Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks

Ziye Jia; Sijie He; Ligang Yuan; Fuhui Zhou; Qihui Wu; Zhu Han; Dusit Niyato

cs.NIFeb 27, 2026

Blockchain-Enabled Routing for Zero-Trust Low-Altitude Intelligent Networks

Authors: Ziye Jia, Sijie He, Ligang Yuan, Fuhui Zhou, Qihui Wu, Zhu Han, Dusit Niyato

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 ·

Abstract

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
$\tau$	0.5 s	$d_{min}$	10 m
$\beta$	0.5	$\varsigma$	20
$P^{tr}_{nm}(t)$	40 dBm	$\sigma^{2}_{nm}(t)$	-110 dBm
$f_{c}$	2.4 Ghz	$c$	3 $\times 10^{8}$ m/s
$\eta_{n^{t}m^{t}}^{\mathrm{LoS}}$	0.1 dB	$\epsilon_{v}$	1 M CPU cycles
$\varrho_{1}$	5.0188	$\partial$	(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

\Lambda

Fig. 4: The minimum time step for identifying malicious UAVs with different trust thresholds.

Panel of Figure 7 — 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.

Panel of Figure 8 — Fig. 6: The evaluation of different $\varsigma$ in terms of the reward and the average E2E delay with 25 demands and 8 UAVs including 2 malicious UAVs.

Panel of Figure 9 — 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.

Panel of Figure 10 — 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.

Panel of Figure 11 — 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.

Joint UAV Flight and Opportunistic Routing under Reinforcement Learning for Delay-Tolerant Networks
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Jul 26, 2026Muhammad Umar Farooq Qaisar, Lin Zhang, Zhen Chen +3UAV Navigation Decentralized MARL
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
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Sep 25, 2026Faisal Al-Kamali, Hussein A. Ammar, Francois Chan +3UAV Communication UAV Trajectory Optimization
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

Aug 5, 2026J/K move · Enter open · S save

Joint UAV Flight and Opportunistic Routing under Reinforcement Learning for Delay-Tolerant Networks

Xiao Wang, Shun-Ren Yang

Department of Computer Science and the Institute of Communications Engineering, National Tsing Hua University, Hsinchu 30013, Taiwan

UAV Trajectory Optimization Decentralized MARL Motion Planning

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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.

Abstract

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