Aug 2026· IEEE Transactions on Cognitive Communications and Networking· Vol 12, pp. 9962-9978· 0 citations· 65 references
Computer Science
TL;DR
Simulation results show that MA-HEAD-Net improves policy-formation efficiency compared with representative MADRL baselines and achieves better AoI performance than both learning-based and heuristic baselines, demonstrating its effectiveness in dynamic UAV-assisted emergency communication scenarios.
Abstract
In post-disaster scenarios, uncrewed aerial vehicles (UAVs) are critical for establishing emergency communication networks. For time-critical rescue missions, information freshness is crucial, as decisions based on outdated data can lead to ineffective control actions. This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services. To characterize heterogeneous emergency services with bursty arrivals and different packet-size requirements, we model packet arrivals using a Markov-modulated Poisson process (MMPP) and adopt finite blocklength (FBL) theory to capture the coupling among transmission duration, packet completion, and AoI evolution. To balance delay-tolerant long-packet transmission and urgent short-packet response, we propose a mini-slot-embedded scheduling mechanism with adaptive checkpoint-interval selection. To solve the joint optimization problem of UAV trajectory control, user scheduling, and checkpoint-interval selection, we propose an adaptive rule-guided multi-agent deep reinforcement learning (MADRL) framework, named Multi-Agent Hybrid Expert-Algorithmic Decision Network (MA-HEAD-Net). MA-HEAD-Net incorporates communication-domain rule priors into a gated multi-head policy, where adaptive gates adjust the influence of rule-prior logits and learned policy logits for different sub-tasks. The policy and gating components are jointly optimized under the multi-agent proximal policy optimization (MAPPO) training framework, enabling rule-guided decision making while preserving the flexibility of policy learning. Simulation results show that MA-HEAD-Net improves policy-formation efficiency compared with representative MADRL baselines and achieves better AoI performance than both learning-based and heuristic baselines, demonstrating its effectiveness in dynamic UAV-assisted emergency communication scenarios.
Maritime unmanned aerial vehicle (UAV) missions such as ship inspection, search and rescue, environmental monitoring, and emergency response often involve multi-wave task releases, time-sensitive deadlines, constrained support vessel positions, and spatially heterogeneous risk. These factors couple task allocation with path planning and make fixed dispatching rules fragile under changing mission profiles. This study develops a hierarchical cooperative planning framework for multiple UAVs over a maritime risk field. A risk-cost A* layer generates feasible routes from support vessels to task points and estimates path length, risk exposure, and sortie duration. A rolling scheduler constructs feasible UAV task candidates, while a scenario-switching-aware LinUCB hyper-heuristic selects online among deadline-first, distance-first, risk-aware, and endurance-balancing rules. A forgetting-update, one-step look-ahead, scenario memory, and lightweight switching detection are used to improve adaptation to mission profile changes. Simulations on a 28 × 40 maritime grid with two support vessels, six UAVs, 40 tasks, and nine release waves show that the proposed framework achieves the highest average effective reward (370.18), the lowest average value regret (0.61), and a best reward ratio of 0.46 over 24 random scenarios. The results should be interpreted as evidence from an idealized simulation benchmark. The main benefit is improved reward robustness under non-stationary and high-risk profiles, rather than uniform gains across all metrics or direct field-deployment validation.
Jian Wu, Shengchang Liu, Wenxi Ni et al.· Drones· 0 citations
Rapid, reliable, and energy-efficient data collection is essential for disaster response, where terrestrial communication networks may be disrupted or unavailable. Unmanned Aerial Vehicles (UAVs) provide a flexible means of collecting critical sensing data, but their operation is constrained by limited onboard energy, stochastic wireless conditions, complex three-dimensional environments, and stringent latency requirements. This paper presents a structured multi-UAV framework that separates mission optimisation into spatial, temporal, and safety layers. In the spatial layer, a three-dimensional Travelling Salesman Problem with Neighbourhoods (3D-TSPN) formulation enables UAVs to collect data by entering valid sensing regions rather than visiting exact sensor coordinates. An Age of Information (AoI)-aware Genetic Algorithm (GA) optimises the sensor-visitation sequence, while Rapidly Exploring Random Tree Connect (RRT-Connect) generates obstacle-aware feasible paths in the three-dimensional environment. In the temporal layer, a Lyapunov-based controller selects between local processing and binary offloading to a single Mobile Edge Computing (MEC) node according to queue backlog, processing delay, energy consumption, information freshness, wireless-link feasibility, and task deadlines. In the safety layer, continuous-time conflict detection and bounded temporal or spatial adjustments are used to monitor and mitigate inter-UAV and obstacle-related risks. The framework is evaluated under stochastic wireless, mobility, computation, and obstacle conditions using 20 independent random seeds. Across the corresponding 20 proposed-policy runs, it achieves a 100% mission-validity rate, complete sensor coverage, no dropped tasks, and zero final collision or near-miss events. Compared with planning-oriented and MEC-oriented baselines, the proposed framework achieves lower information age, average delay, processing delay, energy consumption, and system cost under the evaluated conditions, while maintaining reliable multi-UAV coordination. The layered design also clarifies the contribution of each component: 3D-TSPN provides spatial flexibility, the AoI-aware GA improves route sequencing, RRT-Connect supports obstacle-aware path feasibility, Lyapunov control enables queue-aware processing decisions, and safety monitoring supports coordinated multi-UAV operation. These results indicate that integrating spatial planning, computation control, and safety coordination within a clearly separated layered architecture can provide an effective solution for multi-UAV disaster-response data collection in complex three-dimensional environments.
Rakan Armoush, Shidrokh Goudarzi, Muhammad Nadeem Khan et al.· Italian National Conference...· 0 citations
Unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection is a promising solution for timely information acquisition in post-disaster scenarios with damaged terrestrial infrastructure. However, freshness-aware UAV trajectory planning is challenging due to the coupled effects of heterogeneous ground node priorities, Age of Information (AoI) evolution, continuous UAV control, and safety risks caused by no-fly zones and initially unknown obstacles. In this letter, we formulate the safety-constrained weighted AoI minimization problem as a constrained Markov decision process (CMDP) and propose a safety-constrained twin delayed deep deterministic policy gradient (SC-TD3) algorithm with Lagrangian safety optimization to decouple the AoI-oriented objective from long-term safety-risk control and adaptively balance information freshness and safety risk during policy learning. Simulation results show that SC-TD3 achieves higher accumulated reward and reduces mean weighted AoI by 64.3%–78.2% and 67.1%–73.6% in the CN-ratio and GN-scale tests, respectively, while reducing mean total safety cost by 61.4%–75.6% compared with the strongest benchmark algorithm.
Jinghao Wang, Xu Wang, Jihao Luo et al.· IEEE Wireless Communications...· 0 citations
The three-layer temporal decoupling architecture provides a practical template for safely integrating LLM-assisted planning with real-time multi-agent control in contested environments.
Xingda Li, Jian-Qiang Zhang, Yiping Liu et al.· Drones· 1 citation
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.
Muhammad Umar Farooq Qaisar, Lin Zhang, Zhen Chen et al.· arXiv.org· 0 citations
