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

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

TL;DR

This paper designs the multi-agent double deep Q-network-based routing algorithms to solve the routing with multiple UAV clusters in LAINs, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms.

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.

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

TL;DR

This paper designs the multi-agent double deep Q-network-based routing algorithms to solve the routing with multiple UAV clusters in LAINs, empowered by the soft-hierarchical experience replay buffer and prioritized experience replay mechanisms.

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.
Paper Structure (41 sections, 42 equations, 9 figures, 2 tables, 2 algorithms)

This paper contains 41 sections, 42 equations, 9 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model and Problem Formulation
  4. Zero-Trust Architecture Based Network Model
  5. Network Model
  6. Zero-Trust Architecture Scheme
  7. Joining and Exiting of UAVs
  8. Trust Model
  9. Direct Trust Calculation
  10. Indirect Trust Calculation
  11. Blockchain Model
  12. Role Selection
  13. Full UAV
  14. Light UAV
  15. Primary UAV
  16. ...and 26 more sections

Figures (9)

  • Figure 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.
  • Figure 2: SP-MADDQN algorithm framework for trust routing in LAINs.
  • Figure 3: The minimum time step for identifying malicious UAVs with different simulation parameters in space $\Lambda$.
  • Figure 4: The minimum time step for identifying malicious UAVs with different trust thresholds.
  • Figure 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.
  • ...and 4 more figures