Organizations: the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China · the College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China · the College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China · the Department of Electrical and Computer Engineering at the University of Houston, Houston, TX 77004 USA · the College of Computing and Data Science, Nanyang Technological University, Singapore 639798
Due to the scalability and portability, low-altitude intelligent networks (LAINs) are essential in various fields such as surveillance and disaster rescue. However, in LAINs, unmanned aerial vehicles (UAVs) are characterized by the distributed topology and high mobility, thus vulnerable to security threats, which may degrade routing performances for data transmissions. Hence, how to ensure the routing stability and security of LAINs is challenging. In this paper, we focus on the routing with multiple UAV clusters in LAINs. To minimize the damage caused by potential threats, we present the zero-trust architecture with the software-defined perimeter and blockchain techniques to manage the identify and mobility of UAVs. Besides, we formulate the routing problem to optimize the end-to-end (E2E) delay and transmission success ratio (TSR) simultaneously, which is an integer nonlinear programming problem and intractable to solve. Therefore, we reformulate the problem into a decentralized partially observable Markov decision process. We design the multi-agent double deep Q-network-based routing algorithms to solve the problem, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms. Finally, extensive simulations are conducted and the numerical results demonstrate that the proposed framework reduces the average E2E delay by 59% and improves the TSR by 29% on average compared to benchmarks, while simultaneously enabling faster and more robust identification of low-trust UAVs.
Figures & tables
Reference
Trust Management
Blockchain
Algorithm
Key Characteristic
Our Work
Decentralized (Adaptive weights)
Yes
SP-MADDQN
Provides a comprehensive, adaptive, and
Byzantine-resistant secure routing framework.
[ 11 ]
Decentralized (Fixed weights)
Yes
Security-aware distributed routing protocol
Static trust model lacks dynamic adjustability
and the routing method is non-adaptive.
[ 12 ]
Decentralized (Fixed weights)
No
Adaptive snow geese algorithm
Static trust models lack adaptability, incur high
audit overhead, and are vulnerable to tampering.
TABLE I: Comparisons of Our Adaptive Framework over Existing Secure Routing Schemes for UAV Networks
Fig. 1: An illustration of blockchain-enabled routing for zero-trust LAINs. Part 1) presents the routing scenario in ZTA-based LAINs with the security threats. Part 2) introduces the mobility management of UAVs via the blockchain and SDP techniques. Part 3) illustrates the model of the E2E delay during routing.
Fig. 2: SP-MADDQN algorithm framework for trust routing in LAINs.
Notation
Value
Notation
Value
τ
0.5 s
dmin
10 m
β
0.5
ς
20
Pnmtr(t)
40 dBm
σnm2(t)
-110 dBm
fc
2.4 Ghz
c
3 ×108 m/s
ηntmtLoS
0.1 dB
ϵv
1 M CPU cycles
ϱ1
5.0188
∂
(2,4) G CPU cycles/s
TABLE II: Parameter Notation
Fig. 3: The minimum time step for identifying malicious UAVs with different simulation parameters in space Λ .
Fig. 4: The minimum time step for identifying malicious UAVs with different trust thresholds.
Fig. 5: Comparison of convergence performances for transmitting demands with 8 UAVs and 2 malicious UAVs during training SP-MADDQN. (a) Different learning rates 25 demands. (b) Different demand numbers.
Fig. 6: The evaluation of different ς in terms of the reward and the average E2E delay with 25 demands and 8 UAVs including 2 malicious UAVs.
Fig. 7: Comparison of different algorithms in terms of the convergence performance, and loss values with 25 demands and 8 UAVs including 2 malicious UAVs.
Fig. 8: Comparison of the diverse algorithms in terms of the average E2E delay and TSR with 25 demands and 8 UAVs including 2 malicious UAVs.
Fig. 9: Comparison of the different network scales on performances. (a) The average E2E delay with 2 malicious UAVs. (b) The TSR under various numbers of malicious UAVs.
The growing deployment of delay-tolerant networks (DTNs) has made store-carry-forward (SCF) communication indispensable under sparse connectivity. However, intermittent contacts, finite buffers, and limited message time-to-live (TTL) often give rise to sparse delivery and congestion, leading to substantial end-to-end performance degradation. To address this challenge, this study explores the joint optimization of decentralized opportunistic routing and controllable unmanned aerial vehicle (UAV) flight, aiming to enlarge future contacts through discrete UAV headings while enabling per-node replication under contact-limited observations. Building upon this architecture, we study cooperative factored routing--UAV control under centralized training and decentralized execution (CTDE) and propose JUROR (Joint UAV flight and Opportunistic Routing, based on the proximal policy optimization (PPO) framework. In our design, we first cast the problem as a factored partially observable Markov decision process with sequential motion--routing coupling and a per-step team reward; subsequently, decentralized actors act on local observations while a training-time critic uses global statistics, and an optional multi-horizon hotspot predictor provides auxiliary supervision. Simulation results over four traffic modes demonstrate effective gains over PRoPHET and MaxProp, while retaining contact-limited decentralized execution.
Xiao Wang, Shun-Ren Yang
Department of Computer Science and the Institute of Communications Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan
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 +3
Hangzhou International Innovation Institute of Beihang University, Hangzhou 311115, China · Hangzhou International Innovation Institute of Beihang University, Hangzhou 311115, China, the School of Automation Science and Electrical Engineering at Beihang University, Beijing 100191, China, and the State Key Laboratory of Intelligent Manufacturing Systems Technology, Beijing 100854, China · School of Intelligent Science and Engineering and the School of Transportation, Southeast University, Nanjing 211189, China +3
Ensuring operational safety in threat-prone environments remains a critical challenge for multi-UAV networks serving as aerial base stations. This paper proposes an efficient framework to maximize global energy efficiency (EE) while promoting safe operation through threat-aware clustering and reward-based safety enforcement. The proposed framework is executed in three steps. First, a threat-aware K-means (TAKM) algorithm determines the minimum required UAVs and computes safe initial placements. Second, an optimal matching stage assigns physical UAVs to these centroids to minimize energy expenditure. Third, a threat-aware multi-agent twin delayed deep deterministic policy gradient (MATD3) algorithm dynamically optimizes trajectories, power, and user associations. Simulation results show that the proposed framework achieves zero observed safety violations in the considered scenarios while achieving superior EE and faster convergence than other learning methods and non-clustering baselines. Compared to heuristic optimization, the proposed framework outperforms the greedy particle swarm optimization (GPSO) and achieves performance comparable to that of the optimized PSO (OPSO), while incurring significantly lower online deployment computational complexity. Furthermore, the proposed framework demonstrates effective generalization to unseen user distributions, large UAV fleets, and different threat geometries, while maintaining zero safety violations.
Faisal Al-Kamali, Hussein A. Ammar, Francois Chan +3
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada · Department of Electrical Engineering, Ibb University, Ibb, Yemen · Department of Electrical and Computer Engineering, Royal Military College, Kingston, ON, K7K 7B4, Canada +1