Blockchain-based applications come up with cryptocurrencies, especially Bitcoin, introducing a distributed ledger technologies for peer-to-peer networks and essentially records the transactions in blocks containing hash value of the previous blocks. Block generation constitutes the basis of this technology, and the optimization of such systems is among the most crucial concerns. Determining either the block size or the number of transactions in the block brings out a remarkable problem that has been solved by the miners in recent years. First, higher block size results in higher transaction time, on the other hand, smaller block size has many disadvantages such as security, lower transaction fees, lower transaction numbers in a given time interval, which makes it unable to compete with other currency systems due to this bulky structure and higher block generation time. In this study, multiobjective optimization problem (OP) is proposed by minimizing block generation and transmission time. This multiobjective OP is transformed into a single OP by applying weighted sum method. To determine the optimal block size, particle swarm optimization (PSO) algorithm and whale optimization evolutionary algorithm (WOA) are employed. Although both algorithms have capability to reach optimum block size and corresponding time, WOA achieves better performance than PSO in terms of the convergence speed and output fluctuation. Moreover, analysis of the prediction of optimum block size is carried out under different weights which creates many optimization functions. Experimental results indicate that if higher weight is assigned to the transmission time, then block size decreases sharply. Furthermore, the experimental results reveal that design of the blockchain network and number of nodes in network profoundly affect the block size selection due to the time constraints.
Most of the blockchain-based identity authentication systems are based on public blockchain, which are still essentially traditional centralized identity management and verification methods, making it difficult to meet the needs of trusted access and fine-grained access control in microgrids. Therefore, based on the FISCO BCOS consortium blockchain technology, a distributed identity authentication system supporting multi-center was designed. A DID-based identity management protocol to achieve autonomous control of user identity in a practical scenarios was designed. Distributed trusted access technology for end nodes in microgrids was studied, and privacy-protecting credentials based on zero-knowledge proof were designed. This scheme meets the requirements of trustworthy and verifiable user identity in different privacy security scenarios, and achieves autonomous control of entity identity, fine-grained access control and trusted data exchange. The usability and effectiveness of the proposed algorithm are demonstrated through system experiments and performance analysis.
Abbas Yazdinejad, Elnaz Rabieinejad, Ali Dehghantanha, Reza M. Parizi · 5 authors
With the advancement of information and communication technology, Unmanned Aerial Vehicles (UAV), popularly known as drones, have also increased. The drones have been noted for their wide range of applications such as military, search and rescue operation, disaster detection and monitoring, agriculture, and delivery. Each type of drone has different characteristics and functionality based on its application, making them a security threat for some city zone. Therefore, there is an essential need for efficient drone management based on their type and application in different zones. To do this, we proposed a Machine learning (ML) based Software Defined Network (SDN) drone management framework. In this framework, the SDN controller uses ML with the drone’s radio frequency feature to detect its type and application and, according to its application, authenticate it and assign communication rules. SDN controller records authentication information in a DAG-based Distributed Ledger Technology (DLT) available for other SDN controllers. When a drone desires to migrate to another zone, the destination SDN controller can achieve authentication information by referring to DAG-based DLT, and there is no need for re-authentication. The experimental result shows authentication delay reduction in our proposed framework. Moreover, we adopted ML algorithms includes Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR), to evaluate our proposed framework in drone’s type classification. The result shows that the RF algorithm shows the best performance with 92.81% accuracy in the classification of the drone’s type.
Zhaolong Ning, Handi Chen, Xiaojie Wang, Shupeng Wang · 5 authors
The maturity of the 5th-Generation (5G) communication technology promotes a new round of industrial revolution and supports the high-quality development of economic society. However, owing to the scarce communication resources, costly labor and complex geographic environment, inspecting and maintaining electrical faults accurately and timely in a remote grid is rather challenging. To solve this problem, we comprehensively consider secure and efficient signal transmissions to construct an automatic grid fault inspection system and formulate a multi-objective optimization problem. Due to its complexity, we decompose it into two sub-problems, propose ablockchain-enabledsecuretransmission scheme (BEST) and animprovedmarketmatching (IMM) algorithm, correspondingly. Considering the latency magnitude difference between blockchain verification and intra-domain transmission, the BEST scheme integratesdeepreinforcement learning-basedimprovedproximal policy optimization training algorithm (DRIP) andA*-basedbi-objective multi-destinationoptimization algorithm (ABOO) to achieve the intra-domain secure transmission. Based on the real city topology and the YouTube video service data statistics, our algorithms can optimize the network performance while guaranteeing the security of signal transmissions.
Chaosheng Feng, Bin Liu, Keping Yu, Sotirios K. Goudos · 5 authors
Motivated by Industry 4.0, 5G-enabled unmanned aerial vehicles (UAVs; also known as drones) are widely applied in various industries. However, the open nature of 5G networks threatens the safe sharing of data. In particular, privacy leakage can lead to serious losses for users. As a new machine learning paradigm, federated learning (FL) avoids privacy leakage by allowing data models to be shared instead of raw data. Unfortunately, the traditional FL framework is strongly dependent on a centralized aggregation server, which will cause the system to crash if the server is compromised. Unauthorized participants may launch poisoning attacks, thereby reducing the usability of models. In addition, communication barriers hinder collaboration among a large number of cross-domain devices for learning. To address the abovementioned issues, a blockchain-empowered decentralized horizontal FL framework is proposed. The authentication of cross-domain UAVs is accomplished through multisignature smart contracts. Global model updates are computed by using these smart contracts instead of a centralized server. Extensive experimental results show that the proposed scheme achieves high efficiency of cross-domain authentication and good accuracy.
S. Velliangiri, Rajesh Manoharan, R. Sitharthan, Vani Rajasekar
With the global rollout of fifth-generation communication networks, the development on sixth-generation (6G) communication network has begun. The 6G technology is an emerging technology that will meet the ever-increasing demands of evolving industrial services and applications. This technology consists of varied number of heterogeneous resources and communication protocols to guarantee the seamless access to the services. Because of its heterogeneous nature and authorization delegation, security is a major concern in 6G communication environment. The emerging blockchain technology can able to solve the aforementioned privacy and security challenges. The blockchain technique also offers enhanced services like openness, decentralization, immutability, trust free, and so on. Motivated by the aforementioned facts, this article proposes a novel idea of integration of privacy preserving blockchain framework with 6G communication network. We also propose an integrated system for blockchain radio access network as a reliable and stable model for 6G networking based on blockchain technology with improved efficiency and protection. Furthermore, the critical components of blockchain such as smart contract, consensus protocol, mathematical model, secure data sharing, and auditing are clearly defined and experimental results are analyzed.
In view of the problems of low security, poor reliability, inability to backup automatically, and overreliance on the third party in traditional microgrid data disaster backup schemes based on cloud backup, the edge computing is used to preprocess power big data, and a microgrid data disaster backup scheme based on blockchain in edge computing environment is proposed in this paper. First, the honey encryption (HE) technology and advanced encryption standard (AES) are combined to propose a new encryption algorithm HE-AES, which is used to encrypt the preprocessed data. Second, the Kademlia algorithm is embedded in the edge server to realize the distributed storage and automatic recovery of microgrid data. Finally, the traditional proof of authority (PoA) consensus mechanism is improved partially, and the improved PoA is used to make each node reach consensus and pack blocks on the chain. The scheme can not only realize the data disaster backup automatically but also has high efficiency of data processing, which can provide a new idea for improving the current data disaster backup schemes.
Abstract In order to improve the revenue of attacking mining pools and miners under block withholding attack, we propose the miner revenue optimization algorithm (MROA) based on Pareto artificial bee colony in blockchain network. MROA establishes the revenue optimization model of each attacking mining pool and revenue optimization model of entire attacking mining pools under block withholding attack with the mathematical formulas such as attacking mining pool selection, effective computing power, mining cost and revenue. Then, MROA solves the model by using the modified artificial bee colony algorithm based on the Pareto method. Namely, the employed bee operations include evaluation value calculation, selection probability calculation, crossover operation, mutation operation and Pareto dominance method, and can update each food source. The onlooker bee operations include confirmation probability calculation, crowding degree calculation, neighborhood crossover operation, neighborhood mutation operation and Pareto dominance method, and can find the optimal food source in multidimensional space with smaller distribution density. The scout bee operations delete the local optimal food source that cannot produce new food sources to ensure the diversity of solutions. The simulation results show that no matter how the number of attacking mining pools and the number of miners change, MROA can find a reasonable miner work plan for each attacking mining pool, which increases minimum revenue, average revenue and the evaluation value of optimal solution, and reduces the spacing value and variance of revenue solution set. MROA outperforms the state of the arts such as ABC, NSGA2 and MOPSO.
With the development of terminal technology and the expansion of application fields, the Internet of Things’ application value and service requirements continue to increase. Efficient data transmission is a reliable guarantee for the development and application of the Internet of Things. Blockchain technology provides a solution for storing and delivering distributed data. On this basis, taking the Industrial Internet of Things as the research object, a blockchain-based data transmission optimization method was established. First, an undirected complete graph model is used to describe the network scene. A matrix grid model is used to replace the randomly distributed set of data nodes. Then, a double optimization method is proposed. We designed the mathematical description and modeling method of the lattice matching decision problem and designed the artificial neural network to find the optimal solution to the problem. Finally, an example is used to verify the government data transmission method’s technical performance and packet loss rate. It has achieved at least 20% and 30% improvements in optimizing the network life of static aggregation nodes and data transmission, respectively. While improving the robustness of the network, it also shows a stable advantage in terms of network energy efficiency indicators.
At present, the supply chain finance industry has been in a vigorous development trend, but its development is still hindered by the reasons such as information opacity and information asymmetry. This paper mainly studies the application of blockchain technology in supply chain finance for Beibu Gulf region. According to the industry characteristics of supply chain finance, the blockchain technology is integrated with supply chain finance to construct the supply chain finance alliance architecture based on the blockchain technology and the underlying model and composition technology of Ethereum blockchain system applicable to supply chain finance. Considering the actual operation situation of supply chain finance platform blockchain, using the principal-agent model and incentive theory, supply chain finance accounts receivable mode, for example, the design blockchain financial platform service provider with the core enterprises of supply chain between the incentive mechanism, promote blockchain technology and supply chain finance better ground test, solve the current financial supply chain development bottleneck.
Jun Feng, Laurence T. Yang, Yuxiang Zhu, Nicholaus J. Gati · 5 authors
Deep learning techniques have shown significant success in cyber-physical-social systems (CPSS). As an instance of deep learning models, generative adversarial nets (GAN) model enables powerful and flexible image augmentation, image generation, and classification, thus can be applied to real-world CPSS settings. GAN model training needs a large collection of cyber-physical-social data originating from various CPSS devices. Numerous prevailing GAN models depend on a tacit assumption that several cyber-physical-social data providers present a reliable source to collect training data, which is seldom the case in real CPSS. The existing GAN models also fail to consider multi-dimensional latent structure. In our work, we put forward a novel blockchain-enabled tensor-based conditional deep convolutional GAN (TCDC-GAN) model for cyber-physical-social systems. The blockchain is employed to develop a decentralized and reliable cyber-physical-social data-sharing platform between numerous cyber-physical-social data providers, such that the training data and the model are documented on a ledger that is distributed. Furthermore, a tensor-based generator and a tensor-based discriminator are well designed by employing the tensor model. The results of extensive simulation experiments show the efficacy of the proposed TCDC-GAN model. Compared with the state-of-the-art models, our model gains superior estimation performance.
Generative Adversarial Networks and Image Synthesis
At present, real estate markets hold and contribute a fair share of the Gross Domestic Product (GDP) of any country. The real estate transactions generate revenue in stamp duties, which goes directly into the government kitty. However, this suffers from land document forgery and fraudulent activities due to a lack of an efficient, distributed, and completely digital system. This paper proposes a blockchain-based framework for digitizing property transactions that mitigate the risk of document forgery and other fraudulent activities. Over time, the amount of these transactions can become overwhelming, thus increasing the number of blocks. The proposed framework is based on blockchain technology to decentralize the complete ecosystem. The framework encompasses all major activities for property transactions. The framework uses the InterPlanetary File System (IPFS) that is a Peer-to-Peer (P2P) swarm network, to integrate different region registry offices in the state/country seamlessly. In order to make it secure, a consensus algorithm is proposed that reduces overhead transmissions for multicasting nodes by about 50%. The message exchange communication overhead also reduces by 54.86%. The time taken for the consensus algorithm is around 53.7% lesser than the Proof-of-Work mechanism in which all nodes participate in consensus and 10% lesser than the existing load-based approach.
Summary A secure decision tree twin support vector machine (DT‐TSVM) multi‐classification algorithm has been proposed in this paper for improving the reliability and security of the collected IoT data from multiple data providers. The multiclass secure DT‐TSVM algorithm has been employed to train a machine learning model using the encrypted training dataset. The training dataset is collected via a blockchain platform. A blockchain method has been adopted to construct a secure and reliable distributed platform among dataset providers. The Paillier homomorphic cryptosystem has been applied for encrypting the IoT dataset. Then, the dataset has been recorded on the distributed ledger. The secure DT‐TSVM algorithm's‐based train model effectiveness has been compared with the other two available algorithms, namely the multiclass binary support vector machine (MBSVM) and one‐to‐one SVM algorithms. The experiment results showed that the privacy‐preserving multiclass secure DT‐TSVM‐based model did not reduce the accuracy, but it increased the average precision and recall by 0.53% and 0.44% than MBSVM and 0.82% and 0.71% than one‐to‐one SVM, respectively. Further, the time consumption of data providers and data analysts did not change significantly with the increase of number of data provider.
Nisita Weerasinghe, Tharaka Hewa, Maheshi B. Dissanayake, Mika Ylianttila · 5 authors
Local 5G Operator (L5GO) concept is one of the most prominent versatile applications of the 5G in the near future. The popularity of L5GOs will trigger a greater number of roaming and offloading events between mobile operators. However, existing static and the operator-assisted roaming and offloading procedures are inefficient for L5GO ecosystem due to poor service quality, data privacy issues, data transferring delays, excessive costs for intermediary parties and existence of roaming fraud. To address these challenges, we propose a blockchain / Distributed Ledger Technology (DLT) based service platform for L5GOs to facilitate efficient roaming and offload services. As the key contribution, blockchain-based smart contract scheme is proposed to establish dynamic and automated agreements between operators. By using smart contracts, we introduce several novel features such as universal wallet for subscribers, service quality based L5GO rating system, user-initiated roaming process and the roaming fraud prevention system to improve the operational quality of a L5GO. A prototype of the proposed platform is emulated with the Ethereum blockchain platform and Rinkeby Testnet to evaluate the performance and justify the feasibility of the proposal. Upon an extensive evaluation on the prototype, it was observed that the proposed platform offered benefits such as cost effective, more secure and reliable experience.
Internet of Everything (IoE) has emerged as a promising paradigm for the purpose of connecting and exchanging data among physical objects and humans over the Internet, and it can be widely applied in the fields of industry, transportation, commerce, and education. Recently, the emergence of 6G-enabled cybertwin network architecture provides the technical and theoretical foundation for the realization of IoE paradigm. However, the IoE has three open issues in the 6G-enabled cybertwin architecture, i.e., data authenticity, data storage and node reliability. To address these issues, we propose a blockchain-based decentralized reputation management system (BC-DRMS) for IoE in 6G-enabled Cybertwin architecture. In the proposed BC-DRMS, the traffic data collected from end nodes is stored on the blockchain and the decentralized file system, i.e., InterPlanetary File System (IPFS), to resist data tampering, and then the data is further processed by the edge clouds and core clouds to provide services to users. Also, a multi-level reputation evaluation scheme is designed to compute the reputation scores of IoE nodes to prevent malicious node attacks. The experiment results and analysis demonstrate that, compared to the traditional centralized reputation management systems (CRMS), the proposed BC-DRMS cannot only address the issues of data authenticity and storage, but also provides high reliability for IoE in 6G-enabled cybertwin architecture.
The fifth-generation (5G) wireless communication technology enables high-reliability and low-latency communications for the Intelligent Transportation System (ITS). However, the growingly sophisticated attacks against 5G-enabled ITS (5G-ITS) might cause serious damages to the valuable data generated by various ITS applications. Therefore, establishing a secure 5G-ITS through trust evaluation against potential threats has become a key objective. Furthermore, as a distributed shared ledger and database, Blockchain has the characteristics of non-tampering, traceability, openness and transparency, can support both trust storage and trust verification for trust evaluation. In this paper, we propose a heterogeneous Blockchain based Hierarchical Trust Evaluation strategy, named BHTE, utilizing the federated deep learning technology for 5G-ITS. Specifically, the trusts of ITS users and task distributers are evaluated using the federated deep learning and hierarchical incentive mechanisms are designed for reasonable and fair rewards and punishments. Moreover, the trusts of ITS users and task distributers are stored on heterogeneous and hierarchical blockchains for trust verification. The extensive experiment results show that: (i) the proposed BHTE can achieve reasonable and fair trust evaluations on both ITS users and task distributers; (ii) the BHTE performs excellently with high system throughput and low latency.
The digital content wave has proliferated the financial and industrial sectors. Moreover, with the rise of massive internet-of-things, and automation, technologies like augmented reality (AR) and virtual reality (VR) have emerged as prominent players to drive a range of applications. Currently, sixth-generation (6G) networks support enhanced holographic projection through terahertz (THz) bandwidths, ultra-low latency, and massive device connectivity. However, the data is exchanged between autonomous networks over untrusted channels. Thus, to ensure data security, privacy, and trust among stakeholders, blockchain (BC) opens new dimensions towards intelligent resource management, user access control, audibility, and chronology in stored transactions. Thus, the BC and 6G coalition in future AR/VR applications is an emerging investigative topic. To date, authors have proposed surveys that study the integration of BC and 6G in AR/VR in isolation, and hence a coherent survey is required. Thus, to address the gap, the survey is the first-of-its-kind to investigate and study the coalition of BC and 6G in AR/VR space. Based on the proposed research questions in the survey, a solution taxonomy is presented, and different verticals are studied in detail. Furthermore, an integrative architecture is proposed, and open issues and challenges are presented. Finally, a case study, BvTours, is presented that presents a unique survey on BC-based 6G-assisted AR/VR virtual home tour service. The survey intends to propose future resilient frameworks and architectures for different industry 4.0 verticals and would serve as starting directions for academia, industry stakeholders, and research organizations to study the coalition of BC and 6G in AR/VR in industrial applications, gaming, digital content manufacturing, and digital assets protection in greater detail.
Xiantao Jiang, Zhaowei Ma, F. Richard Yu, Tian Song · 5 authors
In intelligent transportation systems (ITS), video analytics is a potential technology to enhance the safety of the Internet of Vehicles (IoV). However, massive video data transmission and computation-intensive video analytics bring an overwhelming burden for IoV. Furthermore, due to the unstable network connection, the video data are not always reliable, which makes data sharing lack of security and scalability in IoV. In this paper, for video analytics applications, the multi-access edge computing (MEC) and blockchain technologies are integrated into IoV to optimize the transaction throughput as well as reducing the latency of the MEC system. Furthermore, the joint optimization problem is formulated as a Markov decision process (MDP), and the asynchronous advantage actor-critic (A3C) algorithm is adopted to solve this problem. Simulation results show that the proposed approach can fast converge and signifcantly improve the performance of blockchain-enabled IoV with MEC.
Radio spectrum resource is a scarce resource. How to use the limited spectrum resource to provide users with the maximum communication opportunities is worth further study. In fact, the traditional “command and control” mode of spectrum management is very inefficient in the utilization of spectrum, resulting in a great waste of frequency resources. At the same time, in radio management, it is necessary to set up databases of different services to store different monitoring information. Although the system plays a certain control ability, the radio monitoring records form an island of information, and the information of each service does not circulate, which is not conducive to integrated management. In this paper, based on the current radio spectrum resources in dynamic spectrum sharing method of resource sharing, the spectrum value of trust transfer problem, and the information management system each other information, collaborative management difficulties and other issues, put forward a kind of spectrum sharing method based on blockchain and by business combining intelligent contract database, realize data sharing solution.