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A digital twin uses a virtual model of the physical system to fulfill the diverse requirements (e.g., latency, reliability, quality of physical experience) for emerging vehicular network applications. Although a twin-based implementation of vehicular networks can offer performance optimization, modeling a digital twin is a significantly challenging task. Federated learning (FL) is a better privacy-preserving, distributed learning scheme that can be used to model twin models. Although FL can offer performance enhancement, it requires careful design. Therefore, in this article, we present an overview of FL for a twin-based vehicular network. A general architecture for FL-enabled digital twins for a vehicular network is presented. Our proposed architecture consists of two spaces, such as twin space and the physical space. A physical space consists of all the physical entities (e.g., cars and edge servers) required for vehicular networks, whereas, the twin space refers to the logical space that is used for the deployment of twins. A twin space can be implemented either using edge servers and cloud servers. We also outline a few use cases of FL for the twin-based vehicular network. Finally, the article is concluded and an outlook on open challenges is presented.

With the advent of the Internet of Things (IoT) and unmanned aerial vehicles (UAVs) in industrial application scenarios, oil and gas industry (OGI) automation is undergoing a remarkable transformation. Existing monitoring methods like IoT sensor-based surveillance offer accuracy but struggle with transmission inefficiency. Conversely, UAV-based surveillance enables seamless communication but limited sensing capabilities. This article addresses the challenges of latency, energy efficiency, and cost in state-of-the-art leakage detection technologies for OGI pipelines. A three-tier architecture is proposed, integrating IoT, UAVs, and artificial intelligence-empowered edge computing to enhance pipeline surveillance. We aim to propose specialized routing that addresses IoT energy and fault tolerance issues, while UAVs act as relays to transmit data efficiently to control centers, considering factors like UAV energy and data complexity. Intelligent edge services optimize data transmission, prolong UAV lifespan, and manage latency. Various use cases are explored, and open research challenges with potential solutions are presented.