Smart factories promise a world where machines, sensors, and human operators across different organizations exchange data seamlessly, allowing production lines to adapt in real time and supply chains to respond within milliseconds. But that promise carries a hidden cost: every transaction between factories—every material tracking update, every quality record, every machine-to-machine handshake—is a potential point of failure or attack. A new study published in the Journal of Network and Systems Management proposes an answer that blends three technologies often discussed separately: permissioned blockchain, edge computing, and cloud services, woven together into a single hierarchical framework the researchers call XManuChain.
The work, led by Mohammad Iqbal Saryuddin Assaqty of Universitas Nahdlatul Ulama Indonesia, with collaborators at South China University of Technology, the Beijing Institute of Technology, and Fordham University, addresses a problem that has grown sharper as manufacturing shifts toward what researchers term the Internet of Production, or IoP. In this vision, production systems are no longer isolated islands of automation. They are networked ecosystems where data flows between machines on a factory floor, between multiple plants owned by the same company, and between entirely separate organizations collaborating on a shared product. Each of those flows demands trust, and trust is precisely what conventional cloud-centric architectures struggle to guarantee.
The core insight of the paper is that not all manufacturing data transactions are created equal. A temperature reading from a machine inside a single plant has very different trust requirements than a quality certificate shared between two competing suppliers working on the same contract. XManuChain therefore divides the transactional world into three distinct scopes. The local scope covers transactions within one factory. The inter-local scope covers exchanges across multiple factories belonging to the same organization. The cross-domain scope covers communication between different organizations entirely. Rather than forcing all three through the same pipe, the framework assigns each its own authentication pathway, its own agent architecture, and its own relationship to the shared blockchain ledger.
Technically, the framework rests on what the authors describe as a hierarchical agent-based transaction model. Each operational layer is populated by dedicated software agents. User devices—sensors, controllers, human interfaces—are registered and authenticated within their local environment through a local agent. When data must cross the boundary between factories of the same organization, an inter-local agent brokers the exchange, verifying that both endpoints are legitimate members of the broader organizational network. When collaboration extends across organizational boundaries, a cross-domain agent takes over, enforcing stricter identity checks and recording the transaction on the permissioned ledger so that neither party can later dispute what was exchanged or when. This layering means that lightweight devices on the factory floor never need to bear the full cryptographic burden of cross-organizational verification; that responsibility is delegated upward to more capable infrastructure.
The blockchain at the heart of the system is permissioned rather than public. In a permissioned network, every participant is known and vetted before joining, and consensus is achieved among a limited set of trusted nodes rather than through energy-intensive proof-of-work mining. This choice matters enormously for manufacturing, where transaction throughput, latency, and regulatory accountability are paramount. The prototype was built in a Hyperledger Composer and Playground environment, a widely used open-source toolchain for modeling business networks and smart contracts. The researchers used it to validate transaction logic and to confirm that ledger state transitions behaved as intended across all three operational scopes—local, inter-local, and cross-domain workflows all executed correctly in the modeled scenarios.
Of course, a blockchain can guarantee that recorded transactions are tamper-evident, but it cannot by itself guarantee that the entities initiating those transactions are who they claim to be. Authentication is therefore the second pillar of XManuChain. The framework defines a blockchain-backed authentication scheme covering four classes of actors: user devices, local agents, external agents, and cross-domain agents. Each must prove its identity before participating in a transaction, and the proof is anchored to the shared ledger so that credentials cannot be forged or replayed undetected. This design draws on a body of prior work in mutual authentication protocols, including schemes developed for roaming services in global mobility networks and for 5G systems, but adapts those ideas to the specific constraints and trust relationships of collaborative manufacturing.
To assess whether the authentication scheme actually holds up under scrutiny, the team turned to two established formal verification techniques. The first is BAN logic, a classical method introduced by Burrows, Abadi, and Needham in 1990, which allows researchers to reason step by step about what each party in a protocol can believe at each stage of an exchange—whether a shared key is genuinely fresh, whether a message truly originated from the claimed sender, and whether both sides end up with mutual belief in each other’s identity. The second is AVISPA, the Automated Validation of Internet Security Protocols and Applications, a formal tool that models security protocols in a specialized language and searches for attack scenarios that a protocol designer might have overlooked. Together, these analyses examined the authentication properties of the framework across its registration and authentication phases for local, inter-local, and cross-domain scenarios. The reported results indicate that the protocol logic satisfies the intended authentication goals in the modeled settings.
The authors are candid about the limits of what they have demonstrated, and that candor is worth emphasizing. The evaluation was carried out in a browser-based, resource-constrained prototype environment using Hyperledger Composer’s Playground tool, which is designed for modeling and validating business network logic rather than for measuring production-scale throughput. The paper does not yet provide a full distributed deployment specification, meaning that the performance findings should be read as evidence of functional feasibility rather than as definitive benchmarks. A real deployment across multiple factories and organizations would involve consensus overhead, network latency, node failures, and adversarial traffic that a browser-based prototype cannot fully capture. The contribution, as the researchers frame it, lies not in raw performance numbers but in the integration itself—the demonstration that permissioned ledger, edge computing, and cloud services can be composed into a coherent, hierarchically organized transaction model for production collaboration.
That integration is precisely what distinguishes this work from earlier efforts. Blockchain has been proposed for supply chain traceability, for IoT device security, and for federated learning systems. Edge computing has been studied extensively for reducing latency in industrial IoT. Cloud platforms have long been the default home for manufacturing analytics. What has been missing, the authors argue, is a framework that treats these three not as alternatives but as complementary layers of a single security architecture, with the operational scope of each transaction determining which layer handles authentication and recording. In a cross-domain scenario, for instance, the blockchain provides the immutable audit trail that makes disputes resolvable without a trusted third party, while edge infrastructure handles the low-latency data exchange and cloud services support analytics and long-term storage.
The implications for industry are potentially significant. Cross-factory collaboration is becoming the norm in sectors such as aerospace, automotive, and electronics, where no single company owns the entire production process. Yet each new partnership multiplies the number of access-control relationships that must be managed, and each shared data stream expands the attack surface. A framework that standardizes how devices, agents, and organizations authenticate across these boundaries—and that anchors every transaction to a shared, tamper-evident ledger—could reduce the operational burden of securing such collaborations while making them more auditable. The three-scope model also gives organizations a vocabulary for reasoning about which security guarantees apply where, something that flat, one-size-fits-all architectures tend to obscure.
The research builds on a substantial lineage. Earlier work by some of the same authors explored private blockchain approaches for material and product tracking in smart manufacturing, and lightweight authentication schemes using physical uncoable functions for supply chain IoT. Other members of the team have contributed to distributed denial-of-service detection using deep learning and meta-learning techniques, expertise that informs the framework’s awareness of adversarial threats to networked infrastructure. The new paper synthesizes these threads into a unified architecture aimed squarely at the collaborative production scenario.
What comes next will determine whether XManuChain remains an elegant prototype or becomes a practical foundation for industrial deployment. The authors’ own framing suggests a roadmap: a full distributed deployment specification, testing on production-grade Hyperledger Fabric networks with real consensus configurations, and performance evaluation under realistic manufacturing workloads. Questions of scalability—how many agents, how many transactions per second, how large the ledger grows under sustained cross-domain collaboration—remain open. So too do questions of governance: who operates the permissioned network when multiple organizations are involved, and how are membership changes handled. These are not weaknesses of the study so much as the natural boundary of what a single paper can establish.
For now, XManuChain offers something the field has lacked: a carefully structured, formally analyzed template for securing the transactional fabric of collaborative manufacturing. As smart factories multiply and the boundaries between organizations grow increasingly porous, frameworks of this kind—grounded in permissioned ledgers, layered agent architectures, and rigorous protocol verification—may well become part of the standard toolkit for building trust into the industrial internet.
Subject of Research: A permissioned blockchain framework (XManuChain) integrating edge and cloud computing to secure Internet of Production transactions and authentication in collaborative smart manufacturing across local, inter-local, and cross-domain scopes.
Subject of Research: Technology and Engineering
Article Title: XManuChain: A Permissioned Blockchain Framework for Securing IoP Transactions in Production Process Collaboration
Article References: Assaqty, M. I. S., Gao, Y., Hu, X., Alfatemi, A., Fernandy, H., Ali, I., & Zhang, P. (2026). XManuChain: A Permissioned Blockchain Framework for Securing IoP Transactions in Production Process Collaboration. Journal of Network and Systems Management, 34(4), Article 119. https://doi.org/10.1007/s10922-026-10108-8
Image Credits: AI Generated
DOI: 10.1007/s10922-026-10108-8
Keywords: permissioned blockchain, Internet of Production, cross-domain communication, authentication, smart contracts, collaborative manufacturing, edge computing, cloud computing, Hyperledger, industrial IoT
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Denise Maddox. (September 6, 2026). Permissioned Blockchain Framework Secures Industrial IoT Transactions in Manufacturing Collaboration. Scienmag. https://scienmag.com/permissioned-blockchain-framework-secures-industrial-iot-transactions-in-manufacturing-collaboration/
Denise Maddox. “Permissioned Blockchain Framework Secures Industrial IoT Transactions in Manufacturing Collaboration.” Scienmag, 6 September 2026, https://scienmag.com/permissioned-blockchain-framework-secures-industrial-iot-transactions-in-manufacturing-collaboration/. Accessed 6 September 2026.
Denise Maddox. “Permissioned Blockchain Framework Secures Industrial IoT Transactions in Manufacturing Collaboration.” Scienmag. September 6, 2026. https://scienmag.com/permissioned-blockchain-framework-secures-industrial-iot-transactions-in-manufacturing-collaboration/
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Tags: blockchain-based supply chain managementblockchain-based supply chain securitycloud services for industrial applicationscloud services for industrial IoTcollaborative manufacturing networksedge computing in manufacturinghierarchical blockchain frameworkIndustrial IoT securityIoP data exchangeIoT transaction failure preventionmachine-to-machine communication securitymanufacturing collaborationmulti-organizational manufacturing networkspermissioned blockchainreal-time production monitoringsecure factory data transactionstrust management in industrial systems
