Blockchain Papers

Lijuan Liu, Muhammad Shafiq, Vijay Sonawane, Mantripragada Yaswanth Bhanu Murthy · 6 authors

No abstract is available for this record.

Xiaoqiang He, Qianbin Chen, Lun Tang, Weili Wang · 5 authors

Numerous resource-constrained Internet of Things (IoT) devices make the edge IoT consisting of unmanned aerial vehicles (UAVs) vulnerable to network intrusion. Therefore, it is critical to design an effective intrusion detection system (IDS). However, the differences in local data sets among UAVs show small samples and uneven distribution, further reducing the detection accuracy of network intrusion. This article proposes a conditional generative adversarial net (CGAN)-based collaborative intrusion detection algorithm with blockchain-empowered distributed federated learning to solve the above problems. This study introduces long short-term memory (LSTM) into the CGAN training to improve the effect of generative networks. Based on the feature extraction ability of LSTM networks, the generated data with CGAN are used as augmented data and applied in the detection and classification of intrusion data. Distributed federated learning with differential privacy ensures data security and privacy and allows collaborative training of CGAN models using multiple distributed data sets. Blockchain stores and shares the training models to ensure security when the global model’s aggregation and updating. The proposed method has good generalization ability, which can greatly improve the detection of intrusion data.

Geetanjali Rathee, Akshay Kumar, Chaker Abdelaziz Kerrache, Razi Iqbal

Abstract Smart cities equipped with intelligent devices can enhance the lifestyle and quality of humans by automatically and collaboratively acting as a sustainable resource to the ecosystem. In addition, the technological advancement can be further empowered by interconnecting various types of technologies, such as IoT, Artificial Intelligence, drones and robotics which will clearly improve the Quality of Services, energy efficiency and connectivity to the overall system. The integration of drones hovering over smart cities with the other devices in the smart city network brings a lot of benefits. However, it can also lead to various security and privacy concerns in the network. The aim of this article is to put forward a secure and safe smart city communication environment by proposing a trust establishment scheme for the ad hoc Unmanned Aerial Vehicles network. In which, malicious devices can be traced and blocked by analysing and evaluating their historical interactions within the system and calculating their trust values. A behaviour‐based and local trust value scheme is used to analyse the trust of each communicating device that is further associated with a blockchain distributed ledger. The proposed mechanism is measured over various networking and security metrics, including throughput, latency, accuracy and block updating compared to the existing state‐of‐the‐art solutions.

Abegaz Mohammed Seid, Hayla Nahom Abishu, Yasin Habtamu Yacob, Tewodros Alemu Ayall · 6 authors

With the Industrial Internet of Things (IIoT), mobile devices (MDs) and their demands for low-latency data communication are increasing. Due to the limited resources of MDs, such as energy, computation, storage, and bandwidth, IIoT systems cannot meet MDs’ quality of service (QoS) and security requirements. Recently, UAVs have been deployed as aerial base stations in the IIoT network to provide connectivity and share resources with MDs. We consider a resource trading environment where multiple resource providers compete to sell their resources to MDs and maximize their profit by continually adjusting their pricing strategies. Multiple MDs, on the other hand, interact with the environment to make purchasing decisions based on the prices set by resource providers to reduce costs and improve QoS. We propose a novel intelligent resource trading framework that integrates multi-agent deep reinforcement Learning (MADRL), blockchain, and game theory to manage dynamic resource trading environments. A consortium blockchain with a smart contract is deployed to ensure the security and privacy of the resource transactions. We formulated the optimization problem using a Stackelberg game. However, the formulated optimization problem in the multi-agent IIoT environment is complex and dynamic, making it difficult to solve directly. Thus, we transform it into a stochastic game to solve the dynamics of the optimization problem. We propose a dynamic pricing algorithm that combines the Stackelberg game with the MADRL algorithm to solve the formulated stochastic game. The simulation results show that our proposed scheme outperforms others to improve resource trading in UAV-assisted IIoT networks.

Saeed Hamood Alsamhi, Alexey V. Shvetsov, Alexey V. Shvetsov, Ammar Hawbani · 7 authors

The Internet of Drones (IoD) allows drones to collaborate safely while operating in a restricted airspace for numerous applications in Industry 4.0 world. Energy efficiency and sharing sensing data are the main challenges in swarm-drone collaboration for performing complex tasks effectively and efficiently in real-time. Information security of consensus achievement is required for multi-drone collaboration in the presence of Byzantine drones. Byzantine drones may be enough to cause present swarm coordination techniques to collapse, resulting in unpredictable or calamitous results. One or more Byzantine drones may lead to failure in consensus while exploring the environment. Moreover, Blockchain technology is in the early stage for swarm drone collaboration. Therefore, we introduce a novel blockchain-based approach to managing multi-drone collaboration during a swarm operation. Within drone swarms, blockchain technology is utilized as a communication tool to broadcast instructions to the swarm. This paper aims to improve the security of the consensus achievement process of multi-drone collaboration, energy efficiency, and connectivity during the environment’s exploration while maintaining consensus achievement effectiveness. Improving the security of consensus achievement among drones will increase the possibility and stability of multi-drone applications by improving connectivity and energy efficiency in the smart world and solving real environmental issues.

Abdullah Ayub Khan, Asif Ali Laghari, Thippa Reddy Gadekallu, Zaffar Ahmed Shaikh · 8 authors

No abstract is available for this record.

Anita Gehlot, Praveen Kumar Malik, Rajesh Singh, Shaik Vaseem Akram · 5 authors

An intelligent ecosystem with real-time wireless technology is now playing a key role in meeting the sustainability requirements set by the United Nations. Dairy cattle are a major source of milk production all over the world. To meet the food demand of the growing population with maximum productivity, it is necessary for dairy farmers to adopt real-time monitoring technologies. In this study, we will be exploring and assimilating the limitless possibilities for technological interventions in dairy cattle to drastically improve their ecosystem. Intelligent systems for sensing, monitoring, and methods for analysis to be used in applications such as animal health monitoring, animal location tracking, milk quality, and supply chain, feed monitoring and safety, etc., have been discussed briefly. Furthermore, generalized architecture has been proposed that can be directly applied in the future for breakthroughs in research and development linked to data gathering and the processing of applications through edge devices, robots, drones, and blockchain for building intelligent ecosystems. In addition, the article discusses the possibilities and challenges of implementing previous techniques for different activities in dairy cattle. High computing power-based wearable devices, renewable energy harvesting, drone-based furious animal attack detection, and blockchain with IoT assisted systems for the milk supply chain are the vital recommendations addressed in this study for the effective implementation of the intelligent ecosystem in dairy cattle.

Ashwin Verma, Pronaya Bhattacharya, Deepti Saraswat, Sudeep Tanwar · 6 authors

Recently, unmanned aerial vehicles (UAVs) are deployed in Novel Coronavirus Disease-2019 (COVID-19) vaccine distribution process. To address issues of fake vaccine distribution, real-time massive UAV monitoring and control at nodal centers (NCs), the authors propose SanJeeVni, a blockchain (BC)-assisted UAV vaccine distribution at the backdrop of sixth-generation (6G) enhanced ultra-reliable low latency communication (6G-eRLLC) communication. The scheme considers user registration, vaccine request, and distribution through a public Solana BC setup, which assures a scalable transaction rate. Based on vaccine requests at production setups, UAV swarms are triggered with vaccine delivery to NCs. An intelligent edge offloading scheme is proposed to support UAV coordinates and routing path setups. The scheme is compared against fifth-generation (5G) uRLLC communication. In the simulation, we achieve and 86% improvement in service latency, 12.2% energy reduction of UAV with 76.25% more UAV coverage in 6G-eRLLC, and a significant improvement of [Formula: see text]% in storage cost against the Ethereum network, which indicates the scheme efficacy in practical setups.

Qingqing Tang, Zesong Fei, Jianchao Zheng, Bin Li · 6 authors

The introduction of mobile edge computing (MEC) technology in unmanned aerial vehicle (UAV) networks can provide computing services for mobile users with or without communication infrastructure coverage. However, mobile users’ privacy may be leaked during the computation offloading process due to the information interaction between UAVs and the migration of computation tasks between mobile users and UAVs. To this end, we propose a secure aerial computing architecture that integrates MEC and blockchain technology for UAV networks to effectively ensure the security and privacy of computation offloading between UAVs and mobile users. Under this architecture, a problem of joint optimization of user association, UAV trajectory, block processor scheduling, and computation resource allocation is formulated to minimize the weighted sum of the energy consumption and the delay in completing computation tasks and blockchain tasks processing. To handle this intractable issue, we first decouple the optimization variables and then separate the original problem into multiple subproblems to be solved alternately. In addition, we design a block coordinate descent (BCD)-based algorithm for user association and computation resource allocation, and a successive convex approximation (SCA)-based algorithm to optimize the trajectories of UAVs. Simulation results show that the proposed algorithm has better performance.

Xiao Tang, Xunqiang Lan, Lixin Li, Yan Zhang · 5 authors

The Internet of Things (IoT) can be conveniently deployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the clustered IoT data collection with blockchain-based security provisioning. In particular, the UAVs generate candidate blocks based on the collected data, which are then audited through a lightweight proof-of-stake consensus mechanism within the UAV-based blockchain network. To motivate efficient blockchain while reducing the operational cost, a stake pool is constructed at the active UAV while encouraging stake investment from other UAVs with profit sharing. The problem is formulated to maximize the overall profit through the blockchain system in unit time by jointly investigating the IoT transmission, incentives through investment and profit sharing, and UAV deployment strategies. Then, the problem is solved in a distributed manner while being decoupled into two layers. The inner layer incorporates IoT transmission and incentive design, which are tackled with large-system approximation and one-leader-multi-follower Stackelberg game analysis, respectively. The outer layer for UAV deployment is undertaken with a multi-agent deep deterministic policy gradient approach. Results show the convergence of the proposed learning process and the UAV deployment, and also demonstrated is the performance superiority of our proposal as compared with the baselines.

Rui Xing, Zhou Su, Tom H. Luan, Qichao Xu · 6 authors

Vehicular networks which are paralyzed by natural disasters is faced with communication dilemma. Through building a decentralized communication network, unmanned aerial vehicles (UAVs) with high mobility and flexibility are expected to be the solution to the post-disaster vehicular networks. The blockchain technology has been widely used in UAV networks to provision the prompt security of distributed communications. However, existing works ignore the dynamics of the network in that due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blockchain transactions. The delay of synchronization can result in severe security issues. On addressing the issues, this paper proposes UAVs-aided blockchain offline transactions to ensure the security and effectiveness of delay-tolerant blockchain transactions when UAVs are offline. In specific, we consider vehicle-to-vehicle (V2V) charging transactions in post-disaster vehicular networks. By establishing offline channel between charging and discharging electric vehicles (EVs) by hashed time locked contract (HTLC), we design a UAVs aided penalty algorithm with accountable assertions to prevent deposit forging attacks and double-spending attacks. In addition, considering the selfishness of EVs, a Stackelberg game based incentive scheme is developed to encourage EVs to participate the offline transactions and to improve their trust values. By suing the mechanisms above, our proposal addresses the security of offline EV charging, as well as the selfishness of participant. Using extensive simulations, we demonstrate that the proposed scheme can realize secure transactions among EVs and can effectively improve the utilities of EVs through the comparison with conventional schemes.

Yawen Tan, Jiajia Liu, Nei Kato

Representing a new stage in the industrial value chain, Industry 4.0 paves the way to the future eco-systems of industrial innovation, where cyber-physical systems (e.g., smart machines) form its basis by bridging the physical and digital worlds. As one of the most popular groups in intelligent devices, unmanned aerial vehicles (UAVs) have shown great potential for facilitating various industrial sectors. With research on new applications flourishing, the accompanying communication security issue between UAVs is also of concern because of the vital role of communication in enabling the reliable performance of UAV networks. Authentication is recognized as the first defense line for guaranteeing communication security, but traditional authentication schemes are either highly dependent on the central authority or too costly for UAV networks, making them less feasible in practical applications. In this article, we propose a novel authentication scheme for UAV networks using blockchain technology, where blockchain edge nodes are introduced to maintain the ledger, while drones can just be clients to use the distributed blockchain services by calling smart contract APIs. Experimental results illustrate its efficiency, and future research directions are also summarized to guide explorations for realizing more general authentication systems for UAV-assisted industrial applications.

Cong Pu, Andrew Wall, Imtiaz Ahmed, Kim‐Kwang Raymond Choo

Thanks to rapid advancements in microprocessors, battery technologies, and lightweight materials, unmanned aerial vehicles (UAVs), commonly known as drones, have received signif-icant interest in the past few years. As drone-related commercial and civilian applications are flourishing, Internet-of-Drones (IoD) is moving into the fast lane and quickly becoming a highly anticipated network paradigm, where drones and Zone Service Providers (ZSPs) coordinate knowledge sharing in a reliable, accurate, and efficient way. However, for the sake of both strategic and financial value to business and mission critical applications, it is of vital importance to address both data security and privacy preservation issues brought by drones' inherent resource constraints and wide-open wireless medium. In this paper, we propose a secure data collection and storage mechanism, also called SecureIoD, for the IoD environment. In SecureIoD, drones and ZSPs first mutually authenticate each other and establish a secure session key before sharing any sensitive data via an insecure wireless channel. Then, ZSPs pack the collected data into blocks and compete to add their blocks into the blockchain. We also propose a joint Proof-of- Work (PoW) and Proof-of-Stake (PoS) consensus mechanism to select the miner ZSP, where the more transactions are in the block, the easier a ZSP can solve the cryptographic puzzle. We present security verification and analysis to show that SecureIoD can resist various security attacks. Finally, we develop a real-world testbed, implement SecureIoD and existing SDDM and BACSIoD schemes, and carry out extensive simulation experiments for performance evaluation and analysis. Experimental results reveal that not only does SecureIoD have lower computation cost, energy consumption, miner selection time, and communication overhead, but also offer better security features and capabilities.

M. Poongodi, Sami Bourouis, Ahmed Najat Ahmed, M. Vijayaragavan · 7 authors

No abstract is available for this record.

Rubina Akter, Mohtasin Golam, Van‐Sang Doan, Jae‐Min Lee · 5 authors

Unmanned aerial vehicle (UAV) contributes substantial strategic benefits on the Internet of Military Things (IoMT). However, the untrusted party’s misuse of the UAV may violate the security and even demolish the critical operation in the IoMT system. In addition, data manipulation and falsification using unauthorized access are the significant challenges of the IoMT system. In response to this problem, this study proposes a blockchain-integrated convolution neural network (CNN)-based intelligent framework named IoMT-Net for identification and tracking illegal UAV in the IoMT system. Blockchain technology prevents illicit access, data manipulation, and illegal intrusions, as well as stored data on the central control server (CCS). Concurrently, the proposed CNN analyzed the radio-frequency (RF) signal sent by the antenna array element to determine the Direction of Arrival (DoA) for the localization of the illegal UAV. Therefore, a signal model is designed to process the received signal array through IoMT-Net. Moreover, the proposed CNN model is designed with two different functional modules, such as the resource accuracy tradeoff (RAT) module and the unique feature extraction and accuracy boosting (UAB) module, by adopting depthwise and grouped convolution layers. These sparsely connected convolution layers offer high DoA estimation accuracy while maintaining the network more lightweight. In addition, the skip connection is also leveraged into the subunits of RAT and UAB modules for sharing features and handling the vanishing gradients problem. Based on the simulation results, the proposed network achieves superior DoA estimation accuracy (approximately 97.63% accuracy at 10-dB SNR) and outperforms other state-of-the-art models.

Prabhat Kumar, Randhir Kumar, Abhinav Kumar, A. Antony Franklin · 5 authors

Softwarized Unmanned Aerial Vehicles (UAVs) use network programmability concept of Software-Defined Network (SDN) to separate the hardware control layer from the data layer via OpenFlow protocols. The softwarized UAV enable ubiquitous connection, as well as a flexible, cost-effective, and improved method for upgrading all network services without shutting down the entire system. However, the connectivity of UAVs with OpenFlow switches and their heavy reliance on unsecured communication protocols makes the entire network vulnerable. This is a critical concern, particularly in combat surveillance, where eavesdropping, adding, changing, or deleting messages during communications between deployed UAVs and SDN controller is a possible threat. To mitigate the aforementioned issues, this paper presents a novel secure data sharing framework for softwarized UAV environments that incorporates blockchain and Deep Learning (DL). First we present a blockchain-based technique to reg-ister, verify and thereafter validate the communication entities in softwarized UAV environment using smart contract-based Proof-of-Authentication (PoA) consensus mechanism. Additionally, a new deep neural network architecture-based flow analyzer is designed to detect illegitimate transactions. The latter combines a Stacked Contractive Sparse AutoEncoder with Attention-based Long Short-term Memory Neural Network (SCSAE-ALSTM) to improve intrusion detection process. The effectiveness of our framework over several standard baseline methodologies is demonstrated by security analysis and experimental findings.

Zhoujie Wang, Runqun Xiong, Jiahui Jin, Chuan Liang

UAV Ad-hoc Network (UANET) has been widely used in many fields. However, the collaborative communication and data sharing among multiple UAVs in UANET are often attacked and threatened, due to the limited software and hardware capability of UAVs and the openness of wireless network environment. In this work, we introduce blockchain technology into UANET to enhance its security. Instead of directly adopting traditional blockchain which require huge storage, computation and communication resources, we propose a lightweight reputation-based blockchain scheme for resource-constrained UANET, named AirBC. Firstly, we present a lightweight storage strategy by elimination and compression to reduce storage overhead for UAV nodes. Secondly, we propose an improved reputation-enhanced Practical Byzantine Fault Tolerance (PBFT) consensus, as well as a reputation evaluation scheme based on reliable recording of UAV behaviors. In our scheme, UAVs with high reputation are selected into a miner committee to perform the consensus, thus improving efficiency. Meanwhile, the committee is updated at regular intervals to ensure scalability of UANET. Thirdly, we adopt a weighted proposal voting scheme to enhance the ability of group decision-making for UANET. Finally, to evaluate our approach, simulations are conducted and their results demonstrate that AirBC can reduce 63% storage overhead and 69% consensus latency on average for different scale UANET.

Ismaeel Al Ridhawi, Moayad Aloqaily, Fakhri Karray

The monumental growth in the frequency and sophistication of connected moving objects has led to a plethora of solutions to enable continuous, seamless, and reliable connectivity and service delivery. Connected, autonomous, and artificial intelligence (AI)-enabled Internet of Things (IoT) moving devices are playing a significant role in the establishment of reliable wireless connectivity infrastructures for Beyond 5G (B5G) telecommunication networks. The advancements in ad hoc communication and moving platforms are now being recognized as alternative solutions toward connectivity in many complex environments for applications, such as autonomous transportation and robotics. Today’s availability of AI-enabled moving devices, incorporating advanced processing and communication capabilities, together with advanced decentralization-enabling technologies, such as intelligent blockchain, provide alternatives to autonomy for moving platforms in smart cities. In this article, we discuss recent advances in moving IoT devices, such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs), that adapt intelligent connectivity support, data collection, decision making, and blockchain technology to facilitate autonomous configurability and service provisioning for IoT networks.

Muhammad Zawish, Nouman Ashraf, Rafay Iqbal Ansari, Steven Davy · 7 authors

6G envisions artificial intelligence (AI) powered solutions for enhancing the quality of service (QoS) in the network and to ensure optimal utilization of resources. In this work, we propose an architecture based on the combination of unmanned aerial vehicles (UAVs), AI, and blockchain for agricultural supply chain management with the purpose of ensuring traceability and transparency, tracking inventories, and contracts. We propose a solution to facilitate on-device AI by generating a roadmap of models with various resource-accuracy trade-offs. A fully convolutional neural network (FCN) model is used for biomass estimation through images captured by the UAV. Instead of a single compressed FCN model for deployment on UAVs, we motivate the idea of iterative pruning to provide multiple task-specific models with various complexities and accuracy. To alleviate the impact of flight failure in a 6G-enabled dynamic UAV network, the proposed model selection strategy will assist UAVs to update the model based on the runtime resource requirements.

Minghui Dai, Tianshun Wang, Yang Li, Yuan Wu · 6 authors

The wide use of unmanned aerial vehicles provides a promising paradigm for improving air-ground services and applications (e.g., urban sensing, disaster relief) in air-ground integrated networks (AGINs). Digital twin (DT), which is an emerging technology that utilizes data, models, and intelligent algorithms to integrate cyber physical networks and digital virtual models, provides a real-time and dynamic simulation platform for strategy optimization and decision making in AGINs. Due to the openness and massive connectivity of AGINs, the security and reliability services in this system become an important issue. In this article, we investigate the DT envisioned secure federated aerial learning for AGINs via an aerial blockchain approach. Specifically, we propose a layered framework of DT envisioned AGINs, which comprises the construction segment, communication segment, aggregation segment, analysis segment, and operation segment. Based on this framework, we offer the applications of the proposed DT envisioned AGINs. To guarantee the security of data transmission in AGINs, we investigate the aerial blockchain-based approach for ensuring data security. Furthermore, we provide a case study of DT envisioned secure federated aerial computing in AGINs to validate the effectiveness of the proposed approach through designing the aerial blockchain and training model.

Emad H. Abualsauod

No abstract is available for this record.

Anik Islam, Ahmed Al Amin, Soo Young Shin

This letter presents a federated learning-basd data-accumulation scheme that combines drones and blockchain for remote regions where Internet of Things devices face network scarcity and potential cyber threats. The scheme contains a two-phase authentication mechanism in which requests are first validated using a cuckoo filter, followed by a timestamp nonce. Secure accumulation is achieved by validating models using a Hampel filter and loss checks. To increase the privacy of the model, differential privacy is employed before sharing. Finally, the model is stored in the blockchain after consent is obtained from mining nodes. Experiments are performed in a proper environment, and the results confirm the feasibility of the proposed scheme.

Yawen Tan, Jiadai Wang, Jiajia Liu, Nei Kato

Unmanned aerial vehicles (UAVs) have shown great potential in benefiting industries due to their good features, such as the ease of deployment and low maintenance cost. However, the communication security issue remains a serious challenge before the large-scale application of industrial UAVs. The untrusted communication environment can cause the leakage of valuable industrial data or the losing of important cargos that carried by UAVs. Traditional authentication mechanisms for protecting communications include public-key infrastructure-based, ID-based, and certificateless authentication. These mechanisms rely on a central authority and some of them may introduce high-complexity computation that is not suitable for industrial drones. Therefore, aiming at these challenges, we design a blockchain-assisted distributed and lightweight authentication service for industrial UAVs. The blockchain technology supports the distributed and immutable storage of industrial UAVs’ authentication information, and smart contracts enable convenient operations for drones to acquire or update the corresponding information. Security evaluation demonstrates that our scheme is resistant to various attacks and can guarantee trustworthy communications for industrial drones. Extensive experiments also show that our designed authentication service can not only achieve low computation and communication cost for industrial UAVs but also remain robust even if a small proportion of drones are compromised.

Seongjoon Park, Hwangnam Kim

Simultaneous localization and mapping (SLAM) in unmanned vehicles, such as drones, has great usability potential in versatile applications. When operating SLAM in multi-drone scenarios, collecting and sharing the map data and deriving converged maps are major issues (regarded as the bottleneck of the system). This paper presents a novel approach that utilizes the concepts of distributed ledger technology (DLT) for enabling the online map convergence of multiple drones without a centralized station. As DLT allows each agent to secure a collective database of valid transactions, DLT-powered SLAM can let each drone secure global 3D map data and utilize these data for navigation. However, block-based DLT—a so called blockchain—may not fit well to the multi-drone SLAM due to the restricted data structure, discrete consensus, and high power consumption. Thus, we designed a multi-drone SLAM system that constructs a DAG-based map database and sifts the noisy 3D points based on the DLT philosophy, named DAGmap. Considering the differences between currency transactions and data constructions, we designed a new strategy for data organization, validation, and a consensus framework under the philosophy of DAG-based DLT. We carried out a numerical analysis of the proposed system with an off-the-shelf camera and drones.