, and significant data privacy concerns. Federated Learning, known for its privacy-preserving attributes, is a promising technology for implementation within ITSs for IoT-generated Big Data. Nevertheless, the system faces challenges due to the variable nature of devices, the heterogeneity of data, and the dynamic conditions in which ITS operates. Recent efforts to mitigate these challenges focus on the practical selection of an averaging mechanism during the server’s aggregation phase and practical dynamic client training. Despite these efforts, existing research still relies on personalized FL with personalized averaging and client training. This paper presents a personalized architecture, including an optimized Federated Averaging strategy that leverages FL for efficient and real-time Big Data analytics in IoT-enabled ITSs. Various personalization methods are applied to enhance the traditional averaging algorithm. Local fine-tuning and weighted averaging tailor the global model to individual client data. Custom learning rates are utilized to boost the performance further. Regular evaluations are advised to maintain model efficacy. The proposed architecture addresses critical challenges like real-life federated environment settings, data integration, and significant data privacy, offering a comprehensive solution for modern urban transportation systems using Big Data. Using the Udacity Self-Driving Car Dataset foe vehicle detection, we apply the proposed approaches to demonstrate the efficacy of our model. Our empirical findings validate the superiority of our architecture in terms of scalability, real-time decision-making capabilities, and data privacy preservation. We attained accuracy levels of 93.27%, 92.89%, and 92.96% for our proposed model in a Federated Learning architecture with 10 nodes, 20 nodes, and 30 nodes, respectively.

Coronavirus disease (COVID-19), which has caused a global pandemic, continues to have severe effects on human lives worldwide. Characterized by symptoms similar to pneumonia, its rapid spread requires innovative strategies for its early detection and management. In response to this crisis, data science and machine learning (ML) offer crucial solutions to complex problems, including those posed by COVID-19. One cost-effective approach to detect the disease is the use of chest X-rays, which is a common initial testing method. Although existing techniques are useful for detecting COVID-19 using X-rays, there is a need for further improvement in efficiency, particularly in terms of training and execution time. This article introduces an advanced architecture that leverages an ensemble learning technique for COVID-19 detection from chest X-ray images. Using a parallel and distributed framework, the proposed model integrates ensemble learning with big data analytics to facilitate parallel processing. This approach aims to enhance both execution and training times, ensuring a more effective detection process. The model’s efficacy was validated through a comprehensive analysis of predicted and actual values, and its performance was meticulously evaluated for accuracy, precision, recall, and F-measure, and compared to state-of-the-art models. The work presented here not only contributes to the ongoing fight against COVID-19 but also showcases the wider applicability and potential of ensemble learning techniques in healthcare.

The approval of retrieving information on the Internet originates several network securities matters. Intrusion recognition is a critical study in network security to spot unauthorized admission or occurrences on protected networks. Intrusion detection has a fully-fledged reputation in the current era. Research emphasizes several datasets to upsurge system precision and lessen the false-positive proportion. This article proposes a new intrusion detection system using big data analytics and deep learning to address some of the misuse and irregularity detection limitations. The proposed method could identify any odd activities in a network to recognize malicious or unauthorized action and permit a response during a confidentiality break. The proposed system utilizes the big data analytics platform based on parallel and distributed mechanisms. The parallel and distributed platforms improve the training time along with the accuracy. The experimentation appropriately classifies the information as either normal or abnormal. The proposed system has a recognition proportion of 96.11% that pointedly expands overall recognition accuracy related to existing strategies.

Effective requirement collection and prioritization are paramount within the inherently distributed nature of the Internet of Things (IoT) application. Current methods typically categorize IoT application requirements subjectively into inessential, desirable, and mandatory groups. This often leads to prioritization challenges, especially when dealing with requirements of equal importance and when the number of requirements grows. This increases the complexity of the Analytical Hierarchical Process (AHP) to O(n2) dimensions. This research introduces a novel framework that integrates an enhanced AHP with an advanced grouping model to address these issues. This integrated approach mitigates the subjectivity found in traditional grouping methods and efficiently manages larger sets of requirements. The framework consists of two main modules: the Pre-processing Module and the Prioritization Module. The latter includes three units: the Grouping Processing Unit (GPU) for initial classification using a new grouping approach, the Review Processing Unit (RPU) for post-grouping assessment, and the AHP Processing Unit (APU) for final prioritization. This framework is evaluated through a detailed case study, demonstrating its ability to effectively streamline requirement prioritization in IoT applications, thereby enhancing design quality and operational efficiency.

The primary aim of IoT-enabled intelligent transportation is to administer and resolve the issues people confront for the improved transportation system. Technological advancement is an emerging domain because it reduces transportation challenges and makes machine-to-machine communication rapid and easy. The recent expansion in the IoT field provides a significant production perspective during a requirement specification. The existing requirement specification practices of IoT-enabled intelligent transportation systems must be utilized intelligently to comply with the challenges. To overcome these challenging problems, this article presents an inclusive survey of the difficulties relating to the requirements specifications of IoT-enabled transportation systems. The challenges of IoT-enabled intelligent transportation systems in the context of requirements specifications are assessed using specific evaluation criteria (e.g., parameters). This article proposes a clear enhanced framework for the specification of IoT-enabled intelligent transportation systems using a unified approach that extends our previous system. Initially, we reviewed the existing requirement specification work, identified and recognized the challenges, and highlighted how to deal with the difficulties in developing IoT-enabled intelligent transportation systems. The proposed framework is based on integrating proven techniques reported for similar applications. The prosed framework is applied to a specific case study of transportation systems. Moreover, a simple comparative analysis is also carried out based on identified evaluation parameters.