Blockchain for Secure, Transparent, and Scalable Data & Model Sharing in Edge–Cloud Systems

Dear Colleagues,

The rapid proliferation of IoT/IIoT devices, connected and autonomous vehicles (CAV/IoV), and 5G/6G edge–cloud systems is driving an unprecedented demand for secure, transparent, and scalable data—and increasingly, model-sharing. Traditional siloed data pipelines struggle with trust, provenance, privacy, and incentive alignment across heterogeneous stakeholders. Blockchain and distributed ledger technologies (DLTs) provide tamper-evident provenance, verifiable access control, programmable incentives, and auditable governance; when combined with federated/cooperative learning, world-model-driven networked AI, and zero-trust architectures, they enable trustworthy collaboration without centralized custody of data. This Special Issue seeks cutting-edge methods and systems that make data/model exchange provably secure, resource-aware, and scalable, bridging algorithms, systems, and real deployments.

This Special Issue aims to gather original research and reviews on blockchain-empowered mechanisms for secure, transparent, and scalable sharing of data and models across edge–cloud-device ecosystems. We particularly welcome works that integrate communications, embedded/edge computing, networking, and intelligent systems, aligning closely with the journal’s scope on electronics for IoT devices, cyber-physical systems, networking hardware/firmware, edge intelligence, and secure architectures. Submissions should emphasize concrete implementations, system evaluations, and reproducibility, highlighting how blockchain/DLT advances the reliability, efficiency, and trustworthiness of modern electronic and networked systems.

Suggested themes, include, but are not limited to, the following:

• Permissioned/consortium blockchains (Hyperledger, Tendermint, Quorum) for IoT/IIoT, IoV/CAV, and 5G/6G edge–cloud systems;
• Secure data and model sharing: Provenance, auditability, accountable access control, data valuation, and data/model marketplaces;
• Federated, cooperative, and split learning on the edge with blockchain-backed proof-of-contribution, aggregation integrity, and incentive design;
• World-model–driven networked AI and digital twins with on-chain/off-chain governance, versioning, and lifecycle traceability (MLOps/LLMOps);
• Zero-trust architectures: Identity (SSI, DIDs), verifiable credentials, policy enforcement, and cross-domain trust management;
• Privacy-preserving analytics: Secure enclaves (TEE), MPC, homomorphic encryption, differential privacy, and ZK-proofs for verifiable training/inference;
• Verifiable and efficient computing: SNARKs/STARKs, succinct proofs for edge inference, on-chain attestation, and audit of safety constraints;
• Resource-aware optimization under the constraints of computation/memory/energy/bandwidth/latency, and scheduling and placement for edge AI + blockchain;
• Interoperability and scalability: L2 rollups, state channels, cross-chain bridges, Pub/Sub data planes, and oracle designs for real-world signals;
• Semantic/intent-driven communication and content provenance (C2PA) for trustworthy media and sensor streams;
• Security of CPS/ITS: Resilient consensus, fault/attack detection, byzantine-robust learning, and safety certification for autonomous systems;
• Standards, benchmarking, and reproducibility: Datasets, testbeds (vehicular/industrial), emulation frameworks, and open tooling;
• Applications and case studies: Smart manufacturing, energy grids, healthcare, logistics, smart cities, and large-scale pilots.

In this Special Issue, original research articles and reviews are welcome. We encourage submissions that report system prototypes, rigorous evaluations, and real-world deployments, as well as surveys that synthesize emerging foundations, architectures, and best practices at the intersection of blockchain, edge intelligence, and secure data/model sharing.

I look forward to receiving your contributions.

Dr. Dajun Zhang
Dr. Meng Li
Dr. Qi Li
Guest Editors

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