Amit Kumar Mishra, Mohammad Wazid, Devesh Pratap Singh, Ashok Kumar Das · 6 authors
One of the most significant recent advances in technology is the advent of unmanned aerial vehicles (UAVs), i.e., drones. They have widened the scope of possible applications and provided a platform for a wide range of creative responses to a variety of challenges. The Internet of Drones (IoD) is a relatively new concept that has arisen as a consequence of the combination of drones and the Internet. The fifth-generation (5G) and beyond cellular networks (i.e., drones in networks beyond 5G) are promising solutions for achieving safe drone operations and applications. They may have many applications, like surveillance or urban areas, security, surveillance, retaliation, delivering items, smart farming, film production, capturing nature videos, and many more. Due to the fact that it is susceptible to a wide variety of cyber-attacks, there are certain concerns regarding the privacy and security of IoD communications. In this paper, a secure blockchain-enabled authentication key management framework with the big data analytics feature for drones in networks beyond 5G applications is proposed (in short, SBBDA-IoD). The security of SBBDA-IoD against multiple attacks is demonstrated through a detailed security analysis. The Scyther tool is used to perform a formal security verification test on the SBBDA-IoDâs security, confirming the systemâs resistance to various potential attacks. A detailed comparative analysis has identified that SBBDA-IoD outperforms the other schemes by a significant margin. Finally, a real-world implementation of SBBDA-IoD is shown to evaluate its effect on several measures of performance.
The unmanned aerial vehicle (UAV) equipped with mobile-edge computing (MEC) can act as an air base station to provide computing services for Artificial Intelligence of Things (AIoT) devices in remote areas. However, the computation offloading process poses a risk to usersâ privacy due to potential information leaks resulting from interactions between UAVs or migration of data between AIoT devices and UAVs. In this article, we proposed a secure aerial computing network that integrates MEC and blockchain technologies to effectively guarantee privacy and security during computation offloading between AIoT devices and UAVs. Additionally, taking into account task offloading scheduling, radio spectrum resource allocation, and computation resource allocation, a joint optimization problem is formulated to minimize the weighted sum of delay and energy consumption throughout the entire computing process. To tackle this issue, we proposed a block coordinate descent (BCD)-based algorithm to solve the mixed-integer and nonconvex problem. Simulation results demonstrate that the proposed algorithm surpasses other baseline approaches.
Unmanned aerial vehicles (UAVs) will be widely deployed due to their flexibility, mobility, and miniaturization, providing the necessary support for spectrum sharing between different communication systems in the spaceâairâground-integrated IoT network (SAGIN). However, there are potential security threats to spectrum sharing among different communication systems due to the openness of a wireless network, the unreliability of node behavior, and the trust barriers of the networks. In this article, a secure spectrum sharing scheme based on lightweight UAV-blockchain (LUBC) is proposed to address the above security issues. First, a spectrum sharing model based on the overlay mode is developed to improve the spectrum efficiency of SAGIN, where UAVs relay signals from the satellite to the ground users in exchange for spectrum access opportunities and serve their own users simultaneously in the nonorthogonal multiple access (NOMA) mode. Second, a secure spectrum sharing framework based on LUBC is proposed to solve the security and privacy issues of spectrum trading in SAGIN. Then, aiming at maximizing the primary userâs throughput under the premise of meeting the minimum power allocation factor of UAV network, the spectrum auction based on NOMA is formulated as a multirelay selection optimization problem, which is solved by the blockchain-based sequential Vickrey auction mechanism. Finally, the security evaluation and numerical results are conducted to verify the security and effectiveness of the proposed spectrum sharing scheme for SAGIN.
Chengzu Dong, Jingwen Zhou, Qi An, Frank Jiang · 7 authors
In recent years, edge-based intelligent UAV delivery systems have attracted significant interest from both the academic and industrial sectors. One key obstacle faced by these smart UAV delivery systems is data privacy, as they rely on vast amounts of data from users and UAVs for training machine learning models for person re-identification (ReID) purposes. To tackle this issue, federated learning (FL) has been extensively adopted as a promising solution since it only involves sharing and updating model parameters with a central server, without transferring raw data. However, traditional FL still suffers from the problem of having a single point of failure. In this study, we present a performance optimization method for federated person re-identification using benchmark analysis in blockchain-powered edge-based smart UAV delivery systems. Our method integrates a decentralized FL mechanism enabled by blockchain, which eliminates the necessity for a central server and stores private data on a decentralized permissioned blockchain, thus preventing a single point of failure. We employ the person ReID application in intelligent UAV delivery systems as a representative example to drive our research and examine privacy concerns. Additionally, we introduce the Federated Re-identification Consensus (FRC) protocol to address the scalability issue of the blockchain in supporting UAV delivery systems. The efficiency of our proposed method is illustrated through experiments on energy efficiency, confirmation time, and throughput. We also explore the effects of the incentive mechanism and analyze the systemâs resilience under various security attacks. This study offers valuable insights and potential solutions for addressing data privacy and security challenges in the fast-growing domain of smart UAV delivery systems.
The recently accentuated features of augmenting conventional wireless networks with high altitude platform systems (HAPS) have fueled a plethora of applications, which promise to offer new services to ground users, as well to enhance the efficiency and pervasion of existing applications. Cloud-enabled HAPS, which aims to create HAPS-based datacenters that offer cloud services to users, has particularly emerged as a promising key enabler to provide large-scale equitable services from the sky. Although offering cloud services from the HAPS proves to be efficient, its practical deployment at the stratosphere level still faces many challenges such as high energy requirements, physical maintenance, and is particularly prone to security considerations. Safeguarding the cloud-enabled HAPS against various cyberattacks is a necessity to guarantee its safe operation. This paper proposes a blockchain model to secure cloud-enabled HAPS networks that contain a large number of HAPS stations from recurring cyberattacks within the context of the environment and infrastructure monitoring (EIM) application. To this end, the paper first presents a detailed blockchain framework, and describes the ways of integrating the developed framework into the various system components. We then discuss the details of the system implementation, including the storing and consuming of cloud transactions, the generation of new blocks, and the blockchain consensus protocol that is tailored to the EIM requirements. Finally, we present numerical simulations that illustrate the performance of the system in terms of throughput, latency, and resilience to attacks.
Global warming and climate change are responsible for many disasters. Floods pose a serious risk and require immediate management and strategies for optimal response times. Technology can respond in place of humans in emergencies by providing information. As one of these emerging artificial intelligence (AI) technologies, drones are controlled in their amended systems by unmanned aerial vehicles (UAVs). In this study, we propose a secure method of flood detection in Saudi Arabia using a Flood Detection Secure System (FDSS) based on deep active learning (DeepAL) based classification model in federated learning to minimize communication costs and maximize global learning accuracy. We use blockchain-based federated learning and partially homomorphic encryption (PHE) for privacy protection and stochastic gradient descent (SGD) to share optimal solutions. InterPlanetary File System (IPFS) addresses issues with limited block storage and issues posed by high gradients of information transmitted in blockchains. In addition to enhancing security, FDSS can prevent malicious users from compromising or altering data. Utilizing images and IoT data, FDSS can train local models that detect and monitor floods. A homomorphic encryption technique is used to encrypt each locally trained model and gradient to achieve ciphertext-level model aggregation and model filtering, which ensures that the local models can be verified while maintaining privacy. The proposed FDSS enabled us to estimate the flooded areas and track the rapid changes in dam water levels to gauge the flood threat. The proposed methodology is straightforward, easily adaptable, and offers recommendations for Saudi Arabian decision-makers and local administrators to address the growing danger of flooding. This study concludes with a discussion of the proposed method and its challenges in managing floods in remote regions using artificial intelligence and blockchain technology.
The Internet of Drones (IoD) manages and coordinates communications between drones in Internet of Things (IoT) applications. Ensuring security and privacy in unmanned aerial vehicles (UAVs) networks, i.e., drones, is essential to protect data from cyber attacks. In this context, providing authentication is a major challenge due to the fact that drones are devices limited in power capabilities. The problem is aggravated by the dynamism of IoD networks due to the high mobility of drones, being sensitive to packet loss and handovers. Blockchain technology is attractive to address the problem of centralization of existing authentication protocols. In this article, we provide a decentralized, secure, and efficient authentication protocol, based on$\mu $Tesla, that relies on Blockchain to manage drone authentication. We analyze the security and performance of the proposed solution. Simulation results show that the proposed solution outperforms several approaches in the literature, achieving an authentication delay of less than 250 ms with a low information exchange of 1024 bits for 128-bit security level while maintaining low computational requirements.
Hui Xie, Jun Zheng, Teng He, Shengjun Wei · 6 authors
The advent of unmanned aerial vehicle (UAV) swarm technology brings possibilities to help humans complete tasks in no manâs land, such as deserts and rainforests. However, UAV network faces many cyber threats, where attackers can impersonate legitimate entities or tamper with UAV task data. For identity security, most of the existing methods use centralized authentication schemes, which have a single point of failure problem. For data security, the existing methods only secure the task data in the ground system, ignoring the data security in the air network. Therefore, the existing methods are not suitable for ubiquitous UAV scenarios. Blockchain secures data security while eliminating the single point of failure problem, and has been widely used in distributed scenarios. In this article, to secure entity identity and task data, we propose a blockchain-supported secure multi-UAV task management scheme (B-UAVM). Specifically, a three-layer blockchain structure is constructed to secure multitasks, and achieve ubiquitous control of UAV formations. Besides, six types of blocks and three types of transactions are designed to achieve safe processing and storage of task data and entity information. Furthermore, an improved practical byzantine fault tolerance (IPBFT) consensus mechanism and a UAV-formation-action-considered ground station consensus mechanism (UFAGS) are introduced in the Server Network and Ground Control Network, respectively, to accelerate the consensus. The experimental results show that the number of transactions generated per second (TPS) of B-UAVM is about$0.5\times $and$3.7\times $of the existing method when the block size or the number of blockchain nodes increases, respectively.
The advances in the Internet of Things (IoT) have provided generous opportunities for various applications, yet the data security concerns need to be specially addressed due to the limited capability of IoT devices. Toward this issue, we propose to deploy a blockchain system with practical Byzantine fault tolerance (PBFT) consensus for IoT data security provisioning. In particular, we conduct profound analyses regarding the PBFT procedure and elaborate on the computation burden therein. Then, an unmanned aerial vehicle (UAV) is leveraged to offload the computation and assist the blockchain operations. The system energy efficiency is introduced as the ratio of the blockchained IoT data amount and energy consumption for the computation and communication in the network. Further, we maximize the system energy efficiency by the joint design of offloading and transmission strategies, as well as the UAV deployment, within a reinforcement learning framework. Finally, simulation results are provided to corroborate the effectiveness of our proposal.
In 5G and B5G networks, real-time and secure resource allocation with the common telecom infrastructure is challenging. This problem may be more severe when mobile users are growing and connectivity is interrupted by natural disasters or other emergencies. To address the resource allocation problem, the network slicing technique has been applied to assign virtualized resources to multiple network slices, guaranteeing the 5G-RAN quality of service. Moreover, to tackle connectivity interruptions during emergencies, UAVs have been deployed as airborne base stations, providing various services to ground networks. However, this increases the complexity of resource allocation in the shared infrastructure of 5G-RAN. Therefore, this paper proposes a dynamic resource allocation framework that synergies blockchain and multi-agent deep reinforcement learning for multi-UAV-enabled 5G-RAN to allocate resources to smart mobile user equipment (SMUE) with optimal costs. The blockchain ensures the security of virtual resource transactions between SMUEs, infrastructure providers (InPs), and virtual network operators (VNOs). We formulate a virtualized resource allocation problem as a hierarchical Stackelberg game containing InPs, VNOs, and SMUEs, and then transform it into a stochastic game model. Then, we adopt a Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm to solve the formulated problem and obtain the optimal resource allocation policies that maximize the utility function. The simulation results show that the MADDPG method outperforms the state-of-the-art methods in terms of utility optimization and quality of service satisfaction.
C. Suganthi Evangeline, Ashmiya Lenin, Vinoth Babu Kumaravelu
In a smart city environment, Intelligent Transportation System (ITS) enables the vehicle to generate and communicate messages for safety applications. There exists a challenge where the integrity of the message needs to be verified before passing it on to other vehicles. There should be a provision to motivate the honest vehicles who are reporting the true event messages. To achieve this, traffic regulations and event detections can be linked with blockchain technology. Any vehicle violating traffic rules will be issued with a penalty by executing the smart contract. In case any accident occurs, the vehicle nearby to the spot can immediately send the event message to Unmanned Aerial Vehicle (UAV). It will check for its credibility and proceed with rewards. The authenticity of the vehicle inside the smart city area is verified by registering itself with UAVs deployed near the city entrance. This is enabled to reduce the participation of unauthorized vehicles inside the city zone. The Secure Hash Algorithm (SHA256) and Elliptic Curve Digital Signature Algorithm (ECDSA-192) are used for communication. The result of computation time for certificate generation and vehicles involvement rate is presented.
Abdullah Aljumah, Tariq Ahamed Ahanger, Imdad Ullah
Unmanned aerial vehicles, drones, and internet of things (IoT) based devices have acquired significant traction due to their enhanced usefulness. The primary use is aerial surveying of restricted or inaccessible locations. Based on the aforementioned aspects, the current study provides a method based on blockchain technology for ensuring the safety and confidentiality of data collected by virtual circuit-based devices. To test the efficacy of the suggested technique, an IoT-based application is integrated with a simulated vehicle monitoring system. Pentatope-based elliptic curve encryption and secure hash algorithm (SHA) are employed to provide anonymity in data storage. The cloud platform stores technical information, authentication, integrity, and vehicular responses. Additionally, the Ethbalance MetaMask wallet is used for BCN-based transactions. Conspicuously, the suggested technique aids in the prevention of several attacks, including plaintext attacks and ciphertext attacks, on sensitive information. When compared to the state-of-the-art techniques, the outcomes demonstrate the effectiveness and safety of the suggested method in terms of operational cost (2.95 units), scalability (14.98 units), reliability (96.07%), and stability (0.82).
In the next generation modernization plan, the automatic dependent surveillance-broadcast (ADS-B) system plays a pivotal role. However, the ADS-Bâs low level of security and its vulnerabilities have raised valid concerns. The main objectives of this paper are to highlight the limitations of legacy ADS-B systems and to assess the feasibility of using Format-preserving (F), Feistel-based encryption (F), with multiple implementation variances (X) (FFX) algorithms, for enhancing ADS-Bâs security. The offered solution is implemented in a standard software-defined radio (SDR) ADS-B to be utilized in real-time applications. Furthermore, a new proposed blockchain scheme is used as a secured database to manage the cipher key. The metric of message entropy is used to assess an algorithmâs ability to confuse and diffuse predictable ADS-B messages; correlation and serial correlation of plain data and cipher data are deployed to evaluate the proposed methodâs security level. The authors provide both MATLAB simulations and flight test outcomes to demonstrate the feasibility of this approach. Based on our security analysis, ADS-B information can be kept confidential through our scheme. The performance evaluation results reveal that the proposed scheme is achievable, compatible, and efficient for the avionics industry.
The unmanned aerial vehicle (UAV) swarm is widely applied in many fields, such as disaster relief and geological exploration. However, it is highly vulnerable when facing an attack from internal malicious UAVs due to the underlying disconnected, intermittent, and limited (DIL) network environment. Recently, building trusted networks has been considered as a strategy to address security issues. Nevertheless, deploying a trusted network in a swarm faces many challenges imposed by the dynamic topology and limited resources of DIL networks. In order to overcome these challenges, we developed a blockchain-based UAV trusted self-organizing networking mechanism (BC-UTSON) to enhance internal security. Three key technologies are proposed for assisting BC-UTSON in achieving trustworthiness. UAV practical Byzantine fault tolerance (U-PBFT) is proposed to guarantee the lightweight consensus and real-time full-node trustworthiness evaluation under the hierarchical self-organizing networking structure of a UAV swarm. A blockchain-based multiweighted subjective logic (BMWSL) scheme has been designed to identify malicious UAV nodes based on the trustworthiness evaluation, which also assists the trusted path quality-aware dynamic routing (TPDR) mechanism to prevent data from routing through malicious nodes. The experimental results show that the proposed BC-UTSON performs much better than nonblockchain-based mechanisms for the UAV swarm.
With the continuous development of communication technology, drones are playing an important role in many fields, such as power transmission line inspection and agricultural pesticide spraying. In order to protect the data privacy and communication security of drones, many experts are considering blockchain as its enabling technology. However, due to their small size and limited power storage, drones cannot support energy-intensive blockchain applications. In addition, the future 6G communications need to implement an important key performance indicator, namely extremely low-power communications (ELPCs). As a consequence, research into green blockchain is becoming more and more popular. The broadcast of the blockchain is one of the most energy-intensive parts because it entails flooding and there are a lot of unnecessary communication processes. Therefore, in order to make blockchain more suitable for ELPC requirements in 6G communications and unmanned aerial vehicle (UAV) networks, we took the blockchain broadcast as an improvement candidate and designed LECast, a low-energy-consumption protocol. LECast first analyzes the energy consumption model of the communication between two drones and constructs the shortest-path broadcast tree (SPB Tree) for the UAV networks to minimize energy consumption. Meanwhile, to make the sending drone address the receiving drone in a more convenient way, we proposed an extended Huffman coding (EHC) scheme to name the drones. Furthermore, the other issues with the broadcast tree are reliability and security. When a channel fails, subsequent drones cannot smoothly receive the transaction or block data. As a result, we introduced multichannel transmission with splitting data (MTSD); that is, the transaction or block data are divided into segments and transmitted in parallel multiple times over multiple channels. Finally, through the analysis and simulation of LECast in terms of energy consumption, latency, throughput, reliability, security, and coverage rate, the advantages of LECast were confirmed, which could meet the requirements of ELPCs and be well applied to UAV networks.
Unmanned-aerial-vehicle (UAV)-enabled intelligent transportation system (ITS) is an advanced technology that can provide various services including autonomous driving, real-time creation of high-definition maps, and car sharing. In particular, a UAV-enabled ITS can be realized through the combination of traditional vehicular ad hoc networks (VANETs) and UAVs that can act as flying roadside units (RSUs) at the outskirts and monitor road conditions from predefined locations to spot car accidents and any law violations. Notably, to realize these services, real-time communication between UAVs and RSUs must be guaranteed. However, UAVs have limited computing powers, and if extensive computation is required during communication, the provision of real-time ITS services may be hindered. Furthermore, UAVs and RSUs communicate via public channels that are prone to various attacks, such as replay, impersonation, trace, and session key disclosure attacks. Thus, in this article, a secure and lightweight authentication scheme is proposed for UAVs and RSUs using the blockchain technology. The proposed scheme is analyzed using informal and formal methods including BurrowsâAbadiâNikoogadam (BAN) logic, automated validation of internet security protocols and applications (AVISPA) simulation tool, and realâorârandom (RoR) model, and its performance is compared with that of related schemes. The results reveal that the proposed scheme is more efficient and secure as compared to the other competing schemes.
Sana Hafeez, Ahsan Raza Khan, Mohammad Al-Quraan, Lina Mohjazi · 7 authors
Unmanned aerial vehicles (UAVs) have recently established their capacity to provide cost-effective and credible solutions for various real-world scenarios. UAVs provide an immense variety of services due to their autonomy, mobility, adaptability, and communications interoperability. Despite the expansive use of UAVs to support ground communications, data exchanges in those networks are susceptible to security threats because most communication is through radio or Wi-Fi signals, which are easy to hack. While several techniques exist to protect against cyberattacks. Recently emerging technology blockchain could be one of promising ways to enhance data security and user privacy in peer-to-peer UAV networks. Borrowing the superiorities of blockchain, multiple entities can communicate securely, decentralized, and equitably. This article comprehensively overviews privacy and security integration in blockchain-assisted UAV communication. For this goal, we present a set of fundamental analyses and critical requirements that can help build privacy and security models for blockchain and help manage and support decentralized data storage systems. The UAV communication system's security requirements and objectives, including availability, authentication, authorization, confidentiality, integrity, privacy, and non-repudiation, are thoroughly examined to provide a deeper insight. We wrap up with a discussion of open research challenges, the constraints of current UAV standards, and potential future research directions.
Sur Singh Rawat, Youseef Alotaibi, Nitima Malsa, Vimal Gupta
Unmanned aerial vehicles (UAVs), or drones, have revolutionized a wide range of industries, including monitoring, agriculture, surveillance, and supply chain. However, their widespread use also poses significant challenges, such as public safety, privacy, and cybersecurity. Cyberattacks, targeting UAVs have become more frequent, which highlights the need for robust security solutions. Blockchain technology, the foundation of cryptocurrencies has the potential to address these challenges. This study suggests a platform that utilizes blockchain technology to manage drone operations securely and confidentially. By incorporating blockchain technology, the proposed method aims to increase the security and privacy of drone data. The suggested platform stores information on a public blockchain located on Ethereum and leverages the Ganache platform to ensure secure and private blockchain transactions. The MetaMask wallet for Ethbalance is necessary for BCT transactions. The present research finding shows that the proposed approachâs efficiency and security features are superior to existing methods. This study contributes to the development of a secure and efficient system for managing drone operations that could have significant applications in various industries. The proposed platformâs security measures could mitigate privacy concerns, minimize cyber security risk, and enhance public safety, ultimately promoting the widespread adoption of UAVs. The results of the study demonstrate that the blockchain can ensure the fulfillment of core security needs such as authentication, privacy preservation, confidentiality, integrity, and access control.
Unmanned Aerial Vehicles (UAV), often referred to as drones, have the potential to revolutionize the healthcare industry. They offer speed and accessibility in delivering medical supplies, making them the superior mode of transport for inaccessible and remote areas. Though drone-based systems are advantageous, there is a need for an end-to-end system that could provide a secure platform to use drones in a flawless manner to perform transactions. Drones are vulnerable to hacking and data interception, which may lead to the exfiltration of collected data by malicious users. In this study, MedUAV, a prototype for a drone-based decentralized autonomous emergency medicine supply system, is proposed. It leverages the security aspects of blockchain and smart contracts. The proposed system is deployed and tested in the Polygon Main network, and significant conclusions about the performance of the system are discussed.
Livestock monitoring often requires human supervision to guide farm animals to a specific point and the displacement of workers to the places where these animals are, which is likely to be several kilometers away, thus resulting in a repetitive task that requires a significant amount of time and demands the usage of land vehicles capable of moving swiftly through the countryside. In addition to that, data collection about animal behaviour with such procedures is often insufficient and cannot be shared in a secure enough manner. This paper describes how Using Unmanned Aerial Vehicles (UAVs) tailored for this kind of task, when combined with other protocols and software technologies, can provide a useful to mitigate these issues. To prove this end, a functional prototype has been designed, built and tested, offering the operator accurate monitoring of farm facilities and animals. Additionally, security has been conceived as a cornerstone of the presented system from the very beginning. Not only the communication protocols used for this purpose have built-in security layers, but also InterPlanetary File System (IPFS) and blockchain have been used as the technologies that enhance data storage among peers in a network.
Qianqian Pan, Jun Wu, Ali Kashif Bashir, Jianhua Li · 6 authors
With the capability of establishing line-of-sight (LoS) links for devices, drones are generally utilized as aerial base stations to construct coexisting drone-terrestrial networks (CDTNs) for wireless communication. However, the established LoS links are easily blocked, thereby severely decreasing transmission performance. The intelligent reflecting surface (IRS) is a promising technology to improve data transmission in the CDTN by programming propagation channels. However, secure IRS reflection resource allocation is still an open issue. Existing IRS resource allocation methods are mainly based on a centralized third party and are vulnerable to the single point of failure. Furthermore, intelligent allocation of IRS reflection resources is also a key issue. To solve these problems, we propose a blockchain and artificial intelligence (AI) enabled configurable reflection resource allocation approach for the IRS-aided CDTN. First, we establish the IRS-aided communication framework for the CDTN, where a drone-mounted IRS is introduced to improve spatial freedom for data transmission. Second, the blockchain-based reflection resource management mechanism is proposed. In this mechanism, we design allocation transactions, the hierarchical blockchain structure, and smart-contract-enabled resource trading. Third, the AI-based reflection resource allocation mechanism is proposed, including the intelligent reflection elements assignment and deep-reinforcement-learning-driven reflection coefficient configuration. Furthermore, experimental results verify the effectiveness of our proposed approach. Finally, open issues and key challenges of the proposed approach are discussed.