Abdullah Alghamdi, Jiang Zhu, Guocai Yin, Mohammad Shorfuzzaman Ā· 7 authors
Resource constraint Consumer Internet of Things (CIoT) is controlled through gateway devices (e.g., smartphones, computers, etc.) that are connected to Mobile Edge Computing (MEC) servers or cloud regulated by a third party. Recently Machine Learning (ML) has been widely used in automation, consumer behavior analysis, device quality upgradation, etc. Typical ML predicts by analyzing customers' raw data in a centralized system which raises the security and privacy issues such as data leakage, privacy violation, single point of failure, etc. To overcome the problems, Federated Learning (FL) developed an initial solution to ensure services without sharing personal data. In FL, a centralized aggregator collaborates and makes an average for a global model used for the next round of training. However, the centralized aggregator raised the same issues, such as a single point of control leaking the updated model and interrupting the entire process. Additionally, research claims data can be retrieved from model parameters. Beyond that, since the Gateway (GW) device has full access to the raw data, it can also threaten the entire ecosystem. This research contributes a blockchain-controlled, edge intelligence federated learning framework for a distributed learning platform for CIoT. The federated learning platform allows collaborative learning with users' shared data, and the blockchain network replaces the centralized aggregator and ensures secure participation of gateway devices in the ecosystem. Furthermore, blockchain is trustless, immutable, and anonymous, encouraging CIoT end users to participate. We evaluated the framework and federated learning outcomes using the well-known Stanford Cars dataset. Experimental results prove the effectiveness of the proposed framework.
Matthias Babel, Vincent Gramlich, Marc-Fabian Kƶrner, Johannes Sedlmeir Ā· 6 authors
Abstract In the energy transition, there is an urgent need for decreasing overall carbon emissions. Against this background, the purposeful and verifiable tracing of emissions in the energy system is a crucial key element for promoting the deep decarbonization towards a net zero emission economy with a market-based approach. Such an effective tracing system requires end-to-end information flows that link carbon sources and sinks while keeping end consumersā and businessesā sensitive data confidential. In this paper, we illustrate how non-fungible tokens with fractional ownership can help to enable such a system, and how zero-knowledge proofs can address the related privacy issues associated with the fine-granular recording of stakeholdersā emission data. Thus, we contribute to designing a carbon emission tracing system that satisfies verifiability, distinguishability, fractional ownership, and privacy requirements. We implement a proof-of-concept for our approach and discuss its advantages compared to alternative centralized or decentralized architectures that have been proposed in the past. Based on a technical, data privacy, and economic analysis, we conclude that our approach is a more suitable technical backbone for end-to-end digital carbon emission tracing than previously suggested solutions.
Abstract MachineāasāaāService (MaaS) is an emerging service model for industrial appliances. With MaaS, machines are rented instead of being acquired, and their lifecycle is handled by an ecosystem of specialized actors, such as different independent maintenance companies certified for interventions on specific hardware. As the number of actors, clients, and providers involved in a MaaS ecosystem grows, maintaining mutual trust relationships between all involved parties and orchestrating MaaS operations in centralized fashion quickly becomes intractable. We present a blockchainābased approach to providing MaaS in industrial settings where rented machines are equipped with IoT sensors, and where MaaS operations are orchestrated in a transparent, decentralized, and scalable way using a collection of smart contracts deployed over an infrastructure combining the Ethereum and InterPlanetary File System decentralized services. We detail the operations of MaaS, such as the lifecycle of management operations, and report on the performance of a prototype implementation deployed in the cloud.
Autonomous mobile robots (AMRs) use decentralized, AI-driven decision-making processes to providing material handling capabilities in industrial settings. Essay 1 examines how firms organize and engage to mitigate uncertainty during external technology integration (ETI), using an abductive approach with dyadic customer-supplier data to extend prior ETI models by exploring firm engagement, organizational adaptation, and distinct uncertainty types in AMR ETI projects. Essay 2 applies a grounded theory approach to examine AMR integration, using constant comparison and theoretical sampling to develop core categories explaining how suppliers, customers, and users exchange knowledge impacting AMR integration and project performance. Finally, Essay 3 is a conceptual paper examining the importance of end-user adoption by integrating ETI and technology acceptance model (TAM) frameworks, exploring important relationships between managerial interventions, cognitive constructs, user acceptance, and project success in AMR ETIs. As a whole, these essays contribute to the body of knowledge by extending the breadth and depth of current ETI models, emerging a substantive theory of AMR AIU, and extending TAM by grounding managerial interventions and individual cognitive constructs in an AMR context. Managers can use these frameworks to differentiate AMRs and other autonomous collaborative technology from traditional automation, and develop strategies enabling timely and effective AMR implementation.
Actual challenges with data in physical infrastructure include: 1) the adversity of its velocity based on access and retrieval, thus integration; 2) its value as its intrinsic quality; 3) its extensive volume with a limited variety in terms of systems; and finally, 4) its veracity, as data can be modified to obtain an economical advantage. Physical infrastructure design based on Agile project management and minimum viable products provides benefits against the traditional waterfall method. Agile supports an early return on investment that promotes circular reinvesting while making the product more adaptable to variable social-economical environments. However, Agile also presents inherent issues due to its iterative approach. Furthermore, project information requires an efficient record of the aims, requirements, and governance not only for the investors, owners, or users but also to keep evidence in future health & safety and other statutory compliance. In order to address these issues, this article presents a Validation and Verification (V&V) model for data marketplaces with a hierarchical process; each data V&V stage provides a layer of data abstraction, value-added services, and authenticity based on Artificial Intelligence (AI). In addition, this proposed solution applies Distributed Ledger Technology (DLT) for a decentralised approach where each user keeps and maintains the data within a ledger. The presented model is validated in real data marketplace applications: 1) live data for the Newcastle Urban Observatory Smart City Project, where data are collected from sensors embedded within the smart city via APIs; 2) static data for University College London (UCL)āReal EstateāPEARL Project, where different project users and stakeholders introduce data into a Project Information Model (PIM).
Seth Larweh Kodjiku, Yili Fang, Tao Han, Kwame Omono Asamoah Ā· 9 authors
Because of the rise of cryptocurrencies and decentralized apps, blockchain technology has generated a lot of interest. Among these is the emergent blockchain-based crowdsourcing paradigm, which eliminates the centralized conventional mechanism servers in favor of smart contracts for task and reward allocation. However, there are a few crucial challenges that must be resolved properly. For starters, most reputation-based systems favor high-performing employees. Secondly, the crowdsourcing platformās expensive service charges may obstruct the growth of crowdsourcing. Finally, unequal evaluation and reward allocation might lead to job dissatisfaction. As a result, the aforementioned issues will substantially impede the development of blockchain-based crowdsourcing systems. In this study, we introduce ExCrowd, a blockchain-based crowdsourcing system that employs a smart contract as a trustworthy authority to properly select workers, assess inputs, and award incentives while maintaining user privacy. Exploration-based crowdsourcing employs the hyperbolic learning curve model based on the conduct of workers and analyzes worker performance patterns using a decision tree technique. We specifically present the architecture of our framework, on which we establish a concrete scheme. Using a real-world dataset, we implement our model on the Ethereum public test network leveraging its reliability, adaptability, scalability, and rich statefulness. The results of our experiments demonstrate the efficiency, usefulness, and adaptability of our proposed system.
With the fast development of the industrial Internet, its interconnectivity poses new challenges for the cooperation of industrial entities. Cooperation among these entities is built on trust, and trust is based on high-quality industrial products at reasonable prices. A traceability system can play an essential role in objectively reflecting the production process and promoting this trust. However, traditional traceability systems often have data privacy issues. Because traceability data are collected or generated during the production process (namely, production-related data), they could be considered privacy data. Several researchers have introduced privacy protection schemes into the traceability system, such as authentication or encryption. Nevertheless, when a privacy protection scheme is established, the original data are disclosed to the legal user of the system, but the data may still be leaked intentionally or unintentionally. Except for data privacy issues, a traditional traceability system can be vulnerable to network attacks, data unavailability, and reliability issues. The authors conducted a study to overcome these shortcomings, and this paper reports the results. We built a traceability prototype system using a blockchain protocol and a zero-knowledge proof method. First, we built a blockchain to record key production process data, aiming to maintain data reliability and availability. Second, through an analysis of traceability purpose using production knowledge, the traceability purpose could be divided into multiple provable statements. By introducing privacy protection through a zero-knowledge proof, the traceability process was converted to proving relative statements. Finally, the statements were validated by a smart contract that provided openness and reliability during the traceability process. Analysis has shown that our approach could meet the requirements for high security and privacy. In addition, the paper also discusses the calculation cost of the traceability process to show our workās viability. The traceability system described in this paper creates new possibilities for constructing a healthy and reliable trust relationship between production entities to provide further support in the development of the industrial Internet.
Rimvydas Laužikas, Tadas Žižiūnas, Vladislav V. Fomin
The conflict between heritage protection and urban infrastructure development rationales creates a context for inclusion, participation and dialogue of different heritage-related communities. However, developed in the pre-computer age of administrative practice, are often incapable, partially or completely, to accommodate the ānew-eraā community oriented participatory practices. In this article, authors discuss the mutual effects of IT in the process of democratization of urban heritage preservation. The authors create and argue the conceptual model of distributed ledger technologies (DLT) in participatory UHP. The model demonstrates how technologies can become catalysts for democratization in situations when the regulatory and administrative change (on its own) is too inert. The article hypothesizes that novel technological developments which aim at or have the potential for increasing community involvement and democratization of administrative practice, exert their effects directly through technology-based participatory practices.
Retail energy markets are increasingly consumer-oriented, thanks to a growing number of energy plans offered by a plethora of energy suppliers, retailers and intermediaries. To maximize the benefits of competitive retail energy markets, group purchasing is an emerging paradigm that aggregates consumers' purchasing power by coordinating switch decisions to specific energy providers for discounted energy plans. Traditionally, group purchasing is mediated by a trusted third-party, which suffers from the lack of privacy and transparency. In this paper, we introduce a novel paradigm of decentralized privacy-preserving group purchasing, empowered by privacy-preserving blockchain and secure multi-party computation, to enable users to form a coalition for coordinated switch decisions in a decentralized manner, without a trusted third-party. The coordinated switch decisions are determined by a competitive online algorithm, based on users' private consumption data and current energy plan tariffs. Remarkably, no private user consumption data will be revealed to others in the online decision-making process, which is carried out in a transparently verifiable manner to eliminate frauds from dishonest users and supports fair mutual compensations by sharing the switching costs to incentivize group purchasing. We implemented our decentralized group purchasing solution as a smart contract on Solidity-supported blockchain platform (e.g., Ethereum), and provide extensive empirical evaluation.
Abstract With the boom in 5G technology, mobile spatial crowdsourcing has shown great dynamism in industrial mobile communications and edge computing node management. But the traditional crowdsourcing system is not advanced enough to adapt to the new environment. Typically, traditional crowdsourcing workflow is hosted by a centralized crowdsourcing platform. However, the centralized crowdsourcing platform faces the following problems: (1) single point of failure, (2) user privacy leakage, (3) subjective arbitration, (4) additional service fee, and (5) non-transparent task assignment process. To improve those problems, we replaced the centralized crowdsourcing platform with a decentralized blockchain infrastructure. And we analyzed the challenge problems of multi-skilled spatial crowdsourcing tasks in the blockchain crowdsourcing system. In addition, a crowdsourcing task allocation algorithm has been proposed, which implements a transparent task distribution process and can adapt to the computing-constrained environment on the blockchain. Compared with the TSWCrowd blockchain-based crowdsourcing model, our system has a higher task allocation rate under the same conditions. And the experimental result shows our work has good economic feasibility, which decentralizes the crowdsourcing process and significantly reduces the additional consumption of the crowdsourcing process.
Spatial crowdsourcing emerges as a new computing paradigm that enables mobile users to accomplish spatio- temporal tasks in order to solve human-intrinsic problems. Existing crowdsourcing systems critically use centralized servers for interacting with workers and making task assignment decisions. These systems are hence susceptible to issues such as the single point of failure and the lack of operational transparency. Prior work, therefore, turns to blockchain-based decentralized crowdsourcing systems, yet still suffers from problems of lacking efficient task assignment scheme, requiring a deposit to an untrusted system, low block generation speed, and high transaction fees. To address these issues, we design a blockchain-based decentralized framework for spatial crowdsourcing, which we call SC-EOS. Our system does not rely on any trusted servers, while providing efficient and user-customizable task assignment, low monetary cost, and fast block generation. More importantly, it frees users from making a deposit into an untrusted system. Our framework can also be extended and applied to generic crowdsourcing systems. We implemented the proposed system on the EOS blockchain. Trace-driven evaluations involving real users show that our system attains the comparable task assignment performance against a clairvoyant scheme. It also achieves 10× cost savings than an Ethereum-based implementation.
In the Internet of Things (loT) era, edge computing is a promising paradigm to improve the quality of service for latency sensitive applications by filling gaps between the loT devices and the cloud infrastructure. Highly geo-distributed edge computing resources that are managed by independent and competing service providers pose new challenges in terms of resource allocation and effective resource sharing to achieve a globally efficient resource allocation. In this paper, we propose a novel blockchain-based model for allocating computing resources in an edge computing platform that allows service providers to establish resource sharing contracts with edge infrastructure providers apriori using smart contracts in Ethereum. The smart contract in the proposed model acts as the auctioneer and replaces the trusted third-party to handle the auction. The blockchain-based auctioning protocol increases the transparency of the auction-based resource allocation for the participating edge service and infrastructure providers. The design of sealed bids and bid revealing methods in the proposed protocol make it possible for the participating bidders to place their bids without revealing their true valuation of the goods. The truthful auction design and the utility-aware bidding strategies incorporated in the proposed model enables the edge service providers and edge infrastructure providers to maximize their utilities. We implement a prototype of the model on a real blockchain test bed and our extensive experiments demonstrate the effectiveness, scalability and performance efficiency of the proposed approach.
The application of distributed ledger technologies, including blockchain, is rapidly growing in governance, transport, supply chain, and logistics. Today, blockchain technology is promoted as the heart of Smart Cities. This study reviews the potential of blockchain application in water management systems. We surveyed the literature and organized the previous studies based on three main application topics: Smart Water Systems, Water Quality Monitoring, and Storm Water Management. Also, we addressed technical, organizational, social, and institutional challenges that may hinder the adoption of Blockchain technology. Water management systems need to have a long-term commitment plan, update their organizational policies, and acquire relevant knowledge and expertise before successfully adopting any distributed ledger technology.
To understand how distributed ledger technology (DLT) enables people-centered IoT solutions we conducted a systematic literature review of tested implementations since 2017. We created a people-centered classification to analyze 39 implementations. We found that people-centered DLT-IoT architectures are in their infancy and detected no evidence of emerging patterns. We observed that Ethereum is the most used DLT. Fit-for-purpose technologies like IOTA and concepts like Self-Sovereign Identity (SSI) were underrepresented. We noted an increased interest in privacy-preserving and edge-computing mechanisms, and identified three areas for future research. We hope this survey will assist others learning more about people-centered IoT solutions.
Internet backboned crowdsourcing utilizes network-wide resources to solve complicated and large-scale tasks, which are not accomplishable for independent individuals. Existing crowdsourcing platforms are mostly centralized solutions with reliability and trustworthiness fragile to single-point failures on the central servers. The innovation of distributed ledgers as blockchain inspires us to optimize the traditional crowdsourcing procedure with distributed sustainability. We propose a blockchain-based design of the distributed secure crowdsourcing scheme for task distribution and result verification without relying on any third trusted institution. A preference-based task distribution (PTD) mechanism is presented which guarantees the percentage of task distribution and the satisfaction of the chosen workers. Task works are continuously assessed for reputations based on their historical behaviors. Task completion correctness is verified by blockchain consensus in two different scenarios after workers submit their results with reputations. We implement a prototype system based on the Ethereum chain with PTD and solution verification components. With various tasks and scenarios evaluated in the system, the proposed distributed crowdsourcing framework shows system reliability, data security, and scenario feasibility.
Entrepreneurs, enterprises, and governments are using distributed ledger technology (DLT) as a component of complex information systems, and therefore interoperability capabilities are required. Interoperating DLTs enables network effects, synergies and, similarly to the rise of the Internet, it unlocks the full potential of the technology. However, due to the novelty of the area, interoperability mechanisms (IM) are still not well understood, as interoperability is studied in silos. Consequently, choosing the proper IM for a use case is challenging. Our paper has three contributions: first, we systematically study the research area of DLT interoperability by dissecting and analyzing previous work. We study the logical separation of interoperability layers, how a DLT can connect to others (connection mode), the object of interoperation (interoperation mode), and propose a new categorization for IMs. Second, we propose the first interoperability assessment for DLTs that systematically evaluates the interoperability degree of an IM. This framework allows comparing the potentiality, compatibility, and performance among solutions. Finally, we propose two decision models to assist in choosing an IM, considering different requirements. The first decision model assists in choosing the infrastructure of an IM, while the second decision model assists in choosing its functionality.
A mobile crowdsensing system (MCS) utilizes a crowd of users to collect large-scale data using their mobile devices efficiently. The collected data are usually linked with sensitive information, raising the concerns of user privacy leakage. To date, many approaches have been proposed to protect the users' privacy, with the majority relying on a centralized structure, which poses though attack and intrusion vulnerability. Some studies build a distributed platform exploiting a blockchain-type solution, which still requires a fully trusted third party (TTP) to manage a reliable reward distribution in the MCS. Spurred by the deficiencies of current methods, we propose a distributed user privacy protection structure that combines blockchain and a trusted execution environment (TEE). The proposed architecture successfully manages the users' privacy protection and an accurate reward distribution without requiring a TTP. This is because the encryption algorithms ensure data confidentiality and uncouple the correlation between the users' identity and the sensitive information in the collected data. Accordingly, the smart contract signature is used to manage the user deposit and verify the data. Extensive comparative experiments verify the efficiency and effectiveness of the proposed combined blockchain and TEE scheme.
Matevž PustiŔek, Min Chen, Andrej Kos, Anton Kos
Blockchain ecosystems are rapidly maturing and meeting the needs of business environments (e.g., industry, manufacturing, and robotics). The decentralized approaches in industries enable novel business concepts, such as machine autonomy and servitization of manufacturing environments. Introducing the distributed ledger technology principles into the machine sharing and servitization economy faces several challenges, and the integration opens new interesting research questions. Our research focuses on data and event models and secure upgradeable smart contract platforms for machine servitization. Our research indicates that with the proposed approaches, we can efficiently separate on- and off-chain data and assure scalability of the DApp without compromising the trust. We demonstrate that the secure upgradeable smart contract platform, which was adapted for machine servitization, supports the business workflow and, at the same time, assures common identification and authorization of all the participants in the system, including people, devices, and legal entities. We present a hybrid decentralized application (DApp) for the servitization of 3D printing. The solution can be used for or easily adapted to other manufacturing domains. It comprises a modular, upgradeable smart contract platform and off-chain machine, customer and web management, and monitoring interfaces. We pay special attention to the data and event models during the design, which are fundamental for the hybrid data storage and DApp architecture and the responsiveness of off-chain interfaces. The smart contract platform uses a proxy contract to control the access of smart contracts and role-based access control in function calls for blockchain users. We deploy and evaluate the DApp in a consortium blockchain network for performance and privacy. All the actors in the solution, including the machines, are identified by their blockchain accounts and are compeers. Our solution thus facilitates integration with the traditional information-communication systems in terms of the hybrid architectures and security standards for smart contract design comparable to those in traditional software engineering.
Maha Kadadha, Shakti Singh, Rabeb Mizouni, Hadi Otrok
Crowdsourcing is a rapidly growing paradigm that commercial platforms such as Amazon MTurk and UpWork are adopting for allocating tasks to workers. Such frameworks typically employ a centralized infrastructure to implement required mechanisms such as task allocation, submission evaluation, and payment computation. However, centralized deployment comes with unresolved challenges in terms of trust, reliability, and transparency. Blockchain technology has been embraced for the deployment of crowdsourcing frameworks to enable trusted and autonomous execution. Each of the existing Blockchain-based crowdsourcing/ crowdsensing framework targets a specific application context due to the constraint capabilities of Blockchain. In this paper, we propose a context-aware Blockchain-based crowdsourcing framework where the context is defined by task requirements and workersā availability. The proposed framework is developed upon the review of existing works integrating Blockchain and crowdsourcing where the challenges and future directions are identified. The proposed framework has two classes of components: 1)core componentsimplementing the basic framework functionalities, and 2)advanced componentswhich are context and data managers that help improve the framework performance. TheAdvanced Context Manageris designed to monitor the current context and select the mechanisms to run for the core components accordingly. The core components are implemented as smart contracts on Blockchain for autonomous and trusted execution, while the advanced components are implemented spanning Blockchain and the cloud for flexibility and scalability. A case study demonstrating the performance of context-aware task allocation algorithms is presented. It shows how capturing the current system context can help achieve better overall performance based on the objective of the sensing application under consideration.
In the machine learning, data sharing between different participants can increase the amount of data, improve the quality of the dataset, and thereby improve the quality of the model. Under the condition of data supervision, federated learning, as a distributed machine learning, aims to protect data while training models through collaboration among all parties to achieve data sharing and improve model quality. However, there are still some issues. For instance, the lack of trust between the participants makes it impossible to establish a secure and reliable sharing mechanism. In addition, how to fairly share the benefits generated by the model, identify honest participants and punish malicious participants is still a challenge. In this paper, we propose a new federated learning scheme based on blockchain architecture for federated learning data sharing. Moreover, an incentive mechanism based on reputation points and Shaply values is proposed to improve the sustainability of the federated learning system, which provides a credible participation mechanism for data sharing based on federated learning and fair incentives. The experimental results and analysis show that the loss of federated learning is more smooth than that of centralized machine learning.
Distributed Agile Software Development (DASD) is the most important approach for the modern software industry that allows geographically independent software development. In the past, different tools and frameworks were proposed to solve communication and collaboration issues in DASD but they lacked transparency, trust, traceability, and security. These shortcomings resulted in project failure or overdue, customer dissatisfaction, project deal cancellations, and payment clashes between the customers and development teams. This paper addresses and overcomes the major issues of transparency, trust, security, traceability, coordination, and communication in DASD by embedding blockchain technology. We have proposed a novel blockchain-based framework named as AgilePlus which executes the smart contracts on a private ethereum blockchain for acceptance testing, secure payment, verification of developerās payment requirements, and automatic payment distribution into the digital wallets of development teams. The execution of these smart contracts automatically assign penalties to the customers for late or non-payments and also to the developers for overdue tasks. Moreover, we have also solved the blockchainās scalability challenge in AgilePlus by utilizing Interplanetary File System (IPFS) as off-chain storage. Lastly, experimental results prove that the proposed framework enhances transparency, communication, coordination, traceability, security and solves trust issues of both customers and developers in DASD.
Abdullah Yousafzai, Latif U. Khan, Umer Majeed, Owais Hakeem Ā· 5 authors
Federated learning (FL) enables the training of a shared collaborative machine learning model while keeping all the confidential training data on distributed devices. The FL state-of-the-art considers a monopolist FL task publisher. However, we present a FL marketplace where multiple FL task publishers and mobile devices co-exist for a set of diverse and varying learning tasks. Mobile devices participating in the training of FL models provides pay-as-you-go (i.e. using blockchain-based cryptocurrencies) FL training services to the FL task publishers. In the proposed framework, multiple FL task publishers may compete with each other and the participating workers (i.e. mobile devices) can choose one FL task publisher over another for participation in the training of a global model. We utilize code offloading for enabling customized FL pipelines in mobile devices and mitigating the model heterogeneity inherent in varying and changing FL tasks published by the task publishers. Experimental results indicate the efficacy of the proposed framework.
In this work we develop a rewarding framework that can be used to enhance existing crowd-sensing applications. Although a core requirement of such systems is user engagement, people may be reluctant to participate as sensitive information about them may be leaked or inferred from submitted data. The use of monetary rewards can help incentivize participation, thereby increasing not only the amount but also the quality of sensed data. Our framework allows users to submit data and obtain Bitcoin payments in a privacy-preserving manner, preventing curious providers from linking the data or the payments back to the user. At the same time, it prevents malicious user behavior such as double-redeeming attempts, where a user tries to obtain rewards for multiple submissions of the same data. More importantly, it ensures thefairnessof the exchange in a completely trustless manner; by relying on the Blockchain, the trust placed on third parties in traditional fair exchange protocols is eliminated. Finally, our system is highly efficient as most of the protocol steps do not utilize the Blockchain network. When they do, only the simplest of Blockchain transactions are used as opposed to prior works that are based on the use of more complex smart contracts.
With the continuous innovative development and popularization of mobile smart devices , the application of Mobile Crowd Sensing (MCS) continues to be studied extensively. However, existing centralized MCS applications that use servers for task publishing and data collection exhibit common problems, such as single points of failure and security vulnerabilities . Accordingly, we proposed a hybrid blockchain-based identity authentication scheme for MCS called HBIA, which uses blockchain technology to resolve the single-point failure problem. HBIA builds a cluster structure based on factors such as geographical location and balance, and uses it to construct a hybrid blockchain , with the cluster head node and internal cluster node authenticating on the public and private chains, respectively. We also implemented zero-knowledge proof (ZKP) to ensure the privacy of participantsā identities, thus balancing the contradiction between blockchain transparency and security. In addition, HBIA uses the zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) technology to enable off-chain computing and on-chain verification, further reducing the blockchainās workload. Finally,ā HBIA was evaluated based on the pavement crack detection task and tested on the Ethereum public test network known as Ropsten. The test results indicate that the identity authentication scheme proposed in this paper is superior to existing schemes in terms of authentication time.