Gehui Li, Jing Yang, Tao Yu, Fuquan Yang ยท 6 authors
In China, the promulgation of a green power identification system has gradually shifted from electricity power generation to consumption. However, there is no mature digital identification system for green power consumption enterprises. Exploiting the open, transparent, and immutable characteristics of blockchains, this study establishes a digital identification system for green power consumption based on blockchain technology. This system uses expert scoring and the entropy weight method as the evaluation algorithm for the green power consumption chain, issues non-fungible tokens as digital identification for consumption enterprises, and realizes the automatic generation of identification through smart contracts. These functions guarantee the credibility of the certification process and the uniqueness of the generated identification. The results of the experiments show that, in the case of multi-user concurrent requests, the number of concurrent users that the system could handle at optimal processing efficiency was 500, and block generation was stable. The proposed system has high practicability and stability.
A smart contract is a special set of protocols based on blockchain technology to implement the terms oragreements between the parties in the contract. Lots of smart contracts are built and deployed everyday. However, to ensure the safety of smart contracts is still a big challenge. Smart contracts are built to carry out transactions directly related to cryptocurrencies, therefore, the loss of security of smart contracts leads to huge financial losses. Common methods being used to check and to verify smart contract security are heavily dependent on hard rules defined by experts, leading to low detection accuracy and non-scalable, which can be bypassed by experienced attackers. In this paper, we propose to use the combination of Imaging Graph Neural Network With Defined Pattern to detect vulnerabilities in smart contracts. We construct a contract graph that shows the relationship between the main components in a smart contract. Then we extract graph features from normalized graphs, and combine graph features with defined security patterns to create combined features. Finally, we implemented normalization to gray scale image and feed it to the Convolutional Neural Network (CNN) to learn for vulnerability detection. Results show significantly improved accuracy compared to previous methods or other models. Specifically, 96,42%, 90,12%, 79% for reentrancy, timestamp dependence and infinite loop
The education management model refers to the system and processes that colleges and universities use to manage and oversee their academic programs and operations. However, with the advent of digital technologies, there has been a growing trend towards the Internet+ college education management model, which integrates digital technologies into all aspects of college education management. This model includes the use of online learning platforms and tools, such as learning management systems (LMS), to deliver courses and manage student progress. It also includes the use of digital technologies for administrative tasks such as admissions, enrolment, and financial aid. However, the educational management model is subjected to the challenge of security for educational data management. Hence, this paper constructed a secure framework model of the Ethereum SDN Cloud Architecture (ESDNarc). The ESDNarc model uses the Software-defined Network (SDN) for the decentralized management of the network, secure transactions, and improved efficiency. The ESDNarch model incorporates the SDN with the cryptography scheme the secure the data. The constructed model uses the double-hashing Elliptical Curve Cryptography (DHECC) for the data stored in the Ethereum blockchain. The performance of the constructed model is evaluated with the KDD data set. Simulation analysis stated that ESDNarch significantly increases the data security in the cloud model for the attacks in the network.
S. Markkandan, Prem Kumar, R Prathipa, K. Vengatesan ยท 5 authors
6G networks are predicted to provide new prospects for Smart Cities and Internet of Things (IoT) applications because of their global seamless coverage.Therefore, to fulfil the growing need for huge data rates for 6G and greater applications, network capacity must be enhanced.As a result, there is an increase in spectrum demand.Only by successfully sharing existing spectrum and avoiding spectrum underutilization will the increased demand for cellular services be addressed.As a result, for 6G to achieve considerably enhanced network capacity, efficient spectrum management systems must be developed.As a result, maintaining 6G's predicted huge network capacity in such as heterogeneous environment necessitates the shared exploitation of available spectrum resources through dynamic coordination across device and network, which can be accomplished by incorporating SDN into 6G networks.Due to the increased speeds and reliability of 6G networks, users may have to pay more for energy, to overcome these issues, In this paper, a novel proposed a 6G HetNet spectrum management system based on HSA and Smart Contracts.HSA harmonizes network operation by spreading local decision-making and network-wide policy-making processes between BS and the SDN controller, correspondingly, to relieve any possible controller scalability and latency difficulties.And, to tackle the intricacies of service-level agreements, leverage blockchain's smart contract technology, which allows for automation and trustworthy, transparent radio spectrum negotiation among several parties.This proposed solution is dependable, scalable, and implementable, as seen by the results.
Venkatagurunatham Naidu Kollu, Vijayaraj Janarthanan, Muthulakshmi Karupusamy, R. Manikandan
Data sharing is proposed because the issue of data islands hinders advancement of artificial intelligence technology in the 5G era. Sharing high-quality data has a direct impact on how well machine-learning models work, but there will always be misuse and leakage of data. The field of financial technology, or FinTech, has received a lot of attention and is growing quickly. This field has seen the introduction of new terms as a result of its ongoing expansion. One example of such terminology is โFinTechโ. This term is used to describe a variety of procedures utilized frequently in the financial technology industry. This study aims to create a cloud-based intrusion detection system based on IoT federated learning architecture as well as smart contract analysis. This study proposes a novel method for detecting intrusions using a cyber-threat federated graphical authentication system and cloud-based smart contracts in FinTech data. Users are required to create a route on a world map as their credentials under this scheme. We had 120 people participate in the evaluation, 60 of whom had a background in finance or FinTech. The simulation was then carried out in Python using a variety of FinTech cyber-attack datasets for accuracy, precision, recall, F-measure, AUC (Area under the ROC Curve), trust value, scalability, and integrity. The proposed technique attained accuracy of 95%, precision of 85%, RMSE of 59%, recall of 68%, F-measure of 83%, AUC of 79%, trust value of 65%, scalability of 91%, and integrity of 83%.
The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network security. The architecture leverages an adaptive clustering approach based on Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS) for efficient organization of heterogeneous IoT networks. Cluster heads (CH) are selected to manage local authentication and permissions, reducing overhead and latency by minimizing communication distances between CHs and IoT devices. To implement network changes such as node addition, relocation, and deletion, the Network Efficient Whale Optimization (NEWO) algorithm is employed. A localized private blockchain structure facilitates communication between CHs and base stations, providing an authentication mechanism that enhances security and trustworthiness. Simulation results demonstrate the effectiveness of the proposed clustering algorithm compared to existing methodologies. Overall, the lightweight blockchain approach presented in this study strikes a superior balance between network latency and throughput when compared to conventional global blockchain systems. Further analysis of system under test (SUT) behavior was accomplished by running many benchmark rounds at varying transaction sending speeds. Maximum, median, and lowest transaction delays and throughput were measured by generating 1000 transactions for each benchmark. Transactions per second (TPS) rates varied between 20 and 500. Maximum delay rose when throughput reached 100 TPS, while minimum latency maintained a value below 1 s.
Radha Raman Chandan, Awatef Salem Balobaid, Naga Lakshmi Sowjanya Cherukupalli, H L Gururaj ยท 6 authors
Sixth-generation (6G) wireless networking studies have begun with the global implementation of fifth-generation (5G) wireless systems. It is predicted that multiple heterogeneity applications and facilities may be supported by modern wireless communication networks (MWCNs) with improved effectiveness and protection. Nevertheless, a variety of trust-related problems that are commonly disregarded in network architectures prevent us from achieving this objective. In the current world, MWCN transmits a lot of sensitive information. It is essential to protect MWCN users from harmful attacks and offer them a secure transmission to meet their requirements. A malicious node causes a major attack on reliable data during transmission. Blockchain offers a potential answer for confidentiality and safety as an innovative transformative tool that has emerged in the last few years. Blockchain has been extensively investigated in several domains, including mobile networks and the Internet of Things, as a feasible option for system protection. Therefore, a blockchain-based modal, Transaction Verification Denied conflict with spurious node (TVDCSN) methodology, was presented in this study for wireless communication technologies to detect malicious nodes and prevent attacks. In the suggested mode, malicious nodes will be found and removed from the MWCN and intrusion will be prevented before the sensitive information is transferred to the precise recipient. Detection accuracy, attack prevention, security, network overhead, and computation time are the performance metrics used for evaluation. Various performance measures are used to assess the methodโs efficacy, and it is compared with more traditional methods.
Smart contracts are one of the three major characteristics of blockchain, and they are also areas where blockchain has application value and flexibility. In essence, a smart contract is a piece of code implemented in a specific scripting language, which inevitably has the risk of security vulnerabilities. How to accurately and timely detect the vulnerabilities of various smart contracts has become the focus and hot spot of blockchain security research. To detect vulnerabilities in smart contracts, researchers have proposed various analysis methods, including symbolic execution, formal verification and fuzzing. With the rapid development of artificial intelligence technology, more and more deep learning-based methods have been proposed and have achieved good results in several research areas. At present, deep learning-based smart contract vulnerability detection methods have not been investigated and analyzed in detail. This paper first briefly introduces the concept of smart contracts and security events related to smart contract vulnerabilities, then introduces the commonly used smart contract features in deep learning-based methods, and describes the deep learning models commonly used in smart contract vulnerability detection. In addition, in order to further promote the research of deep learning-based smart contract vulnerability detection methods, the recent deep learning-based smart contract vulnerability detection methods are summarized and classified according to their feature extraction forms, and are analyzed and introduced from three perspectives: text processing, static analysis and image processing. Finally, the challenges and future research directions in this field are summarized.
Tharindu Ranathunga, Alan McGibney, Susan Rea, Sourabh Bharti
Traditional federated learning (FL) adopts a client-server architecture where FL clients (e.g., IoT edge devices) train a common global model with the help of a centralized orchestrator (cloud server). However, current approaches are moving away from centralized orchestration toward a decentralized one in order to fully adapt FL for a cross-silo configuration with multiple organizations acting as clients. State-of-the-art decentralized FL mechanisms make at least one of the following assumptions: 1) clients are trusted organizations and cannot inject low-quality model updates for aggregation and 2) client local models can be shared with other clients or a third party for verification of low-quality updates. This article proposes a Blockchain-based decentralized framework for scenarios where participatory organizations are believed to be fully capable of injecting low-quality model updates as they are not willing to expose their local models to any other entity for verification purpose. The proposed decentralized FL framework adopts a novel hierarchical network of aggregators with the ability to punish/reward organizations in proportion to their local model quality updates. The framework is flexible and unlike state-of-the-art solutions, prevents a single entity from possessing the aggregated model in any FL round of training. The proposed framework is tested with respect to off-chain and on-chain performance in two Industry 4.0 use cases: 1) predictive maintenance and 2) product visual inspection. A comparative evaluation against the state-of-the-art reveals the proposed frameworkโs utility in terms of minimizing model convergence time and latency while maximizing accuracy and throughput.
T. Manikandan, Shajahan Basheer, Shitharth Selvarajan, Sara A. Althubiti ยท 7 authors
There can be many inherent issues in the process of managing cloud infrastructure and the platform of the cloud. The platform of the cloud manages cloud software and legality issues in making contracts. The platform also handles the process of managing cloud software services and legal contract-based segmentation. In this paper, we tackle these issues directly with some feasible solutions. For these constraints, the Averaged One-Dependence Estimators (AODE) classifier and the SELECT Applicable Only to Parallel Server (SELECT-APSL ASA) method are proposed to separate the data related to the place. ASA is made up of the AODE and SELECT Applicable Only to Parallel Server. The AODE classifier is used to separate the data from smart city data based on the hybrid data obfuscation technique. The data from the hybrid data obfuscation technique manages 50% of the raw data, and 50% of hospital data is masked using the proposed transmission. The analysis of energy consumption before the cryptosystem shows the total packet delivered by about 71.66% compared with existing algorithms. The analysis of energy consumption after cryptosystem assumption shows 47.34% consumption, compared to existing state-of-the-art algorithms. The average energy consumption before data obfuscation decreased by 2.47%, and the average energy consumption after data obfuscation was reduced by 9.90%. The analysis of the makespan time before data obfuscation decreased by 33.71%. Compared to existing state-of-the-art algorithms, the study of makespan time after data obfuscation decreased by 1.3%. These impressive results show the strength of our methodology.
Smart contract has been the core of blockchain systems and other blockchain-based systems since Blockchain 2.0. Various operations on blockchain are performed through the invocation and execution of smart contracts. This leads to extensive combinations between blockchain, smart contract, Internet of Things (IoT) and Cyber-Physical System (CPS) applications, and then many blockchain-based IoT or CPS applications emerge to provide multiple benefits to the economy and society. In this case, obtaining a better understanding of smart contracts will contribute to the easier operation, higher efficiency and stronger security of those blockchain-based systems and applications. Many existing studies on smart contract analysis are based on similarity calculation and smart contract classification. However, smart contract is a piece of code with special characteristics and most of smart contracts are stored without any category labels, which leads to difficulties of smart contract classification. As the back end of a blockchain-based Decentralized Application (DApp) is one or several smart contracts, DApps with labeled categories and open source codes are applied to achieve a supervised smart contract classification. A three-phase approach is proposed to categorize DApps based on various data features. In this approach, 5,659 DApps with smart contract source codes and pre-tagged categories are first obtained based on massive collected DApps and smart contracts from Ethereum, State of the DApps and DappRadar. Then feature extraction and construction methods are designed to form multi-feature vectors that could present the major characteristics of DApps. Finally, a fused classification model consisting of KNN, XGBoost and random forests is applied to the multi-feature vectors of all DApps for performing DApp classification. The experimental results show that the method is effective. In addition, some positive correlations between feature variables and categories, as well as several user behavior patterns of DApp calls, are found in this paper.
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Blockchain Technology Applications and Security
Advanced Data and IoT Technologies
Advanced Steganography and Watermarking Techniques
Kawther A. AlโDhlan, Hamad A. Alreshidi, Shahbaz Pervez, Zahida Paraveen ยท 8 authors
The adoption of blockchain technology can provide data asset managementโs high security, privacy, and traceability. After a comprehensive investigation of the present blockchain-based data asset management mechanism, it was determined that it is only relevant to a portion of the blockchain system design. To address this issue, a new model of data asset management based on blockchain technology is being developed, which incorporates applications at all levels of the blockchain system. This model implements a network layer node authority control mechanism, a consensus layer consensus mechanism with customizable attributes, improved data query efficiency at the data layer by optimizing the structure and building indexes, intelligent data management, and smart contract layer management. Furthermore, at the transaction layer, sharing information encryption using customizable encryption algorithms is introduced. The experimental results reveal that, when compared to the traditional paradigm, the new blockchain-based data asset management strategy enhances the efficiency of on-chain data queries by 2.33 times.
Sandi Rahmadika, Philip Virgil Astillo, Gaurav Choudhary, Daniel Gerbi Duguma ยท 6 authors
The Internet of Medical Things (IoMT) has risen to prominence as a possible backbone in the health sector, with the ability to improve quality of life by broadening user experience while enabling crucial solutions such as near real-time remote diagnostics. However, privacy and security problems remain largely unresolved in the safety area. Various rule-based methods have been considered to recognize aberrant behaviors in IoMT and have demonstrated high accuracy of misbehavior detection appropriate for lightweight IoT devices. However, most of these solutions have privacy concerns, especially when giving context during misbehavior analysis. Moreover, falsified or modified context generates a high percentage of false positives and sometimes causes a by-pass in misbehavior detection. Relying on the recent powerful consolidation of blockchain and federated learning (FL), we propose an efficient privacy-preserving framework for secure misbehavior detection in lightweight IoMT devices, particularly in the artificial pancreas system (APS). The proposed approach employs privacy-preserving bidirectional long-short term memory (BiLSTM) and augments the security through integrating blockchain technology based on Ethereum smart contract environment. The effectiveness of the proposed model is bench-marked empirically in terms of sustainable privacy preservation, commensurate incentive scheme with an untraceability feature, exhaustiveness, and the compact results of a variant neural network approach. As a result, the proposed model has a 99.93% recall rate, showing that it can detect virtually all possible malicious events in the targeted use case. Furthermore, given an initial ether value of 100, the solution's average gas consumption and Ether spent are 84,456.5 and 0.03157625, respectively.
Baofeng Ji, Mingkun Zhang, Ling Xing, Xiaoli Li ยท 7 authors
The huge increase in the communication network rate has made the application fields and scenarios for vehicular ad hoc networks more abundant and diversified and proposed more requirements for the efficiency and quality of data transmission. To improve the limited communication distance and poor communication quality of the Internet of Vehicles (IoV), an optimal intelligent routing algorithm is proposed in this paper. Combined multi-weight decision algorithm with the greedy perimeter stateless routing protocol, designed and evaluated standardized function for link stability. Linear additive weighting is used to optimize link stability and distance to improve the packet delivery rate of the IoV. The blockchain system is used as the storage structure for relay data, and the smart contract incentive algorithm based on machine learning is used to encourage relay vehicles to provide more communication bandwidth for data packet transmission. The proposed scheme is simulated and analyzed under different scenarios and different parameters. The experimental results demonstrate that the proposed scheme can effectively reduce the packet loss rate and improve system performance.
Abdulrahman Alqarafi, Fadwa Alrowais, Saud S. Alotaibi, Nadhem Nemri ยท 9 authors
Currently, the amount of Internet of Things (IoT) applications is enhanced for processing, analyzing, and managing the created big data from the smart city. Certain other applications of smart cities were location-based services, transportation management, and urban design, amongst others. There are several challenges under these applications containing privacy, data security, mining, and visualization. The blockchain-assisted IoT application (BIoT) is offering new urban computing to secure smart cities. The blockchain is a secure and transparent data-sharing decentralized platform, so BIoT is suggested as the optimum solution to the aforementioned challenges. In this view, this study develops an Optimal Machine Learning-based Intrusion Detection System for Privacy Preserving BIoT with Smart Cities Environment, called OMLIDS-PBIoT technique. The presented OMLIDS-PBIoT technique exploits BC and ML techniques to accomplish security in the smart city environment. For attaining this, the presented OMLIDS-PBIoT technique employs data pre-processing in the initial stage to transform the data into a compatible format. Moreover, a golden eagle optimization (GEO)-based feature selection (FS) model is designed to derive useful feature subsets. In addition, a heap-based optimizer (HBO) with random vector functional link network (RVFL) model was utilized for intrusion classification. Additionally, blockchain technology is exploited for secure data transmission in the IoT-enabled smart city environment. The performance validation of the OMLIDS-PBIoT technique is carried out using benchmark datasets, and the outcomes are inspected under numerous factors. The experimental results demonstrate the superiority of the OMLIDS-PBIoT technique over recent approaches.
Ihab L. Hussein Alsammak, Mohammed F. Alomari, Intedhar Shakir Nasir, Wasan H. Itwee
Recently, with the emergence and growth of the IoT as a promising vehicle for sustainable development, the concept of โsmart citiesโ has advanced significantly. However, many challenges inhibit the development of using IoT applications in smart cities, such as issues of privacy, scalability, trust, security, and centralisation. On a daily basis in smart cities, the IoT generates a large amount of data (big data) which could potentially be used for questionable or suspect purposes by attackers. The weight of the security issues surrounding big data must be acknowledged as the associated technology is continuously developing. To solve this issue, a strategy that secures important and potentially sensitive user information on a distributed blockchain and transmits non-sensitive information to the primary system by controlling the size of the blockchain is proposed. This solution cannot be achieved in traditional blockchain because it requires too many resources. The model is composed of three proposed algorithms: the first aims to allocate data to each user; the second performs the process of searching for data, and the third confirms the communication process. Experiments have proved that this proposed protocol for blockchain has excellent byzantine fault tolerance. The final experimental results of the proposed model established that the algorithms effectively meet the performance requirements.
This document presents the final design of the 5GZORRO high-level architecture, which targets the achievement and implementation of the innovative 5G networks and services vision described above. More specifically, this deliverable is intended as a self-contained document, which merges the original content of deliverables D2.2 and D2.3 (that present the initial and the updated 5GZORRO high-level architecture respectively) and further improves them to align the 5GZORRO architecture functionalities with the feedback from the platform implementation undergoing in WP3 and WP4. With this document, the goal is to have a single source of information for the 5GZORRO high-level architecture, which includes the whole set of services offered, functionalities supported, and operational workflows implemented.<br> In practice, in alignment with the original approach proposed and described in D2.2 and D2.3, the architecture follows a principle of service-based architecture, similar to the 5G Service-based architecture defined in 3GPP and in the ETSI Zero touch network and Service Management. Integrating SDN/NFV and Cloud native orchestration technologies with a Permissioned Distributed Ledger infrastructure, the 5GZORRO architecture offers services for:<br> โข cross-domain network slicing,<br> โข resource and service offering via marketplaces,<br> โข discovery, intelligent selection and trading of resources and Services via Smart Contracts<br> โข zero-touch network slice and service lifecycle management<br> โข cross-stakeholder e-license management<br> โข SLA monitoring & breach prediction<br> โข security and trust across multiple domains.<br> The realization of these services is made possible through the interaction of various functions for slice orchestration, network intelligence and analytics, security and trust, management of virtualized resources, all executed for multi-domain and single domain scopes. Moreover, 5GZORRO leverages many state-of-the-art technologies and standards for virtualization, NFV, Cloud Native platforms and services, zero touch, SDN, distributed ledgers, data lakes, which have been extensively reviewed to summarise the specific positioning of the 5GZORRO innovative proposition.
Bo Zhao, Chenhan Shangguan, Xiaoyan PENG, Yang AN ยท 6 authors
In order to solve the problems of low detection accuracy and high false positive rate of traditional smart contract vulnerability detection methods and less consideration of bytecode level smart contract features in neural networks, a smart contract bytecode vulnerability detection method based on semantic perception graph neural network was proposed. First, in order to generate the control flow graph, the basic blocks divided by the smart contract bytecode were used as the nodes, and the call relationship between the basic blocks was extracted from the bytecode as the edges. Then, control flow graph is transmitted into the graph convolutional network for training to obtain the feature representation of the graph nodes; Afterwards, the contract bytecode instruction sequence is segmented, transformed into a word vector, embedded into a low-dimensional space and transmitted to a long short-term memory network for training. Then, the vector representation of bytecode semantic information was obtained. Finally, the generated node features and semantic features were spliced and transmitted to the full connection layer for dimensionality reduction. Combined with semantic information and node features, the vulnerability detection was carried out for smart contracts. The real smart contracts in public dataset were used for training and testing, and verified in two types of vulnerability classification datasets through traditional methods and artificial tags. The method proposed in this paper was compared with three traditional smart contract vulnerability detection tools and one smart contract vulnerability detection method based on neural network. The experimental results showed that the proposed network greatly improves the performance of network in terms of various indicators, and detects the contracts with vulnerabilities which are not detected by the other four methods. It shows that adding the bytecode semantic information to graph neural network can effectively improve the detection accuracy and reduce the false alarm rate.
In this paper, we consider the Vennia algorithm to conduct in-depth research and analysis on the traceability of dual-chain blockchain agricultural products' E-commerce information. This paper adds a collaborative verification module to the traceability system and carries out a detailed design of information storage, traceability consensus algorithm, and smart contract for agricultural products according to the characteristics of the agricultural products supply chain, among which the collaborative verification module adopts dynamic data storage technology; the ConsiderVinia consensus algorithm is improved by introducing the way of integral penalty mechanism to ensure the block data validity. After a comparative study of the features and differences of the three major blockchain technology platforms, this paper selects the super ledger to implement the agricultural traceability system based on blockchain technology, introduces the partitioning and credit mechanism into the ConsiderVinia algorithm, and elaborates the improvement process of the algorithm. The improved algorithm reduces the malicious behavior of nodes and maintains the system security through a credit mechanism while maintaining the consistency of blockchain. In the event of a transaction dispute, the third-party platform will determine the party at fault based on the transaction records and other evidence and make corresponding punishments and compensations. The experiment proves that the algorithm proposed in this paper can reduce the amount of network data transmission in the process of node consensus, which is better than the ConsiderVinia algorithm in terms of both throughput and latency, improves the consensus efficiency, and alleviates the communication bottleneck caused by the increase of users in blockchain applications, and the solution of applying the blockchain technology to the agricultural products traceability system is practical and feasible. The blockchain-based agricultural products information traceability system solves the problems of information asymmetry, difficult sharing, easy tampering, and storage centralization in the traditional IoT-based agricultural products traceability system and truly realizes the credible and reliable traceability of the whole chain of agricultural products information. The research content and results of this paper have certain theoretical and practical values.
Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing public perceptions of COVID-19, especially on the social media platform and infrastructure. In addition, the unprecedented virality and changing nature of COVID-19 makes call centres to be likely overstressed, which is due to a lack of authentic and unregulated public media information. Furthermore, the lack of data privacy has restricted the sharing of COVID-19 information among health institutions. To resolve the above-mentioned limitations, this paper is proposing a privacy infrastructure based on federated learning and blockchain. The proposed infrastructure has the potentials to enhance the trust and authenticity of public media to disseminate COVID-19 information. Also, the proposed infrastructure can effectively provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security-related attacks.
With the continuous development of urban intelligence, as traffic, power grids, and electric vehicles are new ideas to solve energy shortages and air control problems, they have received widespread attention from the society and strong support from the government. The charging pile is a key hub for data exchange and has typical characteristics of IoT terminals. However, the guidance of the grid connection of electric vehicles is not standardized, and the security and stability of the power grid will inevitably be affected. The blockchain has the characteristics that data is difficult to tamper with and decentralized. Based on these two characteristics, the information recorded by the blockchain is more authentic and reliable. This paper is aimed at realizing the global unified identification and management of large terminal equipment based on ubiquitous power Internet of Things equipment with the help of blockchain technology and at providing safe, efficient, and stable online management services for a large number of devices in the ubiquitous power Internet of Things. In this paper, we analyze the application possibility of blockchain technology in the current electricity market and apply blockchain technology to the electricity market to solve the drawbacks of the electricity market, as well as adding blockchain analytics to the renewable energy electricity market. The experimental data analysis shows that with the increase of the length of the blockchain, the blocking time of the new area increases correspondingly, but the overall performance declines slightly, so this scheme can meet the practical needs of a large number of concurrent access terminals in the ubiquitous power Internet of Things. It can be seen that the successful application of blockchain technology based on the power Internet of Things in electric vehicle charging piles has greatly improved work efficiency.
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.