The coexistence of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in 6G-enabled vehicular networks introduces complex challenges in spectrum sharing, semantic prioritization, and real-time coordination. To address these issues, we propose G-FEDMAP, a graph-based federated multi-agent deep reinforcement learning framework that supports semantic- and intent-aware resource allocation in distributed vehicle-to-everything (V2X) environments. G-FEDMAP integrates GraphSAGE-based spatiotemporal embeddings, shared actor–centralized critic multi-agent proximal policy optimization (MAPPO) training under a centralized training and decentralized execution (CTDE) paradigm, and event-adaptive reward shaping guided by user intent profiles. To preserve data locality and promote scalable collaboration, federated policy coordination is introduced across geographically partitioned vehicular domains. The system dynamically rebalances semantic priorities using intent reprioritization weights, enabling responsiveness to mission-critical context. We evaluate G-FEDMAP in a federated urban vehicular network comprising multiple regions with high agent density, dynamic event intents, and shared spectrum constraints. The proposed framework is tested under diverse communication and traffic conditions, and compared against MAPPO, graph neural network-MAPPO (GNN-MAPPO), and federated PPO variants. G-FEDMAP demonstrates improved V2V delivery success, higher semantic retention, greater intent satisfaction, and better coexistence of V2V and V2I links through graph-based scheduling and federated policy adaptation. These results position G-FEDMAP as a reliable and trustworthy AI-driven solution for future 6G-internet of things (6G-IoT) vehicular networks.

In this paper, we propose a novel semantic communication framework (SCF6) tailored for 6G-enabled vehicular networks, targeting ultra-reliable low-latency communication (URLLC) scenarios. By integrating semantic encoding/decoding with traditional channel processing, our framework optimizes data transmission, focusing on the meaning of the data rather than raw information. To quantify the integrity of the transmitted messages, we employ advanced natural language processing techniques, such as BERT (bidirectional encoder representations from transformers), ensuring semantic similarity between the sent and received information. We formulate an optimization problem that maximizes semantic spectrum efficiency evaluation (SSEE) and success rate (SR) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications under stringent URLLC constraints. The optimization solutions are solved by the multi-agent hierarchical attention-based semantic deep reinforcement learning (MAHAS-DRL) framework, which coordinates resource allocation and spectrum sharing among multiple vehicles. By incorporating hierarchical attention mechanisms at both semantic and channel levels, MAHAS-DRL enhances decision-making, optimizes transmission power, and reduces interference. Extensive simulation results demonstrate that our proposed framework outperforms traditional DRL approaches in terms of spectrum efficiency and reliability while significantly reducing transmission delays, making it ideal for dynamic urban vehicular networks.

This study presents a dynamic UAV swarm framework to support ground networks in disaster zones. The framework leverages Generative AI (GenAI) for real-time hover point generation to guide waypoint-based UAV navigation and realistic task modeling, integrated with graph neural networks (GNN) for safe navigation and obstacle avoidance. A multi-agent graph reinforcement learning (MAGRL) mechanism optimizes UAV coordination, enhancing energy efficiency, task completion, and load balancing in response to environmental changes. The framework's graph attention mechanism further improves inter-UAV communication, enabling adaptive task allocation and efficient coverage of high-risk zones. Extensive simulations show that the integrated GenAI-GNN and MAGRL approach achieves superior performance in task completion, energy savings, and system utility, outperforming benchmarks including MADDPG, GCRL, PSO, and Greedy strategies in dynamic disaster scenarios.

In this study, we propose SCF6, a novel semantic communication framework for 6 G -enabled vehicular networks tailored to ultra-reliable low-latency communication (URLLC) scenarios. SCF6 integrates semantic encoding/decoding with conventional channel processing, optimizing transmission by focusing on data meaning. Leveraging BERT (bidirectional encoder representations from transformers)-based natural language processing, it ensures high semantic similarity between transmitted and received messages. To maximize semantic spectrum efficiency (SSEE) and success rate (SR) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications under strict URLLC constraints, we design a multi-agent hierarchical attention-based semantic deep reinforcement learning (MAHAS-DRL) framework. MAHASDRL coordinates resource allocation and spectrum sharing, embedding hierarchical attention at both semantic and channel levels to enhance decision-making, optimize power control, and reduce interference. Simulations demonstrate SCF6's superiority over traditional DRL methods in spectrum efficiency, reliability, and latency, proving effective for dynamic urban vehicular networks.

In this study, we introduce a digital twin (DT)-assisted IoV framework that combines a semi-synchronous adaptive federated learning (AdFL) method with multi-agent deep reinforcement learning, enhanced by generative artificial intelligence (GenAI) techniques, specifically conditional variational autoencoders (CVAE). This framework optimizes partial task offloading across distributed mobile edge computing (MEC) servers, ensuring scalable and efficient decision-making in diverse vehicular networks. By continuously reflecting the real-time conditions of vehicles and roadside units (RSUs), the DT framework ensures precise resource distribution and adaptive task handling. To handle the complexity of dynamic environments, we develop a global model that includes transformer layers in the federated learning (FL) process, which captures long-range dependencies. A semi-synchronous aggregation mechanism is introduced to maintain a balance between timely updates and model quality. The adaptive federated multi-agent reinforcement learning (AF-MARL) algorithm enables decentralized, collaborative learning among vehicles and RSUs, optimizing overall cost and energy use, reducing delays, and improving task completion rates. Comprehensive simulations show the framework's effectiveness compared to existing methods, emphasizing its potential to revolutionize real-time decision-making in IoV networks.

Uncrewed aerial vehicles (UAVs) are essential for providing communication and computation services in disaster recovery scenarios where traditional infrastructure is compromised. However, challenges related to energy efficiency, real-time adaptability, coverage, load balancing, and safe navigation persist, particularly in dynamic disaster environments. In this study, we propose a comprehensive framework that integrates generative AI (GenAI) with graph neural networks (GNNs) to dynamically generate hover points for waypoint-based UAV navigation and realistic task generation based on environmental conditions. The GNN-based collision avoidance mechanism further ensures safe navigation by allowing UAVs to avoid obstacles and no-fly zones while coordinating with neighboring UAVs in real time. To optimize UAV swarm operations, we introduce a multiagent graph reinforcement learning (MAGRL) framework, enabling UAVs to maximize overall system utility by refining hover point selection, task allocation, and load balancing in response to environmental changes. A graph attention mechanism enhances UAV coordination, improving communication efficiency and decision-making. Extensive simulations show that the proposed GenAI-GNN and MAGRL framework significantly outperforms existing methods in task completion, energy efficiency, and overall system utility in disaster recovery scenarios.

Achieving real-time decision-making and efficient resource management in dynamic, large-scale Internet-of-Vehicles (IoV) networks is a significant challenge due to their inherent complexity and scale. To address this, we propose a digital twin (DT)-assisted IoV framework that integrates a novel semi-synchronous adaptive federated learning (AdFL) approach with multiagent deep reinforcement learning, enhanced by generative artificial intelligence (GenAI) techniques, specifically conditional variational autoencoders (CVAEs). The framework optimizes partial task offloading across distributed mobile-edge computing (MEC) servers, ensuring scalable, efficient, and accurate decision-making in heterogeneous vehicular networks. By continuously mirroring the real-time states of vehicles and roadside units (RSUs), the DT framework enables precise resource allocation and adaptive task management. To tackle the complexities of dynamic environments, we design a global model with transformer layers embedded in the federated learning (FL) process, capturing long-range dependencies. A novel semi-synchronous aggregation mechanism is introduced to balance timely updates with model quality. The proposed adaptive federated multiagent reinforcement learning (AF-MARL) algorithm facilitates decentralized, collaborative learning among vehicles and RSUs, optimizing overall cost, and energy efficiency, reducing delay, and improving task completion rates. Extensive simulations demonstrate the effectiveness of the proposed framework against other existing approaches, highlighting its potential to transform real-time decision-making in IoV networks.

ABSTRACT This study presents an innovative approach to dragline mine operations using 3D virtual reality technology, specifically developed at the virtual reality mines simulation facility at IIT (ISM), Dhanbad. Focused on Northern Coalfields Limited, Singrauli, this research opens up the use of immersive VR for virtual inspection and analysis of dragline operations, aiding its effective deployment. The methodology involves capturing geo‐spatial data using drones and integrating it into a commissioned dragline simulator workbench. The integration process combines drone imagery to accurately reconstruct the physical mine site. Enhanced by the Structure‐from‐Motion technique, the resulting model is a photorealistic point cloud, offering unprecedented visualization accuracy. The developed system enables complex manipulation of datasets, including functions such as scaling, rotation, and translation. It also includes geometric measurement tools for determining length, area, and volume, essential for precise operational planning of draglines. The application of Structure‐from‐Motion‐MultiView Stereo technology is particularly noteworthy for its role in tracking safety concerns and monitoring dragline progress with respect to the stipulated balance diagram. The proposed approach surpasses traditional mine visualization methods, providing superior tools for onsite planning and comprehensive asset management and significant contributions for establishing a new benchmark for monitoring and visualizing the dragline excavation process in the mining industry.

Vehicular crowdsensing (VCS) encounters challenges within social Internet of Vehicles networks, including interdependent behaviors and the necessity for long-term sensing strategies that balance energy efficiency and delay tolerance in dynamic settings. To tackle these obstacles, this study explores a VCS model tailored for social IoV networks, considering dynamic environmental parameters. We further develop a utility model that seamlessly integrates data-quality aware functional and social incentives for each vehicle, ensuring optimal task payoff, efficient energy usage, and minimized processing time within the dynamic social IoV environment. Additionally, we introduce a non-cooperative game between vehicles and propose a multi-agent deep reinforcement learning (DRL)-based solution for the dynamic VCS framework. This enables vehicles to autonomously adjust sensing levels, maximizing both individual and collective utility. Finally, through comparative simulations, we demonstrate the effectiveness of our approach in comparison to baseline methods.

In this study, we present a hybrid deep reinforcement learning (DRL) algorithm, trained using vehicular federated learning (VFL), specifically tailored for dynamic vehicular networks with historical data. Our approach utilizes VFL-based DRL to refine the caching scheme in these networks, focusing on predicting and storing the most effective content nearby to enhance cache efficiency and reduce content request delays. We propose a modified proximal policy optimization (mPPO) based approach for the DRL-based decision-making for caching management, which combines the advantages of proximal policy optimization (PPO) and double deep Q-network (DDQN). Our study encompasses a vehicular framework that includes a central edge node (CEN), roadside units (RSUs), unmanned aerial vehicles (UAVs), and vehicles equipped with historical data. We tackle the challenges posed by varying vehicle density and mobility, non-uniform RSU coverage, and constrained caching capacity. Through comprehensive simulations, we demonstrate that mPPO outperforms conventional DRL methods like PPO and DDQN, as well as heuristic approaches. These results underscore the efficacy of the VFL-based mPPO in dynamic vehicular networks, confirming its potential for real-world applications.

Vehicular crowdsensing (VCS) within the social Internet of Vehicles (IoV) significantly advances urban transportation management by enhancing road safety, traffic efficiency, and the overall driving experience. This article presents an intelligent multiagent deep reinforcement learning (DRL) framework for augmented dynamic task prioritization in a multi-incentive VCS system. Our framework, named intelligent multiagent reinforcement learning (IMARL), leverages augmented intelligence to integrate human-like decision-making processes with autonomous vehicle operations, ensuring more adaptive and robust task management. The proposed IMARL framework offers several key advantages: it dynamically adjusts the sensing levels of each vehicle, ensuring efficient energy usage and minimized processing times, and employs a data-quality aware multi-incentive utility model to capture both functional and social incentives. Additionally, our framework incorporates a layered server architecture, enhancing system resilience and scalability. Simulation results demonstrate the superiority of our approach. IMARL achieves significant improvements in task completion rates, energy consumption, and processing delays compared to other DRL and non-DRL benchmark methods. Furthermore, our approach exhibits strong adaptability to changing environmental conditions, maintaining high performance even in high-density traffic scenarios. These quantified results validate the effectiveness of the proposed framework, highlighting its potential to significantly enhance VCS systems in real-world applications.

In this study, we present a hybrid deep reinforcement learning (DRL) algorithm, trained using vehicular federated learning (VFL), specifically tailored for dynamic vehicular networks with historical data. Our approach utilizes VFL-based DRL to refine the caching scheme in these networks, focusing on predicting and storing the most effective content nearby to enhance cache efficiency and reduce content request delays. We propose a modified proximal policy optimization (mPPO)-based approach for the DRL-based decision making for caching management, which combines the advantages of proximal policy optimization (PPO) and double deep Q-network (DDQN). Our study encompasses a vehicular framework that includes a central edge node (CEN), roadside units (RSUs), unmanned aerial vehicles (UAVs), and vehicles equipped with historical data. We tackle the challenges posed by varying vehicle density and mobility, nonuniform RSU coverage, and constrained caching capacity. Through comprehensive simulations, we demonstrate that the mPPO outperforms the conventional DRL methods like PPO and DDQN, as well as heuristic approaches. These results underscore the efficacy of the VFL-based mPPO in dynamic vehicular networks, confirming its potential as a viable solution for real-world applications.

We present a collection recommender system that can automatically create and recommend collections of items at a user level. Unlike regular recommender systems, which output top-N relevant items, a collection recommender system outputs collections of items such that the items in the collections are relevant to a user, and the items within a collection follow a specific theme. Our system builds on top of the user-item representations learnt by item recommender systems. We employ dimensionality reduction and clustering techniques along with intuitive heuristics to create collections with their ratings and titles. We test these ideas in a real-world setting of music recommendation, within a popular music streaming service. We find that there is a 2.3x increase in recommendation-driven consumption when recommending collections over items. Further, it results in effective utilization of real estate and leads to recommending a more and diverse set of items. To our knowledge, these are first of its kind experiments at such a large scale.