Md Bokhtiar Al Zami, Md Raihan Uddin, Dinh C. Nguyen
Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies, and security vulnerabilities must be solved. This paper proposes an innovative framework that integrates Digital Twin (DT) technology and Zero-Knowledge Federated Learning (zkFed) to tackle these challenges. UAVs act as mobile base stations, allowing scattered devices to train FL models locally and upload model updates for aggregation. By incorporating DT technology, our approach enables real-time system monitoring and predictive maintenance, improving UAV network efficiency. Additionally, Zero-Knowledge Proofs (ZKPs) strengthen security by allowing model verification without exposing sensitive data. To optimize energy efficiency and resource management, we introduce a dynamic allocation strategy that adjusts UAV flight paths, transmission power, and processing rates based on network conditions. Using block coordinate descent and convex optimization techniques, our method significantly reduces system energy consumption by up to 29.6% compared to conventional FL approaches. Simulation results demonstrate improved learning performance, security, and scalability, positioning this framework as a promising solution for next-generation UAV-based intelligent networks.
UAVs (Unmanned Aerial Vehicles) enhance sustainability by enabling precise and efficient environmental monitoring with minimal ecological disruption. This study looks at how integrating artificial intelligence (AI) with blockchain technology can increase operational independence, scalability, and security of UAV systems. Blockchain technology is all about being decentralised and unchangeable, which means it keeps UAV operations secure and reliable by ensuring that data remains intact and preventing any unauthorised changes. Moreover, this research in the importance of blockchain consensus algorithms -Proof of Work (PoW), Proof of Stake (PoS), Practical Byzantine Fault Tolerance (PBFT), and Delegated Proof of Stake (DPoS), specifically for UAV applications. AI integration improves the optimization of processes on the fly and helps to make smarter decisions, leading to plausible improvements in transaction validation and overall network efficiency. The experimental results show how effective the AI-augmented PoS and DPoS algorithms are, highlighting that they are a great fit for scalable and self-sufficient UAV applications.
Hope Leticia Nakayiza, Love Allen Chijioke Ahakonye, Dong‐Seong Kim, Jae Min Lee
The growing deployment of unmanned aerial vehicles (UAVs) in military operations necessitates a secure, scalable, and decentralized approach to airspace management. This paper introduces MilChain-UAV, a blockchain-based traffic control framework tailored for military UAV networks. Built on PureChain, a custom permissioned blockchain network, MilChain-UAV supports autonomous mission governance, real-time path validation, and decentralized collision avoidance. To optimize blockchain efficiency while ensuring auditability, telemetry data is stored off-chain using IPFS, with only the cryptographic hashes anchored on-chain. By replacing centralized controllers with a distributed ledger, the framework enhances resilience against jamming and spoofing while enabling dynamic routing and verifiable behavior logging. Experimentation results demonstrate MilChain- UA V's effectiveness in improving efficiency and scalability in critical military operations, providing a robust solution for autonomous and secure management of military UAV traffic.
Haoxiang Luo, Ruichen Zhang, Yinqiu Liu, Gang Sun · 6 authors
Low-altitude airspace is becoming a new frontier for smart city services and commerce. Networks of drones, electric Vertical Takeoff and Landing (eVTOL) vehicles, and other aircraft, termed Low-Altitude Economic Networks (LAENets), promise to transform urban logistics, aerial sensing, and communication. A key challenge is how to efficiently share and trust the computing utility, termed “computility”, of these aerial devices. We propose treating the computing power on aircraft as tokenized Real-World Assets (RWAs) that can be traded and orchestrated via blockchain. By representing distributed edge computing resources as blockchain tokens, disparate devices can form Low-Altitude Computility Networks (LACNets), collaborative computing clusters in the sky. We first compare blockchain technologies, non-fungible tokens (NFTs), and RWA frameworks to clarify how physical hardware and its computational output can be tokenized as assets. Then, we present an architecture using blockchain to integrate aircraft fleets into a secure, interoperable computing network. Furthermore, a case study models an urban logistics LACNet of delivery drones and air-taxis. Simulation results indicate improvements in task latency, trust assurance, and resource efficiency when leveraging RWA-based coordination. Finally, we discuss future research directions, including AI-driven orchestration, edge AI offloading and collaborative computing, and cross-jurisdictional policy for tokenized assets.
W. Jiang, Zhiqiang Du, Xiaofeng Rong, Yanfang Fu · 6 authors
With the rapid development of 5 G communication technology, small military UAV swarms are increasingly used in reconnaissance, surveillance, and remote sensing fields. Authentication has become a critical factor in ensuring the safe and efficient operation of UAV swarms. Due to their complexity and limitations, traditional methods cannot meet the dynamic deployment and command authority switching requirements of UAV swarms in special wartime environments. Therefore, this study proposes a secure and efficient authentication scheme for UAV swarms based on the CVMerkle tree structure. The scheme integrates distributed digital ledger technology to optimize the authentication process between UAVs and the Airborne Command Center(ACC). This significantly reduces the communication and computational load while improving the security and reliability of authentication. Additionally, the scheme introduces the innovative concept of dynamic authorization in the ACC, which effectively eliminates security threats arising from internal corruption within the core organization. The results of the simulation experiment demonstrate that the scheme offers significant advantages in terms of security, efficiency, and storage requirements. Future research will focus on developing more efficient key management mechanisms and swarm-switching strategies to adapt to the complex and dynamic combat environment, further enhancing the combat effectiveness of UAV swarms.
Ting Ye, Yang Wang, Enrico Zio, Jie Man · 6 authors
Digital twin technology can offer support to ship intelligence by the elaboration of massive data. However, false data may hinder the expected functions and even lead to serious navigation accidents. Therefore, credibility of data is a crucial issue that demands urgent attention. To this end, we propose an information credibility determination scheme suitable for the digital twin framework. This scheme combines the subjective logic model with the Dempster-Shafer theory. Firstly, based on the historical reputation values and interaction records of broadcasting ships, this scheme generates an initial subjective logic model via a smart contract. Subsequently, the model undergoes dynamic updates by taking into account the viewpoints provided by other verified ships. Then, the Dempster-Shafer theory is employed to fuse viewpoints and obtain the final information credibility evaluation results. In addition, a Delegated Proof of Stake consensus algorithm is designed___combined with an evolutionary game mechanism. The aim is to incentivize witness vote ships to select witness ships with high reputation values____to obtain the right to create blocks, thus enhancing overall security and reliability. Simulation results show that the proposed scheme can effectively identify false information, resist the attack of malicious ships, and significantly improve robustness and credibility.
Cooperative multi-UAV clusters have been widely applied in complex mission scenarios due to their flexible task allocation and efficient real-time coordination capabilities. The Air Command Aircraft (ACA), as the core node within the UAV cluster, is responsible for coordinating and managing various tasks within the cluster. When the ACA undergoes fault recovery, a handover operation is required, during which the ACA must re-authenticate its identity with the UAV cluster and re-establish secure communication. However, traditional, centralized identity authentication and ACA handover mechanisms face security risks such as single points of failure and man-in-the-middle attacks. In highly dynamic network environments, single-chain blockchain architectures also suffer from throughput bottlenecks, leading to reduced handover efficiency and increased authentication latency. To address these challenges, this paper proposes a mathematically structured dual-chain framework that utilizes a distributed ledger to decouple the management of identity and authentication information. We formalize the ACA handover process using cryptographic primitives and accumulator functions and validate its security through BAN logic. Furthermore, we conduct quantitative analyses of key performance metrics, including time complexity and communication overhead. The experimental results demonstrate that the proposed approach ensures secure handover while significantly reducing computational burden. The framework also exhibits strong scalability, making it well-suited for large-scale UAV cluster networks.
The evolution of future network and control technologies has enabled unmanned aerial vehicles (UAVs) to collaborate across diverse geographical areas and task domains, enhancing task execution efficiency through data and resource sharing. In response to the increasing demand for cross-domain task allocation and operations for UAVs, establishing robust authentication mechanisms within trusted domains has become a critical foundation for ensuring secure cross-domain access. Despite significant progress in UAV identity authentication and cross-domain access, challenges persist, such as cumbersome and inefficient processes, UAV resource limitations, and establishing trust relationships across different domains. To address these challenges, this paper introduces a dual blockchain-assisted trusted authentication scheme for UAVs' cross-domain access. Our approach utilizes a certificateless signcryption algorithm for lightweight UAV authentication, thereby eliminating the need for certificate management. Then, an efficient credit-based trust model is designed to measure the trustworthiness of data-in-transit and cross-domain entities. Furthermore, blockchain technology is introduced to store the relevant information of UAVs and credibility to assist cross-domain authentication. Theoretical security analysis and extensive simulations have been conducted, demonstrating the effectiveness and efficiency of our proposed scheme.
Unmanned aerial vehicles (UAVs) have recognized as a pivotal technology for advancing wireless augmented reality (AR) applications. However, the considerable energy requirements during the rendering process present a formidable challenge, demanding a precise balance between energy efficiency and latency. Additionally, UAV-enabled systems may face significant security risks in untrusted environments. To solve these issues, we present a secure optimization framework for AR applications, where the blockchain is integrated into the system to provide distributed management and control functions. The critical information during the AR rendering process can be recorded in blockchain promptly to enhance security and privacy. In the proposed framework, a joint optimization problem is formulated to achieve the optimal trade-off between energy consumption and content delivery latency, where the rendering decision, resource allocation, and UAV placement are jointly optimized. Due to the tight coupling variables, the optimization problem is non-convex and difficult to be tackled by adopting the traditional method. To this end, we decouple the formulated problem and design a block coordinate descent (BCD)-based optimization algorithm. In the proposed algorithm, we innovatively combine the Lagrangian multiplier iterative (LMI) method and the deep reinforcement learning (DRL) approach to enhance the solving efficiency by implanting the LMI method into the learning environment of DRL. Simulation results demonstrate that the proposed method can perform well for AR applications compared to other baseline solutions and traditional DRL approaches.
Recently, the promising unmanned aerial vehicle (UAV)-assisted wireless networks (UAWNs) have emerged by advocating the UAVs to provide wireless transmission services. However, owing to the ever-growing volume of data traffic and the untrusted network operation environment, efficiently and securely assigning limited bandwidth for high-quality wireless communication between UAVs and mobile users poses a significant challenge. To address this challenge, we propose a novel secure UAV-bandwidth allocation scheme to provision reliable wireless transmission services for mobile users in UAWNs. Specifically, we first introduce a novel blockchain-empowered framework for secure bandwidth allocation, designed to automate payment processes and deter malicious activities through the immutable logging of transactional and behavioral data. Wherein, a smart contract is designed to regulate the honest behaviors of both mobile users and UAVs during bandwidth allocation with a distributed manner. Besides, a delegated proof-of-stake (DPoS) with reputation consensus protocol is presented to ensure the authenticity and efficiency of the decision-making process. Further, we apply the Stackelberg game theory to model the dynamic of the bandwidth allocation between mobile users and UAVs. In this game, the UAVs act as game leaders to determine the bandwidth price, while each mobile user acts as a game follower, making decision on the bandwidth request. We utilize the backward induction method to derive the optimal strategies of both parties, culminating in the identification of the Stackelberg equilibrium of the formulated game. Finally, extensive simulations are carried out to show the superiority of the proposed scheme over conventional schemes in terms of security, efficiency, and fairness in bandwidth allocation.
Lukas Sparer, Alexander Neulinger, Rigault Bastien, Artur Gonçalves · 7 authors
As the number of unmanned aerial vehicle (UAV) operations is growing rapidly, the risk of collisions increases significantly, making the coordination and verification of flight path compliance crucial. Since many different stakeholders, such as different UAV service suppliers (USS) and UAV operators are involved in an advanced air mobility (AAM) system, the system shall be decentralized and telemetry data shall be measured by the local community using sensor devices. In order to increase system resilience, sub-components of the system are implemented on a blockchain. Smart contracts are used to check whether a UAV has actually navigated the route specified by the USS pre-flight. Due to the restrictions of the system, the flight plan cannot be publicly revealed. Zero-Knowledge proofs (ZKPs) are unfeasible for this use case due to the high number of transactions and computational effort. Therefore, a novel approach has been developed that crosschecks measured telemetry data of UAV flights with flight plans and verifies the correctness without revealing any sensitive flight information. The verification results of UAV telemetry data can further be used to reward UAV operators for complying with the planned flight path.
With the continuous advancement of the Uncrewed Aerial Vehicle (UAV) communication industry, the efficient allocation of scarce spectrum resources to UAVs has become a pressing issue. Given the unique nature of UAVs and the broadcast nature of wireless channels, there are significant instances of unregulated flight operations and malicious attacks, making it essential to ensure the security and fairness of spectrum allocation. The previous work on spectrum sharing schemes seldom considers UAV communication scenarios and fails to consider the transaction environment. This paper employs combinatorial auctions and Stackelberg games to study spectrum trading between multiple base stations (BSs) and UAVs to address these issues. A dynamic, demand-driven spectrum trading model is proposed that accounts for UAVs' flexibility and evolving spectrum needs, maximizing utility for both UAVs and BSs. Subsequently, a blockchain-based spectrum sharing framework is designed, in which the blockchain is made public to trading participants to uphold decentralization. It reflects transactional fairness and reliability through mutual evaluations between the blockchain management platform and trading participants. To simulate a realistic trading environment, we consider specific attack scenarios to assess the credibility guarantee of spectrum trading under blockchain. The simulation results validate the effectiveness of the blockchain-based spectrum trading scheme, enhancing spectrum utilization, ensuring the utility of all entities involved, and maintaining the reliability of the trading process.
Ahmed Alagha, Maha Kadadha, Rabeb Mizouni, Shakti Singh · 6 authors
This paper addresses the challenges of selecting relay nodes and coordinating among them in UAV-assisted Internet-of-Vehicles (IoV). Recently, UAVs have gained popularity as relay nodes to complement vehicles in IoV networks due to their ability to extend coverage through unbounded movement and superior communication capabilities. The selection of UAV relay nodes in IoV employs mechanisms executed either at centralized servers or decentralized nodes, which have two main limitations: 1) the traceability of the selection mechanism execution and 2) the coordination among the selected UAVs, which is currently offered in a centralized manner and is not coupled with the relay selection. Existing UAV coordination methods often rely on optimization methods, which are not adaptable to different environment complexities, or on centralized deep reinforcement learning, which lacks scalability in multi-UAV settings. Overall, there is a need for a comprehensive framework where relay selection and coordination processes are coupled and executed in a transparent and trusted manner. This work proposes a framework empowered by reinforcement learning and Blockchain for UAV-assisted IoV networks. It consists of three main components: a two-sided UAV relay selection mechanism for UAV-assisted IoV, a decentralized Multi-Agent Deep Reinforcement Learning (MDRL) model for efficient and autonomous UAV coordination, and finally, a Blockchain implementation for transparency and traceability in the interactions between vehicles and UAVs. The relay selection considers the two-sided preferences of vehicles and UAVs based on the Quality-of-UAV (QoU) and the Quality-of-Vehicle (QoV). Upon selection of relay UAVs, the coordination between the selected UAVs is enabled through an MDRL model trained to control their mobility and maintain the network coverage and connectivity using Proximal Policy Optimization (PPO). MDRL offers decentralized control and intelligent decision-making for the UAVs to maintain coverage and connectivity over the assigned vehicles. The evaluation results demonstrate that the proposed selection mechanism improves the stability of the selected relays, while MDRL maximizes the coverage and connectivity achieved by the UAVs. Both methods show superior performance compared to several benchmarks.
Unmanned aerial vehicles (UAVs) have witnessed significant growth in various domains, such as agriculture, disaster management, and remote health management systems. However, the use of UAVs necessitates secure and efficient solutions that uphold privacy during task distribution. To address this challenge, this article introduces a novel architecture for privacy-aware task distribution in UAV communication systems. Our approach leverages the benefits of blockchain and smart token-based identification within the proposed architecture, ensuring decentralized, transparent, and tamper-proof operations. By adopting a crowdsourced task distribution model, our approach further optimizes task assignment among UAVs while prioritizing data privacy, user access control, and scalability. The architecture is designed to enhance fault tolerance, enabling seamless operation under dynamic and unpredictable conditions. We present a comprehensive implementation details of a proof-of-concept prototype of our proposed architecture, detailing its design and functionality. The experimental results demonstrate the feasibility, efficiency, and adaptability of our approach in diverse real-world scenarios, highlighting its potential for broader adoption across UAV applications.
The rapid development of the Internet of Things (IoT) and its widespread applications in fog computing environments have underscored the urgent need for secure, scalable, and energy-efficient data exchange mechanisms. This study introduces a hybrid consensus architecture designed to address these challenges by combining Delegated Proof of Stake (DPoS) and Whale Optimization Techniques (WOT). The primary objective of this model is to optimize resource allocation, enhance security, and minimize energy consumption while ensuring scalable and efficient data sharing within fog-based IoT networks. The proposed methodology utilizes DPoS to limit node validation to a select group of trusted delegates, reducing computational overhead and improving scalability by streamlining the consensus process. Meanwhile, WOT enhances decision-making by mimicking the bubble-net feeding behavior of humpback whales, allowing for dynamic and efficient optimization of resource allocation. The integration of these two techniques significantly boosts system performance. Empirical results demonstrate that the hybrid model achieves a 95% increase in security and a 94% improvement in energy efficiency compared to conventional IoT consensus methods. Additionally, the model optimizes processing times, increases data throughput, and minimizes latency, facilitating real-time, low-latency communication that is essential for IoT applications. This combination of DPoS and WOT balances resource utilization and effectively addresses the trade-offs between security, energy efficiency, and scalability. Consequently, the hybrid DPoS-WOT consensus model emerges as a robust and practical solution for secure, efficient, and scalable IoT data sharing in fog computing environments.
Joel Curado, Manila Bhandari, João C. Ferreira, Ana Martins
The maritime supply chain plays a vital role in global trade, but it continues to face major challenges, including transparency issues, fraud, and data privacy concerns. Blockchain technology has emerged as a promising solution to make the supply chain more secure, efficient, and trustworthy across the system. However, it still encounters limitations, especially regarding privacy and the handling of large volumes of data. To address these issues, Zero-Knowledge Proofs (ZKPs) offer a viable solution, enabling the validation of documents and transactions without revealing sensitive information. This helps maintain confidentiality while meeting regulatory requirements, such as those set by the eFTI regulation. This paper investigates blockchain adoption in maritime supply chains with a focus on ZKP integration for secure document verification, fraud mitigation, and regulatory compliance. It evaluates computational overhead, scalability, and adoption barriers, and proposes a framework supported by simulation-based validation using Ethereum and ZoKrates to assess feasibility and performance. By combining ZKPs with blockchain, this approach enhances a secure, transparent, and efficient trade ecosystem, optimising resources and reducing risks. Future research directions are outlined to advance sustainable maritime logistics.
Digital passports for Unmanned Aerial Vehicles (UAVs) are used to create a unified system for tracking and identifying UAVs, which ensures compliance and security. A digital passport holds details like the owner's information, drone model, and activity history, thereby enabling easy tracking and identification of UAVs. However, the absence of decentralized and secure digital passport management systems for UAVs makes it challenging to ensure tamper-proof records and transparent ownership verification across borders. This paper proposes a proof of concept for a decentralized blockchain and Non-Fungible Tokens (NFTs)-based digital passport to improve the transparency, traceability, trust, and security of UAV preoperational certifications. The proposed solution secures UAVs' preoperational stages such as design, manufacturing, and distribution, integrating best practices from the Federal Aviation Administration (FAA) and the European Union Aviation Safety Agency (EASA) for standardized compliance. The proposed NFT-based digital passport consolidates certification records into a single verifiable document, providing access to compliance history. Four smart contracts are developed to enforce role-based access control, and off-chain decentralized storage using the InterPlanetary File System (IPFS) is employed to manage large UAV records. Evaluation through implementation and testing demonstrates the solution's effectiveness in managing UAV certification workflows and enforcing regulatory compliance. Security analysis shows robustness against unauthorized modifications and common vulnerabilities, while cost analysis assesses deployment viability across multiple blockchain networks. A comparison of the proposed solution with existing UAV compliance and certification frameworks shows that it effectively fills the gap by addressing preoperational certification. The smart contract code is publicly available on GitHub.
Recent revolutionary advancements in the services as observed with the use cases of Industry 5.0, consumer electronics 2.0/smart devices 2.0, digital healthcare ecosystem, Internet-of-Things (IoT), advanced digital finance/currency, and Non-Terrestrial Network (NTN) expansion, to name a few, have resulted in a spectacular growth in the number of wireless-connected devices. Subsequently, this has drastically increased the demands for network capacity, channel capacity, reliability, privacy, and security provisions. Despite that, the 5 th Generation (5G) of wireless communication networks has introduced various innovative services such as Ultra-Reliable Low Latency Communication (URLLCs), Massive Machine Type Communication (mMTCs), and Enhanced Mobile Broadband (eMBB). These services only support isolated operations and the requisite reliable service delivery remains a challenge. The Beyond 5G (B5G)/6 th Generation (6G) wireless networks aim at simultaneously providing multiple integrated services through intelligent network operations with ultra-high speed and reliability supporting integrated NTN and terrestrial networks. However, the prospect of such an extensively connected decentralized 3D wireless network also foresees security concerns, underscoring the necessity for seamless and infrastructurefree (decentralized) security solutions. The conventional security mechanisms are considered inadequate to ensure the security provisions of such extensive, decentralized, and heterogeneous networks. Physical Layer Security (PLS) is a promising technique to extend seamless and infrastructure-less security solutions, ensuring the availability, confidentiality, and integrity of legitimate transmissions. This paper provides a comprehensive overview with tutorials and presents the state-of-the-art of PLS, focusing mainly on NTN wireless communications. Furthermore, current research challenges, open issues, and future research directions are also thoroughly discussed in an amalgamation of various emerging 6G technologies. Finally, we provide an overview of implementation challenges in NTN and potential solutions to support the standardization progression of NTN in upcoming releases of 3 rd Generation Partnership Project (3GPP).
The increasing deployment of Unmanned Aerial Vehicles (UAVs) for military, commercial, and logistics applications has raised significant concerns regarding flight path privacy. Conventional UAV communication systems often expose flight path data to third parties, making them vulnerable to tracking, surveillance, and location inference attacks. Existing encryption techniques provide security but fail to ensure complete privacy, as adversaries can still infer movement patterns through metadata analysis. To address these challenges, we propose a zk-SNARK (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge)-based privacy preserving flight path authentication and verification framework. Our approach ensures that a UAV can prove its authorisation, validate its flight path with a control centre, and comply with regulatory constraints without revealing any sensitive trajectory information. By leveraging zk-SNARKs, the UAV can generate cryptographic proofs that verify compliance with predefined flight policies while keeping the exact path and location undisclosed. This method mitigates risks associated with real-time tracking, identity exposure, and unauthorised interception, thereby enhancing UAV operational security in adversarial environments. Our proposed solution balances privacy, security, and computational efficiency, making it suitable for resource-constrained UAVs in both civilian and military applications.
Qichao Xu, Jie Jin, Zhou Su, Ruidong Li · 7 authors
Recently, edge content delivery has been promoted for video applications in unmanned aerial vehicle (UAV)-assisted vehicular networks (UVNs). UAVs could proactively cache (video) contents and transmit them to nearby vehicular users, thereby significantly mitigating delivery latency. However, since UAVs are typically deployed by untrusted third parties, the contents may be illicitly accessed by some curious and even malicious UAVs, compromising users' privacy. Besides, due to the limited resources, UAVs may be unwilling to deliver contents without adequate compensations. To address these issues, in this paper, we propose a novel secure edge content delivery scheme in UVNs. Specifically, we first devise a blockchain-based layered secure edge content delivery framework. With the scalable video coding (SVC) that encodes each content into a base layer and multiple enhancement layers, only the enhancement layers of the content are delivered by UAVs, ensuring that untrusted UAVs without the base layer cannot recover the content to access explicit information. Meanwhile, a lightweight consortium blockchain is utilized to supervise the enhancement layer delivery services of UAVs in a distributed fashion. The delegated proof of stake (DPoS)-based practical Byzantine fault tolerance (PBFT) consensus algorithm is designed to immutably and traceably record content delivery transactions between UAVs and vehicular users. Then, we formulate the content delivery incentive problem as a Stackelberg game, where UAVs act as game leaders to determine content delivery prices and vehicular users act as game followers to determine the number of required enhancement layers. Afterwards, through game analysis using the backward induction approach, the Stackelberg equilibrium is attained as the solution to the formulated problem, where the optimal strategies of both UAVs and vehicular users are derived by the Q-learning algorithm. Finally, extensive simulations are conducted to demonstrate that the proposed scheme can significantly enhance the security of delivered contents and efficiently motivate UAVs to cooperatively deliver contents.
Unmanned aerial vehicles (UAVs) are one of the most popular and effective systems in various industrial applications such as surveillance, security, and infrastructure inspection. It is gradually becoming an essential part of navigation as a consequence of high progress in military and civilian missions. Path planning of UAVs in military and civilian missions or in unknown and restricted environments is one of the biggest problems facing the operation of UAVs. This problem is not only searching for a path from an initial point to the final but also linked to find an optimal among all possible paths and provides collision avoidance. By examining the best path for UAVs, there is a need for the consideration of various other issues such as security and privacy, turning angle, overtake speed of obstacle, etc. The fundamental problem of UAVs is finding an optimal and secure route in a challenging environment. To overcome these challenges, many researchers have used optimization techniques such as ant colony, particle swarm, artificial bee colony, etc. with planning and coordination. In this paper, a blockchain-based solution is used to secure and authenticate UAVs. Hence, we propose a blockchain-based method that uses a genetic algorithm, which solves both constrained and unconstrained optimization problems. The purpose of this technique is to locate the best possible flight path for the UAVs in a three-dimensional setting. In a genetic algorithm, each iteration is designed to surpass the previous one in terms of improvement. To achieve an ideal route, solving the travelling salesman problem is a crucial step in the proposed approach. Consequently, the blockchain technology offers a reliable wireless communication and a dependable network for UAVs path planning, guaranteeing efficient service. Simulation results demonstrate the impact of the proposed scheme. They show that a genetic algorithm is suitable for optimal path planning for UAVs.