# Blockchain-Assisted Adaptive Reconfiguration Method for Trusted UAV Network
###### Abstract  
Due to the high mobility of nodes and the complexity of the mission environment, mission-oriented UAV networks are not only subject to frequent topology changes, but also to the risk of being compromised, hijacked and corrupted. As a result, an operating UAV network is essentially a Byzantine distributed system whose physical structure and node trustworthiness change over time. How to implement the global management of UAV networks to achieve a rational allocation of UAV network resources and reconfiguration of trusted networks is a problem worthy of in-depth study. The method proposed in this paper introduces a lightweight storage blockchain in the UAV network through two-stage consensus, firstly performing data consensus on the local state records of the nodes, then performing decision consensus on the data consensus results using algorithms such as fuzzy K-Modes clustering and global trustworthiness assessment, and finally recording the decision consensus results into a new block as the new configuration information of the UAV network. A lightweight storage blockchain-assisted trusted zone routing protocol (BC_TZRP) is designed to dynamically and adaptively build configurable trusted networks in a way that the blockchain continuously adds new blocks. Using QualNet simulation experimental software, an experimental comparison between the classical routing protocol for mobile self-organizing networks and the traditional consensus algorithm for blockchains is conducted. The results show that the approach has significant advantages in terms of packet delivery rate, routing overhead and average end-to-end delay, and can effectively improve the overall working life and fault tolerance of the UAV network. m 20221 11 2549 11162549 1162  
for Strategic Studies released the \"Military Balance 2022\" statistics, the U.S. military under 20 kg micro and small UAVs more than 10,000. Russia's Concept for the Use of Unmanned Aircraft Systems in the Russian Armed Forces by 2025 comprehensively lays out the development of unmanned aircraft equipment and force construction, and according to the plan, the proportion of unmanned combat systems in the Russian military equipment system will exceed 30% in 2025. In the Israeli-Palestinian conflict in May 2021, the Israeli army used a swarm of miniature multi-rotor drones guided by artificial intelligence systems for the first time, and Israel became the first country to integrate artificial intelligence technology to command a swarm of drones in combat. At India's Army Day parade in 2021, India demonstrated for the first time an offensive drone cluster system consisting of 75 drones that can autonomously identify and shoot down targets [1]. With the boom in UAV technology, anti-UAV technologies [2; 3; 4; 5] have emerged. In addition to traditional methods such as communication/navigation system jamming and signal intrusion, research into anti-drone technologies such as electronic warfare, cyber warfare, drone countermeasures and laser weapons has been vigorously pursued. These new anti-drone technologies can perform GPS decoys, interfere with the normal operation of drones, and even counteract drones, such as a drone countermeasure system based on drone clustering proposed by [PERSON] et al. [6] from the University of Luxembourg. Although some progress has been made in drone clustering technology, drones are currently vulnerable to jamming, trapping and strikes due to their own limited resources, the open nature of wireless communications and the imperfection of aerial countermeasures. Mission-oriented UAV networks operate in highly dynamic, complex and unstructured environments where the size of the network, topology and trustworthiness of the network nodes change over time. Improving the fault tolerance of the network and maintaining its trustworthiness during missions is a major challenge for distributed UAV networks with limited resources and no central support.