Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VANETs) in smart city environments. However, conventional centralized approaches for UAV trajectory planning require continuous global network state aggregation, making them impractical under bandwidth and energy constraints typical of dense urban deployments. In this article, we present TRUAV, a distributed multi-agent reinforcement learning framework based on independent tabular Q-learning for joint UAV trajectory planning and routing enhancement in UAV-aided VANETs. Each UAV is equipped with a local Q-learning agent that operates purely on locally observable information, including vehicle density, packet queue states, and neighbor UAV positions, thereby eliminating the need for global state exchange. A potential-game-inspired reward design encourages spatial diversity and routing-aware UAV positioning among interacting agents while accounting for energy consumption. Numerical simulations over a large urban area with 200 mobile vehicles show that the proposed TRUAV framework achieves network coverage and packet delivery ratios comparable to centralized deep reinforcement learning methods, while also improving relay delay and energy efficiency. Finally, we discuss emerging challenges and future research directions for distributed multi-agent UAV-assisted IoT systems.
This paper investigates the problem of cooperative multiple unmanned aerial vehicles (UAVs) data collection for Internet of Things (IoT) networks in dense urban environments. Unlike existing studies that predominantly rely on idealized spatial models and average-based probabilistic channel models, this work explicitly accounts for realistic 3-D building distributions and deterministically models ground-to-air (G2A) channel blockages. We formulate a joint optimization problem to minimize the total task completion time, subject to stringent system throughput, flight dynamics, and energy constraints. To tackle the highly coupled challenges of node scheduling and trajectory planning, we propose a lightweight two-stage heuristic strategy for dynamic access control, along with a multi-agent reinforcement learning for trajectory planning. Crucially, to overcome the severe sparse-reward bottleneck inherent in complex 3-D obstacle avoidance, we introduce a Pheromone-based Reward Shaping (PRS) mechanism. By mathematically integrating the UAV’s kinematic state with deterministic environmental feedback, PRS effectively transforms the sparse-reward navigation challenge into a dense and smooth gradient, thereby profoundly accelerating policy convergence. Extensive simulations demonstrate that the proposed MATD3-PRS framework significantly outperforms representative baselines, achieving superior performance in task completion time, flight trajectory efficiency, and overall energy saving.
Haitao Chen, Xinfeng Deng, Zhe Wang et al.· IEEE Transactions on Cogniti...· 0 citations
A predictive multi-agent Reinforcement Learning (RL) framework that proactively maintains SLA stability in UAV-enabled MEC through coordinated trajectory control and computation resource allocation and designs an SLA-aware reward function that explicitly penalizes both violation probability and duration across slices.
M. Farhoudi, Zeinab Sasan, Masoud Shokrnezhad et al.· 0 citations
This study introduces a multi agent soft actor-critic (MASAC) framework for UAV path planning and energy aware coordination in a fixed RIS assisted IoT grid, and offers a simulation level benchmark for energy efficient UAV navigation in RIS assisted IoT environments.
Md. Najmul Mowla, D. Asadi, Khaled M. Rabie et al.· Scientific Reports· 0 citations
Unmanned Aerial Vehicles (UAVs) are increasingly deployed as embodied aerial agents in low-altitude economies, forming mobile aerial edge networks that enable flexible computation offloading for vehicles. However, their limited endurance and frequent join/leave behaviours result in highly dynamic topologies, undermining long-term resource availability. Moreover, existing vehicle-centric task scheduling strategies cause resource contention and decision complexity in dense environments. To address these challenges, this paper proposes a hierarchical and scalable reinforcement learning-based scheduling framework (SkySched). In SkySched, UAVs collaboratively make deployment and task scheduling decisions. The framework consists of two tightly coupled modules. First, an adaptive UAV deployment module introduces a capability encoding mechanism that compresses heterogeneous UAV attributes into a unified one-dimensional capability index. This compact representation enables a Scalable Proximal Policy Optimization (SPPO) algorithm to efficiently coordinate UAV positioning, maximizing task coverage and sustaining network-wide computing availability under dynamic topology variations. Second, a hierarchical task scheduling module is designed, where K-means-based Roadside Unit (RSU) clustering enables vertical task offloading, while a SPPO-driven horizontal UAV-to-UAV task redistribution mechanism achieves fine-grained load balancing across the UAV swarm. Simulations demonstrate that SkySched consistently outperforms state-of-the-art methods in terms of task coverage and load fairness, validating its effectiveness as an agentic AI-driven embodied networking solution for UAV-assisted vehicular edge computing.
Meng Yi, V. Lee, Miao Du et al.· IEEE Transactions on Cogniti...· 0 citations
Results show that topology‐aware cooperative learning can provide a scalable and practical solution for reliable UAV communication in future intelligent aerial and 6G‐enabled networks, particularly in dense deployments.
V. Nam, A. Chehri, Weiwei Jiang et al.· Expert systems· 0 citations
The results confirm that the integration of artificial intelligence, energy-aware routing, and UAV trajectory optimization provides an effective and scalable solution for next-generation UAV-assisted IoT systems and establishes a robust foundation for intelligent 6G-enabled wireless sensor networks.
Mojtaba Nasehi· Internet of Things and Cloud...· 0 citations